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On the -differential spectra of two classes of power functions over finite fields

Applicable Algebra in Engineering, Communication and computing 2025年 1-30页

作者： Liu, Qian Huang, Zhiwei Chen, Zhixiong Jiang, Rong Zhang, Liupiao Key Laboratory of Information Security of Network Systems College of Computer and Data Science Fuzhou University Fuzhou China Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Hubei University Wuhan China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Fujian Key Laboratory of Financial Information Processing Putian University Putian China School of Computer Science and Technology Dongguan University of Technology Dongguan China

Recently, a new concept called multiplicative differential was introduced by Ellingsen et al. [7]. As an extension of the differential uniformity, it is theoretically appealing to determine the properties of c-differential uniformity and the corresponding c-differential spectrum. In this paper, based on certain quadratic character sums and two special elliptic curves over $$\mathbb {F}_p$$ , the $$(-1)$$ -differential spectra of the following two classes of power functions over $$\mathbb {F}_{p^n}$$ is completely determined: (1) $$f_1(x)=x^{\frac{p^n+3}{2}}$$ , where $$p>3$$ and $$p\equiv 3\pmod 4$$ ; (2) $$f_2(x)=x^{p^n-3}$$ , where $$p>3$$ . The obtained result shows that the $$(-1)$$ -differential spectra of $$f_1(x)$$ and $$f_2(x)$$ can be expressed explicitly in terms of n. Moreover, an upper bound of the c-differential uniformity of $$f_2(x)$$ is given.

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Multimodal Dialogue Emotion Recognition Based on Label Optimization and Coarse-Grained Assisted Fine-Grained

Multimodal Dialogue Emotion Recognition Based on Label Optim...

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

作者： Zhibo Zhang Zhenyu Yang Yan Huang Baojie Xu Wenyue Hu 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) China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal dialogue emotion recognition integrates data from multiple modalities to accurately identify emotional states in conversations. However, differences in expression and information density across modalities complicate the fusion of features. Traditional methods may introduce redundant information from other utterances, reducing the accuracy of emotion recognition. Existing one-hot labels often fail to capture the full range of emotional expressions, leading to biased results. To address these issues, we propose a model that fuses different modalities within the same utterance to avoid redundancy. It employs a progressive classification process, refining emotion recognition from coarse to fine granularity. Additionally, we use emotion polarity probabilities as weights for fine-grained classification and introduce a multimodal information-rich label that considers both the data and their interactions. Experiments on IEMOCAP and MELD datasets demonstrate the model’s effectiveness, significantly improving dialog emotion recognition accuracy. Our code is available at https://***/r/LOCG-188E.

关键词： Emotion recognition Accuracy Fuses Refining Termination of employment Speech recognition Signal processing Speech processing Optimization Faces

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A hybrid blockchain and AI-based approach for attack protection to secure internet of medical things networks

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Alexandria Engineering Journal 2025年 128卷 223-230页

作者： Hadeel Alsolai Fahd N. Al-Wesabi Ali M. Al-Sharafi Asma Alshuhail Abdulbasit A. Darem Bandar M. Alghamdi Fouad Shoie Alallah Abdulrahman Alzahrani Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Saudi Arabia Department of Computer Science Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information Technology University of Bisha Bisha 67714 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal University Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar 73213 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Saudi Arabia

The Internet of Medical Things (IoMT) has revolutionized healthcare by enabling real-time monitoring, remote diagnosis, and seamless data exchange among medical devices and providers. However, the interconnected and distributed nature of IoMT systems exposes them to significant cybersecurity threats, including data breaches and unauthorized access. This study aims to develop a secure and scalable framework to safeguard IoMT environments using blockchain technology, federated learning, and dynamic consensus algorithms. The proposed approach integrates artificial intelligence to detect threats and employs federated learning to maintain patient privacy while training models collaboratively. Experimental evaluation of the framework shows improved data security, reduced response times, and enhanced system trustworthiness. The results suggest that the integration of blockchain and AI-driven mechanisms offers a robust solution to IoMT cybersecurity challenges, paving the way for more reliable and efficient healthcare systems.

关键词： Internet of medical things (IoMT) Blockchain security Federated learning AI-driven anomaly detection Cyber threat mitigation

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Towards Depth-continuous Scene Representation with Displacement Field for Robust Light Field Depth Estimation

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IEEE Transactions on Multimedia 2025年

作者： Chen, Rongshan Sheng, Hao Yang, Da Cong, Ruixuan Cui, Zhenglong Wang, Sizhe Wang, Tun Zhao, Mingyuan Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang University Key Laboratory of Data Science and Intelligent Computing International Innovation Institute 166 Shuanghongqiao Street Pingyao Town Yuhang District Hangzhou31 1115 China Macao Polytechnic University Faculty of Applied Sciences Macao999078 China

The light field (LF) captures both the spatial and angular information of scenes, enabling accurate depth estimation. However, previous deep learning methods typically model surface depth only while ignoring the continuous nature of depth in 3D scenes. In this paper, we use the displacement field (DF) to describe this continuous property, and propose a novel depth-continuous scene representation for robust LF depth estimation. Experiments demonstrate that our representation enables the network to generate highly detailed depth maps with fewer parameters and faster speeds. Specifically, inspired by the signed distance field in 3D object descriptions, we aim to exploit the intrinsic depth-continuous property of 3D scenes via DF and define a novel depth-continuous scene representation. Then, we introduce a simple, yet general, learning framework for depth-continuous scene embedding, and the proposed network, DepthDF, achieves state-of-the-art performance on both synthetic and real-world LF datasets, ranking 1st on the HCI 4D light field benchmark. Furthermore, previous LF depth estimation methods can also be seamlessly integrated into this framework. Finally, we extend this framework beyond LF depth estimation to various tasks, including multiview stereo depth inference, LF superresolution, and LF salient object detection. Our experiments demonstrate an improved performance when continuous scene representation is applied, suggesting that our framework can potentially provide insights for more fields. © 1999-2012 IEEE.

关键词： Stereo image processing

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A comprehensive approach to mitigate cyberattacks in power systems using an AI-based detection algorithm and the unit commitment corrective action

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Electric Power Systems Research 2025年 247卷

作者： Yuanrong, Jin Alahmari, Saad Sulaiman, Riza Hassan, Ali Hassan Mohammed, Jamal Alwakeel, Ahmed M. Rasheed, Saim Sichuan Vocational College of Information Technology Lizhou District Sichuan Guangyuan China Department of Computer Science Applied College Northern Border University Arar Saudi Arabia Universiti Kebangsaan Malaysia Bangi43600 Malaysia College of Computer and Cyber Sciences University of Prince Mugrin Madinah41499 Saudi Arabia IT Department University of Prince Mugrin Madinah41499 Saudi Arabia Faculty of Computers & Information Technology University of Tabuk Tabuk71491 Saudi Arabia Research Center University of Tabuk Tabuk71491 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University PO Box 80221 Jeddah21589 Saudi Arabia

In smart power systems, cyber-attacks like False Data Injection (FDI) attacks present a significant threat to grid stability and optimal performance operation. This paper proposes an AI-driven approach to detecting anomalous behavior in the system and mitigating cyberattacks. Specifically, it addresses some common attacks known as Load Redistribution (LR) attacks. The proposed detection methodology leverages a deep learning model based on Variational AutoEncoders (VAE) and utilizes anomaly detection to identify cyberattacks and evaluate their severities. A corrective action using Security Constraint Unit Commitment (SCUC) is introduced to counteract worst-case scenarios. Finally, the effectiveness of the proposed approach is demonstrated through a case study on the IEEE 118-bus system. © 2025

关键词： Deep learning

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Energy Generation from Plastic Composites: A Systematic Review of Sustainable Practices and Technologies

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Circular Economy and Sustainability 2025年第2期5卷 1307-1343页

作者： Ametefe, Divine Senanu Ametefe, George Dzorgbenya John, Dah Aliu, Abdulmalik Adozuka Owen, Macaulay M. Shuib, Solehuddin Hamid, Aisha Wireless Communication Technology Group (WiCOT) College of Engineering School of Electrical Engineering Universiti Teknologi MARA (UiTM) Selangor Shah Alam 40450 Malaysia Department of Biotechnology College of Science Engineering and Technology Osun State University Osogbo Nigeria College of Computing Informatics and Mathematics Universiti Teknologi MARA (UiTM) Selangor Puncak Perdana 40150 Malaysia College of Built Environment Universiti Teknologi MARA (UiTM) Selangor Shah Alam 40450 Malaysia Institute of Energy Infrastructure IEI Universiti Tenaga Nasional UNITEN Jalan IKRAM-UNITEN Selangor Kajang 43000 Malaysia School of Mechanical Engineering College of Engineering Universiti Teknologi MARA UiTM Shah Alam 40450 Malaysia School of Chemistry and Environment Faculty of Applied Sciences Universiti Teknologi MARA Selangor Shah Alam 40450 Malaysia

Energy generation from plastic composites offers a viable solution to the dual challenges of plastic waste management and renewable energy production. This study explores the potential of plastic composites for energy generation through key waste-to-energy technologies, including incineration, pyrolysis, gasification, and anaerobic digestion. The study employs a Systematic Literature Review (SLR) methodology, following the PRISMA guidelines, and draws from two primary databases: ScienceDirect and Web of Science. Each method is assessed in terms of energy efficiency, environmental impact, economic feasibility, and scalability, based on studies published between 2020 and the second quarter of 2024. Pyrolysis and gasification emerge as particularly promising technologies due to their flexibility and higher energy recovery potential. Incineration, although widely used and well-established, presents challenges related to emissions, despite improvements in emissions control technologies. Anaerobic digestion, though limited in its application to conventional plastics, provides sustainability benefits when used with biodegradable materials. The analysis reveals regional disparities in research focus, the influence of policy frameworks, and the need for technological innovation to optimize these processes. Ultimately, the findings highlight the importance of integrating energy recovery from plastic waste within circular economy models to achieve sustainable waste management and renewable energy goals. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Circular Economy Plastic Recycling Renewable Energy Production Sustainable Waste Management Waste-to-Energy

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A Fast Exponential Analysis and Variable Projection Based Method for Linear Array Synthesis

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IEEE Antennas and Wireless Propagation Letters 2025年

作者： Sengupta, Ramonika Knaepkens, Ferre Cuyt, Annie Prinsloo, David S. Schäfer, Thomas Smolders, A. Bart Department of Electrical Engineering Eindhoven University of Technology Eindhoven5600 MB Netherlands Faculty of Applied Engineering University of Antwerp Groenenborgerlaan 171 Antwerp2020 Belgium Computing Science and Mathematics Division University of Stirling Scotland StirlingFK9 4LA United Kingdom ASTRON Dwindloo Oude Hoogeveensedjik 4 7991 PD Netherlands SATCUBE Nellickevägen 22 Gothenburg41263 Sweden

This paper presents a fast exponential analysis and variable projection method to reduce the number of elements in linear phased arrays. The numerical examples given in this work demonstrate a reduction of approximately 20% or 10% in the number of elements, with only a slight decrease in directivity of 0.63 dBi observed for a scan range of [−45◦, 45◦]. Compared to the combined matrix pencil and Tabu search algorithm, the proposed technique converges in fewer iterations and exhibits ≈ 5 dB reduction in maximum side lobe level. © 2002-2011 IEEE All rights reserved.

关键词： Antenna phased arrays

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Enhanced Arithmetic Optimization Algorithm for Intrusion Detection in Wireless Sensor Networks

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International Journal of Communication Systems 2025年第10期38卷

作者： Otair, Mohammed Qawaqzeh, Faten Ali Alomari, Saleh Ali Zitar, Raed Abu Migdady, Hazem Saleem, Kashif Smerat, Aseel Alsoud, Anas Ratib Abualigah, Laith Department of Computer Science Faculty of Information Technology Middle East University Amman Jordan School of Information Engineering Sanming University Sanming China Faculty of Science and Information Technology Jadara University Irbid Jordan Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates CSMIS Department Oman College of Management and Technology Barka Oman Department of Computer Science & Engineering College of Applied Studies & Community Service King Saud University Riyadh Saudi Arabia Faculty of Educational Sciences Al-Ahliyya Amman University Amman Jordan Centre for Research Impact & Outcome Institute of Engineering and Technology Chitkara University Punjab Rajpura India Computer Technologies Engineering Mazaya University College Nasiriyah Iraq TECH Harvard John A. Paulson School of Engineering & Applied Sciences Harvard University BostonMA United States Computer Science Department Al al-Bayt University Mafraq Jordan

Wireless sensor networks (WSNs) face significant security challenges because of their resource constraints and exposure to malicious attacks. Traditional intrusion detection systems (IDSs) often suffer from low detection accuracy, high false alarm rates, and long processing times. To address these issues, this paper proposes an enhanced IDS framework based on the arithmetic optimization algorithm (AOA) for feature selection, combined with support vector machine (SVM) for classification. A new cost function is introduced to guide the feature selection process and improve model performance. The approach, named AS_IDS, is evaluated on the NSL-KDD dataset, achieving an accuracy of 96.65%, a detection rate of 98.69%, and a false alarm rate of 0.04% using only 15 features, with a significant reduction in execution time. Comparative results with state-of-the-art methods demonstrate the effectiveness and efficiency of the proposed framework in enhancing intrusion detection in WSNs. © 2025 John Wiley & Sons Ltd.

关键词： Cost functions

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Irreversibility analysis of the radiative flow of cross fluid comprising three distinct nanoparticles across a stretching cylinder with chemical reactions

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Results in Engineering 2025年 26卷

作者： Pasha, Amjad Ali Anjum, Asra Peerusab, Samdani Faizan, M. Ameen, Hawzhen Fateh M. Ali, Farhan Khan, Umair Zaib, A. Hussain, Syed Modassir Aerospace Engineering Department King Abdulaziz University Jeddah21589 Saudi Arabia Mathematics and Computing Skills Unit Preparatory Studies Centre University of Technology and Applied Sciences Salalah P O Box 608 PC:211 Salalah Oman Department of Engineering College of Engineering and Technology University of Technology and Applied Sciences Salalah P O Box 608 PC:211 Salalah Oman Department of Mathematical Sciences Federal Urdu University of Arts Sciences & Technology Gulshan-e-Iqbal Karachi75300 Pakistan Department of Petroleum Engineering Knowledge University Erbil Iraq Department of Petroleum Technology Erbil Technology College Erbil Polytechnic University Kurdistan Region Erbil Iraq Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai602105 India Department of Mathematics Faculty of Science Sakarya University Sakarya Serdivan54050 Turkey Department of Mathematics Faculty of Science Islamic University of Madinah Madinah42351 Saudi Arabia

This study investigates entropy development in a magnetohydrodynamic (MHD) flow of trihybrid nanofluid in a cross fluid, including convective, thermal radiation, and heat source-sink through a stretching cylinder. The liquid that is contained within the enclosure is carboxymethyl cellulose (CMC) water with tri-hybrid nanoparticles MoS2,SiO2 and Ag. Suitable self-similarity variables are utilized to convert the nonlinear differential systems into ordinary ones. This transformation is accomplished through the bvp4c in MATLAB software. The influence of various variables on the velocity, temperature, concentration, entropy production, Bejan number, skin friction, Nusselt number, and the Sherwood number are sketched and elaborated. It is observed that an enhancement in the magnetic field (M) and Weissenberg number (We) reduce the velocity profile, whilst the temperature uplifts due to (We) and (M). Moreover, an increase in the curvature parameter increases the thermal profile. In addition, uplifting the We number ultimately increases the skin friction by approximately 37.4% and heat transmission by 45.21%, whereas the Sherwood number up to 1.98%. Comparison with previously published material confirms the accuracy of the presented numerical computations and supports our current proposed model. In biomedical applications, magnetic and electromagnetic fields play a significant role in radiofrequency ablation (RFA), magnetic resonance imaging (MRI), cancer therapy, tumor therapy, and malaria infection. This theoretical investigation may be productive in biomedical engineering, particularly in cardiology, skin problems treatment, and removing malignancies from the uterus. © 2025 The Author(s)

关键词： Heat radiation

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BFGTP: A BERT-Guided Two-Stage Molecular Representation Learning Framework for Toxicity Prediction

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Hu, Kaimiao He, Yuan Wei, Jianguo Sun, Changming Geng, Jie Wei, Leyi Su, Ran Tianjin University College of Intelligence and Computing Tianjin30000 China CSIRO Data61 SydneyNSW1710 Australia Tianjin University Tianjin Key Laboratory of Cardiovascular Emergency and Critical Care Tianjin Municipal Science and Technology Bureau Department of Cardiology Chest Hospital Tianjin China Macao Polytechnic University Centre for Artificial Intelligence driven Drug Discovery Faculty of Applied Science China Xiamen University School of Informatics Xiamen China

Accurate prediction of molecular toxicity is vital for drug development. Most mainstream methods rely on fingerprints or graph-based feature extraction, the emergence of large language models (LLMs) offers new prospects for molecular representation learning in toxicity prediction. Although several studies attempt to leverage LLMs to integrate molecular sequence data for pretraining molecular representations, certain limitations remain. Current LLM-based approaches usually utilize solely on class embedding features, overlooking the rich information in sequence embedding. Moreover, integrating pre-trained molecular representations with multi-modal molecular data may further enhance performance in toxicity prediction. To address these challenges, we propose BFGTP, a BERT-guided two-stage molecular representation learning framework for toxicity prediction. Firstly, we design independent encoders for molecular descriptions of three modalities, where the fingerprint encoder with dual level attention mechanisms effectively integrates multi-category fingerprints. Then, the two-stage guide strategy is introduced to fully utilize the prior knowledge of LLMs, employing contrastive learning to align and fuse the tri-modal representations and knowledge distillation to align predicted value distributions. BFGTP ultimately combines fingerprint and graph representations to predict molecular toxicity. Experiments on seven toxicity datasets show that BFGTP outperforms baselines, achieving the highest AUC on five datasets and the best average performance across five evaluation metrics. Ablation studies, t-SNE visualization and case study confirm the effectiveness of BFGTP's components and its ability to capture meaningful molecular representations. ©2013 IEEE.

关键词： Contrastive Learning

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