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10th International Conference on digital Arts, Media and technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Lapawong, Werapat Uttama, Surapong Rueangsirarak, Worasak Temdee, Punnarumol Hu, Shanfeng Aslam, Nauman Mae Fah Luang University Digital Transformation Technology School of Applied Digital Technology Chiang Rai Thailand Mae Fah Luang University Computer and Communication Engineering for Capacity Building Research Center Chiang Rai Thailand Northumbria University Department of Computer and Information Sciences Newcastle upon Tyne United Kingdom

ISBN: (纸本)9798331543273

Balance is the result of complex interactions among individuals, tasks, and the environment. It plays a crucial role in daily activities, relying on sensory systems and joint movement strategies to maintain postural stability. Effective balance not only improves movement and physical activities but also enhances overall quality of life. Balance strategies, such as ankle, hip, knee, and stepping strategies, are essential for maintaining stability in various situations and environments. This study focuses on utilizing public data from the Clinical Test of Sensory Interaction on Balance (CTSIB) to assess balance strategies. Traditional methods rely on subjective observations, limiting the identification of nuanced strategy combinations. To overcome this, motion capture (mocap) data and unsupervised machine learning techniques, clustering and dimensionality. reduction using Agglomerative, Gaussian Mixture, and K-Means and classify balance strategies. The results demonstrate the potential of this approach to identify distinct clusters of balance strategies, offering a more detailed, objective, and comprehensive understanding of postural stability. Among the clustering algorithms applied, K-Means proved to be particularly effective, achieving reliable groupings with consistent results. The Silhouette Scores indicated that k=3, identified as the optimal number of clusters from the elbow test, provided the best balance between cohesion and separation for K-Means. This suggests that K-Means delivers interpretable clusters of balance strategies. The three possible outcomes are good balance, A slight unbalance and a poor balance. This innovative methodology advances the field of balance assessment by providing a clear and systematic approach to classifying balance strategies, allowing for better understanding and targeted interventions. © 2025 IEEE.

关键词： K-means clustering

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Superconductivity in Bi1.6Pb0.4Sr2Ca2Cu3O10+δ ceramics with cobalt ferrite (CoFe2O4) nanoparticle addition

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Ceramics International 2025年第12期51卷 16134-16142页

作者： Tran, Duc H. Pham, An T. Binh, Nguyen Thanh Anh, Do Thi Kim Nam, Nguyen Hoang Hong, Nguyen T.M. Cuong, Le Viet Pham, Phong V. Huy, Nguyen Quang Huong, Tran Thi Man, Nguyen K. Lim, Kean Pah Kechik, Mohd Mustafa Awang Chen, Soo Kien Le, Tien Faculty of Physics University of Science Vietnam National University Hanoi100000 Viet Nam Nano and Energy Center University of Science Vietnam National University Hanoi100000 Viet Nam Faculty of Engineering Physics and Nanotechnology VNU University of Engineering and Technology Hanoi100000 Viet Nam University of Science Vietnam National University Hanoi100000 Viet Nam Faculty of Electronics and Computer Engineering University of Economics - Technology for Industries Hanoi100000 Viet Nam Faculty of Electronic Materials and Devices School of Materials Science and Engineering Hanoi University of Science and Technology Hanoi100000 Viet Nam Department of Physics Faculty of Science Universiti Putra Malaysia Selangor Serdang43400 Malaysia University of Science and Technology of Hanoi Vietnam Academy of Science and Technology 18 Hoang Quoc Viet Cau Giay Hanoi100000 Viet Nam

This study explores the use of cobalt ferrite (CoFe2O4) nanoparticles as artificial pinning centers to investigate flux pinning characteristics in the Bi1.6Pb0.4Sr2Ca2Cu3O10+δ ceramics. Polycrystalline samples with the composition (Bi1.6Pb0.4Sr2Ca2Cu3O10+δ)1−x(CoFe2O4)x, where x ranges from 0.000 to 0.010, are synthesized based on the solid-state formation. Structural and surface analyses reveal that the samples predominantly comprise superconducting phases, with volume fractions varying with x. An increase in x leads to a drop in the critical temperature (Tc). However, an improvement in the critical current density (Jc) is observed at x = 0.002, owing to the addition of CoFe2O4. The collective pinning theory indicates a shift from a single-vortex to small bundle vortex pinning at the crossover field (Bsb) in all samples. The dependence of Bsb on temperatures is consistent with δl pinning, attributed to changes in the mean free path of the charge carrier. Geometries of artificial pinning centers are identified as 2D using the Dew–Hughes model. CoFe2O4 nanoparticles are primarily located at the grain boundaries, filling voids and supporting pinning through grain boundaries. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Flux pinning

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AttentionPoolMobileNeXt: An automated construction damage detection model based on a new convolutional neural network and deep feature engineering models

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Multimedia Tools and Applications 2025年第4期84卷 1821-1843页

作者： Aydin, Mehmet Barua, Prabal Datta Chadalavada, Sreenivasulu Dogan, Sengul Tuncer, Turker Chakraborty, Subrata Acharya, Rajendra U. Department of Digital Forensics Engineering Technology Faculty Firat University Elazig Turkey University of Southern Queensland Toowoomba Australia School of Engineering University of Southern Queensland Springfield LakesQLD4300 Australia School of Science and Technology Faculty of Science Agriculture Business and Law University of New England ArmidaleNSW2351 Australia Center for Advanced Modelling and Geospatial Information Systems Faculty of Engineering and IT University of Technology Sydney SydneyNSW2007 Australia School of Mathematics Physics and Computing University of Southern Queensland Toowoomba Australia

In 2023, Turkiye faced a series of devastating earthquakes and these earthquakes affected millions of people due to damaged constructions. These earthquakes demonstrated the urgent need for advanced automated damage detection models to help people. This study introduces a novel solution to address this challenge through the AttentionPoolMobileNeXt model, derived from a modified MobileNetV2 architecture. To rigorously evaluate the effectiveness of the model, we meticulously curated a dataset comprising instances of construction damage classified into five distinct classes. Upon applying this dataset to the AttentionPoolMobileNeXt model, we obtained an accuracy of 97%. In this work, we have created a dataset consisting of five distinct damage classes, and achieved 97% test accuracy using our proposed AttentionPoolMobileNeXt model. Additionally, the study extends its impact by introducing the AttentionPoolMobileNeXt-based Deep Feature engineering (DFE) model, further enhancing the classification performance and interpretability of the system. The presented DFE significantly increased the test classification accuracy from 90.17% to 97%, yielding improvement over the baseline model. AttentionPoolMobileNeXt and its DFE counterpart collectively contribute to advancing the state-of-the-art in automated damage detection, offering valuable insights for disaster response and recovery efforts. © The Author(s) 2024.

关键词： Image classification

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Clustering Single-Cell RNA-seq Data: Impact of Data Binarization on Algorithmic Performance

Clustering Single-Cell RNA-seq Data: Impact of Data Binariza...

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18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Widzisz, Karolina Kania, Mateusz Zyla, Joanna Polański, Andrzej Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Akademicka 16 Gliwice Poland Department of Applied Informatics Silesian University of Technology Akademicka 16 Gliwice Poland Department of Data Science and Engineering Silesian University of Technology Akademicka 16 Gliwice Poland

The primary objective of this study was to test the hypothesis that the binary information on the presence or absence of gene expression can sufficiently capture the inherent heterogeneity within single-cell RNA se quencing (scRNA-seq) data. This hypothesis posits that even without detailed expression levels, valuable insights about cellular diversity can be obtained. Utilizing this method can be particularly advantageous when analyzing large datasets, a common scenario in the field of scRNA-seq. In this paper, we evaluate clustering performance and cluster separability of a variety of model-based algorithms and distance-based methods to an alyze both expression level data and threshold-encoded binarized data. We examined the performance of the Bernoulli-mixture model and Gaussian-mixture model. These were compared against traditional clustering techniques such as hierarchical clustering, K-means, and the Louvain algorithm on a range of scRNA-seq datasets. Our findings reveal that mixture models exhibit a lower dependence on the specific dataset compared to distance-based methods. Mixture models, particularly, demonstrate greater efficacy in accurately estimat ing the number of clusters present within the data. Among analyzed algorithms, the Bernoulli-mixture model stands out, outperforming distance-based approaches in several key aspects. Binary data, presence/absence of gene expression, seem to be indeed adequate to capture the heterogeneity of scRNA-seq data when clus tering with methods specifically designed for binary datasets. The implications of this finding are significant, as it opens up new possibilities for simplifying data analysis in scRNA-seq studies without compromising the accuracy of the results. © 2025 by SCITEPRESS– science and technology Publications, Lda.

关键词： Data accuracy

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Machine learning innovations in CPR: a comprehensive survey on enhanced resuscitation techniques

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Artificial Intelligence Review 2025年第8期58卷 1-41页

作者： Islam, Saidul Rjoub, Gaith Elmekki, Hanae Bentahar, Jamal Pedrycz, Witold Cohen, Robin Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Department of Computer Science 6 G Research Center Khalifa University Abu Dhabi United Arab Emirates Gina Cody School of Engineering and Computer Science Concordia University Montreal Canada Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Research Center of Performance and Productivity Analysis Istinye University Sariyer/Istanbul Turkey David R. Cheriton School of Computer Science University of Waterloo Waterloo Canada

This survey paper explores the transformative role of Machine Learning (ML) and Artificial Intelligence (AI) in Cardiopulmonary Resuscitation (CPR), marking a paradigm shift from conventional, manually driven resuscitation practices to intelligent, data-driven interventions. It examines the evolution of CPR through the lens of predictive modeling, AI-enhanced devices, and real-time decision-making tools that collectively aim to improve resuscitation outcomes and survival rates. Unlike prior surveys that either focus solely on traditional CPR methods or offer general insights into ML applications in healthcare, this work provides a novel interdisciplinary synthesis tailored specifically to the domain of CPR. It presents a comprehensive taxonomy that classifies ML techniques into four key CPR-related tasks: rhythm analysis, outcome prediction, non-invasive blood pressure and chest compression modeling, and real-time detection of pulse and Return of Spontaneous Circulation (ROSC). The paper critically evaluates emerging ML approaches-including Reinforcement Learning (RL) and transformer-based models-while also addressing real-world implementation barriers such as model interpretability, data limitations, and deployment in high-stakes clinical settings. Furthermore, it highlights the role of eXplainable AI (XAI) in fostering clinical trust and adoption. By bridging the gap between resuscitation science and advanced ML techniques, this survey establishes a structured foundation for future research and practical innovation in ML-enhanced CPR. It offers clear insights, identifies unexplored opportunities, and sets a forward-looking research agenda identifying emerging trends and practical implementation challenges aiming to improve both the reliability and effectiveness of CPR in real-world emergencies. © The Author(s) 2025.

关键词： Reinforcement learning

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Facial Action Unit Recognition with Micro-Action-Aware Transformer 2nd

Facial Action Unit Recognition with Micro-Action-Aware Tran...

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2nd CSIG Conference on Emotional Intelligence, CEI 2024

作者： Yuan, Yichen Cheng, Yifan Shao, Zhiwen Dang, Qianwen Chen, Rui Fu, Mingjian Jiang, Shengtian Li, Chunyu Ma, Lizhuang School of Computer Science and Technology China University of Mining and Technology Xuzhou China Mine Digitization Engineering Research Center of the Ministry of Education Xuzhou China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9789819650835

Facial action unit (AU) recognition is a challenging task, due to the subtlety of each AU and the correlations among AUs in global face. However, the learning of local-global features has not been thoroughly exploited in most of the existing methods. In this paper, we propose a novel micro-action-aware transformer to integrate local and global feature extractions, which effectively captures subtle AU details while maintaining the global relational modeling capacity of transformers. Besides, we jointly train facial AU recognition and facial landmark detection, in which the two correlated tasks contribute to each other and further facilitate the learning of local-global AU-related feature. Extensive experiments demonstrate that our approach achieves comparable performance to the state-of-the-art AU recognition methods on the challenging BP4D and GFT benchmarks, and works well for landmark detection. Particularly, our approach achieves average F1 score results of 63.3% and 55.8% on BP4D and GFT datasets, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： facial action unit recognition facial landmark detection Micro-action-aware transformer

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Exploiting the Potential of Self-Supervised Monocular Depth Estimation via Patch-Based Self-Distillation

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IEEE Internet of Things Journal 2025年第11期12卷 18042-18055页

作者： Yu, Ting Pan, Shan Chen, Wei Tian, Zijian Wang, Zehua Yu, F. Richard Leung, Victor C. M. Postdoctoral Research Station China RongTong Academy of Sciences Group Corporation Limited Beijing100022 China China RongTong Artificial Intelligence Research Center Beijing100022 China China University of Mining and Technology School of Artificial Intelligence Beijing100083 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China School of Artificial Intelligence Beijing100083 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

Perceiving scene depth and 3D structure is one of the key tasks for Internet of Video Things (IoVT) devices to understand and interact with the environment. Self-supervised monocular depth estimation has demonstrated significant potential in leveraging large-scale unlabeled datasets to achieve competitive performance, thereby playing an increasingly important role in depth estimation. Despite recent methods providing additional supervisory signals through self-distillation strategies to improve depth estimation, an effective method for generating pseudo-depth labels suitable for addressing occlusion issues among elements far from the camera remains unexplored. To address this limitation, we propose a Patch-based Self-Distillation Learning Framework to exploit the potential of self-supervised monocular depth estimation in recovering fine-grained scene depth. In the proposed framework, elements far from the camera within the input image are enlarged by enlarging and cropping operations in the patch-based self-distillation branch. Guided by photometric consistency, the model learns the detailed occlusion relationships among elements from the enlarged patches, producing patch depth maps with fine structures. In the main branch, which takes full-scale images as input, patch depth maps serve as pseudo-depth labels through self-distillation loss to provide additional supervisory signals for regions where photometric consistency fails to offer effective supervision. This forces the depth estimation network to recover fine structures of elements far from the camera in full-scale input images. Regarding the architecture of the depth estimation network, we introduce a bin-center prediction. In this prediction, a global aggregator based on self-attention provides additional scene structure queries for adaptive scene depth discretization. Finally, to encourage the model to explore more general cues for depth inference beyond road plane cues, we propose a PatchMix data augmentati

关键词： Image enhancement

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Numerical Simulation Study on the Thickness of Melt Film on the Surface of Spinning Wheel During Centrifugal Spinning of Metallurgical Solid Waste

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Journal of Sustainable Metallurgy 2025年 1-16页

作者： Chen, Wei Yu, Jianyu Zhao, Kai Wang, Baoxiang Zhen, Changliang Wang, Weixing Chen, Ying College of Metallurgy and Energy North China University of Science and Technology No. 21 Bohai Avenue Tangshan063200 China College of Materials Science and Engineering North China University of Science and Technology No. 21 Bohai Avenue Tangshan063200 China Department of Computer Science The University of Sheffield SheffieldS10 2TN United Kingdom Comprehensive Testing and Analysis Center North China University of Science and Technology No. 21 Bohai Avenue Tangshan063200 China

Melt fiberization using a spinning wheel is a method for the high-value-added treatment of solid waste from metallurgical processes. In this paper, the volume of fluid (VOF) method was employed to numerically simulate the fluid spreading process on the spinning wheel surface, and physical experiments were conducted to validate the simulation's effectiveness. The thickness and velocity distribution characteristics of the liquid film on the wheel surface were analyzed, revealing the influence of wheel speed and fluid viscosity on the liquid film. Through regression analysis of the data, a predictive equation for the liquid film thickness at the contact position was established. The liquid film thickness on the wheel surface decreases exponentially with increasing circumferential angle, and there is a significant radial velocity gradient within the film. The radial velocity gradient is inversely proportional to the fluid viscosity—the higher the viscosity, the smaller the radial velocity gradient in the liquid film. A predictive equation for the liquid film thickness at the contact position was developed based on the data, expressed as Tl = 1.34 × Re−0.31 × q6.63, with a correlation coefficient of 0.89. © The Minerals, Metals & Materials Society 2025.

关键词： Metal refining

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Optimal Location of Renewable Energy Generators in Transmission and Distribution System of Deregulated Power Sector:A Review

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Energy engineering 2025年第3期122卷 823-859页

作者： Digambar Singh Najat Elgeberi Mohammad Aljaidi Ramesh Kumar Rabia Emhamed Al Mamlook Manish Kumar Singla Department Electrical Engineering Government Polytechnic CollegeBTE LucknowEtah207120India University of Nevada RenoNV 89557USA Department of Computer Science Zarqa UniversityZarqa13110Jordan Department of Interdisciplinary Courses in Engineering Chitkara University Institute of Engineering&TechnologyChitkara UniversityChandigarh160002India Jadara University Research Center Jadara UniversityIrbid00962Jordan Department of Industrial Engineering Al Zawiya UniversityAl-ZawiyaP.O.Box 16418Libya Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Department of Biosciences Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India

The literature on multi-attribute optimization for renewable energy source(RES)placement in deregulated power markets is extensive and diverse in *** study focuses on the most relevant publications directly addressing the research problem at ***,while the body of work on optimal location and sizing of renewable energy generators(REGs)in balanced distribution systems is substantial,only the most pertinent sources are cited,aligning closely with the study’s objective function.A comprehensive literature review reveals several key research areas:RES integration,RES-related optimization techniques,strategic placement of wind and solar generation,and RES promotion in deregulated powermarkets,particularly within transmission ***,the optimal location and sizing of REGs in both balanced and unbalanced distribution systems have been extensively *** demonstrate significant potential for standalone applications in remote areas lacking conventional transmission and distribution *** presents a thorough review of current modeling and optimization approaches for RES-based distribution system location and ***,it examines the optimal positioning,sizing,and performance of hybrid and standalone renewable energy *** paper provides a comprehensive review of current modeling and optimization approaches for the location and sizing of Renewable Energy Sources(RESs)in distribution systems,focusing on both balanced and unbalanced networks.

关键词： Optimization of RESs distributed generation modeling and selection of RESs hybrid systems standalone systems optimal location

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Location Privacy Preservation Crowdsensing With Federated Reinforcement Learning

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 1877-1894页

作者： You, Zhichao Dong, Xuewen Liu, Ximeng Gao, Sheng Wang, Yongzhi Shen, Yulong Xidian University School of Computer Science and Technology The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education Xi'an710071 China Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'an710071 China Fuzhou University College of Computer and Data Science Fuzhou350025 China Central University of Finance and Economics School of Information Beijing102206 China California State Polytechnic University Pomona Department of Computer Information Systems PomonaCA91768 United States Xidian University School of Computer Science and Technology Xi'an710071 China Shaanxi Key Laboratory of Network and System Security Xi'an710071 China

Crowdsensing has become a popular method of sensing data collection while facing the problem of protecting participants' location privacy. Existing location-privacy crowdsensing mechanisms focus on static tasks and participants without considering sensing tasks' time requirements and participants' mobility, which cannot achieve satisfactory collected data quality and task completion in crowdsensing with dynamic tasks and participants. Inspired by this, we proposed a location-preservation crowdsensing mechanism, FedSense, considering dynamic tasks and participants based on federated learning (FL) and reinforcement learning (RL). In FedSense, through RL's outstanding decision-making ability, participants select sensing tasks to perform by well-trained RL models without uploading location information to servers for task allocation. We propose an independent tasks selection environment that defines actions, states, and rewards of RL to enable FedSense to achieve satisfactory task completion and data quality while preserving location privacy. Besides, FedSense applies an asynchronous FL aggregation algorithm that reduces participants' network stabilization and device computing ability requirements. Analysis proves that participants' location information does not leave the local device during the model training and task selection process, effectively avoiding privacy leakage. Simulation shows that compared with existing location-preservation crowdsensing mechanisms, FedSense achieves the highest task completion and sensing accuracy for dynamic tasks and participants. © 2024 IEEE.

关键词： Data privacy

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