Temporal-Sequence Offline Reinforcement Learning for Transition Control of a Novel Tilt-Wing Unmanned Aerial Vehicle by Shiji Jin, Wenjie Zhao

“…Experimental results demonstrate that TSCQ significantly outperforms gain scheduling, Model Predictive Control (MPC), and Batch-Constrained Q-learning (BCQ), reducing the RMSE of pitch angle by up to 53.3% and vertical velocity RMSE by approximately 33%. …”
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Synthesis of silica nanoparticles from blast furnace slag as cost-effective adsorbent for efficient azo-dye removal by Abdelghaffar S. Dhmees, Nagla M. Khaleel, Sawsan A. Mahmoud

“…The sorbent materials were characterized by X-ray diffraction (XRD), X-ray fluorescence (XRF), N2 adsorption-desorption isotherms, dynamic light scattering (DLS), scanning electron microscopy (SEM), and Energy Dispersive X-ray Analysis (EDAX). …”
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Big Data-Driven 3D Visualization Analysis System for Promoting Regional-Scale Digital Geological Exploration by Yiping Tian, Jiongqi Wu, Genshen Chen, Gang Liu, Xialin Zhang

“…The system integrates three core technological components: (1) a heterogeneous cloud resource scheduling method employing an optimized CMMN algorithm with unified cloud API standardization to enhance task distribution efficiency; (2) a block model-based dynamic data aggregation approach utilizing semantic unification and attribute mapping for multi-source geological data integration; (3) a GPU-accelerated rendering framework implementing occlusion culling and batch processing to optimize 3D visualization performance. …”
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Federated Learning With Automated Dual-Level Hyperparameter Tuning by Rakib Ul Haque, Panagiotis Markopoulos

“…Similar to centralized learning, determining hyperparameters (HPs) like learning rate and batch size remains challenging yet critical for model performance. …”
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MCC: Multi-Cluster Contrastive Semi-Supervised Segmentation Framework for Echocardiogram Videos by Yu-Jen Chen, Shr-Shiun Lin, Yiyu Shi, Tsung-Yi Ho, Xiaowei Xu

“…Experimental results on two public echocardiography datasets (MCE and EchoNet-Dynamic) demonstrate the effectiveness of our method, achieving state-of-the-art performance. …”
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Cost-aware cloud workflow scheduling using DRL and simulated annealing by Yan Gu, Feng Cheng, Lijie Yang, Junhui Xu, Xiaomin Chen, Long Cheng

“…Cloud workloads are highly dynamic and complex, making task scheduling in cloud computing a challenging problem. …”
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Comprehensive Micromagnetic Modeling: Practical Techniques, Applications, and Emerging Challenges by Ahmet Bahadir Trabzon, Taichi Goto, Mehmet Cengiz Onbasli

“…Micromagnetic models play a pivotal role in advancing magnetic materials and spintronic device designs, enabling dynamic and equilibrium studies essential for next-generation information processing and neuromorphic computing. …”
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Advanced Sales Route Optimization Through Enhanced Genetic Algorithms and Real-Time Navigation Systems by Wilmer Clemente Cunuhay Cuchipe, Johnny Bajaña Zajia, Byron Oviedo, Cristian Zambrano-Vega

“…Although GAAM-TS incurs higher computational costs, it is best suited for offline or batch optimization scenarios, whereas GA-AM provides a balanced alternative for near-real-time applications. …”
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Mission Sequence Model and Deep Reinforcement Learning-Based Replanning Method for Multi-Satellite Observation by Peiyan Li, Peixing Cui, Huiquan Wang

“…In this paper, we tackle the problem of multi-satellite rapid mission replanning under dynamic batch-arrival observation requests. The objective is to maximize overall observation revenue while minimizing disruptions to the original scheme. …”
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Methanogens EDeath Induced by Sulphide and its Kinetic Modelling by Oanh Thi Phung, Meng Sun, Mitsuharu Terashima, Rajeev Goel, Hidenari Yasui

“…Using cultures enriched with either acetate or formate as a sole electron donor, sets of 1-week batch inhibition tests were performed to analyse the dynamic change of the living microorganism concentrations under the varied sulphide concentrations between zero and 400 mg-S L∁E. …”
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Learning from low precision samples by Ji In Choi, Madeleine Georges, Jung Ah Shin, Olivia Wang, Tiffany Zhu, Tapan Shah

“… • We propose 2 future research areas, • dynamic quantizer update where the model is trained using streaming data and the quantizer is updated after each batch and • precision re-allocation under budget constraints where different precision is used for different features.…”
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A Unified Approach to Video Anomaly Detection: Advancements in Feature Extraction, Weak Supervision, and Strategies for Class Imbalance by Rui Z. Barbosa, Hugo S. Oliveira

“…” Novel strategies like the Sample-Batch Selection (SBS) dynamic segment selection and pre-trained image-text models, including Contrastive Language-Image Pre-training (CLIP) and ViTamin encoder, significantly improve anomaly detection. …”
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Stimuli-responsive hydrogels, their mass transfer, intermolecular interactions, and applications in biomedical devices by Parker M. Toews, Ashwin Velraj, Jeffrey S. Bates

“…Synthesis and fabrication challenges, such as batch consistency, storage stability, degradation, and inconsistent mechanical swelling behavior, are addressed. …”
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Prediction of Remaining Service Life of Miniature Circuit Breakers Based on Wiener Process by Lin Ma, Linming Hou, Puquan He, Changxian Wang, Zhenhua Xie, Yao Wang

“…The parameters of the electrical performance degradation model are identified through the synergistic driving of the electrical life simulation data and the experimental data, the remaining electrical life prediction is realized based on the degradation data of the same batch of products, and the maximum prediction error of the proposed method is controlled within 15%.…”
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Model-Free Adaptive Iterative Learning Control of High-Order Pseudo Partial Derivatives for Nonlinear Systems by Wei Cao, Ting Wang, Jinjie Qiao, Buning Chai

“…Firstly, the nonlinear system is initially transformed into a dynamic linearized data model, taking into account external non-repetitive disturbances by the iterative dynamic linearized method. …”
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Event-Based Visual/Inertial Odometry for UAV Indoor Navigation by Ahmed Elamin, Ahmed El-Rabbany, Sunil Jacob

“…It is complex and challenging due to dynamic scenes, limited space, and, more importantly, the unavailability of global navigation satellite system (GNSS) signals. …”
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An incremental data-driven approach for carbon emission prediction and optimization of heat treatment processes by Qian Yi, Xin Wu, Junkang Zhuo, Congbo Li, Chuanjiang Li, Huajun Cao

“…Finally, using real data collected in enterprises for four consecutive years, it is verified that the predictive model can be effectively applied to the working conditions of multi-variety, small batch, and incremental heat treatment data.…”
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Data on battery health and performance: Analysing Samsung INR21700-50E cells with advanced feature engineering by Sahar Qaadan, Aiman Alshare, Alexander Popp, Myrel Tiemann, Utz Spaeth, Benedikt Schmuelling

“…This dataset provides a comprehensive collection of detailed measurements from 256 Samsung INR21700-50E cells, spanning 32 batches. It uniquely combines raw data and engineered features derived from charge-discharge cycles and Hybrid Pulse Power Characterization tests. …”
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Long-Term Prediction of Biological Wastewater Treatment Process Behavior via Wiener-Laguerre Network Model by Yasaman Sanayei, Naz Chaibakhsh, Ali Chaibakhsh, Ali Reza Pendashteh, Norli Ismail, Tjoon Tow Teng

“…A Wiener-Laguerre model with artificial neural network (ANN) as its nonlinear static part was employed to describe the dynamic behavior of a sequencing batch reactor (SBR) used for the treatment of dye-containing wastewater. …”
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Research on multi-objective energy optimization design for multi-story residential buildings in Suzhou region based on artificial neural networks by Zhongcheng Duan, Leilei Wang, Binhao Li, Gang Yao

“…The NSGA-III algorithm was then dynamically linked with the ANN, iterating based on predefined objectives and adjusting inputs to produce 200 Pareto front solutions. …”
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