Predicting chronic pain using wearable devices: a scoping review of sensor capabilities, data security, and standards compliance by Johannes C. Ayena, Johannes C. Ayena, Amina Bouayed, Amina Bouayed, Myriam Ben Arous, Myriam Ben Arous, Youssef Ouakrim, Youssef Ouakrim, Karim Loulou, Karim Loulou, Darine Ameyed, Isabelle Savard, Leila El Kamel, Neila Mezghani, Neila Mezghani

“…Data extraction focused on device types, sensor quality, compliance with health standards, and the predictive algorithms employed.ResultsWearable devices show promise in correlating physiological markers with CP, but few studies integrate predictive models. …”
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Preoperative prediction of recurrence risk factors in operable cervical cancer based on clinical-radiomics features by Xue Du, Xue Du, Chunbao Chen, Lu Yang, Yu Cui, Min Li

“…ObjectiveTo investigate the value of preoperative prediction of risk factors for recurrence of operable cervical cancer based on the radiomics features of biparametric magnetic resonance imaging (bp-MRI) combined with clinical features.MethodA retrospective collection of cervical cancer cases undergoing radical hysterectomy + pelvic and/or para-aortic lymph node dissection at the Affiliated Hospital of North Sichuan Medical College was conducted. …”
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Evaluating machine learning models comprehensively for predicting maximum power from photovoltaic systems by Samir A. Hamad, Mohamed A. Ghalib, Amr Munshi, Majid Alotaibi, Mostafa A. Ebied

“…To achieve this, the research explores various ML algorithms, such as Linear Regression (LR), Ridge Regression (RR), Lasso Regression (Lasso R), Bayesian Regression (BR), Decision Tree Regression (DTR), Gradient Boosting Regression (GBR), and Artificial Neural Networks (ANN), to predict the MPP of PV systems. …”
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Predicting adolescent psychopathology from early life factors: A machine learning tutorial by Faizaan Siddique, Brian K. Lee

“…The best performing elastic net models included prenatal and family history factors (Sensitivity 0.654, Specificity 0.713; AUC 0.742, F1-score 0.401) and prenatal, family, history, and sociodemographic factors (Sensitivity 0.668, Specificity 0.704; AUC 0.745, F1-score 0.402). Across all 5 ML algorithms, family history factors (e.g., either parent had nervous breakdowns, trouble holding jobs/fights/police encounters, and counseling for mental issues) and sociodemographic covariates (e.g., maternal age, child's sex, caregiver income and caregiver education) tended to be better predictors of adolescent psychopathology. …”
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Research on Traffic Accident Severity Level Prediction Model Based on Improved Machine Learning by Jiming Tang, Yao Huang, Dingli Liu, Liuyuan Xiong, Rongwei Bu

“…Decision tree, XGBoost, and random forest algorithms, respectively, were applied for the secondary prediction. …”
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Advancing Credit Rating Prediction: The Role of Machine Learning in Corporate Credit Rating Assessment by Nazário Augusto de Oliveira, Leonardo Fernando Cruz Basso

“…This study investigated the potential of machine-learning (ML) algorithms as a more precise and adaptable alternative for credit rating predictions. …”
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Optimized Model Predictive Control-Based Path Planning for Multiple Wheeled Mobile Robots in Uncertain Environments by Yang She, Chao Song, Zetian Sun, Bo Li

“…Addressing the path planning problem for multiple wheeled mobile robots (WMRs) in uncertain environments, this paper proposes a multi-WMR path planning algorithm based on the fusion of artificial potential field and model predictive control. …”
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Nonlinear Recurrent Neural Network Predictive Control for Energy Distribution of a Fuel Cell Powered Robot by Qihong Chen, Rong Long, Shuhai Quan, Liyan Zhang

“…Because the dynamic of the system is viewed as operating- state- dependent time varying local linear behavior in this frame, a linear constrained model predictive control algorithm is developed to optimize the power splitting between the fuel cell and ultracapacitor. …”
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Energy Management Strategy for Hybrid Electric Vehicles Based on Adaptive Equivalent Ratio-Model Predictive Control by Farah Mahdi Ali, Nizar Hadi Abbas

“…This paper develops an optimal energy management algorithm based on Model Predictive Control that can produce optimal control parameters for power distribution between the battery unit and generator. …”
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Machine Learning-Based Modelling and Predictive Maintenance of Turning Operation under Cooling/Lubrication for Manufacturing Systems by Gurpreet Singh, Jothi Prabha Appadurai, Varatharaju Perumal, K. Kavita, T. Ch Anil Kumar, DVSSSV Prasad, A. Azhagu Jaisudhan Pazhani, K. Umamaheswari

“…These three processes are developed to predict the machine learning force, cutting power, and cutting pressure by controlling primary factors (cutting speed, depth of cut, and feed rate). …”
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Assessing the predictive power of immune microenvironment-related genes and clinical immune-inflammatory parameters in breast cancer by Shangyi Lu, Shun Liang, Fucai Chen, Jianyuan Meng, Junwei Chen, Yi Lu, Gangjian Zhu

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Research on Feed Forward Adaptive-generalized Model Predictive Control for High-speed Train Automatic Operation by LUO Yuan, YI Jie, BAI Jinlei, ZHANG Zhengfang

“…Furthermore, a feed forward generalized prediction speed tracking control algorithm is designed based on the multi-object function, aiming to solve controller overshoot due to additional resistance changes and enhance control convergence rates. …”
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Improved Modulated Model Predictive Control: Reducing Total Harmonic Distortion, Computational Burden, and Parameter Sensitivity by Seyed Rasul Eftekhari, Ali Mosallanejad, Hamidreza Pairo, Jose Rodriguez

“…This paper presents an improved modulated model predictive control (M2PC) method, which is implemented on synchronous reluctance motor (SynRM) drives, significantly enhancing performance by streamlining the control algorithm. …”
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Evaluation of the effectiveness of an intervention strategy in a control simulation experiment through comparison with model predictive control by R. Nagai, Y. Bai, M. Ogura, M. Ogura, S. Kotsuki, S. Kotsuki, N. Wakamiya

“…This study evaluates CSE's efficacy against model predictive control (MPC), a well-established method in control engineering. …”
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Predictive modeling of Bifidobacterium animalis subsp. lactis Bb-12 growth in cow’s, goat’s and soy milk by Vedran Slačanac, Mirela Lučan, Jovica Hardi, Kristina Habschied, Vinko Krstanović, Goran Gagula

“…The aim of this study was to use a predictive model to analyse the growth of a probiotic strain Bifidobacterium animalis subsp. lactis Bb-12 in cow’s, goat’s and soy milk. …”
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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

“…Long-horizon finite control set model predictive control (FCS-MPC) is known for its superior performance, particularly when applied to complex, higher order systems, such as grid-connected converters with <inline-formula><tex-math notation="LaTeX">$LCL$</tex-math></inline-formula> filters. …”
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A reliable mixed-integer linear programming formulation for data-driven model predictive control in buildings by Peter Klanatsky, François Veynandt, Christian Heschl

“…Integrating renewable energy sources into buildings requires advanced control strategies to enhance demand-side flexibility. Data-driven Model Predictive Control (DMPC) has shown significant promise in this area. …”
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Hierarchical predictive optimal control for range extension of EV with ANN based torque control for IPMSM drives by Lekshmi S, Lal Priya P S

“…The approach involves a hierarchical control scheme that integrates a model predictive controller with a torque vectoring algorithm to optimize torque demand and accurately anticipate road-specific torque requirements. …”
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Extended State Observer-Based Robust Model Predictive Velocity Control for Permanent Magnet Synchronous Motor by Eunji Lee, Yonghao Gui, Sesun You, Jun Moon, Wonhee Kim

“…This article proposes an extended state observer based robust model predictive velocity control to decrease system prediction error under parameter uncertainties for permanent magnet synchronous motor (PMSM). …”
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Predictive analysis of solubility data with pressure and temperature in assessing nanomedicine preparation via supercritical carbon dioxide by Hashem O. Alsaab, Yusuf S. Althobaiti

“…Three models, namely Automatic Relevance Determination Regression (ARD), Gaussian process regression (GPR), and Linear Regression (LR) were designed and tuned to build predictive models. The ADABOOST ensemble technique was applied to strengthen the predictive capabilities of the models, while hyperparameter tuning was conducted using the Jellyfish Optimization (JO) algorithm. …”
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