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3rd International Conference on Intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Punia, Sushma Saharia, Ankur Ismail, Yaseera Petruccione, Francesco Bourdine, Anton V. Demidov, Vladimir V. Morozov, Oleg G. Yin, Juan Singh, Ghanshyam Tiwari, Manish Optoelectronics and Photonics Research Lab Department of Electronics and Communication Engineering Manipal University Jaipur Jaipur303007 India Department of Physics Stellenbosch University Stellenbosch7600 South Africa School of Data Science and Computational Thinking Stellenbosch University Stellenbosch7602 South Africa Stellenbosch South Africa Department of Communication Lines Povolzhskiy State University of Telecommunications and Informatics 23 Lev Tolstoy Street Samara Oblast443010 Russia JSC Scientific Production Association State Optical Institute Named after Vavilov S.I. 36/1 Babushkin Street St. Petersburg192171 Russia Department of Photonics and Communication Links Saint Petersburg State University of Telecommunications Named after M.A. Bonch-Bruevich 22 Bolshevikov Avenue St. Petersburg193232 Russia Department of Radiophotonics and Microwave Technologies Kazan National Research State University named after A.N. Tupolev-KAI 10 Karl Marx Street Kazan420111 Russia Division of Quantum Physics and Quantum Information University of Science and Technology of China 99 Xiupu Road Pudong District Shanghai200093 China Department of ECE Malaviya National Institute of Technology Jaipur Jaipur302017 India

ISBN: (纸本)9789819784639

This work is intended to present a nested Photonic Crystal Fiber (PCF) for OAM mode propagation. The proposed PCF comprises of a dense flint SF6 and a very light flint LLF1. The finite-element method (FEM) is used for the modal analysis to obtain propagating HE/EH modes. The proposed fiber has shown flat dispersion during the propagation of multiple orbital angular momentum (OAM) modes. Furthermore, the designed PCF’s nonlinearity (gamma) and confinement loss (CL) are calculated to prove the practical feasibility of the design. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Photonic crystal fibers

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Machine learning for modelling unstructured grid data in computational physics: A review

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Information Fusion 2025年 123卷

作者： Cheng, Sibo Bocquet, Marc Ding, Weiping Finn, Tobias Sebastian Fu, Rui Fu, Jinlong Guo, Yike Johnson, Eleda Li, Siyi Liu, Che Moro, Eric Newton Pan, Jie Piggott, Matthew Quilodran, Cesar Sharma, Prakhar Wang, Kun Xiao, Dunhui Xue, Xiao Zeng, Yong Zhang, Mingrui Zhou, Hao Zhu, Kewei Arcucci, Rossella CEREA ENPC EDF R&D Institut Polytechnique de Paris Île-de-France France School of Artificial Intelligence and Computer Science Nantong University Jiangsu Nantong226019 China School of Mathematical Sciences Key Laboratory of Intelligent Computing and Applications Tongji University Shanghai200092 China Faculty of Data Science City University of Macau 999078 China School of Engineering and Materials Science Faculty of Science and Engineering Queen Mary University of London LondonE1 4NS United Kingdom Zienkiewicz Centre for Modelling Data and AI Faculty of Science and Engineering Swansea University SwanseaSA1 8EN United Kingdom Department of Computer Science and Engineering Hong Kong university of science and technology Hong Kong Department of Earth Science & Engineering Imperial College London LondonSW7 2AZ United Kingdom Tianjin Key Laboratory of Imaging and Sensing Microelectronics Technology School of Microelectronics Tianjin University Tianjin300072 China T2N 1N4 Canada T2N 1N4 Canada Undaunted Grantham Institute for Climate Change and the Environment Imperial College London LondonSW7 2AZ United Kingdom Culham Campus AbingdonOX14 3DB United Kingdom Centre for Computational Science Department of Chemistry University College London LondonWC1H 0AJ United Kingdom H3G 1M8 Canada Australia Department of Chemical Engineering University College London LondonWC1E 6BT United Kingdom

Unstructured grid data are essential for modelling complex geometries and dynamics in computational physics. Yet, their inherent irregularity presents significant challenges for conventional machine learning (ML) techniques. This paper provides a comprehensive review of advanced ML methodologies designed to handle unstructured grid data in high-dimensional dynamical systems. Key approaches discussed include graph neural networks, transformer models with spatial attention mechanisms, interpolation-integrated ML methods, and meshless techniques such as physics-informed neural networks. These methodologies have proven effective across diverse fields, including fluid dynamics and environmental simulations. This review is intended as a guidebook for computational scientists seeking to apply ML approaches to unstructured grid data in their domains, as well as for ML researchers looking to address challenges in computational physics. It places special focus on how ML methods can overcome the inherent limitations of traditional numerical techniques and, conversely, how insights from computational physics can inform ML development. For this purpose, we mainly focus in this review on recent papers from the past decade that reflect strong interactions between computational physics and deep learning methods. To support benchmarking, this review also provides a summary of open-access datasets of unstructured grid data in computational physics. Finally, emerging directions such as generative models with unstructured data, reinforcement learning for mesh generation, and hybrid physics-data-driven paradigms are discussed to inspire future advancements in this evolving field. © 2025

关键词： Reinforcement learning

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A Tutorial on Federated Learning from Theory to Practice:Foundations,Software Frameworks,Exemplary Use Cases,and Selected Trends

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 824-850页

作者： M.Victoria Luzón Nuria Rodríguez-Barroso Alberto Argente-Garrido Daniel Jiménez-López Jose M.Moyano Javier Del Ser Weiping Ding Francisco Herrera Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Communications Engineering University of the Basque Country(UPV/EHU)and also with TECNALIABasque Research&Technology Alliance(BRTA)Spain School of Information Science and Technology Nantong UniversityNantong 226019China

When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized *** allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third *** paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data *** virtue of FL,models can be learned from all such distributed data sources while preserving data *** aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software ***,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL *** ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.

关键词： data privacy distributed machine learning federated learning software frameworks

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CanSeer: a translational methodology for developing personalized cancer models and therapeutics

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Scientific Reports 2025年第1期15卷 1-22页

作者： Butt, Rida Nasir Amina, Bibi Sultan, Muhammad Umer Tanveer, Zain Bin Gondal, Mahnoor Naseer Hussain, Risham Khan, Salaar Akbar, Rida Nasir, Zainab Khalid, Muhammad Farhan Channan-Khan, Asher Alban Faisal, Amir Shoaib, Muhammad Chaudhary, Safee Ullah Biomedical Informatics and Engineering Research Laboratory (BIRL) Syed Babar Ali School of Science and Engineering Department of Life Sciences Lahore University of Management Sciences Lahore 54792 Pakistan Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor 48109 MI United States Michigan Center for Translational Pathology University of Michigan Ann Arbor 48109 MI United States Data Science Institute Lancaster University Lancaster LA1 4YW United Kingdom Center for Eukaryotic Gene Regulation Department of Biochemistry and Molecular Biology Penn State University University Park 16802 PA United States Mayo Clinic School of Medicine New York University NY United States Cancer Therapeutics Lab Syed Babar Ali School of Science and Engineering Department of Life Sciences Lahore University of Management Sciences Lahore 54792 Pakistan Epigenome and Genome Integrity Lab (EaGIL) Syed Babar Ali School of Science and Engineering Department of Life Sciences Lahore University of Management Sciences Lahore 54792 Pakistan

computational modeling and analysis of biomolecular network models annotated with omics data are emerging as a versatile tool for designing personalized therapies. Current endeavors aimed at employing in silico models towards personalized cancer therapeutics remain limited in providing all-in-one approach that ascertains actionable targets, re-positions FDA (Food and Drug Administration) approved drugs, furnishes quantitative cues on therapy responses such as efficacy and cytotoxic effect, and identifies novel drug combinations. Here we propose “CanSeer”—a methodology for developing personalized therapeutics. CanSeer employs patient-specific genetic alterations and RNA-seq data to annotate in silico models followed by dynamical network analyses towards assessment of treatment responses. To exemplify, three use cases involving paired samples, unpaired samples, and cancer samples only, of lung squamous cell carcinoma (LUSC) patients are provided. CanSeer reveals the effectiveness of repositioned drugs along with the identification of several novel LUSC treatment combinations including Afuresertib + Palbociclib, Dinaciclib + Trametinib, Afatinib + Oxaliplatin, Ulixertinib + Olaparib, etc. © The Author(s) 2025.

关键词： Biomolecular network modeling Dynamical analysis of in silico models Integrative omics Personalized mono- and combinatorial therapies Therapeutic screening Translational cancer systems biology

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Vacancy-Type Defects in HfO2 Layers and Their Role in Amorphous-to-Crystalline Transition Studied by Monoenergetic Positron Beams

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IEEE Transactions on Semiconductor Manufacturing 2025年

作者： Uedono, Akira Kawakami, Kazuya Kuribara, Tatsunori Hasunuma, Ryu Harashima, Yosuke Shigeta, Yasuteru Ni, Zeyuan Yan, Ruolin Nakamura, Hideo Notake, Akira Moriya, Tsuyoshi Michishio, Koji Ishibashi, Shoji University of Tsukuba Faculty of Pure and Applied Science Tsukuba Ibaraki305-8573 Japan Nara Institute of Science and Technology Graduate School of Science and Technology Ikoma Nara630-0192 Japan Tokyo Electron Technology Solutions Ltd S-Technology Development Center Nirasaki Yamanashi650 Japan Tokyo Electron Technology Solutions Ltd Development Department1 Nirasaki Yamanashi407-0192 Japan Tokyo Electron Ltd Digital Design Center Sapporo Hokkaido060-0003 Japan Tokyo Electron Technology Solutions Ltd Software and Data Engineering Division Nirasaki Yamanashi407-0192 Japan National Institute of Advanced Industrial Science and Technology Research Institute for Measurement and Analytical Instrumentation Tsukuba Ibaraki305-8568 Japan National Institute of Advanced Industrial Science and Technology Research Center for Computational Design of Advanced Functional Materials Tsukuba Ibaraki305-8568 Japan

The annealing properties of vacancies in HfO2 layers deposited on Si substrates and their role in amorphous-to-crystalline transition were studied with monoenergetic positron beams. HfO2 layers with a thickness of 4.30 nm were fabricated by the atomic layer deposition technique. The major vacancytype defects in these layers were identified as a Hf vacancy (VHf) coupled with multiple oxygen vacancies (VOs) and larger vacancy clusters. After annealing at 500°C, the concentration of vacancy clusters started to increase, which was attributed to the agglomeration of intrinsic open spaces in the amorphous phase upon the phase transition from amorphous to monoclinic crystalline phases. The amorphous-crystalline transition started near the interface between the HfO2 layer and bottom electrodes (TiN). After the crystallization, the concentration of vacancy clusters decreased as the annealing temperature increased. © 2025 IEEE.

关键词： Hafnium oxides

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Cosmological phase-space analysis of f(G)-theories of gravity

arXiv

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arXiv 2025年

作者： Papagiannopoulos, Giannis Luongo, Orlando Leon, Genly Paliathanasis, Andronikos Department of Physics National & Kapodistrian University of Athens Zografou Campus GR 157 73 Athens Greece School of Science and Technology University of Camerino Via Madonna delle Carceri Camerino62032 Italy Department of Nanoscale Science and Engineering University at Albany-SUNY AlbanyNY12222 United States INAF - Osservatorio Astronomico di Brera Milano Italy Sezione di Perugia Perugia06123 Italy Al-Farabi Kazakh National University Al-Farabi av. 71 Almaty050040 Kazakhstan Departamento de Matemáticas Universidad Católica del Norte Avda. Angamos 0610 Casilla Antofagasta1280 Chile Institute of Systems Science Department of Mathematics Faculty of Applied Sciences Durban University of Technology Durban4000 South Africa School for Data Science and Computational Thinking Department of Mathematical Sciences Stellenbosch University Stellenbosch7602 South Africa South Africa

The impact of topological terms that modify the Hilbert-Einstein action is here explored by virtue of a further f(G) contribution. In particular, we investigate the phase-space stability and critical points of an equivalent scalar field representation that makes use of a massive field, whose potential is function of the topological correction. To do so, we introduce to the gravitational Action Integral a Lagrange multiplier and model the modified Friedmann equations by virtue of new non-dimensional variables. We single out dimensionless variables that permit a priori the Hubble rate change of sign, enabling regions in which the Hubble parameter either vanishes or becomes negative. In this respect, we thus analyze the various possibilities associated with a universe characterized by such topological contributions and find the attractors, saddle points and regions of stability, in general. The overall analysis is carried out considering the exponential potential first and then shifting to more complicated cases, where the underlying variables do not simplify. We compute the eigenvalues of our coupled differential equations and, accordingly, the stability of the system, in both a spatially-flat and non-flat universe. Quite remarkably, regardless of the spatial curvature, we show that a stable de Sitter-like phase that can model current time appears only a small fraction of the entire phase-space, suggesting that the model under exam is unlikely in describing the whole universe dynamics, i.e., the topological terms appear disfavored in framing the entire evolution of the universe. © 2025, CC BY.

关键词： Topology

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Large language models design sequencedefined macromolecules via evolutionary optimization

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npj computational Materials 2024年第1期10卷 446-453页

作者： Wesley F.Reinhart Antonia Statt Department of Materials Science and Engineering Pennsylvania State UniversityUniversity Park16802 PAUSA Institute for Computational and Data Sciences Pennsylvania State UniversityUniversity Park16802 PAUSA Department of Materials Science and Engineering Grainger College of EngineeringUniversity of Illinois Urbana-ChampaignChampaign61801 ILUSA

We demonstrate the ability of a large language model to perform evolutionary optimization for materials ***’s Claude 3.5 model outperforms an active learning scheme with handcrafted surrogate models and an evolutionary algorithm in selecting monomer sequences to produce targeted morphologies in macromolecular *** pre-trained language models can potentially reduce the need for hyperparameter tuning while offering new capabilities such as *** model performs this task effectively with orwithout context about the task itself,but domain-specific context sometimes results in faster convergence to good ***,when this context is withheld,the model infers an approximate notion of the task(e.g.,calling it a protein folding problem).This work provides evidence of Claude 3.5’s ability to act as an evolutionary optimizer,a recently discovered emergent behavior of large language models,and demonstrates a practical use case in the study and design of soft materials.

关键词： offering evolutionary faster

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Investigation on the influence of atomic potentials on the above threshold ionization

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Chinese Physics B 2014年第5期23卷 238-243页

作者：田原野李苏宇魏珊珊郭福明曾思良陈基根杨玉军 Institute of Atomic and Molecular Physics Jilin University Data Center for High Energy Density Physics Institute of Applied Physics and Computational Mathematics Science and Technology Computation Physics Laboratory Institute of Applied Physics and Computational Mathematics Department of Physics and Materials Engineering Taizhou University

We systematically investigate the influence of atomic potentials on the above-threshold ionization （ATI） spectra in one-dimensional （1D） cases and compare them with the three-dimensional （3D） case by numerically solving the time-dependent Schrrdinger equation. It is found that the direct ionization plateau and the rescattering plateau of the ATI spectrum in the 3D case can be well reproduced by the 1D ATI spectra calculated from the supersolid-core potential and the soft-core potential, respectively. By analyzing the factors that affect the yield of the ATI spectrum, we propose a modified-potential with which we can reproduce the overall 3D ATI spectrum. In addition, the influence of the incident laser intensities and frequencies on the ATI spectra calculated from the proposed modified potential is studied.

关键词： above threshold ionization model potential angular distribution of photoelectron

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Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-based Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Yi Wang, Ze Song, Yu Jia, Ziyu Wang, Boyu Jung, Tzyy-Ping Wan, Feng Macau University of Science and Technology Macao Centre for Mathematical Sciences Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovation Engineering 999078 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin300384 China Chinese Academy of Sciences Beijing Key Laboratory of Brainnetome and Brain-Computer Interface and Brainnetome Center Institute of Automation Beijing100045 China Western University Department of Computer Science Brain Mind Institute LondonONN6A 3K7 Canada University of California at San Diego Swartz Center for Computational Neuroscience Institute for Neural Computation La Jolla CA92093 United States University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China University of Macau Centre for Cognitive and Brain Sciences Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation 999078 China

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-confidence individuals within the source neighborhood. We have conducted extensive experiments on the public SEED dataset and our collected the Hearing-Impaired EEG dataset (HIED). The experimental results underscore the efficacy of our proposed NSAL-DGAT approach, showcasing state-of-the-art accuracy in subject-dependent as well as subject-independent scenarios. The source code is available at https://***/YYingDL/NSAL-DGAT. © 2010-2012 IEEE.

关键词： Speech analysis

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Mesoscale Wind Farm Placement via Linear Optimization Constrained by Power System and Techno-economics

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Journal of Modern Power Systems and Clean Energy 2021年第2期9卷 356-366页

作者： Ali Erduman Bahri Uzunoglu Bedri Kekezoglu Ali Durusu the Electrical Engineering Department Hakkari UniversityHakkari30000 Turkey Department of Engineering Sciences Department of Electrical EngineeringComputational and Data-enabled Science and Engineering in Energy SystemsUppsala University75121 UppsalaSweden Department of Mathematics Florida State UniversityTallahasseeFL 32310USA the Electrical Engineering Department Yildiz Technical UniversityIstanbul34220 Turkey

The objective of this study is to develop a wind farm placement and investment methodology based on a linear optimization *** problem has a major significance for the investment success for the projects of renewable energy such as wind *** this study,a mesoscale approach is adopted whereby the wind farm location is investigated in comparison with a microscale approach where the location of each individual turbine is *** study focuses on the placement of a wind farm by economical optimization constrained by the power system,wind resources,and *** optimization is introduced in this context at the power system which is constrained by wind farm planning.

关键词： Generation expansion grid integration mesoscale wind farm linear optimization wind power

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