bluuebunny/crossref_metadata_2025 huggingface dataset:
Indoor formaldehyde monitoring system based on fog computing
Authors: Chen Hao , Li Sixuan , Zhang Jie , Peng Xiaoxiang
Published: August 2019

With the enhancement of people’s awareness of environmental protection, environmental monitoring technology has gradually entered into all aspects of people’s life and work. However, due to the high demand of real-time monitoring for data transmission delay, the traditional cloud computing architecture cannot meet the demand, and the fog computing architecture emerges at the historic moment. In this paper, the traditional cloud computing architecture is improved by using the fog computing architecture combined with the function as a service mode of the serverless architecture, and an environment monitoring architecture based on fog computing is proposed. Taking indoor formaldehyde monitoring as an example, the delay of the system is significantly improved, and the resource utilization efficiency is higher and the energy consumption is lower. It solves the deficiency of traditional cloud computing architecture and better meets the demand of real-time environmental monitoring.
An original design of fresh agricultural products e-commerce platform based on Mobile Internet
Authors: Shen Hongmin
Published: August 2019

With the development of Mobile Internet, it provides a good technological environment for the development of agricultural e-commerce. The fresh agricultural products e-commerce is to design, planning and control of the information flow, the trade flow, the fund flow and the material flow of the fresh agricultural products with a view to strengthening competitiveness. Because of the previous view, the paper design the functions of the e-commerce platform suitable for fresh agricultural products. According to the function design, we design the app prototype of the fresh agricultural products e-commerce platform, which can more accurately express the functions of the e-commerce platform.
Movement authority security modeling and verification based on fault statechart
Authors: Yu Fan , Tang Minan , Hao Binbin , Wang Qianqian
Published: August 2019

Communication Based Train Control System (CBTC) has become the development trend of train control system, and generating safe and reasonable movement authority is the key to ensure the normal operation of the whole system. Combined with the security of the system, a security analysis method based on fault tree information to construct fault Statechart is proposed. The fault tree that generates the abnormality of the movement authority is analyzed, and it is represented as a form that the Statechart can describe, thereby establishing a fault statechart of the movement authority generating abnormality. Finally, the formal modeling of the fault statechart is carried out by using the time automaton theory, and the fault state unreachable is taken as the attribute of the specification for inspection. The results show that the method of combining fault Statechart and time automata proposed in the paper is feasible and suitable for the analysis and verification of safety critical systems.
A Multiple LRU List Buffer Management Algorithm
Authors: Wu Xiang , Cai Delin , Guan Shujie
Published: August 2019

Paper[1] proposes LLRU algorithm. The buffer is divided into four categories in LLRU: cold clean LRU list, cold dirty LRU list, hot clean LRU list and hot dirty LRU list. But the cold clean LRU list may be empty when the algorithm runs for a while. Then a new page just reading in buffer will be victim when the LLRU selects the victim page, leading to a clean page hard to turn into a hot page. The AM-LRU proposed in this paper ensures that the cold clean page has the chance to turn into a hot page by setting the minimum length of the cold clean LRU list and adopting secondary opportunity replacement strategy for the victim page from the hot area. As a result, compared with LLRU, the hit rate of AM-LRU can be improved and the flash reads counts, writes counts and erases counts can be reduced.
Research on Multi-objective Project Scheduling Model Based on Multi-skilled Workers
Authors: Zhaoyue Zhang , Zhenqiang Bao , Yun Kan , Yuqing Zhu
Published: August 2019

There are many kinds of skills for the workers in the project group, but the present scheduling methods do not consider that the workers have different proficiency in different skills. In this paper, a scheduling model of human resources project is established, which takes the worker’s skill proficiency into account and the project duration and labor cost are considered as the optimization objectives. The improved non-dominated sorting genetic algorithm (NSGAII) is used to solve the model. The simulation results show that the model can significantly improve the scheduling efficiency of the project team, and managers can make scheduling decisions according to preference in a series of solutions.
Short-term Forecast of Shanghai Composite Index in China Using BP Neural Network
Authors: Guo Qiushi
Published: August 2019

This article analyses the historical statistics of the Shanghai Composite Index over the last 25 years, namely the characteristics and influencing factors. The article also predicts the trend of a 60-day time sequence of Shanghai composite index after 30th, Nov, 2018, using BP neural network model. The BP model fits the historical statistics well after consecutive training. The prediction result of the BP neural model indicates three characteristics of the trend in next 60 days: (1) the price will go up; (2) fluctuation frequency will reduce; (3) fluctuation range of the price will remain low.

By analysing the relevant daily data of Shanghai Composite Index from 1993 to 2018, it is found that the forecast for Stock Market is very challengeable due to the intrinsic indeterminacy of the stock market and the complexity of the variables. To train and test the BP neural network model, this research adopts the trading record of 4465 transaction records as the training group, the next 935 trading days as the testing group and 935 trading days as verification group. The input variable in this BP neural network is Shanghai Composite Index (SZCindex), and the expected Shanghai Composite Index (EXSZCindex) is output variable. The BP neural network model is constructed as a three-layer neural network model.

In the analysis of the daily closing price of the Shanghai Stock Exchange Index, this paper uses the goodness of fit to evaluate the BP neural network model and to continuously adjust the weight of the parameters. The results show that the training goodness of fit is 0.99898, and the correction goodness is 0.99897. The test goodness is 0.99904, and the overall goodness of fit is 0.99899, which indicates that the BP neural network has a rather good fitting effect. It is predicted that the price of the next 60 days from November 30, 2018 will rise to the highest point of 2623.874.

In the end, the rationality of the predicted results is analysed in this paper. Via the analysis of the obtained data, it is found that there is no over-fit. The images of predicted values and real values are drawn by using the data from subjective and objective analysis.
Speed estimation of PMSM using SRUKF algorithm
Authors: Li Jingnan , Gao Yuan , Hong Shuai , Zhang Yin
Published: August 2019

Speed estimation is a key technology to realize sensorless control for the PMSM. Based on the unscented Kalman filter (UKF) method without linearization of nonlinear system equations, this letter provides square root unscented Kalman filter (SRUKF) algorithm that operates through iterating the square roots of the covariance matrixes obtained by QR decomposition and Cholesky decomposition. The presented method can further improve speed estimation performance through decreasing the effect of truncation error and enhancing the convergence and stability of algorithm. Simulation results of sensorless control system demonstrate the feasibility and effectiveness of the proposed algorithm.
Bayesian Network Parameter Learning Algorithm for Target Damage Assessment
Authors: Du Xiuli , Fan Guichuan , Lv Yana , Qiu Shaoming
Published: August 2019

Aiming at the problem that the existing methods of target damage assessment based on Bayesian network mainly determine the structural parameters of Bayesian network by giving conditional probability tables based on expert experience, which results in too subjective and having large errors in the evaluation results, an improved learning algorithm of conditional probability tables of Bayesian network is proposed in this paper. We divided the E step of the EM algorithm (Expectation Maximization Algorithm) into three steps. Firstly, the range of the missing variable is determined by expert experience, then the Gibbs sampling algorithm is used to complete the sample set, and finally the sample is weighted. The proposed algorithm is compared with EM algorithm, Gibbs algorithm, EM and Gibbs algorithm. The experimental results show that the proposed algorithm has good stability and high precision.
A Novel Algorithm to Differential Scanning Calorimeter Temperature Control Based on Liquid Nitrogen Feedforward Technique
Authors: Yang YU , Jingtao HU , Yang Jia , Laixing Wei , Wei Sun
Published: August 2019

Differential scanning calorimeter (DSC) is an important instrument in the thermal analysis which could be pervasively applied to property analysis of inorganic compounds, organic compounds and drugs. An effective temperature control technique could ensure DSC thermal experimental accuracy. Unlike the traditional temperature control methods which have limited temperature control ranges and change rates, this paper presents a novel algorithm which exploits the liquid nitrogen feedforward technique to control the temperature of DSC. To be specific, the proposed approach fully takes advantage of the characteristic of the liquid nitrogen that has the ability to rapidly decrease the objective temperature. Furthermore, a cold-heat synergistic feedforward-feedback control is utilized. On one hand, the feedforward controller is able to avoid the singularities for the amount of liquid nitrogen. On the other hand, the feedback controller is able to compensate for the temperature errors due to external disturbances. The proposed algorithm has been applied to the DSC. The experimental results and analysis prove that the control method is able to change the temperature accurately within a wider range.
Image Stitching Algorithm for Drones Based on SURF-GHT
Authors: Duan Haohua , Liu Yangyang , Huang Hexiang , Wang Zhengtao , Zhao Hongwei
Published: August 2019

Aiming at the problem that the amount of single remote sensing picture information is limited due to the limited angular range of single drone, this paper proposes SURF-GHT algorithm based on SURF algorithm and generalized Hough transform based on the existing image stitching algorithm. After the feature points are extracted by the SURF algorithm for the pictures to be spliced, the decision tree forest in the generalized Hough transform is used to filter the set of feature points, and then the fading image fusion algorithm is used for image splicing. The experimental results show that the SURF-GHT algorithm improves the quality of image stitching on the basis of higher stitching efficiency, and better satisfies the subsequent identification of remote sensing images of drones.
Discretization method based on binary ant colony and variable precision rough set
Authors: Song Jian , Jiang Yu , Li Dong , Bao Yangwanying
Published: August 2019

Discretization plays an important role in data pre-processing and has been used in fields such as artificial intelligence and data mining. A new discretization algorithm based on binary ant colony and variable precision rough set is proposed in this paper. Binary ant network is first built using candidate breakpoints, then global optimal breakpoints is searched in the network. The fitness function is established by the number of breakpoints and approximation classification accuracy of variable precision rough set. This method is compared with other algorithms using C4.5 classifier on the WEKA (Waikato Environment for Knowledge Analysis) platform and seven UCI data sets. The results indicate that the proposed method performs well.
Implementation of Vehicle Traffic Statistics Algorithm Based on ViBe Image Processing
Authors: Lu Mingyu , Tang Zhihua
Published: August 2019

This project uses all kinds of main roads and intersections of UAV aerial photography cities to obtain information on traffic flow and real-time traffic conditions of various main roads, and feeds back information to unmanned airborne image processing devices through big data and clouds. The extracted image is processed and analyzed, and then a traffic flow safety prediction and evaluation model database based on road vehicle conflict is established.Through the analysis of the data, relevant departments can better plan the layout of traffic facilities on the road, so that the traffic facilities can be fully utilized, relieve traffic pressure, and reduce the incidence of traffic accidents.
Experimental study on crop disease detection based on deep learning
Authors: Xia Xiaoling , Wu Yongbo , Lu Qinyang , Fan Changqi
Published: August 2019

At present, deep learning has been widely used in our daily life, and the recognition of crop disease degree based on deep learning has gradually entered the public’s vision. The degree of crop diseases can usually be judged by the characteristics of leaf colour, shape and spot. According to the data set provided by AI Challenge Competition, the degree of crop diseases can be divided into many situations. According to the current open source data, the recognition accuracy rate on this data set has not been able to exceed 90%. In order to achieve better recognition results, on the basis of the convolutional neural network CNN, the transfer learning method is used to load the residual network ResNet pre-training model and SeNet + ResNet to carry out deeper network training to improve the efficiency and accuracy of recognition. This paper carries out many experiments under the condition of controlling variables. The experimental results show that Resnet can play a good role in this data set with training parameters and achieve 83% accuracy, while SeNet + ResNet can achieve 90% accuracy without using pre-training parameters, and 87% accuracy of test set recognition. It can be seen that the SeNet + ResNet method has better effects in image classification and recognition tasks in this data set.
Manipulator Meta-Imitation Learning Algorithm with Memory Weight Integration
Authors: Yin Mingjun , Zeng Qingshan
Published: August 2019

Versatility is one of the key characteristics of general agent. In order to enable the manipulator to quickly and effectively acquire the ability to perform multiple tasks in an unknown environment, a large capacity model is essential. In this paper, the memory weight integration term adapted to meta-learning algorithm is proposed. By adjusting the plasticity of neurons, the manipulator can learn to learn more effectively in the process of learning multi-task and improve the forgetting problem of multi-task learning. Then, this paper combines the memory weight integration with meta-imitation learning, so that the manipulator can acquire new skills from a single demonstration task. Finally, a 7-DoF manipulator in PusherEnv experiment is used to explore the influence of different integration coefficients on the algorithm. The results show that the memory weight integration can effectively improve the success rate of tasks.
Research on Library Information Visualization Retrieval Technology Based on Readers’ Needs
Authors: Hu Jianfei , Wang Di , Zhang Bin
Published: August 2019

With the advancement of social informatization and the widespread application of network, the resources accumulated by libraries are becoming larger and larger. In addition to the need to store, transmit, retrieve and classify massive data, there is an urgent need to understand the relationship between data and its development trend. With the continuous development of modern information technology, digital libraries have also made great progress. And to introduce visual information retrieval technology into digital libraries is an inevitable trend in the future development. This paper first analyses the characteristics of readers’ information retrieval needs in the new period, and then introduce library information visual presentation technologies to design the library information resource visualization retrieval system. The system can visually display the collection structure, popular books, readers’ interests and so on, to truly reflect the structure, coverage, utilization and other indicators of books resource, and provide convenience for the visual sharing of library information resources.
Research on Damage Evaluation of Radar Target Based on Deep Learning
Authors: Xu Pengcheng , Shu Jiansheng
Published: August 2019

In view of the evaluation of the damage effect of a radar on a damaged object, considering the nonlinearity and complexity of the target damage assessment, deep learning can be used to evaluate the radar target damage effect. Determine the network parameters and network node structure characteristics according to the characteristics of damage information; use the appropriate software and tools to build a modular network model; train the damage assessment network to make the network have strong experts Experience has led to a neural network with expert experience.
A scale transform and deformation target tracking algorithm based on correlation filtering
Authors: Hu Cong , Xia Wei , Chen Peng , Huang Shun , Guo Hui , Jin Xing
Published: August 2019

In order to improve the robustness of the correlation filtering (CF) tracking algorithm and overcome the problem that the traditional correlation filtering method can not deal with the target’s scale and deformation change, a feature-adaptive scale adaptive correlation filter tracking algorithm is proposed. Firstly, the target is characterized; then the output filter is calculated by using the correlation filter; finally, the image blocks of different scales are intercepted from the target position of the current frame, and the optimal estimation of the target scale is obtained by the adaptive scale pool model. The experiment selects multiple video sequences for testing and compares the proposed algorithm with other target tracking methods. The experimental results show that the average performance is better than the comparison method.
Construction of Artificial Intelligence-Based Interactive Oral English Teaching Platform based on application problems of present intelligent products
Authors: Zhou Junping
Published: August 2019

This paper investigated the application status of current intelligent oral English learning products, summarized the most favourable products applied by college English learners and analysed their common feature, advantages as well as problems. The survey result shows that the present intelligent oral English teaching products have the merits such as rich teaching resources, correct voice scoring system and individualized assessment and interesting game bonus etc. However, there are also some demerits like lack of words choice judgement, real-time scoring and indicating system as well as interactive teaching platform required by both teachers and learners etc. Based on the above problems, the paper put forward an Artificial Intelligence-Based Interactive Oral English Teaching Platform, which includes all-round spoken corpus that can be updated by teachers, students and AI system all together, an On-line Teaching Assistant System aimed for monitoring, controlling and interacting with students in learning process and a modified scoring system offering real-time assessment for students’ speaking performance, with a view to improving students’ oral English proficiency in a better and more effective way.
Prediction of Data Stream Based on Gaussian Process Regression with Online Free Variational Inference Approximation
Authors: Wang Xin , Duan Jiangyong , Yan Zhen
Published: August 2019

Online prediction of data stream is currently being used in various fields. The prediction method based on Gaussian Process Regression has obvious advantages since its outputs have a probabilistic significance, which is suitable for dealing with nonlinear and complex regression problem. However, the characteristics of the data stream are real-time and online. If only using the historical data of the previous time as the training sample to construct the prediction model, the model will not be accurately predicted once the distribution of the new data changes. To this end, we employ the Online Free Variational Inference approximation to build the prediction model. The key idea is to introduce a variational distribution and maximizing the Kullback-Leibler Divergence between the variational distribution and the true value of the output’s posterior distribution. Further, we use telemetry data to verify the validity of the Online Free Variational Inference approximation and the experiment shows that the online data stream can be predicted well by the method employed in the paper.
A Survey of Knowledge Reasoning based on KG
Authors: Lu Rui , Cai Zhiping , Zhao Shan
Published: August 2019

Knowledge Reasoning(KR) has become the core issue in the field of Artificial Intelligence(AI) and even Natural Language Processing(NLP). KR based on Knowledge Graph(KG) is based on existing KG’s facts. It uses some inference models and algorithms to infer new unknown knowledge and targets at improving the completeness and accuracy of KG. This article presents a brief overview of KR based on KG, expounds the connotation and research scope of it, judges the two main research directions(Knowledge Graph Completion(KGC) and Question Answering over Knowledge Graph (QA-KG)) of current KR and summarizes the four main technical methods. A series of latest results of current research on KR are also listed in this paper. Finally, we look forward to the future improvement of KR.
Pinball loss based extreme learning machines
Authors: Wang Kuaini , Ding Xiaoshuai
Published: August 2019

Extreme Learning Machine (ELM) is a novel machine learning method by training single hidden layer feedforward neural network. It employs squared loss function to minimize the mean squares error, which is sensitive to noises and outliers. In this paper, pinball loss function with quantile error is introduced into ELM in order to improve the robustness of ELM. An ELM model based on squared pinball loss function (SPELM) and an ELM model based on pinball loss function (PELM) are proposed. The corresponding optimization problem are solved by iterative reweighted algorithm. Three simulated datasets and nine Benchmark datasets are used to verify the validity of the proposed models. It is concluded that the proposed SPELM and PELM are superior to other comparisons, especially for datasets containing larger proportion of outliers.
Fast algorithm for infrared transmittance in marine environment
Authors: Zhong-wei Chen
Published: August 2019

Because Modtran software is not convenient to connect with other software programs, a fast algorithm for infrared transmittance in marine environment was established. The relevant meteorological parameters were obtained by actually measuring the marine environment of a certain sea area. The suitable parameters of Modtran to calculate the infrared transmittance of 3~5μm and 8~12μm at different distances were selected. Using the exponential function relationship, this paper constructed a fast algorithm in 3~5μm and 8~12μm infrared transmittance of marine environment. The comparison between the fast algorithm and the Modtran calculation shows that the maximum error of the 3~5μm infrared transmittance calculation method is only 2.16%, and the maximum error of 8~12μm is 0.16%. The formula of the fast algorithm has high precision and can meet the requirements of fast calculation of infrared radiation transmittance.
Research on Electric Heating Shielding and Sunlight Visualization of LCD Display Based on Machine Vision
Authors: Wei Chen
Published: August 2019

Due to the widespread popularity of interactive electronic products, the requirement for flat panel display becomes higher and higher, so the visibility of display screen becomes particularly important. Therefore, based on machine vision, the author studies the electric heating shielding of LCD and the visibility in sunlight. The results show that high brightness is the only way for airborne LCD to be visible under direct sunlight, especially in open cockpit. Machine vision, as an advanced automated inspection technology in the field of information science and technology, can improve production efficiency and industrial manufacturing. Improves the contrast of the display in strong sunlight, enabling the LCD to be visible in the sun. At the same time, the production yield is improved, the quality is more stable, and the reliability is higher, which enhances the competitiveness of the liquid crystal display in the fields of high-end and outdoor.
A Hybrid Intelligent Directional Push Model for Service Platform Based on Deep Learning
Authors: Shuai Yong , Song Tailiang , Wang Yong , Xia Qing , Su Xinyi
Published: August 2019

In order to improve the push precision and user satisfaction of the service platform, this paper proposes an intelligent directional push model based on the existing problems of the intelligent search model and the actual characteristics of the service platform. Firstly, use data preparation method to finish collecting, integrating, cleaning, conversion and protocol of the user association data in the service platform. Then deep learning model is used to build a local service platform semantic library. Thirdly, use jieba algorithm to match the user-associated data with the local service platform semantic library, set the weights of input data based on its importance, use the normalization algorithm to obtain the access matching matrix of the target users. Finally the collaborative filtering algorithm is used to calculate the user matching degree. The case analysis proves that the model in this paper has higher accuracy.
Identification method of suspicious targets in submarine cable boundary based on multi-source data fusion
Authors: Yang ZuPei , Wang Xin
Published: August 2019

This paper studies a new all-round visualized cable zone monitoring system and target identification method. Specifically,a mobile robot vision system based on distributed control was designed for improving the efficiency of video processing. Secondly,the saliency measure based on background priors was used for image preprocessing and excluded the background regions which were less important. Finally,the recognition accuracy of the target was improved according to the dynamic template matching to images which had been processed. The experimental results show that the method can meet the requirements of real-time and veracity in the process of mobile robot’s target recognition,the method show good effectiveness.
Study on the method of SLAM initialization for monocular vision
Authors: Xu Guanghui , Zhang Qingsong , Li Na
Published: August 2019

Visual SLAM is considered as a key technology to realize the autonomous positioning and navigation of mobile robots, and also a hot research technology in the fields of unmanned driving, augmented reality and smart home. Among them, monocular camera based visual SLAM is one of the hotspots in the field of visual SLAM. In today’s mainstream visual SLAM, PTAM needs to manually select two images to complete the initial keyframe trajectory and map point estimation process, which limits the practical application of the system and reduces the success rate of initialization. In addition, ORB_SLAM system adopts the statistical model selection method to realize the automatic initialization process. This paper first introduces the definition of the homography matrix and the fundamental matrix and the implementation algorithm, and then elaborates on the use of a certain scoring strategy to select the model method for initialization.
Research on bat algorithms for inversion of particle size distribution in spectral extinction method
Authors: Wang Li , Huang Qianghuang , Wang Su , Yu Huiming , Xu Fuxin
Published: August 2019

The development of rapid and effective inversion algorithm is an important subject in the field of particle size distribution (PSD) measurement. An intelligent bat algorithm has been proposed to solve the problem of independent mode inversion in particle size distribution reconstruction based on spectral extinction method. The improved algorithm is more suitable for particle size distribution inversion under extinction method by enriching its behavior mode, improving local search mode and adding greedy strategy. In simulations, uniform spherical particles that obey the unimodal, bimodal and multimodal R-R distribution were used, and the scattering intensity values of the target function was added to the random noise of ±0%, ±3%, ±5%, ±8% respectively. In the inversion calculation of the improved bat algorithm, 400, 800 and 1000 iterations were performed for different distributions, respectively. Compared with modified pattern search method, the improved bat algorithm has strong noise resistance, moderate inversion time, independent of initial solution and high inversion accuracy for complex distribution. Compared with genetic algorithm, the improved bat algorithm has fast convergence speed, high accuracy and short time-consuming. Considering the practical application, the algorithm is suitable for field measurement and has a wide application prospect.
Research on PCB Cost Data System Based on Cloud Platform
Authors: Jinhai Yu
Published: August 2019

With the rapid development of electronic manufacturing industry in China, defect detection of printed circuit board (PCB) is particularly important in order to improve its quality and reduce its production cost. The cost of PCB has always been discussed in depth by practitioners, but there are few fixed models for replication, although confidentiality is the key reason. However, due to the long process of PCB and the variety of products, it is really difficult to be comprehensive. Therefore, based on the cloud platform, the author studies the PCB cost data system. Research shows that the cloud-based PCB cost data system allows management to master energy consumption data more effectively and in real time, effectively grasp the monitoring reports of production costs and energy consumption, and control the production equipment in a safe state in real time. Improve management capabilities.
Analysis of Bicycle-sharing Operation and Location Selection of Urban Retail Stores
Authors: Shang Rui
Published: August 2019

Bicycle-sharing is the product of the new era, which has brought a variety of life styles to the society. Generally speaking, the conventional considerations of retail store location include: urban factors, population factors, traffic factors, competitors and other related factors. Under the circumstance of sharing bicycle operation, the location methodology of retail stores has developed and changed. Besides recognizing Bicycle-sharing, people should carefully analyze the basic characteristics of bicycle-sharing, and then make a location decision suitable for the development of retail stores. In short, the location of retail stores should keep pace with the times and change the way of life in the new era with the sharing bicycle as a corresponding change.
Behavior Analysis of College Students on Using Microblog in Big Data Environment
Authors: Yang Peng , Li Wei
Published: August 2019

With the development of information technology in big data environment, the “closed” campus has gradually turned into an open campus, which brings a series of new opportunities and challenges for the student management of college. Social platforms such as Microblog can provide students with a broader space for learning and communication, students are no longer limited to being managed, and they are increasingly inclined to their own opinions. In this paper, a distributed Microblog spider platform is studied and implemented for the behavior of student users. First, the data support can be provided for behavior analysis by the crawling of user information and content of Microblog. Secondly, the combing, integrating, analyzing and mining of the crawled data can make the useful value behind the information fully utilized in the student management work.
An Approximate Calculation Method of Broadband Impedance of GIS Pipe Brackets
Authors: Qian Wenxiao , Ji Jianfei , Liu Haibo , Zhang Ping , Xu Keqiang , Cao Shuyun , Jiao Chongqing
Published: August 2019

In Gas Insulated Switchgear (GIS) substation, the grounding bracket of GIS pipe is used to support GIS pipe, meanwhile, it’s a part of grounding system connecting GIS pipe to grounding grid. The frequency of transient enclosure voltage(TEV) generated during switching operation of disconnector in gas insulated switchgear (GIS) with no-load short busbar can be up to tens or even hundreds of megahertz(MHz). Because of the impact of skin effect and proximity effect, the impedance of bracket will vary considerably with frequency. A simple and effective method of broadband impedance modeling was proposed based on electromagnetic field numerical calculation method and the physical structure of the grounding bracket in this paper. At last, the validity of the method was verified through calculating and analyzing some typical grounding bracket.
Consumption behavior evaluation of college students based on PSO-DTSVM
Authors: Chen Hui , Wang Xinze , Zhou Yan , Zhao Shasha , Zhang Dengyin
Published: August 2019

Blind and reckless consumption of the college students has become a common phenomenon. To give a correctly guiding, a decision tree support vector machine (DTSVM) based on particle swarm optimization (PSO) used to evaluate college students consumption behaviour is proposed in this paper. Firstly, the classification standard of college students consumption was derived with the investigation and data analysis. Then, the consumption ability and habits for different students are divided into four categories. Lastly, the targeted consumption index is fed back for the corresponding students. Experiment results show that a classification accuracy of 99.79% can be achieved, which will provide better guidance for the college students’ consumption behaviour.
Electronic Medical Record of University Hospital Based on Deep Learning
Authors: Cao Wanshan , Su Jing , Peng Zhibin , Xu Weihong , Liu Qingjian
Published: August 2019

Because the traditional paper medical record has the disadvantages of illegible handwriting and being difficult to save, we design the electronic medical record system for the university hospital. Additionally, the neural network model is used to realize auxiliary diagnosis and help doctors make decisions, which can provide effective reference for preventive health care, health education, and medical services in school hospitals. The system is putting into use in our university and has effectively improved the work efficiency of our school hospital.
Crystal growth of (Bi, Pb)2Sr2Ca2Cu3O thin films fabricated via post annealing
Authors: Mori Zon , Koba Shinichiro
Published: August 2019

Post-annealing of superconducting Bi2Sr2Can-1CunOx (BSCCO) thin films deposited by sputtering is essential for obtaining a single 2223 phase. However, optimum annealing temperature is limited to a very narrow range, needing to pass liquid phase for 2223 crystallization while not exceeding re-evaporation point. In this study, we optimized annealing conditions and observed the crystal growth process. As a result, single phase 2223 thin films with critical temperature of 108 K were obtained at 850 °C, while mixed phase thin films of 2223 and 2212 were grown above 855 °C. This is due to the re-evaporation of the thin film compounds, and these results reflect the recrystallization process after melting.
Utility of Routine Frozen Section of Thyroid Nodules Classified as Follicular Neoplasm
Authors: Grisales Jhorman , Sanabria Alvaro
Published: August 2019

Objectives

To evaluate the diagnostic performance of frozen section in thyroid nodules classified as follicular neoplasm.

Methods

A diagnostic test meta-analysis was designed. Studies that assessed frozen section in patients with thyroid nodules and a fine-needle aspiration biopsy result of Bethesda IV were selected. The outcomes measured were the number of false- and true-positive and -negative results. We used the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) instrument for methodological quality assessment and a bivariate mixed-effects regression framework and a likelihood-based estimation of the exact binomial approach.

Results

Forty-six studies from 1991 to 2018 were included. Most studies had moderate methodological quality. The overall sensitivity and specificity were 43% (95% confidence internal [CI], 0.34-0.53) and 100% (95% CI, 0.99-1.00), respectively. The hierarchic summary receiver operating characteristic curve showed an area under the curve of 0.91 (95% CI, 0.80-0.97).

Conclusions

Frozen section demonstrates moderate diagnostic performance in patients with follicular neoplasm, and its utility for making intraoperative decisions is limited. Its routine use should be discouraged.
Avoiding sexual interference: herkogamy and dichogamy in style dimorphic flowers of Narcissus broussonetii (Amaryllidaceae)
Authors: Barranco Daniel , Arroyo Juan , Santos-Gally Rocío
Published: August 2019

Spatial (herkogamy) or temporal (dichogamy) separation of sex organs are mechanisms considered to restrict self-pollination and promote outcrossing. Additionally, avoidance of self-interference is proposed to be the driving force for the evolution of these mechanisms, particularly in self-incompatible species. However, species with anthers and stigmas at different levels may increase the rate of imprecise pollen transfer, resulting in pollen discounting. Non-reciprocal stylar dimorphism has been considered a transitional, unstable stage towards the evolution of reciprocal style dimorphism (distyly), to simultaneously avoid interference and lack of precision. In this study we investigate the spatial and temporal separation of sex organs in a population of the style dimorphic and self-incompatible Narcissus broussonetii and their consequences in the reciprocity between the sex organs of morphs and their fecundity. First, we evaluated the relative growth of sex organs after anthesis. Then, we studied the stigma receptivity along the flower lifespan including its effect on seed production in both morphs. Finally, given the weak reciprocity between the sex organs of morphs of this species, we estimated population genetic diversity parameters in Long- and Short-styled plants to explore differences between them as a result of rates of inbreeding due to different mating strategies. We observed that Long-styled plants and Short-styled plants present different strategies to avoid sexual interference and both of them had negative consequences in the reciprocity between the sex organs of morphs. Long-styled plants exhibited a delay in stigma receptivity and a higher growth rate of the style after anthesis, while Short-styled plants presented higher herkogamy and no delay in stigma receptivity. These findings suggest that the avoidance of self-interference, in stylar dimorphic Narcissus species, seems to be more critical than improving of reciprocity between the sex organs of morphs. This might explain why reciprocal herkogamy (distyly) is rare in the genus.
Alternating EM algorithm for a bilinear model in isoform quantification from RNA-seq data
Authors: Deng Wenjiang , Mou Tian , Kalari Krishna R , Niu Nifang , Wang Liewei , Pawitan Yudi , Vu Trung Nghia
Published: August 2019

Motivation

Estimation of isoform-level gene expression from RNA-seq data depends on simplifying assumptions, such as uniform read distribution, that are easily violated in real data. Such violations typically lead to biased estimates. Most existing methods provide bias correction step(s), which is based on biological considerations—such as GC content—and applied in single samples separately. The main problem is that not all biases are known.

Results

We have developed a novel method called XAEM based on a more flexible and robust statistical model. Existing methods are essentially based on a linear model Xβ, where the design matrix X is known and is computed based on the simplifying assumptions. In contrast XAEM considers Xβ as a bilinear model with both X and β unknown. Joint estimation of X and β is made possible by a simultaneous analysis of multi-sample RNA-seq data. Compared to existing methods, XAEM automatically performs empirical correction of potentially unknown biases. We use an alternating expectation-maximization (AEM) algorithm, alternating between estimation of X and β. For speed XAEM utilizes quasi-mapping for read alignment, thus leading to a fast algorithm. Overall XAEM performs favorably compared to recent advanced methods. For simulated datasets, XAEM obtains higher accuracy for multiple-isoform genes. In a differential-expression analysis of a real single-cell RNA-seq dataset, XAEM achieves substantially better rediscovery rates in independent validation sets.

Availability and implementation

The method and pipeline are implemented as a tool and freely available for use at http://fafner.meb.ki.se/biostatwiki/xaem/.

Supplementary information

Supplementary data are available at Bioinformatics online.
Reconciling Conflicting Paradigms of Biodiversity Conservation: Human Intervention and Rewilding
Authors: Van Meerbeek Koenraad , Muys Bart , Schowanek Simon D , Svenning Jens-Christian
Published: August 2019

There are strong opposing views among conservationists about whether we have to intervene to safeguard our natural heritage or not. In the Western European tradition, human intervention has been dominating, whereas, elsewhere, rewilding aimed at restoring self-regulating ecosystems has often been preferred. However, cultural rather than ecological differences are at the root of these opposing paradigms, leading to management strategies that are not always optimal for biodiversity conservation. In the present article, we propose a framework based on the relationship between ecosystem dynamics and the human footprint, including land-use legacies, to guide the mixture of rewilding and intervention practices in order to ensure a biodiverse future. We argue that these paradigms are not conflicting but complementary and advocate for rewilding where possible, human intervention where needed.
Changes in Transcript, Metabolite, and Antibody Reactivity During the Early Protective Immune Response in Humans to Mycobacterium tuberculosis Infection
Authors: Weiner January , Domaszewska Teresa , Donkor Simon , Kaufmann Stefan H E , Hill Philip C , Sutherland Jayne S
Published: August 2019

Background

Strategies to prevent Mycobacterium tuberculosis (Mtb) infection are urgently required. In this study, we aimed to identify correlates of protection against Mtb infection.

Methods

Two groups of Mtb-exposed contacts of tuberculosis (TB) patients were recruited and classified according to their Mtb infection status using the tuberculin skin test (TST; cohort 1) or QuantiFERON (QFT; cohort 2). A negative reading at baseline with a positive reading at follow-up classified TST or QFT converters and a negative reading at both time points classified TST or QFT nonconverters. Ribonucleic acid sequencing, Mtb proteome arrays, and metabolic profiling were performed.

Results

Several genes were found to be differentially expressed at baseline between converters and nonconverters. Gene set enrichment analysis revealed a distinct B-cell gene signature in TST nonconverters compared to converters. When infection status was defined by QFT, enrichment of type I interferon was observed. A remarkable area under the curve (AUC) of 1.0 was observed for IgA reactivity to Rv0134 and an AUC of 0.98 for IgA reactivity to both Rv0629c and Rv2188c. IgG reactivity to Rv3223c resulted in an AUC of 0.96 and was markedly higher compared to TST nonconverters. We also identified several differences in metabolite profiles, including changes in biomarkers of inflammation, fatty acid metabolism, and bile acids. Pantothenate (vitamin B5) was significantly increased in TST nonconverters compared to converters at baseline (q = 0.0060).

Conclusions

These data provide new insights into the early protective response to Mtb infection and possible avenues to interfere with Mtb infection, including vitamin B5 supplementation.

Analysis of blood from highly exposed household contacts from The Gambia who never develop latent Mycobacterium tuberculosis infection shows distinct transcriptomic, antibody, and metabolomic profiles compared to those who develop latent tuberculosis infection but prior to any signs of infection.
Emergence of Delafloxacin-Resistant Staphylococcus aureus in Brooklyn, New York
Authors: Iregui Alejandro , Khan Zeb , Malik Saquib , Landman David , Quale John
Published: August 2019

Delafloxacin is an option for infections due to methicillin-resistant Staphylococcus aureus. In 2017, 22% of isolates from 7 hospitals in Brooklyn, New York, were nonsusceptible to delafloxacin. Isolates belonging to ST105, a strain associated with healthcare-related infections, predominated. Resistance was also found in ST8, a strain (USA300) associated with community-associated infections.
Monotone Lagrangians in Flag Varieties
Authors: Cho Yunhyung , Kim Yoosik
Published: August 2019

In this paper, we give a formula for the Maslov index of a gradient holomorphic disk, which is a relative version of the Chern number formula of a gradient holomorphic sphere for a Hamiltonian $S^1$-action. Using the formula, we classify all monotone Lagrangian fibers of Gelfand–Cetlin systems on partial flag manifolds.
Detection of atypical porcine pestivirus genome in newborn piglets affected by congenital tremor and high preweaning mortality1
Authors: Sutton Kylee M , Lahmers Kevin K , Harris Seth P , Wijesena Hiruni R , Mote Benny E , Kachman Stephen D , Borza Tudor , Ciobanu Daniel C
Published: August 2019

Recently, piglets from a high-health status farm began exhibiting congenital tremors, high preweaning mortality and incidence of splayed legs. Postmortem histological examination identified a small number of scattered white matter vacuoles in the cerebellum and underlying brainstem of affected piglets. Presence of potential viral sources associated with this neurologic condition was initially infirmed using quantitative PCR for atypical porcine pestivirus (APPV), porcine teschovirus, and porcine sapelovirus. Using metagenomic analysis, APPV was identified as the main microbial species in serum obtained from piglets affected by congenital tremor. These piglets had higher preweaning mortality rates (46.4% vs. 15.3%) and incidence of splayed legs (33.0% vs. 0.8 %) compared to unaffected piglets. Piglets affected by congenital tremor had higher viral titer (P < 0.15) and larger birth weights (P < 0.05) compared to normal litter mates. Whole-genome sequencing and genome assembly of the novel APPV strain (MK728876) was carried out using Oxford Nanopore and related bioinformatics pipelines. Phylogenic analysis demonstrated that this strain along with other completely sequenced APPV strains were grouped into 2 clades, both including strains-inducing congenital tremor. Strains appear to cluster based on region but there were still significant differences within regions. Future research needs to address potential underdiagnosis due to genetic diversity but also to understand mode of transmission, variation in virulence, and the role of host genetics in APPV susceptibility.