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Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

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Mathematical analysis and stability of thermally magnetized ternary hybrid nanofluid over porous moving wedge with suction/injection and entropy generation

Results in Engineering

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Results in engineering 2025年 26卷

作者： Zeeshan Khan, Waris Kouki, Marouan Boukhris, Imed Arishi, Ali Garalleh, Hakim AL Safi, Mohammad Sediq Department of Mathematics and Statistics Bacha Khan University Charsadda KP Pakistan Department of Mathematics and Statistics Hazara University KP Mansehra21120 Pakistan Department of Information System Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia Department of Physics Faculty of Science King Khalid University AbhaP.O. Box 960 Saudi Arabia Department of Industrial Engineering King Khalid University Abha61421 Saudi Arabia Department of Mathematical Science College of Engineering University of Business and Technology Jeddah21361 Saudi Arabia Department Computer Science Allamah University Kabul Afghanistan

Present study investigates the ternary hybrid nanofluid to analyze thermally magnetized flow over a moving wedge with wall permeability and convective boundary conditions. Using stability analysis and suction and injection give the novel contributions to the study. Water host fluid containing titania, silver, and graphene nanoparticles is used. A self-similarity approach is applied to convert the Partial Differential Equations into dimensionless Ordinary Differential Equations. Numerical solution has been obtained through bvp4c method. Duality has been observed due to suction and injection. From the stability analysis it is perceived that first solution is stable while the second solution is unstable. The impact of embedded factors like magnetic factor, wedge angle parameter Prandtl and Eckert numbers, Suction parameter, and thermal radiation on flow characteristics, temperature distribution, and the entropy generation are demonstrated. Entropy generation is compared for the convectional fluid, nanofluid, hybrid nanofluid, and THNF. Similarly, entropy generation for the downhill flow (m=−0.8), flat flow (m=0), Wedge flow (m=0.2), and stagnation flow (m=1) has been investigated for both suction (S=1) and injection (S=−1) and it has been observed that entropy generation is higher fir the THNF compared to other flows. Additionally, for confirmation, the present study is compared with published work. © 2025 The Author(s)

关键词： Heat radiation

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STGCN-LSTM FOR OLYMPIC MEDAL PREDICTION: DYNAMIC POWER MODELING AND CAUSAL POLICY OPTIMIZATION

arXiv

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arXiv 2025年

作者： Wang, Yiquan Wang, Jiaying Huang, Tin-Yeh Yang, Jingyi Wang, Guanjie Xu, Zihao College of Mathematics and System Science Xinjiang University Xinjiang Urumqi830046 China Intelligent Software Research Center Institute of Software Chinese Academy of Sciences Beijing100190 China Shenzhen X-Institute Shenzhen518055 China College of Architecture and Environment Sichuan University Chengdu610065 China Department of Industrial and System Engineering Faculty of Engineering The Hong Kong Polytechnic University 999077 Hong Kong School of Design and Art Hunan Institute of Engineering Xiangtan411104 China School of Computer and Artificial Intelligence Huaihua University Huaihua418099 China Faculty of Science and Engeerning The University of Nottingham-Ningbo Ningbo315100 China

This paper proposes a novel hybrid model, STGCN-LSTM, to forecast Olympic medal distributions by integrating the spatio-temporal relationships among countries and the long-term dependencies of national performance. The Spatial-Temporal Graph Convolution Network (STGCN) captures geographic and interactive factors-such as coaching exchange and socio-economic links-while the Long Short-Term Memory (LSTM) module models historical trends in medal counts, economic data, and demographics. To address zero-inflated outputs (i.e., the disparity between countries that consistently yield wins and those never having won medals), a Zero-Inflated Compound Poisson (ZICP) framework is incorporated to separate random zeros from structural zeros, providing a clearer view of potential breakthrough performances. Validation includes historical backtracking, policy shock simulations, and causal inference checks, confirming the robustness of the proposed method. Results shed light on the influence of coaching mobility, event specialization, and strategic investment on medal forecasts, offering a data-driven foundation for optimizing sports policies and resource allocation in diverse Olympic contexts. © 2025, CC BY-NC-SA.

关键词： Resource allocation

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A secure and trustworthy blockchain-assisted edge computing architecture for industrial internet of things

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Scientific Reports 2025年第1期15卷 1-17页

作者： Asaithambi, Sasikumar Nallusamy, Senthilkumar Yang, Jing Prajapat, Sunil Kumar, Gyanendra Rathore, Pramod Singh Department of Data Science and Business Systems Faculty of Engineering and Technology SRM Institute of Science & Technology Tamil Nadu Kattankulathur 603203 India Centre for Applied Nanomaterials Chennai Institute of Technology Tamil Nadu Chennai 600069 India Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur 50603 Malaysia Department of Mathematics Central University of Himachal Pradesh Dharamshala 176215 India Department of IoT and Intelligent Systems Manipal University Jaipur Jaipur 303007 India Department of Computer and Communication Engineering Manipal University Jaipur Jaipur 303007 India

The Industrial Internet of Things (IIoT) seeks to improve smart factory productivity by leveraging automation and scalability. For automation in industry, optimization, collaboration and protection, and scalability, the IoTs paradigm, technology for communication and information, and intelligent systems are integrated as a single organism. This article presents a blockchain-assisted safe data-sharing mechanism that provides security requirements in the industry using IoT. End-to-end authentication is developed based on the blockchain’s reputation, and the smart contract is used to validate nodes’ security measures. Through integrity verification and categorization in node terminals and industry, the blockchain paradigm manages data collection and dissemination. An efficient proof of authentication (PoAh) consensus mechanism is created using the blockchain network to build a collaborative network to preserve logs and verification data in the industrial IoT. It accomplishes trustworthy authentication and endpoint activity tracing, and edge computing is used in blockchain nodes to offer device authentication processes that utilize smart contracts and PoAh. According to an experimental study, the proposed architecture lowers the authentication time and obtains a high response rate. The proposed system’s service time shows the efficiency of PoAh-based blockchain architecture for industrial IoT compared with existing works. Finally, we evaluated various block sizes to ensure an efficient transaction rate. The outcomes demonstrate the practicality of the suggested and put-into-practice architecture, distinguished by enhanced data audibility, device data ownership, security, and privacy while utilizing decentralized storage. © The Author(s) 2025.

关键词： Authentication Blockchain Edge computing Industrial internet of things PoAh

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Development and modeling of advanced systems Na2SnBr6-based perovskite solar cells: a comprehensive study on electron transport layers

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Optics Express 2025年第10期33卷 21152-21172页

作者： Dipta Roy Md Selim Reza Avijit Ghosh Anup Kumar Roy Bivash Ranjan Chowdhury Md Aktarujjaman H A Alrafai Abeer A Hassan Department of Electrical and Computer Engineering California State University at Northridge Northridge CA 91330 USA Department of Electrical and Electronic Engineering Begum Rokeya University Rangpur-5400 Bangladesh Phillip M. Drayer Department of Electrical and Computer Engineering Lamar University Beaumont TX 77710 USA Department of Management Information System International American University Los Angeles CA 90010 USA Department of Information Technology Management Webster University St. Louis MO 63119 USA Department of Chemistry Faculty of Science King Khalid University PO Box 9004 Abha 61413 Saudi Arabia

This study simulates a n-i-p planar heterojunction for Na 2 SnBr 6 -based perovskite solar cells (PSCs) , with Na 2 SnBr 6 as the absorber layer, fluorine-doped tin oxide (FTO) as the substrate, and In 2 S 3 , IGZO, and SnS 2 as electron transport layers (ETLs). Gold (Au) is used as the back contact. Na 2 SnBr 6 was chosen for its low cost, non-toxicity, tunable band gap, excellent electrical properties, and stability. The SCAPS-1D simulator was employed to model the solar cell performance under AM 1.5 G light irradiation, focusing on factors like doping concentration, layer thickness, defect density, and interface defects across three device configurations: Device I (Au/Na 2 SnBr 6 / In 2 S 3 /FTO/Al), Device II (Au/ Na 2 SnBr 6 /IGZO/FTO/Al), and Device III (Au/ Na 2 SnBr 6 /SnS 2 /FTO/Al). Device III achieved the highest PCE of 31.35%, FF of 85.95%, V OC of 0.7926 V, and J SC of 46.01 mA/cm 2 2 , making it a promising candidate for high-efficiency, lead-free perovskite solar cells. Devices I and II showed lower efficiencies of 27.47% and 30.98%, respectively. The study also analyzed quantum efficiency (QE), carrier dynamics, and recombination rates, highlighting the potential of Device III in advancing Na 2 SnBr 6 -based hybrid perovskite solar technologies for future solar energy applications.

关键词： Material properties Solar cell efficiency Solar cells Solar energy Thin film solar cells Transparent conducting oxides

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qing Liu Qian Gao Jun Fan Zhiqiang Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China China Telecom Digital Intelligence Techonology Co Ltd Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN— a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model’s effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets.

关键词： Measurement Bridges Contrastive learning Signal processing Data processing Vectors Space exploration Resource management Speech processing Mutual information

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Comprehensive investigation of rooftop photovoltaic power plants with monocrystalline polycrystalline and thin-film technologies for exergy economic and environmental assessments

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Scientific Reports 2025年第1期15卷 1-15页

作者： Lekbir, Abdelhak Hassani, Samir Mohamad Radzi, Putri Nor Liyana Mekhilef, Saad Tey, Kok Soon Samatar, Abdullahi Mohamed Alshammari, Obaid Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur 50603 Malaysia Power Electronics and Renewable Energy Research Laboratory (PEARL) Department of Electrical Engineering Universiti Malaya Kuala Lumpur 50603 Malaysia Unité de Recherche Appliquée en Energies Renouvelables CDER Ghardaia 47133 Algeria School of Engineering Swinburne University of Technology Hawthorn 3122 VIC Australia Department of Electrical Engineering Faculty of Engineering Hormuud University Mogadishu Somalia Department of Electrical Engineering College of Engineering University of Hail Hail 2240 Saudi Arabia

This research aims to conduct an exergy, economic, and environmental analysis of a 6.57 kWp rooftop photovoltaic (PV) power plant that combines different PV technologies, comprising 2 kWp of poly-crystalline (p-Si), 1... 详细信息

This research aims to conduct an exergy, economic, and environmental analysis of a 6.57 kW_p rooftop photovoltaic (PV) power plant that combines different PV technologies, comprising 2 kW_p of poly-crystalline (p-Si), 1.87 kW_p of mono-crystalline (m-Si), and 2.7 kW_p of thin-film amorphous silicon (a-Si) technologies. A comprehensive assessment was conducted to evaluate the environmental and techno-economic parameters of a PV plant system. This study is based on the performance data obtained over four years of energy production under the weather conditions of Kuala Lumpur, Malaysia. The embodied energy required for the manufacturing of the PV power plant was estimated using embodied energy indices available in the literature. Additionally, a detailed economic evaluation was conducted based on the electricity costs in Malaysia. Moreover, the environmental impact was assessed over the plant’s life cycle, considering the emission factors of coal power plants. Results indicate that the exergy payback time for the different technologies i.e., m-Si, p-Si, a-Si, when operated individually, and when combined within a single PV system, are found to be ~, and years, respectively. Over its life cycle, it was found that the PV plant emits about. Emission breakdown analysis has revealed that the manufacturing process of the m-Si, p-Si, a-Si, and the monitoring systems contribute to,,, and of emissions, respectively. However, the PV power plant could offset about annually, equivalent to the emission of a car over approximately 3 years. This study offers critical insights into the exergy efficiency, environmental impact, and economic viability of a grid-connected rooftop PV power plant that integrates multiple PV technologies under tropical climate conditions. © The Author(s) 2025.

关键词： CO2 emission Economic assessment Environmental benefits Life cycle PV material

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AI-optimized electrochemical aptasensors for stable, reproducible detection of neurodegenerative diseases, cancer, and coronavirus

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Heliyon 2025年第1期11卷 e41338页

作者： Tayfour Ahmed, Amira Elsir Dhahi, Th.S. Attia, Tahani A. Elhassan Ali, Fawzia Awad Elobaid, Mohamed Elshaikh Adam, Tijjani Gopinath, Subash C.B. Department of Information System College of Science & Arts King Khalid University Asser Mohyel Saudi Arabia Health and Medical Technicals College Southern Technical University Basrah Iraq Department of Computer Engineering College of Computer Science and Engineering University of Ha'il Saudi Arabia DEEE Faculty of Engineering University of Khartoum Sudan Imam Abdulrahman Bin Faisal University Applied College Computer Department Saudi Arabia Faculty of Electronic Engineering & Technology Universiti Malaysia Perlis Perlis Arau 02600 Malaysia Institute of Nano Electronic Engineering Universiti Malaysia Perlis Perlis Kangar 01000 Malaysia Micro System Technology Centre of Excellence (CoE) Universiti Malaysia Perlis (UniMAP) Perlis Malaysia Center for Global Health Research Saveetha Medical College & Hospital Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam Tamil Nadu Chennai 602 105 India Faculty of Chemical Engineering & Technology Universiti Malaysia Perlis (UniMAP) Perlis Arau 02600 Malaysia

AI-optimized electrochemical aptasensors are transforming diagnostic testing by offering high sensitivity, selectivity, and rapid response times. Leveraging data-driven AI techniques, these sensors provide a non-invasive, cost-effective alternative to traditional methods, with applications in detecting molecular biomarkers for neurodegenerative diseases, cancer, and coronavirus. The performance metrics outlined in the comparative table illustrate the significant advancements enabled by AI integration. Sensitivity increases from 60 to 75 % in ordinary aptasensors to 85–95 %, while specificity improves from 70-80 % to 90–98 %. This enhanced performance allows for ultra-low detection limits, such as 10 fM for carcinoembryonic antigen (CEA) and 20 fM for mucin-1 (MUC1) using Electrochemical Impedance Spectroscopy (EIS), and 1 pM for prostate-specific antigen (PSA) with Differential Pulse Voltammetry (DPV). Similarly, Square Wave Voltammetry (SWV) and potentiometric sensors have detected alpha-fetoprotein (AFP) at 5 fM and epithelial cell adhesion molecule (EpCAM) at 100 fM, respectively. AI integration also enhances reproducibility, reduces false positives and negatives (from 15-20 % to 5–10 %), and significantly decreases response times (from 10-15 s to 2–3 s). These advancements improve data processing speeds (from 10 to 20 min per sample to 2–5 min) and calibration accuracy (<2 % margin of error compared to 5–10 %), while expanding application scope to multi-target biomarker detection. This review highlights how these advancements position AI-optimized electrochemical aptasensors as powerful tools for personalized treatment, point-of-care testing, and continuous health monitoring. Despite a higher cost ($500-$1,500/unit), their enhanced portability and diagnostic performance promise to revolutionize healthcare, environmental monitoring, and food safety, ultimately improving public health outcomes. © 2024

关键词： AI Aptasensors Cancer Coronavirus Electrochemical Integrated biosensor Molecular cells Neurodegenerative

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An efficient binary salp swarm algorithm for user selection in multiuser MIMO antenna systems

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Scientific Reports 2025年第1期15卷 1-17页

作者： Sasikumar, A. Ravi, Logesh Devarajan, Malathi Almazyad, Abdulaziz S. De, Shuvodeep Xiong, Guojiang Mousavirad, Seyed Jalaleddin Mohamed, Ali Wagdy Department of Data Science and Business Systems Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India Centre for Advanced Data Science Tamil Nadu Chennai 600127 India School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Chennai 600127 India School of Computer Science and Engineering Vellore Institute of Technology Chennai 600127 India Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia Virginia Tech Blacksburg United States Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical Engineering Guizhou University Guiyang 550025 China Department of Computer and Electrical Engineering Mid Sweden University Sundsvall Sweden Operations Research Department Faculty of Graduate Studies for Statistical Research Cairo University Giza 12613 Egypt Applied Science Research Center Applied Science Private University Amman 11931 Jordan Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India

The past ten years have seen notable research activity and significant advancements in multiuser multiple-input multiple-output (MU-MIMO) antennas. An MU-MIMO antenna system must accommodate many subscribers without additional bandwidth or energy. User scheduling becomes a critical strategy to take advantage of multiuser heterogeneity and acquire maximum gain in systems where the total number of recipients exceeds the number of transmitting antennas. Due to their high computational cost, many user selection methods currently in use, such as greedy algorithms and exhaustive search are unsuitable for MU-MIMO systems. A suitable scheduling mechanism is essential for the various users in an MU-MIMO system to utilise bandwidth and enhance the system’s total rate effectively. In this article, we proposed a user and antenna scheduling with a population-based meta-heuristic approach, namely the binary salp swarm algorithm (binary SSA), to increase the system sum rate with low computing complexity. We specifically used a population-based meta-heuristics optimisation technique to simulate the user scheduling problem in MU-MIMO systems, characterising complicated issues with binary decisions. Additionally, binary SSA significantly outperforms existing population-based models, such as the binary bat algorithm (binary BA), PSO, SSA, FPA and binary flower pollination algorithm (binary FPA), regarding system throughput/sum rate. The proposed binary SSA technique also effectively achieves a system sum rate compared to a random search scheme and other existing suboptimal scheduling methods. Compared to binary BA and binary FPA approaches, the binary SSA has a higher convergence rate and superior searching capabilities. The simulation outcomes show the proposed binary SSA-based scheduling scheme delivers noticeable performance benefits. © The Author(s) 2025.

关键词： Antenna Binary salp swarm algorithm Metaheuristics optimization Multiuser MIMO User scheduling

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Cellular neural networks with multiple-valued output and its application

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND computer scienceS 1996年第10期E79A卷 1658-1663页

作者： Kanagawa, A Kawabata, H Takahashi, H Faculty of Computer Science and System Engineering Okayama Prefectural University Soja-shi 719-11 Japan

Various applications of cellular neural network (CNN) are reported such as a feature extraction of the patterns, an extraction of the edges or corners of a figure, noise exclusion, searching in maze and so forth. In this paper, we propose a cellular neural network whose each cell has more than two output levels. By using the output function which has several saturated levels, each cell turns to have several output states. The multiple-valued CNN enhances its associative memory function so as to express various kinds of aspects. We report an application of the enhanced associative memory function to a diagnosis of the liver troubles.

关键词： cellular neural network associative memory classification problem

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