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A Study of the Sensitivity of the Fuel Consumption to Driving Strategy by Micro Simulation

Procedia computer Science 2019年 157卷 375-381页

作者： Fergyanto E. Gunawan Benfano Soewito Nico Surantha Tuga Mauritsius Nobumasa Sekishita Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Information Systems Information Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480 Department of Mechanical Engineering Toyohashi University of Technology Toyohashi Aichi 441-8580 Japan

Global warming due to excessive emission greenhouse gases is a significant issue faced by humanity, and collectively, cars and trucks contribute about 20% to the entire global-warming gases. For a reason, recently, many attempts have been endured to reduce the emission and to increase fuel efficiency, including vehicle platooning, better driving strategy, and increasing engine efficiency. This study intends to provide a more detail assessment regarding the effects of the driving strategy to fuel consumption. For the purpose, the vehicle dynamics are quantified by employing the car-following model based on the optimal velocity model. The fuel consumption is estimated from the regression model of Ahn 18 . The result suggests that the braking distance strongly affects fuel consumption.

关键词： Optimal Velocity Model Micro Simulation Car-Following Model Fuel Efficiency

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Performance Analysis and Optimization of the Vector-Kronecker Product Multiplication

Performance Analysis and Optimization of the Vector-Kronecke...

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International Symposium on computer Architecture and High Performance Computing (SBAC-PAD)

作者： Alexandre Azevedo Cristiana Bentes Maria Clicia Castro Claude Tadonki Computational Sciences Program State University of Rio de Janeiro Department of Systems Engineering State University of Rio de Janeiro Department of Informatics and Computer Science State University of Rio de Janeiro CRI (Centre de Recherche en Informatique) MINES ParisTech

ISBN: (数字)9781728199245

ISBN: (纸本)9781728199252

The Kronecker product, also called tensor product, is a fundamental matrix algebra operation, used to model complex systems using structured descriptions. This operation needs to be computed efficiently, since it is a critical kernel for iterative algorithms. In this work, we focus on the vector-kronecker product operation, where we present an in-depth performance analysis of a sequential and a parallel algorithm previously proposed. Based on this analysis, we proposed three optimizations: changing the memory access pattern, reducing load imbalance and manually vectorizing some portions of the code with Intel SSE4.2 intrinsics. The obtained results show better cache usage and load balance, thus improving the performance, especially for larger matrices.

关键词： Complexity theory Optimization program processors Computational modeling Tensors Parallel algorithms Scalability

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A robotic and virtual testing platform highlighting the promise of soft wearable actuators for wrist tremor suppression

Device

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Device 2025年

作者： Alona Shagan Shomron Christina Chase-Markopoulou Johannes R. Walter Johanna Sellhorn-Timm Yitian Shao Tobias Nadler Audrey Benson Isabell Wochner Ellen H. Rumley Isabel Wurster Philipp Klocke Daniel Weiss Syn Schmitt Christoph Keplinger Daniel F.B. Haeufle Robotic Materials Department Max Planck Institute for Intelligent Systems Stuttgart Germany Institute for Modelling and Simulation of Biomechanical Systems University of Stuttgart Stuttgart Germany Stuttgart Center for Simulation Science (SimTech) University of Stuttgart Stuttgart Germany Hertie Institute for Clinical Brain Research University of Tübingen Tübingen Germany Haptic Intelligence Department Max Planck Institute for Intelligent Systems Stuttgart Germany Institute for Computer Engineering (ZITI) University of Heidelberg Heidelberg Germany Paul M. Rady Department of Mechanical Engineering University of Colorado Boulder CO USA Center for Neurology Department of Neurodegenerative Diseases University of Tübingen Tübingen Germany Center for Bionic Intelligence (BITS) Tübingen and Stuttgart Germany Material Science and Engineering Program University of Colorado Boulder CO USA Werner Reichardt Center for Integrative Neuroscience University of Tübingen Tübingen Germany

Nearly 80 million people in the world deal with medical conditions that cause involuntary periodic movements known as tremors. Wearable soft robotic devices offer a potential solution for actively suppressing these tremors. However, existing prototypes face limitations in actuation performance and complex testing procedures. We present a comprehensive approach for the rapid evaluation of emerging wearable tremor-suppression technologies. This method combines reproducing patient-recorded tremor episodes and measuring tremor suppression in a robotic platform, termed a “mechanical patient”, with validation of the achieved suppression performance of soft actuators via biomechanical modeling, thereby avoiding time-consuming clinical testing in the early stages of development. Using this approach, we highlight that an antagonistic pair of slim and lightweight electrohydraulic actuators can effectively suppress clinically relevant tremors between 2 and 8 Hz. The biomechanical model confirms that the pair of actuators generates adequate suppression forces for all tested tremors.

关键词： tremor suppression mechanical patient soft actuators wearable devices biomechanical simulation DTI-4: Validate

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A PARALLEL VARIATIONAL MESH QUALITY IMPROVEMENT METHOD FOR TETRAHEDRAL MESHES 28

A PARALLEL VARIATIONAL MESH QUALITY IMPROVEMENT METHOD FOR T...

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28th International Meshing Roundtable, IMR 2019

作者： Shontz, Suzanne M. Varilla, M.A.L. Huang, Weizhang Department of Electrical Engineering and Computer Science Bioengineering Program Information and Telecommunication Technology Center University of Kansas LawrenceKS United States Husky Injection Molding Systems Ltd. Luxembourg Luxembourg Department of Mathematics University of Kansas LawrenceKS United States

ISBN: (纸本)9781733489003

There are numerous large-scale applications requiring mesh adaptivity, e.g., computational uid dynamics and weather prediction. Parallel processing is needed for simulations involving large-scale adaptive meshes. In this paper, we propose a parallel variational mesh quality improvement algorithm for use with distributed memory machines. Our method parallelizes the serial variational mesh quality improvement method by Huang and Kamenski. Their approach is based on the use of the Moving Mesh PDE method to adapt the mesh based on the minimization of an energy functional for mesh equidistribution and alignment. This leads to a system of ordinary differential equations (ODEs) to be solved which determine where to move the interior mesh nodes. An efficient solution is obtained by solving the ODEs on subregions of the mesh with overlapped communication and computation. Strong and weak scaling experiments on up to 128 cores for meshes with up to 160M elements demonstrate excellent results. © 2019 Proceedings of the 28th International Meshing Roundtable, IMR 2019. All rights reserved.

关键词： Ordinary differential equations

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Biomedical data and AI

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Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Deep Learning-Derived Optimal Aviation Strategies to Control Pandemics

arXiv

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arXiv 2022年

作者： Rizvi, Syed Awasthi, Akash Peláez, Maria J. Wang, Zhihui Cristini, Vittorio Van Nguyen, Hien Dogra, Prashant Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States Mathematics in Medicine Program Department of Medicine Houston Methodist Research Institute HoustonTX77030 United States Department of Physiology and Biophysics Weill Cornell Medical College New YorkNY10065 United States Neal Cancer Center Houston Methodist Research Institute HoustonTX77030 United States Department of Imaging Physics The University of Texas M.D. Anderson Cancer Center HoustonTX77230 United States Physiology Biophysics and Systems Biology Program Graduate School of Medical Sciences Weill Cornell Medicine New YorkNY10065 United States Research in Physiology and Biophysics Department of Physiology and Biophysics Weill Cornell Medical College New YorkNY United States

The COVID-19 pandemic has affected countries across the world, demanding drastic public health policies to mitigate the spread of infection, leading to economic crisis as a collateral damage. In this work, we investigated the impact of human mobility (described via international commercial flights) on COVID-19 infection dynamics at the global scale. For this, we developed a graph neural network-based framework referred to as Dynamic Connectivity GraphSAGE (DCSAGE), which operates over spatiotemporal graphs and is well-suited for dynamically changing adjacency information. To obtain insights on the relative impact of different geographical locations, due to their associated air traffic, on the evolution of the pandemic, we conducted local sensitivity analysis on our model through node perturbation experiments. From our analyses, we identified Western Europe, North America, and Middle East as the leading geographical locations fueling the pandemic, attributed to the enormity of air traffic originating or transiting through these regions. We used these observations to identify tangible air traffic reduction strategies that can have a high impact on controlling the pandemic, with minimal interference to human mobility. Our work provides a robust deep learning-based tool to study global pandemics and is of key relevance to policy makers to take informed decisions regarding air traffic restrictions during future outbreaks. © 2022, CC BY-NC-SA.

关键词： COVID-19

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Colloidal Structure and Proton Conductivity of the Gel within the Electrosensory Organs of Cartilaginous Fishes

SSRN

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SSRN 2021年

作者： Phillips, Molly Wheeler, Alauna Robinson, Matthew J. Leppert, Valerie Jia, Manping Rolandi, Marco Hirst, Linda S. Amemiya, Chris T. Department of Biology University of Washington SeattleWA98195 United States Department of Molecular and Cell Biology University of California Merced MercedCA95343 United States Department of Physics University of California Merced MercedCA95343 United States Department of Materials Science and Engineering University of California Merced MercedCA95343 United States Department of Electrical and Computer Engineering Baskin School of Engineering University of California Santa Cruz Santa CruzCA95064 United States Quantitative and Systems Biology Program University of California Merced MercedCA95343 United States

Cartilaginous fishes possess gel-filled tubular sensory organs called Ampullae of Lorenzini (AoL) that are used to detect electric fields. Although recent studies have identified various components of AoL gel, it has remained unclear how the molecules are structurally arranged and how their structure influences the function of the organs. Here we present the first structural description of AoL gel using microscopy and small angle x-ray scattering, revealing that the material is colloidal in nature. To assess the relative function of the gel’s protein constituents, we compared the microscopic structure, x-ray scattering, and proton conductivity properties of the gel before and after digestion with proteolytic enzymes. We discovered that while proteins were largely responsible for conferring the stiffness of the gel, their removal did not diminish proton conductivity. The findings described here lay the groundwork for more detailed studies into the specific interactions of molecules inside AoL gel at the nanoscale. © 2021, The Authors. All rights reserved.

关键词： Fish

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Epigenetic factor competition reshapes the EMT landscape

arXiv

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arXiv 2022年

作者： Ali Al-Radhawi, M. Tripathi, Shubham Zhang, Yun Sontag, Eduardo D. Levine, Herbert Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States Center for Theoretical Biological Physics Northeastern University BostonMA02115 United States PhD Program in Systems Synthetic and Physical Biology Rice University HoustonTX77005 United States Whitehead Institute for Biomedical Research CambridgeMA02142 United States Department of Bioengineering Northeastern University BostonMA02115 United States Department of Physics Northeastern University BostonMA02115 United States Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States State Key Laboratory of Molecular Oncology National Cancer Center National Clinical Research Center for Cancer Cancer Hospital Chinese Academy of Medical Sciences Peking Union Medical College Beijing100021 China

The emergence of and transitions between distinct phenotypes in isogenic cells can be attributed to the intricate interplay of epigenetic marks, external signals, and gene regulatory elements. These elements include chromatin remodelers, histone modifiers, transcription factors, and regulatory RNAs. Mathematical models known as Gene Regulatory Networks (GRNs) are an increasingly important tool to unravel the workings of such complex networks. In such models, epigenetic factors are usually proposed to act on the chromatin regions directly involved in the expression of relevant genes. However, it has been well-established that these factors operate globally and compete with each other for targets genome-wide. Therefore, a perturbation of the activity of a regulator can redistribute epigenetic marks across the genome and modulate the levels of competing regulators. In this paper, we propose a conceptual and mathematical modeling framework that incorporates both local and global competition effects between antagonistic epigenetic regulators in addition to local transcription factors, and show the counter-intuitive consequences of such interactions. We apply our approach to recent experimental findings on the Epithelial-Mesenchymal Transition (EMT). We show that it can explain the puzzling experimental data as well provide new verifiable predictions. © 2022, CC BY-NC-ND.

关键词： Transcription factors

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Prediction of traffic congestion based on time series dataset number of vehicles using neural network algorithm

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AIP Conference Proceedings 2024年第1期2982卷

作者： Prasetyo Wibowo Yunanto Rahmat Gernowo Oky Dwi Nurhayati Doctoral Program of Information System Diponegoro University Jl. Imam Bardjo SH No.5 Semarang Indonesia Information Systems and Technology Universitas Negeri Jakarta Jl. Rawamangun Muka Jakarta Indonesia Department of Physics Diponegoro University Jl. Prof. H. Soedarto Semarang Indonesia Computer Engineering Department Diponegoro University Jl. Prof. H. Soedarto Semarang Indonesia

The increasing number of vehicles that was not accompanied by an adequate road infrastructure readiness causes traffic jams. Traffic jams often occur repeatedly every day, especially at certain times, such as when people are going to and from work. The same traffic jams can also occur at the beginning or the end of the week; this usually repeats every week. As a result, if the congestion dataset is known, the repeating traffic congestion for daily and weekly congestion can be anticipated. In this study, traffic congestion predictions are modeled using the Neural Network Algorithm based on traffic congestion data collected within 24 hours for one to two weeks. The parameters used in optimizing Neural Network performance are learning rate, momentum, and epochs (training cycles). Based on these experiments, the Neural Network Algorithm successfully modeled traffic density patterns which are recurring congestion patterns, with quite good results.

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A review of hidden markov models and recurrent neural networks for event detection and localization in biomedical signals

arXiv

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arXiv 2020年

作者： Khalifa, Yassin Mandic, Danilo Sejdić, Ervin Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA United States Department of Electrical and Computer Engineering Imperial College LondonSW7 2BT United Kingdom Department of Bioengineering University of Pittsburgh PittsburghPA United States Department of Biomedical Informatics University of Pittsburgh PittsburghPA United States Intelligent Systems Program University of Pittsburgh PittsburghPA United States

Biomedical signals carry signature rhythms of complex physiological processes that control our daily bodily activity. The properties of these rhythms indicate the nature of interaction dynamics among physiological processes that maintain a homeostasis. Abnormalities associated with diseases or disorders usually appear as disruptions in the structure of the rhythms which makes isolating these rhythms and the ability to differentiate between them, indispensable. computer aided diagnosis systems are ubiquitous nowadays in almost every medical facility and more closely in wearable technology, and rhythm or event detection is the first of many intelligent steps that they perform. How these rhythms are isolated? How to develop a model that can describe the transition between processes in time? Many methods exist in the literature that address these questions and perform the decoding of biomedical signals into separate rhythms. In here, we demystify the most effective methods that are used for detection and isolation of rhythms or events in time series and highlight the way in which they were applied to different biomedical signals and how they contribute to information fusion. The key strengths and limitations of these methods are also discussed as well as the challenges encountered with application in biomedical signals. Copyright © 2020, The Authors. All rights reserved.

关键词： Biomedical signal processing

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