The crystallization of amorphous nifedipine was studied using hot-stage microscopy (HSM), powder X-ray diffractometry (PXRD), and differential scanning calorimetry (DSC). The kinetic data obtained from DSC studies under isothermal and nonisothermal conditions were examined using both model-fitting and model-free approaches. Evaluation of 16 different models showed that model A4 (Avrami-Erofeev, n = 4) to be most appropriate for crystallization in the conversion range 0.05-0.80. This choice was based on the goodness of fit, the residual plots, and the guidance provided by the model-free approach. The model-free approach indicated that the activation energy decreases slightly as the crystallization proceeds. This variation of the activation energy with the extent of conversion determines the range of conversion over which a model can be fit, and the magnitude of the activation energy helps in the selection of the best model. The model-free approach gives much better predictions than the model of best fit and allows the experimental kinetic function to be numerically evaluated. At the early stage (alpha = 0-0.6), the numerically reconstructed model is almost identical to A4, but gradually approaches A3 (Avrami-Erofeev, n = 3) as the crystallization progresses (alpha = 0.6-0.8) and deviates from both models near the end of the reaction. This behavior may be explained by the relative contributions of nucleation and crystal growth at different stages of the reaction.
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http://dx.doi.org/10.1002/jps.10425 | DOI Listing |
NPP Digit Psychiatry Neurosci
January 2025
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY USA.
Reinforcement learning studies propose that decision-making is guided by a tradeoff between computationally cheaper model-free (habitual) control and costly model-based (goal-directed) control. Greater model-based control is typically used under highly rewarding conditions to minimize risk and maximize gain. Although prior studies have shown impairments in sensitivity to reward value in individuals with frequent alcohol use, it is unclear how these individuals arbitrate between model-free and model-based control based on the magnitude of reward incentives.
View Article and Find Full Text PDFSci Rep
January 2025
Multidisciplinary Center for Infrastructure Engineering, Shenyang University of Technology, Shenyang, 110870, China.
The current research introduces a model-free ultra-local model (MFULM) controller that utilizes the multi-agent on-policy reinforcement learning (MAOPRL) technique for remotely regulating blood pressure through precise drug dosing in a closed-loop system. Within the closed-loop system, there exists a MFULM controller, an observer, and an intelligent MAOPRL algorithm. Initially, a flexible MFULM controller is created to make adjustments to blood pressure and medication dosages.
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January 2025
Department of Electrical Engineering, Chosun University, Gwangju, 61452, South Korea.
The study presents an intelligent, model-free current control strategy that eliminates the need for explicit plant models while efficiently reducing the effect of plant parameter perturbation. By employing a data-driven approach with fewer input features, the proposed scheme reduces the computational burden during training while maintaining high control performance. Unlike conventional model predictive current control (MPCC), which is computationally expensive because of solving optimization problems at each sample time, and requires precise plant models, the proposed method enhances system performance by addressing plant model discrepancies through data-driven techniques.
View Article and Find Full Text PDFJ Magn Reson
December 2024
Department of Low-Temperature Physics, Faculty of Mathematics and Physics, Charles University, V Holešovičkách 747/2, 180 00 Prague 8, Czech Republic.
PCA-based denoising usually implies either discarding a number of high-index principal components (PCs) of a data matrix or their attenuation according to a regularization model. This work introduces an alternative, model-free, approach to high-index PC attenuation that seeks to average values of PC vectors as if they were expected from noise perturbation of data. According to the perturbation theory, the average PCs are attenuated versions of the clean PCs of noiseless data - the higher the noise-related content in a PC vector, the lower is its average's norm.
View Article and Find Full Text PDFJ Environ Manage
December 2024
College of Mechanical Engineering, Quzhou University, Quzhou, 324000, China.
Plastic blends were co-pyrolyzed under non-isothermal conditions in a thermogravimetric (TG) analyzer. The co-pyrolysis characteristics and kinetic triplet, i.e.
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