Research on Efficiency of Financial Supports in Agricultural Industrialization in China

The agricultural industry development in China has been very successful, but there exist some problems, such as weak financial support strength. With the help of DEA-Malmquist index method, this paper evaluated the efficiency of the agricultural industrialization＇s financial supports, made a deep study of its influencing factors, which have an extremely important influence on the perfect agricultural industrialization＇s development.

CO_2-triggered gelation for mobility control and channeling blocking during CO_2 flooding processes

CO2 flooding is regarded as an important method for enhanced oil recovery （EOR） and greenhouse gas control. However, the heterogeneity prevalently dis- tributed in reservoirs inhibits the performance of this technology. The sweep efficiency can be significantly reduced especially in the presence of ＂thief zones＂. Hence, gas channeling blocking and mobility control are important technical issues for the success of CO2 injection. Normally, crosslinked gels have the potential to block gas channels, but the gelation time control poses challenges to this method. In this study, a new method for selectively blocking CO2 channeling is proposed, which is based on a type of CO2-sensitive gel system （modified polyacry- lamide-methenamine-resorcinol gel system） to form gel in situ. A CO2-sensitive gel system is when gelation or solidification will be triggered by CO2 in the reservoir to block gas channels. The CO2-sensitivity of the gel system was demonstrated in parallel bottle tests of gel in N2 and CO2 atmospheres. Sand pack flow experiments were con- ducted to investigate the shutoff capacity of the gel system under different conditions. The injectivity of the gel system was studied via viscosity measurements. The results indi- cate that this gel system was sensitive to CO2 and had good performance of channeling blocking in porous media. Advantageous viscosity-temperature characteristics were achieved in this work. The effectiveness for EOR in heterogeneous formations based on this gel system was demonstrated using displacement tests conducted in double sand packs. The experimental results can provide guideli- nes for the deployment of theCO2-sensitive gel system for field applications.

Efficient and Secure Authenticated Quantum Dialogue Protocols over Collective-Noise Channels

Based on the deterministic secure quantum communication, we present a novel quantum dialogue protocol with- out information leakage over the collective noise channel. The logical qubits and four-qubit decoherence-free states are introduced for resisting against collective-dephasing noise, collective-rotation noise and all kinds of unitary collective noise, respectively. Compared with the existing similar protocols, the analyses on security and information-theoretical emciency show that the proposed protocol is more secure and emeient.

Deep learning-based automated grading of visual impairment in cataract patients using fundus images

Cataract is the leading cause of visual impairment globally.The scarcity and uneven distribution of ophthalmologists seriously hinder early visual impairment grading for cataract patients in the clin-ic.In this study,a deep learning-based automated grading system of visual impairment in cataract patients is proposed using a multi-scale efficient channel attention convolutional neural network(MECA_CNN).First,the efficient channel attention mechanism is applied in the MECA_CNN to extract multi-scale features of fundus images,which can effectively focus on lesion-related regions.Then,the asymmetric convolutional modules are embedded in the residual unit to reduce the infor-mation loss of fine-grained features in fundus images.In addition,the asymmetric loss function is applied to address the problem of a higher false-negative rate and weak generalization ability caused by the imbalanced dataset.A total of 7299 fundus images derived from two clinical centers are em-ployed to develop and evaluate the MECA_CNN for identifying mild visual impairment caused by cataract(MVICC),moderate to severe visual impairment caused by cataract(MSVICC),and nor-mal sample.The experimental results demonstrate that the MECA_CNN provides clinically meaning-ful performance for visual impairment grading in the internal test dataset:MVICC(accuracy,sensi-tivity,and specificity;91.3%,89.9%,and 92%),MSVICC(93.2%,78.5%,and 96.7%),and normal sample(98.1%,98.0%,and 98.1%).The comparable performance in the external test dataset is achieved,further verifying the effectiveness and generalizability of the MECA_CNN model.This study provides a deep learning-based practical system for the automated grading of visu-al impairment in cataract patients,facilitating the formulation of treatment strategies in a timely man-ner and improving patients’vision prognosis.

Priority-based adaptive transmission algorithm for medical devices in wireless body area networks(WBANs)

A wireless body area network offers cost-effective solutions for healthcare infrastructure. An adaptive transmission algorithm is designed to handle channel efficiency, which adjusts packet size according to the difference in feature-point values that indicate biomedical signal characteristics. Furthermore, we propose a priority-adjustment method that enhances quality of service while guaranteeing signal integrity. A large number of simulations were carried out for performance evaluation. We use electrocardiogram and electromyogram signals as reference biomedical signals for performance verification. From the simulation results, we find that the average packet latency of proposed scheme is enhanced by 30% compared to conventional method. The simulation results also demonstrate that the proposed algorithm achieves significant performance improvement in terms of drop rates of high-priority packets around 0.3%-0.9 %.

Improved edge lightweight YOLOv4 and its application in on-site power system work

A“cloud-edge-end”collaborative system architecture is adopted for real-time security management of power system on-site work,and mobile edge computing equipment utilizes lightweight intelligent recognition algorithms for on-site risk assessment and alert.Owing to its lightweight and fast speed,YOLOv4-Tiny is often deployed on edge computing equipment for real-time video stream detection;however,its accuracy is relatively low.This study proposes an improved YOLOv4-Tiny algorithm based on attention mechanism and optimized training methods,achieving higher accuracy without compromising the speed.Specifically,a convolution block attention module branch is added to the backbone network to enhance the feature extraction capability and an efficient channel attention mechanism is added in the neck network to improve feature utilization.Moreover,three optimized training methods:transfer learning,mosaic data augmentation,and label smoothing are used to improve the training effect of this improved algorithm.Finally,an edge computing equipment experimental platform equipped with an NVIDIA Jetson Xavier NX chip is established and the newly developed algorithm is tested on it.According to the results,the speed of the improved YOLOv4-Tiny algorithm in detecting on-site dress code compliance datasets is 17.25 FPS,and the mean average precision(mAP)is increased from 70.89%to 85.03%.

Disease Recognition of Apple Leaf Using Lightweight Multi-Scale Network with ECANet

To solve the problem of difficulty in identifying apple diseases in the natural environment and the low application rate of deep learning recognition networks,a lightweight ResNet(LW-ResNet)model for apple disease recognition is proposed.Based on the deep residual network(ResNet18),the multi-scale feature extraction layer is constructed by group convolution to realize the compression model and improve the extraction ability of different sizes of lesion features.By improving the identity mapping structure to reduce information loss.By introducing the efficient channel attention module(ECANet)to suppress noise from a complex background.The experimental results show that the average precision,recall and F1-score of the LW-ResNet on the test set are 97.80%,97.92%and 97.85%,respectively.The parameter memory is 2.32 MB,which is 94%less than that of ResNet18.Compared with the classic lightweight networks SqueezeNet and MobileNetV2,LW-ResNet has obvious advantages in recognition performance,speed,parameter memory requirement and time complexity.The proposed model has the advantages of low computational cost,low storage cost,strong real-time performance,high identification accuracy,and strong practicability,which can meet the needs of real-time identification task of apple leaf disease on resource-constrained devices.

An insider user authentication method based on improved temporal convolutional network

With the rapid development of information technology,information system security and insider threat detection have become important topics for organizational management.In the current network environment,user behavioral bio-data presents the characteristics of nonlinearity and temporal sequence.Most of the existing research on authentication based on user behavioral biometrics adopts the method of manual feature extraction.They do not adequately capture the nonlinear and time-sequential dependencies of behavioral bio-data,and also do not adequately reflect the personalized usage characteristics of users,leading to bottlenecks in the performance of the authentication algorithm.In order to solve the above problems,this paper proposes a Temporal Convolutional Network method based on an Efficient Channel Attention mechanism(ECA-TCN)to extract user mouse dynamics features and constructs an one-class Support Vector Machine(OCSVM)for each user for authentication.Experimental results show that compared with four existing deep learning algorithms,the method retains more adequate key information and improves the classification performance of the neural network.In the final authentication,the Area Under the Curve(AUC)can reach 96%.

RESERVOIR SEDIMENTATION AND TRANSFORMATION OF MORPHO-LOGY IN THE LOWER YELLOW RIVER DURING 10 YEAR'S INITIAL OPERATION OF THE XIAOLANGDI RESERVOIR

The Xiaolangdi Hydro-Project is one of the large projects on the main stem of the Middle Yellow River. It has been operated for more than 10 years, since its impoundment in October, 1999. The reservoir has trapped 2.833 × 10^9 m3 of sediment, and caused the total erosion of 1.891 × 10^9t in the Lower Yellow River from October, 1999 through October, 2010. Not only the serious atrophied situation of the Lower Yellow River （LYR） has been resuscitating, but also many new phenomena of sediment transport and behaviors of channel re-establishing are coming into being. They are illustrated and discussed in detail in this paper.

AM-PSPNet:Pyramid Scene Parsing Network Based on Attentional Mechanism for Image Semantic Segmentation

In this paper,AM-PSPNet is proposed for image semantic segmentation.AM-PSPNet embeds the efficient channel attention(ECA)module in the feature extraction stage of the convolutional network and makes the network pay more attention to the channels with obvious classification characteristics through end-to-end learning.To recognize the edges of objects and small objects more effectively,AM-PSPNet proposes a deep guidance fusion(DGF)module to generate global contextual attention maps to guide the expression of shallow information.The average crossover ratio of the proposed algorithm on the Pascal VOC 2012 dataset and Cityscapes dataset reaches 78.8%and 69.1%,respectively.Comparedwith the other four network models,the accuracy and average crossover ratio of AM-PSPNet are improved.