Atom-number fluctuation and macroscopic quantum entanglement in dipole spinor condensates

published_or_final_versio

Comprehensive entropy weight observability-controllability risk analysis and its application to water resource decision-making

Decision making for water resource planning is often related to social, economic and environmental factors. There are various methods for making decisions about water resource planning alternatives and measures with various shortcomings. A comprehensive entropy weight observability-controllability risk analysis approach is presented in this study. Computing methods for entropy weight (EW) and subjective weight (SW) are put forward based on information entropy theory and experimental psychology principles, respectively. Comprehensive weight (CW) consisting of EW and SW is determined. The values of observability-controllability risk (Roc) and gain by comparison (Gbc) are obtained based on the CWs. The quantitative analysis of alternatives and measures is achieved based on Roc and Gbc. A case study on selection of water resource planning alternatives and measures in the Yellow River Basin, China, was performed. Results demonstrate that the approach presented in this study can achieve optimal decision-making results

Quantum spin liquid states in the two dimensional kagome antiferromagnets, ZnxCu4-x(OD)6Cl2

A three-dimensional system of interacting spins typically develops static long-range order when it is cooled. If the spins are quantum (S = 1/2), however, novel quantum paramagnetic states may appear. The most highly sought state among them is the resonating valence bond (RVB) state in which every pair of neighboring quantum spins form entangled spin singlets (valence bonds) and the singlets are quantum mechanically resonating amongst all the possible highly degenerate pairing states. Here we provide experimental evidence for such quantum paramagnetic states existing in frustrated antiferromagnets, ZnxCu4-x(OD)6Cl2, where the S = 1/2 magnetic Cu2+ moments form layers of a two-dimensional kagome lattice. We find that in Cu4(OD)6Cl2, where distorted kagome planes are weakly coupled to each other, a dispersionless excitation mode appears in the magnetic excitation spectrum below ~ 20 K, whose characteristics resemble those of quantum spin singlets in a solid state, known as a valence bond solid (VBS), that breaks translational symmetry. Doping nonmagnetic Zn2+ ions reduces the distortion of the kagome lattice, and weakens the interplane coupling but also dilutes the magnetic occupancy of the kagome lattice. The VBS state is suppressed and for ZnCu3(OD)6Cl2 where the kagome planes are undistorted and 90% occupied by the Cu2+ ions, the low energy spin fluctuations in the spin liquid phase become featureless

PET/NIRF/MRI triple functional iron oxide nanoparticles

Engineered nanoparticles with theranostic functions have attracted a lot of attention for their potential role in the dawning era of personalized medicine. Iron oxide nanoparticles (IONPs), with their advantages of being non-toxic, biodegradable and inexpensive, are candidate platforms for the buildup of theranostic nanostructures; however, progress in using them has been limited largely due to inefficient drug loading and delivery. In the current study, we utilized dopamine to modify the surface of IONPs, yielding nanoconjugates that can be easily encapsulated into human serum albumin (HSA) matrices (clinically utilized drug carriers). This nanosystem is well-suited for dual encapsulation of IONPs and drug molecules, because the encapsulation is achieved in a way that is similar to common drug loading. To assess the biophysical characteristics of this novel nanosystem, the HSA coated IONPs (HSA-IONPs) were dually labeled with (64)Cu-DOTA and Cy5.5, and tested in a subcutaneous U87MG xenograft mouse model. In vivo positron emission tomography (PET)/near-infrared fluorescence (NIRF)/magnetic resonance imaging (MRI) tri-modality imaging, and ex vivo analyses and histological examinations were carefully conducted to investigate the in vivo behavior of the nanostructures. With the compact HSA coating, the HSA-IONPs manifested a prolonged circulation half-life; more impressively, they showed massive accumulation in lesions, high extravasation rate, and low uptake of the particles by macrophages at the tumor area. Published by Elsevier Ltd

Capture barrier of Sn-related DX centers in AlGaAs epilayers

Thermal capture and emission processes of Sn-related DX centers in AlxGa1-xAs (x = 0.26) were measured by a constant capacitance (CC) voltage transient in various temperatures, By employing a Laplace defect spectroscopic (LDS) method, the non-exponential transients were decomposed into several discrete exponential components. The results shown that more exponential components appeared in the small emission rate region as capture period increased. This indicates that electrons preferentially fill shallow energy levels due to their lower capture barriers. Discrete exponential components of the capture process were identified and four of their barriers were preliminarily measured to be about 0.14, 0.15, 0.16, and 0.17 eV, respectively

Towards Intelligent Crowd Behavior Understanding through the STFD Descriptor Exploration

Realizing the automated and online detection of crowd anomalies from surveillance CCTVs is a research-intensive and application-demanding task. This research proposes a novel technique for detecting crowd abnormalities through analyzing the spatial and temporal features of input video signals. This integrated solution defines an image descriptor (named spatio-temporal feature descriptor - STFD) that reflects the global motion information of crowds over time. A CNN has then been adopted to classify dominant or large-scale crowd abnormal behaviors. The work reported has focused on: 1) detecting moving objects in online (or near real-time) manner through spatio-temporal segmentations of crowds that is defined by the similarity of group trajectory structures in temporal space and the foreground blocks based on Gaussian Mixture Model (GMM) in spatial space; 2) dividing multiple clustered groups based on the spectral clustering method by considering image pixels from spatio-temporal segmentation regions as dynamic particles; 3) generating the STFD descriptor instances by calculating the attributes (i.e., collectiveness, stability, conflict and crowd density) of particles in the corresponding groups; 4) inputting generated STFD descriptor instances into the devised convolutional neural network (CNN) to detect suspicious crowd behaviors. The test and evaluation of the devised models and techniques have selected the PETS database as the primary experimental data sets. Results against benchmarking models and systems have shown promising advancements of this novel approach in terms of accuracy and efficiency for detecting crowd anomalies

Towards Intelligent Crowd Behavior Understanding through the STFD Descriptor Exploration

Realizing the automated and online detection of crowd anomalies from surveillance CCTVs is a research-intensive and application-demanding task. This research proposes a novel technique for detecting crowd abnormalities through analyzing the spatial and temporal features of input video signals. This integrated solution defines an image descriptor (named spatio-temporal feature descriptor - STFD) that reflects the global motion information of crowds over time. A CNN has then been adopted to classify dominant or large-scale crowd abnormal behaviors. The work reported has focused on: 1) detecting moving objects in online (or near real-time) manner through spatio-temporal segmentations of crowds that is defined by the similarity of group trajectory structures in temporal space and the foreground blocks based on Gaussian Mixture Model (GMM) in spatial space; 2) dividing multiple clustered groups based on the spectral clustering method by considering image pixels from spatio-temporal segmentation regions as dynamic particles; 3) generating the STFD descriptor instances by calculating the attributes (i.e., collectiveness, stability, conflict and crowd density) of particles in the corresponding groups; 4) inputting generated STFD descriptor instances into the devised convolutional neural network (CNN) to detect suspicious crowd behaviors. The test and evaluation of the devised models and techniques have selected the PETS database as the primary experimental data sets. Results against benchmarking models and systems have shown promising advancements of this novel approach in terms of accuracy and efficiency for detecting crowd anomalies