Resource-constrained project scheduling problem: Review of recent developments by Sahar Khajesaeedi, Seyed Jafar Sadjadi, Farnaz Barzinpour, Reza Tavakkoli Moghaddam

“…Furthermore, the application of machine learning and sustainability-driven models has expanded the practical scope of RCPSP in dynamic and complex environments. …”
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An ensemble learning approach for predicting nanoparticle dispersion properties in cutting fluids using a small sample dataset by Abhishek Shrotriya, Vinay Vakharia, Himanshu Borade, Rakesh Chaudhari, Jay Vora

“…It is observed that the coolants enhanced by nanoparticles may outperform traditional cutting fluids in terms of cooling and lubricating effectiveness during machining operations. This work underscores the role of optimized models in advancing the development of high-performance, sustainable cutting fluids for industrial applications.…”
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Coal mining equipment lifecycle management system architecture and key technology by Yihui PANG, Jinglong BI, Pengzhe YUAN, Baofu ZHAO, Ziwei DING

“…The main influencing factors of coal mining equipment selection optimization design were analyzed, and a calculation method for comprehensive mining equipment selection optimization was studied. …”
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Utilizing deep learning for intelligent monitoring and early warning of slope disasters in public space design by Wang Ting, Ying Wang

“…Earlier methods, including rule-based and empirical approaches, use fixed thresholds to assess risk factors such as soil moisture, slope angle, and seismic activity. Although machine learning models like decision trees and support vector machines have improved predictions using historical data, their scalability and adaptability remain constrained due to the need for intensive feature engineering and their limited ability to model complex nonlinear dynamics.MethodsThis study introduces a novel framework utilizing Deep Learning techniques to enable intelligent, real-time monitoring and early warning of slope disasters. …”
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Bidirectional f-Divergence-Based Deep Generative Method for Imputing Missing Values in Time-Series Data by Wen-Shan Liu, Tong Si, Aldas Kriauciunas, Marcus Snell, Haijun Gong

“…First, the use of f-divergence provides a flexible and adaptable framework for optimizing the model across diverse imputation tasks, enhancing its versatility. …”
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Hyperclustering: High-Order Deep/Shallow Feature Clustering for Subway Shield Tunneling Water Leakage Detection by Jianjun Xu, Xing Yuan, Lixiao Zheng, Da Lin

“…Existing tunneling water leakage detection techniques primarily focus on traditional machine learning models, such as decision trees, SVMs, and random forests. …”
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Classification and Recognition of Soybean Quality Based on Hyperspectral Imaging and Random Forest Methods by Man Chen, Zhichang Chang, Chengqian Jin, Gong Cheng, Shiguo Wang, Youliang Ni

“…The model parameters were optimized using particle swarm optimization (PSO) and differential evolution (DE) algorithms to improve performance. …”
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Nonlinear Control of Photovoltaic (PV) Solar-Powered Centrifugal Pump for Irrigation System: A Case Study of Fadak Farm in Karbala by Mays Mousawi, Ali Abdul Razzaq Altahir, Asseel Majeed AL- Gaheeshi

“…A solar generator provides an asynchronous three-phase machine coupled to a centrifugal pump as part of the system. …”
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Enhancing Privacy in IoT-Enabled Digital Infrastructure: Evaluating Federated Learning for Intrusion and Fraud Detection by Amogh Deshmukh, Peplluis Esteva de la Rosa, Raul Villamarin Rodriguez, Sandeep Dasari

“…To mitigate this, solutions using federated averaging (FedAvg), federated proximal (FedProx), and federated optimization methods have been proposed. These methods work with data locality during training at local clients without exposing data, while maintaining global convergence to enhance the privacy of local models within the framework. …”
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Mix design and performance prediction of EPS lightweight structural concrete based on orthogonal experimentation by Qianhui Zhang

“…Abstract This study explores mix proportion design and mechanical property prediction of EPS lightweight structural concrete using orthogonal experimentation and machine learning models. The research systematically analyzed the effects of EPS content, water-to-binder ratio, and POM fiber content on compressive strength, splitting tensile strength, thermal conductivity, and frost resistance. …”
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Deep learning for property prediction of natural fiber polymer composites by Ivan P. Malashin, Dmitry Martysyuk, Vladimir Nelyub, Aleksei Borodulin, Andrei Gantimurov, Vadim Tynchenko

“…Best DNN model architecture (four hidden layers (128–64–32–16 neurons), ReLU activation, 20% dropout, a batch size of 64, and the AdamW optimizer with a learning rate of $$10^{-3}$$ ) obtained through hyperparameter optimization using Optuna, delivered the best performance (R $$^2$$ up to 0.89) and MAE reductions of 9–12% compared to gradient boosting, driven by the DNN’s ability to capture nonlinear synergies between fiber-matrix interactions, surface treatments, and processing parameters while aligning architectural complexity with multiscale material behavior.…”
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Stochastic Gradient Descent for Kernel-Based Maximum Correntropy Criterion by Tiankai Li, Baobin Wang, Chaoquan Peng, Hong Yin

“…In comparison with the classical least squares method (LS), which takes only the second-order moment of models into consideration and belongs to the convex optimization problem, MCC captures the high-order information of models that play crucial roles in robust learning, which is usually accompanied by solving the non-convexity optimization problems. …”
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Active ramp-down control and trajectory design for tokamaks with neural differential equations and reinforcement learning by Allen M. Wang, Cristina Rea, Oswin So, Charles Dawson, Darren T. Garnier, Chuchu Fan

“…The policy training environment is a hybrid physics and machine learning model trained on simulations of the SPARC primary reference discharge (PRD) ramp-down, an upcoming burning plasma scenario which we use as a testbed. …”
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Smart Contract Security in Decentralized Finance: Enhancing Vulnerability Detection with Reinforcement Learning by Jose Juan de Leon, Cenchuan Zhang, Christos-Spyridon Koulouris, Francesca Medda, Rahul

“…The PPO model exhibits more stable and consistent learning patterns and achieves higher overall rewards. …”
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Data-Efficient Sowing Position Estimation for Agricultural Robots Combining Image Analysis and Expert Knowledge by Shuntaro Aotake, Takuya Otani, Masatoshi Funabashi, Atsuo Takanishi

“…We collected 84 RGB-depth images from seven field sites, labeled by synecological farming practitioners of varying proficiency levels, and trained a regression model to estimate optimal sowing positions and seeding quantities. …”
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Theoretical and computational investigations on estimation of viscosity of ionic liquids for green adsorbent: Effect of temperature and composition by Zhaoxiong Han

“…The models are optimized using the Whale Optimization Algorithm (WOA) to fine-tune hyperparameters, enhancing their predictive performance. …”
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Holistic Electric Powertrain Component Design for Battery Electric Vehicles in an Early Development Phase by Nico Rosenberger, Silvan Deininger, Jan Koloch, Markus Lienkamp

“…This study presents a holistic electric powertrain component design model, including the high-voltage battery, power electronics, electric machine, and transmission, which is meant to be used as a foundation for further development. …”
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Enhancing IoT cybersecurity through lean-based hybrid feature selection and ensemble learning: A visual analytics approach to intrusion detection. by Islam Zada, Esraa Omran, Salman Jan, Hessa Alfraihi, Seetah Alsalamah, Abdullah Alshahrani, Shaukat Hayat, Nguyen Phi

“…In this paper, a Lean-based hybrid Intrusion Detection framework using Particle Swarm Optimization and Genetic Algorithm (PSO-GA) to select the features and Extreme Learning Machine and Bootstrap Aggregation (ELM-BA) to classify the features is introduced. …”
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The effect of axial geometrical variations on the steady state characteristics of oil lubricated journal bearings using titanium dioxide nanoparticles as lubricant additives by H. Awad, Khaled M. Abdou, E. Saber

“…Because of recent advancements in numerically controlled machine tools, accurate machining of complex shapes is now a realistic operation. …”
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Enhancing medical AI with retrieval-augmented generation: A mini narrative review by Omid Kohandel Gargari, Gholamreza Habibi

“…Retrieval-augmented generation (RAG) is a powerful technique in artificial intelligence (AI) and machine learning that enhances the capabilities of large language models (LLMs) by integrating external data sources, allowing for more accurate, contextually relevant responses. …”
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