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A recently developed methodology for the calculation of the dynamic heat capacity from simulation is applied to the east Ising model. Results show stretched exponential relaxation with the stretching exponent, beta, decreasing with decreasing temperature. For low temperatures, the logarithm of the relaxation time is approximately proportional to the inverse of the temperature squared, which is the theoretical limiting behavior predicted by theories of facilitated dynamics. In addition, an analytical approach is employed where the overall relaxation is a composite of relaxation processes of subdomains, each with their own characteristic time. Using a Markov chain method, these times are computed both numerically and in closed form. The Markov chain results are seen to match the simulations at low temperatures and high frequencies. The dynamics of the east model are tracked very well by this analytic procedure, and it is possible to associate features of the spectrum of the dynamic heat capacity with specific domain relaxation events.
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http://dx.doi.org/10.1063/1.3469767 | DOI Listing |
PLoS One
September 2025
Computational Chemistry Laboratory, Chemistry Department, Faculty of Science, Minia University, Minia, Egypt.
Polar protic and aprotic solvents can effectively simulate the maturation of breast carcinoma cells. Herein, the influence of polar protic solvents (water and ethanol) and aprotic solvents (acetone and DMSO) on the properties of 3-(dimethylaminomethyl)-5-nitroindole (DAMNI) was investigated using density functional theory (DFT) computations. Thermodynamic parameters retrieved from the vibrational analysis indicated that the DAMNI's entropy, heat capacity, and enthalpy increased with rising temperature.
View Article and Find Full Text PDFPhys Rev Lett
August 2025
University of York, School of Physics, Engineering and Technology, York YO10 5DD, United Kingdom.
We propose a model that is able to reproduce the type-II ultrafast demagnetization dynamics observed in 2D magnets. The spin system is coupled to the electronic thermal bath and is treated with atomistic spin dynamics, while the electron and phonon heat baths are described phenomenologically by coupled equations via the two-temperature model. Our proposed two-temperature model takes into account the effect of the heated substrate, which for 2D systems results in a slow demagnetization regime.
View Article and Find Full Text PDFMicrobiol Spectr
September 2025
International Centre for Diarrheal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh.
Enterotoxigenic (ETEC), a leading cause of diarrhea, is defined by heat-stable (ST) and/or heat-labile (LT) toxins and associated colonization factors (CFs). However, there is still a knowledge gap in understanding ETEC's evolution, particularly in endemic regions like Bangladesh. This study investigates the genomic attributes contributing to the rise of ETEC-associated diarrhea in Bangladesh during 2022-2023.
View Article and Find Full Text PDFEnviron Monit Assess
September 2025
Institute of Earth Sciences, Southern Federal University, Rostov-On-Don, Russia.
Sustainable urban development requires actionable insights into the thermal consequences of land transformation. This study examines the impact of land use and land cover (LULC) changes on land surface temperature (LST) in Ho Chi Minh city, Vietnam, between 1998 and 2024. Using Google Earth Engine (GEE), three machine learning algorithms-random forest (RF), support vector machine (SVM), and classification and regression tree (CART)-were applied for LULC classification.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
September 2025
Max Planck Institute for Solar System Research, Göttingen 37077, Germany.
Turbulent convection governs heat transport in both natural and industrial settings, yet optimizing it under extreme conditions remains a significant challenge. Traditional control strategies, such as predefined temperature modulation, struggle to achieve substantial enhancement. Here, we introduce a deep reinforcement learning (DRL) framework that autonomously discovers optimal control policies to maximize heat transfer in turbulent Rayleigh-Bénard convection.
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