Topology Unveiled: A New Horizon for Economic and Financial Modeling by Yicheng Wei, Junzo Watada, Zijin Wang

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Multiobjective optimization of buffer capacity allocation in multiproduct unreliable production lines using improved adaptive NSGA-II algorithm by jianguo duan, nan xie, Haochen Li, Qinglei Zhang

“…Based on the theoretical production rate in system and system state entropy, a mathematical model for buffer capacity optimization was established and optimized using a specific genetic algorithm. …”
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Groundwater–CO₂ emissions relationship in Dutch peatlands derived by machine learning using airborne and ground-based eddy covariance data by L. M. van der Poel, L. V. Bataille, B. Kruijt, W. Franssen, W. Jans, J. Biermann, A. Rietman, A. J. V. Buzacott, Y. van der Velde, R. Boelens, R. W. A. Hutjes

“…Using spatiotemporal data, we train and optimize a boosted regression tree (BRT) machine learning algorithm to predict immediate CO<span class="inline-formula">₂</span> fluxes and use Shapley values and various simulations to interpret the model's outputs. …”
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Advancing flood susceptibility prediction: A comparative assessment and scalability analysis of machine learning algorithms via artificial intelligence in high‐risk regions of Paki... by Mirza Waleed, Muhammad Sajjad

“…This study addresses the need for accurate and scalable FSM by systematically evaluating the performance of 14 machine learning (ML) models in high‐risk areas of Pakistan. …”
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Artificial intelligence applied to bed regulation in Rio Grande do Norte: Data analysis and application of machine learning on the "RegulaRN Leitos Gerais" platform. by Tiago de Oliveira Barreto, Fernando Lucas de Oliveira Farias, Nicolas Vinícius Rodrigues Veras, Pablo Holanda Cardoso, Gleyson José Pinheiro Caldeira Silva, Chander de Oliveira Pinheiro, Maria Valéria Bezerra Medina, Felipe Ricardo Dos Santos Fernandes, Ingridy Marina Pierre Barbalho, Lyane Ramalho Cortez, João Paulo Queiroz Dos Santos, Antonio Higor Freire de Morais, Gustavo Fontoura de Souza, Guilherme Medeiros Machado, Márcia Jacyntha Nunes Rodrigues Lucena, Ricardo Alexsandro de Medeiros Valentim

“…This study explored different machine learning models in the RegulaRN database, from October 2021 to January 2024, totaling 47,056 regulations. …”
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Prediction of matrilineal specific patatin-like protein governing in-vivo maternal haploid induction in maize using support vector machine and di-peptide composition by Suman Dutta, Rajkumar U. Zunjare, Anirban Sil, Dwijesh Chandra Mishra, Alka Arora, Nisrita Gain, Gulab Chand, Rashmi Chhabra, Vignesh Muthusamy, Firoz Hossain

“…So far, no online tool is available that can classify unknown proteins into patatin-like proteins. Here, we aimed to optimize a machine learning-based algorithm to predict the patatin-like phospholipase activity of unknown proteins. …”
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SIDDA: SInkhorn Dynamic Domain Adaptation for image classification with equivariant neural networks by Sneh Pandya, Purvik Patel, Brian D Nord, Mike Walmsley, Aleksandra Ćiprijanović

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Deep-Learning-Driven Insights into Nitrogen Leaching for Sustainable Land Use and Agricultural Practices by Caixia Hu, Jie Li, Yaxu Pang, Lan Luo, Fang Liu, Wenhao Wu, Yan Xu, Houyu Li, Bingcang Tan, Guilong Zhang

“…A total of 509 observational data points regarding nitrate leaching in northern China were collected, capturing the spatial and temporal variations across crops such as winter wheat, maize, and greenhouse vegetables. A machine learning (ML) model for predicting nitrate leaching was then developed, with the random forest (RF) model outperforming the support vector machine (SVM), extreme gradient boosting (XGBoost), and convolutional neural network (CNN) models, achieving an R² of 0.75. …”
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A New Hybrid Model for Underwater Acoustic Signal Prediction by Guohui Li, Wanni Chang, Hong Yang

“…In addition, an artificial bee colony (ABC) algorithm is used to optimize model performance by adjusting the parameters of SVR. …”
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STUDY ON THE MICROMECHANICAL MODEL OF FORCE-MAGNETIC COUPLING FOR FERROGEL MATERIALS by SHI PingAn, WAN Qiang, ZHANG CanYang, XU YangGuang, LI Xu

“…The microstructure and mechanical properties of the sample are analyzed and tested by using X ray diffractometer（ XRD）and MTS testing machine. Based on the analysis of the microstructure and morphology features and the compression performance data,a stress-magnetization coupled model was established in accordance with the magnetic interaction between particles,and some factors influencing on magneto-induced effect and macroscopic response of ferrogel are analyzed theoretically,including the magnetic particle content,size ratio,magnetic field strength and compress strain etc. …”
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Combined influence of crushed brick powder and recycled concrete aggregate on the mechanical, durability and microstructural properties of eco-concrete: An experimental and machine... by Md. Habibur Rahman Sobuz, Mahmudur Hossain Khan, Md. Rakibul Islam, Md. Kawsarul Islam Kabbo, Abdullah Alzlfawi, M Jameel, Md. Munir Hayet Khan

“…Additionally, the study evaluates machine learning algorithms such as extreme gradient boosting (XG Boost), random forest (RF), and bagging model (BAG) for predicting the mechanical strength of concrete specimens. …”
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Machine learning to predict virological failure among HIV patients on antiretroviral therapy in the University of Gondar Comprehensive and Specialized Hospital, in Amhara Region, E... by Daniel Niguse Mamo, Tesfahun Melese Yilma, Makda Fekadie Tewelgne, Yakub Sebastian, Tilahun Bizuayehu, Mequannent Sharew Melaku, Agmasie Damtew Walle

“…Then, seven supervised classification machine-learning algorithms for model development were trained. …”
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Pt/ZnO and Pt/few-layer graphene/ZnO Schottky devices with Al ohmic contacts using Atlas simulation and machine learning by Shonak Bansal, Abha Kiran Rajpoot, G. Chamundeswari, Krishna Prakash, Parvataneni Rajendra Kumar, Ahmed Nabih Zaki Rashed, Mohamed S. Soliman, Mohammad Tariqul Islam

“…Furthermore, a comprehensive comparative analysis of various machine-learning regression models is conducted, validating the simulation findings and providing a predictive framework for optimizing the photodetector's performance. …”
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Kinetic Pattern Recognition in Home-Based Knee Rehabilitation Using Machine Learning Clustering Methods on the Slider Digital Physiotherapy Device: Prospective Observational Study by Clement Twumasi, Mikail Aktas, Nicholas Santoni

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An Extended Model for the UAVs-Assisted Multiperiodic Crowd Tracking Problem by Skander Htiouech, Khalil Chebil, Mahdi Khemakhem, Fidaa Abed, Monaji H. Alkiani

“…The behavior of crowds can be predicted using machine learning techniques. Based on this assumption, we proposed a new mixed integer linear programming (MILP) model, called MILP-MPCT, to solve the MPCT. …”
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Vibration Principles Research of Novel Power Electronic Module as Dynamic Vibration Absorber for Chassis-By-Wire by Xiaoyu Ding, Wei Wang, Xinbo Chen

“…Based on the vibration system model, the mechanical principles are analyzed and the design parameters are mathematically optimized. …”
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Research on the rock cutting performance and feasibility verification of small-scale rotary cutting test for disc cutter by Zilong Yang, Yong Hu, Mingxu Xu, Hao Pang, Youpeng Gu, Baicheng Zheng

“…The predictive capability of the proposed model and CSM model is validated using 72 sets of full-scale test data involving the same types of rock, and the predictions of the proposed model are closer to the test data.…”
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Height of Hydraulic Fracture Zone Based on PSO_LSSVM Model by Hebin Zhang, Tingting Wang, Bin Wu, Haijun Feng

“…At the same time, this study develops a particle swarm optimization algorithm based on adaptive inertia weight and a least squares support vector machine model to achieve height prediction of water conducting fracture zones. …”
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Modeling student satisfaction in online learning using random forest by Jinlei Li, Xiaowei Chen

“…This study contributes theoretically by integrating cognitive-affective dimensions, methodologically by demonstrating the utility of machine learning in modeling nonlinear interactions, and practically by providing actionable insights for platform optimization. …”
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An accurate model to predict drilling fluid density at wellbore conditions by Mohammad Ali Ahmadi, Seyed Reza Shadizadeh, Kalpit Shah, Alireza Bahadori

“…Moreover, two competitive machine learning models including fuzzy inference system (FIS) model and a hybrid of genetic algorithm (GA) and FIS (called GA-FIS) method were employed. …”
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