Microcontroller-Driven Battery Management in Hybrid Energy Systems: A Systematic Review of Applications, Control Strategies, and Emerging Trends by Ripfumelo Humphrey Malele, Bongumsa Mendu, Bessie Baakanyang Monchusi

“…Tools like Maximum Power Point Tracking (MPPT) and Pulse Width Modulation (PWM) help improve efficiency, while microcontrollers like Arduino and TMS320F28379D balance cost and performance depending on the project. …”
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Estimation of Flood Inundation Area Using Soil Moisture Active Passive Fractional Water Data with an LSTM Model by Rekzi D. Febrian, Wanyub Kim, Yangwon Lee, Jinsoo Kim, Minha Choi

“…Accurate flood monitoring and forecasting techniques are important and continue to be developed for improved disaster preparedness and mitigation. Flood estimation using satellite observations with deep learning algorithms is effective in detecting flood patterns and environmental relationships that may be overlooked by conventional methods. …”
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Multi-Agent Meta Reinforcement Learning for Reliable and Low-Latency Distributed Inference in Resource-Constrained UAV Swarms by Marwan Dhuheir, Aiman Erbad, Bechir Hamdaoui, Samir Brahim Belhaouari, Mohsen Guizani, Thang X. Vu

“…Our approach is tested on CNN networks and benchmarked against state-of-the-art conventional reinforcement learning algorithms. Extensive simulations show that our model outperforms competitive methods by around 29% in terms of latency and around 23% in terms of transmission power improvements while delivering results comparable to the traditional LDTP optimization solution by around 9% in terms of latency.…”
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New Hybrid Method for Buffer Positioning and Production Control Using DDMRP Logic in Smart Manufacturing by Sahar Habbadi, Ismail El Mouayni, Brahim Herrou, Souhail Sekkat

“…To identify the optimal buffer locations, a Genetic Algorithm (GA) is employed. …”
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Large Language Model and Digital Twins Empowered Asynchronous Federated Learning for Secure Data Sharing in Intelligent Labeling by Xuanzhu Sheng, Chao Yu, Xiaolong Cui, Yang Zhou

“…By analysising and comparing and with other existing asynchronous federated learning algorithms, the experimental results show that our proposed method outperforms other algorithms in terms of performance, such as model accuracy and running time. …”
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Adaptive data driven multi period power supply recovery method for distribution networks by Xi Ye, Meng Yang, Zhihong Yang, Qing Xiang, Yazhuo Li

“…Finally, simulations on the improved IEEE-33 bus system and actual example systems verify that the adaptive data driven power supply recovery model for distribution networks can reduce conservatism and improve the robustness of the optimization results. …”
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Adaptive Feedforward Vibration Control of Helicopter Cabin Floor Driven by Piezoelectric Stack Actuators: Modeling, Simulation and Experiments by Laishou Song, Yingquan Wang, Xiaoyu Shen

“…A scale helicopter airframe model, preserving the local geometric similarity of the cabin floor structure, is developed and optimized to capture the low-order global dynamic characteristics of a reference airframe. …”
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Research on Lightweight Dynamic Security Protocol for Intelligent In-Vehicle CAN Bus by Yuanhao Wang, Yinan Xu, Zhiquan Liu, Suya Liu, Yujing Wu

“…To address these issues, we propose an improved dynamic compression algorithm that achieves higher compression rates and efficiency by optimizing header information processing during data reorganization. …”
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Theoretical analysis of MOFs for pharmaceutical applications by using machine learning models to predict loading capacity and cell viability by Bader Huwaimel, Saad Alqarni

“…Principal Component Analysis (PCA) was applied to reduce dimensionality, and the Water Cycle Algorithm was used to optimize hyperparameters. Evaluation metrics, including R2, Root Mean Squared Error (RMSE), and maximum error, indicated that the QR-MLP model outperformed the other models, achieving test R2 scores of 0.99917 for Drug Loading Capacity and 0.99111 for Cell Viability. …”
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Real-time urban regional route planning model for connected vehicles based on V2X communication by Pangwei Wang, Hui Deng, Juan Zhang, Mingfang Zhang

“…Advancement in the novel technology of connected vehicles has presented opportunities and challenges for smart urban transport and land use. To improve the capacity of urban transport and optimize land-use planning, a novel real-time regional route planning model based on vehicle to X communication (V2X) is presented in this paper. …”
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BERT-OntoSent: combining BERT language model with sentiment ontology for enhanced sentiment analysis on social media by Abdelweheb Gueddes, Mohamed Ali Mahjoub

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An interpretable machine learning model for predicting mortality risk in adult ICU patients with acute respiratory distress syndrome by Wanyi Li, Hangyu Zhou, Yingxue Zou

“…This study used eight machine learning algorithms to construct predictive models. Recursive feature elimination with cross-validation is used to screen features, and cross-validation-based Bayesian optimization is used to filter the features used to find the optimal combination of hyperparameters for the model. …”
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Environmental Risk Mitigation via Deep Learning Modeling of Compressive Strength in Green Concrete Incorporating Incinerator Ash by Amin Amraee, Seyed Azim Hosseini, Farshid Farokhizadeh, Mohammad Hassan Haeri

“…A database for deep learning modeling was created using Convolutional Neural Networks (CNNs) and the Multi-Verse Optimizer (MVO) algorithm. …”
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Techno-economic modeling and analysis of a PV EV charged with battery energy storage system (BESS) on Kalimantan Island by Aripriharta, Satria Adiguna, Arif N. Afandi, Muhammad Cahyo Bagaskoro

“…This research analyses the optimal combination of solar panels and battery capacity in 11 cities in Kalimantan using particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms to maximize energy output, reduce levelised energy costs, and maximally reduce carbon emissions. …”
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Process-based modeling framework for sustainable irrigation management at the regional scale: integrating rice production, water use, and greenhouse gas emissions by Y. Bo, H. Liang, T. Li, F. Zhou, F. Zhou, F. Zhou

“…Here, we propose an advancing framework that addresses these problems by integrating a process-based soil–crop model with vital physiological effects, a novel method for model upscaling, and the non-dominated sorting genetic algorithm II (NSGA-II) multi-objective optimization algorithm at a parallel computing platform. …”
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Machine Learning-Based Prediction of Resilience in Green Agricultural Supply Chains: Influencing Factors Analysis and Model Construction by Daqing Wu, Tianhao Li, Hangqi Cai, Shousong Cai

“…Secondly, by integrating configurational analysis with machine learning, it innovatively constructs a resilience level prediction model based on fsQCA-XGBoost. The research findings are as follows: (1) fsQCA identifies a total of four high-resilience pathways, verifying the core proposition of “multiple conjunctural causality” in complex adaptive system theory; (2) compared with single algorithms such as Random Forest, Decision Tree, AdaBoost, ExtraTrees, and XGBoost, the fsQCA-XGBoost prediction method proposed in this paper achieves an optimization of 66% and over 150% in recall rate and positive sample identification, respectively. …”
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A Novel Energy Consumption Prediction Model Integrating Real-Time Traffic State Recognition and Velocity Prediction of BEVs by Yue Li, Yu Jiang, Jianhua Guo, Dong Xie

“…Consequently, we propose an improved Fuzzy C-Means (FCM) clustering algorithm that use historical traffic data and dynamic traffic information accurately identify traffic conditions. …”
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Non-destructive assessment of hemp seed vigor using machine learning and deep learning models with hyperspectral imaging by Damrongvudhi Onwimol, Pongsan Chakranon, Kris Wonggasem, Papis Wongchaisuwat

“…To simplify the analysis and reduce computational complexity, a subset of key spectral wavelengths was selected using a successive projection algorithm. Deep learning models were trained on these selected wavelengths to directly learn patterns from the raw spectral data. …”
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A Novel LSTM Architecture for Automatic Modulation Recognition: Comparative Analysis With Conventional Machine Learning and RNN-Based Approaches by Sam Ansari, Soliman Mahmoud, Sohaib Majzoub, Eqab Almajali, Anwar Jarndal, Talal Bonny

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Modeling Worldwide Tree Biodiversity Using Canopy Structure Metrics from Global Ecosystem Dynamics Investigation Data by Jin Xu, Kjirsten Coleman, Volker C. Radeloff, Melissa Songer, Qiongyu Huang

“…With the launch of NASA’s Global Ecosystem Dynamics Investigation (GEDI), we evaluated the efficacy of space-borne lidar metrics in predicting tree species richness globally and explored whether integrating spectral vegetation metrics with space-borne lidar data could improve model performances. Using Forest Global Earth Observatory (ForestGEO) data, we developed three models using the random forest algorithm to predict global tree species richness across climate zones, including a dynamic habitat index (DHI)-only model, a GEDI-only model, and a combined GEDI-DHI model. …”
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