SuperSalt: equivariant neural network force fields for multicomponent molten salts system by Chen Shen, Siamak Attarian, Yixuan Zhang, Hongbin Zhang, Mark Asta, Izabela Szlufarska, Dane Morgan

“…We further illustrate how Bayesian optimization combined with SuperSalt can accelerate the discovery of optimal salt compositions with desired properties. …”
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Electricity Theft Detection in a Smart Grid Using Hybrid Deep Learning-Based Data Analysis Technique by Camille Franklin Mbey, Jacques Bikai, Felix Ghislain Yem Souhe, Vinny Junior Foba Kakeu, Alexandre Teplaira Boum

“…Thus, the proposed hybrid model is based on the support vector machine (SVM) and a particle swarm optimization (PSO) algorithm to detect energy fraudsters in the network. …”
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A Consistency Evaluation Method for Digital Twins in Manufacturing by Li Sun, Ziyiming Jiao, Shihao Wang, Feng Jiang, Xianghui Li

“…In this paper, the consistency evaluation method is carried out from three dimensions of model consistency, data consistency, and interaction consistency. …”
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Learning in Quantum Common-Interest Games and the Separability Problem by Wayne Lin, Georgios Piliouras, Ryann Sim, Antonios Varvitsiotis

“…Learning in games has emerged as a powerful tool for machine learning with numerous applications. Quantum games model interactions between strategic players who have access to quantum resources, and several recent works have studied learning in the competitive regime of quantum zero-sum games. …”
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On the Perspectives of Image-to-Lidar Constraints in Dynamic Network Optimisation by K. Mouzakidou, T. Stoltz, L. V. Jospin, D. A. Cucci, J. Skaloud

“…Stemming from a special form of graph-based optimization, the dynamic networks allow rigorous modeling of spatio-temporal constraints and thus provide the common framework for optimizing original observations from inertial systems with those coming from optical sensors. …”
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A Comprehensive Overview of the Development and Research of Energy Savings of Electric Wheel Loader by Xiaotao Fei, Zuo Cheng, Shaw Voon Wong, Muhammad Amin Azman, Dawei Wang, Xiuxian Zhang, Qiuchen Shao, Qingqiu Lin

“…The analysis reveals that integrating optimal control strategies with machine learning algorithms—such as model predictive control (MPC)—is a critical pathway to achieving energy-efficient and assisted driving in next-generation EWLs. …”
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Analytical first-screening of distributed generation hosting capacity via extreme-value theory by Xin Ma, Han Wu, Yue Yuan, Wenhui Shi, Yuehui Huang

“…Against the backdrop of large-scale interconnection requests for distributed generation (DG), there is a growing demand for rapid assessment of distribution network hosting capacity (DGHC). Conventional optimization and machine learning approaches often require high-resolution data and need substantial computational costs, making them impractical for engineering timelines. …”
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Prediction of Carbonate Reservoir Porosity Based on CNN-BiLSTM-Transformer by Yingqiang Qi, Shuiliang Luo, Song Tang, Jifu Ruan, Da Gao, Qianqian Liu, Sheng Li

“…Compared with traditional machine learning and deep learning models, the improved model better captures domain-specific information, resulting in an R² increase of 0.23 and reductions in RMSE and MAE by 0.016 and 0.014, respectively. …”
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Prediction of lithium-ion battery SOC based on IGA-GRU and the fusion of multi-head attention mechanism by Pei Tang, Minnan Jiang, Weikai Xu, Zhengyu Ding, Mao Lv

“…Compared with the traditional parameter optimization approach, this paper uses the immune genetic algorithm to find the optimal hyperparameters of the model, which on the one hand has a wider choice of parameters, and on the other hand has been improved for the genetic algorithm is easy to fall into the local optimal solution, so as to improve the SOC estimation accuracy of the GRU model. …”
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Client Selection in Federated Learning on Resource-Constrained Devices: A Game Theory Approach by Zohra Dakhia, Massimo Merenda

“…Federated Learning (FL), a key paradigm in privacy-preserving and distributed machine learning (ML), enables collaborative model training across decentralized data sources without requiring raw data exchange. …”
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Research on green supply chain finance risk identification based on two-stage deep learning by Ying Liu, Sizhe Li, Chunmei Yu, Mingli Lv

“…Finally, to model low-order feature interactions, we integrate the Support Vector Machine (SVM) algorithm. …”
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Preventing Health Risks Through Intelligent Medical Appointment Management Using Disease and Attendance Propensity by Fredy Troncoso-Espinosa, Guillermo Latorre-Nunez, Catalina Valenzuela-Nunez, Juan San Martin-Duran

“…For its development, the methodology begins with the formulation of an optimization model, using assumed values for disease and attendance propensity to establish an initial, efficient scheduling of appointments. …”
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Friction in Cylindrical Joints by Andrei Marius Mihalache, Vasile Merticaru, Vasile Ermolai, Oana Dodun, Gheorghe Nagiț, Adelina Hrițuc, Marius Ionuț Rîpanu, Laurențiu Slătineanu

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Remaining Available Energy Prediction for Energy Storage Batteries Based on Interpretable Generalized Additive Neural Network by Ji Qi, Pengrui Li, Yifan Dong, Zhicheng Fu, Zhanguo Wang, Yong Yi, Jie Tian

“…This optimization improves both the accuracy and runtime efficiency of the model. …”
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Privacy-Preserving Glycemic Management in Type 1 Diabetes: Development and Validation of a Multiobjective Federated Reinforcement Learning Framework by Fatemeh Sarani Rad, Juan Li

“…ResultsThe PRIMO-FRL model achieved a robust overall TIR of 76.54%, with adults demonstrating the highest TIR at 81.48%, followed by children at 77.78% and adolescents at 70.37%. …”
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Trustworthy Reputation for Federated Learning in O-RAN Using Blockchain and Smart Contracts by Farhana Javed, Josep Mangues-Bafalluy, Engin Zeydan, Luis Blanco

“…We introduce a blockchain-based reputation mechanism that evaluates the accuracy, integrity, and quality of participants’ model updates within the FL process. Smart contracts automate critical tasks-such as participant registration, model update verification, and reputation scoring-ensuring that data inputs directly influence accountability in a tamper-proof, transparent manner. …”
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ANN-based prediction of 17α-ethinylestradiol removal using green magnetic nanoparticles synthesized from celery extract by Saba N. Fayyadh, Nurfaizah A. Tahrim, Wan Nur Aini Wan Mokhtar

“…This experimental and modelling methodology is demonstrated to be a cost-effective and sustainable approach to removing emerging contaminants from freshwater, thereby reducing reliance on trial-and-error experiments through machine learning-based optimization.…”
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Increasing neural network robustness improves match to macaque V1 eigenspectrum, spatial frequency preference and predictivity. by Nathan C L Kong, Eshed Margalit, Justin L Gardner, Anthony M Norcia

“…Our results are consistent with other findings that there is a misalignment between human and machine perception. They also suggest that it may be useful to penalize slow-decaying eigenspectra or to bias models to extract features of lower spatial frequencies during task-optimization in order to improve robustness and V1 neural response predictivity.…”
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Study on User Fraud Identification of PV Expansion Based on a Bottom-Up Approach of a DELM Algorithm Improved by SSA for a Power Distribution Network by Wang Jinpeng, Wei Haojie, Dou Shunyao, Jeremy-Gillbanks, Zhao Xin

“…Next, a Sparrow Search Algorithm (SSA) was applied to optimize the weight parameters of the Deep Extreme Learning Machine (DELM). …”
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Dynamic Cohort Formation with Hierarchical Blockchain Using GDP for Enhanced FL by Sunila Fatima Ahmad, Zahra Abbas, Madiha Haider Syed, Adeel Anjum, Semeen Rehman

“…Federated Learning (FL) can be defined as an effective solution for using the benefits of machine learning (ML) in distributed systems, in which the data of the clients remain protected. …”
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