@article {pmid38638340,
year = {2024},
author = {Abbas, Q and Alyas, T and Alghamdi, T and Alkhodre, AB and Albouq, S and Niazi, M and Tabassum, N},
title = {Redefining governance: a critical analysis of sustainability transformation in e-governance.},
journal = {Frontiers in big data},
volume = {7},
number = {},
pages = {1349116},
pmid = {38638340},
issn = {2624-909X},
abstract = {With the rapid growth of information and communication technologies, governments worldwide are embracing digital transformation to enhance service delivery and governance practices. In the rapidly evolving landscape of information technology (IT), secure data management stands as a cornerstone for organizations aiming to safeguard sensitive information. Robust data modeling techniques are pivotal in structuring and organizing data, ensuring its integrity, and facilitating efficient retrieval and analysis. As the world increasingly emphasizes sustainability, integrating eco-friendly practices into data management processes becomes imperative. This study focuses on the specific context of Pakistan and investigates the potential of cloud computing in advancing e-governance capabilities. Cloud computing offers scalability, cost efficiency, and enhanced data security, making it an ideal technology for digital transformation. Through an extensive literature review, analysis of case studies, and interviews with stakeholders, this research explores the current state of e-governance in Pakistan, identifies the challenges faced, and proposes a framework for leveraging cloud computing to overcome these challenges. The findings reveal that cloud computing can significantly enhance the accessibility, scalability, and cost-effectiveness of e-governance services, thereby improving citizen engagement and satisfaction. This study provides valuable insights for policymakers, government agencies, and researchers interested in the digital transformation of e-governance in Pakistan and offers a roadmap for leveraging cloud computing technologies in similar contexts. The findings contribute to the growing body of knowledge on e-governance and cloud computing, supporting the advancement of digital governance practices globally. This research identifies monitoring parameters necessary to establish a sustainable e-governance system incorporating big data and cloud computing. The proposed framework, Monitoring and Assessment System using Cloud (MASC), is validated through secondary data analysis and successfully fulfills the research objectives. By leveraging big data and cloud computing, governments can revolutionize their digital governance practices, driving transformative changes and enhancing efficiency and effectiveness in public administration.},
}

@article {pmid38628614,
year = {2024},
author = {Wang, TH and Kao, CC and Chang, TH},
title = {Ensemble Machine Learning for Predicting 90-Day Outcomes and Analyzing Risk Factors in Acute Kidney Injury Requiring Dialysis.},
journal = {Journal of multidisciplinary healthcare},
volume = {17},
number = {},
pages = {1589-1602},
pmid = {38628614},
issn = {1178-2390},
abstract = {PURPOSE: Our objectives were to (1) employ ensemble machine learning algorithms utilizing real-world clinical data to predict 90-day prognosis, including dialysis dependence and mortality, following the first hospitalized dialysis and (2) identify the significant factors associated with overall outcomes.

PATIENTS AND METHODS: We identified hospitalized patients with Acute kidney injury requiring dialysis (AKI-D) from a dataset of the Taipei Medical University Clinical Research Database (TMUCRD) from January 2008 to December 2020. The extracted data comprise demographics, comorbidities, medications, and laboratory parameters. Ensemble machine learning models were developed utilizing real-world clinical data through the Google Cloud Platform.

RESULTS: The Study Analyzed 1080 Patients in the Dialysis-Dependent Module, Out of Which 616 Received Regular Dialysis After 90 Days. Our Ensemble Model, Consisting of 25 Feedforward Neural Network Models, Demonstrated the Best Performance with an Auroc of 0.846. We Identified the Baseline Creatinine Value, Assessed at Least 90 Days Before the Initial Dialysis, as the Most Crucial Factor. We selected 2358 patients, 984 of whom were deceased after 90 days, for the survival module. The ensemble model, comprising 15 feedforward neural network models and 10 gradient-boosted decision tree models, achieved superior performance with an AUROC of 0.865. The pre-dialysis creatinine value, tested within 90 days prior to the initial dialysis, was identified as the most significant factor.

CONCLUSION: Ensemble machine learning models outperform logistic regression models in predicting outcomes of AKI-D, compared to existing literature. Our study, which includes a large sample size from three different hospitals, supports the significance of the creatinine value tested before the first hospitalized dialysis in determining overall prognosis. Healthcare providers could benefit from utilizing our validated prediction model to improve clinical decision-making and enhance patient care for the high-risk population.},
}

@article {pmid38628390,
year = {2024},
author = {Fujinami, H and Kuraishi, S and Teramoto, A and Shimada, S and Takahashi, S and Ando, T and Yasuda, I},
title = {Development of a novel endoscopic hemostasis-assisted navigation AI system in the standardization of post-ESD coagulation.},
journal = {Endoscopy international open},
volume = {12},
number = {4},
pages = {E520-E525},
pmid = {38628390},
issn = {2364-3722},
abstract = {Background and study aims While gastric endoscopic submucosal dissection (ESD) has become a treatment with fewer complications, delayed bleeding remains a challenge. Post-ESD coagulation (PEC) is performed to prevent delayed bleeding. Therefore, we developed an artificial intelligence (AI) to detect vessels that require PEC in real time. Materials and methods Training data were extracted from 153 gastric ESD videos with sufficient images taken with a second-look endoscopy (SLE) and annotated as follows: (1) vessels that showed bleeding during SLE without PEC; (2) vessels that did not bleed during SLE with PEC; and (3) vessels that did not bleed even without PEC. The training model was created using Google Cloud Vertex AI and a program was created to display the vessels requiring PEC in real time using a bounding box. The evaluation of this AI was verified with 12 unlearned test videos, including four cases that required additional coagulation during SLE. Results The results of the test video validation indicated that 109 vessels on the ulcer required cauterization. Of these, 80 vessels (73.4%) were correctly determined as not requiring additional treatment. However, 25 vessels (22.9%), which did not require PEC, were overestimated. In the four videos that required additional coagulation in SLE, AI was able to detect all bleeding vessels. Conclusions The effectiveness and safety of this endoscopic treatment-assisted AI system that identifies visible vessels requiring PEC should be confirmed in future studies.},
}

@article {pmid38625954,
year = {2024},
author = {Frimpong, T and Hayfron Acquah, JB and Missah, YM and Dawson, JK and Ayawli, BBK and Baah, P and Sam, SA},
title = {Securing cloud data using secret key 4 optimization algorithm (SK4OA) with a non-linearity run time trend.},
journal = {PloS one},
volume = {19},
number = {4},
pages = {e0301760},
pmid = {38625954},
issn = {1932-6203},
mesh = {*Algorithms ; *Information Storage and Retrieval ; Cloud Computing ; Computer Security ; Microcomputers ; },
abstract = {Cloud computing alludes to the on-demand availability of personal computer framework resources, primarily information storage and processing power, without the customer's direct personal involvement. Cloud computing has developed dramatically among many organizations due to its benefits such as cost savings, resource pooling, broad network access, and ease of management; nonetheless, security has been a major concern. Researchers have proposed several cryptographic methods to offer cloud data security; however, their execution times are linear and longer. A Security Key 4 Optimization Algorithm (SK4OA) with a non-linear run time is proposed in this paper. The secret key of SK4OA determines the run time rather than the size of the data as such is able to transmit large volumes of data with minimal bandwidth and able to resist security attacks like brute force since its execution timings are unpredictable. A data set from Kaggle was used to determine the algorithm's mean and standard deviation after thirty (30) times of execution. Data sizes of 3KB, 5KB, 8KB, 12KB, and 16 KB were used in this study. There was an empirical analysis done against RC4, Salsa20, and Chacha20 based on encryption time, decryption time, throughput and memory utilization. The analysis showed that SK4OA generated lowest mean non-linear run time of 5.545±2.785 when 16KB of data was executed. Additionally, SK4OA's standard deviation was greater, indicating that the observed data varied far from the mean. However, RC4, Salsa20, and Chacha20 showed smaller standard deviations making them more clustered around the mean resulting in predictable run times.},
}

*Algorithms
*Information Storage and Retrieval
Cloud Computing
Computer Security
Microcomputers

@article {pmid38610575,
year = {2024},
author = {Ocampo, AF and Fida, MR and Elmokashfi, A and Bryhni, H},
title = {Assessing the Cloud-RAN in the Linux Kernel: Sharing Computing and Network Resources.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {7},
pages = {},
pmid = {38610575},
issn = {1424-8220},
abstract = {Cloud-based Radio Access Network (Cloud-RAN) leverages virtualization to enable the coexistence of multiple virtual Base Band Units (vBBUs) with collocated workloads on a single edge computer, aiming for economic and operational efficiency. However, this coexistence can cause performance degradation in vBBUs due to resource contention. In this paper, we conduct an empirical analysis of vBBU performance on a Linux RT-Kernel, highlighting the impact of resource sharing with user-space tasks and Kernel threads. Furthermore, we evaluate CPU management strategies such as CPU affinity and CPU isolation as potential solutions to these performance challenges. Our results highlight that the implementation of CPU affinity can significantly reduce throughput variability by up to 40%, decrease vBBU's NACK ratios, and reduce vBBU scheduling latency within the Linux RT-Kernel. Collectively, these findings underscore the potential of CPU management strategies to enhance vBBU performance in Cloud-RAN environments, enabling more efficient and stable network operations. The paper concludes with a discussion on the efficient realization of Cloud-RAN, elucidating the benefits of implementing proposed CPU affinity allocations. The demonstrated enhancements, including reduced scheduling latency and improved end-to-end throughput, affirm the practicality and efficacy of the proposed strategies for optimizing Cloud-RAN deployments.},
}

@article {pmid38610476,
year = {2024},
author = {Liang, YP and Chang, CM and Chung, CC},
title = {Implementation of Lightweight Convolutional Neural Networks with an Early Exit Mechanism Utilizing 40 nm CMOS Process for Fire Detection in Unmanned Aerial Vehicles.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {7},
pages = {},
pmid = {38610476},
issn = {1424-8220},
support = {MOST-111-2221- E-194-049-//Ministry of Science and Technology of Taiwan/ ; },
abstract = {The advancement of unmanned aerial vehicles (UAVs) enables early detection of numerous disasters. Efforts have been made to automate the monitoring of data from UAVs, with machine learning methods recently attracting significant interest. These solutions often face challenges with high computational costs and energy usage. Conventionally, data from UAVs are processed using cloud computing, where they are sent to the cloud for analysis. However, this method might not meet the real-time needs of disaster relief scenarios. In contrast, edge computing provides real-time processing at the site but still struggles with computational and energy efficiency issues. To overcome these obstacles and enhance resource utilization, this paper presents a convolutional neural network (CNN) model with an early exit mechanism designed for fire detection in UAVs. This model is implemented using TSMC 40 nm CMOS technology, which aids in hardware acceleration. Notably, the neural network has a modest parameter count of 11.2 k. In the hardware computation part, the CNN circuit completes fire detection in approximately 230,000 cycles. Power-gating techniques are also used to turn off inactive memory, contributing to reduced power consumption. The experimental results show that this neural network reaches a maximum accuracy of 81.49% in the hardware implementation stage. After automatic layout and routing, the CNN hardware accelerator can operate at 300 MHz, consuming 117 mW of power.},
}

@article {pmid38610447,
year = {2024},
author = {Gomes, B and Soares, C and Torres, JM and Karmali, K and Karmali, S and Moreira, RS and Sobral, P},
title = {An Efficient Edge Computing-Enabled Network for Used Cooking Oil Collection.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {7},
pages = {},
pmid = {38610447},
issn = {1424-8220},
abstract = {In Portugal, more than 98% of domestic cooking oil is disposed of improperly every day. This avoids recycling/reconverting into another energy. Is also may become a potential harmful contaminant of soil and water. Driven by the utility of recycled cooking oil, and leveraging the exponential growth of ubiquitous computing approaches, we propose an IoT smart solution for domestic used cooking oil (UCO) collection bins. We call this approach SWAN, which stands for Smart Waste Accumulation Network. It is deployed and evaluated in Portugal. It consists of a countrywide network of collection bin units, available in public areas. Two metrics are considered to evaluate the system's success: (i) user engagement, and (ii) used cooking oil collection efficiency. The presented system should (i) perform under scenarios of temporary communication network failures, and (ii) be scalable to accommodate an ever-growing number of installed collection units. Thus, we choose a disruptive approach from the traditional cloud computing paradigm. It relies on edge node infrastructure to process, store, and act upon the locally collected data. The communication appears as a delay-tolerant task, i.e., an edge computing solution. We conduct a comparative analysis revealing the benefits of the edge computing enabled collection bin vs. a cloud computing solution. The studied period considers four years of collected data. An exponential increase in the amount of used cooking oil collected is identified, with the developed solution being responsible for surpassing the national collection totals of previous years. During the same period, we also improved the collection process as we were able to more accurately estimate the optimal collection and system's maintenance intervals.},
}

@article {pmid38610327,
year = {2024},
author = {Armijo, A and Zamora-Sánchez, D},
title = {Integration of Railway Bridge Structural Health Monitoring into the Internet of Things with a Digital Twin: A Case Study.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {7},
pages = {},
pmid = {38610327},
issn = {1424-8220},
support = {ZL-2020/00902//Basque Government/ ; GA10112353//European Commission/ ; GA10108395//European Commission/ ; },
abstract = {Structural health monitoring (SHM) is critical for ensuring the safety of infrastructure such as bridges. This article presents a digital twin solution for the SHM of railway bridges using low-cost wireless accelerometers and machine learning (ML). The system architecture combines on-premises edge computing and cloud analytics to enable efficient real-time monitoring and complete storage of relevant time-history datasets. After train crossings, the accelerometers stream raw vibration data, which are processed in the frequency domain and analyzed using machine learning to detect anomalies that indicate potential structural issues. The digital twin approach is demonstrated on an in-service railway bridge for which vibration data were collected over two years under normal operating conditions. By learning allowable ranges for vibration patterns, the digital twin model identifies abnormal spectral peaks that indicate potential changes in structural integrity. The long-term pilot proves that this affordable SHM system can provide automated and real-time warnings of bridge damage and also supports the use of in-house-designed sensors with lower cost and edge computing capabilities such as those used in the demonstration. The successful on-premises-cloud hybrid implementation provides a cost effective and scalable model for expanding monitoring to thousands of railway bridges, democratizing SHM to improve safety by avoiding catastrophic failures.},
}

@article {pmid38610235,
year = {2024},
author = {Gaffurini, M and Flammini, A and Ferrari, P and Fernandes Carvalho, D and Godoy, EP and Sisinni, E},
title = {End-to-End Emulation of LoRaWAN Architecture and Infrastructure in Complex Smart City Scenarios Exploiting Containers.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {7},
pages = {},
pmid = {38610235},
issn = {1424-8220},
support = {1033 17/06/2022, CN00000023//European Union/ ; },
abstract = {In a LoRaWAN network, the backend is generally distributed as Software as a Service (SaaS) based on container technology, and recently, a containerized version of the LoRaWAN node stack is also available. Exploiting the disaggregation of LoRaWAN components, this paper focuses on the emulation of complex end-to-end architecture and infrastructures for smart city scenarios, leveraging on lightweight virtualization technology. The fundamental metrics to gain insights and evaluate the scaling complexity of the emulated scenario are defined. Then, the methodology is applied to use cases taken from a real LoRaWAN application in a smart city with hundreds of nodes. As a result, the proposed approach based on containers allows for the following: (i) deployments of functionalities on diverse distributed hosts; (ii) the use of the very same SW running on real nodes; (iii) the simple configuration and management of the emulation process; (iv) affordable costs. Both premise and cloud servers are considered as emulation platforms to evaluate the resource request and emulation cost of the proposed approach. For instance, emulating one hour of an entire LoRaWAN network with hundreds of nodes requires very affordable hardware that, if realized with a cloud-based computing platform, may cost less than USD 1.},
}

@article {pmid38609681,
year = {2024},
author = {Gupta, P and Shukla, DP},
title = {Demi-decadal land use land cover change analysis of Mizoram, India, with topographic correction using machine learning algorithm.},
journal = {Environmental science and pollution research international},
volume = {},
number = {},
pages = {},
pmid = {38609681},
issn = {1614-7499},
abstract = {Mizoram (India) is part of UNESCO's biodiversity hotspots in India that is primarily populated by tribes who engage in shifting agriculture. Hence, the land use land cover (LULC) pattern of the state is frequently changing. We have used Landsat 5 and 8 satellite images to prepare LULC maps from 2000 to 2020 in every 5 years. The atmospherically corrected images were pre-processed for removal of cloud cover and then classified into six classes: waterbodies, farmland, settlement, open forest, dense forest, and bare land. We applied four machine learning (ML) algorithms for classification, namely, random forest (RF), classification and regression tree (CART), minimum distance (MD), and support vector machine (SVM) for the images from 2000 to 2020. With 80% training and 20% testing data, we found that the RF classifier works best with the most accuracy than other classifiers. The average overall accuracy (OA) and Kappa coefficient (KC) from 2000 to 2020 were 84.00% and 0.79 when the RF classifier was used. When using SVM, CART, and MD, the average OA and KC were 78.06%, 0.73; 78.60%, 0.72; and 73.32%, 0.65, respectively. We utilised three methods of topographic correction, namely, C-correction, SCS (sun canopy sensor) correction, and SCS + C correction to reduce the misclassification due to shadow effects. SCS + C correction worked best for this region; hence, we prepared LULC maps on SCS + C corrected satellite image. Hence, we have used RF classifier for LULC preparation demi-decadal from 2000 to 2020. The OA for 2000, 2005, 2010, 2015, and 2020 was found to be 84%, 81%, 81%, 85%, and 89%, respectively, using RF. The dense forest decreased from 2000 to 2020 with an increase in open forest, settlement, and agriculture; nevertheless, when Farmland was low, there was an increase in the barren land. The results were significantly improved with the topographic correction, and misclassification was quite less.},
}

@article {pmid38609409,
year = {2024},
author = {Zhang, Y and Geng, H and Su, L and He, S and Lu, L},
title = {An efficient polynomial-based verifiable computation scheme on multi-source outsourced data.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {8512},
pmid = {38609409},
issn = {2045-2322},
support = {U22B2038//Research and Verification of Key Technologies for Secure and Efficient Federated Learning/ ; },
abstract = {With the development of cloud computing, users are more inclined to outsource complex computing tasks to cloud servers with strong computing capacity, and the cloud returns the final calculation results. However, the cloud is not completely trustworthy, which may leak the data of user and even return incorrect calculations on purpose. Therefore, it is important to verify the results of computing tasks without revealing the privacy of the users. Among all the computing tasks, the polynomial calculation is widely used in information security, linear algebra, signal processing and other fields. Most existing polynomial-based verifiable computation schemes require that the input of the polynomial function must come from a single data source, which means that the data must be signed by a single user. However, the input of the polynomial may come from multiple users in the practical application. In order to solve this problem, the researchers have proposed some schemes for multi-source outsourced data, but these schemes have the common problem of low efficiency. To improve the efficiency, this paper proposes an efficient polynomial-based verifiable computation scheme on multi-source outsourced data. We optimize the polynomials using Horner's method to increase the speed of verification, in which the addition gate and the multiplication gate can be interleaved to represent the polynomial function. In order to adapt to this structure, we design the corresponding homomorphic verification tag, so that the input of the polynomial can come from multiple data sources. We prove the correctness and rationality of the scheme, and carry out numerical analysis and evaluation research to verify the efficiency of the scheme. The experimental indicate that data contributors can sign 1000 new data in merely 2 s, while the verification of a delegated polynomial function with a power of 100 requires only 18 ms. These results confirm that the proposed scheme is better than the existing scheme.},
}

@article {pmid38606391,
year = {2024},
author = {Li, S and Nair, R and Naqvi, SM},
title = {Acoustic and Text Features Analysis for Adult ADHD Screening: A Data-Driven Approach Utilizing DIVA Interview.},
journal = {IEEE journal of translational engineering in health and medicine},
volume = {12},
number = {},
pages = {359-370},
pmid = {38606391},
issn = {2168-2372},
mesh = {Adult ; Humans ; *Attention Deficit Disorder with Hyperactivity/diagnosis ; Treatment Outcome ; Magnetic Resonance Imaging ; },
abstract = {Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder commonly seen in childhood that leads to behavioural changes in social development and communication patterns, often continues into undiagnosed adulthood due to a global shortage of psychiatrists, resulting in delayed diagnoses with lasting consequences on individual's well-being and the societal impact. Recently, machine learning methodologies have been incorporated into healthcare systems to facilitate the diagnosis and enhance the potential prediction of treatment outcomes for mental health conditions. In ADHD detection, the previous research focused on utilizing functional magnetic resonance imaging (fMRI) or Electroencephalography (EEG) signals, which require costly equipment and trained personnel for data collection. In recent years, speech and text modalities have garnered increasing attention due to their cost-effectiveness and non-wearable sensing in data collection. In this research, conducted in collaboration with the Cumbria, Northumberland, Tyne and Wear NHS Foundation Trust, we gathered audio data from both ADHD patients and normal controls based on the clinically popular Diagnostic Interview for ADHD in adults (DIVA). Subsequently, we transformed the speech data into text modalities through the utilization of the Google Cloud Speech API. We extracted both acoustic and text features from the data, encompassing traditional acoustic features (e.g., MFCC), specialized feature sets (e.g., eGeMAPS), as well as deep-learned linguistic and semantic features derived from pre-trained deep learning models. These features are employed in conjunction with a support vector machine for ADHD classification, yielding promising outcomes in the utilization of audio and text data for effective adult ADHD screening. Clinical impact: This research introduces a transformative approach in ADHD diagnosis, employing speech and text analysis to facilitate early and more accessible detection, particularly beneficial in areas with limited psychiatric resources. Clinical and Translational Impact Statement: The successful application of machine learning techniques in analyzing audio and text data for ADHD screening represents a significant advancement in mental health diagnostics, paving the way for its integration into clinical settings and potentially improving patient outcomes on a broader scale.},
}

Adult
Humans
*Attention Deficit Disorder with Hyperactivity/diagnosis
Treatment Outcome
Magnetic Resonance Imaging

@article {pmid38601602,
year = {2024},
author = {Sachdeva, S and Bhatia, S and Al Harrasi, A and Shah, YA and Anwer, K and Philip, AK and Shah, SFA and Khan, A and Ahsan Halim, S},
title = {Unraveling the role of cloud computing in health care system and biomedical sciences.},
journal = {Heliyon},
volume = {10},
number = {7},
pages = {e29044},
pmid = {38601602},
issn = {2405-8440},
abstract = {Cloud computing has emerged as a transformative force in healthcare and biomedical sciences, offering scalable, on-demand resources for managing vast amounts of data. This review explores the integration of cloud computing within these fields, highlighting its pivotal role in enhancing data management, security, and accessibility. We examine the application of cloud computing in various healthcare domains, including electronic medical records, telemedicine, and personalized patient care, as well as its impact on bioinformatics research, particularly in genomics, proteomics, and metabolomics. The review also addresses the challenges and ethical considerations associated with cloud-based healthcare solutions, such as data privacy and cybersecurity. By providing a comprehensive overview, we aim to assist readers in understanding the significance of cloud computing in modern medical applications and its potential to revolutionize both patient care and biomedical research.},
}

@article {pmid38591672,
year = {2024},
author = {Hicks, CB and Martinez, TJ},
title = {Massively scalable workflows for quantum chemistry: BigChem and ChemCloud.},
journal = {The Journal of chemical physics},
volume = {160},
number = {14},
pages = {},
doi = {10.1063/5.0190834},
pmid = {38591672},
issn = {1089-7690},
abstract = {Electronic structure theory, i.e., quantum chemistry, is the fundamental building block for many problems in computational chemistry. We present a new distributed computing framework (BigChem), which allows for an efficient solution of many quantum chemistry problems in parallel. BigChem is designed to be easily composable and leverages industry-standard middleware (e.g., Celery, RabbitMQ, and Redis) for distributed approaches to large scale problems. BigChem can harness any collection of worker nodes, including ones on cloud providers (such as AWS or Azure), local clusters, or supercomputer centers (and any mixture of these). BigChem builds upon MolSSI packages, such as QCEngine to standardize the operation of numerous computational chemistry programs, demonstrated here with Psi4, xtb, geomeTRIC, and TeraChem. BigChem delivers full utilization of compute resources at scale, offers a programable canvas for designing sophisticated quantum chemistry workflows, and is fault tolerant to node failures and network disruptions. We demonstrate linear scalability of BigChem running computational chemistry workloads on up to 125 GPUs. Finally, we present ChemCloud, a web API to BigChem and successor to TeraChem Cloud. ChemCloud delivers scalable and secure access to BigChem over the Internet.},
}

@article {pmid38589881,
year = {2024},
author = {Holl, F and Clarke, L and Raffort, T and Serres, E and Archer, L and Saaristo, P},
title = {The Red Cross Red Crescent Health Information System (RCHIS): an electronic medical records and health information management system for the red cross red crescent emergency response units.},
journal = {Conflict and health},
volume = {18},
number = {1},
pages = {28},
pmid = {38589881},
issn = {1752-1505},
abstract = {BACKGROUND: The Red Cross and Red Crescent Movement (RCRC) utilizes specialized Emergency Response Units (ERUs) for international disaster response. However, data collection and reporting within ERUs have been time-consuming and paper-based. The Red Cross Red Crescent Health Information System (RCHIS) was developed to improve clinical documentation and reporting, ensuring accuracy and ease of use while increasing compliance with reporting standards.

CASE PRESENTATION: RCHIS is an Electronic Medical Record (EMR) and Health Information System (HIS) designed for RCRC ERUs. It can be accessed on Android tablets or Windows laptops, both online and offline. The system securely stores data on Microsoft Azure cloud, with synchronization facilitated through a local ERU server. The functional architecture covers all clinical functions of ERU clinics and hospitals, incorporating user-friendly features. A pilot study was conducted with the Portuguese Red Cross (PRC) during a large-scale event. Thirteen super users were trained and subsequently trained the staff. During the four-day pilot, 77 user accounts were created, and 243 patient files were documented. Feedback indicated that RCHIS was easy to use, requiring minimal training time, and had sufficient training for full utilization. Real-time reporting facilitated coordination with the civil defense authority.

CONCLUSIONS: The development and pilot use of RCHIS demonstrated its feasibility and efficacy within RCRC ERUs. The system addressed the need for an EMR and HIS solution, enabling comprehensive clinical documentation and supporting administrative reporting functions. The pilot study validated the training of trainers' approach and paved the way for further domestic use of RCHIS. RCHIS has the potential to improve patient safety, quality of care, and reporting efficiency within ERUs. Automated reporting reduces the burden on ERU leadership, while electronic compilation enhances record completeness and correctness. Ongoing feedback collection and feature development continue to enhance RCHIS's functionality. Further trainings took place in 2023 and preparations for international deployments are under way. RCHIS represents a significant step toward improved emergency medical care and coordination within the RCRC and has implications for similar systems in other Emergency Medical Teams.},
}

@article {pmid38586319,
year = {2024},
author = {Chen, A and Yu, S and Yang, X and Huang, D and Ren, Y},
title = {IoT data security in outsourced databases: A survey of verifiable database.},
journal = {Heliyon},
volume = {10},
number = {7},
pages = {e28117},
pmid = {38586319},
issn = {2405-8440},
abstract = {With the swift advancement of cloud computing and the Internet of Things (IoT), to address the issue of massive data storage, IoT devices opt to offload their data to cloud servers so as to alleviate the pressure of resident storage and computation. However, storing local data in an outsourced database is bound to face the danger of tampering. To handle the above problem, a verifiable database (VDB), which was initially suggested in 2011, has garnered sustained interest from researchers. The concept of VDB enables resource-limited clients to securely outsource extremely large databases to untrusted servers, where users can retrieve database records and modify them by allocating new values, and any attempts at tampering will be detected. This paper provides a systematic summary of VDB. First, a definition of VDB is given, along with correctness and security proofs. And the VDB based on commitment constructions is introduced separately, mainly divided into vector commitments and polynomial commitments. Then VDB schemes based on delegated polynomial functions are introduced, mainly in combination with Merkle trees and forward-secure symmetric searchable encryption. We then classify the current VDB schemes relying on four different assumptions. Besides, we classify the established VDB schemes built upon two different groups. Finally, we introduce the applications and future development of VDB. To our knowledge, this is the first VDB review paper to date.},
}

@article {pmid38585837,
year = {2024},
author = {Mimar, S and Paul, AS and Lucarelli, N and Border, S and Santo, BA and Naglah, A and Barisoni, L and Hodgin, J and Rosenberg, AZ and Clapp, W and Sarder, P and , },
title = {ComPRePS: An Automated Cloud-based Image Analysis tool to democratize AI in Digital Pathology.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {38585837},
support = {R01 DK118431/DK/NIDDK NIH HHS/United States ; R21 DK128668/DK/NIDDK NIH HHS/United States ; R01 DK114485/DK/NIDDK NIH HHS/United States ; U01 DK133090/DK/NIDDK NIH HHS/United States ; R01 DK129541/DK/NIDDK NIH HHS/United States ; OT2 OD033753/OD/NIH HHS/United States ; },
abstract = {Artificial intelligence (AI) has extensive applications in a wide range of disciplines including healthcare and clinical practice. Advances in high-resolution whole-slide brightfield microscopy allow for the digitization of histologically stained tissue sections, producing gigapixel-scale whole-slide images (WSI). The significant improvement in computing and revolution of deep neural network (DNN)-based AI technologies over the last decade allow us to integrate massively parallelized computational power, cutting-edge AI algorithms, and big data storage, management, and processing. Applied to WSIs, AI has created opportunities for improved disease diagnostics and prognostics with the ultimate goal of enhancing precision medicine and resulting patient care. The National Institutes of Health (NIH) has recognized the importance of developing standardized principles for data management and discovery for the advancement of science and proposed the Findable, Accessible, Interoperable, Reusable, (FAIR) Data Principles[1] with the goal of building a modernized biomedical data resource ecosystem to establish collaborative research communities. In line with this mission and to democratize AI-based image analysis in digital pathology, we propose ComPRePS: an end-to-end automated Computational Renal Pathology Suite which combines massive scalability, on-demand cloud computing, and an easy-to-use web-based user interface for data upload, storage, management, slide-level visualization, and domain expert interaction. Moreover, our platform is equipped with both in-house and collaborator developed sophisticated AI algorithms in the back-end server for image analysis to identify clinically relevant micro-anatomic functional tissue units (FTU) and to extract image features.},
}

@article {pmid38584872,
year = {2024},
author = {Copeland, CJ and Roddy, JW and Schmidt, AK and Secor, PR and Wheeler, TJ},
title = {VIBES: a workflow for annotating and visualizing viral sequences integrated into bacterial genomes.},
journal = {NAR genomics and bioinformatics},
volume = {6},
number = {2},
pages = {lqae030},
pmid = {38584872},
issn = {2631-9268},
support = {R01 AI138981/AI/NIAID NIH HHS/United States ; R01 GM132600/GM/NIGMS NIH HHS/United States ; },
abstract = {Bacteriophages are viruses that infect bacteria. Many bacteriophages integrate their genomes into the bacterial chromosome and become prophages. Prophages may substantially burden or benefit host bacteria fitness, acting in some cases as parasites and in others as mutualists. Some prophages have been demonstrated to increase host virulence. The increasing ease of bacterial genome sequencing provides an opportunity to deeply explore prophage prevalence and insertion sites. Here we present VIBES (Viral Integrations in Bacterial genomES), a workflow intended to automate prophage annotation in complete bacterial genome sequences. VIBES provides additional context to prophage annotations by annotating bacterial genes and viral proteins in user-provided bacterial and viral genomes. The VIBES pipeline is implemented as a Nextflow-driven workflow, providing a simple, unified interface for execution on local, cluster and cloud computing environments. For each step of the pipeline, a container including all necessary software dependencies is provided. VIBES produces results in simple tab-separated format and generates intuitive and interactive visualizations for data exploration. Despite VIBES's primary emphasis on prophage annotation, its generic alignment-based design allows it to be deployed as a general-purpose sequence similarity search manager. We demonstrate the utility of the VIBES prophage annotation workflow by searching for 178 Pf phage genomes across 1072 Pseudomonas spp. genomes.},
}

@article {pmid38578775,
year = {2024},
author = {Nawaz Tareen, F and Alvi, AN and Alsamani, B and Alkhathami, M and Alsadie, D and Alosaimi, N},
title = {EOTE-FSC: An efficient offloaded task execution for fog enabled smart cities.},
journal = {PloS one},
volume = {19},
number = {4},
pages = {e0298363},
pmid = {38578775},
issn = {1932-6203},
mesh = {Cities ; *Algorithms ; *Communication ; Health Facilities ; Information Science ; },
abstract = {Smart cities provide ease in lifestyle to their community members with the help of Information and Communication Technology (ICT). It provides better water, waste and energy management, enhances the security and safety of its citizens and offers better health facilities. Most of these applications are based on IoT-based sensor networks, that are deployed in different areas of applications according to their demand. Due to limited processing capabilities, sensor nodes cannot process multiple tasks simultaneously and need to offload some of their tasks to remotely placed cloud servers, which may cause delays. To reduce the delay, computing nodes are placed in different vicinitys acting as fog-computing nodes are used, to execute the offloaded tasks. It has been observed that the offloaded tasks are not uniformly received by fog computing nodes and some fog nodes may receive more tasks as some may receive less number of tasks. This may cause an increase in overall task execution time. Furthermore, these tasks comprise different priority levels and must be executed before their deadline. In this work, an Efficient Offloaded Task Execution for Fog enabled Smart cities (EOTE - FSC) is proposed. EOTE - FSC proposes a load balancing mechanism by modifying the greedy algorithm to efficiently distribute the offloaded tasks to its attached fog nodes to reduce the overall task execution time. This results in the successful execution of most of the tasks within their deadline. In addition, EOTE - FSC modifies the task sequencing with a deadline algorithm for the fog node to optimally execute the offloaded tasks in such a way that most of the high-priority tasks are entertained. The load balancing results of EOTE - FSC are compared with state-of-the-art well-known Round Robin, Greedy, Round Robin with longest job first, and Round Robin with shortest job first algorithms. However, fog computing results of EOTE - FSC are compared with the First Come First Serve algorithm. The results show that the EOTE - FSC effectively offloaded the tasks on fog nodes and the maximum load on the fog computing nodes is reduced up to 29%, 27.3%, 23%, and 24.4% as compared to Round Robin, Greedy, Round Robin with LJF and Round Robin with SJF algorithms respectively. However, task execution in the proposed EOTE - FSC executes a maximum number of offloaded high-priority tasks as compared to the FCFS algorithm within the same computing capacity of fog nodes.},
}

Cities
*Algorithms
*Communication
Health Facilities
Information Science

@article {pmid38568312,
year = {2024},
author = {Khan, NS and Roy, SK and Talukdar, S and Billah, M and Iqbal, A and Zzaman, RU and Chowdhury, A and Mahtab, SB and Mallick, J},
title = {Empowering real-time flood impact assessment through the integration of machine learning and Google Earth Engine: a comprehensive approach.},
journal = {Environmental science and pollution research international},
volume = {},
number = {},
pages = {},
pmid = {38568312},
issn = {1614-7499},
abstract = {Floods cause substantial losses to life and property, especially in flood-prone regions like northwestern Bangladesh. Timely and precise evaluation of flood impacts is critical for effective flood management and decision-making. This research demonstrates an integrated approach utilizing machine learning and Google Earth Engine to enable real-time flood assessment. Synthetic aperture radar (SAR) data from Sentinel-1 and the Google Earth Engine platform were employed to generate near real-time flood maps of the 2020 flood in Kurigram and Lalmonirhat. An automatic thresholding technique quantified flooded areas. For land use/land cover (LULC) analysis, Sentinel-2's high resolution and machine learning models like artificial neural networks (ANN), random forests (RF) and support vector machines (SVM) were leveraged. ANN delivered the best LULC mapping with 0.94 accuracy based on metrics like accuracy, kappa, mean F1 score, mean sensitivity, mean specificity, mean positive predictive value, mean negative value, mean precision, mean recall, mean detection rate and mean balanced accuracy. Results showed over 600,000 people exposed at peak inundation in July-about 17% of the population. The machine learning-enabled LULC maps reliably identified vulnerable areas to prioritize flood management. Over half of croplands flooded in July. This research demonstrates the potential of integrating SAR, machine learning and cloud computing to empower authorities through real-time monitoring and accurate LULC mapping essential for effective flood response. The proposed comprehensive methodology can assist stakeholders in developing data-driven flood management strategies to reduce impacts.},
}

@article {pmid38560228,
year = {2024},
author = {Gheni, HM and AbdulRahaim, LA and Abdellatif, A},
title = {Real-time driver identification in IoV: A deep learning and cloud integration approach.},
journal = {Heliyon},
volume = {10},
number = {7},
pages = {e28109},
pmid = {38560228},
issn = {2405-8440},
abstract = {The Internet of Vehicles (IoV) emerges as a pivotal extension of the Internet of Things (IoT), specifically geared towards transforming the automotive landscape. In this evolving ecosystem, the demand for a seamless end-to-end system becomes paramount for enhancing operational efficiency and safety. Hence, this study introduces an innovative method for real-time driver identification by integrating cloud computing with deep learning. Utilizing the integrated capabilities of Google Cloud, Thingsboard, and Apache Kafka, the developed solution tailored for IoV technology is adept at managing real-time data collection, processing, prediction, and visualization, with resilience against sensor data anomalies. Also, this research suggests an appropriate method for driver identification by utilizing a combination of Convolutional Neural Networks (CNN) and multi-head self-attention in the proposed approach. The proposed model is validated on two datasets: Security and collected. Moreover, the results show that the proposed model surpassed the previous works by achieving an accuracy and F1 score of 99.95%. Even when challenged with data anomalies, this model maintains a high accuracy of 96.2%. By achieving accurate driver identification results, the proposed end-to-end IoV system can aid in optimizing fleet management, vehicle security, personalized driving experiences, insurance, and risk assessment. This emphasizes its potential for road safety and managing transportation more effectively.},
}

@article {pmid38559152,
year = {2024},
author = {Li, Y and Xue, F and Li, B and Yang, Y and Fan, Z and Shu, J and Yang, X and Wang, X and Lin, J and Copana, C and Zhao, B},
title = {Analyzing bivariate cross-trait genetic architecture in GWAS summary statistics with the BIGA cloud computing platform.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {38559152},
abstract = {As large-scale biobanks provide increasing access to deep phenotyping and genomic data, genome-wide association studies (GWAS) are rapidly uncovering the genetic architecture behind various complex traits and diseases. GWAS publications typically make their summary-level data (GWAS summary statistics) publicly available, enabling further exploration of genetic overlaps between phenotypes gathered from different studies and cohorts. However, systematically analyzing high-dimensional GWAS summary statistics for thousands of phenotypes can be both logistically challenging and computationally demanding. In this paper, we introduce BIGA (https://bigagwas.org/), a website that aims to offer unified data analysis pipelines and processed data resources for cross-trait genetic architecture analyses using GWAS summary statistics. We have developed a framework to implement statistical genetics tools on a cloud computing platform, combined with extensive curated GWAS data resources. Through BIGA, users can upload data, submit jobs, and share results, providing the research community with a convenient tool for consolidating GWAS data and generating new insights.},
}

@article {pmid38559026,
year = {2024},
author = {Marini, S and Barquero, A and Wadhwani, AA and Bian, J and Ruiz, J and Boucher, C and Prosperi, M},
title = {OCTOPUS: Disk-based, Multiplatform, Mobile-friendly Metagenomics Classifier.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {38559026},
support = {R01 AI141810/AI/NIAID NIH HHS/United States ; R01 AI145552/AI/NIAID NIH HHS/United States ; R01 AI170187/AI/NIAID NIH HHS/United States ; },
abstract = {Portable genomic sequencers such as Oxford Nanopore's MinION enable real-time applications in both clinical and environmental health, e.g., detection of bacterial outbreaks. However, there is a bottleneck in the downstream analytics when bioinformatics pipelines are unavailable, e.g., when cloud processing is unreachable due to absence of Internet connection, or only low-end computing devices can be carried on site. For instance, metagenomics classifiers usually require a large amount of memory or specific operating systems/libraries. In this work, we present a platform-friendly software for portable metagenomic analysis of Nanopore data, the Oligomer-based Classifier of Taxonomic Operational and Pan-genome Units via Singletons (OCTOPUS). OCTOPUS is written in Java, reimplements several features of the popular Kraken2 and KrakenUniq software, with original components for improving metagenomics classification on incomplete/sampled reference databases (e.g., selection of bacteria of public health priority), making it ideal for running on smartphones or tablets. We indexed both OCTOPUS and Kraken2 on a bacterial database with ~4,000 reference genomes, then simulated a positive (bacterial genomes from the same species, but different genomes) and two negative (viral, mammalian) Nanopore test sets. On the bacterial test set OCTOPUS yielded sensitivity and precision comparable to Kraken2 (94.4% and 99.8% versus 94.5% and 99.1%, respectively). On non-bacterial sequences (mammals and viral), OCTOPUS dramatically decreased (4- to 16-fold) the false positive rate when compared to Kraken2 (2.1% and 0.7% versus 8.2% and 11.2%, respectively). We also developed customized databases including viruses, and the World Health Organization's set of bacteria of concern for drug resistance, tested with real Nanopore data on an Android smartphone. OCTOPUS is publicly available at https://github.com/DataIntellSystLab/OCTOPUS and https://github.com/Ruiz-HCI-Lab/OctopusMobile.},
}

@article {pmid38555378,
year = {2024},
author = {Du, J and Dong, G and Ning, J and Xu, Z and Yang, R},
title = {Identity-based controlled delegated outsourcing data integrity auditing scheme.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {7582},
pmid = {38555378},
issn = {2045-2322},
support = {2023SKY007//Yunnan Minzu University Graduate Research Innovation Fund Project/ ; 61662089//National Natural Science Foundation of China/ ; },
abstract = {With the continuous development of cloud computing, the application of cloud storage has become more and more popular. To ensure the integrity and availability of cloud data, scholars have proposed several cloud data auditing schemes. Still, most need help with outsourced data integrity, controlled outsourcing, and source file auditing. Therefore, we propose a controlled delegation outsourcing data integrity auditing scheme based on the identity-based encryption model. Our proposed scheme allows users to specify a dedicated agent to assist in uploading data to the cloud. These authorized proxies use recognizable identities for authentication and authorization, thus avoiding the need for cumbersome certificate management in a secure distributed computing system. While solving the above problems, our scheme adopts a bucket-based red-black tree structure to efficiently realize the dynamic updating of data, which can complete the updating of data and rebalancing of structural updates constantly and realize the high efficiency of data operations. We define the security model of the scheme in detail and prove the scheme's security under the difficult problem assumption. In the performance analysis section, the proposed scheme is analyzed experimentally in comparison with other schemes, and the results show that the proposed scheme is efficient and secure.},
}

@article {pmid38546988,
year = {2024},
author = {Chen, X and Xu, G and Xu, X and Jiang, H and Tian, Z and Ma, T},
title = {Multicenter Hierarchical Federated Learning With Fault-Tolerance Mechanisms for Resilient Edge Computing Networks.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2024.3362974},
pmid = {38546988},
issn = {2162-2388},
abstract = {In the realm of federated learning (FL), the conventional dual-layered architecture, comprising a central parameter server and peripheral devices, often encounters challenges due to its significant reliance on the central server for communication and security. This dependence becomes particularly problematic in scenarios involving potential malfunctions of devices and servers. While existing device-edge-cloud hierarchical FL (HFL) models alleviate some dependence on central servers and reduce communication overheads, they primarily focus on load balancing within edge computing networks and fall short of achieving complete decentralization and edge-centric model aggregation. Addressing these limitations, we introduce the multicenter HFL (MCHFL) framework. This innovative framework replaces the traditional single central server architecture with a distributed network of robust global aggregation centers located at the edge, inherently enhancing fault tolerance crucial for maintaining operational integrity amidst edge network disruptions. Our comprehensive experiments with the MNIST, FashionMNIST, and CIFAR-10 datasets demonstrate the MCHFL's superior performance. Notably, even under high paralysis ratios of up to 50%, the MCHFL maintains high accuracy levels, with maximum accuracy reductions of only 2.60%, 5.12%, and 16.73% on these datasets, respectively. This performance significantly surpasses the notable accuracy declines observed in traditional single-center models under similar conditions. To the best of our knowledge, the MCHFL is the first edge multicenter FL framework with theoretical underpinnings. Our extensive experimental results across various datasets validate the MCHFL's effectiveness, showcasing its higher accuracy, faster convergence speed, and stronger robustness compared to single-center models, thereby establishing it as a pioneering paradigm in edge multicenter FL.},
}

@article {pmid38545518,
year = {2024},
author = {Lock, C and Toh, EMS and Keong, NC},
title = {Structural volumetric and Periodic Table DTI patterns in Complex Normal Pressure Hydrocephalus-Toward the principles of a translational taxonomy.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1188533},
pmid = {38545518},
issn = {1662-5161},
abstract = {INTRODUCTION: We previously proposed a novel taxonomic framework to describe the diffusion tensor imaging (DTI) profiles of white matter tracts by their diffusivity and neural properties. We have shown the relevance of this strategy toward interpreting brain tissue signatures in Classic Normal Pressure Hydrocephalus vs. comparator cohorts of mild traumatic brain injury and Alzheimer's disease. In this iteration of the Periodic Table of DTI Elements, we examined patterns of tissue distortion in Complex NPH (CoNPH) and validated the methodology against an open-access dataset of healthy subjects, to expand its accessibility to a larger community.

METHODS: DTI measures for 12 patients with CoNPH with multiple comorbidities and 45 cognitively normal controls from the ADNI database were derived using the image processing pipeline on the brainlife.io open cloud computing platform. Using the Periodic Table algorithm, DTI profiles for CoNPH vs. controls were mapped according to injury patterns.

RESULTS: Structural volumes in most structures tested were significantly lower and the lateral ventricles higher in CoNPH vs. controls. In CoNPH, significantly lower fractional anisotropy (FA) and higher mean, axial, and radial diffusivities (MD, L1, and L2 and 3, respectively) were observed in white matter related to the lateral ventricles. Most diffusivity measures across supratentorial and infratentorial structures were significantly higher in CoNPH, with the largest differences in the cerebellum cortex. In subcortical deep gray matter structures, CoNPH and controls differed most significantly in the hippocampus, with the CoNPH group having a significantly lower FA and higher MD, L1, and L2 and 3. Cerebral and cerebellar white matter demonstrated more potential reversibility of injury compared to cerebral and cerebellar cortices.

DISCUSSION: The findings of widespread and significant reductions in subcortical deep gray matter structures, in comparison to healthy controls, support the hypothesis that Complex NPH cohorts retain imaging features associated with Classic NPH. The use of the algorithm of the Periodic Table allowed for greater consistency in the interpretation of DTI results by focusing on patterns of injury rather than an over-reliance on the interrogation of individual measures by statistical significance alone. Our aim is to provide a prototype that could be refined for an approach toward the concept of a "translational taxonomy."},
}

@article {pmid38544154,
year = {2024},
author = {Kang, S and Lee, S and Jung, Y},
title = {Design of Network-on-Chip-Based Restricted Coulomb Energy Neural Network Accelerator on FPGA Device.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {6},
pages = {},
pmid = {38544154},
issn = {1424-8220},
support = {00144288, 00144290//Ministry of Trade, Industry and Energy/ ; },
abstract = {Sensor applications in internet of things (IoT) systems, coupled with artificial intelligence (AI) technology, are becoming an increasingly significant part of modern life. For low-latency AI computation in IoT systems, there is a growing preference for edge-based computing over cloud-based alternatives. The restricted coulomb energy neural network (RCE-NN) is a machine learning algorithm well-suited for implementation on edge devices due to its simple learning and recognition scheme. In addition, because the RCE-NN generates neurons as needed, it is easy to adjust the network structure and learn additional data. Therefore, the RCE-NN can provide edge-based real-time processing for various sensor applications. However, previous RCE-NN accelerators have limited scalability when the number of neurons increases. In this paper, we propose a network-on-chip (NoC)-based RCE-NN accelerator and present the results of implementation on a field-programmable gate array (FPGA). NoC is an effective solution for managing massive interconnections. The proposed RCE-NN accelerator utilizes a hierarchical-star (H-star) topology, which efficiently handles a large number of neurons, along with routers specifically designed for the RCE-NN. These approaches result in only a slight decrease in the maximum operating frequency as the number of neurons increases. Consequently, the maximum operating frequency of the proposed RCE-NN accelerator with 512 neurons increased by 126.1% compared to a previous RCE-NN accelerator. This enhancement was verified with two datasets for gas and sign language recognition, achieving accelerations of up to 54.8% in learning time and up to 45.7% in recognition time. The NoC scheme of the proposed RCE-NN accelerator is an appropriate solution to ensure the scalability of the neural network while providing high-performance on-chip learning and recognition.},
}

@article {pmid38544035,
year = {2024},
author = {Zhan, Y and Xie, W and Shi, R and Huang, Y and Zheng, X},
title = {Dynamic Privacy-Preserving Anonymous Authentication Scheme for Condition-Matching in Fog-Cloud-Based VANETs.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {6},
pages = {},
pmid = {38544035},
issn = {1424-8220},
support = {61872091//National Natural Science Foundation of China/ ; JCKY20 19102C001//National Defense Basic Research Program of China/ ; 62372110//National Natural Science Foundation of China/ ; 2023J02008//Fujian Provincial Natural Science of Foundation/ ; 2020B0101090005//Key-Area Research and Development Program of Guangdong Province/ ; YSPTZX202145//The specific research fund of The Innovation Platform for Academician of Hainan Province/ ; 2022HZ022022//Major Special Project for Industrial Science and Technology in Fujian Province/ ; 2022H0012//Industrial Guiding Project in Fujian/ ; 2022L3003//Special Project of Central Finance Guiding Local Development/ ; },
abstract = {Secure group communication in Vehicle Ad hoc Networks (VANETs) over open channels remains a challenging task. To enable secure group communications with conditional privacy, it is necessary to establish a secure session using Authenticated Key Agreement (AKA). However, existing AKAs suffer from problems such as cross-domain dynamic group session key negotiation and heavy computational burdens on the Trusted Authority (TA) and vehicles. To address these challenges, we propose a dynamic privacy-preserving anonymous authentication scheme for condition matching in fog-cloud-based VANETs. The scheme employs general Elliptic Curve Cryptosystem (ECC) technology and fog-cloud computing methods to decrease computational overhead for On-Board Units (OBUs) and supports multiple TAs for improved service quality and robustness. Furthermore, certificateless technology alleviates TAs of key management burdens. The security analysis indicates that our solution satisfies the communication security and privacy requirements. Experimental simulations verify that our method achieves optimal overall performance with lower computational costs and smaller communication overhead compared to state-of-the-art solutions.},
}

@article {pmid38540411,
year = {2024},
author = {Yuan, DY and Park, JH and Li, Z and Thomas, R and Hwang, DM and Fu, L},
title = {A New Cloud-Native Tool for Pharmacogenetic Analysis.},
journal = {Genes},
volume = {15},
number = {3},
pages = {},
pmid = {38540411},
issn = {2073-4425},
support = {LMMD Strategic Innovation Fund//Sunnybrook Health Sciences Centre/ ; },
mesh = {Humans ; *Pharmacogenomic Testing ; *Pharmacogenetics/methods ; High-Throughput Nucleotide Sequencing/methods ; Genomics/methods ; Computational Biology ; },
abstract = {BACKGROUND: The advancement of next-generation sequencing (NGS) technologies provides opportunities for large-scale Pharmacogenetic (PGx) studies and pre-emptive PGx testing to cover a wide range of genotypes present in diverse populations. However, NGS-based PGx testing is limited by the lack of comprehensive computational tools to support genetic data analysis and clinical decisions.

METHODS: Bioinformatics utilities specialized for human genomics and the latest cloud-based technologies were used to develop a bioinformatics pipeline for analyzing the genomic sequence data and reporting PGx genotypes. A database was created and integrated in the pipeline for filtering the actionable PGx variants and clinical interpretations. Strict quality verification procedures were conducted on variant calls with the whole genome sequencing (WGS) dataset of the 1000 Genomes Project (G1K). The accuracy of PGx allele identification was validated using the WGS dataset of the Pharmacogenetics Reference Materials from the Centers for Disease Control and Prevention (CDC).

RESULTS: The newly created bioinformatics pipeline, Pgxtools, can analyze genomic sequence data, identify actionable variants in 13 PGx relevant genes, and generate reports annotated with specific interpretations and recommendations based on clinical practice guidelines. Verified with two independent methods, we have found that Pgxtools consistently identifies variants more accurately than the results in the G1K dataset on GRCh37 and GRCh38.

CONCLUSIONS: Pgxtools provides an integrated workflow for large-scale genomic data analysis and PGx clinical decision support. Implemented with cloud-native technologies, it is highly portable in a wide variety of environments from a single laptop to High-Performance Computing (HPC) clusters and cloud platforms for different production scales and requirements.},
}

Humans
*Pharmacogenomic Testing
*Pharmacogenetics/methods
High-Throughput Nucleotide Sequencing/methods
Genomics/methods
Computational Biology

@article {pmid38535044,
year = {2024},
author = {Kukkar, A and Kumar, Y and Sandhu, JK and Kaur, M and Walia, TS and Amoon, M},
title = {DengueFog: A Fog Computing-Enabled Weighted Random Forest-Based Smart Health Monitoring System for Automatic Dengue Prediction.},
journal = {Diagnostics (Basel, Switzerland)},
volume = {14},
number = {6},
pages = {},
pmid = {38535044},
issn = {2075-4418},
abstract = {Dengue is a distinctive and fatal infectious disease that spreads through female mosquitoes called Aedes aegypti. It is a notable concern for developing countries due to its low diagnosis rate. Dengue has the most astounding mortality level as compared to other diseases due to tremendous platelet depletion. Hence, it can be categorized as a life-threatening fever as compared to the same class of fevers. Additionally, it has been shown that dengue fever shares many of the same symptoms as other flu-based fevers. On the other hand, the research community is closely monitoring the popular research fields related to IoT, fog, and cloud computing for the diagnosis and prediction of diseases. IoT, fog, and cloud-based technologies are used for constructing a number of health care systems. Accordingly, in this study, a DengueFog monitoring system was created based on fog computing for prediction and detection of dengue sickness. Additionally, the proposed DengueFog system includes a weighted random forest (WRF) classifier to monitor and predict the dengue infection. The proposed system's efficacy was evaluated using data on dengue infection. This dataset was gathered between 2016 and 2018 from several hospitals in the Delhi-NCR region. The accuracy, F-value, recall, precision, error rate, and specificity metrics were used to assess the simulation results of the suggested monitoring system. It was demonstrated that the proposed DengueFog monitoring system with WRF outperforms the traditional classifiers.},
}

@article {pmid38531975,
year = {2024},
author = {Ali, I and Wassif, K and Bayomi, H},
title = {Dimensionality reduction for images of IoT using machine learning.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {7205},
pmid = {38531975},
issn = {2045-2322},
abstract = {Sensors, wearables, mobile devices, and other Internet of Things (IoT) devices are becoming increasingly integrated into all aspects of our lives. They are capable of gathering enormous amounts of data, such as image data, which can then be sent to the cloud for processing. However, this results in an increase in network traffic and latency. To overcome these difficulties, edge computing has been proposed as a paradigm for computing that brings processing closer to the location where data is produced. This paper explores the merging of cloud and edge computing for IoT and investigates approaches using machine learning for dimensionality reduction of images on the edge, employing the autoencoder deep learning-based approach and principal component analysis (PCA). The encoded data is then sent to the cloud server, where it is used directly for any machine learning task without significantly impacting the accuracy of the data processed in the cloud. The proposed approach has been evaluated on an object detection task using a set of 4000 images randomly chosen from three datasets: COCO, human detection, and HDA datasets. Results show that a 77% reduction in data did not have a significant impact on the object detection task's accuracy.},
}

@article {pmid38531933,
year = {2024},
author = {Huettmann, F and Andrews, P and Steiner, M and Das, AK and Philip, J and Mi, C and Bryans, N and Barker, B},
title = {A super SDM (species distribution model) 'in the cloud' for better habitat-association inference with a 'big data' application of the Great Gray Owl for Alaska.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {7213},
pmid = {38531933},
issn = {2045-2322},
support = {-EWHALE lab//University of Alaska Fairbanks/ ; -EWHALE lab//University of Alaska Fairbanks/ ; },
abstract = {The currently available distribution and range maps for the Great Grey Owl (GGOW; Strix nebulosa) are ambiguous, contradictory, imprecise, outdated, often hand-drawn and thus not quantified, not based on data or scientific. In this study, we present a proof of concept with a biological application for technical and biological workflow progress on latest global open access 'Big Data' sharing, Open-source methods of R and geographic information systems (OGIS and QGIS) assessed with six recent multi-evidence citizen-science sightings of the GGOW. This proposed workflow can be applied for quantified inference for any species-habitat model such as typically applied with species distribution models (SDMs). Using Random Forest-an ensemble-type model of Machine Learning following Leo Breiman's approach of inference from predictions-we present a Super SDM for GGOWs in Alaska running on Oracle Cloud Infrastructure (OCI). These Super SDMs were based on best publicly available data (410 occurrences + 1% new assessment sightings) and over 100 environmental GIS habitat predictors ('Big Data'). The compiled global open access data and the associated workflow overcome for the first time the limitations of traditionally used PC and laptops. It breaks new ground and has real-world implications for conservation and land management for GGOW, for Alaska, and for other species worldwide as a 'new' baseline. As this research field remains dynamic, Super SDMs can have limits, are not the ultimate and final statement on species-habitat associations yet, but they summarize all publicly available data and information on a topic in a quantified and testable fashion allowing fine-tuning and improvements as needed. At minimum, they allow for low-cost rapid assessment and a great leap forward to be more ecological and inclusive of all information at-hand. Using GGOWs, here we aim to correct the perception of this species towards a more inclusive, holistic, and scientifically correct assessment of this urban-adapted owl in the Anthropocene, rather than a mysterious wilderness-inhabiting species (aka 'Phantom of the North'). Such a Super SDM was never created for any bird species before and opens new perspectives for impact assessment policy and global sustainability.},
}

@article {pmid38528619,
year = {2024},
author = {Budge, J and Carrell, T and Yaqub, M and Wafa, H and Waltham, M and Pilecka, I and Kelly, J and Murphy, C and Palmer, S and Wang, Y and Clough, RE},
title = {The ARIA trial protocol: a randomised controlled trial to assess the clinical, technical, and cost-effectiveness of a cloud-based, ARtificially Intelligent image fusion system in comparison to standard treatment to guide endovascular Aortic aneurysm repair.},
journal = {Trials},
volume = {25},
number = {1},
pages = {214},
pmid = {38528619},
issn = {1745-6215},
support = {NIHR201004//Invention for Innovation Programme/ ; },
mesh = {Humans ; *Aortic Aneurysm, Abdominal/diagnostic imaging/surgery ; Cost-Benefit Analysis ; Cloud Computing ; *Endovascular Procedures/methods ; *Blood Vessel Prosthesis Implantation/adverse effects ; Treatment Outcome ; Retrospective Studies ; Randomized Controlled Trials as Topic ; Multicenter Studies as Topic ; },
abstract = {BACKGROUND: Endovascular repair of aortic aneurysmal disease is established due to perceived advantages in patient survival, reduced postoperative complications, and shorter hospital lengths of stay. High spatial and contrast resolution 3D CT angiography images are used to plan the procedures and inform device selection and manufacture, but in standard care, the surgery is performed using image-guidance from 2D X-ray fluoroscopy with injection of nephrotoxic contrast material to visualise the blood vessels. This study aims to assess the benefit to patients, practitioners, and the health service of a novel image fusion medical device (Cydar EV), which allows this high-resolution 3D information to be available to operators at the time of surgery.

METHODS: The trial is a multi-centre, open label, two-armed randomised controlled clinical trial of 340 patient, randomised 1:1 to either standard treatment in endovascular aneurysm repair or treatment using Cydar EV, a CE-marked medical device comprising of cloud computing, augmented intelligence, and computer vision. The primary outcome is procedural time, with secondary outcomes of procedural efficiency, technical effectiveness, patient outcomes, and cost-effectiveness. Patients with a clinical diagnosis of AAA or TAAA suitable for endovascular repair and able to provide written informed consent will be invited to participate.

DISCUSSION: This trial is the first randomised controlled trial evaluating advanced image fusion technology in endovascular aortic surgery and is well placed to evaluate the effect of this technology on patient outcomes and cost to the NHS.

TRIAL REGISTRATION: ISRCTN13832085. Dec. 3, 2021.},
}

Humans
*Aortic Aneurysm, Abdominal/diagnostic imaging/surgery
Cost-Benefit Analysis
Cloud Computing
*Endovascular Procedures/methods
*Blood Vessel Prosthesis Implantation/adverse effects
Treatment Outcome
Retrospective Studies
Randomized Controlled Trials as Topic
Multicenter Studies as Topic

@article {pmid38528564,
year = {2024},
author = {Zhang, S and Li, H and Jing, Q and Shen, W and Luo, W and Dai, R},
title = {Anesthesia decision analysis using a cloud-based big data platform.},
journal = {European journal of medical research},
volume = {29},
number = {1},
pages = {201},
pmid = {38528564},
issn = {2047-783X},
support = {2022JJ70061//Natural Science Foundation of Hunan Province/ ; 22A0011//Key Fund Project of Hunan Provincial Department of Education/ ; W20243113//Health Commission of Hunan Province/ ; 82103641 and 82071347//National Natural Science Foundation of China/ ; },
mesh = {Humans ; Big Data ; *Anesthesiology ; Cloud Computing ; *Anesthesia ; *Anesthetics ; Decision Support Techniques ; },
abstract = {Big data technologies have proliferated since the dawn of the cloud-computing era. Traditional data storage, extraction, transformation, and analysis technologies have thus become unsuitable for the large volume, diversity, high processing speed, and low value density of big data in medical strategies, which require the development of novel big data application technologies. In this regard, we investigated the most recent big data platform breakthroughs in anesthesiology and designed an anesthesia decision model based on a cloud system for storing and analyzing massive amounts of data from anesthetic records. The presented Anesthesia Decision Analysis Platform performs distributed computing on medical records via several programming tools, and provides services such as keyword search, data filtering, and basic statistics to reduce inaccurate and subjective judgments by decision-makers. Importantly, it can potentially to improve anesthetic strategy and create individualized anesthesia decisions, lowering the likelihood of perioperative complications.},
}

Humans
Big Data
*Anesthesiology
Cloud Computing
*Anesthesia
*Anesthetics
Decision Support Techniques

@article {pmid38524844,
year = {2024},
author = {Mukuka, A},
title = {Data on mathematics teacher educators' proficiency and willingness to use technology: A structural equation modelling analysis.},
journal = {Data in brief},
volume = {54},
number = {},
pages = {110307},
pmid = {38524844},
issn = {2352-3409},
abstract = {The role of Mathematics Teacher Educators (MTEs) in preparing future teachers to effectively integrate technology into their mathematics instruction is of paramount importance yet remains an underexplored domain. Technology has the potential to enhance the development of 21st-century skills, such as problem-solving and critical thinking, which are essential for students in the era of the fourth industrial revolution. However, the rapid evolution of technology and the emergence of new trends like data analytics, the Internet of Things, machine learning, cloud computing, and artificial intelligence present new challenges in the realm of mathematics teaching and learning. Consequently, MTEs need to equip prospective teachers with the knowledge and skills to harness technology in innovative ways within their future mathematics classrooms. This paper presents and describes data from a survey of 104 MTEs in Zambia. The study focuses on MTEs' proficiency, perceived usefulness, perceived ease of use, and willingness to incorporate technology in their classrooms. This data-driven article aims to unveil patterns and trends within the dataset, with the objective of offering insights rather than drawing definitive conclusions. The article also highlights the data collection process and outlines the procedure for assessing the measurement model of the hypothesised relationships among variables through structural equation modelling analysis. The data described in this article not only sheds light on the current landscape but also serves as a valuable resource for mathematics teacher training institutions and other stakeholders seeking to understand the requisites for MTEs to foster technological skills among prospective teachers of mathematics.},
}

@article {pmid38520921,
year = {2024},
author = {Tadi, AA and Alhadidi, D and Rueda, L},
title = {PPPCT: Privacy-Preserving framework for Parallel Clustering Transcriptomics data.},
journal = {Computers in biology and medicine},
volume = {173},
number = {},
pages = {108351},
doi = {10.1016/j.compbiomed.2024.108351},
pmid = {38520921},
issn = {1879-0534},
mesh = {Humans ; *Privacy ; *Software ; Algorithms ; Gene Expression Profiling ; Cluster Analysis ; Sequence Analysis, RNA ; },
abstract = {Single-cell transcriptomics data provides crucial insights into patients' health, yet poses significant privacy concerns. Genomic data privacy attacks can have deep implications, encompassing not only the patients' health information but also extending widely to compromise their families'. Moreover, the permanence of leaked data exacerbates the challenges, making retraction an impossibility. While extensive efforts have been directed towards clustering single-cell transcriptomics data, addressing critical challenges, especially in the realm of privacy, remains pivotal. This paper introduces an efficient, fast, privacy-preserving approach for clustering single-cell RNA-sequencing (scRNA-seq) datasets. The key contributions include ensuring data privacy, achieving high-quality clustering, accommodating the high dimensionality inherent in the datasets, and maintaining reasonable computation time for big-scale datasets. Our proposed approach utilizes the map-reduce scheme to parallelize clustering, addressing intensive calculation challenges. Intel Software Guard eXtension (SGX) processors are used to ensure the security of sensitive code and data during processing. Additionally, the approach incorporates a logarithm transformation as a preprocessing step, employs non-negative matrix factorization for dimensionality reduction, and utilizes parallel k-means for clustering. The approach fully leverages the computing capabilities of all processing resources within a secure private cloud environment. Experimental results demonstrate the efficacy of our approach in preserving patient privacy while surpassing state-of-the-art methods in both clustering quality and computation time. Our method consistently achieves a minimum of 7% higher Adjusted Rand Index (ARI) than existing approaches, contingent on dataset size. Additionally, due to parallel computations and dimensionality reduction, our approach exhibits efficiency, converging to very good results in less than 10 seconds for a scRNA-seq dataset with 5000 genes and 6000 cells when prioritizing privacy and under two seconds without privacy considerations. Availability and implementation Code and datasets availability: https://github.com/University-of-Windsor/PPPCT.},
}

Humans
*Privacy
*Software
Algorithms
Gene Expression Profiling
Cluster Analysis
Sequence Analysis, RNA

@article {pmid38514837,
year = {2024},
author = {Hajiaghabozorgi, M and Fischbach, M and Albrecht, M and Wang, W and Myers, CL},
title = {BridGE: a pathway-based analysis tool for detecting genetic interactions from GWAS.},
journal = {Nature protocols},
volume = {},
number = {},
pages = {},
pmid = {38514837},
issn = {1750-2799},
support = {R21CA235352//U.S. Department of Health & Human Services | NIH | Center for Information Technology (Center for Information Technology, National Institutes of Health)/ ; R01HG005084//U.S. Department of Health & Human Services | NIH | Center for Information Technology (Center for Information Technology, National Institutes of Health)/ ; R01HG005853//U.S. Department of Health & Human Services | NIH | Center for Information Technology (Center for Information Technology, National Institutes of Health)/ ; BAND-19-615151//Weston Brain Institute/ ; },
abstract = {Genetic interactions have the potential to modulate phenotypes, including human disease. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions; however, traditional methods for identifying them, which tend to focus on testing individual variant pairs, lack statistical power. In this protocol, we describe a novel computational approach, called Bridging Gene sets with Epistasis (BridGE), for discovering genetic interactions between biological pathways from GWAS data. We present a Python-based implementation of BridGE along with instructions for its application to a typical human GWAS cohort. The major stages include initial data processing and quality control, construction of a variant-level genetic interaction network, measurement of pathway-level genetic interactions, evaluation of statistical significance using sample permutations and generation of results in a standardized output format. The BridGE software pipeline includes options for running the analysis on multiple cores and multiple nodes for users who have access to computing clusters or a cloud computing environment. In a cluster computing environment with 10 nodes and 100 GB of memory per node, the method can be run in less than 24 h for typical human GWAS cohorts. Using BridGE requires knowledge of running Python programs and basic shell script programming experience.},
}

@article {pmid38506901,
year = {2024},
author = {Sahu, KS and Dubin, JA and Majowicz, SE and Liu, S and Morita, PP},
title = {Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things-Based Thermostat Data and Google Mobility Insights.},
journal = {JMIR public health and surveillance},
volume = {10},
number = {},
pages = {e46903},
pmid = {38506901},
issn = {2369-2960},
mesh = {Humans ; Pandemics ; *Internet of Things ; Search Engine ; *COVID-19/epidemiology ; Alberta/epidemiology ; Health Policy ; },
abstract = {BACKGROUND: The COVID-19 pandemic necessitated public health policies to limit human mobility and curb infection spread. Human mobility, which is often underestimated, plays a pivotal role in health outcomes, impacting both infectious and chronic diseases. Collecting precise mobility data is vital for understanding human behavior and informing public health strategies. Google's GPS-based location tracking, which is compiled in Google Mobility Reports, became the gold standard for monitoring outdoor mobility during the pandemic. However, indoor mobility remains underexplored.

OBJECTIVE: This study investigates in-home mobility data from ecobee's smart thermostats in Canada (February 2020 to February 2021) and compares it directly with Google's residential mobility data. By assessing the suitability of smart thermostat data, we aim to shed light on indoor mobility patterns, contributing valuable insights to public health research and strategies.

METHODS: Motion sensor data were acquired from the ecobee "Donate Your Data" initiative via Google's BigQuery cloud platform. Concurrently, residential mobility data were sourced from the Google Mobility Report. This study centered on 4 Canadian provinces-Ontario, Quebec, Alberta, and British Columbia-during the period from February 15, 2020, to February 14, 2021. Data processing, analysis, and visualization were conducted on the Microsoft Azure platform using Python (Python Software Foundation) and R programming languages (R Foundation for Statistical Computing). Our investigation involved assessing changes in mobility relative to the baseline in both data sets, with the strength of this relationship assessed using Pearson and Spearman correlation coefficients. We scrutinized daily, weekly, and monthly variations in mobility patterns across the data sets and performed anomaly detection for further insights.

RESULTS: The results revealed noteworthy week-to-week and month-to-month shifts in population mobility within the chosen provinces, aligning with pandemic-driven policy adjustments. Notably, the ecobee data exhibited a robust correlation with Google's data set. Examination of Google's daily patterns detected more pronounced mobility fluctuations during weekdays, a trend not mirrored in the ecobee data. Anomaly detection successfully identified substantial mobility deviations coinciding with policy modifications and cultural events.

CONCLUSIONS: This study's findings illustrate the substantial influence of the Canadian stay-at-home and work-from-home policies on population mobility. This impact was discernible through both Google's out-of-house residential mobility data and ecobee's in-house smart thermostat data. As such, we deduce that smart thermostats represent a valid tool for facilitating intelligent monitoring of population mobility in response to policy-driven shifts.},
}

Humans
Pandemics
*Internet of Things
Search Engine
*COVID-19/epidemiology
Alberta/epidemiology
Health Policy

@article {pmid38495592,
year = {2024},
author = {Wang, H and Chen, H and Wang, Y},
title = {Analysis of Hot Topics Regarding Global Smart Elderly Care Research - 1997-2021.},
journal = {China CDC weekly},
volume = {6},
number = {9},
pages = {157-161},
pmid = {38495592},
issn = {2096-7071},
abstract = {With the assistance of the internet, big data, cloud computing, and other technologies, the concept of smart elderly care has emerged.

WHAT IS ADDED BY THIS REPORT?: This study presents information on the countries or regions that have conducted research on smart elderly care, as well as identifies global hotspots and development trends in this field.

The results of this study suggest that future research should focus on fall detection, health monitoring, and guidance systems that are user-friendly and contribute to the creation of smarter safer communities for the well-being of the elderly.},
}

@article {pmid38495055,
year = {2024},
author = {Li, J and Xiong, Y and Feng, S and Pan, C and Guo, X},
title = {CloudProteoAnalyzer: scalable processing of big data from proteomics using cloud computing.},
journal = {Bioinformatics advances},
volume = {4},
number = {1},
pages = {vbae024},
pmid = {38495055},
issn = {2635-0041},
support = {R01 AT011618/AT/NCCIH NIH HHS/United States ; },
abstract = {SUMMARY: Shotgun proteomics is widely used in many system biology studies to determine the global protein expression profiles of tissues, cultures, and microbiomes. Many non-distributed computer algorithms have been developed for users to process proteomics data on their local computers. However, the amount of data acquired in a typical proteomics study has grown rapidly in recent years, owing to the increasing throughput of mass spectrometry and the expanding scale of study designs. This presents a big data challenge for researchers to process proteomics data in a timely manner. To overcome this challenge, we developed a cloud-based parallel computing application to offer end-to-end proteomics data analysis software as a service (SaaS). A web interface was provided to users to upload mass spectrometry-based proteomics data, configure parameters, submit jobs, and monitor job status. The data processing was distributed across multiple nodes in a supercomputer to achieve scalability for large datasets. Our study demonstrated SaaS for proteomics as a viable solution for the community to scale up the data processing using cloud computing.

This application is available online at https://sipros.oscer.ou.edu/ or https://sipros.unt.edu for free use. The source code is available at https://github.com/Biocomputing-Research-Group/CloudProteoAnalyzer under the GPL version 3.0 license.},
}

@article {pmid38491365,
year = {2024},
author = {Clements, J and Goina, C and Hubbard, PM and Kawase, T and Olbris, DJ and Otsuna, H and Svirskas, R and Rokicki, K},
title = {NeuronBridge: an intuitive web application for neuronal morphology search across large data sets.},
journal = {BMC bioinformatics},
volume = {25},
number = {1},
pages = {114},
pmid = {38491365},
issn = {1471-2105},
mesh = {Animals ; *Software ; Neurons ; *Connectome ; Microscopy, Electron ; Drosophila ; },
abstract = {BACKGROUND: Neuroscience research in Drosophila is benefiting from large-scale connectomics efforts using electron microscopy (EM) to reveal all the neurons in a brain and their connections. To exploit this knowledge base, researchers relate a connectome's structure to neuronal function, often by studying individual neuron cell types. Vast libraries of fly driver lines expressing fluorescent reporter genes in sets of neurons have been created and imaged using confocal light microscopy (LM), enabling the targeting of neurons for experimentation. However, creating a fly line for driving gene expression within a single neuron found in an EM connectome remains a challenge, as it typically requires identifying a pair of driver lines where only the neuron of interest is expressed in both. This task and other emerging scientific workflows require finding similar neurons across large data sets imaged using different modalities.

RESULTS: Here, we present NeuronBridge, a web application for easily and rapidly finding putative morphological matches between large data sets of neurons imaged using different modalities. We describe the functionality and construction of the NeuronBridge service, including its user-friendly graphical user interface (GUI), extensible data model, serverless cloud architecture, and massively parallel image search engine.

CONCLUSIONS: NeuronBridge fills a critical gap in the Drosophila research workflow and is used by hundreds of neuroscience researchers around the world. We offer our software code, open APIs, and processed data sets for integration and reuse, and provide the application as a service at http://neuronbridge.janelia.org .},
}

Animals
*Software
Neurons
*Connectome
Microscopy, Electron
Drosophila

@article {pmid38475170,
year = {2024},
author = {Tripathi, A and Waqas, A and Venkatesan, K and Yilmaz, Y and Rasool, G},
title = {Building Flexible, Scalable, and Machine Learning-Ready Multimodal Oncology Datasets.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {5},
pages = {},
pmid = {38475170},
issn = {1424-8220},
support = {2234836//National Science Foundation/ ; 2234468//National Science Foundation/ ; 1903466//National Science Foundation/ ; },
mesh = {Humans ; Reproducibility of Results ; *Neoplasms ; },
abstract = {The advancements in data acquisition, storage, and processing techniques have resulted in the rapid growth of heterogeneous medical data. Integrating radiological scans, histopathology images, and molecular information with clinical data is essential for developing a holistic understanding of the disease and optimizing treatment. The need for integrating data from multiple sources is further pronounced in complex diseases such as cancer for enabling precision medicine and personalized treatments. This work proposes Multimodal Integration of Oncology Data System (MINDS)-a flexible, scalable, and cost-effective metadata framework for efficiently fusing disparate data from public sources such as the Cancer Research Data Commons (CRDC) into an interconnected, patient-centric framework. MINDS consolidates over 41,000 cases from across repositories while achieving a high compression ratio relative to the 3.78 PB source data size. It offers sub-5-s query response times for interactive exploration. MINDS offers an interface for exploring relationships across data types and building cohorts for developing large-scale multimodal machine learning models. By harmonizing multimodal data, MINDS aims to potentially empower researchers with greater analytical ability to uncover diagnostic and prognostic insights and enable evidence-based personalized care. MINDS tracks granular end-to-end data provenance, ensuring reproducibility and transparency. The cloud-native architecture of MINDS can handle exponential data growth in a secure, cost-optimized manner while ensuring substantial storage optimization, replication avoidance, and dynamic access capabilities. Auto-scaling, access controls, and other mechanisms guarantee pipelines' scalability and security. MINDS overcomes the limitations of existing biomedical data silos via an interoperable metadata-driven approach that represents a pivotal step toward the future of oncology data integration.},
}

Humans
Reproducibility of Results
*Neoplasms

@article {pmid38475051,
year = {2024},
author = {Gaba, P and Raw, RS and Kaiwartya, O and Aljaidi, M},
title = {B-SAFE: Blockchain-Enabled Security Architecture for Connected Vehicle Fog Environment.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {5},
pages = {},
pmid = {38475051},
issn = {1424-8220},
support = {00//nottingham trent university/ ; },
abstract = {Vehicles are no longer stand-alone mechanical entities due to the advancements in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication-centric Internet of Connected Vehicles (IoV) frameworks. However, the advancement in connected vehicles leads to another serious security threat, online vehicle hijacking, where the steering control of vehicles can be hacked online. The feasibility of traditional security solutions in IoV environments is very limited, considering the intermittent network connectivity to cloud servers and vehicle-centric computing capability constraints. In this context, this paper presents a Blockchain-enabled Security Architecture for a connected vehicular Fog networking Environment (B-SAFE). Firstly, blockchain security and vehicular fog networking are introduced as preliminaries of the framework. Secondly, a three-layer architecture of B-SAFE is presented, focusing on vehicular communication, blockchain at fog nodes, and the cloud as trust and reward management for vehicles. Thirdly, details of the blockchain implementation at fog nodes is presented, along with a flowchart and algorithm. The performance of the evaluation of the proposed framework B-SAFE attests to the benefits in terms of trust, reward points, and threshold calculation.},
}

@article {pmid38474954,
year = {2024},
author = {Vercheval, N and Royen, R and Munteanu, A and Pižurica, A},
title = {PCGen: A Fully Parallelizable Point Cloud Generative Model.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {5},
pages = {},
pmid = {38474954},
issn = {1424-8220},
support = {174B0911//Flanders AI Research Programme/ ; G094122N//Fonds Wetenschappelijk Onderzoek (FWO) project/ ; },
abstract = {Generative models have the potential to revolutionize 3D extended reality. A primary obstacle is that augmented and virtual reality need real-time computing. Current state-of-the-art point cloud random generation methods are not fast enough for these applications. We introduce a vector-quantized variational autoencoder model (VQVAE) that can synthesize high-quality point clouds in milliseconds. Unlike previous work in VQVAEs, our model offers a compact sample representation suitable for conditional generation and data exploration with potential applications in rapid prototyping. We achieve this result by combining architectural improvements with an innovative approach for probabilistic random generation. First, we rethink current parallel point cloud autoencoder structures, and we propose several solutions to improve robustness, efficiency and reconstruction quality. Notable contributions in the decoder architecture include an innovative computation layer to process the shape semantic information, an attention mechanism that helps the model focus on different areas and a filter to cover possible sampling errors. Secondly, we introduce a parallel sampling strategy for VQVAE models consisting of a double encoding system, where a variational autoencoder learns how to generate the complex discrete distribution of the VQVAE, not only allowing quick inference but also describing the shape with a few global variables. We compare the proposed decoder and our VQVAE model with established and concurrent work, and we prove, one by one, the validity of the single contributions.},
}

@article {pmid38474952,
year = {2024},
author = {AlSaleh, I and Al-Samawi, A and Nissirat, L},
title = {Novel Machine Learning Approach for DDoS Cloud Detection: Bayesian-Based CNN and Data Fusion Enhancements.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {5},
pages = {},
pmid = {38474952},
issn = {1424-8220},
support = {GRANT5,340//King Faisal University/ ; },
abstract = {Cloud computing has revolutionized the information technology landscape, offering businesses the flexibility to adapt to diverse business models without the need for costly on-site servers and network infrastructure. A recent survey reveals that 95% of enterprises have already embraced cloud technology, with 79% of their workloads migrating to cloud environments. However, the deployment of cloud technology introduces significant cybersecurity risks, including network security vulnerabilities, data access control challenges, and the ever-looming threat of cyber-attacks such as Distributed Denial of Service (DDoS) attacks, which pose substantial risks to both cloud and network security. While Intrusion Detection Systems (IDS) have traditionally been employed for DDoS attack detection, prior studies have been constrained by various limitations. In response to these challenges, we present an innovative machine learning approach for DDoS cloud detection, known as the Bayesian-based Convolutional Neural Network (BaysCNN) model. Leveraging the CICDDoS2019 dataset, which encompasses 88 features, we employ Principal Component Analysis (PCA) for dimensionality reduction. Our BaysCNN model comprises 19 layers of analysis, forming the basis for training and validation. Our experimental findings conclusively demonstrate that the BaysCNN model significantly enhances the accuracy of DDoS cloud detection, achieving an impressive average accuracy rate of 99.66% across 13 multi-class attacks. To further elevate the model's performance, we introduce the Data Fusion BaysFusCNN approach, encompassing 27 layers. By leveraging Bayesian methods to estimate uncertainties and integrating features from multiple sources, this approach attains an even higher average accuracy of 99.79% across the same 13 multi-class attacks. Our proposed methodology not only offers valuable insights for the development of robust machine learning-based intrusion detection systems but also enhances the reliability and scalability of IDS in cloud computing environments. This empowers organizations to proactively mitigate security risks and fortify their defenses against malicious cyber-attacks.},
}

@article {pmid38469580,
year = {2024},
author = {Yakubu, B and Appiah, EM and Adu, AF},
title = {Pangenome Analysis of Helicobacter pylori Isolates from Selected Areas of Africa Indicated Diverse Antibiotic Resistance and Virulence Genes.},
journal = {International journal of genomics},
volume = {2024},
number = {},
pages = {5536117},
pmid = {38469580},
issn = {2314-4378},
abstract = {The challenge facing Helicobacter pylori (H. pylori) infection management in some parts of Africa is the evolution of drug-resistant species, the lack of gold standard in diagnostic methods, and the ineffectiveness of current vaccines against the bacteria. It is being established that even though clinical consequences linked to the bacteria vary geographically, there is rather a generic approach to treatment. This situation has remained problematic in the successful fight against the bacteria in parts of Africa. As a result, this study compared the genomes of selected H. pylori isolates from selected areas of Africa and evaluated their virulence and antibiotic drug resistance, those that are highly pathogenic and are associated with specific clinical outcomes and those that are less virulent and rarely associated with clinical outcomes. 146 genomes of H. pylori isolated from selected locations of Africa were sampled, and bioinformatic tools such as Abricate, CARD RGI, MLST, Prokka, Roary, Phandango, Google Sheets, and iTOLS were used to compare the isolates and their antibiotic resistance or susceptibility. Over 20 k virulence and AMR genes were observed. About 95% of the isolates were genetically diverse, 90% of the isolates harbored shell genes, and 50% harbored cloud and core genes. Some isolates did not retain the cagA and vacA genes. Clarithromycin, metronidazole, amoxicillin, and tinidazole were resistant to most AMR genes (vacA, cagA, oip, and bab). Conclusion. This study found both virulence and AMR genes in all H. pylori strains in all the selected geographies around Africa with differing quantities. MLST, Pangenome, and ORF analyses showed disparities among the isolates. This in general could imply diversities in terms of genetics, evolution, and protein production. Therefore, generic administration of antibiotics such as clarithromycin, amoxicillin, and erythromycin as treatment methods in the African subregion could be contributing to the spread of the bacterium's antibiotic resistance.},
}

@article {pmid38468957,
year = {2024},
author = {Tripathy, SS and Bebortta, S and Chowdhary, CL and Mukherjee, T and Kim, S and Shafi, J and Ijaz, MF},
title = {FedHealthFog: A federated learning-enabled approach towards healthcare analytics over fog computing platform.},
journal = {Heliyon},
volume = {10},
number = {5},
pages = {e26416},
pmid = {38468957},
issn = {2405-8440},
abstract = {The emergence of federated learning (FL) technique in fog-enabled healthcare system has leveraged enhanced privacy towards safeguarding sensitive patient information over heterogeneous computing platforms. In this paper, we introduce the FedHealthFog framework, which was meticulously developed to overcome the difficulties of distributed learning in resource-constrained IoT-enabled healthcare systems, particularly those sensitive to delays and energy efficiency. Conventional federated learning approaches face challenges stemming from substantial compute requirements and significant communication costs. This is primarily due to their reliance on a singular server for the aggregation of global data, which results in inefficient training models. We present a transformational approach to address these problems by elevating strategically placed fog nodes to the position of local aggregators within the federated learning architecture. A sophisticated greedy heuristic technique is used to optimize the choice of a fog node as the global aggregator in each communication cycle between edge devices and the cloud. The FedHealthFog system notably accounts for drop in communication latency of 87.01%, 26.90%, and 71.74%, and energy consumption of 57.98%, 34.36%, and 35.37% respectively, for three benchmark algorithms analyzed in this study. The effectiveness of FedHealthFog is strongly supported by outcomes of our experiments compared to cutting-edge alternatives while simultaneously reducing number of global aggregation cycles. These findings highlight FedHealthFog's potential to transform federated learning in resource-constrained IoT environments for delay-sensitive applications.},
}

@article {pmid38466691,
year = {2024},
author = {Shafi, I and Din, S and Farooq, S and Díez, IT and Breñosa, J and Espinosa, JCM and Ashraf, I},
title = {Design and development of patient health tracking, monitoring and big data storage using Internet of Things and real time cloud computing.},
journal = {PloS one},
volume = {19},
number = {3},
pages = {e0298582},
pmid = {38466691},
issn = {1932-6203},
mesh = {Humans ; *Cloud Computing ; *Internet of Things ; Pandemics ; Monitoring, Physiologic ; Information Storage and Retrieval ; },
abstract = {With the outbreak of the COVID-19 pandemic, social isolation and quarantine have become commonplace across the world. IoT health monitoring solutions eliminate the need for regular doctor visits and interactions among patients and medical personnel. Many patients in wards or intensive care units require continuous monitoring of their health. Continuous patient monitoring is a hectic practice in hospitals with limited staff; in a pandemic situation like COVID-19, it becomes much more difficult practice when hospitals are working at full capacity and there is still a risk of medical workers being infected. In this study, we propose an Internet of Things (IoT)-based patient health monitoring system that collects real-time data on important health indicators such as pulse rate, blood oxygen saturation, and body temperature but can be expanded to include more parameters. Our system is comprised of a hardware component that collects and transmits data from sensors to a cloud-based storage system, where it can be accessed and analyzed by healthcare specialists. The ESP-32 microcontroller interfaces with the multiple sensors and wirelessly transmits the collected data to the cloud storage system. A pulse oximeter is utilized in our system to measure blood oxygen saturation and body temperature, as well as a heart rate monitor to measure pulse rate. A web-based interface is also implemented, allowing healthcare practitioners to access and visualize the collected data in real-time, making remote patient monitoring easier. Overall, our IoT-based patient health monitoring system represents a significant advancement in remote patient monitoring, allowing healthcare practitioners to access real-time data on important health metrics and detect potential health issues before they escalate.},
}

Humans
*Cloud Computing
*Internet of Things
Pandemics
Monitoring, Physiologic
Information Storage and Retrieval

@article {pmid38460568,
year = {2024},
author = {Ghiandoni, GM and Evertsson, E and Riley, DJ and Tyrchan, C and Rathi, PC},
title = {Augmenting DMTA using predictive AI modelling at AstraZeneca.},
journal = {Drug discovery today},
volume = {29},
number = {4},
pages = {103945},
doi = {10.1016/j.drudis.2024.103945},
pmid = {38460568},
issn = {1878-5832},
mesh = {*Artificial Intelligence ; *Biological Assay ; Drug Discovery ; },
abstract = {Design-Make-Test-Analyse (DMTA) is the discovery cycle through which molecules are designed, synthesised, and assayed to produce data that in turn are analysed to inform the next iteration. The process is repeated until viable drug candidates are identified, often requiring many cycles before reaching a sweet spot. The advent of artificial intelligence (AI) and cloud computing presents an opportunity to innovate drug discovery to reduce the number of cycles needed to yield a candidate. Here, we present the Predictive Insight Platform (PIP), a cloud-native modelling platform developed at AstraZeneca. The impact of PIP in each step of DMTA, as well as its architecture, integration, and usage, are discussed and used to provide insights into the future of drug discovery.},
}

*Artificial Intelligence
*Biological Assay
Drug Discovery

@article {pmid38455562,
year = {2024},
author = {Gokool, S and Mahomed, M and Brewer, K and Naiken, V and Clulow, A and Sibanda, M and Mabhaudhi, T},
title = {Crop mapping in smallholder farms using unmanned aerial vehicle imagery and geospatial cloud computing infrastructure.},
journal = {Heliyon},
volume = {10},
number = {5},
pages = {e26913},
pmid = {38455562},
issn = {2405-8440},
abstract = {Smallholder farms are major contributors to agricultural production, food security, and socio-economic growth in many developing countries. However, they generally lack the resources to fully maximize their potential. Subsequently they require innovative, evidence-based and lower-cost solutions to optimize their productivity. Recently, precision agricultural practices facilitated by unmanned aerial vehicles (UAVs) have gained traction in the agricultural sector and have great potential for smallholder farm applications. Furthermore, advances in geospatial cloud computing have opened new and exciting possibilities in the remote sensing arena. In light of these recent developments, the focus of this study was to explore and demonstrate the utility of using the advanced image processing capabilities of the Google Earth Engine (GEE) geospatial cloud computing platform to process and analyse a very high spatial resolution multispectral UAV image for mapping land use land cover (LULC) within smallholder farms. The results showed that LULC could be mapped at a 0.50 m spatial resolution with an overall accuracy of 91%. Overall, we found GEE to be an extremely useful platform for conducting advanced image analysis on UAV imagery and rapid communication of results. Notwithstanding the limitations of the study, the findings presented herein are quite promising and clearly demonstrate how modern agricultural practices can be implemented to facilitate improved agricultural management in smallholder farmers.},
}

@article {pmid38453988,
year = {2024},
author = {Inam, S and Kanwal, S and Firdous, R and Hajjej, F},
title = {Blockchain based medical image encryption using Arnold's cat map in a cloud environment.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {5678},
pmid = {38453988},
issn = {2045-2322},
abstract = {Improved software for processing medical images has inspired tremendous interest in modern medicine in recent years. Modern healthcare equipment generates huge amounts of data, such as scanned medical images and computerized patient information, which must be secured for future use. Diversity in the healthcare industry, namely in the form of medical data, is one of the largest challenges for researchers. Cloud environment and the Block chain technology have both demonstrated their own use. The purpose of this study is to combine both technologies for safe and secure transaction. Storing or sending medical data through public clouds exposes information into potential eavesdropping, data breaches and unauthorized access. Encrypting data before transmission is crucial to mitigate these security risks. As a result, a Blockchain based Chaotic Arnold's cat map Encryption Scheme (BCAES) is proposed in this paper. The BCAES first encrypts the image using Arnold's cat map encryption scheme and then sends the encrypted image into Cloud Server and stores the signed document of plain image into blockchain. As blockchain is often considered more secure due to its distributed nature and consensus mechanism, data receiver will ensure data integrity and authenticity of image after decryption using signed document stored into the blockchain. Various analysis techniques have been used to examine the proposed scheme. The results of analysis like key sensitivity analysis, key space analysis, Information Entropy, histogram correlation of adjacent pixels, Number of Pixel Change Rate, Peak Signal Noise Ratio, Unified Average Changing Intensity, and similarity analysis like Mean Square Error, and Structural Similarity Index Measure illustrated that our proposed scheme is an efficient encryption scheme as compared to some recent literature. Our current achievements surpass all previous endeavors, setting a new standard of excellence.},
}

@article {pmid38452470,
year = {2024},
author = {Zhong, C and Darbandi, M and Nassr, M and Latifian, A and Hosseinzadeh, M and Jafari Navimipour, N},
title = {A new cloud-based method for composition of healthcare services using deep reinforcement learning and Kalman filtering.},
journal = {Computers in biology and medicine},
volume = {172},
number = {},
pages = {108152},
doi = {10.1016/j.compbiomed.2024.108152},
pmid = {38452470},
issn = {1879-0534},
mesh = {Humans ; *Cloud Computing ; Reproducibility of Results ; *Delivery of Health Care ; },
abstract = {Healthcare has significantly contributed to the well-being of individuals around the globe; nevertheless, further benefits could be derived from a more streamlined healthcare system without incurring additional costs. Recently, the main attributes of cloud computing, such as on-demand service, high scalability, and virtualization, have brought many benefits across many areas, especially in medical services. It is considered an important element in healthcare services, enhancing the performance and efficacy of the services. The current state of the healthcare industry requires the supply of healthcare products and services, increasing its viability for everyone involved. Developing new approaches for discovering and selecting healthcare services in the cloud has become more critical due to the rising popularity of these kinds of services. As a result of the diverse array of healthcare services, service composition enables the execution of intricate operations by integrating multiple services' functionalities into a single procedure. However, many methods in this field encounter several issues, such as high energy consumption, cost, and response time. This article introduces a novel layered method for selecting and evaluating healthcare services to find optimal service selection and composition solutions based on Deep Reinforcement Learning (Deep RL), Kalman filtering, and repeated training, addressing the aforementioned issues. The results revealed that the proposed method has achieved acceptable results in terms of availability, reliability, energy consumption, and response time when compared to other methods.},
}

Humans
*Cloud Computing
Reproducibility of Results
*Delivery of Health Care

@article {pmid38449567,
year = {2024},
author = {Wang, J and Yin, J and Nguyen, MH and Wang, J and Xu, W},
title = {Editorial: Big scientific data analytics on HPC and cloud.},
journal = {Frontiers in big data},
volume = {7},
number = {},
pages = {1353988},
doi = {10.3389/fdata.2024.1353988},
pmid = {38449567},
issn = {2624-909X},
}

@article {pmid38449564,
year = {2024},
author = {Saad, M and Enam, RN and Qureshi, R},
title = {Optimizing multi-objective task scheduling in fog computing with GA-PSO algorithm for big data application.},
journal = {Frontiers in big data},
volume = {7},
number = {},
pages = {1358486},
pmid = {38449564},
issn = {2624-909X},
abstract = {As the volume and velocity of Big Data continue to grow, traditional cloud computing approaches struggle to meet the demands of real-time processing and low latency. Fog computing, with its distributed network of edge devices, emerges as a compelling solution. However, efficient task scheduling in fog computing remains a challenge due to its inherently multi-objective nature, balancing factors like execution time, response time, and resource utilization. This paper proposes a hybrid Genetic Algorithm (GA)-Particle Swarm Optimization (PSO) algorithm to optimize multi-objective task scheduling in fog computing environments. The hybrid approach combines the strengths of GA and PSO, achieving effective exploration and exploitation of the search space, leading to improved performance compared to traditional single-algorithm approaches. The proposed hybrid algorithm results improved the execution time by 85.68% when compared with GA algorithm, by 84% when compared with Hybrid PWOA and by 51.03% when compared with PSO algorithm as well as it improved the response time by 67.28% when compared with GA algorithm, by 54.24% when compared with Hybrid PWOA and by 75.40% when compared with PSO algorithm as well as it improved the completion time by 68.69% when compared with GA algorithm, by 98.91% when compared with Hybrid PWOA and by 75.90% when compared with PSO algorithm when various tasks inputs are given. The proposed hybrid algorithm results also improved the execution time by 84.87% when compared with GA algorithm, by 88.64% when compared with Hybrid PWOA and by 85.07% when compared with PSO algorithm it improved the response time by 65.92% when compared with GA algorithm, by 80.51% when compared with Hybrid PWOA and by 85.26% when compared with PSO algorithm as well as it improved the completion time by 67.60% when compared with GA algorithm, by 81.34% when compared with Hybrid PWOA and by 85.23% when compared with PSO algorithm when various fog nodes are given.},
}

@article {pmid38435622,
year = {2024},
author = {Mehmood, T and Latif, S and Jamail, NSM and Malik, A and Latif, R},
title = {LSTMDD: an optimized LSTM-based drift detector for concept drift in dynamic cloud computing.},
journal = {PeerJ. Computer science},
volume = {10},
number = {},
pages = {e1827},
pmid = {38435622},
issn = {2376-5992},
abstract = {This study aims to investigate the problem of concept drift in cloud computing and emphasizes the importance of early detection for enabling optimum resource utilization and offering an effective solution. The analysis includes synthetic and real-world cloud datasets, stressing the need for appropriate drift detectors tailored to the cloud domain. A modified version of Long Short-Term Memory (LSTM) called the LSTM Drift Detector (LSTMDD) is proposed and compared with other top drift detection techniques using prediction error as the primary evaluation metric. LSTMDD is optimized to improve performance in detecting anomalies in non-Gaussian distributed cloud environments. The experiments show that LSTMDD outperforms other methods for gradual and sudden drift in the cloud domain. The findings suggest that machine learning techniques such as LSTMDD could be a promising approach to addressing the problem of concept drift in cloud computing, leading to more efficient resource allocation and improved performance.},
}

@article {pmid38429324,
year = {2024},
author = {Yin, X and Fang, W and Liu, Z and Liu, D},
title = {A novel multi-scale CNN and Bi-LSTM arbitration dense network model for low-rate DDoS attack detection.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {5111},
pmid = {38429324},
issn = {2045-2322},
support = {2021GX056//the Key Technologies R\&D Program of Weifang/ ; 2023GX063//the Key Technologies R\&D Program of Weifang/ ; KJRC2021002//the Foundation for the Talents by the Weifang University of Science and Technology/ ; ZR2021MF086//the Natural Science Foundation of Shandong Province/ ; 2019GNC106034//the Key R\&D Program of Shandong Province under Grant/ ; },
abstract = {Low-rate distributed denial of service attacks, as known as LDDoS attacks, pose the notorious security risks in cloud computing network. They overload the cloud servers and degrade network service quality with the stealthy strategy. Furthermore, this kind of small ratio and pulse-like abnormal traffic leads to a serious data scale problem. As a result, the existing models for detecting minority and adversary LDDoS attacks are insufficient in both detection accuracy and time consumption. This paper proposes a novel multi-scale Convolutional Neural Networks (CNN) and bidirectional Long-short Term Memory (bi-LSTM) arbitration dense network model (called MSCBL-ADN) for learning and detecting LDDoS attack behaviors under the condition of limited dataset and time consumption. The MSCBL-ADN incorporates CNN for preliminary spatial feature extraction and embedding-based bi-LSTM for time relationship extraction. And then, it employs arbitration network to re-weigh feature importance for higher accuracy. At last, it uses 2-block dense connection network to perform final classification. The experimental results conducted on popular ISCX-2016-SlowDos dataset have demonstrated that the proposed MSCBL-ADN model has a significant improvement with high detection accuracy and superior time performance over the state-of-the-art models.},
}

@article {pmid38421498,
year = {2024},
author = {Mahato, T and Parida, BR and Bar, S},
title = {Assessing tea plantations biophysical and biochemical characteristics in Northeast India using satellite data.},
journal = {Environmental monitoring and assessment},
volume = {196},
number = {3},
pages = {327},
pmid = {38421498},
issn = {1573-2959},
support = {F.4-5(209-FRP)/2015/BSR//University Grants Commission/ ; },
mesh = {*Environmental Monitoring ; *Camellia sinensis ; India ; Nitrogen ; Tea ; },
abstract = {Despite advancements in using multi-temporal satellite data to assess long-term changes in Northeast India's tea plantations, a research gap exists in understanding the intricate interplay between biophysical and biochemical characteristics. Further exploration is crucial for precise, sustainable monitoring and management. In this study, satellite-derived vegetation indices and near-proximal sensor data were deployed to deduce various physico-chemical characteristics and to evaluate the health conditions of tea plantations in northeast India. The districts, such as Sonitpur, Jorhat, Sibsagar, Dibrugarh, and Tinsukia in Assam were selected, which are the major contributors to the tea industry in India. The Sentinel-2A (2022) data was processed in the Google Earth Engine (GEE) cloud platform and utilized for analyzing tea plantations biochemical and biophysical properties. Leaf chlorophyll (Cab) and nitrogen contents are determined using the Normalized Area Over Reflectance Curve (NAOC) index and flavanol contents, respectively. Biophysical and biochemical parameters of the tea assessed during the spring season (March-April) 2022 revealed that tea plantations located in Tinsukia and Dibrugarh were much healthier than the other districts in Assam which are evident from satellite-derived Enhanced Vegetation Index (EVI), Modified Soil Adjusted Vegetation Index (MSAVI), Leaf Area Index (LAI), and Fraction of Absorbed Photosynthetically Active Radiation (fPAR), including the Cab and nitrogen contents. The Cab of healthy tea plants varied from 25 to 35 µg/cm[2]. Pearson correlation among satellite-derived Cab and nitrogen with field measurements showed R[2] of 0.61-0.62 (p-value < 0.001). This study offered vital information about land alternations and tea health conditions, which can be crucial for conservation, monitoring, and management practices.},
}

*Environmental Monitoring
*Camellia sinensis
India
Nitrogen
Tea

@article {pmid38420486,
year = {2024},
author = {Liu, X and Wider, W and Fauzi, MA and Jiang, L and Udang, LN and Hossain, SFA},
title = {The evolution of smart hotels: A bibliometric review of the past, present and future trends.},
journal = {Heliyon},
volume = {10},
number = {4},
pages = {e26472},
pmid = {38420486},
issn = {2405-8440},
abstract = {This study provides a bibliometric analysis of smart hotel research, drawing from 613 publications in the Web of Science (WoS) database to examine scholarly trends and developments in this dynamic field. Smart hotels, characterized by integrating advanced technologies such as AI, IoT, cloud computing, and big data, aim to redefine customer experiences and operational efficiency. Utilizing co-citation and co-word analysis techniques, the research delves into the depth of literature from past to future trends. In co-citation analysis, clusters including "Sustainable Hotel and Green Hotel", "Theories Integration in Smart Hotel Research", and "Consumers' Decisions about Green Hotels" underscore the pivotal areas of past and current research. Co-word analysis further reveals emergent trend clusters: "The New Era of Sustainable Tourism", "Elevating Standards and Guest Loyalty", and "Hotels' New Sustainable Blueprint in Modern Travel". These clusters reflect the industry's evolving focus on sustainability and technology-enhanced guest experiences. Theoretically, this research bridges gaps in smart hotel literature, proposing new frameworks for understanding customer decisions amid technological advancements and environmental responsibilities. Practically, it offers valuable insights for hotel managers, guiding technology integration strategies for enhanced efficiency and customer loyalty while underscoring the critical role of green strategies and sustainability.},
}

@article {pmid38420393,
year = {2024},
author = {Mukred, M and Mokhtar, UA and Hawash, B and AlSalman, H and Zohaib, M},
title = {The adoption and use of learning analytics tools to improve decision making in higher learning institutions: An extension of technology acceptance model.},
journal = {Heliyon},
volume = {10},
number = {4},
pages = {e26315},
pmid = {38420393},
issn = {2405-8440},
abstract = {Learning Analytics Tools (LATs) can be used for informed decision-making regarding teaching strategies and their continuous enhancement. Therefore, LATs must be adopted in higher learning institutions, but several factors hinder its implementation, primarily due to the lack of an implementation model. Therefore, in this study, the focus is directed towards examining LATs adoption in Higher Learning Institutions (HLIs), with emphasis on the determinants of the adoption process. The study mainly aims to design a model of LAT adoption and use it in the above context to improve the institutions' decision-making and accordingly, the study adopted an extended version of Technology Acceptance Model (TAM) as the underpinning theory. Five experts validated the employed survey instrument, and 500 questionnaire copies were distributed through e-mails, from which 275 copies were retrieved from Saudi employees working at public HLIs. Data gathered was exposed to Partial Least Square-Structural Equation Modeling (PLS-SEM) for analysis and to test the proposed conceptual model. Based on the findings, the perceived usefulness of LAT plays a significant role as a determinant of its adoption. Other variables include top management support, financial support, and the government's role in LATs acceptance and adoption among HLIs. The findings also supported the contribution of LAT adoption and acceptance towards making informed decisions and highlighted the need for big data facility and cloud computing ability towards LATs usefulness. The findings have significant implications towards LATs implementation success among HLIs, providing clear insights into the factors that can enhance its adoption and acceptance. They also lay the basis for future studies in the area to validate further the effect of LATs on decision-making among HLIs institutions. Furthermore, the obtained findings are expected to serve as practical implications for policy makers and educational leaders in their objective to implement LAT using a multi-layered method that considers other aspects in addition to the perceptions of the individual user.},
}

@article {pmid38409183,
year = {2024},
author = {Grossman, RL and Boyles, RR and Davis-Dusenbery, BN and Haddock, A and Heath, AP and O'Connor, BD and Resnick, AC and Taylor, DM and Ahalt, S},
title = {A Framework for the Interoperability of Cloud Platforms: Towards FAIR Data in SAFE Environments.},
journal = {Scientific data},
volume = {11},
number = {1},
pages = {241},
pmid = {38409183},
issn = {2052-4463},
support = {HHSN261201400008C/CA/NCI NIH HHS/United States ; },
mesh = {*Cloud Computing ; *Electronic Health Records ; },
abstract = {As the number of cloud platforms supporting scientific research grows, there is an increasing need to support interoperability between two or more cloud platforms. A well accepted core concept is to make data in cloud platforms Findable, Accessible, Interoperable and Reusable (FAIR). We introduce a companion concept that applies to cloud-based computing environments that we call a Secure and Authorized FAIR Environment (SAFE). SAFE environments require data and platform governance structures and are designed to support the interoperability of sensitive or controlled access data, such as biomedical data. A SAFE environment is a cloud platform that has been approved through a defined data and platform governance process as authorized to hold data from another cloud platform and exposes appropriate APIs for the two platforms to interoperate.},
}

*Cloud Computing
*Electronic Health Records

@article {pmid38404043,
year = {2024},
author = {Rusinovich, Y and Rusinovich, V and Buhayenka, A and Liashko, V and Sabanov, A and Holstein, DJF and Aldmour, S and Doss, M and Branzan, D},
title = {Classification of anatomic patterns of peripheral artery disease with automated machine learning (AutoML).},
journal = {Vascular},
volume = {},
number = {},
pages = {17085381241236571},
doi = {10.1177/17085381241236571},
pmid = {38404043},
issn = {1708-539X},
abstract = {AIM: The aim of this study was to investigate the potential of novel automated machine learning (AutoML) in vascular medicine by developing a discriminative artificial intelligence (AI) model for the classification of anatomical patterns of peripheral artery disease (PAD).

MATERIAL AND METHODS: Random open-source angiograms of lower limbs were collected using a web-indexed search. An experienced researcher in vascular medicine labelled the angiograms according to the most applicable grade of femoropopliteal disease in the Global Limb Anatomic Staging System (GLASS). An AutoML model was trained using the Vertex AI (Google Cloud) platform to classify the angiograms according to the GLASS grade with a multi-label algorithm. Following deployment, we conducted a test using 25 random angiograms (five from each GLASS grade). Model tuning through incremental training by introducing new angiograms was executed to the limit of the allocated quota following the initial evaluation to determine its effect on the software's performance.

RESULTS: We collected 323 angiograms to create the AutoML model. Among these, 80 angiograms were labelled as grade 0 of femoropopliteal disease in GLASS, 114 as grade 1, 34 as grade 2, 25 as grade 3 and 70 as grade 4. After 4.5 h of training, the AI model was deployed. The AI self-assessed average precision was 0.77 (0 is minimal and 1 is maximal). During the testing phase, the AI model successfully determined the GLASS grade in 100% of the cases. The agreement with the researcher was almost perfect with the number of observed agreements being 22 (88%), Kappa = 0.85 (95% CI 0.69-1.0). The best results were achieved in predicting GLASS grade 0 and grade 4 (initial precision: 0.76 and 0.84). However, the AI model exhibited poorer results in classifying GLASS grade 3 (initial precision: 0.2) compared to other grades. Disagreements between the AI and the researcher were associated with the low resolution of the test images. Incremental training expanded the initial dataset by 23% to a total of 417 images, which improved the model's average precision by 11% to 0.86.

CONCLUSION: After a brief training period with a limited dataset, AutoML has demonstrated its potential in identifying and classifying the anatomical patterns of PAD, operating unhindered by the factors that can affect human analysts, such as fatigue or lack of experience. This technology bears the potential to revolutionize outcome prediction and standardize evidence-based revascularization strategies for patients with PAD, leveraging its adaptability and ability to continuously improve with additional data. The pursuit of further research in AutoML within the field of vascular medicine is both promising and warranted. However, it necessitates additional financial support to realize its full potential.},
}

@article {pmid38403304,
year = {2024},
author = {Wu, ZF and Yang, SJ and Yang, YQ and Wang, ZQ and Ai, L and Zhu, GH and Zhu, WF},
title = {[Current situation and development trend of digital traditional Chinese medicine pharmacy].},
journal = {Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica},
volume = {49},
number = {2},
pages = {285-293},
doi = {10.19540/j.cnki.cjcmm.20230904.301},
pmid = {38403304},
issn = {1001-5302},
mesh = {Humans ; Medicine, Chinese Traditional ; Artificial Intelligence ; Technology, Pharmaceutical ; Drug Industry ; *Pharmacy ; *Drugs, Chinese Herbal ; },
abstract = {The 21st century is a highly information-driven era, and traditional Chinese medicine(TCM) pharmacy is also moving towards digitization and informatization. New technologies such as artificial intelligence and big data with information technology as the core are being integrated into various aspects of drug research, manufacturing, evaluation, and application, promoting interaction between these stages and improving the quality and efficiency of TCM preparations. This, in turn, provides better healthcare services to the general population. The deep integration of emerging technologies such as artificial intelligence, big data, and cloud computing with the TCM pharmaceutical industry will innovate TCM pharmaceutical technology, accelerate the research and industrialization process of TCM pharmacy, provide cutting-edge technological support to the global scientific community, boost the efficiency of the TCM industry, and promote economic and social development. Drawing from recent developments in TCM pharmacy in China, this paper discussed the current research status and future trends in digital TCM pharmacy, aiming to provide a reference for future research in this field.},
}

Humans
Medicine, Chinese Traditional
Artificial Intelligence
Technology, Pharmaceutical
Drug Industry
*Pharmacy
*Drugs, Chinese Herbal

@article {pmid38400504,
year = {2024},
author = {Alasmary, H},
title = {ScalableDigitalHealth (SDH): An IoT-Based Scalable Framework for Remote Patient Monitoring.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {4},
pages = {},
pmid = {38400504},
issn = {1424-8220},
support = {The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through large group Research Project under grant number RGP2/312/44//King Khalid University/ ; },
mesh = {Aged ; Humans ; *Awareness ; *Benchmarking ; Blood Pressure ; Body Temperature ; Monitoring, Physiologic ; },
abstract = {Addressing the increasing demand for remote patient monitoring, especially among the elderly and mobility-impaired, this study proposes the "ScalableDigitalHealth" (SDH) framework. The framework integrates smart digital health solutions with latency-aware edge computing autoscaling, providing a novel approach to remote patient monitoring. By leveraging IoT technology and application autoscaling, the "SDH" enables the real-time tracking of critical health parameters, such as ECG, body temperature, blood pressure, and oxygen saturation. These vital metrics are efficiently transmitted in real time to AWS cloud storage through a layered networking architecture. The contributions are two-fold: (1) establishing real-time remote patient monitoring and (2) developing a scalable architecture that features latency-aware horizontal pod autoscaling for containerized healthcare applications. The architecture incorporates a scalable IoT-based architecture and an innovative microservice autoscaling strategy in edge computing, driven by dynamic latency thresholds and enhanced by the integration of custom metrics. This work ensures heightened accessibility, cost-efficiency, and rapid responsiveness to patient needs, marking a significant leap forward in the field. By dynamically adjusting pod numbers based on latency, the system optimizes system responsiveness, particularly in edge computing's proximity-based processing. This innovative fusion of technologies not only revolutionizes remote healthcare delivery but also enhances Kubernetes performance, preventing unresponsiveness during high usage.},
}

Aged
Humans
*Awareness
*Benchmarking
Blood Pressure
Body Temperature
Monitoring, Physiologic

@article {pmid38400486,
year = {2024},
author = {Dhiman, P and Saini, N and Gulzar, Y and Turaev, S and Kaur, A and Nisa, KU and Hamid, Y},
title = {A Review and Comparative Analysis of Relevant Approaches of Zero Trust Network Model.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {4},
pages = {},
pmid = {38400486},
issn = {1424-8220},
support = {This research was funded by the United Arab Emirates UAEU-ZU Joint Research Grant G00003819 (Fund No.: 12R138) Emirates Center for Mobility Research.//United Arab Emirates University/ ; },
abstract = {The Zero Trust safety architecture emerged as an intriguing approach for overcoming the shortcomings of standard network security solutions. This extensive survey study provides a meticulous explanation of the underlying principles of Zero Trust, as well as an assessment of the many strategies and possibilities for effective implementation. The survey begins by examining the role of authentication and access control within Zero Trust Architectures, and subsequently investigates innovative authentication, as well as access control solutions across different scenarios. It more deeply explores traditional techniques for encryption, micro-segmentation, and security automation, emphasizing their importance in achieving a secure Zero Trust environment. Zero Trust Architecture is explained in brief, along with the Taxonomy of Zero Trust Network Features. This review article provides useful insights into the Zero Trust paradigm, its approaches, problems, and future research objectives for scholars, practitioners, and policymakers. This survey contributes to the growth and implementation of secure network architectures in critical infrastructures by developing a deeper knowledge of Zero Trust.},
}

@article {pmid38400360,
year = {2024},
author = {Li, W and Zhou, H and Lu, Z and Kamarthi, S},
title = {Navigating the Evolution of Digital Twins Research through Keyword Co-Occurence Network Analysis.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {4},
pages = {},
pmid = {38400360},
issn = {1424-8220},
abstract = {Digital twin technology has become increasingly popular and has revolutionized data integration and system modeling across various industries, such as manufacturing, energy, and healthcare. This study aims to explore the evolving research landscape of digital twins using Keyword Co-occurrence Network (KCN) analysis. We analyze metadata from 9639 peer-reviewed articles published between 2000 and 2023. The results unfold in two parts. The first part examines trends and keyword interconnection over time, and the second part maps sensing technology keywords to six application areas. This study reveals that research on digital twins is rapidly diversifying, with focused themes such as predictive and decision-making functions. Additionally, there is an emphasis on real-time data and point cloud technologies. The advent of federated learning and edge computing also highlights a shift toward distributed computation, prioritizing data privacy. This study confirms that digital twins have evolved into complex systems that can conduct predictive operations through advanced sensing technologies. The discussion also identifies challenges in sensor selection and empirical knowledge integration.},
}

@article {pmid38400338,
year = {2024},
author = {Wiryasaputra, R and Huang, CY and Lin, YJ and Yang, CT},
title = {An IoT Real-Time Potable Water Quality Monitoring and Prediction Model Based on Cloud Computing Architecture.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {4},
pages = {},
pmid = {38400338},
issn = {1424-8220},
support = {112-2622-E-029-003,112-2621-M-029-004, and 110-2221-E-029-020-MY3//the National Science and Technology Council (NSTC), Taiwan R.O.C./ ; },
mesh = {Humans ; Artificial Intelligence ; Cloud Computing ; *Drinking Water ; *Internet of Things ; Data Accuracy ; },
abstract = {In order to achieve the Sustainable Development Goals (SDG), it is imperative to ensure the safety of drinking water. The characteristics of each drinkable water, encompassing taste, aroma, and appearance, are unique. Inadequate water infrastructure and treatment can affect these features and may also threaten public health. This study utilizes the Internet of Things (IoT) in developing a monitoring system, particularly for water quality, to reduce the risk of contracting diseases. Water quality components data, such as water temperature, alkalinity or acidity, and contaminants, were obtained through a series of linked sensors. An Arduino microcontroller board acquired all the data and the Narrow Band-IoT (NB-IoT) transmitted them to the web server. Due to limited human resources to observe the water quality physically, the monitoring was complemented by real-time notifications alerts via a telephone text messaging application. The water quality data were monitored using Grafana in web mode, and the binary classifiers of machine learning techniques were applied to predict whether the water was drinkable or not based on the data collected, which were stored in a database. The non-decision tree, as well as the decision tree, were evaluated based on the improvements of the artificial intelligence framework. With a ratio of 60% for data training: at 20% for data validation, and 10% for data testing, the performance of the decision tree (DT) model was more prominent in comparison with the Gradient Boosting (GB), Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) modeling approaches. Through the monitoring and prediction of results, the authorities can sample the water sources every two weeks.},
}

Humans
Artificial Intelligence
Cloud Computing
*Drinking Water
*Internet of Things
Data Accuracy

@article {pmid38400323,
year = {2024},
author = {Pan, S and Huang, C and Fan, J and Shi, Z and Tong, J and Wang, H},
title = {Optimizing Internet of Things Fog Computing: Through Lyapunov-Based Long Short-Term Memory Particle Swarm Optimization Algorithm for Energy Consumption Optimization.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {4},
pages = {},
pmid = {38400323},
issn = {1424-8220},
abstract = {In the era of continuous development in Internet of Things (IoT) technology, smart services are penetrating various facets of societal life, leading to a growing demand for interconnected devices. Many contemporary devices are no longer mere data producers but also consumers of data. As a result, massive amounts of data are transmitted to the cloud, but the latency generated in edge-to-cloud communication is unacceptable for many tasks. In response to this, this paper introduces a novel contribution-a layered computing network built on the principles of fog computing, accompanied by a newly devised algorithm designed to optimize user tasks and allocate computing resources within rechargeable networks. The proposed algorithm, a synergy of Lyapunov-based, dynamic Long Short-Term Memory (LSTM) networks, and Particle Swarm Optimization (PSO), allows for predictive task allocation. The fog servers dynamically train LSTM networks to effectively forecast the data features of user tasks, facilitating proper unload decisions based on task priorities. In response to the challenge of slower hardware upgrades in edge devices compared to user demands, the algorithm optimizes the utilization of low-power devices and addresses performance limitations. Additionally, this paper considers the unique characteristics of rechargeable networks, where computing nodes acquire energy through charging. Utilizing Lyapunov functions for dynamic resource control enables nodes with abundant resources to maximize their potential, significantly reducing energy consumption and enhancing overall performance. The simulation results demonstrate that our algorithm surpasses traditional methods in terms of energy efficiency and resource allocation optimization. Despite the limitations of prediction accuracy in Fog Servers (FS), the proposed results significantly promote overall performance. The proposed approach improves the efficiency and the user experience of Internet of Things systems in terms of latency and energy consumption.},
}

@article {pmid38400319,
year = {2024},
author = {Brata, KC and Funabiki, N and Panduman, YYF and Fajrianti, ED},
title = {An Enhancement of Outdoor Location-Based Augmented Reality Anchor Precision through VSLAM and Google Street View.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {4},
pages = {},
pmid = {38400319},
issn = {1424-8220},
abstract = {Outdoor Location-Based Augmented Reality (LAR) applications require precise positioning for seamless integrations of virtual content into immersive experiences. However, common solutions in outdoor LAR applications rely on traditional smartphone sensor fusion methods, such as the Global Positioning System (GPS) and compasses, which often lack the accuracy needed for precise AR content alignments. In this paper, we introduce an innovative approach to enhance LAR anchor precision in outdoor environments. We leveraged Visual Simultaneous Localization and Mapping (VSLAM) technology, in combination with innovative cloud-based methodologies, and harnessed the extensive visual reference database of Google Street View (GSV), to address the accuracy limitation problems. For the evaluation, 10 Point of Interest (POI) locations were used as anchor point coordinates in the experiments. We compared the accuracies between our approach and the common sensor fusion LAR solution comprehensively involving accuracy benchmarking and running load performance testing. The results demonstrate substantial enhancements in overall positioning accuracies compared to conventional GPS-based approaches for aligning AR anchor content in the real world.},
}

@article {pmid38376453,
year = {2024},
author = {Horstmann, A and Riggs, S and Chaban, Y and Clare, DK and de Freitas, G and Farmer, D and Howe, A and Morris, KL and Hatton, D},
title = {A service-based approach to cryoEM facility processing pipelines at eBIC.},
journal = {Acta crystallographica. Section D, Structural biology},
volume = {80},
number = {Pt 3},
pages = {174-180},
pmid = {38376453},
issn = {2059-7983},
mesh = {*Software ; *Image Processing, Computer-Assisted/methods ; Cryoelectron Microscopy/methods ; Workflow ; Cloud Computing ; },
abstract = {Electron cryo-microscopy image-processing workflows are typically composed of elements that may, broadly speaking, be categorized as high-throughput workloads which transition to high-performance workloads as preprocessed data are aggregated. The high-throughput elements are of particular importance in the context of live processing, where an optimal response is highly coupled to the temporal profile of the data collection. In other words, each movie should be processed as quickly as possible at the earliest opportunity. The high level of disconnected parallelization in the high-throughput problem directly allows a completely scalable solution across a distributed computer system, with the only technical obstacle being an efficient and reliable implementation. The cloud computing frameworks primarily developed for the deployment of high-availability web applications provide an environment with a number of appealing features for such high-throughput processing tasks. Here, an implementation of an early-stage processing pipeline for electron cryotomography experiments using a service-based architecture deployed on a Kubernetes cluster is discussed in order to demonstrate the benefits of this approach and how it may be extended to scenarios of considerably increased complexity.},
}

*Software
*Image Processing, Computer-Assisted/methods
Cryoelectron Microscopy/methods
Workflow
Cloud Computing

@article {pmid38370642,
year = {2024},
author = {McMurry, AJ and Gottlieb, DI and Miller, TA and Jones, JR and Atreja, A and Crago, J and Desai, PM and Dixon, BE and Garber, M and Ignatov, V and Kirchner, LA and Payne, PRO and Saldanha, AJ and Shankar, PRV and Solad, YV and Sprouse, EA and Terry, M and Wilcox, AB and Mandl, KD},
title = {Cumulus: A federated EHR-based learning system powered by FHIR and AI.},
journal = {medRxiv : the preprint server for health sciences},
volume = {},
number = {},
pages = {},
pmid = {38370642},
support = {NU38OT000286/OT/OSTLTS CDC HHS/United States ; U01 TR002623/TR/NCATS NIH HHS/United States ; U01 TR002997/TR/NCATS NIH HHS/United States ; U18DP006500/ACL/ACL HHS/United States ; },
abstract = {OBJECTIVE: To address challenges in large-scale electronic health record (EHR) data exchange, we sought to develop, deploy, and test an open source, cloud-hosted app 'listener' that accesses standardized data across the SMART/HL7 Bulk FHIR Access application programming interface (API).

METHODS: We advance a model for scalable, federated, data sharing and learning. Cumulus software is designed to address key technology and policy desiderata including local utility, control, and administrative simplicity as well as privacy preservation during robust data sharing, and AI for processing unstructured text.

RESULTS: Cumulus relies on containerized, cloud-hosted software, installed within a healthcare organization's security envelope. Cumulus accesses EHR data via the Bulk FHIR interface and streamlines automated processing and sharing. The modular design enables use of the latest AI and natural language processing tools and supports provider autonomy and administrative simplicity. In an initial test, Cumulus was deployed across five healthcare systems each partnered with public health. Cumulus output is patient counts which were aggregated into a table stratifying variables of interest to enable population health studies. All code is available open source. A policy stipulating that only aggregate data leave the institution greatly facilitated data sharing agreements.

DISCUSSION AND CONCLUSION: Cumulus addresses barriers to data sharing based on (1) federally required support for standard APIs (2), increasing use of cloud computing, and (3) advances in AI. There is potential for scalability to support learning across myriad network configurations and use cases.},
}

@article {pmid38370229,
year = {2024},
author = {Yadav, N and Pattabiraman, B and Tummuru, NR and Soundharajan, BS and Kasiviswanathan, KS and Adeloye, AJ and Sen, S and Maurya, M and Vijayalakshmanan, S},
title = {Toward improving water-energy-food nexus through dynamic energy management of solar powered automated irrigation system.},
journal = {Heliyon},
volume = {10},
number = {4},
pages = {e25359},
pmid = {38370229},
issn = {2405-8440},
abstract = {This paper focuses on developing a water and energy-saving reliable irrigation system using state-of-the-art computing, communication, and optimal energy management framework. The framework integrates real-time soil moisture and weather forecasting information to decide the time of irrigation and quantity of water required for potato crops, which is made available to the users across a region through the cloud-based irrigation decision support system. This is accomplished through various modules such as data acquisition, soil moisture forecasting, smart irrigation scheduling, and energy management scheme. The main emphasizes is on the electrical segment which demonstrates an energy management scheme for PV-battery based grid-connected system to operate the irrigation system valves and water pump. The proposed scheme is verified through simulation and dSpace-based real-time experiment studies. Overall, the proposed energy management system demonstrates an improvement in the optimal onsite solar power generation and storage capacity to power the solar pump which save the electrical energy as well as the water in order to establish an improved solar-irrigation system. Finally, the proposed system achieved water and energy savings of around 9.24 % for potato crop with full irrigation enhancing the Water-Energy-Food Nexus at field scale.},
}

@article {pmid38365804,
year = {2024},
author = {Beteri, J and Lyimo, JG and Msinde, JV},
title = {The influence of climatic and environmental variables on sunflower planting season suitability in Tanzania.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {3906},
pmid = {38365804},
issn = {2045-2322},
mesh = {Seasons ; *Helianthus ; Tanzania ; Temperature ; Plants ; *Asteraceae ; },
abstract = {Crop survival and growth requires identification of correlations between appropriate suitable planting season and relevant climatic and environmental characteristics. Climatic and environmental conditions may cause water and heat stress at critical stages of crop development and thus affecting planting suitability. Consequently, this may affect crop yield and productivity. This study assesses the influence of climate and environmental variables on rain-fed sunflower planting season suitability in Tanzania. Data on rainfall, temperature, slope, elevation, soil and land use/or cover were accessed from publicly available sources using Google Earth Engine. This is a cloud-based geospatial computing platform for remote sensed datasets. Tanzania sunflower production calendar of 2022 was adopted to mark the start and end limits of planting across the country. The default climate and environmental parameters from FAO database were used. In addition, Pearson correlation was used to evaluate the relationship between rainfall, temperature over Normalized Difference Vegetation Index (NDVI) from 2000 to 2020 at five-year interval for January-April and June-September, for high and poor suitability season. The results showed that planting suitability of sunflower in Tanzania is driven more by rainfall than temperature. It was revealed that intra-annual planting suitability increases gradually from short to long- rain season and diminishes towards dry season of the year. January-April planting season window showing highest suitability (41.65%), whereas June-September indicating lowest suitability (0.05%). Though, not statistically significant, rainfall and NDVI were positively correlated with r = 0.65 and 0.75 whereas negative correlation existed between temperature and NDVI with r = -- 0.6 and - 0.77. We recommend sunflower subsector interventions that consider appropriate intra-regional and seasonal diversity as an important adaptive mechanism to ensure high sunflower yields.},
}

Seasons
*Helianthus
Tanzania
Temperature
Plants
*Asteraceae

@article {pmid38360949,
year = {2024},
author = {Periola, AA and Alonge, AA and Ogudo, KA},
title = {Ocean warming events resilience capability in underwater computing platforms.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {3781},
pmid = {38360949},
issn = {2045-2322},
abstract = {Underwater data centers (UDCs) use the ocean's cold-water resources for free cooling and have low cooling costs. However, UDC cooling is affected by marine heat waves, and underwater seismic events thereby affecting UDC functioning continuity. Though feasible, the use of reservoirs for UDC cooling is non-scalable due to the high computing overhead, and inability to support continuity for long duration marine heat waves. The presented research proposes a mobile UDC (capable of migration) to address this challenge. The proposed UDC migrates from high underwater ground displacement ocean regions to regions having no or small underwater ground displacement. It supports multiple client underwater applications without requiring clients to develop, deploy, and launch own UDCs. The manner of resource utilization is influenced by the client's service level agreement. Hence, the proposed UDC provides resilient services to the clients and the requiring applications. Analysis shows that using the mobile UDC instead of the existing reservoir UDC approach enhances the operational duration and power usage effectiveness by 8.9-48.5% and 55.6-70.7% on average, respectively. In addition, the overhead is reduced by an average of 95.8-99.4%.},
}

@article {pmid38355983,
year = {2024},
author = {Kashyap, P and Shivgan, K and Patil, S and Raja, BR and Mahajan, S and Banerjee, S and Tallur, S},
title = {Unsupervised deep learning framework for temperature-compensated damage assessment using ultrasonic guided waves on edge device.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {3751},
pmid = {38355983},
issn = {2045-2322},
support = {RD/0118-ISROC00-006//Indian Space Research Organisation/ ; CRG/2021/001959//Science and Engineering Research Board/ ; },
abstract = {Fueled by the rapid development of machine learning (ML) and greater access to cloud computing and graphics processing units, various deep learning based models have been proposed for improving performance of ultrasonic guided wave structural health monitoring (GW-SHM) systems, especially to counter complexity and heterogeneity in data due to varying environmental factors (e.g., temperature) and types of damages. Such models typically comprise of millions of trainable parameters, and therefore add to cost of deployment due to requirements of cloud connectivity and processing, thus limiting the scale of deployment of GW-SHM. In this work, we propose an alternative solution that leverages TinyML framework for development of light-weight ML models that could be directly deployed on embedded edge devices. The utility of our solution is illustrated by presenting an unsupervised learning framework for damage detection in honeycomb composite sandwich structure with disbond and delamination type of damages, validated using data generated by finite element simulations and experiments performed at various temperatures in the range 0-90 °C. We demonstrate a fully-integrated solution using a Xilinx Artix-7 FPGA for data acquisition and control, and edge-inference of damage. Despite the limited number of features, the lightweight model shows reasonably high accuracy, thereby enabling detection of small size defects with improved sensitivity on an edge device for online GW-SHM.},
}

@article {pmid38351164,
year = {2024},
author = {Feng, Q and Niu, B and Ren, Y and Su, S and Wang, J and Shi, H and Yang, J and Han, M},
title = {A 10-m national-scale map of ground-mounted photovoltaic power stations in China of 2020.},
journal = {Scientific data},
volume = {11},
number = {1},
pages = {198},
pmid = {38351164},
issn = {2052-4463},
support = {42001367//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {We provide a remote sensing derived dataset for large-scale ground-mounted photovoltaic (PV) power stations in China of 2020, which has high spatial resolution of 10 meters. The dataset is based on the Google Earth Engine (GEE) cloud computing platform via random forest classifier and active learning strategy. Specifically, ground samples are carefully collected across China via both field survey and visual interpretation. Afterwards, spectral and texture features are calculated from publicly available Sentinel-2 imagery. Meanwhile, topographic features consisting of slope and aspect that are sensitive to PV locations are also included, aiming to construct a multi-dimensional and discriminative feature space. Finally, the trained random forest model is adopted to predict PV power stations of China parallelly on GEE. Technical validation has been carefully performed across China which achieved a satisfactory accuracy over 89%. Above all, as the first publicly released 10-m national-scale distribution dataset of China's ground-mounted PV power stations, it can provide data references for relevant researchers in fields such as energy, land, remote sensing and environmental sciences.},
}

@article {pmid38351065,
year = {2024},
author = {Chuntakaruk, H and Hengphasatporn, K and Shigeta, Y and Aonbangkhen, C and Lee, VS and Khotavivattana, T and Rungrotmongkol, T and Hannongbua, S},
title = {FMO-guided design of darunavir analogs as HIV-1 protease inhibitors.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {3639},
pmid = {38351065},
issn = {2045-2322},
mesh = {Humans ; Darunavir/pharmacology ; *HIV Protease Inhibitors/pharmacology/chemistry ; *HIV-1/genetics ; Molecular Docking Simulation ; Sulfonamides/pharmacology ; *HIV Infections ; Viral Proteins/genetics ; HIV Protease/metabolism ; Mutation ; Drug Resistance, Viral/genetics ; },
abstract = {The prevalence of HIV-1 infection continues to pose a significant global public health issue, highlighting the need for antiretroviral drugs that target viral proteins to reduce viral replication. One such target is HIV-1 protease (PR), responsible for cleaving viral polyproteins, leading to the maturation of viral proteins. While darunavir (DRV) is a potent HIV-1 PR inhibitor, drug resistance can arise due to mutations in HIV-1 PR. To address this issue, we developed a novel approach using the fragment molecular orbital (FMO) method and structure-based drug design to create DRV analogs. Using combinatorial programming, we generated novel analogs freely accessible via an on-the-cloud mode implemented in Google Colab, Combined Analog generator Tool (CAT). The designed analogs underwent cascade screening through molecular docking with HIV-1 PR wild-type and major mutations at the active site. Molecular dynamics (MD) simulations confirmed the assess ligand binding and susceptibility of screened designed analogs. Our findings indicate that the three designed analogs guided by FMO, 19-0-14-3, 19-8-10-0, and 19-8-14-3, are superior to DRV and have the potential to serve as efficient PR inhibitors. These findings demonstrate the effectiveness of our approach and its potential to be used in further studies for developing new antiretroviral drugs.},
}

Humans
Darunavir/pharmacology
*HIV Protease Inhibitors/pharmacology/chemistry
*HIV-1/genetics
Molecular Docking Simulation
Sulfonamides/pharmacology
*HIV Infections
Viral Proteins/genetics
HIV Protease/metabolism
Mutation
Drug Resistance, Viral/genetics

@article {pmid38350039,
year = {2024},
author = {Bell, J and Decker, B and Eichmann, A and Palkovich, C and Reji, C},
title = {Effectiveness of Virtual Reality for Upper Extremity Function and Motor Performance of Children With Cerebral Palsy: A Systematic Review.},
journal = {The American journal of occupational therapy : official publication of the American Occupational Therapy Association},
volume = {78},
number = {2},
pages = {},
doi = {10.5014/ajot.2024.050374},
pmid = {38350039},
issn = {0272-9490},
mesh = {Child ; Humans ; Young Adult ; Adult ; *Cerebral Palsy ; Upper Extremity ; *Virtual Reality ; Language ; },
abstract = {IMPORTANCE: Research on the functional and motor performance impact of virtual reality (VR) as an intervention tool for children with cerebral palsy (CP) is limited.

OBJECTIVE: To understand whether VR is an effective intervention to improve upper extremity (UE) function and motor performance of children diagnosed with CP.

DATA SOURCES: Databases used in the search were EBSCOhost, One Search, PubMed, Cloud Source, CINAHL, SPORTDiscus, and Google Scholar.

Studies published from 2006 to 2021 were included if children had a diagnosis of CP and were age 21 yr or younger, VR was used as an intervention, and measures of UE function and motor performance were used.

FINDINGS: Twenty-one studies were included, and the results provided promising evidence for improvements in areas of UE function, motor performance, and fine motor skills when VR is used as an intervention. To yield noticeable UE improvements in children with CP, VR should be implemented for 30 to 60 min/session and for at least 360 min over more than 3 wk. Additional areas of improvement include gross motor skills, functional mobility, occupational performance, and intrinsic factors.

CONCLUSIONS AND RELEVANCE: The use of VR as an intervention for children with CP to improve UE function and motor performance is supported. More randomized controlled trials with larger sample sizes focusing on similar outcomes and intervention frequencies are needed to determine the most effective type of VR for use in clinical occupational therapy. Plain-Language Summary: This systematic review explains how virtual reality (VR) has been used as an intervention with children with cerebral palsy (CP). The review synthesizes the results of 21 research studies of children who had a diagnosis of CP and who were 21 years old or younger. The findings support using VR to improve upper extremity performance, motor performance, and fine motor skills. The findings also show that occupational therapy practitioners should use a VR intervention at a minimum frequency of 30 to 60 minutes per session and for at least 360 minutes over more than 3 weeks to yield noticeable improvements in upper extremity, motor performance, and fine motor skills for children with CP.},
}

Child
Humans
Young Adult
Adult
*Cerebral Palsy
Upper Extremity
*Virtual Reality
Language

@article {pmid38347885,
year = {2024},
author = {Bhattacharjee, T and Kiwuwa-Muyingo, S and Kanjala, C and Maoyi, ML and Amadi, D and Ochola, M and Kadengye, D and Gregory, A and Kiragga, A and Taylor, A and Greenfield, J and Slaymaker, E and Todd, J and , },
title = {INSPIRE datahub: a pan-African integrated suite of services for harmonising longitudinal population health data using OHDSI tools.},
journal = {Frontiers in digital health},
volume = {6},
number = {},
pages = {1329630},
pmid = {38347885},
issn = {2673-253X},
abstract = {INTRODUCTION: Population health data integration remains a critical challenge in low- and middle-income countries (LMIC), hindering the generation of actionable insights to inform policy and decision-making. This paper proposes a pan-African, Findable, Accessible, Interoperable, and Reusable (FAIR) research architecture and infrastructure named the INSPIRE datahub. This cloud-based Platform-as-a-Service (PaaS) and on-premises setup aims to enhance the discovery, integration, and analysis of clinical, population-based surveys, and other health data sources.

METHODS: The INSPIRE datahub, part of the Implementation Network for Sharing Population Information from Research Entities (INSPIRE), employs the Observational Health Data Sciences and Informatics (OHDSI) open-source stack of tools and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to harmonise data from African longitudinal population studies. Operating on Microsoft Azure and Amazon Web Services cloud platforms, and on on-premises servers, the architecture offers adaptability and scalability for other cloud providers and technology infrastructure. The OHDSI-based tools enable a comprehensive suite of services for data pipeline development, profiling, mapping, extraction, transformation, loading, documentation, anonymization, and analysis.

RESULTS: The INSPIRE datahub's "On-ramp" services facilitate the integration of data and metadata from diverse sources into the OMOP CDM. The datahub supports the implementation of OMOP CDM across data producers, harmonizing source data semantically with standard vocabularies and structurally conforming to OMOP table structures. Leveraging OHDSI tools, the datahub performs quality assessment and analysis of the transformed data. It ensures FAIR data by establishing metadata flows, capturing provenance throughout the ETL processes, and providing accessible metadata for potential users. The ETL provenance is documented in a machine- and human-readable Implementation Guide (IG), enhancing transparency and usability.

CONCLUSION: The pan-African INSPIRE datahub presents a scalable and systematic solution for integrating health data in LMICs. By adhering to FAIR principles and leveraging established standards like OMOP CDM, this architecture addresses the current gap in generating evidence to support policy and decision-making for improving the well-being of LMIC populations. The federated research network provisions allow data producers to maintain control over their data, fostering collaboration while respecting data privacy and security concerns. A use-case demonstrated the pipeline using OHDSI and other open-source tools.},
}

@article {pmid38345858,
year = {2024},
author = {Zandesh, Z},
title = {Privacy, Security, and Legal Issues in the Health Cloud: Structured Review for Taxonomy Development.},
journal = {JMIR formative research},
volume = {8},
number = {},
pages = {e38372},
pmid = {38345858},
issn = {2561-326X},
abstract = {BACKGROUND: Privacy in our digital world is a very complicated topic, especially when meeting cloud computing technological achievements with its multidimensional context. Here, privacy is an extended concept that is sometimes referred to as legal, philosophical, or even technical. Consequently, there is a need to harmonize it with other aspects in health care in order to provide a new ecosystem. This new ecosystem can lead to a paradigm shift involving the reconstruction and redesign of some of the most important and essential requirements like privacy concepts, legal issues, and security services. Cloud computing in the health domain has markedly contributed to other technologies, such as mobile health, health Internet of Things, and wireless body area networks, with their increasing numbers of embedded applications. Other dependent applications, which are usually used in health businesses like social networks, or some newly introduced applications have issues regarding privacy transparency boundaries and privacy-preserving principles, which have made policy making difficult in the field.

OBJECTIVE: One way to overcome this challenge is to develop a taxonomy to identify all relevant factors. A taxonomy serves to bring conceptual clarity to the set of alternatives in in-person health care delivery. This study aimed to construct a comprehensive taxonomy for privacy in the health cloud, which also provides a prospective landscape for privacy in related technologies.

METHODS: A search was performed for relevant published English papers in databases, including Web of Science, IEEE Digital Library, Google Scholar, Scopus, and PubMed. A total of 2042 papers were related to the health cloud privacy concept according to predefined keywords and search strings. Taxonomy designing was performed using the deductive methodology.

RESULTS: This taxonomy has 3 layers. The first layer has 4 main dimensions, including cloud, data, device, and legal. The second layer has 15 components, and the final layer has related subcategories (n=57). This taxonomy covers some related concepts, such as privacy, security, confidentiality, and legal issues, which are categorized here and defined by their expansion and distinctive boundaries. The main merits of this taxonomy are its ability to clarify privacy terms for different scenarios and signalize the privacy multidisciplinary objectification in eHealth.

CONCLUSIONS: This taxonomy can cover health industry requirements with its specifications like health data and scenarios, which are considered as the most complicated among businesses and industries. Therefore, the use of this taxonomy could be generalized and customized to other domains and businesses that have less complications. Moreover, this taxonomy has different stockholders, including people, organizations, and systems. If the antecedent effort in the taxonomy is proven, subject matter experts could enhance the extent of privacy in the health cloud by verifying, evaluating, and revising this taxonomy.},
}

@article {pmid38345524,
year = {2024},
author = {McCoy, ES and Park, SK and Patel, RP and Ryan, DF and Mullen, ZJ and Nesbitt, JJ and Lopez, JE and Taylor-Blake, B and Vanden, KA and Krantz, JL and Hu, W and Garris, RL and Snyder, MG and Lima, LV and Sotocinal, SG and Austin, JS and Kashlan, AD and Shah, S and Trocinski, AK and Pudipeddi, SS and Major, RM and Bazick, HO and Klein, MR and Mogil, JS and Wu, G and Zylka, MJ},
title = {Development of PainFace software to simplify, standardize, and scale up mouse grimace analyses.},
journal = {Pain},
volume = {},
number = {},
pages = {},
doi = {10.1097/j.pain.0000000000003187},
pmid = {38345524},
issn = {1872-6623},
support = {R01NS114259/NS/NINDS NIH HHS/United States ; },
abstract = {Facial grimacing is used to quantify spontaneous pain in mice and other mammals, but scoring relies on humans with different levels of proficiency. Here, we developed a cloud-based software platform called PainFace (http://painface.net) that uses machine learning to detect 4 facial action units of the mouse grimace scale (orbitals, nose, ears, whiskers) and score facial grimaces of black-coated C57BL/6 male and female mice on a 0 to 8 scale. Platform accuracy was validated in 2 different laboratories, with 3 conditions that evoke grimacing-laparotomy surgery, bilateral hindpaw injection of carrageenan, and intraplantar injection of formalin. PainFace can generate up to 1 grimace score per second from a standard 30 frames/s video, making it possible to quantify facial grimacing over time, and operates at a speed that scales with computing power. By analyzing the frequency distribution of grimace scores, we found that mice spent 7x more time in a "high grimace" state following laparotomy surgery relative to sham surgery controls. Our study shows that PainFace reproducibly quantifies facial grimaces indicative of nonevoked spontaneous pain and enables laboratories to standardize and scale-up facial grimace analyses.},
}

@article {pmid38344670,
year = {2024},
author = {Simpson, RL and Lee, JA and Li, Y and Kang, YJ and Tsui, C and Cimiotti, JP},
title = {Medicare meets the cloud: the development of a secure platform for the storage and analysis of claims data.},
journal = {JAMIA open},
volume = {7},
number = {1},
pages = {ooae007},
pmid = {38344670},
issn = {2574-2531},
support = {R01 HS026232/HS/AHRQ HHS/United States ; },
abstract = {INTRODUCTION: Cloud-based solutions are a modern-day necessity for data intense computing. This case report describes in detail the development and implementation of Amazon Web Services (AWS) at Emory-a secure, reliable, and scalable platform to store and analyze identifiable research data from the Centers for Medicare and Medicaid Services (CMS).

MATERIALS AND METHODS: Interdisciplinary teams from CMS, MBL Technologies, and Emory University collaborated to ensure compliance with CMS policy that consolidates laws, regulations, and other drivers of information security and privacy.

RESULTS: A dedicated team of individuals ensured successful transition from a physical storage server to a cloud-based environment. This included implementing access controls, vulnerability scanning, and audit logs that are reviewed regularly with a remediation plan. User adaptation required specific training to overcome the challenges of cloud computing.

CONCLUSION: Challenges created opportunities for lessons learned through the creation of an end-product accepted by CMS and shared across disciplines university-wide.},
}

@article {pmid38339714,
year = {2024},
author = {González-Herbón, R and González-Mateos, G and Rodríguez-Ossorio, JR and Domínguez, M and Alonso, S and Fuertes, JJ},
title = {An Approach to Develop Digital Twins in Industry.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {3},
pages = {},
pmid = {38339714},
issn = {1424-8220},
support = {Grant PID2020-117890RB-I00//Ministerio de Ciencia e Innovación/ ; },
abstract = {The industry is currently undergoing a digital revolution driven by the integration of several enabling technologies. These include automation, robotics, cloud computing, industrial cybersecurity, systems integration, digital twins, etc. Of particular note is the increasing use of digital twins, which offer significant added value by providing realistic and fully functional process simulations. This paper proposes an approach for developing digital twins in industrial environments. The novelty lies in not only focusing on obtaining the model of the industrial system and integrating virtual reality and/or augmented reality but also in emphasizing the importance of incorporating other enabled technologies of Industry 4.0, such as system integration, connectivity with standard and specific industrial protocols, cloud services, or new industrial automation systems, to enhance the capabilities of the digital twin. Furthermore, a proposal of the software tools that can be used to achieve this incorporation is made. Unity is chosen as the real-time 3D development tool for its cross-platform capability and streamlined industrial system modeling. The integration of augmented reality is facilitated by the Vuforia SDK. Node-RED is selected as the system integration option, and communications are carried out with MQTT protocol. Finally, cloud-based services are recommended for effective data storage and processing. Furthermore, this approach has been used to develop a digital twin of a robotic electro-pneumatic cell.},
}

@article {pmid38339672,
year = {2024},
author = {Lu, Y and Zhou, L and Zhang, A and Zha, S and Zhuo, X and Ge, S},
title = {Application of Deep Learning and Intelligent Sensing Analysis in Smart Home.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {3},
pages = {},
pmid = {38339672},
issn = {1424-8220},
abstract = {Deep learning technology can improve sensing efficiency and has the ability to discover potential patterns in data; the efficiency of user behavior recognition in the field of smart homes has been further improved, making the recognition process more intelligent and humanized. This paper analyzes the optical sensors commonly used in smart homes and their working principles through case studies and explores the technical framework of user behavior recognition based on optical sensors. At the same time, CiteSpace (Basic version 6.2.R6) software is used to visualize and analyze the related literature, elaborate the main research hotspots and evolutionary changes of optical sensor-based smart home user behavior recognition, and summarize the future research trends. Finally, fully utilizing the advantages of cloud computing technology, such as scalability and on-demand services, combining typical life situations and the requirements of smart home users, a smart home data collection and processing technology framework based on elderly fall monitoring scenarios is designed. Based on the comprehensive research results, the application and positive impact of optical sensors in smart home user behavior recognition were analyzed, and inspiration was provided for future smart home user experience research.},
}

@article {pmid38339591,
year = {2024},
author = {Ehtisham, M and Hassan, MU and Al-Awady, AA and Ali, A and Junaid, M and Khan, J and Abdelrahman Ali, YA and Akram, M},
title = {Internet of Vehicles (IoV)-Based Task Scheduling Approach Using Fuzzy Logic Technique in Fog Computing Enables Vehicular Ad Hoc Network (VANET).},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {3},
pages = {},
pmid = {38339591},
issn = {1424-8220},
support = {NU/IFC/2/SERC/-/47//Najran University/ ; Authors would like to acknowledge the support of the Deputy for Research and Innovation Ministry of Education, Kingdom of Saudi Arabia, for this research through a grant (NU/IFC/2/SERC/-/47) under the Institutional Funding Committee at Najran University,//Najran University/ ; },
abstract = {The intelligent transportation system (ITS) relies heavily on the vehicular ad hoc network (VANET) and the internet of vehicles (IoVs), which combine cloud and fog to improve task processing capabilities. As a cloud extension, the fog processes' infrastructure is close to VANET, fostering an environment favorable to smart cars with IT equipment and effective task management oversight. Vehicle processing power, bandwidth, time, and high-speed mobility are all limited in VANET. It is critical to satisfy the vehicles' requirements for minimal latency and fast reaction times while offloading duties to the fog layer. We proposed a fuzzy logic-based task scheduling system in VANET to minimize latency and improve the enhanced response time when offloading tasks in the IoV. The proposed method effectively transfers workloads to the fog computing layer while considering the constrained resources of car nodes. After choosing a suitable processing unit, the algorithm sends the job and its associated resources to the fog layer. The dataset is related to crisp values for fog computing for system utilization, latency, and task deadline time for over 5000 values. The task execution, latency, deadline of task, storage, CPU, and bandwidth utilizations are used for fuzzy set values. We proved the effectiveness of our proposed task scheduling framework via simulation tests, outperforming current algorithms in terms of task ratio by 13%, decreasing average turnaround time by 9%, minimizing makespan time by 15%, and effectively overcoming average latency time within the network parameters. The proposed technique shows better results and responses than previous techniques by scheduling the tasks toward fog layers with less response time and minimizing the overall time from task submission to completion.},
}

@article {pmid38339582,
year = {2024},
author = {Hassan, MU and Al-Awady, AA and Ali, A and Iqbal, MM and Akram, M and Jamil, H},
title = {Smart Resource Allocation in Mobile Cloud Next-Generation Network (NGN) Orchestration with Context-Aware Data and Machine Learning for the Cost Optimization of Microservice Applications.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {3},
pages = {},
pmid = {38339582},
issn = {1424-8220},
support = {NU/IFC/2/SERC/-/47//Deputy for Research and Innovation Ministry of Education, Kingdom of Saudi Arabia/ ; },
abstract = {Mobile cloud computing (MCC) provides resources to users to handle smart mobile applications. In MCC, task scheduling is the solution for mobile users' context-aware computation resource-rich applications. Most existing approaches have achieved a moderate service reliability rate due to a lack of instance-centric resource estimations and task offloading, a statistical NP-hard problem. The current intelligent scheduling process cannot address NP-hard problems due to traditional task offloading approaches. To address this problem, the authors design an efficient context-aware service offloading approach based on instance-centric measurements. The revised machine learning model/algorithm employs task adaptation to make decisions regarding task offloading. The proposed MCVS scheduling algorithm predicts the usage rates of individual microservices for a practical task scheduling scheme, considering mobile device time, cost, network, location, and central processing unit (CPU) power to train data. One notable feature of the microservice software architecture is its capacity to facilitate the scalability, flexibility, and independent deployment of individual components. A series of simulation results show the efficiency of the proposed technique based on offloading, CPU usage, and execution time metrics. The experimental results efficiently show the learning rate in training and testing in comparison with existing approaches, showing efficient training and task offloading phases. The proposed system has lower costs and uses less energy to offload microservices in MCC. Graphical results are presented to define the effectiveness of the proposed model. For a service arrival rate of 80%, the proposed model achieves an average 4.5% service offloading rate and 0.18% CPU usage rate compared with state-of-the-art approaches. The proposed method demonstrates efficiency in terms of cost and energy savings for microservice offloading in mobile cloud computing (MCC).},
}

@article {pmid38339552,
year = {2024},
author = {Parracciani, C and Gigante, D and Bonini, F and Grassi, A and Morbidini, L and Pauselli, M and Valenti, B and Lilli, E and Antonielli, F and Vizzari, M},
title = {Leveraging Google Earth Engine for a More Effective Grassland Management: A Decision Support Application Perspective.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {3},
pages = {},
pmid = {38339552},
issn = {1424-8220},
mesh = {Animals ; Humans ; *Ecosystem ; *Grassland ; Search Engine ; Biodiversity ; Agriculture ; Livestock ; },
abstract = {Grasslands cover a substantial portion of the earth's surface and agricultural land and is crucial for human well-being and livestock farming. Ranchers and grassland management authorities face challenges in effectively controlling herders' grazing behavior and grassland utilization due to underdeveloped infrastructure and poor communication in pastoral areas. Cloud-based grazing management and decision support systems (DSS) are needed to address this issue, promote sustainable grassland use, and preserve their ecosystem services. These systems should enable rapid and large-scale grassland growth and utilization monitoring, providing a basis for decision-making in managing grazing and grassland areas. In this context, this study contributes to the objectives of the EU LIFE IMAGINE project, aiming to develop a Web-GIS app for conserving and monitoring Umbria's grasslands and promoting more informed decisions for more sustainable livestock management. The app, called "Praterie" and developed in Google Earth Engine, utilizes historical Sentinel-2 satellite data and harmonic modeling of the EVI (Enhanced Vegetation Index) to estimate vegetation growth curves and maturity periods for the forthcoming vegetation cycle. The app is updated in quasi-real time and enables users to visualize estimates for the upcoming vegetation cycle, including the maximum greenness, the days remaining to the subsequent maturity period, the accuracy of the harmonic models, and the grassland greenness status in the previous 10 days. Even though future additional developments can improve the informative value of the Praterie app, this platform can contribute to optimizing livestock management and biodiversity conservation by providing timely and accurate data about grassland status and growth curves.},
}

Animals
Humans
*Ecosystem
*Grassland
Search Engine
Biodiversity
Agriculture
Livestock

@article {pmid38339545,
year = {2024},
author = {Gragnaniello, M and Borghese, A and Marrazzo, VR and Maresca, L and Breglio, G and Irace, A and Riccio, M},
title = {Real-Time Myocardial Infarction Detection Approaches with a Microcontroller-Based Edge-AI Device.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {3},
pages = {},
pmid = {38339545},
issn = {1424-8220},
support = {PNC0000007//Italian Ministry for Universities and Research (MUR)/ ; },
mesh = {Humans ; *Myocardial Infarction/diagnosis ; Heart ; *Heart Diseases ; Myocardium ; Algorithms ; },
abstract = {Myocardial Infarction (MI), commonly known as heart attack, is a cardiac condition characterized by damage to a portion of the heart, specifically the myocardium, due to the disruption of blood flow. Given its recurring and often asymptomatic nature, there is the need for continuous monitoring using wearable devices. This paper proposes a single-microcontroller-based system designed for the automatic detection of MI based on the Edge Computing paradigm. Two solutions for MI detection are evaluated, based on Machine Learning (ML) and Deep Learning (DL) techniques. The developed algorithms are based on two different approaches currently available in the literature, and they are optimized for deployment on low-resource hardware. A feasibility assessment of their implementation on a single 32-bit microcontroller with an ARM Cortex-M4 core was examined, and a comparison in terms of accuracy, inference time, and memory usage was detailed. For ML techniques, significant data processing for feature extraction, coupled with a simpler Neural Network (NN) is involved. On the other hand, the second method, based on DL, employs a Spectrogram Analysis for feature extraction and a Convolutional Neural Network (CNN) with a longer inference time and higher memory utilization. Both methods employ the same low power hardware reaching an accuracy of 89.40% and 94.76%, respectively. The final prototype is an energy-efficient system capable of real-time detection of MI without the need to connect to remote servers or the cloud. All processing is performed at the edge, enabling NN inference on the same microcontroller.},
}

Humans
*Myocardial Infarction/diagnosis
Heart
*Heart Diseases
Myocardium
Algorithms

@article {pmid38332408,
year = {2024},
author = {Huang, Z and Herbozo Contreras, LF and Yu, L and Truong, ND and Nikpour, A and Kavehei, O},
title = {S4D-ECG: A Shallow State-of-the-Art Model for Cardiac Abnormality Classification.},
journal = {Cardiovascular engineering and technology},
volume = {},
number = {},
pages = {},
pmid = {38332408},
issn = {1869-4098},
abstract = {PURPOSE: This study introduces an algorithm specifically designed for processing unprocessed 12-lead electrocardiogram (ECG) data, with the primary aim of detecting cardiac abnormalities.

METHODS: The proposed model integrates Diagonal State Space Sequence (S4D) model into its architecture, leveraging its effectiveness in capturing dynamics within time-series data. The S4D model is designed with stacked S4D layers for processing raw input data and a simplified decoder using a dense layer for predicting abnormality types. Experimental optimization determines the optimal number of S4D layers, striking a balance between computational efficiency and predictive performance. This comprehensive approach ensures the model's suitability for real-time processing on hardware devices with limited capabilities, offering a streamlined yet effective solution for heart monitoring.

RESULTS: Among the notable features of this algorithm is its strong resilience to noise, enabling the algorithm to achieve an average F1-score of 81.2% and an AUROC of 95.5% in generalization. The model underwent testing specifically on the lead II ECG signal, exhibiting consistent performance with an F1-score of 79.5% and an AUROC of 95.7%.

CONCLUSION: It is characterized by the elimination of pre-processing features and the availability of a low-complexity architecture that makes it suitable for implementation on numerous computing devices because it is easily implementable. Consequently, this algorithm exhibits considerable potential for practical applications in analyzing real-world ECG data. This model can be placed on the cloud for diagnosis. The model was also tested on lead II of the ECG alone and has demonstrated promising results, supporting its potential for on-device application.},
}

@article {pmid38327871,
year = {2024},
author = {Schönherr, S and Schachtl-Riess, JF and Di Maio, S and Filosi, M and Mark, M and Lamina, C and Fuchsberger, C and Kronenberg, F and Forer, L},
title = {Performing highly parallelized and reproducible GWAS analysis on biobank-scale data.},
journal = {NAR genomics and bioinformatics},
volume = {6},
number = {1},
pages = {lqae015},
pmid = {38327871},
issn = {2631-9268},
abstract = {Genome-wide association studies (GWAS) are transforming genetic research and enable the detection of novel genotype-phenotype relationships. In the last two decades, over 60 000 genetic associations across thousands of traits have been discovered using a GWAS approach. Due to increasing sample sizes, researchers are increasingly faced with computational challenges. A reproducible, modular and extensible pipeline with a focus on parallelization is essential to simplify data analysis and to allow researchers to devote their time to other essential tasks. Here we present nf-gwas, a Nextflow pipeline to run biobank-scale GWAS analysis. The pipeline automatically performs numerous pre- and post-processing steps, integrates regression modeling from the REGENIE package and supports single-variant, gene-based and interaction testing. It includes an extensive reporting functionality that allows to inspect thousands of phenotypes and navigate interactive Manhattan plots directly in the web browser. The pipeline is tested using the unit-style testing framework nf-test, a crucial requirement in clinical and pharmaceutical settings. Furthermore, we validated the pipeline against published GWAS datasets and benchmarked the pipeline on high-performance computing and cloud infrastructures to provide cost estimations to end users. nf-gwas is a highly parallelized, scalable and well-tested Nextflow pipeline to perform GWAS analysis in a reproducible manner.},
}

@article {pmid38324613,
year = {2024},
author = {Swetnam, TL and Antin, PB and Bartelme, R and Bucksch, A and Camhy, D and Chism, G and Choi, I and Cooksey, AM and Cosi, M and Cowen, C and Culshaw-Maurer, M and Davey, R and Davey, S and Devisetty, U and Edgin, T and Edmonds, A and Fedorov, D and Frady, J and Fonner, J and Gillan, JK and Hossain, I and Joyce, B and Lang, K and Lee, T and Littin, S and McEwen, I and Merchant, N and Micklos, D and Nelson, A and Ramsey, A and Roberts, S and Sarando, P and Skidmore, E and Song, J and Sprinkle, MM and Srinivasan, S and Stanzione, D and Strootman, JD and Stryeck, S and Tuteja, R and Vaughn, M and Wali, M and Wall, M and Walls, R and Wang, L and Wickizer, T and Williams, J and Wregglesworth, J and Lyons, E},
title = {CyVerse: Cyberinfrastructure for open science.},
journal = {PLoS computational biology},
volume = {20},
number = {2},
pages = {e1011270},
pmid = {38324613},
issn = {1553-7358},
mesh = {Humans ; *Artificial Intelligence ; *Software ; Cloud Computing ; Publishing ; },
abstract = {CyVerse, the largest publicly-funded open-source research cyberinfrastructure for life sciences, has played a crucial role in advancing data-driven research since the 2010s. As the technology landscape evolved with the emergence of cloud computing platforms, machine learning and artificial intelligence (AI) applications, CyVerse has enabled access by providing interfaces, Software as a Service (SaaS), and cloud-native Infrastructure as Code (IaC) to leverage new technologies. CyVerse services enable researchers to integrate institutional and private computational resources, custom software, perform analyses, and publish data in accordance with open science principles. Over the past 13 years, CyVerse has registered more than 124,000 verified accounts from 160 countries and was used for over 1,600 peer-reviewed publications. Since 2011, 45,000 students and researchers have been trained to use CyVerse. The platform has been replicated and deployed in three countries outside the US, with additional private deployments on commercial clouds for US government agencies and multinational corporations. In this manuscript, we present a strategic blueprint for creating and managing SaaS cyberinfrastructure and IaC as free and open-source software.},
}

Humans
*Artificial Intelligence
*Software
Cloud Computing
Publishing

@article {pmid38323147,
year = {2024},
author = {Lewis, EC and Zhu, S and Oladimeji, AT and Igusa, T and Martin, NM and Poirier, L and Trujillo, AJ and Reznar, MM and Gittelsohn, J},
title = {Design of an innovative digital application to facilitate access to healthy foods in low-income urban settings.},
journal = {mHealth},
volume = {10},
number = {},
pages = {2},
pmid = {38323147},
issn = {2306-9740},
support = {R34 HL145368/HL/NHLBI NIH HHS/United States ; T32 DK062707/DK/NIDDK NIH HHS/United States ; },
abstract = {BACKGROUND: Under-resourced urban minority communities in the United States are characterized by food environments with low access to healthy foods, high food insecurity, and high rates of diet-related chronic disease. In Baltimore, Maryland, low access to healthy food largely results from a distribution gap between small food sources (retailers) and their suppliers. Digital interventions have the potential to address this gap, while keeping costs low.

METHODS: In this paper, we describe the technical (I) front-end design and (II) back-end development process of the Baltimore Urban food Distribution (BUD) application (app). We identify and detail four main phases of the process: (I) information architecture; (II) low and high-fidelity wireframes; (III) prototype; and (IV) back-end components, while considering formative research and a pre-pilot test of a preliminary version of the BUD app.

RESULTS: Our lessons learned provide valuable insight into developing a stable app with a user-friendly experience and interface, and accessible cloud computing services for advanced technical features.

CONCLUSIONS: Next steps will involve a pilot trial of the app in Baltimore, and eventually, other urban and rural settings nationwide. Once iterative feedback is incorporated into the app, all code will be made publicly available via an open source repository to encourage adaptation for desired communities.

TRIAL REGISTRATION: ClinicalTrials.gov NCT05010018.},
}

@article {pmid38321247,
year = {2024},
author = {Pacios, D and Vázquez-Poletti, JL and Dhuri, DB and Atri, D and Moreno-Vozmediano, R and Lillis, RJ and Schetakis, N and Gómez-Sanz, J and Iorio, AD and Vázquez, L},
title = {A serverless computing architecture for Martian aurora detection with the Emirates Mars Mission.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {3029},
pmid = {38321247},
issn = {2045-2322},
support = {101007638//Horizon 2020 Framework Programme/ ; G1502//New York University Abu Dhabi/ ; S1560//Advanced Technology Research Council/ ; },
abstract = {Remote sensing technologies are experiencing a surge in adoption for monitoring Earth's environment, demanding more efficient and scalable methods for image analysis. This paper presents a new approach for the Emirates Mars Mission (Hope probe); A serverless computing architecture designed to analyze images of Martian auroras, a key aspect in understanding the Martian atmosphere. Harnessing the power of OpenCV and machine learning algorithms, our architecture offers image classification, object detection, and segmentation in a swift and cost-effective manner. Leveraging the scalability and elasticity of cloud computing, this innovative system is capable of managing high volumes of image data, adapting to fluctuating workloads. This technology, applied to the study of Martian auroras within the HOPE Mission, not only solves a complex problem but also paves the way for future applications in the broad field of remote sensing.},
}

@article {pmid38315519,
year = {2024},
author = {Xu, J},
title = {The Current Status and Promotional Strategies for Cloud Migration of Hospital Information Systems in China: Strengths, Weaknesses, Opportunities, and Threats Analysis.},
journal = {JMIR medical informatics},
volume = {12},
number = {},
pages = {e52080},
pmid = {38315519},
issn = {2291-9694},
abstract = {BACKGROUND: In the 21st century, Chinese hospitals have witnessed innovative medical business models, such as online diagnosis and treatment, cross-regional multidepartment consultation, and real-time sharing of medical test results, that surpass traditional hospital information systems (HISs). The introduction of cloud computing provides an excellent opportunity for hospitals to address these challenges. However, there is currently no comprehensive research assessing the cloud migration of HISs in China. This lack may hinder the widespread adoption and secure implementation of cloud computing in hospitals.

OBJECTIVE: The objective of this study is to comprehensively assess external and internal factors influencing the cloud migration of HISs in China and propose promotional strategies.

METHODS: Academic articles from January 1, 2007, to February 21, 2023, on the topic were searched in PubMed and HuiyiMd databases, and relevant documents such as national policy documents, white papers, and survey reports were collected from authoritative sources for analysis. A systematic assessment of factors influencing cloud migration of HISs in China was conducted by combining a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis and literature review methods. Then, various promotional strategies based on different combinations of external and internal factors were proposed.

RESULTS: After conducting a thorough search and review, this study included 94 academic articles and 37 relevant documents. The analysis of these documents reveals the increasing application of and research on cloud computing in Chinese hospitals, and that it has expanded to 22 disciplinary domains. However, more than half (n=49, 52%) of the documents primarily focused on task-specific cloud-based systems in hospitals, while only 22% (n=21 articles) discussed integrated cloud platforms shared across the entire hospital, medical alliance, or region. The SWOT analysis showed that cloud computing adoption in Chinese hospitals benefits from policy support, capital investment, and social demand for new technology. However, it also faces threats like loss of digital sovereignty, supplier competition, cyber risks, and insufficient supervision. Factors driving cloud migration for HISs include medical big data analytics and use, interdisciplinary collaboration, health-centered medical service provision, and successful cases. Barriers include system complexity, security threats, lack of strategic planning and resource allocation, relevant personnel shortages, and inadequate investment. This study proposes 4 promotional strategies: encouraging more hospitals to migrate, enhancing hospitals' capabilities for migration, establishing a provincial-level unified medical hybrid multi-cloud platform, strengthening legal frameworks, and providing robust technical support.

CONCLUSIONS: Cloud computing is an innovative technology that has gained significant attention from both the Chinese government and the global community. In order to effectively support the rapid growth of a novel, health-centered medical industry, it is imperative for Chinese health authorities and hospitals to seize this opportunity by implementing comprehensive strategies aimed at encouraging hospitals to migrate their HISs to the cloud.},
}

@article {pmid38312948,
year = {2024},
author = {Ssekagiri, A and Jjingo, D and Bbosa, N and Bugembe, DL and Kateete, DP and Jordan, IK and Kaleebu, P and Ssemwanga, D},
title = {HIVseqDB: a portable resource for NGS and sample metadata integration for HIV-1 drug resistance analysis.},
journal = {Bioinformatics advances},
volume = {4},
number = {1},
pages = {vbae008},
pmid = {38312948},
issn = {2635-0041},
support = {MC_UU_00027/5/MRC_/Medical Research Council/United Kingdom ; },
abstract = {SUMMARY: Human immunodeficiency virus (HIV) remains a public health threat, with drug resistance being a major concern in HIV treatment. Next-generation sequencing (NGS) is a powerful tool for identifying low-abundance drug resistance mutations (LA-DRMs) that conventional Sanger sequencing cannot reliably detect. To fully understand the significance of LA-DRMs, it is necessary to integrate NGS data with clinical and demographic data. However, freely available tools for NGS-based HIV-1 drug resistance analysis do not integrate these data. This poses a challenge in interpretation of the impact of LA-DRMs, mainly for resource-limited settings due to the shortage of bioinformatics expertise. To address this challenge, we present HIVseqDB, a portable, secure, and user-friendly resource for integrating NGS data with associated clinical and demographic data for analysis of HIV drug resistance. HIVseqDB currently supports uploading of NGS data and associated sample data, HIV-1 drug resistance data analysis, browsing of uploaded data, and browsing and visualizing of analysis results. Each function of HIVseqDB corresponds to an individual Django application. This ensures efficient incorporation of additional features with minimal effort. HIVseqDB can be deployed on various computing environments, such as on-premises high-performance computing facilities and cloud-based platforms.

HIVseqDB is available at https://github.com/AlfredUg/HIVseqDB. A deployed instance of HIVseqDB is available at https://hivseqdb.org.},
}

@article {pmid38308984,
year = {2024},
author = {Lan, L and Wang, YG and Chen, HS and Gao, XR and Wang, XK and Yan, XF},
title = {Improving on mapping long-term surface water with a novel framework based on the Landsat imagery series.},
journal = {Journal of environmental management},
volume = {353},
number = {},
pages = {120202},
doi = {10.1016/j.jenvman.2024.120202},
pmid = {38308984},
issn = {1095-8630},
mesh = {*Water ; *Environmental Monitoring/methods ; Satellite Imagery ; Environment ; Algorithms ; },
abstract = {Surface water plays a crucial role in the ecological environment and societal development. Remote sensing detection serves as a significant approach to understand the temporal and spatial change in surface water series (SWS) and to directly construct long-term SWS. Limited by various factors such as cloud, cloud shadow, and problematic satellite sensor monitoring, the existent surface water mapping datasets might be short and incomplete due to losing raw information on certain dates. Improved algorithms are desired to increase the completeness and quality of SWS datasets. The present study proposes an automated framework to detect SWS, based on the Google Earth Engine and Landsat satellite imagery. This framework incorporates implementing a raw image filtering algorithm to increase available images, thereby expanding the completeness. It improves OTSU thresholding by replacing anomaly thresholds with the median value, thus enhancing the accuracy of SWS datasets. Gaps caused by Landsat7 ETM + SLC-off are respired with the random forest algorithm and morphological operations. The results show that this novel framework effectively expands the long-term series of SWS for three surface water bodies with distinct geomorphological patterns. The evaluation of confusion matrices suggests the good performance of extracting surface water, with the overall accuracy ranging from 0.96 to 0.97, and user's accuracy between 0.96 and 0.98, producer's accuracy ranging from 0.83 to 0.89, and Matthews correlation coefficient ranging from 0.87 to 0.9 for several spectral water indices (NDWI, MNDWI, ANNDWI, and AWEI). Compared with the Global Reservoirs Surface Area Dynamics (GRSAD) dataset, our constructed datasets promote greater completeness of SWS datasets by 27.01%-91.89% for the selected water bodies. The proposed framework for detecting SWS shows good potential in enlarging and completing long-term global-scale SWS datasets, capable of supporting assessments of surface-water-related environmental management and disaster prevention.},
}

*Water
*Environmental Monitoring/methods
Satellite Imagery
Environment
Algorithms

@article {pmid38303478,
year = {2024},
author = {Lv, W and Chen, J and Cheng, S and Qiu, X and Li, D},
title = {QoS-driven resource allocation in fog radio access network: A VR service perspective.},
journal = {Mathematical biosciences and engineering : MBE},
volume = {21},
number = {1},
pages = {1573-1589},
doi = {10.3934/mbe.2024068},
pmid = {38303478},
issn = {1551-0018},
abstract = {While immersive media services represented by virtual reality (VR) are booming, They are facing fundamental challenges, i.e., soaring multimedia applications, large operation costs and scarce spectrum resources. It is difficult to simultaneously address these service challenges in a conventional radio access network (RAN) system. These problems motivated us to explore a quality-of-service (QoS)-driven resource allocation framework from VR service perspective based on the fog radio access network (F-RAN) architecture. We elaborated details of deployment on the caching allocation, dynamic base station (BS) clustering, statistical beamforming and cost strategy under the QoS constraints in the F-RAN architecture. The key solutions aimed to break through the bottleneck of the network design and to deep integrate the network-computing resources from different perspectives of cloud, network, edge, terminal and use of collaboration and integration. Accordingly, we provided a tailored algorithm to solve the corresponding formulation problem. This is the first design of VR services based on caching and statistical beamforming under the F-RAN. A case study provided to demonstrate the advantage of our proposed framework compared with existing schemes. Finally, we concluded the article and discussed possible open research problems.},
}

@article {pmid38303438,
year = {2024},
author = {Niu, Q and Li, H and Liu, Y and Qin, Z and Zhang, LB and Chen, J and Lyu, Z},
title = {Toward the Internet of Medical Things: Architecture, trends and challenges.},
journal = {Mathematical biosciences and engineering : MBE},
volume = {21},
number = {1},
pages = {650-678},
doi = {10.3934/mbe.2024028},
pmid = {38303438},
issn = {1551-0018},
mesh = {*Artificial Intelligence ; Big Data ; Cloud Computing ; Internet ; *Internet of Things ; },
abstract = {In recent years, the growing pervasiveness of wearable technology has created new opportunities for medical and emergency rescue operations to protect users' health and safety, such as cost-effective medical solutions, more convenient healthcare and quick hospital treatments, which make it easier for the Internet of Medical Things (IoMT) to evolve. The study first presents an overview of the IoMT before introducing the IoMT architecture. Later, it portrays an overview of the core technologies of the IoMT, including cloud computing, big data and artificial intelligence, and it elucidates their utilization within the healthcare system. Further, several emerging challenges, such as cost-effectiveness, security, privacy, accuracy and power consumption, are discussed, and potential solutions for these challenges are also suggested.},
}

*Artificial Intelligence
Big Data
Cloud Computing
Internet
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@article {pmid38301786,
year = {2024},
author = {Shrestha, N and Kolarik, NE and Brandt, JS},
title = {Mesic vegetation persistence: A new approach for monitoring spatial and temporal changes in water availability in dryland regions using cloud computing and the sentinel and Landsat constellations.},
journal = {The Science of the total environment},
volume = {917},
number = {},
pages = {170491},
doi = {10.1016/j.scitotenv.2024.170491},
pmid = {38301786},
issn = {1879-1026},
abstract = {Climate change and anthropogenic activity pose severe threats to water availability in drylands. A better understanding of water availability response to these threats could improve our ability to adapt and mitigate climate and anthropogenic effects. Here, we present a Mesic Vegetation Persistence (MVP) workflow that takes every usable image in the Sentinel (10-m) and Landsat (30-m) archives to generate a dense time-series of water availability that is continuously updated as new images become available in Google Earth Engine. MVP takes advantage of the fact that mesic vegetation can be used as a proxy of available water in drylands. Our MVP workflow combines a novel moisture-based index (moisture change index - MCI) with a vegetation index (Modified Chlorophyll Absorption Ratio Vegetation Index (MCARI2)). MCI is the difference in soil moisture condition between an individual pixel's state and the dry and wet reference reflectance in the image, derived using 5th and 95th percentiles of the visible and shortwave infra-red drought index (VSDI). We produced and validated our MVP products across drylands of the western U.S., covering a broad range of elevation, land use, and ecoregions. MVP outperforms NDVI, a commonly-employed index for mesic ecosystem health, in both rangeland and forested ecosystems, and in mesic habitats with particularly high and low vegetation cover. We applied our MVP product at case study sites and found that MVP more accurately characterizes differences in mesic persistence, late-season water availability, and restoration success compared to NDVI. MVP could be applied as an indicator of change in a variety of contexts to provide a greater understanding of how water availability changes as a result of climate and management. Our MVP product for the western U.S. is freely available within a Google Earth Engine Web App, and the MVP workflow is replicable for other dryland regions.},
}

@article {pmid38293581,
year = {2024},
author = {Zurqani, HA},
title = {The first generation of a regional-scale 1-m forest canopy cover dataset using machine learning and google earth engine cloud computing platform: A case study of Arkansas, USA.},
journal = {Data in brief},
volume = {52},
number = {},
pages = {109986},
pmid = {38293581},
issn = {2352-3409},
abstract = {Forest canopy cover (FCC) is essential in forest assessment and management, affecting ecosystem services such as carbon sequestration, wildlife habitat, and water regulation. Ongoing advancements in techniques for accurately and efficiently mapping and extracting FCC information require a thorough evaluation of their validity and reliability. The primary objectives of this study are to: (1) create a large-scale forest FCC dataset with a 1-meter spatial resolution, (2) assess the regional spatial distribution of FCC at a regional scale, and (3) investigate differences in FCC areas among the Global Forest Change (Hansen et al., 2013) and U.S. Forest Service Tree Canopy Cover products at various spatial scales in Arkansas (i.e., county and city levels). This study utilized high-resolution aerial imagery and a machine learning algorithm processed and analyzed using the Google Earth Engine cloud computing platform to produce the FCC dataset. The accuracy of this dataset was validated using one-third of the reference locations obtained from the Global Forest Change (Hansen et al., 2013) dataset and the National Agriculture Imagery Program (NAIP) aerial imagery with a 0.6-m spatial resolution. The results showed that the dataset successfully identified FCC at a 1-m resolution in the study area, with overall accuracy ranging between 83.31% and 94.35% per county. Spatial comparison results between the produced FCC dataset and the Hansen et al., 2013 and USFS products indicated a strong positive correlation, with R[2] values ranging between 0.94 and 0.98 for county and city levels. This dataset provides valuable information for monitoring, forecasting, and managing forest resources in Arkansas and beyond. The methodology followed in this study enhances efficiency, cost-effectiveness, and scalability, as it enables the processing of large-scale datasets with high computational demands in a cloud-based environment. It also demonstrates that machine learning and cloud computing technologies can generate high-resolution forest cover datasets, which might be helpful in other regions of the world.},
}

@article {pmid38292471,
year = {2024},
author = {Li, W and Zhang, Z and Xie, B and He, Y and He, K and Qiu, H and Lu, Z and Jiang, C and Pan, X and He, Y and Hu, W and Liu, W and Que, T and Hu, Y},
title = {HiOmics: A cloud-based one-stop platform for the comprehensive analysis of large-scale omics data.},
journal = {Computational and structural biotechnology journal},
volume = {23},
number = {},
pages = {659-668},
pmid = {38292471},
issn = {2001-0370},
abstract = {Analyzing the vast amount of omics data generated comprehensively by high-throughput sequencing technology is of utmost importance for scientists. In this context, we propose HiOmics, a cloud-based platform equipped with nearly 300 plugins designed for the comprehensive analysis and visualization of omics data. HiOmics utilizes the Element Plus framework to craft a user-friendly interface and harnesses Docker container technology to ensure the reliability and reproducibility of data analysis results. Furthermore, HiOmics employs the Workflow Description Language and Cromwell engine to construct workflows, ensuring the portability of data analysis and simplifying the examination of intricate data. Additionally, HiOmics has developed DataCheck, a tool based on Golang, which verifies and converts data formats. Finally, by leveraging the object storage technology and batch computing capabilities of public cloud platforms, HiOmics enables the storage and processing of large-scale data while maintaining resource independence among users.},
}