Design Improvement of Permanent Magnet Motor Using Single- and Multi-Objective Approaches by Cvetkovski Goga, Petkovska Lidija

“…Engineers and academics have been actively involved in optimising these systems to achieve better performance, efficiency, and cost-effectiveness. Optimising electrical machines, including permanent magnet motors, is a complex task. …”
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Performance analysis of high-rate generalized space code index modulation system by BIAN Lina, ZHANG Qianyu, WU Jiayan

“…The traditional modulation symbols were replaced with the additional phase by the HR-GS-CIM system, and the genetic algorithm (GA) was used to optimize the design of the additional phase, which could acquire high energy efficiency advantage and performance advantage. At the same time, the methods of generalized and spatial indexing were introduced to jointly map select the additional phase, spreading code, and antenna lead, and a low-complexity joint mapping selection algorithm was adopted to greatly improve the system’s spectral efficiency. …”
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RLEAFS: Reinforcement Learning-Based Energy Aware Forwarding Strategy for NDN-Based IoT Networks by Naeem Ali Askar, Adib Habbal

“…Our Strategy integrates Q learning algorithm into path selection procedure, focusing on minimizing energy consumption and extending network lifetime while maintaining efficient data delivery. …”
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Multi-Feature Fusion-Based Speech Disorder Classification Using MobileNetV3-EfficientNetB7, Linformer-Performer, and SHAP-Aware XGBoost by Abdul Rahaman Wahab Sait, Suresh Sankaranarayanan, P. Gouthaman

“…Traditional speech disorders (SD) detection relies on subjective analysis, resulting in inconsistent outcome. …”
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TWO-PARAMETER IRT MODEL APPLICATION TO ASSESS PROBABILISTIC CHARACTERISTICS OF PROHIBITED ITEMS DETECTION BY AVIATION SECURITY SCREENERS by A. K. Volkov, D. V. Aidarkin, A. K. Volkov

“…The main drawback is the complexity of ICAO recommendations implementation concerning taking into account of shadow x-ray image complexity factors during preparation and evaluation of prohibited items detection efficiency by aviation security screeners. …”
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The coupling of a high-efficiency aerosol collector with electrospray ionisation–Orbitrap mass spectrometry as a novel tool for real-time chemical characterisation of fine and ultr... by Y.-S. Lau, Z. Ristovski, B. Miljevic

“…The proposed setup consists of a custom-built high-efficiency aerosol collector (HEAC) used to collect aerosol samples into a working fluid and an electrospray ionisation (ESI) Orbitrap mass spectrometer (MS) for the subsequent chemical analysis of the liquid sample. …”
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Network security situation assessment based on dual attention mechanism and HHO-ResNeXt by Dongmei Zhao, Guoqing Ji, Shuiguang Zeng

“…The traditional convolutional neural network (CNN) has a limited receptive field and cannot accurately identify the importance of each channel, making it difficult to solve increasingly complex network security problems. To solve these problems, this paper combines ResNeXt with the Efficient Channel Attention (ECA) module and the Contextual Transformer (COT) block to construct a model to assess network conditions. …”
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A new three-dimensional mesh modeling method for geological bodies containing complex faults in geological engineering by Wenli Gao, Xinming Lu, Shengzhe Hou, Tianyu Zhang, Wenting Liu

“…This strategy transforms the integration of complex three-dimensional models into a triangulation problem for simple two-dimensional polygons, ensuring topologically consistent and computationally efficient merging. …”
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Surface Charge Transfer Enhanced Cobalt‐Phthalocyanine Crystals for Efficient CO2‐to‐CO Electroreduction with Large Current Density Exceeding 1000 mA cm−2 by Tengyi Liu, Di Zhang, Yutaro Hirai, Koju Ito, Kosuke Ishibashi, Naoto Todoroki, Yasutaka Matsuo, Junya Yoshida, Shimpei Ono, Hao Li, Hiroshi Yabu

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Experimental Evaluation and Thermodynamic Analysis of Magnetic Fe₃O₄@La-Zr-MOFs for Highly Efficient Fluoride and Phosphate Removal by Ziyi Zhang, Xinyun Chen, Yongyi Yu, Wenbin Pan, Ruilai Liu, Jiangyan Song, Jiapeng Hu

“…The magnetic Fe₃O₄@La-Zr-MOFs exhibited a magnetic recovery efficiency of 93%, and they could maintain outstanding adsorption performance at a broad range of pH values and superior selectivity for phosphate and fluoride ions. …”
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Hybrid Deep Learning Approach for Accurate Detection and Multiclass Classification of Broken Conductor Faults in Power Distribution Systems by Firas Saadoon Mohammed Al-Jumaili, Mustafa Onat

“…The developed CVNN framework was built with TensorFlow’s Sequential API, which provided efficient computing of complex-valued inputs and weights and, thus, the proper handling of the intricate signals often encountered in power systems. …”
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PPFL-DCS: Privacy-Preserving Federated Learning Using Neural Transformer and Leveraging Dynamic Client Selection to Accommodate Data Diversity by Nakul Mehta, Nitesh Bharot, John G. Breslin, Priyanka Verma

“…Extensive experiments demonstrate that PPFL-DCS achieves a high detection accuracy of 97.424% for cyber threats in industrial CPSs, and highlight its efficiency over state-of-the-art techniques.…”
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GhostConv+CA-YOLOv8n: a lightweight network for rice pest detection based on the aggregation of low-level features in real-world complex backgrounds by Fei Li, Yang Lu, Qiang Ma, Shuxin Yin, Rui Zhao

“…Deep learning models for rice pest detection often face performance degradation in real-world field environments due to complex backgrounds and limited computational resources. …”
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A Novel Fault Diagnosis Method Using FCEEMD-Based Multi-Complexity Low-Dimensional Features and Directed Acyclic Graph LSTSVM by Rongrong Lu, Miao Xu, Chengjiang Zhou, Zhaodong Zhang, Kairong Tan, Yuhuan Sun, Yuran Wang, Min Mao

“…Rolling bearings, as critical components of rotating machinery, significantly influence equipment reliability and operational efficiency. Accurate fault diagnosis is therefore crucial for maintaining industrial production safety and continuity. …”
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NEW ORGANIZATION PROCESS OF FEATURE SELECTION BY FILTER WITH CORRELATION-BASED FEATURES SELECTION METHOD by Olga Solovei

“…The goal of the work is to increase the efficiency of feature selection by Filter with CFS by proposing a new organization process of feature selection. …”
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EnsembleXAI-Motor: A Lightweight Framework for Fault Classification in Electric Vehicle Drive Motors Using Feature Selection, Ensemble Learning, and Explainable AI by Md. Ehsanul Haque, Mahe Zabin, Jia Uddin

“…As electric vehicles (EVs) are growing, the fault diagnosis in their drive motor becomes more important to have optimal performance and safety. Traditional fault detection methods suffer mainly from high false positive and false negative rates, computational complexity, and lack of transparency in decision-making methods. …”
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Cross-attention swin-transformer for detailed segmentation of ancient architectural color patterns by Lv Yongyin, Yu Caixia

“…Traditional approaches, including convolutional neural networks (CNNs) and standard transformer-based models, have achieved significant success; however, they often face challenges in capturing fine-grained details and maintaining efficiency across diverse datasets. These methods struggle with balancing precision and computational efficiency, especially when dealing with complex patterns and high-resolution images.MethodsTo address these limitations, we propose a novel segmentation model that integrates a hierarchical vision transformer backbone with multi-scale self-attention, cascaded attention decoding, and diffusion-based robustness enhancement. …”
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Hierarchical proxy consensus optimization for IoV based on blockchain and trust value by Baoqin ZHAI, Jian WANG, Lei HAN, Jiqiang LIU, Jiahao HE, Tianhao LIU

“…With the rapid development of Internet of vehicles, 5G and artificial intelligence technologies, intelligent transportation has become the development trend of transportation technology.As a vehicle-vehicle and vehicle-road information interaction platform, the Internet of vehicles is the basic support platform for intelligent traffic information sharing and processing.At the same time, the security of Internet of vehicles has attracted much attention, especially data security which may cause user privacy leakage.The blockchain technology has become a solution, but it still faces new challenges in efficiency, security and other aspects.With the increase of vehicle nodes and information, how to efficiently achieve information consensus in high-speed vehicle moving environment has become a key problem.Then a bottom-up RSU (road side unit) chain consensus protocol was proposed based on blockchain and trust value.Several typical consensus structures were compared, and bottom-up two-layer consensus structure was adopted according to the actual scenarios of the Internet of vehicles.Moreover, a group leader node election algorithm was proposed which is based on node participation, work completion and message value.The system security was ensured by assigning trust value to each vehicle.Following the consensus structure and algorithm work mentioned above, the specific process of the protocol was comprehensively described, which was divided into six steps: region division, group leader node selection, local consensus, leader primary node selection, global consensus, and intra-domain broadcast.Then the experiments were analyzed from four aspects: security, communication complexity, consensus algorithm delay and fault tolerance rate.Experiments showed that, compared with other schemes, the proposed protocol can effectively reduce communication complexity and shorten consensus delay under the condition of resisting conspiracy attack, witch attack and other attacks.On the premise of security, the protocol improves fault tolerance rate and enables more nodes to participate in information sharing to satisfy the requirements of Internet of vehicles scenarios.…”
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Integrating SMED and Industry 4.0 to optimize processes with plithogenic n-SuperHyperGraphs by Julio Cesar Méndez Bravo, Claudia Jeaneth Bolanos Piedrahita, Manuel Alberto Méndez Bravo, Luis Manuel Pilacuan-Bonete

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A fuzzy-optimized multi-level random forest (FOMRF) model for the classification of the impact of technostress by Gabriel James, Ifeoma, David, John, Samuel, Enefiok, Imeh Umoren, Ubong Etuk, Aloysius, Anietie, Saviour, Chikodili

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