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computational Medicine: What Electrophysiologists Should Know to Stay Ahead of the Curve

CURRENT CARDIOLOGY REPORTS 2024年第12期26卷 1393-1403页

作者： Magoon, Matthew J. Nazer, Babak Akoum, Nazem Boyle, Patrick M. Univ Washington Dept Bioengn Seattle WA 98195 USA Univ Washington Med Div Cardiol Seattle WA USA

Purpose of Review Technology drives the field of cardiac electrophysiology. Recent computational advances will bring exciting changes. To stay ahead of the curve, we recommend electrophysiologists develop a robust appreciation for novel computational techniques, including deterministic, statistical, and hybrid models. Recent Findings In clinical applications, deterministic models use biophysically detailed simulations to offer patient-specific insights. Statistical techniques like machine learning and artificial intelligence recognize patterns in data. Emerging clinical tools are exploring avenues to combine all the above methodologies. We review three ways that computational medicine will aid electrophysiologists by: (1) improving personalized risk assessments, (2) weighing treatment options, and (3) guiding ablation procedures. Leveraging clinical data that are often readily available, computational models will offer valuable insights to improve arrhythmia patient care. Summary As emerging tools promote personalized medicine, physicians must continue to critically evaluate technology-driven tools they consider using to ensure their appropriate implementation.

关键词： computational medicine Arrhythmias Cardiac electrophysiology computational modeling Machine learning Precision medicine

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A computational and experimental approach to evaluate thermal conductivity and diffusivity of steel composite metal foam

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JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY 2023年第21期148卷 11589-11606页

作者： Chacko, Zubin Amoafo-Yeboah, Nigel Cance, John Rabiei, Afsaneh North Carolina State Univ Dept Mech & Aerosp Engn Raleigh NC 27606 USA

This study aims to comprehensively investigate the thermal properties of steel-steel composite metal foams (S-S CMFs) compared to traditional bulk steel across a wide temperature range. To achieve this, a combination of computational modeling and laser flash (LF) experimental methods was employed to characterize the thermal behavior of S-S CMF. Initially, the thermal conductivities of S-S CMF were analyzed using computational fluid dynamics software ANSYS Fluent within the temperature range of 300-600 & DEG;C. These computational results were then compared to experimental data obtained from the high-temperature guarded-comparative longitudinal heat flow (GCH) technique used in a previous study, ensuring their accuracy. The comparison indicated an average deviation of only 6% across the investigated temperature range. Subsequently, the computational model was extended to predict the thermal conductivity of S-S CMF up to 1000 & DEG;C. Thermal conductivity measurements were taken using the LF experimental technique to validate these predictions at elevated temperatures, covering the temperature range from room temperature to 1000 & DEG;C. The experimental findings revealed that the thermal conductivity of S-S CMF was six times lower than that of bulk 316L stainless steel, while its thermal diffusivity was half that of bulk steel. Finally, an uncertainty analysis was performed to assess the reliability and accuracy of both the computational model and the LF experimental data. The results showed uncertainties of 3.6% and 5.5% at a 95% confidence level for the computational and LF approaches. Notably, these values were lower than the reported uncertainty in thermal conductivity obtained through the GCH technique in the earlier study (7-7.5%). Additionally, the comparison between the computational and experimental results from the LF technique demonstrated a mere 2% deviation throughout the investigated temperature range, highlighting the superior accuracy of the LF techni

关键词： Composite metal foam computational modeling ANSYS Fluent Laser flash technique Thermal diffusivity Thermal conductivity

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iMRSA-Fuse: A Fast and Accurate computational Approach for Predicting Anti-MRSA Peptides by Fusing Multi-View Information

IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATIC...

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IEEE TRANSACTIONS ON computational BIOLOGY AND BIOINFORMATICS 2025年第1期22卷 2-12页

作者： Charoenkwan, Phasit Schaduangrat, Nalini Moni, Mohammad Ali Shoombuatong, Watshara Chiang Mai Univ Coll Arts Media & Technol Modern Management & Informat Technol Chiang Mai 50200 Thailand Mahidol Univ Fac Med Technol Ctr Res Innovat & Biomed Informat Bangkok 10700 Thailand Charles Sturt Univ AI & Cyber Futures Inst AI & Digital Hlth Technol Bathurst NSW 2795 Australia

Methicillin-resistant S. aureus (MRSA) has prominently emerged among the recognized causes of community-acquired and hospital infections. We proposed a novel computational approach, iMRSA-Fuse, based on a multi-view feature fusion strategy for fast and accurate anti-MRSA peptide identification. In iMRSA-Fuse, we explored and integrated 12 different sequence-based feature descriptors from multiple perspectives, in conjunction with 12 popular machine learning (ML) algorithms, to construct multi-view features that were able to fully capture the useful information of anti-MRSA peptides. Additionally, we applied our customized genetic algorithm to determine a set of multi-view features to enhance its discriminative ability. Based on a series of comparative results, our multi-view features exhibited the most discriminative ability compared to several conventional feature descriptors. Moreover, concerning the independent test dataset, iMRSA-Fuse achieved the best balanced accuracy (BACC) and Matthew's correlation coefficient (MCC) of 0.997 and 0.981, respectively with an increase of 3.93 and 7.78%, respectively. Finally, to facilitate the large-scale identification of candidate anti-MRSA peptides, a user-friendly web server of the iMRSA-Fuse model is constructed and is freely accessible at https://***/iMRSA-Fuse. We anticipate that this new computational approach will be effectively applied to screen and prioritize candidate peptides that might exhibit the great anti-MRSA activities.

关键词： Peptides Amino acids Encoding Bioinformatics Biological system modeling Feature extraction computational modeling Accuracy Random forests Immune system Anti-MRSA peptide sequence analysis bioinformatics machine learning feature representation multi-view feature

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A Channel Pruning Optimization With Layer-Wise Sensitivity in a Single-Shot Manner Under computational Constraints

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IEEE ACCESS 2023年 11卷 7043-7055页

作者： Jeon, Minsu Kim, Taewoo Lee, Changha Youn, Chan-Hyun Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon South Korea

In the constrained computing environments such as mobile device or satellite on-board system, various computational factors of hardware resource can restrict the processing of deep learning (DL) services. Recent DL models such as satellite image analysis mainly require larger resource memory occupation for intermediate feature map footprint than the given memory specification of hardware resource and larger computational overhead (in FLOP) to meet service-level objective in the sense of hardware accelerator. As one of the solutions, we propose a new method of controlling the layer-wise channel pruning in a single-shot manner that can decide how much channels to prune in each layer by observing dataset once without full pretraining. To improve the robustness of the performance degradation, we also propose a layer-wise sensitivity and formulate the optimization problems for deciding layer-wise pruning ratio under target computational constraints. In the paper, the optimal conditions are theoretically derived, and the practical optimum searching schemes are proposed using the optimal conditions. On the empirical evaluation, the proposed methods show robustness on performance degradation, and present feasibility on DL serving under constrained computing environments by reducing memory occupation, providing acceleration effect and throughput improvement while keeping the accuracy performance.

关键词： computational modeling Sensitivity Degradation Memory management Satellites Analytical models Adaptation models Single-shot pruning channel pruning lottery ticket hypothesis DL model compression

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Cooperative computational Offloading in Mobile Edge Computing for Vehicles: A Model-Based DNN Approach

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第3期72卷 3376-3391页

作者： Munawar, Suleman Ali, Zaiwar Waqas, Muhammad Tu, Shanshan Hassan, Syed Ali Abbas, Ghulam Ghulam Ishaq Khan Inst Engn Sci & Technol Fac Comp Sci & Engn Topi 23460 Pakistan Univ Bahrain Coll Informat Technol Dept Comp Engn Sakhir 32038 Bahrain Edith Cowan Univ Sch Engn Perth WA 6027 Australia Beijing Univ Technol Fac Informat Technol Engn Res Ctr Intelligent Percept & Autonomous Cont Beijing 100021 Peoples R China Natl Univ Sci & Technol Sch Elect Engn & Comp Sci Islamabad 44000 Pakistan

Many advancements are being made in vehicular networks, such as self-driving, dynamic route scheduling, real-time traffic condition monitoring, and on-board infotainment services. However, these services require high computation power and precision and can be met using mobile edge computing (MEC) mechanisms for vehicular networks. MEC operates through the edge servers available at the roadside, also known as roadside units (RSU). MEC is very useful for vehicular networks because it has extremely low latency and supports operations that require near-real-time access to rapidly changing data. This paper proposes an efficient computational offloading, smart division of tasks, and cooperation among RSUs to increase service performance and decrease the delay in a vehicular network via MEC. The computational delay is further reduced by parallel processing. In the division of tasks, each task is divided into two sub-components which are fed to a deep neural network (DNN) for training. Consequently, this reduces the overall time delay and overhead. We also adopt an efficient routing policy to deliver the results through the shortest path to improve service reliability. The offloading, computing, division, and routing policies are formulated, and a model-based DNN approach is used to obtain an optimal solution. Simulation results prove that our proposed approach is suitable in a dynamic environment. We also compare our results with the existing state-of-the-art, showing that our proposed approach outperforms the existing schemes.

关键词： Task analysis Servers Delay effects computational modeling Routing Delays Cloud computing Deep neural network (DNN) computational offloading vehicular networks smart task division routing policy mobile edge computing

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Toward Smart and Efficient Service Systems: computational Layered Federated Learning Framework

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IEEE NETWORK 2023年第6期37卷 264-271页

作者： Shi, Yanhang Li, Xue Chen, Siguang Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing 210003 Peoples R China Nanjing Med Univ Dept Dermatol Womens Hosp Nanjing Matern & Child Hlth Care Hosp Nanjing 210004 Peoples R China

As increasing concerns have arisen on privacy leakage in data-driven smart services, federated learning (FL) has been introduced to collaboratively learn an efficient model across multiple participants without sharing raw data. However, existing FL schemes hinge on participating nodes to perform intensive on- device training and network communication, which is a significant burden for energy-constrained mobile devices. In this work, we present the computational layered FL (CLFL) framework to enable resource-constrained devices to perform computation-efficient on-device training and lightweight message transmitting. We first introduce the network structure and key aspects of the entities in the framework. Then, we give the implementation principles of CLFL, and present two instance schemes that allow devices to participate in joint training without the need for direct gradient computation or continuous data transmission. In order to more intuitively reflect the performance and efficiency of the proposed methods, we carry out a preliminary implementation and give the comparison with traditional FL. Finally, for the future exploration, we present four related research challenges of CLFL and offer possible solutions.

关键词： Training computational modeling Task analysis Solid modeling Servers Metadata Collaboration Federated learning Data privacy

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Data Driven Model Predictive Control for Modular Multilevel Converters With Reduced computational Complexity

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IEEE ACCESS 2023年 11卷 42113-42123页

作者： Raja, Muneeb Masood Wang, Haoran Arshad, Muhammad Haseeb Kish, Gregory J. Zhao, Qing Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2R3 Canada

Model predictive control (MPC) has become increasingly popular among researchers for modular multilevel converters (MMCs) due to its ability to incorporate multiobjective control and provide superior dynamic response. However, it is computationally challenging to implement it on MMCs when the number of submodules is increased. This paper proposes a finite control set (FCS) model predictive control (MPC) with reduced computational complexity for modular multilevel converters (MMCs). To accomplish this goal, a reduced order data-driven model is obtained using sparse identification of nonlinear systems (SINDy) by incorporating the input terms in the load current and circulating current dynamics. As a result, the need to use the arm voltages or the submodule capacitor voltages dynamic equations as in the case of a conventional FCS-MPC is eliminated. To improve the output current total harmonic distortion (THD) and reduce the effect of higher switching frequencies caused by the FCS-MPC, an updated cost function is proposed. The effectiveness of the proposed technique is validated by simulation and experimental results.

关键词： Mathematical models Capacitors Voltage control Switching frequency Switches Control systems computational modeling FCS-MPC MMCs data-driven control SINDy power converters

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computational Intelligent Sensor-Rank Consolidation Approach for Industrial Internet of Things (IIoT)

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IEEE INTERNET OF THINGS JOURNAL 2023年第3期10卷 2121-2130页

作者： Mekala, M. S. Rizwan, Patan Khan, Mohammad S. Yeungnam Univ Dept Informat & Commun Engn Gyongsan 38541 South Korea Yeungnam Univ RLRC Autonomous Vehicle Parts & Mat Innovat Gyongsan 38541 South Korea Velagapudi Ramakrishna Siddhartha Engn Coll Dept Comp Sci & Engn Vijayawada 520007 India East Tennessee State Univ Dept Comp Johnson City TN 37614 USA

Continues field monitoring and searching sensor data remains an imminent element emphasizes the influence of the Internet of Things (IoT). Most of the existing systems are concede spatial coordinates or semantic keywords to retrieve the entail data, which are not comprehensive constraints because of sensor cohesion, unique localization haphazardness. To address this issue, we propose deep-learning-inspired sensor-rank consolidation (DLi-SRC) system that enables 3-set of algorithms. First, sensor cohesion algorithm based on Lyapunov approach to accelerate sensor stability. Second, sensor unique localization algorithm based on rank-inferior measurement index to avoid redundancy data and data loss. Third, a heuristic directive algorithm to improve entail data search efficiency, which returns appropriate ranked sensor results as per searching specifications. We examined thorough simulations to describe the DLi-SRC effectiveness. The outcomes reveal that our approach has significant performance gain, such as search efficiency, service quality, sensor existence rate enhancement by 91%, and sensor energy gain by 49% than benchmark standard approaches.

关键词： Sensors Location awareness Indexes Stability criteria Monitoring Heuristic algorithms computational modeling Big data analytics cloud computing Lyapunov approach optimal measurement analysis rank-inferior measurement (RIM) index

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Serious Games and AI: Challenges and Opportunities for computational Social Science

引用

IEEE ACCESS 2023年 11卷 62051-62061页

作者： Perez, Jaime Castro, Mario Lopez, Gregorio Univ Pontificia Comillas Inst Res Technol ICAI Engn Sch Madrid 28015 Spain

The gaming industry plays a crucial role in the realm of entertainment within our society. However, from Monopoly to Flight Simulators, serious games have also been appealing tools for learning a new language, conveying values, or training skills. The resurgence of Artificial Intelligence (AI) and data science in the last decade presents a unique window of opportunity for its integration into video games. This integration is of particular interest due to the vast amount of data that can be collected through a game, which is needed to feed the AI algorithms. This paper aims to identify relevant research paths in the intersection of serious games, AI, and computational social science, particularly in their utilization as novel research tools to comprehend human behavior and society. To provide a comprehensive context, we also present an overview of the serious game research field identifying the most prominent application areas and analyzing applications of AI in serious games that hold great potential for computational social science research. The goal of our work is to establish a valuable framework for researchers interested in utilizing serious games as a novel tool for AI-supported social research.

关键词： Serious games Artificial intelligence Behavioral sciences Video games Recruitment computational modeling Social sciences Games artificial intelligence computational social science novel research tools human behaviour

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Social Knowledge Enhances Collective Safety: computational Models and Simulations

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IEEE TRANSACTIONS ON computational SOCIAL SYSTEMS 2023年第2期10卷 807-818页

作者： Lu, Peng Li, Yan Wen, Feier Chen, Dianhan Cent South Univ Dept Sociol Changsha 410017 Peoples R China Cent South Univ Dept Artificial Intelligence Changsha 410017 Peoples R China Beijing Inst Gen Artificial Intelligence Social Syst Ctr Beijing 100085 Peoples R China

Under common risks (such as mass shootings), people tend to be selfish and not cooperative, which increases the deaths and injuries of civilians. If some are encouraged to fight bravely against the shooters, the deaths and injuries can be reduced substantially. This forms social knowledge, which helps to enhance social safety. However, we cannot use field experiments to calculate social knowledge and evaluate effects. Alternatively, we use an agent-based model to simulate real cases and use counterfactual outcomes to evaluate the beneficial effect of social knowledge. We obtain the optimal solution (with the best matches), based on which we calculate and obtain the social knowledge matrix. Then, we add this matrix into simulations to infer social outcomes. It suggests that social safety has been enhanced substantially: the living chance of shooters has been reduced sharply;the civilian survival rate has been enhanced substantially;and the living chance of heroes themselves has been increased as well. Thus, social safety can be improved, and the effects can be evaluated by computational methods.

关键词： computational modeling Terrorism Injuries Sociology Safety Logic gates Buildings Agent-based model counterforce social knowledge terrorism attacks

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