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SSRN 2024年

作者： Park, Jinkyu Shim, Yoonsu Chang, Joon Ha Kim, Se-Hee Kang, Yongku Lee, Jin Woong Jung, Dae Soo Yuk, Jong Min Lee, Chan-Woo Suk, Jungdon Advanced Energy Materials Research Center Advanced Materials Division Korea Research Institute of Chemical Technology 141 Gajeong-ro Yuseong-gu Daejeon34114 Korea Republic of Department of Materials Science and Engineering Korea Advanced Institute of Science and Technology 291 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of Department of Advanced Materials University of Science and Technology 217 Gajeong-ro Yuseong-gu Daejeon34113 Korea Republic of Research Institute of Industrial Science and Technology Korea Republic of Energy Storage Materials Center Korea Institute of Ceramic Engineering and Technology Gyeongnam Jinju52851 Korea Republic of Computational Science & Engineering Laboratory Korea Institute of Energy Research 152 Gajeong-ro Yuseong-Gu Daejeon34129 Korea Republic of

Lithium all-solid-state batteries (ASSBs) are a promising technology for achieving high energy density, long cycle life, and safe rechargeable battery systems. Among these, Li ASSBs using solid polymer electrolytes (SPEs) have gained attention owing to their processability, lightweight nature, flexibility, and favorable electrode contacts. However, SPEs have low ionic conductivity, low Li+ transference number, and lack mechanical strength, limiting cell performance. Therefore, the present study focuses on the development of hybrid polymer electrolytes (HPEs) by incorporating Li1+xAlxTi2-x(PO4)3 (LATP) of Li/Na superionic conductor-type materials into SPEs. The HPEs with LATP 10 wt% exhibited significant improvements with an ionic conductivity of 7.23 × 10-4 S cm-1 at 45 ℃ and a Li+ transference number of 0.61. Density functional theory calculations supported the enhanced Li-ion migration through the polymer-LATP interface achieved by the optimized LATP content. The addition of LATP particles enhanced the mechanical strength of the electrolytes, effectively suppressing dendrite growth, resulting in a 65.42% capacity retention after 320 cycles, highlighting the crucial role of high-performance HPEs in advanced ASSBs. By addressing the limitations of SPEs, HPEs offer promising opportunities to unlock the full potential of solid-state battery technologies for various energy storage systems. © 2024, The Authors. All rights reserved.

关键词： Ionic conductivity

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Optimizations of reinforcing particulates and processing parameters for stir casting of aluminium metal matrix composites for sustainable properties

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Materials Today: Proceedings 2022年 68卷 1172-1179页

作者： Kumar, Jatinder Kumar, Vipan Sharma, Shubham Chohan, Jasgurpreet Kumar, Raman Singh, Sandeep Obaid, Ahmed J. Akram, Shaik Vaseem Department of Mechanical Engineering St. Soldier Institute of Engineering and Technology Punjab Jalandhar144011 India Department of Mechanical Engineering IK Gujral Punjab Technical University Jalandhar-Kapurthala Road Punjab Kapurthala144603 India Chandigarh University Gharuan140413 India Department of Civil Engineering Chandigarh University Gharuan140413 India Faculty of Computer Science and Mathematics University of Kufa Iraq Division of Research and Innovation Uttaranchal University Uttarakhand Dehradun248007 India

Due to its exceptional characteristics, aluminium matrix composites are used in defence, automotive, and structural applications. Stir casting for SiC and Gr reinforced AMCs was optimised. SiC weight was kept constant during manufacturing, while Gr was 1, 3, and 5% by weight. Stir casting was used to formulate AMCs by varying graphite amounts, stirring time, and stirring speed according to Taguchi's L9 OA. Hardness, tensile strength, and porosity levels of composites were examined. Taguchi analysis and Grey Relational Analysis optimised stir casting. Stirring speed, duration, and graphite content were found to affect AMC hardness. Graphite particles influence AMC strength more than stirring speed and time. GRA results show that include Gr in AMCs is the most important component impacting hybrid AMC characteristics, contributing 63.83 percent to optimal response values. Stirring speed and time contribute 17.27% and 11.1% to optimal response values. Comparing anticipated and experimental GRG validates results. © 2022

关键词： Silicon carbide

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Detection of small holes by the scale-invariant robust density-aware distance (RDAD) filtration

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Journal of Applied and Computational Topology 2024年第6期8卷 1793-1836页

作者： Siu, Chunyin Samorodnitsky, Gennady Yu, Christina Lee Yao, Andrey Center for Applied Mathematics Cornell University Frank H.T. Rhodes Hall Ithaca 14853 NY United States School of Operations Research and Information Engineering Cornell University Frank H.T. Rhodes Hall Ithaca 14853 NY United States Department of Computer Science University of Wisconsin – Madison CS Building 1210 W. Dayton St Madison 53703 WI United States

A novel topological-data-analytical (TDA) method is proposed to distinguish, from noise, small holes surrounded by high-density regions of a probability density function. The proposed method is robust against additive noise and outliers. Traditional TDA tools, like those based on the distance filtration, often struggle to distinguish small features from noise, because both have short persistences. An alternative filtration, called the Robust Density-Aware Distance filtration, is proposed to prolong the persistences of small holes of high-density regions. This is achieved by weighting the distance function by the density in the sense of Bell et al. The concept of distance-to-measure is incorporated to enhance stability and mitigate noise. The persistence-prolonging property and robustness of the proposed filtration are rigorously established, and numerical experiments are presented to demonstrate the proposed filtration’s utility in identifying small holes. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Distance-to-measure Random topology Topological bootstrapping Topological data analysis Topological inference Weighted filtration

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Time and Space Complexity of Deterministic and Nondeterministic Decision Trees: Local Approach

Time and Space Complexity of Deterministic and Nondeterminis...

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IEEE International Conference on Big Data

作者： Kerven Durdymyradov Mikhail Moshkov Computer Electrical and Mathematical Sciences & Engineering Division and Computational Bioscience Research Center King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

Extensive research has been conducted on rough set theory, specifically focusing on the study of decision trees (DTs) and decision rule systems (DRSs). This theory addresses several important questions, including the relationship between DTs and DRSs, the tradeoff between time and space for DTs, and the tradeoff for *** objective of this paper is to investigate infinite binary information systems (ISs), each consisting of an infinite set (universe) and an infinite set of two-valued functions (features) defined on the universe. We examine the concept of a task within an IS, which is described by a finite number of features and a mapping that assigns a decision to each combination of feature values. Our analysis focuses on deterministic and nondeterministic DTs (DDTs and NDTs) as algorithms for task-solving, with the restriction that only features from the task description are *** serve as representations of DRSs and can sometimes have lower space complexity compared to the original DRSs. We study the time and space complexity of DTs, specifically examining the depth and the number of nodes in these trees. The obtained results allow us to classify the set of all ISs into three families, enabling the identification of nontrivial relationships between DDTs and DRSs represented by NDTs. For each family, we investigate the tradeoff between time and space for both DDTs and NDTs.

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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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A Survey of Artificial Intelligence Solutions in Response to the COVID-19 Pandemic in Saudi Arabia 18

A Survey of Artificial Intelligence Solutions in Response to...

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18th International Learning and Technology Conference, L and T 2021

作者： Aljizawi, Jailan Dalloul, Dina Ghryani, Layal Aldabbagh, Shaymaa Brahimi, Tayeb Computer Science Department College of Engineering Effat University Jeddah Saudi Arabia Energy and Technology Research Center Natural Science Mathematics and Technology Unit College of Engineering Effat University Jeddah Saudi Arabia

Artificial Intelligence (AI) has a significant impact on the course of the COVID-19 pandemic from various aspects. This paper aims to explore the trends of AI applications and how they help predict and prevent the progress of COVID-19 in Saudi Arabia. The method used in this study is based on a narrative review of recent literature on AI and COVID-19 and on a survey conducted on 211 participants. Results show that AI is a crucial element to overcoming COVID 19. The use of COVID-19 related Apps that helped reduce the pandemic spread was more common among the age group participants ranging from 15 - 30. The study concluded that COVID-19 made a positive impact on everyone, and by the use of technology, things got much more comfortable to help in enduring any long-term health consequences. © 2021 The Authors. Published by Elsevier B.V.

关键词： COVID-19

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Experimental evaluation onto the machinability of sustainable Al/SiC/Gr hybrid composite using grey relational analysis

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Materials Today: Proceedings 2022年 68卷 A7-A15页

作者： Kumar, Jatinder Gagandeep Sharma, Shubham Chohan, Jasgurpreet Kumar, Raman Singh, Sandeep Obaid, Ahmed J. Joshi, Ankita Department of Mechanical Engineering St. Soldier Institute of Engineering and Technology Punjab Jalandhar 144011 India Department of Mechanical Engineering IK Gujral Punjab Technical University Jalandhar-Kapurthala Road Punjab Kapurthala 144603 India Chandigarh University Gharuan 140413 India Department of Civil Engineering Chandigarh University Gharuan 140413 India Faculty of Computer Science and Mathematics University of Kufa Iraq Division of Research and Innovation Uttaranchal University Uttarakhand Dehradun 248007 India

The utilization of Aluminium Matrix Composites (AMCs) has undergone a tremendous increase due to its distinct characteristics. However, the inclusion of reinforcement in the aluminium matrix reduces its machinability due to the presence of hard reinforcements in it. This research article refers to the fabrication and evaluation of machining behaviour of newly developed SiC and Graphite reinforced AMC. AMCs were developed through the stir casting route. Al 6061 alloy was selected as the base matrix, which was reinforced with silicon carbide Graphite. Uncoated and multiple layer coated carbide tools were used to evaluate the turning of AMC at various speeds (60, 90 and 120 m/min) and feeds (0.07, 1.0 and 0.13 mm/rev) in the form of surface roughness, material cutting rate and tool wear rate by employing Grey Relational Analysis. Results indicated that feed rate is the highly prominent parameters affecting process performance, followed by spindle speed and tool type. The optimum set of input conditions is to turn Al-SiC-Gr hybrid AMC at a speed of 120 m/min feed of 0.1 mm/rev with a coated carbide tool. The predicted value of Grey Relational Grades was also determined to validate results. © 2022

关键词： Silicon carbide

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advanced eHealth with Explainable AI: Secured by Blockchain with AI-Empowered Block Sensitivity for Adaptive Authentication

Advanced eHealth with Explainable AI: Secured by Blockchain ...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Joy Dutta Hossien B. Eldeeb Tu Dac Ho Department of Computer Science C2PS Research Center Khalifa University Abu Dhabi UAE Electrical Engineering Division University of Cambridge Cambridge UK Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology (NTNU) Trondheim Norway

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

This paper presents an innovative, yet secure, eHealth framework that leverages Explainable Artificial Intelligence (XAI) and blockchain technology to enhance transparency and security in the IoT-edge-cloud continuum. The framework incorporates SHapley Additive exPlanations (SHAP) to provide real-time, model-agnostic explanations for AI predictions, enabling personalized health monitoring and informed decisionmaking in healthcare. To strengthen data security, a consortium blockchain is employed, and AI is utilized to identify block data sensitivity at the edge within blockchain-integrated IoT architectures using Random Forest (RF) algorithm. This approach achieves high accuracy in validating block sensitivity, enabling efficient selection of authentication mechanisms in the proof of authentication (PoAh) consensus within the consortium blockchain. This ensures heightened protection for sensitive data and contributes to improved overall blockchain performance. The proposed framework is evaluated in an edge computing environment and demonstrates significant potential for advancing security and authentication in eHealth, representing a substantial advancement in healthcare technology.

关键词： Technological innovation Sensitivity Explainable AI Data security Scalability Authentication Electronic healthcare Consensus protocol Reliability Protection

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A Meta-Analysis of Artificial Intelligence Applications for Tracking COVID-19: The Case of the U.A.E. 18

A Meta-Analysis of Artificial Intelligence Applications for ...

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18th International Learning and Technology Conference, L and T 2021

作者： Haneya, Hala Alkaf, Dhekra Bajammal, Faigah Brahimi, Tayeb Computer Science Department College of Engineering Effat University Jeddah Saudi Arabia Energy and Technology Research Center Natural Science Mathematics and Technology Unit College of Engineering Effat University Jeddah Saudi Arabia

Coronavirus disease (COVID-19) is an infectious respiratory disease that was first found in Wuhan, China, on December 31, 2019. It is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of November 10, 2020, more than fifty million cases have been confirmed, and more than one million deaths have been reported globally. This situation has created a serious challenge for all countries to institute a variety of control measures to track and slow down the spread of the virus and prevent the increasing number of deaths. In recent years, there has been an ongoing interest in using Artificial Intelligence (A.I.) in healthcare to create new treatments and detecting diseases. The objective of this study is to analyze the application and the impact of A.I. on the breakout of COVID-19 and discuss the contribution of A.I. to the fight against the pandemic based on the most recent applications used in the United Arab Emirates, including Dubai Police Movement Restriction Monitoring System, Taxis Preventive Measures Compliance System, Mobile App "Wai-Eye,"Smart Helmets, Virtual Doctor, and The Department of Health - Abu Dhabi (DoH) Remote Healthcare App. The method used in this study is based on a meta-analysis of recent COVID-19 studies from various databases such as scienceDirect, Sage Journals, SpringerLink, researchGate, Emerald Open research, and IEEE Xplore. The COVID-19 data was based on Johns Hopkins University center for Systems science and engineering (JHU CCSE). Results showed that A.I. applications provided the necessary prevention of the spread of COVID-19, assisted in monitoring restrictions and preventive measures violations, and provided remote healthcare, which directly impacted the number of hospital visits amidst the lockdown. The study concluded that A.I. has proven to be effective in supporting governments in fighting the pandemic. © 2021 The Authors. Published by Elsevier B.V.

关键词： COVID-19

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Parallel Approximations for High-Dimensional Multivariate Normal Probability Computation in Confidence Region Detection Applications

arXiv

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arXiv 2024年

作者： Zhang, Xiran Abdulah, Sameh Cao, Jian Ltaief, Hatem Sun, Ying Genton, Marc G. Keyes, David E. Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Saudi Arabia Statistics Program King Abdullah University of Science and Technology Saudi Arabia Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Mathematics The University of Houston HoustonTX United States

Addressing the statistical challenge of computing the multivariate normal (MVN) probability in high dimensions holds significant potential for enhancing various applications. For example, the critical task of detecting confidence regions where a process probability surpasses a specific threshold is essential in diverse applications, such as pinpointing tumor locations in magnetic resonance imaging (MRI) scan images, determining hydraulic parameters in groundwater flow issues, and forecasting regional wind power to optimize wind turbine placement, among numerous others. One common way to compute high-dimensional MVN probabilities is the Separation-of- Variables (SOV) algorithm. This algorithm is known for its high computational complexity of O(n3) and space complexity of O(n2), mainly due to a Cholesky factorization operation for an n × n covariance matrix, where n represents the dimensionality of the MVN problem. This work proposes a high-performance computing framework that allows scaling the SOV algorithm and, subsequently, the confidence region detection algorithm. The framework leverages parallel linear algebra algorithms with a task-based programming model to achieve performance scalability in computing process probabilities, especially on largescale systems. In addition, we enhance our implementation by incorporating Tile Low-Rank (TLR) approximation techniques to reduce algorithmic complexity without compromising the necessary accuracy. To evaluate the performance and accuracy of our framework, we conduct assessments using simulated data and a wind speed dataset. Our proposed implementation effectively handles high-dimensional multivariate normal (MVN) probability computations on shared and distributed-memory systems using finite precision arithmetics and TLR approximation computation. Performance results show a significant speedup of up to 20X in solving the MVN problem using TLR approximation compared to the reference dense solution without sacrificing the

关键词： Statistics

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