Semiclassical results are usually expected to be valid in the semiclassical regime. An interesting question is, in models in which appropriate effective Planck constants can be introduced, to what extent will a semiclassical prediction stay valid when the effective Planck constant is increased? In this paper, we numerically study this problem, focusing on semiclassical predictions for the decay of the quantum Loschmidt echo in deep quantum regions. Our numerical simulations, carried out in the chaotic regime in the sawtooth model and in the kicked rotator model and also in the critical region of a one-dimensional Ising chain in transverse field, show that the semiclassical predictions may work even in deep quantum regions, particularly for perturbation strength in the so-called Fermi-Golden-Rule regime.
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http://dx.doi.org/10.1103/PhysRevE.86.066203 | DOI Listing |
J Chem Inf Model
January 2025
Industrial and Molecular Pharmaceutics, Purdue University, West Lafayette, Indiana 47907, United States.
Drug-drug interaction can lead to diminished therapeutic effects or increased toxicity, posing significant risks, especially in polypharmacy, and cytochrome P450 plays an indispensable role in this interaction. Cytochrome P450, responsible for the metabolism and detoxification of most drugs, metabolizes about 90% of Food and Drug Administration-approved drugs, making early detection of potential drug-drug interactions. Over the years, in-silico modeling has become a valuable tool for predicting drug-drug interactions.
View Article and Find Full Text PDFACS Phys Chem Au
January 2025
Instituto de Ciência e Tecnologia, Universidade Federal de São Paulo, São José dos Campos, 12247-014 São Paulo, Brazil.
The unique properties and versatile applications of natural deep eutectic solvents (NaDES) have sparked significant interest in the field of green chemistry. Comprised of natural components that form liquids at room temperature through strong noncovalent electrostatic interaction, these solvents are cost-effective, nontoxic, and versatile. Betaine chloride-based NaDES, in particular, have shown promise in biocatalysis and sugar extraction due to their excellent properties.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Industrial Systems Institute (ISI), Athena Research and Innovation Center, 26504 Patras, Greece.
The integration of deep learning (DL) into image processing has driven transformative advancements, enabling capabilities far beyond the reach of traditional methodologies. This survey offers an in-depth exploration of the DL approaches that have redefined image processing, tracing their evolution from early innovations to the latest state-of-the-art developments. It also analyzes the progression of architectural designs and learning paradigms that have significantly enhanced the ability to process and interpret complex visual data.
View Article and Find Full Text PDFCancers (Basel)
January 2025
Intense Laser Irradiation Laboratory, National Institute of Optics, National Research Council of Italy, 56124 Pisa, Italy.
The use of very high energy electron (VHEE) beams, with energies between 50 and 400 MeV, has drawn considerable interest in radiotherapy due to their deep tissue penetration, sharp beam edges, and low sensitivity to tissue density. VHEE beams can be precisely steered with magnetic components, positioning VHEE therapy as a cost-effective option between photon and proton therapies. However, the clinical implementation of VHEE therapy (VHEET) requires advances in several areas: developing compact, stable, and efficient accelerators; creating sophisticated treatment planning software; and establishing clinically validated protocols.
View Article and Find Full Text PDFAnal Chem
January 2025
The School of Information Sciences and Technology, Northwest University, Xi'an 710127, P.R.China.
Digital fluorescence immunoassay (DFI) based on random dispersion magnetic beads (MBs) is one of the powerful methods for ultrasensitive determination of protein biomarkers. However, in the DFI, improving the limit of detection (LOD) is challenging since the ratio of signal-to-background and the speed of manual counting beads are low. Herein, we developed a deep-learning network (ATTBeadNet) by utilizing a new hybrid attention mechanism within a UNet3+ framework for accurately and fast counting the MBs and proposed a DFI using CdS quantum dots (QDs) with narrow peak and optical stability as reported at first time.
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