Design of Gain-Scheduled Lateral Controllers for Autonomous Vehicles by Ákos Bokor, Ádám Szabó, Szilárd Aradi, László Palkovics

“…This study evaluates their effectiveness in real-time navigation scenarios within a simulated environment by applying gain-scheduled linear quadratic regulators and model predictive control. The results show that while traditional controllers, such as Pure Pursuit, perform adequately under constant speed conditions, adaptive model-based algorithms significantly enhance the performance, especially in dynamic driving situations involving speed variations.…”
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Combining CFD and AI/ML Modeling to Improve the Performance of Polypropylene Fluidized Bed Reactors by Nayef Ghasem

“…It also combines CFD with artificial intelligence and machine learning (AI/ML) algorithms, like artificial neural networks (ANN), resulting in a powerful predictive tool for accurately predicting reactor metrics based on operating conditions. …”
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Innovative approaches to optimizing the road safety monitoring system by Shevtsova A.G., Bychkova C.A., Podkopaev A.V., Nechaev O.Y.

“…The article presents a statistical analysis of innovative approaches to optimizing the system for monitoring the level of accidents and social risk, which is aimed at improving the predictive capabilities of the existing system for monitoring road safety indicators on the State Traffic Safety Inspectorate website. …”
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A Review of Generative Design Using Machine Learning for Additive Manufacturing by Parankush Koul

“…The scalability and predictability of artificial intelligence (AI) models make handling huge data easy and enable scale-up of production without compromising quality. …”
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Optimising 3D-printed carbon fibre composites using machine learning: Balancing strength and efficiency by José Humberto S. Almeida, Jr., Guilherme Ferreira Gomes

“…Experimental validation confirmed the predicted failure modes, including interlaminar shear and delamination. …”
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Data-Driven Modeling and Enhancement of Surface Quality in Milling Based on Sound Signals by Paschalis Charalampous

“…These signals are then transformed into Mel-Frequency Cepstral Coefficients (MFCCs), which yield a compact time–frequency representation optimized for human auditory perception. Next, several AI algorithms were trained in order to classify these MFCCs into predefined surface roughness categories. …”
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Intelligent energy management of microgrids using machine learning: Leveraging random forest models for solar and wind power by Hasanur Zaman Anonto, Md Ismail Hossain, Abu Shufian, Md. Shaoran Sayem, S M Tanvir Hassan Shovon, Protik Parvez Sheikh, Sadman Shahriar Alam

“…Integrating grid-stability measures, that is, voltage and frequency variation into the predictive model, the framework enhances the accuracy of the energy dispatch and storage plans. …”
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Machine learning model for preoperative classification of stromal subtypes in salivary gland pleomorphic adenoma based on ultrasound histogram analysis by Huan-Zhong Su, Dao-Hui Yang, Long-Cheng Hong, Yu-Hui Wu, Kun Yu, Zuo-Bing Zhang, Xiao-Dong Zhang

“…The least absolute shrinkage and selection operator (LASSO) regression identified optimal features, which were then utilized to build predictive models using logistic regression (LR) and eight ML algorithms. …”
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Exploring pesticide risk in autism via integrative machine learning and network toxicology by Ling Qi, Jingran Yang, Qiao Niu, Jianan Li

“…Each combination of 1–23 targets was used to construct predictive models using eight different machine learning algorithms. …”
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Computational Molecular Modeling of Pin1 Inhibition Activity of Quinazoline, Benzophenone, and Pyrimidine Derivatives by Nicolás Cabrera, Jose R. Mora, Edgar A. Marquez

“…In order to improve the prediction of the pIC50 values, the aggregation of the individual models was performed through the construction of an ensemble, and the most robust one was constructed by two individual models (LR3 and RF1) by applying the IBK algorithm, and a substantial improvement in predictive performance is reflected in the values of R2ADJ = 0.982, Q2CV = 0.962, and Q2EXT = 0.918. …”
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Antiviral therapy can effectively suppress irAEs in HBV positive hepatocellular carcinoma treated with ICIs: validation based on multi machine learning by Shuxian Pan, Zibing Wang

“…The accuracy of the model is verified in the DCA curve.ResultsA total of 274 HBV-related liver cancer patients were enrolled in the study. Predictive models were constructed using three machine learning algorithms to analyze and statistically evaluate clinical characteristics, including immune cell data. …”
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A manganese metabolism-related gene signature stratifies prognosis and immunotherapy efficacy in kidney cancer by Yang Liu, Hao Ye, Ruoxuan Zhang, Xiaolong Liu, Ranlu Liu

“…We constructed a clinical nomogram incorporating the MMCG risk score and other clinical parameters, which demonstrated highly accurate predictive capabilities. Immune infiltration analysis and immune therapy response predictions indicated that patients in Cluster I and the high-risk group showed low responses to immune therapy. …”
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Diagnostic Utility of ¹⁸F-FDG PET/CT in Infective Endocarditis by Corina-Ioana Anton, Alice-Elena Munteanu, Mihaela Raluca Mititelu, Militaru Alexandru Ștefan, Cosmin-Alexandru Buzilă, Adrian Streinu-Cercel

“…PET/CT reclassified 13 patients from possible to definite IE, demonstrating an overall sensitivity of 83.3%, specificity of 93.7%, positive predictive value (PPV) of 83.3%, and negative predictive value (NPV) of 93.7%. …”
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Identification of a novel ferroptosis-induced immunogenic cell death related signature based on a machine learning framework in colorectal cancer by Feng Zhu, Xin Liu, Huiyuan Li, Jianfeng Li, Hongzhang Liu, Yusheng Wang

“…The RSF algorithm was chosen for further analysis due to its strong predictive performance, making it a robust tool for our study. …”
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Analysis of shared pathogenic mechanisms and drug targets in myocardial infarction and gastric cancer based on transcriptomics and machine learning by Junyang Ma, Junyang Ma, Shufu Hou, Xinxin Gu, Peng Guo, Jiankang Zhu

“…The nomogram constructed based on 8 genes with diagnostic value had good predictive performance.…”
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Comprehensive Review on the Control of Heat Pumps for Energy Flexibility in Distribution Networks by Gustavo L. Aschidamini, Mina Pavlovic, Bradley A. Reinholz, Malcolm S. Metcalfe, Taco Niet, Mariana Resener

“…Real-time optimization is achieved through model predictive control (MPC), which relies on a predictive model to optimize decisions over a time horizon, and reinforcement learning (RL), which takes a model-free approach by learning optimal strategies through direct interaction with the environment. …”
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The unwell patient with advanced chronic liver disease: when to use each score? by Oliver Moore, Wai-See Ma, Scott Read, Jacob George, Golo Ahlenstiel

“…Incorporating artificial intelligence to personalise predictive algorithms may provide the most effective prognostication for all clinical phenotypes. …”
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Modeling the variables that influence substance consumption of people who experience homelessness in Colombia by Leandro González Támara, Sandra Patricia Barragán Moreno

“…A two-stage quantitative methodology was applied: (1) descriptive analysis of the demographic and socioeconomic characteristics of the homeless population, and (2) predictive modeling using random forest algorithms to identify key variables associated with substance use. …”
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Coral reef detection using ICESat-2 and machine learning by Gabrielle A. Trudeau, Kim Lowell, Jennifer A. Dijkstra

“…These obstacles were mitigated through the integration of algorithmically derived pseudo-rugosity and slope metrics as innovative proxies for seafloor complexity, significantly improving predictive performance. …”
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Advances in the application of nomograms for patients with gastric cancer associated with peritoneal metastasis by Shiyang Jin, Zeshen Wang, Qiancheng Wang, Zhenglong Li, Xirui Liu, Kuan Wang

“…Abstract This review elucidates advancements in nomogram applications for predicting peritoneal metastasis (PM) and prognostication in gastric cancer (GC). …”
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