Smart solar concentrators for building integrated photovoltaic façades

In this study a novel static concentrating photovoltaic (PV) system, suitable for use in windows or glazing façades, has been designed. The developed smart Concentrating PV (CPV) system is lightweight, low cost and able to generate electricity. Additionally, this system automatically responds to climate by varying the balance of electricity generated from the PV with the amount of solar light and heat permitted through it into the building. It therefore offers the potential to contribute to, and control, energy consumption within buildings. A comprehensive optical analysis of the smart CPV is undertaken via 3-D ray tracing technique. To obtain optimal overall optical performance of the novel smart CPV analysis has been based upon all necessary design parameters including the average reflectivity of the thermotropic reflective layer, the glazing cover dimension, the glazing cover materials as well as the dimensions of the solar cells. In addition, a hydroxypropyl cellulose (HPC) hydrogel polymer, suitable for use as the reflective thermotropic layer for the smart CPV system, was synthesized and experimentally studied

GaN directional couplers for integrated quantum photonics

Large cross-section GaN waveguides are proposed as a suitable architecture to achieve integrated quantum photonic circuits. Directional couplers with this geometry have been designed with aid of the beam propagation method and fabricated using inductively coupled plasma etching. Scanning electron microscopy inspection shows high quality facets for end coupling and a well defined gap between rib pairs in the coupling region. Optical characterization at 800 nm shows single-mode operation and coupling-length-dependent splitting ratios. Two photon interference of degenerate photon pairs has been observed in the directional coupler by measurement of the Hong-Ou-Mandel dip with 96% visibility.Comment: 4 pages, 5 figure

NeutronOrch: Rethinking Sample-based GNN Training under CPU-GPU Heterogeneous Environments

Graph Neural Networks (GNNs) have demonstrated outstanding performance in various applications. Existing frameworks utilize CPU-GPU heterogeneous environments to train GNN models and integrate mini-batch and sampling techniques to overcome the GPU memory limitation. In CPU-GPU heterogeneous environments, we can divide sample-based GNN training into three steps: sample, gather, and train. Existing GNN systems use different task orchestrating methods to employ each step on CPU or GPU. After extensive experiments and analysis, we find that existing task orchestrating methods fail to fully utilize the heterogeneous resources, limited by inefficient CPU processing or GPU resource contention. In this paper, we propose NeutronOrch, a system for sample-based GNN training that incorporates a layer-based task orchestrating method and ensures balanced utilization of the CPU and GPU. NeutronOrch decouples the training process by layer and pushes down the training task of the bottom layer to the CPU. This significantly reduces the computational load and memory footprint of GPU training. To avoid inefficient CPU processing, NeutronOrch only offloads the training of frequently accessed vertices to the CPU and lets GPU reuse their embeddings with bounded staleness. Furthermore, NeutronOrch provides a fine-grained pipeline design for the layer-based task orchestrating method, fully overlapping different tasks on heterogeneous resources while strictly guaranteeing bounded staleness. The experimental results show that compared with the state-of-the-art GNN systems, NeutronOrch can achieve up to 11.51x performance speedup

NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams

Existing Graph Neural Network (GNN) training frameworks have been designed to help developers easily create performant GNN implementations. However, most existing GNN frameworks assume that the input graphs are static, but ignore that most real-world graphs are constantly evolving. Though many dynamic GNN models have emerged to learn from evolving graphs, the training process of these dynamic GNNs is dramatically different from traditional GNNs in that it captures both the spatial and temporal dependencies of graph updates. This poses new challenges for designing dynamic GNN training frameworks. First, the traditional batched training method fails to capture real-time structural evolution information. Second, the time-dependent nature makes parallel training hard to design. Third, it lacks system supports for users to efficiently implement dynamic GNNs. In this paper, we present NeutronStream, a framework for training dynamic GNN models. NeutronStream abstracts the input dynamic graph into a chronologically updated stream of events and processes the stream with an optimized sliding window to incrementally capture the spatial-temporal dependencies of events. Furthermore, NeutronStream provides a parallel execution engine to tackle the sequential event processing challenge to achieve high performance. NeutronStream also integrates a built-in graph storage structure that supports dynamic updates and provides a set of easy-to-use APIs that allow users to express their dynamic GNNs. Our experimental results demonstrate that, compared to state-of-the-art dynamic GNN implementations, NeutronStream achieves speedups ranging from 1.48X to 5.87X and an average accuracy improvement of 3.97%.Comment: 12 pages, 15 figure

Ultrafast Switching from the Charge Density Wave Phase to a Metastable Metallic State in 1T-TiSe2_2

The ultrafast electronic structures of the charge density wave material 1T-TiSe$_2$ were investigated by high-resolution time- and angle-resolved photoemission spectroscopy. We found that the quasiparticle populations drove ultrafast electronic phase transitions in 1T-TiSe$_2$ within 100 fs after photoexcitation, and a metastable metallic state, which was significantly different from the equilibrium normal phase, was evidenced far below the charge density wave transition temperature. Detailed time- and pump-fluence-dependent experiments revealed that the photoinduced metastable metallic state was a result of the halted motion of the atoms through the coherent electron-phonon coupling process, and the lifetime of this state was prolonged to picoseconds with the highest pump fluence used in this study. Ultrafast electronic dynamics were well captured by the time-dependent Ginzburg-Landau model. Our work demonstrates a mechanism for realizing novel electronic states by photoinducing coherent motion of atoms in the lattice.Comment: 13 Pages, 10 figure

Evolution of Maximum Bending Strain on Poisson's Ratio Distribution

In recent years, new flexible functional materials have attracted increasing interest, but there is a lack of the designing mechanisms of flexibility design with superstructures. In traditional engineering mechanics, the maximum bending strain (MBS) was considered universal for describing the bendable properties of a given material, leading to the universal designing method of lowering the dimension such as thin membranes designed flexible functional materials.In this work, the MBS was found only applicable for materials with uniformly distributed Poisson's ratio, while the MBS increases with the thickness of the given material in case there is a variation Poisson's ratio in different areas. This means the MBS can be enhanced by certain Poisson's ratio design in the future to achieve better flexibility of thick materials. Here, the inorganic freestanding nanofiber membranes, which have a nonconstant Poisson's ratio response on stress/strain for creating nonuniformly distributed Poisson's ratio were proven applicable for designing larger MBS and lower Young's modulus for thicker samples

Analysis of risk factors related to the progression rate of hemifacial spasm

IntroductionAlthough there have been many researches on the etiology and risk factors with the onset of hemifacial spasm, researches on the risk factors related to progression rate are limited. This study aims to analyze the risk factors related to the progression rate of hemifacial spasm.MethodsThe study enrolled 142 patients who underwent microvascular decompression for hemifacial spasm. Based on the duration and severity of symptoms, patients were classified into rapid progression group and slow progression group. To analyze risk factors, univariate and multivariate logistic regression analyses were conducted. Of 142 patients with hemifacial spasm, 90(63.3%) were classified as rapid progression group, 52(36.7%) were classified as slow progression group.ResultsIn the univariate analysis, there were significant statistical differences between the two groups in terms of age of onset (P = 0.021), facial nerve angle (P < 0.01), hypertension (P = 0.01), presence of APOE ε4 expression (P < 0.01) and different degrees of brainstem compression in the Root Entry Zone (P < 0.01). In the multivariable analyses, there were significant statistical differences between the two groups in terms of age of symptom onset (P < 0.01 OR = 6.591), APOE ε4 (P < 0.01 OR = 5.691), brainstem compression (P = 0.006 OR = 5.620), and facial nerve angle (P < 0.01 OR = 5.758). Furthermore, we found no significant correlation between the severity of facial spasms and the progression rate of the disease (t = 2.47, P = 0.12>0.05).ConclusionAccording to our study, patients with facial nerve angle ≤ 96.5°, severer compression of the brainstem by offending vessels, an onset age > 45 years and positive expression of APOE ε4, may experience faster progression of hemifacial spasm

Absence of metallicity and bias-dependent resistivity in low-carrier-density EuCd2As2

EuCd2As2 was theoretically predicted to be a minimal model of Weyl semimetals with a single pair of Weyl points in the ferromagnet state. However, the heavily p-doped EuCd2As2 crystals in previous experiments prevent direct identification of the semimetal hypothesis. Here we present a comprehensive magneto-transport study of high-quality EuCd2As2 crystals with ultralow bulk carrier density (10^13 cm-3). In contrast to the general expectation of a Weyl semimetal phase, EuCd2As2 shows insulating behavior in both antiferromagnetic and ferromagnetic states as well as surface-dominated conduction from band bending. Moreover, the application of a dc bias current can dramatically modulate the resistance by over one order of magnitude, and induce a periodic resistance oscillation due to the geometric resonance. Such nonlinear transport results from the highly nonequilibrium state induced by electrical field near the band edge. Our results suggest an insulating phase in EuCd2As2 and put a strong constraint on the underlying mechanism of anomalous transport properties in this system.Comment: 13 pages, 4 figure

Metallic vanadium disulfide nanosheets as a platform material for multifunctional electrode applications

Nano-thick metallic transition metal dichalcogenides such as VS$_{2}$ are essential building blocks for constructing next-generation electronic and energy-storage applications, as well as for exploring unique physical issues associated with the dimensionality effect. However, such 2D layered materials have yet to be achieved through either mechanical exfoliation or bottom-up synthesis. Herein, we report a facile chemical vapor deposition route for direct production of crystalline VS$_{2}$ nanosheets with sub-10 nm thicknesses and domain sizes of tens of micrometers. The obtained nanosheets feature spontaneous superlattice periodicities and excellent electrical conductivities (~3$\times$10$^{3}$ S cm$^{-1}$), which has enabled a variety of applications such as contact electrodes for monolayer MoS$_{2}$ with contact resistances of ~1/4 to that of Ni/Au metals, and as supercapacitor electrodes in aqueous electrolytes showing specific capacitances as high as 8.6$\times$10$^{2}$ F g$^{-1}$. This work provides fresh insights into the delicate structure-property relationship and the broad application prospects of such metallic 2D materials.Comment: 23 pages, 5 figue