Thermal comfort and energy related occupancy behavior in Dutch residential dwellings by Anastasios Ioannou

“…The first part of this thesis was based on dynamic building simulations in combination with a Monte Carlo statistical analysis, which tried to shed light to the most influential parameters, including occupancy related ones, that affect the energy consumption and comfort (a factor that is believed to be integral to the energy related behavior of people in buildings). …”
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Facial emotion based smartphone addiction detection and prevention using deep learning and video based learning by C. Joseph, P. Uma Maheswari

“…Experimental results demonstrate significant improvements in students’ behavior and reductions in smartphone usage post-intervention. …”
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Temporal dynamics of negative emotional memory reprocessing during sleep by Serik Tabarak, Ximei Zhu, Peng Li, Frederik D. Weber, Le Shi, Yimiao Gong, Kai Yuan, Yanping Bao, Tengteng Fan, Suxia Li, Jie Shi, Lin Lu, Jiahui Deng

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Reinforcement Learning-Based Control for Robotic Flexible Element Disassembly by Benjamín Tapia Sal Paz, Gorka Sorrosal, Aitziber Mancisidor, Carlos Calleja, Itziar Cabanes

“…However, automating disassembly, especially for flexible elements such as cables and rubber seals, poses significant challenges due to their nonlinear behavior and dynamic properties. Traditional control systems struggle to handle these tasks efficiently, requiring adaptable solutions that can operate in unstructured environments that provide online adaptation. …”
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Carbon stock dynamics of forest to oil palm plantation conversion for ecosystem rehabilitation planning by D. Frianto, E. Sutrisno, A. Wahyudi, E. Novriyanti, W.C. Adinugroho, A.S. Yunianto, H. Kurniawan, H. Khotimah, A. Windyoningrum, I.W.S. Dharmawan, H.L. Tata, S. Suharti, H.H. Rachmat, E.M. Lim

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A Survey on UAV Control with Multi-Agent Reinforcement Learning by Chijioke C. Ekechi, Tarek Elfouly, Ali Alouani, Tamer Khattab

“…A potentially effective technique used for UAV fleet operation is Multi-Agent Reinforcement Learning (MARL). MARL offers a powerful framework for addressing these challenges by enabling UAVs to learn optimal behaviors through interaction with the environment and each other. …”
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Leveraging System Dynamics to Predict the Commercialization Success of Emerging Energy Technologies: Lessons from Wind Energy by Svetlana Lawrence, Daniel R. Herber, Kamran Eftekhari Shahroudi

“…The developed model yielded outcomes that confirmed the hypothesized dynamics of wind energy system diffusion through a quantitative comparison of installed capacity and highlighted the significant influence of resource availability, federal incentives (production tax credits), and technological learning on capacity growth and cost reduction. …”
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TRACE: A trust-aware incentive mechanism for federated learning in IoMT by Bing Li, Jianfeng Lu, Shuqin Cao, Yanan Jin, Zhiwei Ye, Hu Liu

“…TRACE not only encourages client participation but also guarantees truthful behavior by dynamically rewarding verifiable contributions. …”
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Dataset on droplet spreading and rebound behavior of water and viscous water-glycerol mixtures on superhydrophobic surfaces with laser-made channelsMendeley Data by Matic Može, Samo Jereb, Robert Lovšin, Jure Berce, Matevž Zupančič, Iztok Golobič

“…It can help validate theoretical and numerical models of droplet spreading, retracting, and rebounding from poorly wettable surfaces, optimize superhydrophobic surfaces for applications such as self-cleaning and drag reduction, and contribute to machine learning models predicting droplet behavior. …”
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Active learning-enhanced yttrium oxyhydride thin films for photoinduced insulator-to-metal transitions by Yijing Song, Dongqing Liu, Haifeng Cheng, Yan Jia, Mei Zu, Tingting Shi, Kaixuan Ding, Wenxia Zhang, Zhuo Chen, Yan Huang, Zhen Meng

“…The active learning-assisted multi-objective optimization strategy demonstrated superior experimental efficiency compared to conventional random sampling approaches, achieving an approximately 20-40 fold reduction in all experimental iterations. …”
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Joint learning equation of state surfaces with uncertainty-aware physically regularized neural networks by Dongyang Kuang, Shiwei Li, Buxuan Wang, Chao Xiong, Shichang Zhang, Yanyao Zhang

“…To overcome these limitations, we propose EOSNN, a neural network based physics informed deep learning method that jointly learns multiple EOS surfaces from diverse data sources, including static and dynamic compression and ab initio calculations. …”
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Energy Demand Response in a Food-Processing Plant: A Deep Reinforcement Learning Approach by Philipp Wohlgenannt, Sebastian Hegenbart, Elias Eder, Mohan Kolhe, Peter Kepplinger

“…Moreover, while MILP’s computation time increases considerably with the number of binary variables, RL efficiently learns dynamic system behavior and scales to more complex systems without significant performance degradation. …”
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e-Health Strategy for Surgical Prioritization: A Methodology Based on Digital Twins and Reinforcement Learning by Fabián Silva-Aravena, Jenny Morales, Manoj Jayabalan

“…We generate prioritization scores by modeling clinical, economic, behavioral, and social variables in real time and optimize access through a reinforcement learning engine designed to maximize long-term system performance. …”
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Using Machine Learning to Develop a Surrogate Model for Simulating Multispecies Contaminant Transport in Groundwater by Thu-Uyen Nguyen, Heejun Suk, Ching-Ping Liang, Yu-Chieh Ho, Jui-Sheng Chen

“…Model validation reveals that the ANN surrogate accurately reproduces the spatial–temporal concentration profiles of both original and degradation species, capturing key dynamic behaviors with high precision. Notably, the ANN model achieves up to a 100-fold reduction in computational time compared to traditional analytical or semi-analytical solutions. …”
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Innovative framework for fault detection and system resilience in hydropower operations using digital twins and deep learning by Jun Tan, Raoof Mohammed Radhi, Kimia Shirini, Sina Samadi Gharehveran, Zamen Parisooz, Mohsen Khosravi, Hossein Azarinfar

“…The integration of Digital Twins allowed for precise real-time modeling of system behavior, while Deep Learning algorithms effectively identified and predicted faults. …”
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Honeybee colony soundscapes: Decoding distance-based cues and environmental stressors by Nayan Di, Chunjing Zhu, Zongwen Hu, Muhammad Zahid Sharif, Baizhong Yu, Fanglin Liu

“…The introduction of ethyl acetate and acetone caused minor reductions in classification accuracy but had divergent impacts on foraging dynamics: ethyl acetate enhanced landing efficiency, whereas acetone disrupted foraging activity. …”
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Investigating Catching Hotspots of Fishing Boats: A Framework Using BeiDou Big Data and Deep Learning Algorithms by Fen Wang, Xingyu Liu, Tanxue Chen, Hongxiang Feng, Qin Lin

“…The CNN-BiLSTM hybrid model emerged as optimal for fishing behavior classification, achieving 89.98% accuracy and an 87.72% F1 score through synergistic spatiotemporal feature extraction. …”
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Damage Detection of Seismically Excited Buildings Using Neural Network Arrays with Branch Pruning Optimization by Jau-Yu Chou, Chia-Ming Chang, Chieh-Yu Liu

“…A synthetic dataset is first generated from a simplified building model that includes floor flexural behavior and reflects the target dynamics of the structures. …”
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Attention Mechanism with Spatial-Temporal Joint Deep Learning Model for the Forecasting of Short-Term Passenger Flow Distribution at the Railway Station by Zhicheng Dai, Dewei Li, Shiqing Feng

“…We conduct a comparative analysis of the prediction performance and time complexity of the proposed architecture against existing baseline models, demonstrating superior performance and robustness exhibited by the ST-Bi-LSTM model (achieving a reduction in RMSE of over 10%). This study facilitates the transition of station management from passive response to active prediction of station passenger flow dynamics.…”
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Data-driven insights of flow over heated elliptic cylinders: Machine learning and CFD perspectives on non-Newtonian forced convection by Anika Tahsin Meem, Md. Zhangir Hossain, Hasina Akter, Md. Mamun Molla

“…At n=0.9, a notable 73.91% reduction in CL is observed as g∗ increases, emphasizing geometric sensitivity in force dynamics. …”
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