Full-chain comprehensive assessment and multi-scenario simulation of geological disaster vulnerability based on the VSD framework: a case study of Yunnan province in China by Li Xu, Shucheng Tan, Runyang Li

“…Furthermore, the Ordered Weighted Averaging (OWA) algorithm and the Partical Swarm Optimization-Support Vector Machine (PSO-SVM) model were combined to simulate future GDV scenarios for 2030–2050 under three development preferences: environment oriented, status quo, and economically oriented. …”
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Studying the influence of correction codes on coherent reception of M-PSK signals in the presence of noise and harmonic interference by V. D. Nguyen

“…The methods of statistical radio engineering, optimal signal reception theory and computer simulation modeling were used.Results. …”
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Risk prediction and analysis of gallbladder polyps with deep neural network by Kerong Yuan, Xiaofeng Zhang, Qian Yang, Xuesong Deng, Zhe Deng, Xiangyun Liao, Weixin Si

“…This predictive model can be employed to differentiate the nature of polyps before surgery, enhancing diagnostic accuracy. …”
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Investigations into Picture Defogging Techniques Based on Dark Channel Prior and Retinex Theory by Lihong Yang, Zhi Zeng, Hang Ge, Yao Li, Shurui Ge, Kai Hu

“…The method involves building a two-stage optimization framework: in the first stage, global contrast enhancement is achieved by Retinex preprocessing, which effectively improves the detail information regarding the dark area and the accuracy of the transmittance map and atmospheric light intensity estimation; in the second stage, an a priori compensation model for the dark channel is constructed, and a depth-map-guided transmittance correction mechanism is introduced to obtain a refined transmittance map. …”
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Multi-Agent Mapping and Tracking-Based Electrical Vehicles with Unknown Environment Exploration by Chafaa Hamrouni, Aarif Alutaybi, Ghofrane Ouerfelli

“…When rapid deliveries are required, the algorithm prioritizes faster routes, whereas, for flexible schedules, it optimizes energy conservation. …”
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Leveraging Spectral Neighborhood Information for Corn Yield Prediction with Spatial-Lagged Machine Learning Modeling: Can Neighborhood Information Outperform Vegetation Indices? by Efrain Noa-Yarasca, Javier M. Osorio Leyton, Chad B. Hajda, Kabindra Adhikari, Douglas R. Smith

“…Key predictors included spatially lagged spectral bands (e.g., Green_lag, NIR_lag, RedEdge_lag) and VIs (e.g., CREI, GCI, NCPI, ARI, CCCI), highlighting the value of integrating neighborhood data for improved corn yield prediction. This study underscores the importance of spatial context in corn yield prediction and lays the foundation for future research across diverse agricultural settings, focusing on optimizing neighborhood size, integrating spatial and spectral data, and refining spatial dependencies through localized search algorithms.…”
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Machine-learning-based reconstruction of long-term global terrestrial water storage anomalies from observed, satellite and land-surface model data by N. Mandal, P. Das, K. Chanda, K. Chanda

“…The most effective machine learning (ML) algorithms among convolutional neural network (CNN), support vector regression (SVR), extra trees regressor (ETR) and stacking ensemble regression (SER) models are evaluated at each grid cell to achieve optimal reproducibility. …”
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Enhancing cotton irrigation with distributional actor–critic reinforcement learning by Yi Chen, Meiwei Lin, Zhuo Yu, Weihong Sun, Weiguo Fu, Liang He

“…This study introduces a novel intelligent irrigation approach based on distributional reinforcement learning, ensuring that the algorithm simultaneously considers weather, soil, and crop conditions to make optimal irrigation decisions for long-term benefits. …”
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Сontrol of Intelligent Transport System in Minsk by D. V. Kapskiy, D. V. Navoy, P. A. Pegin

“…The paper considers algorithms for searching a maximum traffic volume of road vehicles in a traffic light cycle with a distributed intensity pulse and optimization of shifts under coordinated traffic flow control. …”
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Enhancing grid-connected PV-EV charging station performance through a real-time dynamic power management using model predictive control by Aziz Watil, Hamid Chojaa

“…This paper presents a novel station manager algorithm for grid-connected PV-EV charging stations, designed to address key challenges in current systems. …”
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A Dual-Variable Selection Framework for Enhancing Forest Aboveground Biomass Estimation via Multi-Source Remote Sensing by Dapeng Chen, Hongbin Luo, Zhi Liu, Jie Pan, Yong Wu, Er Wang, Chi Lu, Lei Wang, Weibin Wang, Guanglong Ou

“…A dual-variable selection strategy based on SHapley Additive exPlanations (SHAP) was developed, and a genetic algorithm (GA) was used to optimize the parameters of five machine learning models—elastic net (EN), least absolute shrinkage and selection operator (Lasso), support vector regression (SVR), Random Forest (RF), and Categorical Boosting (CatBoost)—to estimate the AGB of <i>Pinus kesiya</i> var. …”
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An Efficient Anomalous Sound Detection System for Microcontrollers by Yi-Cheng Lo, Tsung-Lin Tsai, Chieh-Wen Yang, An-Yeu Wu

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Nodal Carbon Emission Factor Prediction for Power Systems Based on MDBO-CNN-LSTM by Lihua Zhong, Feng Pan, Yuyao Yang, Lei Feng, Haiming Shao, Jiafu Wang

“…In this study, we address these limitations by improving population initialization, search mechanisms, and iteration strategies and developing a hybrid strategy Modified Dung Beetle Optimization (MDBO) algorithm. …”
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A Stackelberg Trust-Based Human–Robot Collaboration Framework for Warehouse Picking by Yang Liu, Fuqiang Guo, Yan Ma

“…An iterative Stackelberg trust strategy generation (ISTSG) algorithm is designed to achieve the optimal long-term collaboration benefits between humans and robots, which is solved by the Bellman equation. …”
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The authors would like to thank the staff of the Organisation of Transportation and Transport Management Department by B. S. Trofimov, I. S. Trofimova

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Bridging the Gap: A Review of Machine Learning in Water Quality Control by Herlina Abdul Rahim, Nur Athirah Syafiqah Noramli, Indrabayu

“…Emerging solutions like federated learning (35% accuracy improvement in pan-African E. coli prediction) and explainable AI (SHAP-guided nitrate models) address these barriers. …”
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A Metaheuristic Framework for Cost-Effective Renewable Energy Planning: Integrating Green Bonds and Fiscal Incentives by Juan D. Saldarriaga-Loaiza, Johnatan M. Rodríguez-Serna, Jesús M. López-Lezama, Nicolás Muñoz-Galeano, Sergio D. Saldarriaga-Zuluaga

“…To do this, we use three optimization techniques to identify solutions that lower electricity generation costs: Teaching Learning, Harmony Search, and the Shuffled Frog Leaping Algorithm. …”
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Analysis of a Four-Legged Robot Kinematics during Rotational Movements of Its Body by M. J. Fernando, G. R. Saypulaev, M. R. Saypulaev

“…To provide high precision in controlling their movement, it is required to develop mathematical models and algorithms for planning the robot movement along various trajectories. …”
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Predicting Pathological Complete Response Following Neoadjuvant Therapy in Patients With Breast Cancer: Development of Machine Learning–Based Prediction Models in a Retrospective S... by Chun-Chi Lai, Cheng-Yu Chen, Tzu-Hao Chang

“…ConclusionsThis study suggests that incorporating breast sonography into models with clinical and laboratory data may modestly improve pCR prediction. …”
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Lightweight blasthole image detection and positioning method by Shan PAN, Ting YU, Zhongwen YUE, Zijian TIAN, Qingyu JIN

“…To tackle the issues of large parameter size and low frames per second (FPS) in the blasthole image detection and localization algorithm network model, a lightweight Sc_C2f module is proposed to optimize the network structure. …”
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