Remote Sensing Techniques for Assessing Snow Avalanche Formation Factors and Building Hazard Monitoring Systems by Natalya Denissova, Serik Nurakynov, Olga Petrova, Daniker Chepashev, Gulzhan Daumova, Alena Yelisseyeva

“…As climate change accelerates shifts in snowfall and temperature patterns, it is increasingly important to improve our ability to monitor and predict avalanches. This review explores the use of remote sensing technologies in understanding key geomorphological, geobotanical, and meteorological factors that contribute to avalanche formation. …”
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The intelligent fault identification method based on multi-source information fusion and deep learning by Dashu Guo, Xiaoshuang Yang, Peng Peng, Lei Zhu, Handong He

“…Second, the importance of each influencing factor is predicted using 4 machine learning methods. Finally, fault identification is carried out on the fault identification map, which is fused with multi-source feature information, using the Convolutional Neural Network Model. …”
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Photovoltaic Power Estimation for Energy Management Systems Addressing NMOT Removal with Simplified Thermal Models by Juan G. Marroquín-Pimentel, Manuel Madrigal-Martínez, Juan C. Olivares-Galvan, Alma L. Núñez-González

“…The proposed estimation algorithm converts specific Numerical Weather Prediction data and solar module specifications into photovoltaic power output, which can be used in energy management applications to provide economic and ecological benefits. …”
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Modeling the Effect of Greenways’ Multilevel Visual Characteristics on Thermal Perception in Summer Based on Bayesian Network and Computer Vision by Yongrong Zheng, Siren Lan, Jiayi Zhao, Yuhan Liu, Songjun He, Chang Liu

“…Using the hill climbing algorithm in R to construct a Bayesian network, model validation results indicated prediction accuracies of 0.799 for TS and 0.838 for TP. …”
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A neural network design for black-box identification of converter impedance models in arbitrary operating conditions by CHEN Bing, ZHAO Chongbin, JIANG Qirong, WANG Xu, WANG Fangming

“…In the model verification phase, the network is fed with set operating conditions, achieving highly accurate identification of stable operating conditions and offline prediction.…”
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AI-Driven Optimization of Multilayer Thin-Film Structures for Advanced Optical Applications by Emad Alkhazraji, Sani Mukhtar, Haitham Khaled, Jaime Viegas

“…The FCNN model is trained to predict reflectance spectra with high accuracy over a wide design space defined by the thicknesses of individual layers. …”
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A Multi-Stage Approach for External Trucks and Yard Cranes Scheduling with CO₂ Emissions Considerations in Container Terminals by Ahmed Talaat, Mohamed Gheith, Amr Eltawil

“…Specifically, it employs a K-means clustering algorithm to reduce the number of necessary truck trips for container handling. …”
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A Survey on UAV Control with Multi-Agent Reinforcement Learning by Chijioke C. Ekechi, Tarek Elfouly, Ali Alouani, Tamer Khattab

“…Unmanned Aerial Vehicles (UAVs) have become increasingly prevalent in both governmental and civilian applications, offering significant reductions in operational costs by minimizing human involvement. …”
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Development of a prognostic model for chemotherapy response and identification of TNFAIP2 as a target in colorectal cancer by Cheng Zhou, Chuan-Hai Xu, Min Xu, Xin-Kun Huang, Bin Zhu

“…Abstract Oxaliplatin, a key chemotherapeutic agent, often induces resistance in colorectal cancer (CRC) treatment, highlighting the urgent need for reliable biomarkers to predict treatment efficacy. In this study, we aimed to identify key genes associated with oxaliplatin resistance in CRC and to evaluate their potential as prognostic biomarkers. …”
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Deep Learning-Based Atmospheric Visibility Detection by Yawei Qu, Yuxin Fang, Shengxuan Ji, Cheng Yuan, Hao Wu, Shengbo Zhu, Haoran Qin, Fan Que

“…This paper systematically reviews the applications of various deep learning models—Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and Transformer networks—in visibility estimation, prediction, and enhancement. Each model’s characteristics and application methods are discussed, highlighting the efficiency of CNNs in spatial feature extraction, RNNs in temporal tracking, GANs in image restoration, and Transformers in capturing long-range dependencies. …”
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Digital mapping of soil organic carbon stocks in the forest lands of Dominican Republic by Efraín Duarte, Erick Zagal, Juan A. Barrera, Francis Dube, Fabio Casco, Alexander J. Hernández

“…Model A reported the lowest prediction error and uncertainty with an R2 of 0.83, an RMSE of 35.02 Mg C ha−1. …”
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Statistical Analysis of High-Resolution Coherent Monopulse Radar Sea Clutter by Hong Zhu, Qingping Wang, Ning Tai, Jingjian Huang, Naichang Yuan

“…A statistical analysis that properly characterizes sea clutter processes is indispensable both for optimum detection algorithm design and for performance prediction problems in maritime surveillance applications. …”
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Energy-efficient outdoor air flow control in ventilation systems by A. B. Sulin, A. A. Nikitin, T. V. Ryabova, S. S. Muraveinikov, I. N. Sankina

“…The expected microclimate parameters predicted assessment in real time opens up the possibility of using such elements and algorithms for controlling the ventilation and air conditioning system, which provide the required air quality with minimal energy consumption. …”
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A Novel Method of Parameter Identification for Lithium-Ion Batteries Based on Elite Opposition-Based Learning Snake Optimization by Wuke Li, Ying Xiong, Shiqi Zhang, Xi Fan, Rui Wang, Patrick Wong

“…Traditional evolutionary algorithms often fail to balance global and local search. …”
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Evaluating ecological impacts through geospatial analysis of land use land cover dynamics in Jarmet wetland ecosystems in Western Ethiopia by Amanuel Kumsa Bojer, Ayad M. Fadhil Al-Quraishi, Saralees Nadarajah

“…Conversely, grasslands, forested areas, wetlands, and shrublands experienced reductions of 21.3%, 14.7%, 7.2%, and 6.8%, respectively. …”
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Exploring Heterogeneity in Car-Following Behaviors Based on Driver Visual Characteristics: Modeling and Calibration by Congcong Bai, Jun Jing, Bokun Liu, Wenbin Yao, Chengcheng Yang, Adjé Jérémie Alagbé, Sheng Jin

“…The driver’s visual characteristic variables were then incorporated to improve the full velocity difference (FVD) model. Genetic algorithms were employed to calibrate the model under different car-following types, revealing pronounced behavioral variations. …”
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Design of Cloud-Based Complex VR 3D Scene Interaction Based on Attribute Preferences by Ze Yu Yang, Yeom Jun Young

“…Key findings indicate that the RL-based adaptive interface significantly improved user interaction, with reductions in latency up to 30% and increased task completion rates by 50%. …”
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Task shifting for non-communicable disease management in low and middle income countries--a systematic review. by Rohina Joshi, Mohammed Alim, Andre Pascal Kengne, Stephen Jan, Pallab K Maulik, David Peiris, Anushka A Patel

“…Factors such as training of NPHWs, provision of algorithms and protocols for screening, treatment and drug titration were the main enablers of the task-shifting intervention. …”
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EXPLORING AUTOMATED SUMMARIZATION: FROM EXTRACTION TO ABSTRACTION by Svetlana G. Sorokina

“…Summaries generated by AI models offer new opportunities for analyzing machine-generated language and provide valuable data for studying how algorithms process, condense, and restructure human language.…”
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Chaotic chimp-mountain gazelle optimized FOPID control for frequency regulation in islanded airport microgrids with heterogeneous energy systems by Odelu P., Chandan Kumar Shiva, Sachidananda Sen, Vedik Basetti, Chandra Sekhar Reddy

“…To address these issues, this study proposes a Chaotic Chimp Mountain Gazelle Optimizer (CCMGO) algorithm. The CCMGO algorithm integrates the exploration capabilities of the Chimp Optimization Algorithm (ChOA) with the fast convergence of the Mountain Gazelle Optimizer (MGO), which is further enhanced by chaotic mapping to improve search diversity and avoid local optima. …”
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