Review of Fault Detection and Diagnosis Methods in Power Plants: Algorithms, Architectures, and Trends by Camelia Adela Maican, Cristina Floriana Pană, Daniela Maria Pătrașcu-Pană, Virginia Maria Rădulescu

“…The analysis reveals a significant transition from traditional statistical methods to machine learning (ML) and deep learning (DL) models, with over 70% of recent studies employing AI-driven approaches. …”
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Educational status influences cognitive-motor learning in older adults: going to university provides greater protection against aging than going to high school by Mariana Callil Voos, Maria Elisa Pimentel Piemonte, Letícia Lessa Mansur, Fátima Aparecida Caromano, Sonia Maria Dozzi Brucki, Luiz Eduardo Ribeiro do Valle

“…ABSTRACT Objective: To investigate if middle-aged and older adults with a higher education would differ from those with an average education in cognitive-motor tasks involving lower limb function. Methods: A walking version of the Trail Making Test (Walking Executive Function Task, [WEFT]) was used. …”
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Predicting grip strength-related frailty in middle-aged and older Chinese adults using interpretable machine learning models: a prospective cohort study by Lisheng Yu, Lisheng Yu, Shunshun Cao, Botian Song, Yangyang Hu

“…The SHAP summary plot showed that all features predicted frailty in order of importance, with cognitive function being considered the most important predictive feature. …”
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Efficient handover authentication protocol for cloud-edge collaborative UAV networks by RUI Li, ZHANG Xiongwei, YANG Jibin, XU Weiguang

“…For current unmanned aerial vehicle (UAV) network handover authentication protocols in cloud-edge colla-borative environments suffered from low efficiency and lack of physical security, an efficient handover authentication protocol based on physical unclonable function was proposed, which achieved lightweight handover authentication for UAV between different ground stations within a cloud-edge architecture. …”
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A Local–Global Graph KAN for Multi‐Class Prediction of PPI by Minghui Liu, Ying Qu

“…To mitigate data imbalance, an asymmetric loss function is utilized to better handle minority classes and improve overall prediction accuracy. …”
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The Application of Compound Control Algorithm in Photovoltaic System MPPT by WANG Shenghui, LI Yilun, ZHENG Hong, GAO Shan

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LPFFNet: Lightweight Prior Feature Fusion Network for SAR Ship Detection by Xiaozhen Ren, Peiyuan Zhou, Xiaqiong Fan, Chengguo Feng, Peng Li

“…At present, many deep learning-based ship detection methods have improved the detection accuracy but also increased the complexity and computational cost. …”
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Performance Assessment of Solar-Transformer-Consumption System Using Neural Network Approach by Ritu Gupta, Ekata, C.M. Batra

“…MATLAB code for FFBP algorithm is built to improve the values of reliability and cost function by minimizing the error up to 0.0001 precision. …”
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Hygienic assessment of the state of health of senior pupils at the 5-day school week by V. R. Kuchma, I. K. Rapoport, S. B. Sokolova, A. Yu. Makarova

“…Activities aimed at maintaining the health in the educational institution should be aimed at creating a hygienically sound learning environments. The aim of the research was to study the effect of the mode in general education schools on 5-day training week on the State of health of pupils 10-11 classes, and also features the day students. …”
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GuardianMPC: Backdoor-Resilient Neural Network Computation by Mohammad Hashemi, Domenic Forte, Fatemeh Ganji

“…The rapid growth of deep learning (DL) has raised serious concerns about users’ data and neural network (NN) models’ security and privacy, particularly the risk of backdoor insertion when outsourcing the training or employing pre-trained models. …”
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Minimizing Total Weighted Completion Time on Single Machine with Past-Sequence-Dependent Setup Times and Exponential Time-Dependent and Position-Dependent Learning Effects by Kaibiao Sun, Hongxing Li

“…This paper addresses a single-machine problem in which the past-sequence-dependent (p-s-d) setup times and exponential time-dependent and position-dependent learning effects are considered. By the exponential time-dependent learning effect, it means that the processing time of a job is defined by an exponent function of the total actual processing time of the already processed jobs. …”
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DRBD-YOLOv8: A Lightweight and Efficient Anti-UAV Detection Model by Panpan Jiang, Xiaohua Yang, Yaping Wan, Tiejun Zeng, Mingxing Nie, Zhenghai Liu

“…Furthermore, DN-ShapeIoU, a novel loss function, has been established to enhance detection accuracy, and depthwise separable convolutions have been included to decrease computational complexity. …”
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A pose generation model for animated characters based on DCNN and PFNN by Boli Wang

“…Therefore, to reduce the cost of generating 3D animated characters and improve the similarity between animated characters and real humans, a 3D action recognition and animated character generation model based on ResNet and phase function neural network is proposed. The experiment outcomes denote that the raised model begins to converge at 50 iterations, with a minimum loss value of 0.13. …”
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An Overview of Distributed Firewalls and Controllers Intended for Mobile Cloud Computing by Cyril Godwin Suetor, Daniele Scrimieri, Amna Qureshi, Irfan-Ullah Awan

“…It explores the complexities involved in merging Software-Defined Networking (SDN), Network Function Virtualisation (NFV), and CASB into a cohesive system, focusing on the need to resolve interoperability issues and maintain low latency and high throughput while balancing performance across distributed firewalls and controllers. …”
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Innovative Ghost Channel Spatial Attention Network with Adaptive Activation for Efficient Rice Disease Identification by Yang Zhou, Yang Yang, Dongze Wang, Yuting Zhai, Haoxu Li, Yanlei Xu

“…Through combining the advantages of Mish and ReLU, we designed the Mish-ReLU Adaptive Activation Function (MAAF), enhancing the model’s generalization capacity and convergence speed. …”
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Method for Augmenting Side-Scan Sonar Seafloor Sediment Image Dataset Based on BCEL1-CBAM-INGAN by Haixing Xia, Yang Cui, Shaohua Jin, Gang Bian, Wei Zhang, Chengyang Peng

“…Secondly, a BCEL1 loss function (combining binary cross-entropy and L1 loss functions) is introduced into the discriminator, enabling it to focus on both global image consistency and finer distinctions for better generation results. …”
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Metal-organic framework UiO-66 dose-dependently mitigates oxidative stress and astrocyte activation in the rat hippocampus by Ali Dankoob, Moein Shirzad, Fatemeh Rabiei, Hamid Askari, Shahram Ghasemi, Aliakbar Rajabzadeh, Maryam Ghasemi-Kasman

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Promoting physical activity in middle-aged and older adults through digital intervention: matching message frame and recipient profile through deep reinforcement learning by Patrizia Catellani, Marco Piastra

“…Finally, by applying Deep Reinforcement Learning to develop an automatic strategy to quickly profile recipients and select the most effective messages . …”
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HTCNN-Attn: a fine-grained hierarchical multi-label deep learning model for disaster emergency information intelligent extraction from social media by Shanshan Li, Qingjie Liu, Xiaoling Sun

“…The model employs a hierarchical loss function to enforce label consistency across three levels: binary disaster filtering (Level 1), mid-level category classification (Level 2), and fine-grained subcategory extraction (Level 3). …”
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Application of Machine Learning Methods for Identifying Wave Aberrations from Combined Intensity Patterns Generated Using a Multi-Order Diffractive Spatial Filter by Paval. A. Khorin, Aleksey P. Dzyuba, Aleksey V. Chernykh, Muhammad A. Butt, Svetlana N. Khonina

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