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There are many problems in the process of collecting paper data using single data source, such as insufficient data comprehensiveness and limited data collection speed due to website access frequency limitation. Aiming at these problems, this paper proposed a paper data crawling technology for multi-data sources. Firstly, it used the four Chinese document service websites-How Net, Wanfang Data, Weipu, and Chaoxing as data sources, completed the task of crawling and parsing list page data for the search keywords. Then it used the task scheduling strategy to remove repeated data and balance the tasks. Finally, it used multi-threads for each data source to crawl, parse and store the detail information of the papers, and built a website for search and display. Experiments show that under the same crawling and parsing speed, this technology can complete the paper information collection task more comprehensively and efficiently, which proves the effectiveness of this technology. [ABSTRACT FROM AUTHOR]

This study sought to identify the opinions of the Egyptian academic medical elite in the treatment of news sites for preventive medicine crises (corona vaccine model) and their assessment of this treatment and their trends towards it. The study relied on the method of media survey and it used the tool of electronic and paper questionnaire to collect data. The Egyptian academic medical elite chose the site of the seventh day with a relative weight of (61) in the front of the sites that were keen to follow it in the treatment of the Corona vaccine, and then sky news Arabic at a relative weight (57), and then the website BBC Arabic at a relative weight (56), and also chose breaking news form first, which is keen to follow it in the treatment of corona vaccine at a relative weight of (80.3), and then live broadcast at a relative weight (62), and then news reports at a relative weight (61). (48.5%) of respondents considered corona vaccine treatment on news sites to be fairly in-depth, (37.9%) felt superficial, and (13.6%) felt it was in-depth. A statistically significant correlation between the assessment of the Egyptian academic medical elite to address corona vaccine news sites and the degree of follow-up to them has been established. A statistically significant correlation between the degree of follow-up of the Egyptian academic medical elite to treat corona vaccine on news sites and their levels of confidence has also been established, while a statistically significant correlation between the elite's follow-up score for corona vaccine treatment on news sites and the effects of follow-up has not been established. [ABSTRACT FROM AUTHOR]

Lung cancer is the malignant tumor with the highest incidence and mortality rate worldwide. For lung adenocarcinoma, identifying specific gene mutations, fusions, and giving corresponding targeted drugs can greatly improve the survival time of the patients. Among them, anaplastic lymphoma kinase (ALK) fusion occurs in 3%-7% of non-small cell lung cancer (NSCLC). In clinical practice, a variety of detection methods can be used to determine the ALK fusion status, but false negative test results are possible. This paper retrospectively analyzed the diagnosis and treatment of a patient with lung adenocarcinoma, judged the ALK fusion status by various detection methods. Among them, immunohistochemistry (IHC)(Ventana D5F3), RNA based next-generation sequencing (RNA-based NGS) confirmed positive echinoderm microtubule associated protein like 4 (EML4)-ALK fusion, while DNA-based NGS was negative. This paper analyzed the detection methods of ALK fusion, in order to clarify which detection method is the most accurate and simple to choose in different clinical cases and guide the subsequent treatment. [ABSTRACT FROM AUTHOR]

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[Objectives] Peak laying period is a critical period of layer breeding. The construction of a sub-health evaluation index system for laying hens during peak laying period is not only conducive to accurately evaluating the health of flocks and carrying out accurate feeding and management adjustments according to the health status of the flocks, and it can also provide reference indicators and scientific basis for non-destructive monitoring of the health status of laying hens during the peak laying period. [Methods] Based on the research on laying hen farms, data collection and expert meetings, this paper initially constructed a set of evaluation indexes for sub-health of laying hens during peak laying period from four aspects, namely, health status, behavioural performance, production performance, environment and flock information of hens, and used the expert consultation method to screen the indexes to establish the evaluation index system, and determined the indexes' weights through the hierarchical analysis method. [Results] A total of 14 experts were invited for consultation, and the effective recovery of the expert consultation questionnaire was 100%, the expert authority coefficient was 0.875, and the Kendall's coordination coefficient was 0.386, which indicated that the experts' motivation, authority, and coordination of their opinions were all better, and the final established evaluation index system included 4 first-level and 27 second-level indicators. The weights of the four first-level indicators, namely, health status, behavioural performance, production performance and henhouse environment, were 0.254, 0.245, 0.253 and 0.248, respectively. [Conclusions] This study constructed the evaluation index system of sub-health of laying hens during the peak laying period, with better reliability of expert consultation and more comprehensive coverage of evaluation indexes. [ABSTRACT FROM AUTHOR]

In recent years, with the diversification of data acquisition, multi-view learning has become more and more important. Most multi-view clustering methods obtain the similarity between samples through self-representation or local structures. However, these methods do not consider the influence of noise and the clustering structure of the overall sample. This way may lead to a large error in the clustering study. To address this issue, this paper proposes a Multiview Latent subspace Clustering with Cluster structure and Local similarity（MLC2L）, which combines shared information on different views and suppresses the presence of possible noise through latent representations. Besides, it simultaneously explores the clustering structure and local similarity in the latent space, so the samples can be promoted to achieve the purpose of homogeneous aggregation and heterogeneous separation. Further, this paper introduces an alternate direction iterative optimization algorithm to quickly solve the objective function. The experimental results in six real datasets show that the proposed method is optimal for five evaluation metrics on MSRC-v1, UCI, and 100Leaves, and four out of five metrics on 3Sources, WebKB, and Prokaryotic datasets. Extensive experimental results demonstrate the effectiveness of the MLC2L, which combines local structure and overall clustering structure, in multiview clustering tasks. [ABSTRACT FROM AUTHOR]

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This article reports on the nursing experience in caring for a 23 year old man who suffered a spinal cord injury after a car accident. Even after long-term rehabilitation therapy, the patient felt weakness in his lower limbs and neuralgia. Therefore, he was admitted in our hospital for spinal cord stimulation surgery. Nursing care for the patient lasted from September 16 to November 06, 2019. Gordon's functional health patterns assessment, penand- paper communication, systematic observation, as well as the medical records were utilized for data collection. According to the results of the data analysis, major health problems encountered by the nurse included pain, impaired physical mobility, and anxiety. During care, the patient's level of pain was assessed via the pain scale, and subsequently the patient and family were instructed to utilize relaxation techniques, massages, and medication. Rehabilitation activities were arranged for muscle strength training, such as the use of Redcord training and tilting a table at 65 degrees for 20-30 mins to maintain the patient's physical activity and enhance muscle strength. We provided active, caring companionship, guided the patient to express his inner feelings, enhanced his positive self-concept to help him actively cooperate with the rehabilitation. We hope that this nursing experience can serve as a future reference for nursing staff with similar cases. [ABSTRACT FROM AUTHOR]

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At present, although gait recognition methods based on deep learning have made some progress, data collection and changes in gait appearance are still challenges to achieving accurate gait recognition. In order to improve the network ' s ability to capture temporal and spatial gait information, this paper proposed a two-stream network architecture based on gait contour flow and gait feature differential flow. The gait contour stream toke the gait contour map as input to extract the spatial gait information contained in the gait contour sequence, and the gait feature difference stream used the gait feature difference map as input to capture the dynamic information between adjacent gait frames . At the same time, to make full use of the global and local information in the gait sequence, this paper proposed a multi-scale pyramid mapping ( MPM) module and inserted it into each single-stream network to enhance the network ' s ability to extract global and local gait information. The average recognition accuracy of this method on the gait datasets CASIA-B and OU-MVLP reaches 87. 0% and 85 . 5%, respectively, which shows that the two-stream network architecture and MPM module can effectively capture the spatiotemporal gait information in the gait sequence. [ABSTRACT FROM AUTHOR]

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Federated learning is a novel distributed machine learning approach, which provides a privacy protection way to learn a shared model without sharing each client's private data. However, the existing frameworks of federated learning only work for supervised learning wherein each client's data is labeled. Since collecting labeled data is difficult and expensive to obtain in the real world, the assumption of federated learning is not valid. To solve this problem, this paper proposed a semi-supervised federated learning model named ANN-SSFL based on an AutoEncoder neural network. The proposed model was extended from classical federated learning and allowed clients who might not have labeled data to participate the federated learning. The latent features which could be identified by the classifier were obtained by AutoEncoder neural network from unlabeled data, therefore unlabeled data could provide their data information to make their contributions. This paper conducted experiments on MNIST data sets. The experimental results show that the proposed ANN-SSFL is practical and effective. When the number of supervised clients remains unchanged, adding unsupervised clients can improve the accuracy of classical federated learning. [ABSTRACT FROM AUTHOR]

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In view of the application scenarios that multiple UAVs cooperate with each other to complete the field sensor data collection task, it is particularly important to establish a path planning problem model for multiple UAVs with accurate energy consumption model. This paper presented the MUPP-AEC problem. The MUPP-AEC toke into account the differences in energy consumption under U AV acceleration, deceleration, cruising in constant speed and turning. For solving the MUPP-AEC, this paper proposed the DBSO-OS. In DBSO-OS, this paper proposed individual space integer encoding and the phased greedy decoding strategy with 2-opt,and defined the perturbation operator and individual update operator discretely. The individual update operators adopted the new individual generation strategy utilizing the random inversion transformation and the partial matching transformation. The experimental results show that the proposed algorithm can effectively solve the MUPP-AEC. The proposed discrete brainstorm operator is superior to the traditional brainstorm operators in terms of global convergence ability, convergence precision, and stability. In the small and medium-sized test cases and the large test cases, the proposed algorithm is better than the compared algorithms. [ABSTRACT FROM AUTHOR]

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Mastering the migration and distribution of large nekton is the basis for analyzing their population dynamics, and carrying out population conservation and management. However, large nekton has strong swimming ability, wide range of activities and difficulty in recapture, so the traditional tagging technology has great limitations. Satellite Archival Tags technique has become one of the effective technical means to study the population changes and behavioral habits of deep-sea large nekton because of its features of not relying on retrieval, large data collection volume and high accuracy. Currently, mainstream Satellite Archival Tags are developed based on the Argos satellite system and transmitted via satellite channels, which has significant advantages over traditional tags that rely on sign recovery. This paper reviews the working principle, development history and main types of Satellite Archival Tags, introduces the main application progress, summarizes the problems and presents the research outlook, aiming to provide reference for the study of population dynamics and behavioral habits of large nekton. [ABSTRACT FROM AUTHOR]

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Mobile traffic classification/ clustering is an important foundation for mobile network traffic management. However, the mobile network traffic data used by different papers were collected from different network environment. In addition, the labels and the flow statistical features of mobile traffic were different from papers. These experimental results couldn't be directly compared. This paper collected the traffic data generated by App based on MobileGT system. The two kinds of labels were built on these data (App level and function level),and two kinds of flow statistical features were independently extracted on these traffic data. This paper comprehensively researched the machine learning techniques on the traffic data with different labels and different flow statistical features. The experimental results show that the uni-direction flow based features are better than bidirection flow based features, random forest and AdaBoost are better on classifying mobile traffic data, and K-means is better on clustering mobile traffic data. [ABSTRACT FROM AUTHOR]

With the advancement of data acquisition technology, spatiotemporal data with geographic location information are growing rapidly, so it is urgent to explore effective spatiotemporal data modeling methods. Spatiotemporal sequence prediction is one of the basic methods in spatiotemporal data modeling and widely used in many fields. At present, there is no Chinese literature on its review, so it is of great significance to summarize these methods. For the spatiotemporal sequence prediction problem, this paper reviewed its application background and development history. Then it introduced some related definitions and characteristics. According to its category, the spatiotemporal sequence prediction methods, based on traditional methods, classical machine learning methods and deep learning methods are analyzed respectively as well as their application scope, advantages and disadvantages. Finally, this paper made a review and prospect over the research directions of spatiotemporal sequence prediction, laying a theoretical foundation for researchers to further study this problem [ABSTRACT FROM AUTHOR]

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