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Impact of glioma peritumoral edema, tumor size, and tumor location on alternating electric fields (AEF) therapy in realistic 3D rat glioma models: a computational study

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PHYSICS IN MEDICINE AND BIOLOGY 2024年第8期69卷 085015-085015页

作者： Nguyen, Ha Schubert, Keith E. Pohling, Christoph Chang, Edwin Yamamoto, Vicky Zeng, Yuping Nie, Ying Van Buskirk, Samuel Schulte, Reinhard W. Patel, Chirag B. Baylor Univ Baylor TX 76706 USA Loma Linda Univ Loma Linda CA 92350 USA Stanford Univ Stanford CA 94305 USA Univ Southern Calif Keck Sch Med Los Angeles CA 90033 USA Univ Delaware Newark DE 19716 USA Univ Texas San Antonio San Antonio TX 78249 USA Univ Texas MD Anderson Canc Ctr Houston Houston TX 77030 USA Univ Texas MD Anderson UTHealth Grad Sch Biomed Sci Houston Houston TX 77030 USA

Objective. Alternating electric fields (AEF) therapy is a treatment modality for patients with glioblastoma. Tumor characteristics such as size, location, and extent of peritumoral edema may affect the AEF strength and distribution. We evaluated the sensitivity of the AEFs in a realistic 3D rat glioma model with respect to these properties. Approach. The electric properties of the peritumoral edema were varied based on calculated and literature-reported values. Models with different tumor composition, size, and location were created. The resulting AEFs were evaluated in 3D rat glioma models. Main results. In all cases, a pair of 5 mm diameter electrodes induced an average field strength >1 V cm(-1). The simulation results showed that a negative relationship between edema conductivity and field strength was found. As the tumor core size was increased, the average field strength increased while the fraction of the shell achieving >1.5 V cm(-1) decreased. Increasing peritumoral edema thickness decreased the shell's mean field strength. Compared to rostrally/caudally, shifting the tumor location laterally/medially and ventrally (with respect to the electrodes) caused higher deviation in field strength. Significance. This study identifies tumor properties that are key drivers influencing AEF strength and distribution. The findings might be potential preclinical implications.

关键词： alternating electric fields (AEF) tumor treating fields (TTFields) glioblastoma edema computational modeling sensitivity analysis small animals

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A computational model elucidating mechanisms and variability in theta burst stimulation responses

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JOURNAL OF computational NEUROSCIENCE 2024年第3期52卷 183-196页

作者： Farahani, Mohammadreza Vasheghani Shariatpanahi, Seyed Peyman Goliaei, Bahram Univ Tehran Inst Biochem & Biophys POB 13145-1384 Tehran Iran

Theta burst stimulation (TBS) is a form of repetitive transcranial magnetic stimulation (rTMS) with unknown underlying mechanisms and highly variable responses across subjects. To investigate these issues, we developed a simple computational model. Our model consisted of two neurons linked by an excitatory synapse that incorporates two mechanisms: short-term plasticity (STP) and spike-timing-dependent plasticity (STDP). We applied a variable-amplitude current through I-clamp with a TBS time pattern to the pre- and post-synaptic neurons, simulating synaptic plasticity. We analyzed the results and provided an explanation for the effects of TBS, as well as the variability of responses to it. Our findings suggest that the interplay of STP and STDP mechanisms determines the direction of plasticity, which selectively affects synapses in extended neurons and underlies functional effects. Our model describes how the timing, number, and intensity of pulses delivered to neurons during rTMS contribute to induced plasticity. This not only successfully explains the different effects of intermittent TBS (iTBS) and continuous TBS (cTBS), but also predicts the results of other protocols such as 10 Hz rTMS. We propose that the variability in responses to TBS can be attributed to the variable span of neuronal thresholds across individuals and sessions. Our model suggests a biologically plausible mechanism for the diverse responses to TBS protocols and aligns with experimental data on iTBS and cTBS outcomes. This model could potentially aid in improving TBS and rTMS protocols and customizing treatments for patients, brain areas, and brain disorders.

关键词： Transcranial magnetic stimulation (TMS) Long-term potentiation (LTP) Long-term depression (LTD) Theta burst stimulation (TBS) computational modeling TBS variability of responses

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An Improved and Efficient computational Offloading Method Based on ADMM Strategy in Cloud-Edge Collaborative Computing Environment for Resilient Industry 5.0

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 1392-1402页

作者： Shu, Wanneng Nie, Shaoliang Jian, Wencheng Ge, Xin South Cent Minzu Univ Coll Comp Sci Wuhan 430074 Peoples R China

With the rapid growth of data demand and the rise of the Industry 5.0, cloud-edge collaborative computing achieves intelligent connectivity and data sharing between devices for intelligent management and optimization of the production process. computational offloading, as a key technology in the cloud-edge collaborative computing environment, provides higher-quality user services and realizes higher data requirements for intelligent manufacturing and production. In this paper, we proposes an Improved Efficient Alternating Direction Method of Multipliers (IEADMM) for computational offloading in cloud-edge collaborative computing environment, aiming to reduce the total system cost with the optimization goal of minimizing the total time delay. The proposed IEADMM fully utilizes the Alternating Direction Method of Multipliers ADMM) to minimize the total delay by increasing the convergence speed. The experimental result shows that our proposed IEADMM has good performance and practicality for computational offloading in cloud-edge collaborative computing environment, and can be widely applied in practical application scenarios such as intelligent monitoring, Internet of Things (IoT), mobile games, etc.

关键词： Task analysis Servers Costs computational modeling Symbols Optical fiber networks Improved efficient alternating direction method of multipliers cloud-edge collaborative computing MEC server computational offloading resilient industry 50

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computational Analysis of Upscaled Fibrotic Liver Multi-Lobule Flows and Metabolism

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PROCESSES 2024年第9期12卷 1789页

作者： Coombe, Dennis Wallace, Cooper Rezania, Vahid Tuszynski, Jack A. Comp Modeling Grp Inc Calgary AB T2L 2M1 Canada MacEwan Univ Phys Sci Dept Edmonton AB T5J 4S2 Canada Univ Alberta Dept Phys Edmonton AB T6G 2E1 Canada Politecn Torino DIMEAS I-10129 Turin Italy Silesian Tech Univ PL-44100 Gliwice Poland

The modeling of fibrotic effects on fluid flow and metabolism in the liver can be computationally challenging. This paper combines innovative concepts based on fundamental physics to address such issues at the level of the liver functional unit, the lobule, and upscales and extends this to a multi-lobule tissue scale analysis. Fibrosis effects on fluid flow and metabolism are introduced using percolation theory and its consequences are explored for single lobule and multi-lobule patterns, without and with distortion.

关键词： liver metabolic processes computational modeling fibrosis lobule fluid flows

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Enabling Efficient Vehicle-Road Cooperation Through AIoT: A Deep Learning Approach to computational Offloading

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IEEE INTERNET OF THINGS JOURNAL 2024年第22期11卷 36127-36139页

作者： Wang, Xin Alassafi, Madini O. Alsaadi, Fawaz E. Xue, Xingsi Zou, Longhao Liu, Zhonghua Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Peoples R China King Abdulaziz Univ Dept Informat Technol Jeddah 80221 Saudi Arabia King Abdulaziz Univ Fac Comp & Informat Technol Jeddah 80221 Saudi Arabia Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350118 Peoples R China Peng Cheng Lab Dept Broadband Commun Shenzhen 518057 Peoples R China China Med Univ Shengjing Hosp Dept Comp Ctr Shenyang 110004 Peoples R China

The integration of Artificial Intelligence with the Internet of Things significantly enhances the functionality of vehicle-road cooperation (VRC) systems by enabling smarter, real-time decision-making and resource optimization across interconnected vehicular networks. To tackle the challenges associated with resource constraints, this study introduces a method where vehicle users can offload tasks to nearby roadside units (RSUs) or service-oriented vehicles to ensure timely application execution. However, this task offloading introduces additional transmission delays and energy expenditures. Consequently, this article first conceptualizes the computation offloading problem, aiming to minimize the total task processing time and energy consumption under the constraints of resources provided by RSUs and service-oriented vehicles. We model the computation offloading issue within the VRC framework as a Markov decision process (MDP) and propose a multiagent reinforcement learning-based resource scheduling method. Each vehicle, acting as an intelligent agent, interacts with and influences decisions within this environment. The method integrates the twin delayed deep deterministic policy gradient algorithm to train deep neural networks for deciding on task offloading and computational resource allocation. Simulation results demonstrate that compared to existing algorithms, the proposed method more effectively utilizes the computational resources available through RSUs and service-oriented vehicles within the VRC system. It achieves joint optimization of latency and energy consumption, thus validating the efficacy of the proposed approach in enhancing the operational efficiency and sustainability of urban transportation systems.

关键词： Task analysis Internet of Things Artificial intelligence Collaboration computational modeling Servers Energy consumption Augmented Intelligence of Things (AIoT) deep reinforcement learning resource scheduling twin delayed deep deterministic policy gradient (TD3) vehicle-road cooperation (VRC) system

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Evaluating the computational Advantages of the Variational Quantum Circuit Model in Financial Fraud Detection

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IEEE ACCESS 2024年 12卷 102918-102940页

作者： Tudisco, Antonio Volpe, Deborah Ranieri, Giacomo Curato, Gianbiagio Ricossa, Davide Graziano, Mariagrazia Corbelletto, Davide Politecn Torino Dept Elect & Telecommun I-10129 Turin Italy Intesa Sanpaolo I-10121 Turin Italy Politecn Torino Dept Appl Sci & Technol I-10129 Turin Italy

Home banking and digital payments diffusion has greatly increased in recent years. As a result, fraud has also dramatically grown, resulting in the loss of billions of dollars worldwide every year. Therefore, banks and financial institutions are required to offer clients increasingly effective and sophisticated services for illegal transaction detection. Machine learning strategies are commonly employed for this crucial application. However, classical models are not satisfactory enough in highly unbalanced classification tasks like fraud detection. Quantum machine learning, working intrinsically in a higher dimensional computation space thanks to superposition and entanglement, can express more complex models than its classical counterpart and thus could provide a significant advantage in identifying potential illegal transactions. This work aims to analyze the potential of quantum classifiers in the fraud detection context, focusing on the Variational Quantum Circuit (VQC) model. The study has been led by exploiting a dataset based on real transactions provided by Intesa Sanpaolo of 500000 items with 15 features. Considering the limitations of contemporary Noisy Intermediate-Scale Quantum (NISQ) computers and quantum simulators, the dataset has been reduced in the number of transactions and features, exploiting Principal Component Analysis (PCA). The results obtained have been compared on equal terms with those of the most commonly employed classical methods, such as Logistic Regression, Random Forest, XGBoost, Support Vector Machine, and Neural Networks, obtaining a better classification quality in terms of recall. Even though this work is preliminary, the results are encouraging and prove the quantum models' potential in highly unbalanced classification tasks.

关键词： Machine learning Integrated circuit modeling computational modeling Random forests Qubit Fraud Analytical models Quantum computing Neural networks Quantum machine learning fraud detection quantum data encoding variational quantum circuits anomaly detection anti-fraud engine quantum computing transaction classification hybrid quantum-classical computing angle encoding quantum neural network

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Toward Rapid and Optimal Strategy for Swarm Conflict: A computational Game Approach

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第3期60卷 3108-3120页

作者： Zhang, Tao Zhu, Yiji Ma, Dongying Li, Chaoyong Wang, Xiaodong Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China Beijing Inst Elect Syst Engn Beijing 100854 Peoples R China

The decision and control problem for swarm operations is crucial for autonomous military conflict management. In this article, the underlying decision and control problem is treated as a noncooperative game problem, in which the underlying target assignment problem is generalized to be a graph-theoretic problem. We introduce an algorithm to seek the desired Nash equilibrium with the help of the parallel maximum weight matching algorithm. Then, we prove that the proposed solution is epsilon-Nash with guaranteed computational efficiency, and is well suited for the swarm conflict. Simulation results verified the effectiveness of the proposed solutions.

关键词： Games Approximation algorithms computational modeling Military computing Analytical models Nash equilibrium Task analysis Game theory search algorithm swarm operation

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Stimuli-Responsive Polymer Networks: Application, Design, and computational Exploration

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ACS APPLIED POLYMER MATERIALS 2024年第23期6卷 14204-14228页

作者： Rosario, Alexander J. Ma, Boran Univ Southern Mississippi Sch Polymer Sci & Engn Hattiesburg MS 39406 USA

Polymer networks are an extensively studied class of materials, a subset of which has been developed to respond to specific stimuli. These stimuli-responses can be harnessed for enhanced or innovative functionalities, rendering stimuli-responsive polymer network materials highly desired for a myriad of applications ranging from sensing to drug delivery. This review provides a summary of recent progress in design and application of stimuli-responsive polymer network materials, including both chemically and physically cross-linked networks. Temperature, light, pH, chemical, mechanical, and more stimuli are discussed in the context of achieving desirable responses and functionalities in polymer networks. Furthermore, application areas for these materials and their various responses are also delved into with a particular focus on soft robotics and biomedical applications. In addition, computational methodologies are introduced as indispensable, complementary strategies to experimental characterization and analysis for further advancement and exploration within this domain. Techniques such as density functional theory, molecular dynamics simulations, Monte Carlo simulations, and finite element analysis are discussed. Ultimately, insights obtained from the perspectives of chemistry, stimuli, materials design, and computational methods offer a promising trajectory for the ongoing evolution and potential impact of this field.

关键词： Stimulus-Response Polymer Network Materials Simulation computational modeling Materials Design

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computational modeling of driver distraction by integrating cognitive and agent-based traffic simulation models

Computational modeling of driver distraction by integrating ...

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2014 International Conference on Computing in Civil and Building Engineering

作者： Nourzad, Seyed Hossein Hosseini Salvucci, Dario D. Pradhan, Anu Department of Civil Architectural and Environmental Engineering Drexel University PhiladelphiaPA United States College of Computing and Informatics Drexel University PhiladelphiaPA United States

ISBN: (纸本)9780784413616

The U.S. National Highway Traffic Safety Administration has reported that the major cause of vehicle crashes in the United States has been driver inattention and distraction. Among various types of distraction, cell-phone dialing and text messaging have been shown to significantly degrade driving performance. Existing studies have mainly focused on driver distraction scenarios with one or a few vehicles, and have generally not looked at large-scale simulation of many vehicles. In contrast, currently available software for simulating traffic with many vehicles (e.g., CORSIM and VISSIM) has generally not considered the effects of driver distraction on driver/vehicle behavior. To overcome these limitations, we propose a computational modeling framework that integrates a cognitive model of distraction (i.e., Distract-R) and an agent-based traffic (micro-) simulation model (i.e., VISSIM). The framework aims to enable transportation modelers to easily set up virtual experiments and evaluate the impact of distracted drivers on large-scale networks. We validate the framework using two existing experimental data sets. Preliminary results indicate that the framework can account for statistically significant changes in speed fluctuation and headway distance in the presence of a significant number of distracted drivers. © ASCE 2014.

关键词： Distracted Driving computational modeling Traffic simulation Traffic Accidents distractability Frameworks speed ripple vehicle Drivers

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Toward Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep computational Models for Inverse Inference

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2024年第7期43卷 2466-2478页

作者： Li, Lei Camps, Julia Wang, Zhinuo (Jenny) Beetz, Marcel Banerjee, Abhirup Rodriguez, Blanca Grau, Vicente Univ Oxford Inst Biomed Engn Dept Engn Sci Oxford OX3 7DQ England Univ Oxford Dept Comp Sci Oxford OX1 3QD England

Cardiac digital twins (CDTs) have the potential to offer individualized evaluation of cardiac function in a non-invasive manner, making them a promising approach for personalized diagnosis and treatment planning of myocardial infarction (MI). The inference of accurate myocardial tissue properties is crucial in creating a reliable CDT of MI. In this work, we investigate the feasibility of inferring myocardial tissue properties from the electrocardiogram (ECG) within a CDT platform. The platform integrates multi-modal data, such as cardiac MRI and ECG, to enhance the accuracy and reliability of the inferred tissue properties. We perform a sensitivity analysis based on computer simulations, systematically exploring the effects of infarct location, size, degree of transmurality, and electrical activity alteration on the simulated QRS complex of ECG, to establish the limits of the approach. We subsequently present a novel deep computational model, comprising a dual-branch variational autoencoder and an inference model, to infer infarct location and distribution from the simulated QRS. The proposed model achieves mean Dice scores of $ computational.{457} \pm computational.{317} $ and $ computational.{302} \pm computational.{273} $ for the inference of left ventricle scars and border zone, respectively. The sensitivity analysis enhances our understanding of the complex relationship between infarct characteristics and electrophysiological features. The in silico experimental results show that the model can effectively capture the relationship for the inverse inference, with promising potential for clinical application in the future. The code is available at https://***/lileitech/MI_inverse_inference.

关键词： Magnetic resonance imaging Electrocardiography Myocardium Image reconstruction computational modeling Inverse problems Heart Cardiac digital twins cardiac MRI electrophysiology inverse problem multi-modal integration

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