Predictive integrated modelling of the hybrid and baseline scenarios of JT-60SA in view of the second operational phase by S. Gabriellini, V.K. Zotta, L. Garzotti, N. Aiba, J.-F. Artaud, G. Giruzzi, G. Pucella, C. Sozzi, D. Taylor, T. Wakatsuki, L. Burla, C. Leoni, R. Gatto, the JT-60SA integrated project team

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CBFs-Based Model Predictive Control for Obstacle Avoidance With Tilt Angle Limitation for Ball-Balancing Robots by Minh Duc Pham, Duc Cuong Vu, Thi Thuy Hang Nguyen, Thi-van-Anh Nguyen, Minh Nhat Vu, Tung Lam Nguyen

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An Improved Multiobjective Model-Predictive Control Method for Active Neutral-Point-Clamped Five-Level Converter by Qiguang Ren, Chang Liu, Xiangyang Xing, Rui Zhang, Chenghui Zhang

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Enhanced Inverse Model Predictive Control for EV Chargers: Solution for DC–DC Side by Abdullah Berkay Bayindir, Ali Sharida, Sertac Bayhan, Haitham Abu-Rub

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Long-Horizon Direct Model Predictive Control for Medium-Voltage Converters Connected to a Distorted Grid by Andrei Tregubov, Petros Karamanakos, Ludovico Ortombina

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Lane-Keeping Assistance System Based on Model Predictive Control With Smooth Transitions Between Operational Modes by Younsung Hong, Jae-Sung Moon, Yunhyoung Hwang

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A predictive machine-learning model for clinical decision-making in washed microbiota transplantation on ulcerative colitis by Sheng Zhang, Gaochen Lu, Weihong Wang, Qianqian Li, Rui Wang, Zulun Zhang, Xia Wu, Chenchen Liang, Yujie Liu, Pan Li, Quan Wen, Bota Cui, Faming Zhang

“…Besides, the voting ensembles exhibited an area under curve (AUC) of 0.769 ± 0.019 [accuracy, 0.754; F1-score, 0.845] in the internal validation; the AUC of the external validation was 0.614 ± 0.017 [accuracy, 0.801; F1-score, 0.887]. Additionally, the model was available at https://wmtpredict.streamlit.app.ConclusionsThis study pioneered the development of a machine learning model to predict the one-month clinical response of WMT on UC. …”
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Design of a Predictive Controller for the Current Loop of the Tubular Linear Motor Using a Discrete Model by Nguyen Trung Thanh, Nguyen Minh Cuong, Le Thi Thuy Ngan, Duong Quoc Tuan

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Estimation of soil moisture levels at field capacity and wilting point by predictive models on a regional scale by P. Pérez Cutillas, G. González Barberá, Carmelo Conesa García

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Estimation of soil moisture levels at field capacity and wilting point by predictive models on a regional scale by P. Pérez Cutillas, G. González Barberá, Carmelo Conesa García

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Early Detection of Lung Cancer Using Predictive Modeling Incorporating CTGAN Features and Tree-Based Learning by Abdulrahman Alzahrani

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Robust Invariant Set Design for Reliable Stopping in Adaptive Cruise Control via Model Predictive Control by Shila Alizadehghobadi, Reza Ehsani

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Development and validation of a predictive model for critical illness in adult patients requiring hospitalization for COVID-19. by Neha Paranjape, Lauren L Staples, Christina Y Stradwick, Herman Gene Ray, Ian J Saldanha

“…We developed a free, web-based calculator to facilitate use of the prediction model (https://icucovid19.shinyapps.io/ICUCOVID19/).…”
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Balance Technologies for Calculating and Predictive Estimation of Labor Resources for Regional Socio-Economic Systems by V. A. Kurzenev, V. T. Perekrest, I. V. Perekrest

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Development of a predictive model for systemic lupus erythematosus incidence risk based on environmental exposure factors by Qianjin Lu, Ying Zhang, Cheng Zhao, Yu Lei, Hui Jin, Qilin Li

“…Leave-one-out cross-validation confirmed that the ForestMDG model had the best accuracy (0.8338). Finally, we developed a dynamic nomogram for practical use, which is accessible via the following link: https://yingzhang99321.shinyapps.io/dynnomapp/.Conclusion We created a user-friendly dynamic nomogram for predicting the relative risk of SLE onset based on occupational and living environmental exposures.Trial registration number ChiCTR2000038187.…”
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Prediction and Analysis of Borosilicate Glass Surface Deformation Induced by Flame Jet by Biling Wang, Yutang Zhai, Xinming Zhang, Xinxin Qi, Weijie Fu

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Predictive modelling and ranking: Azadirachta indica compounds through indices and multi-criteria decision-making techniques by D. S. Anuradha, B. Jaganathan

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A spatial predictive model for malaria resurgence in central Greece integrating entomological, environmental and social data. by Panagiotis Pergantas, Andreas Tsatsaris, Chrisovalantis Malesios, Georgia Kriparakou, Nikolaos Demiris, Yiannis Tselentis

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Development of a Predictive Model for Hepatitis B Virus (HBV) Status Using Gender and Serum Biomarkers by Stanley Obinna Ezeadichie, Joy E. Ikekpeazu, Amechi Uchenna Katchy, Mima Wariso, Chioma Cordelia Ugwu, Osah Martins Onwuka

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Clinical characteristics, outcomes, and predictive modeling of patients diagnosed with immune checkpoint inhibitor therapy-related pneumonitis by Antonious Hazim, Irene Riestra Guiance, Jacob Shreve, Gordon Ruan, Damian McGlothlin, Allison LeMahieu, Robert Haemmerle, Keith Mcconn, Richard C. Godby, Lisa Kottschade, Anna Schwecke, Casey Fazer-Posorske, Tobias Peikert, Eric Edell, Konstantinos Leventakos, Ashley Egan

“…The grading of pneumonitis was defined in accordance with ASCO guidelines (Schneider et al. in J Clin Oncol 39(36):4073–4126, 2021. https://doi.org/10.1200/JCO.21.01440 ). Predictive modeling was performed using gradient boosting machine learning technology, XGBoost (Chen in 1(4):1, 2015), to conduct binary classification and model reverse engineering using Shapley statistics (Lundberg and Lee in Adv Neural Inf Process Syst 30, 2017). …”
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