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STAR-RIS-NOMA Assisted Power Constrained Covert Communication Systems

IEEE Transactions on Vehicular technology 2025年

作者： Lei, Yuxin Huang, Gaojian Li, Xingwang Tian, Zhifa Mumtaz, Shahid Asif, Muhammad Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454003 China City University of Macau Faculty of Data Science 999078 China Henan Polytechnic University School of Surveying and Land Information Engineering Jiaozuo454003 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454099 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Silesian University of Technology Department of Applied Informatics Gliwice Poland Department of Engineering Nottingham Trent University United Kingdom Jiangsu University School of Computer Science and Communication Engineering Zhenjiang China

In this paper, a covert communication system assisted by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in non-orthogonal multiple access (NOMA) networks is proposed. When the maximum channel power gain from the transmitter in the main network to the users in adjacent networks is smaller than the threshold, the transmitter sends the NOMA signals to the users with the assistance of STAR-RIS. Moreover, channel estimation errors and imperfect successive interference cancellation (SIC) are considered in the system analysis. To characterize the covert performance, the detection error probability (DEP) and the average minimum detection error probability (AMDEP) are first derived. Then, the closed-form solutions of the outage probability (OP) for the users are obtained. Finally, the maximum effective covert rate (ECR) subject to the covertness constraint and non-interruption constraint is studied. The numerical simulation results show that: 1) the detection performance of Willie (warden) deteriorates as the channel estimation errors increase;2) when the transmitter power exceeds the threshold, communication in the main network is interrupted;3) under a certain transmit power, the maximum ECR increases with the increasing of the number of STAR-RIS reflection elements. © 2025 IEEE.

关键词： Channel estimation

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Method for modelling the number of HIV and AIDS cases using least square spline Biresponse nonparametric negative binomial regression

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MethodsX 2025年 14卷 103336页

作者： Ramadan, Arip Chamidah, Nur Budiantara, I Nyoman Lestari, Budi Aydin, Dursun Doctoral Study Program of Mathematics and Natural Sciences Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Surabaya 60231 Indonesia Department of Mathematics Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia Research Group of Statistical Modeling in Life Science Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia Statistics Department Faculty of Science and Data Analytics Sepuluh Nopember Institute of Technology Surabaya 60111 Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences The University of Jember Jember 68121 Indonesia Department of Statistics Faculty of Science Muğla Sıtkı Koçman University Muğla 48000 Turkey Research Scholar at Department of Mathematics University of Wisconsin Oshkosh Algoma Blvd Oshkosh 54901 WI United States

We propose a new statistical modeling method to analyze functional relationship between predictor variables and correlated response variables, and apply it for modeling the number of HIV and AIDS cases influenced by early-age marriages and contraceptive users. The proposed method is LS-Spline BNNBR that is a Biresponse Nonparametric Negative Binomial Regression (BNNBR) based on least squares spline (LS-Spline). To validate the proposed method, we compare it with classical method, namely Ordinary Least Square Biresponse Parametric Negative Binomial Regression (OLS-BPNBR). The results show that the LS-Spline BNNBR method provided deviance value of 7.542334 which is smaller than that of OLS-BPNBR method, namely 7.993935. Thus, the proposed method has better prediction fit than the classical method. • This study discusses new statistical modeling method for the number of HIV and AIDS cases influenced by early-age marriages and contraceptive users where the response variables are correlated with each other. • The model estimation results show that the number of HIV and AIDS cases in Indonesia will decrease if the early-age marriages percentage increases and the contraceptive users percentage decreases. • The results are useful to provide a scientific basis for making policies to control the number of HIV and AIDS cases in Indonesia. © 2025 The Authors

关键词： AIDS BLSSNNBR model Contraceptive user Early-age marriage HIV Least square spline

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Enhancing Safety in Autonomous Maritime Transportation Systems with Real-Time AI Agents

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Applied sciences (Switzerland) 2025年第9期15卷 4986-4986页

作者： Durlik, Irmina Miller, Tymoteusz Kostecka, Ewelina Kozlovska, Polina Ślączka, Wojciech Faculty of Navigation Maritime University of Szczecin Waly Chrobrego 1-2 Szczecin70-500 Poland Institute of Marine and Environmental Sciences University of Szczecin Wąska 13 Szczecin71-415 Poland Faculty of Data Science and Information Technology INTI International University Putra Nilai Persiaran Perdana BBNPutra Nilai Negeri Sembilan Nilai71800 Malaysia Faculty of Mechatronics and Electrical Engineering Maritime University of Szczecin Waly Chrobrego 1-2 Szczecin70-500 Poland Faculty of Economics Finance and Management University of Szczecin Szczecin71-415 Poland

The maritime transportation sector is undergoing a profound shift with the emergence of autonomous vessels powered by real-time artificial intelligence (AI) agents. This article investigates the pivotal role of these agents in enhancing the safety, efficiency, and sustainability of autonomous maritime systems. Following a structured literature review, we examine the architecture of real-time AI agents, including sensor integration, communication systems, and computational infrastructure. We distinguish maritime AI agents from conventional systems by emphasizing their specialized functions, real-time processing demands, and resilience in dynamic environments. Key safety mechanisms—such as collision avoidance, anomaly detection, emergency coordination, and fail-safe operations—are analyzed to demonstrate how AI agents contribute to operational reliability. The study also explores regulatory compliance, focusing on emission control, real-time monitoring, and data governance. Implementation challenges, including limited onboard computational power, legal and ethical constraints, and interoperability issues, are addressed with practical solutions such as edge AI and modular architectures. Finally, the article outlines future research directions involving smart port integration, scalable AI models, and emerging technologies like federated and explainable AI. This work highlights the transformative potential of AI agents in advancing autonomous maritime transportation. © 2025 by the authors.

关键词： Emergency services

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Topic Modelling using Transfer Learning: Issues and Challenges

Topic Modelling using Transfer Learning: Issues and Challeng...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Rama Krishna K Kaipa Sandhya Praveen Gujjar J Raghavendra M Devadas Vani Hiremani Preethi Department of Artificial Intelligence and Machine Learning Impact college of Engineering and Applied Sciences Bengaluru India Department of Data Science Impact college of Engineering and applied sciences Bengaluru India Faculty of Management Studies JAIN (Deemed-to-be University) Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education (MAHE) Manipal India Symbiosis Institute of Technology Symbiosis International (Deemed) University Pune India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

A machine learning method, transfer learning, uses information from one job or area to enhance effectiveness in a related but distinct task or area. Transfer learning enables using pre-trained models that were previously learned using a sizable and varied dataset in the original domain instead of creating a model from the beginning for the intended activity. Usually, using a lesser dataset, the pre-trained model's representations and knowledge may be improved upon or applied to the intended goal or area. In natural language processing (NLP) and machine learning, topic modeling is an approach that finds and extracts the fundamental concepts or subjects from a group of documents or textual data. Despite prior experience with the aspects, this unsupervised learning method seeks to uncover the text's latent semantic structure. This paper's primary goal is to investigate the possibilities of transfer learning for text topic modeling. Additionally, to assess the correctness of the pre-trained model's performance. The paper also describes the issues and challenges associated with the text for performing the topic modeling using transfer learning. This paper delves into the methodologies and techniques of topic modeling using transfer learning. It explores how this novel approach transcends the constraints of conventional topic modeling, enabling the extraction of meaningful topics from data-rich source domains to improve topic modeling performance in data-scarce or specialized target domains. By integrating the principles of transfer learning into the topic modeling framework, we unlock the potential to enhance the interpretability, accuracy, and applicability of topic modeling across diverse domains and applications. This paper sheds light on the transformative power of transfer learning in topic modeling.

关键词： Performance evaluation Adaptation models Accuracy Computational modeling Transfer learning Semantics data models Natural language processing data mining Unsupervised learning

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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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Disease and Medications Text Visualization Using Scattertext

Disease and Medications Text Visualization Using Scattertext

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Rama Krishna K Kaipa Sandhya Praveen Gujjar J Raghavendra M Devadas Vani Hiremani Sapna R Department of Artificial Intelligence and Machine Learning Impact college of Engineering and Applied Sciences Bengaluru India Department of Data Science Impact college of Engineering and applied sciences Bengaluru India Faculty of Management Studies JAIN (Deemed-to-be University) Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education (MAHE) Manipal India Symbiosis Institute of Technology Symbiosis International (Deemed) University Pune India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Before text data can be analysed and visualised, it must be thoroughly cleaned due to its messy nature. data visualizations use the data to tell an engaging and simple-to-read story. That is what the Scattertext tool does. Scatter text allows which terms stand out in your corpora and how their frequency varies across categories. The dataset used in this paper includes reviews of drugs. This dataset offers a useful window into how people discuss their thoughts and experiences using various drugs. The drug names, user ratings, and the actual written reviews are among the details included in the dataset. This dataset consists of drug reviews collected from various sources, hence expressing a wide range of opinions and feelings. Each row in this dataset represents the experience a user had with a given medication and hence allows us to explore those emotions that are associated with any particular set of drugs. Understanding opinion expressed in the review about drugs might go a long way in helping decision-making processes in both healthcare and pharmaceuticals. The scatter plot distinguishes between labelled points and unlabelled ones by using a custom algorithm for Scattertext. By hovering their cursor over the labelled areas done client-side, users can interactively see which terms correspond to which points.

关键词： Drugs Reviews Decision making data visualization Medical services Market research Feature extraction Diseases Intelligent control

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Streamlining Eye-Tracking and Observational data for Field Study Visual Analysis 25

Streamlining Eye-Tracking and Observational Data for Field S...

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Proceedings of the 2025 Symposium on Eye Tracking Research and Applications

作者： Yidan Zhang Nethara Athukorala Ziying Liang Yidan Qiao Simran - Yu Xuan Yio Lee Lawrence Benjamin Tag Mor Vered Michael Wybrow Sarah Goodwin Department of Human-Centred Computing Monash University Melbourne Australia Monash University Melbourne Victoria Australia Faculty of Information Technology Monash University Melbourne Victoria Australia Monash University Melboure Victoria Australia Faculty of Information Technology Monash University Melboure Victoria Australia Monash Business School Monash University Melbourne Australia School of Computer Science and Engineering University of New South Wales Sydney New South Wales Australia Dept of Data Science & AI Monash University Clayton VIC Australia Department of Human-Centred Computing Monash University Melbourne VIC Australia Monash University Melbourne Australia

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Learning and Generating Diverse Residential Load Patterns Using GAN with Weakly-Supervised Training and Weight Selection

arXiv

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arXiv 2025年

作者： Liang, Xinyu Wang, Hao Department of Data Science and AI Faculty of Information Technology Monash Energy Institute Monash University MelbourneVIC3800 Australia

The scarcity of high-quality residential load data can pose obstacles for decarbonizing the residential sector as well as effective grid planning and operation. The above challenges have motivated research into generating synthetic load data, but existing methods faced limitations in terms of scalability, diversity, and similarity. This paper proposes a Generative Adversarial Network-based Synthetic Residential Load Pattern (RLP-GAN) generation model, a novel weakly-supervised GAN framework, leveraging an over-complete autoencoder to capture dependencies within complex and diverse load patterns and learn household-level data distribution at scale. We incorporate a model weight selection method to address the mode collapse problem and generate load patterns with high diversity. We develop a holistic evaluation method to validate the effectiveness of RLP-GAN using real-world data of 417 households. The results demonstrate that RLP-GAN outperforms state-of-the-art models in capturing temporal dependencies and generating load patterns with higher similarity to real data. Furthermore, we have publicly released the RLP-GAN generated synthetic dataset1, which comprises one million synthetic residential load pattern profiles. © 2025, CC BY.

关键词： Deep learning

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Chemical Bond Management of FA-Based Mixed Halide Perovskites for Stable and High-Efficiency Solar Cells

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Advanced Energy Materials 2025年第0期

作者： Liu, Na Chen, Shuyan Liu, Xingyu Zhu, Cheng Xu, Fan Song, Tinglu Cai, Wanqing Jiang, Yuqun Zhang, Xuan Vasiliev, Roman B. Chang, Shuai Li, Shukui Chen, Qi Wei, Guodan Faculty of Materials Science MSU-BIT University Shenzhen518172 China Institute of Materials Research Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518000 China Advanced Research Institute of Multidisciplinary Sciences Beijing Institute of Technology Beijing100081 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China Experimental Center of Advanced Materials School of Materials Science and Engineering Beijing Institute of Technology Beijing100081 China Institute of Data and Information Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518000 China Department of Materials Science Department of Chemistry Lomonosov Moscow State University Moscow119991 Russia

The unavoidable migration of organic cation within formamidinium (FA)-based mixed halide perovskite leads to severe phase segregation and device degradation. The intrinsic weak chemical bond between organic cation and [PbI6]4− octahedra can easily break during device operation, resulting in the formation of cation vacancies and undesirable structural transformation. In this work, a pyrrolidine compound is incorporated, with a strong electron-withdrawing fluorine substitution, which strengthened the lattice bond between organic cation and [PbI6]4− octahedra. Meanwhile, the 1D/3D heterojunction films are also achieved due to the chemical reaction between PbI2 and pyrrolidine, successfully constructing a new 1D perovskite such as PYFPbI3. The resultant hetero-perovskite films retained their photoactive-α phase even after eight days of ambient exposure, demonstrating superior phase stability without any post-encapsulation. More importantly, the ion-migration channels inside the perovskite lattice are effectively blocked by 1D/3D heterojunctions. The resultant rigid and flexible solar cells exhibited an enhanced power conversion efficiency (PCE) from the initial 24.48% to 25.39%, as well as 23.86% to 24.26%, respectively, which are among the highest records in 1D/3D-based works. Furthermore, the unencapsulated devices retained 90% of their initial PCE during maximum power point tracking for over 350 hours under continuous illuminations. © 2025 Wiley-VCH GmbH.

关键词： Perovskite

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Cybersecurity Enhancement in IoT-Enabled Public Health information Systems Using Deep Learning Techniques

Cybersecurity Enhancement in IoT-Enabled Public Health Infor...

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AI-Driven Smart Healthcare for Society 5.0

作者： Mahesh Kumar Mishra Lalit Mohan Trivedi Mansoor Farooq Divya Nimma Malik Bader Alazzam B Kiran Bala Sr SAP Consultant Delta System and Software Inc Texas USA Department of ASH Moradabad Institute of Technology Moradabad UP India Department of Cybersecurity American University of Bahrain Bahrain PhD in Computational Science University of Southern Mississippi Data Analyst in UMMC USA Faculty of Information Technology Jadara University Irbid Jordan Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331536336

ISBN: (纸本)9798331536343

The rising use of IoT devices in Public Health information Systems (PHIS) has revolutionized the way patient surveillance and record keeping is being done in real-time, but this has exposed systems to serious cybersecurity threats. Generated by using traditional approaches, like rule-based detection and basic artificial neural network-based models, is not capable of dealing with the dynamic and diverse nature of threats in IoT networks. These approaches are often inadequate in providing a detailed analysis of the attack especially because they do not have the ability to learn from sequences of features. This work presents an improved predictive analytics model using CNN with BiLSTM to improve cybersecurity in IoT connected PHIS. The CNN component performs feature extraction on spatial elements in the data received while the BiLSTM uncovers temporal dependencies and sequence of attacks, which enhance threats identification accuracy. The process of the proposed methodology includes dataset acquisition and cleansing of IoT healthcare datasets and finally, the training and testing of the model with the help of relevant parameter such as accuracy, precision, and recall values. Qualitative comparison with the baseline models also shows that the proposed hybrid CNN-BiLSTM model performs better, and it has fewer false alarms to detect a range of cyber threats. Accordingly, the results point to a feasible solution for enhancing security and reliability of IoT-based healthcare applications to protect patients’ record from cyber threats.

关键词： Training Accuracy Surveillance Bidirectional long short term memory Threat assessment Internet of Things Computer security Public healthcare information systems Testing

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