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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Enhanced Inverse Model Predictive Control for EV Chargers: Solution for DC&#x2013;DC Side by Abdullah Berkay Bayindir, Ali Sharida, Sertac Bayhan, Haitham Abu-Rub

“…To address this challenge, the proposed method employs an adaptive estimation strategy utilizing a recursive least square algorithm for online dynamic model estimation. This real-time estimated model enables IMPC to predict optimal switching states with improved reliability. …”
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Improving the Performance of Steering by Wire Using a Model Predictive Controller Enhanced with Particle Swarm Optimisation by Taher Alhassany, Ali Hussien Mary, Furat Ibrahim Hussein, Mohammed Ghufran Khidhir Abboosh, Muhammad Umar Fareed

“…The current study introduces an innovation of a model that integrates model predictive control (MPC) with particle swarm optimisation (PSO) to improve the performance of SBW systems. …”
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Learning-Based Model Predictive Control for Legged Robots with Battery–Supercapacitor Hybrid Energy Storage System by Boyu Shu, Zhiwu Huang, Wanwan Ren, Yue Wu, Heng Li

“…This article proposes a learning-based model predictive control (MPC) energy management strategy for legged robots with battery–supercapacitor hybrid energy storage systems containing a power prediction unit and an MPC with learning-based adaptive weights. …”
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Predictive modeling of graves’ orbitopathy activity based on meibomian glands analysis using in vivo confocal microscopy by Zixuan Su, Yayan You, Shengnan Cheng, Jiahui Huang, Xueqing Liang, Xinghua Wang, Fagang Jiang

“…Subsequently, a logistic regression model was constructed to predict GO disease activity. Results A total of 45 GO patients, corresponding to 90 eyes, were included in this study. …”
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Modeling and control of automatic voltage regulation for a hydropower plant using advanced model predictive control by Ebunle Akupan Rene, Willy Stephen Tounsi Fokui

“…This study examined the modeling and control of AVR in hydroelectric power plants using model predictive control (MPC), which utilizes an extensive mathematical model of the voltage regulation system to optimize the control actions over a defined prediction horizon. …”
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Intelligent predictive networks for MHD nanofluid with carbon nanotubes and thermal conductivity along a porous medium by Hafiz Muhammad Shahbaz, Iftikhar Ahmad

“…The reduced MSE reveals that predictions of model are less likely to deviate from the true values and are more accurate this validates the proposed approach.…”
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Integrated multi-omics analysis and predictive modeling of heart failure using sepsis-related gene signature. by Yiping Lang, Tianyu Liang, Fei Li

“…This study aimed to develop a predictive model for HF based on sepsis-related gene signatures.…”
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Multivariate forecasting of dengue infection in Bangladesh: evaluating the influence of data downscaling on machine learning predictive accuracy by Mahadee Al Mobin

“…This study introduces a rigorous multivariate time series analysis, integrating meteorological factors with state-of-the-art machine learning (ML) models, to predict DENV case trends across different temporal scales. …”
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The predictive value of radiomics and deep learning for synchronous distant metastasis in clear cell renal cell carcinoma by Wan-Bin He, Chuan Zhou, Zhi-Jun Yang, Yun-Feng Zhang, Wen-Bo Zhang, Han He, Jia Wang, Feng-Hai Zhou

“…Abstract Objective The objective of this research was to devise and authenticate a predictive model that employs CT radiomics and deep learning methodologies for the accurate prediction of synchronous distant metastasis (SDM) in clear cell renal cell carcinoma (ccRCC). …”
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Predictive modeling for the adsorptive and photocatalytic removal of phenolic contaminants from water using artificial neural networks by Shahzar Hafeez, Ayesha Ishaq, Azeem Intisar, Tariq Mahmood, Muhammad Imran Din, Ejaz Ahmed, Muhammad Rizwan Tariq, Muhammad Amin Abid

“…The comparison of ANNs with other AI techniques revealed that ANNs have better predictability for mitigation of most of the phenolic contaminants. …”
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Machine learning based predictive modeling and risk factors for prolonged SARS-CoV-2 shedding by Yani Zhang, Qiankun Li, Haijun Duan, Liang Tan, Ying Cao, Junxin Chen

“…This study involved a large cohort of 56,878 hospitalized patients, and we leveraged the XGBoost algorithm to establish a predictive model based on these features. …”
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Evaluation of predictive performance of modeling hyperuricemia using medical big data: comparison of data preprocessing methods by Luwei Li, Xian Huang, Cijin Yan, Shuzhan He, Sishuai Cheng, WenJie Yang

“…Then, the continuous variables in the raw data were assigned values to become categorical variables, and statistical analysis was performed using the same algorithm to obtain the predicted values of the two models. …”
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Autonomic nervous system development-related signature as a novel predictive biomarker for immunotherapy in pan-cancers by Cunen Wu, Cunen Wu, Cunen Wu, Cunen Wu, Weiwei Xue, Yuwen Zhuang, Dayue Darrel Duan, Dayue Darrel Duan, Zhou Zhou, Zhou Zhou, Xiaoxiao Wang, Zhenfeng Wu, Jin-yong Zhou, Xiangkun Huan, Ruiping Wang, Haibo Cheng, Haibo Cheng

“…Differentially expressed genes (DEGs) were validated using RT-PCR and immunohistochemical (IHC) analyses of clinical samples.ResultsAnalysis of scRNA-seq datasets and autonomic nervous system development (ANSD) scores revealed 20 genes comprising a novel ANSD-related differential signature (ANSDR.Sig). A pan-cancer predictive model for ICI prognosis based on ANSDR.Sig was constructed, with the random forest algorithm yielding the most robust performance. …”
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Predictive machine-learning model for screening iron deficiency without anaemia: a retrospective cohort study by Girish N Nadkarni, Orly Efros, Eyal Klang, Shelly Soffer, Gili Kenet, Aya Mudrik, Renana Robinson

“…The primary hypothesis was that an ML model could achieve better accuracy in identifying low ferritin levels (&lt;30 ng/mL) in non-anaemic patients compared with traditional methods.Design A retrospective cohort study.Setting Data were derived from secondary and tertiary care facilities within the eight-hospital Mount Sinai Health System, an urban academic health system.Participants The study included 211 486 adult patients (aged ≥18 years) with normal haemoglobin levels (≥130 g/L for men and ≥120 g/L for women) and recorded ferritin measurements.Primary and secondary outcome measures The primary outcome was the prediction of low ferritin levels (&lt;30 ng/mL) using extreme gradient-boosted decision trees, an ML algorithm suited for structured clinical data. …”
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Exploring the gut microbiota associated with peripheral nerve invasion in colorectal cancer patients and constructing predictive models by Chuanbin Chen, Qingmin Chen, Shenghai Liu, Guoxi Li, Jiawei Zhao, Jingting Huang, Tianyi Ye, Xinting Yang, Zigui Huang, Zhen Wang, Fuhai He, Mingjian Qin, Chenyan Long, Binzhe Tang, Yongqi Huang, Weizhong Tang, Jungang Liu, Xiaoliang Huang

“…Finally, we successfully developed a predictive model to predict PNI in CRC patients through leveraging microbial biomarkers. …”
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