Scaling laws enable the determination of physicochemical properties of molecules and materials as a function of their size, density, number of electrons or other easily accessible descriptors. Such relations can be counterintuitive and nonlinear, and ultimately yield much needed insight into quantum mechanics of many-particle systems. In this work, we show on the basis of single-particle models, multielectron atoms and molecules that the dipole polarizability of quantum systems is generally proportional to the fourth power of a characteristic length, computed from the ground-state wave function. This four-dimensional (4D) scaling is independent of the ratio of bound-to-bound and bound-to-continuum electronic transitions and applies to many-electron atoms when a correlated length metric is used. Finally, this scaling law is applied to predict the polarizability of molecules by electrostatically coupled atoms-in-molecules approach, obtaining approximately 8% absolute and relative accuracy with respect to hybrid density functional theory (DFT) on the QM7-X data set of organic molecules, providing an efficient and scalable model for the anisotropic polarizability tensors of extended (bio)molecules.
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http://dx.doi.org/10.1021/acs.jctc.4c00582 | DOI Listing |
Recent advances in near-field interference detection, inspired by the non-Hermitian coupling-induced directional sensing of Ormia ochracea, have demonstrated the potential of paired semiconductor nanowires for compact light field detection without optical filters. However, practical implementation faces significant challenges including limited active area, architectural scaling constraints, and incomplete characterization of angular and polarization information. Here, we demonstrate a filterless vector light field photodetector, leveraging the angle- and polarization-sensitive near-field interference of non-Hermitian semiconductor nanostructures.
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January 2025
Department of Medical Life Sciences, College of Medicine, The Catholic University of Korea, Seoul, 06591, Korea.
Human cerebral organoids serve as a quintessential model for deciphering the complexities of brain development in a three-dimensional milieu. However, imaging these organoids, particularly when they exceed several millimeters in size, has been curtailed by the technical impediments such as phototoxicity, slow imaging speeds, and inadequate resolution and imaging depth. Addressing these pivotal challenges, our study has pioneered a high-speed scanning microscope, synergistically coupled with advanced computational image processing.
View Article and Find Full Text PDFBMC Cancer
January 2025
Breast Surgery Department, Hangzhou Institute of Medicine, Zhejiang Cancer Hospital, Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
Adjuvant endocrine therapy (AET) is essential for improving survival and reducing mortality and recurrence rates in breast cancer (BrCa) patients. However, the adherence to AET among BrCa patients is poor, and there is no scale to measure adherence to AET or the reasons for non-adherence among BrCa patients in mainland China. The aim of this study was to assess the psychometric properties of the simple Chinese version of the Medication Adherence Reasons (MAR) scale in BrCa patients undergoing AET.
View Article and Find Full Text PDFStroke
February 2025
South Western Sydney Clinical School University of New South Wales, Department of Neurology Liverpool Hospital, Ingham Institute of Applied Medical Research, Australia (C.C., L.L., M.P.).
Background: Vascular territory mapping (VTM) software estimates which intracerebral vessel provides predominant arterial flow to a brain voxel. The presence of antegrade flow in the setting of acute middle cerebral artery (MCA) occlusion is associated with improved outcomes. We identify whether VTM software is a determinant of antegrade flow in patients with proximal MCA occlusion.
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November 2024
Second Target Station, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA.
We introduce a computational framework that integrates artificial intelligence (AI), machine learning, and high-performance computing to enable real-time steering of neutron scattering experiments using an edge-to-exascale workflow. Focusing on time-of-flight neutron event data at the Spallation Neutron Source, our approach combines temporal processing of four-dimensional neutron event data with predictive modeling for multidimensional crystallography. At the core of this workflow is the Temporal Fusion Transformer model, which provides voxel-level precision in predicting 3D neutron scattering patterns.
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