Research on Some Phenomenon of E-Government Service Capacity Distribution in Mainland China Based on Multi-channel Perspective

In the context of the government\u27s increasing emphasis on e-government services, this is an urgent need for empirical research of large sample and multi-channels. Therefore, based on the government website, WeChat, Micro-blog, app, by using the existing mature evaluation index system, this paper analyzes e-government service capacity of the city above prefecture- level and provincial. Then, this paper selects the administrative level, economic level, regional balance as the differentiation attribute. It is found that both administrative level and economic level are positively correlated with government service capacity in all the channels. The channel capacity distribution varies related to attribute of administrative and economic, government type of city and province, but it is not restricted by level and region. It provides direction and intensity management to balance and promote channel service capacity for China government

EWT: Efficient Wavelet-Transformer for Single Image Denoising

Transformer-based image denoising methods have achieved encouraging results in the past year. However, it must uses linear operations to model long-range dependencies, which greatly increases model inference time and consumes GPU storage space. Compared with convolutional neural network-based methods, current Transformer-based image denoising methods cannot achieve a balance between performance improvement and resource consumption. In this paper, we propose an Efficient Wavelet Transformer (EWT) for image denoising. Specifically, we use Discrete Wavelet Transform (DWT) and Inverse Wavelet Transform (IWT) for downsampling and upsampling, respectively. This method can fully preserve the image features while reducing the image resolution, thereby greatly reducing the device resource consumption of the Transformer model. Furthermore, we propose a novel Dual-stream Feature Extraction Block (DFEB) to extract image features at different levels, which can further reduce model inference time and GPU memory usage. Experiments show that our method speeds up the original Transformer by more than 80%, reduces GPU memory usage by more than 60%, and achieves excellent denoising results. All code will be public.Comment: 12 pages, 11 figur

A Multifunctional Array System Based on Adjustable-Phase Antenna for Wireless communications

In this work, an innovative method for controlling the current distribution of the radiating patch by adjusting the input phase is investigated to achieve both pattern and polarization reconfigurable characteristics for the multifunction. A compact and low-profile antenna with four fed ports is designed to implement the proposed method, which can operate linear, right-hand circular polarization (RHCP) and left-hand circular polarization (LHCP) with different beam directions in the operating band from 4.0 to 5.0 GHz. Even more, a four-by-four passive planar array is designed and fabricated based on this antenna element, which can scan the coverage of 70{\deg} with low gain fluctuation and low sidelobe with dual-polarization. Meanwhile, it can realize the wide-angle scanning capability up to 60{\deg} with low sidelobe with RHCP and LHCP. More important, the dual- and triple-beam with different directions can be obtained by the proposed array. Good agreement has been shown between measured and simulated results. Therefore, the proposed antenna is a good solution for wireless communication systems because of its simple-configuration, multifunction, and beamforming capability

The effect of calcium nitrate on the hydration of calcium aluminate cement at different curing temperatures

Phase conversion in calcium aluminate cements (CAC) induces significant volumetric instability; it would result in an increase in porosity and decrease in strength in CAC. In this study, calcium nitrate (CN) as a phase conversion inhibitor, the effect of CN on the hydration of CAC at different curing temperatures was studied. Xray diffraction, thermal analysis, SEM, isothermal calorimetry and the compressive strength were conducted on the CAC dosages of 0%, 5%, 10% and 15%CN cured at 20�, 30�, 40� and 50�. The results show CN can retard CAC hydration, alter the characters of the hydrates of CAC systems and avoid the conversion process. With increasing dosage of CN and curing temperature, the hydration products formed is different.in CAC systems with CN, NO3-AFm and NO3-AFt are more preferred than CAH10 and C2AH8 and are more thermostable than those typically hydrates. In the presence of CN, The phase conversion to a large extent can be avoided and the compressive strength is significantly improved. The CN dosage has a very important effect on CAC systems with CN. In this study, the optimum dosage for CN is 10 percent. This study may provide a new insight into avoiding the unstable phase conversion in calcium aluminate cements

CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution

Recently, deep convolution neural networks (CNNs) steered face super-resolution methods have achieved great progress in restoring degraded facial details by jointly training with facial priors. However, these methods have some obvious limitations. On the one hand, multi-task joint learning requires additional marking on the dataset, and the introduced prior network will significantly increase the computational cost of the model. On the other hand, the limited receptive field of CNN will reduce the fidelity and naturalness of the reconstructed facial images, resulting in suboptimal reconstructed images. In this work, we propose an efficient CNN-Transformer Cooperation Network (CTCNet) for face super-resolution tasks, which uses the multi-scale connected encoder-decoder architecture as the backbone. Specifically, we first devise a novel Local-Global Feature Cooperation Module (LGCM), which is composed of a Facial Structure Attention Unit (FSAU) and a Transformer block, to promote the consistency of local facial detail and global facial structure restoration simultaneously. Then, we design an efficient Local Feature Refinement Module (LFRM) to enhance the local facial structure information. Finally, to further improve the restoration of fine facial details, we present a Multi-scale Feature Fusion Unit (MFFU) to adaptively fuse the features from different stages in the encoder procedure. Comprehensive evaluations on various datasets have assessed that the proposed CTCNet can outperform other state-of-the-art methods significantly.Comment: 12 pages, 10 figures, 8 table