Assessing the Influence of Citizen Emotions on E-Government Satisfaction in Service Recovery Situations

E-government involves many aspects of public administration ranging from introducing new technology to provide citizens with access to programs, services and information via the online channel. Citizen’s emotion plays a significant role in e-government satisfaction, but much less is known about citizen emotions. The purpose of this study is to explore how citizen emotions affect satisfaction in e-government service recovery. The citizen\u27s psychology mechanism under the service recovery circumstance is carefully studied, and a conceptual model among the key constructs of citizen’s emotion and citizen satisfaction of e-government is developed. The method of Structural Equation Modeling (SEM) is adopted to verify the internal quality of the proposed measurement model. The empirical results indicated that positive emotions have a significant impact on citizen satisfaction of e-government and no significant impact of negative emotions. Furthermore, our study identifies that perceived justice and perceived service quality that influence citizen satisfaction

Proximity Enhanced Quantum Spin Hall State in Graphene

Graphene is the first model system of two-dimensional topological insulator (TI), also known as quantum spin Hall (QSH) insulator. The QSH effect in graphene, however, has eluded direct experimental detection because of its extremely small energy gap due to the weak spin-orbit coupling. Here we predict by ab initio calculations a giant (three orders of magnitude) proximity induced enhancement of the TI energy gap in the graphene layer that is sandwiched between thin slabs of Sb2Te3 (or MoTe2). This gap (1.5 meV) is accessible by existing experimental techniques, and it can be further enhanced by tuning the interlayer distance via compression. We reveal by a tight-binding study that the QSH state in graphene is driven by the Kane-Mele interaction in competition with Kekul\'e deformation and symmetry breaking. The present work identifies a new family of graphene-based TIs with an observable and controllable bulk energy gap in the graphene layer, thus opening a new avenue for direct verification and exploration of the long-sought QSH effect in graphene.Comment: 4 figures in Carbon, 201

Opening Band Gap without Breaking Lattice Symmetry: A New Route toward Robust Graphene-Based Nanoelectronics

Developing graphene-based nanoelectronics hinges on opening a band gap in the electronic structure of graphene, which is commonly achieved by breaking the inversion symmetry of the graphene lattice via an electric field (gate bias) or asymmetric doping of graphene layers. Here we introduce a new design strategy that places a bilayer graphene sheet sandwiched between two cladding layers of materials that possess strong spin-orbit coupling (e.g., Bi2Te3). Our ab initio and tight-binding calculations show that proximity enhanced spin-orbit coupling effect opens a large (44 meV) band gap in bilayer graphene without breaking its lattice symmetry, and the band gap can be effectively tuned by interlayer stacking pattern and significantly enhanced by interlayer compression. The feasibility of this quantum-well structure is demonstrated by recent experimental realization of high-quality heterojunctions between graphene and Bi2Te3, and this design also conforms to existing fabrication techniques in the semiconductor industry. The proposed quantum-well structure is expected to be especially robust since it does not require an external power supply to open and maintain a band gap, and the cladding layers provide protection against environmental degradation of the graphene layer in its device applications

Graphene-based topological insulator with an intrinsic bulk band gap above room temperature

Topological insulators (TIs) represent a new quantum state of matter characterized by robust gapless states inside the insulating bulk gap. The metallic edge states of a two-dimensional (2D) TI, known as quantum spin Hall (QSH) effect, are immune to backscattering and carry fully spin-polarized dissipationless currents. However, existing 2D TIs realized in HgTe and InAs/GaSb suffer from small bulk gaps (<10 meV) well below room temperature, thus limiting their application in electronic and spintronic devices. Here, we report a new 2D TI comprising a graphene layer sandwiched between two Bi2Se3 slabs that exhibits a large intrinsic bulk band gap of 30 to 50 meV, making it viable for room-temperature applications. Distinct from previous strategies for enhancing the intrinsic spin-orbit coupling effect of the graphene lattice, the present graphene-based TI operates on a new mechanism of strong inversion between graphene Dirac bands and Bi2Se3 conduction bands. Strain engineering leads to effective control and substantial enhancement of the bulk gap. Recently reported synthesis of smooth graphene/Bi2Se3 interfaces demonstrates feasibility of experimental realization of this new 2D TI structure, which holds great promise for nanoscale device applications.Comment: 3 figures, 1 tabl

Unsupervised Deraining: Where Contrastive Learning Meets Self-similarity

Image deraining is a typical low-level image restoration task, which aims at decomposing the rainy image into two distinguishable layers: the clean image layer and the rain layer. Most of the existing learning-based deraining methods are supervisedly trained on synthetic rainy-clean pairs. The domain gap between the synthetic and real rains makes them less generalized to different real rainy scenes. Moreover, the existing methods mainly utilize the property of the two layers independently, while few of them have considered the mutually exclusive relationship between the two layers. In this work, we propose a novel non-local contrastive learning (NLCL) method for unsupervised image deraining. Consequently, we not only utilize the intrinsic self-similarity property within samples but also the mutually exclusive property between the two layers, so as to better differ the rain layer from the clean image. Specifically, the non-local self-similarity image layer patches as the positives are pulled together and similar rain layer patches as the negatives are pushed away. Thus the similar positive/negative samples that are close in the original space benefit us to enrich more discriminative representation. Apart from the self-similarity sampling strategy, we analyze how to choose an appropriate feature encoder in NLCL. Extensive experiments on different real rainy datasets demonstrate that the proposed method obtains state-of-the-art performance in real deraining.Comment: 10 pages, 10 figures, accept to 2022CVP

Identification of initial fault time for bearing based on monitoring indicator, WEMD and Infogram

Rolling element bearing is a core component in the rotating machine. The performance of the whole machine is mainly dominated by the performance condition of the rolling element bearing. The Initial Fault Time (IFT) is a beginning landmark of the unhealthy condition of bearings. In order to identify accurately and rapidly the IFT under the weak fault signatures and heavy background noise, an identification method of the IFT is proposed by the monitoring indicator and envelope analysis with Weighted Empirical Mode Decomposition (WEMD) and Infogram. The monitoring indicator is constructed by the variation coefficient of the summation of the multiple standardized statistical features of the vibration signal. The approximate IFT can be obtained by the minimum before the early stage of the continuous increase in the monitoring indicator. Whereafter, a more accurate IFT can be detected by envelope analysis with WEMD and Infogram based on interval-halving backtracking strategy. The proposed method is verified by the tested dataset provided by Intelligent Maintenance System (IMS). The results show that the proposed method is efficient, rapid and simple for identifying the IFT

Unsupervised Deraining: Where Asymmetric Contrastive Learning Meets Self-similarity

Most of the existing learning-based deraining methods are supervisedly trained on synthetic rainy-clean pairs. The domain gap between the synthetic and real rain makes them less generalized to complex real rainy scenes. Moreover, the existing methods mainly utilize the property of the image or rain layers independently, while few of them have considered their mutually exclusive relationship. To solve above dilemma, we explore the intrinsic intra-similarity within each layer and inter-exclusiveness between two layers and propose an unsupervised non-local contrastive learning (NLCL) deraining method. The non-local self-similarity image patches as the positives are tightly pulled together, rain patches as the negatives are remarkably pushed away, and vice versa. On one hand, the intrinsic self-similarity knowledge within positive/negative samples of each layer benefits us to discover more compact representation; on the other hand, the mutually exclusive property between the two layers enriches the discriminative decomposition. Thus, the internal self-similarity within each layer (similarity) and the external exclusive relationship of the two layers (dissimilarity) serving as a generic image prior jointly facilitate us to unsupervisedly differentiate the rain from clean image. We further discover that the intrinsic dimension of the non-local image patches is generally higher than that of the rain patches. This motivates us to design an asymmetric contrastive loss to precisely model the compactness discrepancy of the two layers for better discriminative decomposition. In addition, considering that the existing real rain datasets are of low quality, either small scale or downloaded from the internet, we collect a real large-scale dataset under various rainy kinds of weather that contains high-resolution rainy images.Comment: 16 pages, 15 figures. arXiv admin note: substantial text overlap with arXiv:2203.1150

Evaluation of the deposition and distribution of spray droplets in citrus orchards by plant protection drones

Plant protection drone spraying technology is widely used to prevent and control crop diseases and pests due to its advantages of being unaffected by crop growth patterns and terrain restrictions, high operational efficiency, and low labor requirements. The operational parameters of plant protection drones significantly impact the distribution of spray droplets, thereby affecting pesticide utilization. In this study, a field experiment was conducted to determine the working modes of two representative plant protection drones and an electric backpack sprayer as a control to explore the characteristics of droplet deposition with different spray volumes in the citrus canopy. The results showed that the spraying volume significantly affected the number of droplets and the spray coverage. The number of droplets and the spray coverage area on the leaf surface were significantly increased by increasing the spray volume from 60 L/ha to 120 L/ha in plant protection drones. Particularly for the DJI T30, the mid-lower canopy showed a spray coverage increase of 52.5%. The droplet density demonstrated the most significant variations in the lower inner canopy, ranging from 18.7 droplets/cm2 to 41.7 droplets/cm2 by XAG V40. From the deposition distribution on fruit trees, the plant protection drones exhibit good penetration ability, as the droplets can achieve a relatively even distribution in different canopy layers of citrus trees. The droplet distribution uniformity inside the canopy is similar for XAG V40 and DJI T30, with a variation coefficient of approximately 50%-100%. Compared to the plant protection drones, the knapsack electric sprayer is suitable for pest and disease control in the mid-lower canopy, but they face challenges of insufficient deposition capability in the upper canopy and overall poor spray uniformity. The distribution of deposition determined in this study provides data support for the selection of spraying agents for fruit trees by plant protection drones and for the control of different pests and diseases

Pressure-stabilized divalent ozonide CaO3 and its impact on Earth's oxygen cycles.

High pressure can drastically alter chemical bonding and produce exotic compounds that defy conventional wisdom. Especially significant are compounds pertaining to oxygen cycles inside Earth, which hold key to understanding major geological events that impact the environment essential to life on Earth. Here we report the discovery of pressure-stabilized divalent ozonide CaO3 crystal that exhibits intriguing bonding and oxidation states with profound geological implications. Our computational study identifies a crystalline phase of CaO3 by reaction of CaO and O2 at high pressure and high temperature conditions; ensuing experiments synthesize this rare compound under compression in a diamond anvil cell with laser heating. High-pressure x-ray diffraction data show that CaO3 crystal forms at 35 GPa and persists down to 20 GPa on decompression. Analysis of charge states reveals a formal oxidation state of -2 for ozone anions in CaO3. These findings unravel the ozonide chemistry at high pressure and offer insights for elucidating prominent seismic anomalies and oxygen cycles in Earth's interior. We further predict multiple reactions producing CaO3 by geologically abundant mineral precursors at various depths in Earth's mantle

New Family of Robust 2D Topological Insulators in van der Waals Heterostructures

We predict a new family of robust two-dimensional (2D) topological insulators in van der Waals heterostructures comprising graphene and chalcogenides BiTeX (X=Cl, Br and I). The layered structures of both constituent materials produce a naturally smooth interface that is conducive to proximity induced new topological states. First principles calculations reveal intrinsic topologically nontrivial bulk energy gaps as large as 70-80 meV, which can be further enhanced up to 120 meV by compression. The strong spin-orbit coupling in BiTeX has a significant influence on the graphene Dirac states, resulting in the topologically nontrivial band structure, which is confirmed by calculated nontrivial Z2 index and an explicit demonstration of metallic edge states. Such heterostructures offer an unique Dirac transport system that combines the 2D Dirac states from graphene and 1D Dirac edge states from the topological insulator, and it offers new ideas for innovative device designs