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International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Maryam Nikoupour Mohammad Reza Keyvanpour Seyed Vahab Shojaedini Department of Computer Engineering Faculty of Engineering Data Mining Laboratory Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Electrical Engineering Iranian Research Organization for Science and Technology Tehran Iran

Seizures that take place repeatedly and without provocation are referred to as epilepsy. Epilepsy can be diagnosed with electroencephalography (EEG). One of the most influential challenges of the past few years has been the use of deep learning algorithms to replace manual inspection of medical signals by specialists, such as epilepsy signal classification. This paper presents a multi-label classification approach for epileptic seizures using deep learning. UCI machine learning repository’s epileptic seizure dataset has been used to classify epileptic seizure patients. 178 features are present in each of the 11500 samples in the dataset. Based on a variety of criteria, the proposed method may have a positive impact on epilepsy diagnosis, in most cases by approximately 6% compared with existing methods utilizing long short-term memory (LSTM) and autoencoder. It is possible thus to develop and apply gated recurrent unit-based methods with good potential for categorizing EEG signals for epilepsy diagnosis based on gated recurrent unit (GRU)-CNN based methods.

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A Multi-Agent Digital Twin Framework for AI-Driven Fitness Coaching 25

A Multi-Agent Digital Twin Framework for AI-Driven Fitness C...

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Proceedings of the 2025 ACM International Conference on Interactive Media Experiences

作者： Monica (Monireh) Vahdati Kamran Gholizadeh HamlAbadi Fedwa Laamarti Abdulmotaleb El Saddik Multimedia Communications Research Laboratory (MCRLab) School of Electrical Engineering and Computer Science University of Ottawa Ottawa Ontario Canada Computer Vision Department Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi United Arab Emirates and Multimedia Communications Research Laboratory (MCRLab) School of Electrical Engineering and Computer Science University of Ottawa Ottawa Ontario Canada

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Integrating Sustainability and Industry 4.0: A Framework for Sustainable Logistics 4.0

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Circular Economy and Sustainability 2025年 1-39页

作者： Di Nardo, Mario Gallab, Maryam Murino, Teresa Wu, Jing Pandey, Shatrudhan Facoltàdi Ingegneria Università Telematica Pegaso Rome 00186 Italy MIS-LISTD Laboratory Computer Science Department Mines-Rabat School (ENSMR) Rabat 10000 Morocco Department of Chemical Materials and Industrial Production Engineering University of Naples “Federico II” Naples Italy Department of Electrical and Photonics Engineering Technical University of Denmark Kgs. Lyngby 2800 Denmark Marwadi University Research Center Faculty of Management Studies Marwadi University Gujarat Rajkot 360003 India

As society becomes more aware of environmental and social issues, supply chain stakeholders increasingly consider these factors alongside efficiency, cost, and time. The logistics industry must adapt by integrating Industry 4.0 technologies and sustainability practices. Sustainable Logistics 4.0, though nascent, is gaining traction for enhancing sustainability through digital interventions. This study conducted a comprehensive literature review and bibliometric network analysis using data extracted from the Scopus and Google Scholar databases, covering the period from 2013 to 2023, focusing on the integration of sustainability, Industry 4.0, and logistics. Noting a gap in conceptual frameworks, the paper performs a SWOT analysis to identify key factors and proposes a framework for digitalizing sustainable logistics and addressing emerging challenges. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Circular Economy Industry 4.0 Logistics Sustainability TBL

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Molecular architecture of mammalian pyruvate dehydrogenase complex

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Protein & Cell 2025年第1期16卷 72-78页

作者： Maofei Chen Yutong Song Sensen Zhang Yitang Zhang Xudong Chen Minghui Zhang Meng Han Xin Gao Sai Li Maojun Yang Tsinghua-Peking Center for Life Sciences Beijing Frontier Research Center for Biological StructureSchool of Life SciencesTsinghua UniversityBeijing 100084China Ministry of Education Key Laboratory of Protein Science Tsinghua UniversityBeijing 100084China State Key Laboratory of Membrane Biology Tsinghua UniversityBeijing 100084China Department of Biochemistry and Molecular Biology School of Basic Medical SciencesShenzhen University Health Science CenterShenzhen 518060China Protein Research Technology Center Protein Chemistry and Omics PlatformSchool of Life SciencesTsinghua UniversityBeijing 100084China Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Kingdom of Saudi Arabia Cryo-EM Facility Center Southern University of Science and TechnologyShenzhen 518055China Beijing Life Science Academy Beijing 102209China

Dear Editor,Pyruvate dehydrogenase complex(PDHc) is a large multienzyme assembly(Mr = 4–10 million Daltons) consisting of three essential components: pyruvate dehydrogenase(E1p), dihydrolipoyl transacetylase(E2p), and dihydrolipoyl dehydrogenase(E3). These three enzymes perform distinct functions sequentially to catalyze the oxidative decarboxylation of pyruvate with formation of nicotinamide adenine dinucleotide(NADH) and acetyl-coenzyme A(Patel and Roche, 1990).

关键词： sequentially oxidative Dalton

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A Surrogate-Assisted Evolutionary Algorithm for Expensive Dynamic Multimodal Optimzation

A Surrogate-Assisted Evolutionary Algorithm for Expensive Dy...

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Congress on Evolutionary Computation

作者： Xunfeng Wu Songbai Liu Junkai Ji Lijia Ma Victor C. M. Leung College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Surrogate-assisted evolutionary algorithms (SAEAs) have demonstrated promising optimization performance in addressing expensive dynamic optimization problems or expensive multimodal optimization problems. However, none of existing SAEAs are designed specifically for tackling expensive dynamic multimodal optimization problems (EDMMOPs). Therefore, in this paper, a first SAEA for tackling EDMMOPs is proposed. First, a nearest density clustering is designed to divide the population into a number of subpopulations, enhancing the diversity of the population. Then, a surrogate-assisted evolutionary optimizer is developed to construct surrogate models for each subpopulation and evolve all solutions in subpopulations by means of the built surrogate models, accelerating the population's converge towards several optimal solutions rapidly. Finally, a transfer learning-based prediction is devised to generate initial samples for next environment by leveraging the stored training samples in the previous environments. To assess the performance of our proposed algorithm, a set of complex benchmark problems is adopted, and the experimental results confirm its superior performance over several competitive algorithms on most test cases.

关键词： Training Heuristic algorithms Computational modeling Sociology Clustering algorithms Evolutionary computation Benchmark testing

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Temperature Dependent Implantation of As-Isoelectronic Impurity in GaN Micro and Nanostructures

Temperature Dependent Implantation of As-Isoelectronic Impur...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Aadil Waseem Clarence Chan Xihang Wu Yujie Liu Yifan Shen Manos Kapritsos Theodore B Norris Xiuling Li Chandara Family Department of Electrical and Computer Engineering Microelectronic Research Center The University of Texas at Austin Austin Tx USA Department of Electrical and Computer Engineering Holonyak Micro and Nanotechnology Laboratory University of Illinois Urbana-Champaign Urbana Illinois USA Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor Michigan USA

To enhance solar photo absorption of III-N semiconductors, we demonstrate Arsenic implantation at elevated temperatures, improving doping activation and reducing crystal damage.

ISBN: (纸本)9798350369311

To enhance solar photo absorption of III-N semiconductors, we demonstrate Arsenic implantation at elevated temperatures, improving doping activation and reducing crystal damage.

关键词： Temperature dependence Photonic band gap Impurities Arsenic Metals Lattices Crystals Electro-optic effects Doping profiles Nanostructures

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Non-Classical Quantum State Generation using Superconducting-Nanostrip Photon-Number Resolving Detector with Optical Sampling

Non-Classical Quantum State Generation using Superconducting...

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2024 Frontiers in Optics, FiO 2024

作者： Endo, Mamoru Takahashi, Kazuma Nomura, Takefumi Sonoyama, Tatsuki Miki, Shigehito Yabuno, Masahiro Terai, Hirotaka Kashiwazaki, Takahiro Inoue, Asuka Umeki, Takeshi Nehra, Rajveer Takase, Kan Asavanant, Warit Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan Advanced ICT Research Institute National Institute of Information and Communications Technology Japan Department of Electrical and Computer Engineering University of Massachusetts Amherst AmherstMA01003 United States Department of Physics University of Massachusetts Amherst AmherstMA01003 United States College of Information and Computer Science University of Massachusetts Amherst AmherstMA01003 United States

Superconducting-nanostrip photon detectors with optical sampling method now function as true photon-number resolving detectors in real-time without multiplexing. We applied this technique for quantum state generation ... 详细信息

关键词： Quantum computers

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Modeling Multi-Threaded Aggregated I/O for Asynchronous Checkpointing on HPC Systems

Modeling Multi-Threaded Aggregated I/O for Asynchronous Chec...

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International Symposium on Parallel and Distributed computing

作者： Mikaila J. Gossman Bogdan Nicolae Jon C. Calhoun Holcombe Department of Electrical and Computing Engineering Clemson University Clemson SC Mathematics and Computer Science Division Argonne National Laboratory Lemont IL

HPC systems encompass more components with each new generation. As a result, the process of interacting with stable storage systems like parallel file systems (PFS) becomes increasingly difficult. Larger systems often result in more frequent failures, increasing the need and frequency to incorporate fault-tolerant mechanisms. One example is checkpoint-restart (C/R), where applications or systems save their data to non-volatile storage devices, such as a PFS. On failure, the system or application is restored to a saved state and computation continues. Today, asynchronous C/R is gaining traction for its ability to checkpoint data to permanent storage concurrently with the application. However, asynchronous C/R brings about many new challenges. For starters, asynchronous C/R introduces complex resource contention between the application and the C/R implementation. Additionally, some implementations adopt file-per-process writing strategies, which overwhelm PFS’ at high core counts. In this work, we explore how multi-threaded POSIX I/O impacts aggregated throughput. To this extent we characterize the influence of different I/O parameters, such as the number of writer threads and how they access storage devices, has on aggregated I/O. We use the information gathered in this study to identify best practices when performing aggregated I/O as a first step in designing an efficient I/O aggregation scheme for asynchronous C/R.

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Fire Security Systems Analysis and Internet of Things Implications

Fire Security Systems Analysis and Internet of Things Implic...

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IEEE International Conference on Electro-Information Technology

作者： Susana Rodriguez Fuerte Xinrui Yu Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing Research Laboratory Illinois Institute of Technology Chicago IL U.S.A.

This paper presents the current use of the Internet of Things (IoT) in fire evacuation and extinction. It examines the different approaches to the problem and technologies like Building Information Modeling (BIM) and mathematical algorithms that can be used to determine the optimal evacuation route. It also evaluates existing fire security solutions such as smoke, flame, motion, and gas sensors, LED lights, buzzers, and SMS modules. Entities that specialize in Residential and Commercial Security Systems and Home Automation are also discussed, along with the services they offer. The main objective of this study is to understand current systems and resources regarding fire evacuation and extinction systems and to analyze different developments in smart buildings to create an efficient system for fire detection and evacuation.

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DPP-HSS: Towards Fast and Scalable Hypervolume Subset Selection for Many-objective Optimization

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IEEE Transactions on Evolutionary Computation 2024年

作者： Gong, Cheng Nan, Yang Shang, Ke Guo, Ping Ishibuchi, Hisao Zhang, Qingfu City University of Hong Kong Department of Computer Science Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

Hypervolume subset selection (HSS) has received significant attention since it has a strong connection with evolutionary multi-objective optimization (EMO), such as environment selection and post-processing to identify representative solutions for decision-makers. The goal of HSS is to find the optimal subset that maximizes the hypervolume indicator subject to a given cardinality constraint. However, existing HSS algorithms or related methods are not efficient in achieving good performance in high-dimensional objective spaces. This is primarily because HSS problems become NP-hard when the number of objectives exceeds two, and the calculation of hypervolume contribution is very time-consuming. To efficiently solve HSS problems while maintaining a good solution quality, we propose a fast and scalable hypervolume subset selection method for many-objective optimization based on the determinantal point process (DPP), named DPP-HSS, which is fully free of hypervolume contribution calculation. Specifically, DPP-HSS constructs a hypervolume kernel matrix by extracting the convergence and diversity representations of each solution for a given HSS problem. This matrix is then used to build a DPP model. Subsequently, the original HSS problem is reformulated as a new maximization optimization problem based on the constructed model. A greedy DPP-based hypervolume subset selection algorithm is implemented to solve this transformed problem. Extensive experiments show that the proposed DPP-HSS achieves significant speedup and good hypervolume performance in comparison with state-of-the-art HSS algorithms on benchmark problems. Furthermore, DPP-HSS demonstrates very good scalability with respect to the number of objectives. © 1997-2012 IEEE.

关键词： Optimization algorithms

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