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Prediction-powered inference (PPI) [1] and its subsequent development called PPI++ [2] provide a novel approach to standard statistical estimation leveraging machine learning systems to enhance unlabeled data with predictions. We use this paradigm in clinical trials. The predictions are provided by disease progression models, providing prognostic scores for all the participants as a function of baseline covariates.
View Article and Find Full Text PDFWhen a Small Amount of Comonomer Is Enough: Tailoring the Critical Solution Temperature of LCST-Type Thermoresponsive Random Copolymers by PEG Methyl Ether Methacrylate with 1100 g/mol Molecular Weight.
Materials (Basel)
January 2025
Polymer Chemistry and Physics Research Group, Institute of Materials and Environmental Chemistry, HUN-REN Research Centre for Natural Sciences, Magyar Tudósok Körútja 2, H-1117 Budapest, Hungary.
Tuning the critical solution temperature (CST) of thermoresponsive polymers is essential to exploit their immense potential in various applications. In the present study, the effect of PEG-methyl ether methacrylate with a higher molecular weight of 1100 g/mol (mPEGMA) as a comonomer was investigated for its suitability for the CST adjustment of LCST-type polymers. Accordingly, a library of mPEGMA-based copolymers was established with varying compositions () using four main comonomers, namely di(ethylene glycol) ethyl ether acrylate, -isopropyl acrylamide and methacrylamide, and mPEGMA, with different CST values (cloud points, , and clearing points, , by turbidimetry).
View Article and Find Full Text PDFEnhanced disturbance mitigation in nonlinear time delay systems with applications to double boost converters in microgrid environments.
Sci Rep
January 2025
Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, 560035, Karnataka, India.
This study proposes an enhanced Smith Predictor (ESP) based Modified repetitive control (MRC) technique to develop a precise tracking performance of the input delayed double boost converter model, which is highly influenced by disturbances and saturation nonlinearity. A group of inequalities is derived on the basis of Lyapunov stability concept, which are in the form of linear-matrix-inequalities (LMIs) to guarantee the asymptotic stability and tracking performance of the considered delayed double boost converter. To be more specific, the proposed Lyapunov method not only provides the asymptotic stability of the system but also design the controller parameters when the feasibility of the derived LMIs are analyzed.
View Article and Find Full Text PDFAsymptotic BLSCP models in uncertain and asymmetric environments-Take the charging station location problem.
PLoS One
January 2025
School of Statistics, Jilin University of Finance and Economics, Changchun, Jilin, China.
The rapid development of the field of vehicles exposes many problems of charging station, the most common is the uncertainty and asymmetry of its task volume, so the flexibility of location and scale design is crucial. This paper proposes the asymptotic BLSCP model, suitable for uncertain and asymmetric environments, which balances facility workload and minimizes cost through the forward or backward asymptotic methods of candidate center locations and flexible allocation of jurisdictions. Additionally, the findings suggest these methods possess considerable potential for application and generalization.
View Article and Find Full Text PDFA Simple Sensitivity Analysis Method for Unmeasured Confounders via Linear Programming With Estimating Equation Constraints.
Stat Med
February 2025
Department of Biomedical Statistics, Graduate School of Medicine, Osaka University, Osaka, Japan.
In estimating the average treatment effect in observational studies, the influence of confounders should be appropriately addressed. To this end, the propensity score is widely used. If the propensity scores are known for all the subjects, bias due to confounders can be adjusted by using the inverse probability weighting (IPW) by the propensity score.
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