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Boosting bulk photovoltaic effect in transition metal dichalcogenide by edge semimetal contact

Light(science & Applications) 2025年第1期14卷 197-205页

作者： Shuang Qiao Jihong Liu Chengdong Yao Ni Yang Fangyuan Zheng Wanqing Meng Yi Wan Philip C.Y.Chow Dong-Keun Ki Lijie Zhang Yumeng Shi Lain-Jong Li Hebei Key Laboratory of Optic-Electronic Information and Materials College of Physics Science and TechnologyHebei University071002BaodingChina Department of Mechanical Engineering The University of Hong KongHong KongChina Department of Physics and HK Institute of Quantum Science&Technology The University of Hong KongHong KongChina Key Laboratory of Carbon Materials of Zhejiang Province College of Chemistry and Materials EngineeringWenzhou UniversityWenzhouChina Key Laboratory of Luminescence and Optical Information Ministry of EducationSchool of Physical Science and EngineeringBeijing Jiaotong University100044BeijingChina

Oxide materials with a non-centrosymmetric structure exhibit bulk photovoltaic effect(BPVE)but with a low cell *** the past few years,relatively larger BPVE coefficients have been reported for two-dimensional(2D)layers and stacks with asymmety-induced spontaneous ***,we report a crucial breakthrough in boosting the BPVE in 3R-MoS2 by adopting edge contact(EC)geometry using bismuth semimetal *** clear contrast to the typically used top contact(TC)geometry,the EC metal which strongly adheres to the edges and the subtrates can induce a pronounced tensile strain to the 3R-MoS2,and the lateral contact geometry allows to completely access to in-plane polarization from underneath layers reachable by light,leading to>100 times of BPVE enhancement in *** further design a 3R-MoS2/WSe2 heterojunction to demonstrate constructive coupling of BPVE with the conventional photovoltaic effect,indicating their potential in photodetectors and photovoltaic devices.

关键词： effect geometry breakthrough

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A survey of distributed algorithms for aggregative games

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IEEE/CAA Journal of Automatica Sinica 2025年第5期12卷 859-871页

作者： Li, Huaqing Li, Jun Ran, Liang Zheng, Lifeng Huang, Tingwen Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing The College of Electronic and Information Engineering Southwest University Chongqing400715 China College of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China School of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518107 China

Game theory-based models and design tools have gained substantial prominence for controlling and optimizing behavior within distributed engineering systems due to the inherent distribution of decisions among individuals. In non-cooperative settings, aggregative games serve as a mathematical framework model for the interdependent optimal decision-making problem among a group of non-cooperative players. In such scenarios, each player's decision is influenced by an aggregation of all players' decisions. Nash equilibrium (NE) seeking in aggregative games has emerged as a vibrant topic driven by applications that harness the aggregation property. This paper presents a comprehensive overview of the current research on aggregative games with a focus on communication topology. A systematic classification is conducted on distributed algorithm research based on communication topologies such as undirected networks, directed networks, and time-varying networks. Furthermore, it sorts out the challenges and compares the algorithms' convergence performance. It also delves into real-world applications of distributed optimization techniques grounded in aggregative games. Finally, it proposes several challenges that can guide future research directions. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Nash equilibrium

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Multi-View Test-Time Adaptation for Semantic Segmentation in Clinical Cataract Surgery

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IEEE Transactions on Medical Imaging 2025年第5期44卷 2307-2318页

作者： Li, Heng Ou, Mingyang Li, Haojin Qiu, Zhongxi Niu, Ke Fu, Huazhu Liu, Jiang Southern University of Science and Technology Research Institute of Trustworthy Autonomous Systems Shenzhen518055 China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China Beijing Information Science and Technology University Computer School Beijing100192 China Institute of High Performance Computing Agency for Science Technology and Research Fusionopolis Way 138632 Singapore Southern University of Science and Technology Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Shenzhen518055 China

Cataract surgery, a widely performed operation worldwide, is incorporating semantic segmentation to advance computer-assisted intervention. However, the tissue appearance and illumination in cataract surgery often differ among clinical centers, intensifying the issue of domain shifts. While domain adaptation offers remedies to the shifts, the necessity for data centralization raises additional privacy concerns. To overcome these challenges, we propose a Multi-view Test-time Adaptation algorithm (MUTA) to segment cataract surgical scenes, which leverages multi-view learning to enhance model training within the source domain and model adaptation within the target domain. In the training phase, the segmentation model is equipped with multi-view decoders to boost its robustness against variations in cataract surgery. During the inference phase, test-time adaptation is implemented using multi-view knowledge distillation, enabling model updates in clinics without data centralization or privacy concerns. We conducted experiments in a simulated cross-center scenario using several cataract surgery datasets to evaluate the effectiveness of MUTA. Through comparisons and investigations, we have validated that MUTA effectively learns a robust source model and adapts the model to target data during the practical inference phase. © 1982-2012 IEEE.

关键词： Semantic Segmentation

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Co-regulation effect of solvation and interface of pyridine derivative enabling highly reversible zinc anode

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材料科学技术（英文版） 2025年第1期204卷 1-9页

作者： Binrui Xu Guangbin Wang Yong Liu Quanan Li Fengzhang Ren Jianmin Ma School of Information Engineering Henan University of Science and TechnologyLuoyang 471023China School of Materials Science and Engineering Provincial and Ministerial Coconstruction of Collaborative Innovation Center for Non-ferrous Metal New Materials and Advanced Processing TechnologyHenan University of Science and TechnologyLuoyang 471023China Longmen laboratory Luoyang 471000China School of Chemistry Tiangong UniversityTianjin 300387China

The poor reversibility and stability of Zn anodes greatly restrict the practical application of aqueous Zn-ion batteries(AZIBs),resulting from the uncontrollable dendrite growth and H2O-induced side reactions during *** additive modification is considered one of the most effective and simplest methods for solving the aforementioned ***,the pyridine derivatives(PD)including 2,4-dihydroxypyridine(2,4-DHP),2,3-dihydroxypyridine(2,3-DHP),and 2-hydroxypyrdine(2-DHP),were em-ployed as novel electrolyte additives in ZnSO4 *** density functional theory calculation and experimental findings demonstrated that the incorporation of PD additives into the electrolyte effectively modulates the solvation structure of hydrated Zn ions,thereby suppressing side reactions in ***-ditionally,the adsorption of PD molecules on the zinc anode surface contributed to uniform Zn deposi-tion and dendrite growth ***,a 2,4-DHP-modified Zn/Zn symmetrical cell achieved an extremely long cyclic stability up to 5650 h at 1 mA ***,the Zn/NH4V4O10 full cell with 2,4-DHP-containing electrolyte exhibited an outstanding initial capacity of 204 mAh g-1,with a no-table capacity retention of 79％after 1000 cycles at 5 A ***,this study expands the selection of electrolyte additives for AZIBs,and the working mechanism of PD additives provides new insights for electrolyte modification enabling highly reversible zinc anode.

关键词： Zn anode Pyridine derivative Electrolyte additive Solvation regulation Interface modification

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A deep learning framework for automatic assessment of presence in virtual reality using multimodal behavioral cues

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Neural Computing and Applications 2025年第8期37卷 6283-6303页

作者： Pannattee, Peerawat Shimada, Shogo Yem, Vibol Nishiuchi, Nobuyuki Graduate School of Systems Design Tokyo Metropolitan University Tokyo191-0065 Japan Graduate School of Science and Technology Degree Program in Systems and Information Engineering University of Tsukuba Ibaraki3058573 Japan

Effective development of virtual reality (VR) applications is heavily reliant on the evaluation of user experience (UX). However, traditional methods such as questionnaires have inherent limitations which hinder their ability to capture nuanced behavioral responses and impede the agility of VR content creation: They are time-consuming, burdensome for users, and require significant human effort for interpretation. This study introduces an automated framework aimed at addressing the limitations of questionnaire-based evaluations to assess UX in VR. Our primary focus is to validate the concept of this framework through assessment of the sense of presence (SOP), a crucial psychological perception with significant impact on VR UX. Our proposed framework utilizes a deep neural network (DNN) to analyze patterns in multimodal behavioral cues, including facial expressions, head movements, and hand movements, to predict scores from the Igroup Presence Questionnaire (IPQ). Additionally, we introduce two statistical profiles: the Visual Entropy Profile (VEP), which offers insights into visual complexity by depicting scene entropy, and the Experiential Presence Profile (EPP), which is designed to capture users’ historical SOP levels to enable personalized baseline and sensitivity estimation. The proposed framework achieves a significant correlation between actual and predicted IPQ scores, with a Spearman’s rank correlation coefficient of 0.7303, showcasing the potential of DNNs in analyzing complex behavioral signals and automating SOP assessment. This study represents a pioneering effort in leveraging DNNs for the automatic assessment of SOP and paves the way for future advances in automatically assessing VR UX and unlocking new opportunities in the field. © The Author(s) 2025.

关键词： Virtual environments

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Modulation of inactive Li_(2)O via iodinated MOF nanocapsules interfacial transformation engineering for high-performance solid electrolyte interphase

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Journal of Energy Chemistry 2025年第5期104卷 482-493页

作者： Xingxing Zhang Qingmei Su Gaohui Du Bingshe Xu Xuehan Hou Xiaowei Yang Weihao Shi Zhuo Chen Yang Shi Yujie Lv Wenhuan Huang School of Materials Science&Engineering Shaanxi University of Science and TechnologyXi’an 710021ShaanxiChina Key Laboratory of Chemical Additives for China National Light Industry College of Chemistry and Chemical EngineeringShaanxi University of Science and TechnologyXi’an 710021ShaanxiChina Materials Institute of Atomic and Molecular Science School of Physics&Information ScienceShaanxi University of Science and TechnologyXi’an 710021ShaanxiChina School of Chemistry and Chemical Engineering Shanghai Jiaotong UniversityShanghai 200240China

Lithium(Li)deposition and nucleation at solid electrolyte interphase(SEI)is the main origin for the capacity decay in Li metal batteries(LMBs).SEI conversion with enhanced electrochemical and mechanical properties is an effective approach to achieve uniform nucleation of Li^(+)and stabilize the lithium metal ***,complex interfacial reaction mechanisms and interface compatibility issues hinder the development of SEI conversion strategies for stabilizing lithium metal ***,we presented the release of I_(3)^(-)in–NH_(2)-modified metal–organic frameworks for a Li metal surface SEI phase conversion ***–NH_(2)group in MOF pores induced the formation of I_(3)^(-)from I_(2),which was further spontaneously reacted with inactive Li_(2)O transforming into high-performance LiI and LiIO_(3)***,theoretical calculation provided deeply insight into the unique reconstructed interfacial formation and electrochemical mechanism of rich LiI and LiIO_(3)*** a result,the Li^(+)deposition and nucleation were improved,facilitating the transport kinetics of Li^(+)and inhibiting the growth of lithium *** assembled solid-state Li||LiFePO_(4)full cells exhibited superior long-term stability of 800 cycles and high Coulombic efficiency(>99%),Li||LiNi_(0.8)Co_(0.1)Mn_(0.1)O_(2)pouch cell also displayed superior practical performance over 200 cycles at 2 C,high loading of 5 mg cm^(-2)and safety *** innovative SEI design strategy promotes the development of high-performance solid-state Li metal batteries.

关键词： Solid electrolyte interphase(SEI) SEI phase conversion MOF Nano-capsule Solid-state electrolytes Li metal battery

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Single-level Discrete Two Dimensional Wavelet Transform Based Multiscale Deep Learning Framework for Two-Wheeler Helmet Classification

EAI Endorsed Transactions on Industrial Networks and Intelli...

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EAI Endorsed Transactions on Industrial Networks and Intelligent systems 2025年第2期12卷 1-13页

作者： Annadurai, Amrutha Prusty, Manas Ranjan Pandey, Trilok Nath Patra, Subhra Rani School of Computer Science and Engineering Vellore Institute of Technology Chennai India Centre for Cyber Physical Systems Vellore Institute of Technology Chennai India Information Systems and Operations Management University of Texas at Arlington Texas 76019 United States

INTRODUCTION: A robust method is proposed in this paper to detect helmet usage in two-wheeler riders to enhance road safety. OBJECTIVES: This involves a custom made dataset that contains 1000 images captured under diverse real-world scenarios, including variations in helmet size, colour, and lighting conditions. This dataset has two classes namely with helmet and without helmet. METHODS: The proposed helmet classification approach utilizes the Multi-Scale Deep Convolutional Neural Network (CNN) framework cascaded with Long Short-Term Memory (LSTM) network. Initially the Multi-Scale Deep CNN extracts modes by applying Single-level Discrete 2D Wavelet Transform (dwt2) to decompose the original images. In particular, four different modes are used for segmenting a single image namely approximation, horizontal detail, vertical detail and diagonal detail. After feeding the segmented images into a Multi-Scale Deep CNN model, it is cascaded with an LSTM network. RESULTS: The proposed model achieved accuracies of 99.20% and 95.99% using both 5-Fold Cross-Validation (CV) and Hold-out CV methods, respectively. CONCLUSION: This result was better than the CNN-LSTM, dwt2-LSTM and a tailor made CNN model. © 2025 A. Annadurai et al.

关键词： Convolutional neural networks

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KCl acts as a flux to assist the growth of sub-millimeter-scale metallic 2D non-layered molybdenum dioxide

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Rare Metals 2025年第1期44卷 404-416页

作者： Li-Ying Deng Qing Zhang Wang-Yang Li Xiao-Yuan Ye Yi-Fan Zhao Shen-Zhong Chen Yu-Lan Wang Xing-Hui Wang Hui-Peng Chen Zhi-Yang Yu Qun Yan Shu-Ying Cheng Tai-Liang Guo Wen-Ping Hu Feng Ding Jie Sun National and Local United Engineering Laboratory of Flat Panel Display Technology College of Physics and Information EngineeringFuzhou UniversityFuzhou350100China Fujian Key Laboratory of Agricultural Information Sensing Technology College of Mechanical and Electrical EngineeringFujian Agriculture and Forestry UniversityFuzhou350002China Key Laboratory of Organic Integrated Circuit Department of ChemistrySchool of ScienceMinistry of Education and Tianjin Key Laboratory of Molecular Optoelectronic SciencesTianjin UniversityTianjin300072China Collaborative Innovation Center of Chemical Science and Engineering(Tianjin) Tianjin300072China College of Physics and Information Engineering Institute of Micro-Nano Devices and Solar CellsFuzhou UniversityFuzhou350108China State Key Laboratory of Photocatalysis on Energy and Environment College of ChemistryFuzhou UniversityFuzhou350116China Faculty of Materials Science and Engineering/Institute of Technology for Carbon Neutrality Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518000China

Two-dimensional(2D)metal oxides(2DMOs),such as MoO_(2),have made impressive strides in recent years,and their applicability in a number of fields such as electronic devices,optoelectronic devices and lasers has been ***,2DMOs present challenges in their synthesis using conventional methods due to their non-van der Waals *** report that KCl acts as a flux to prepare large-area 2DMOs with sub-millimeter *** systematically investigate the effects of temperature,homogeneous time and cooling rate on the products in the flux method,demonstrating that in this reaction a saturated homogenous solution is obtained upon the melting of the salt and ***,the cooling rate was adjusted to regulate the thickness of the target crystals,leading to the precipitation of 2D non-layered material from the supersaturated solution;by applying this method,the highly crystalline non-layered 2D MoO_(2)flakes with so far the largest lateral size of up to sub-millimeter scale(~464μm)were *** studies have revealed that the 2D MoO_(2)features metallic properties,with an excellent sheet resistance as low as 99Ω·square^(-1 )at room temperature,and exhibits a property of charge density wave in the measurement of resistivity as a function of temperature.

关键词： 2D non-layered materials Metallic Metal oxide Thickness modulation Flux method

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Tree comprehensive diversity-based pruned deep forest

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International Journal of Machine Learning and Cybernetics 2025年 1-20页

作者： Ding, Jiaman Jiang, Xiangyu Jia, Lianyin Fu, Xiaodong Jiang, Ying Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming650500 China Artificial Intelligence Key Laboratory of Yunnan Province Kunming University of Science and Technology Yunnan Kunming650500 China

Deep Forest (DF) is proposed as a deep learning model based on non-differentiable modules, which extends the deep learning paradigm by incorporating non-neural network structures and has demonstrated remarkable performance in several fields. DF utilizes a cascade structure where each layer contains one or more sets of random forests. However, some of these forests may include decision trees with high redundancy and suboptimal performance. Additionally, the feature representations in DF are composed of prediction class probability vectors, and these prediction-based representations require the retention of a large amount of information about the forest models for subsequent testing. These two characteristics of DF make it prone to issues related to high time, memory, and storage costs, and may even lead to performance degradation. To address these challenges, we design a novel pruning strategy aimed at optimizing random forests in cascade layers and propose a new deep forest model called Tree Comprehensive Diversity-based Pruned Deep Forest (TCDPDF). Unlike most methods that rely on model prediction results to measure diversity, we explore a diversity measure based on the morphological structure of decision trees. Furthermore, we propose a tree comprehensive diversity measure for the pruning strategy by balancing both structural diversity and prediction accuracy of decision trees. We conduct experiments on 12 public datasets, and the results demonstrate that TCDPDF achieves competitive performance, significantly reduces the model’s high computational complexity, and further enhances its generalization ability. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Deep learning

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A Review on Binary Code Analysis Datasets 18th

A Review on Binary Code Analysis Datasets

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18th International Conference on Wireless Artificial Intelligent Computing systems and Applications, WASA 2024

作者： Huang, Zhijian Song, Shuguang Liu, Han Kuang, Hongyu Zhang, Jingjing Hu, Pengfei National Key Laboratory of Science and Technology on Information System Security Systems Engineering Institute Academy of Military Sciences Beijing China School of Computer Science and Technology Shandong University Qingdao China

ISBN: (纸本)9783031714696

Binary code analysis serves as the foundation for research in vulnerability discovery, software protection, and malicious code analysis. However, analyzing binary files is challenging due to the lack of high-level semantic information, leading to heavy dependence on analysts’ expertise and significantly impacting the efficiency of binary code analysis. Recent years has witnessed the blossom of machine learning models for binary analysis, but few researches address the problem of binary code datasets. In this paper, we review all the existing and available datasets, and make classification according to their application. We set up experiments to illustrate how dataset quality could affect the performance of machine learning models in binary function recognition. Based on the experimental evaluation, we present a discussion on the ground truth as well as quality evaluation problems for binary code datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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