bluuebunny/crossref_metadata_2025_split huggingface dataset:
Novel Dual Therapy: A Paradigm Shift in Anticoagulation in Patients of Atrial Fibrillation Undergoing Percutaneous Coronary Intervention
Authors: Pradhan Akshyaya , Bhandari Monika , Vishwakarma Pravesh , Sethi Rishi 
Published: October 2020

AbstractPatients with atrial fibrillation (AF) on long-term oral anticoagulation (OAC) either have underlying coronary artery disease or suffer from acute coronary syndromes necessitating a percutaneous coronary intervention (PCI). In such a scenario, an amalgamation of antiplatelet and antithrombotic therapy (conventionally called as “triple therapy”) is obligatory for preventing coronary ischemia and stroke. But such ischemic benefits are accrued at the cost of increased bleeding. We also now know that bleeding events following PCI are related to increased mortality. Balancing the bleeding and ischemic risks is often a clinical dilemma. With the advent of novel oral anticoagulants (NOAC's) with preserved efficacy and attenuated bleeding rates, anticoagulation in AF is undergoing paradigm shift. The spotlight is now shifting from conventional triple therapy (vitamin-K antagonist + dual antiplatelet therapy [VKA + DAPT]) to novel dual therapy (NOAC + single antiplatelet therapy [SAPT]) in situation of anticoagulated AF patients undergoing PCI. Such a strategy aims to ameliorate the higher bleeding risk with conventional VKA's while retaining the ischemic benefits. In this review, we briefly discuss the need for combination therapy, trials of novel dual therapy, strategies for mitigating bleeding, the current guidelines, and the future perspectives in AF undergoing PCI with stent(s).
Melatonin Administration from 2000 to 2020 to Human Newborns with Hypoxic-Ischemic Encephalopathy
Authors: D'angelo Gabriella , Cannavò Laura , Reiter Russel J. , Gitto Eloisa 
Published: October 2020

Hypoxic-ischemic encephalopathy (HIE) is the main cause of long-term neurodevelopmental morbidity in term born infants worldwide. Melatonin is a hormone with antioxidant and anti-inflammatory effects that make it a promising molecule for the treatment of perinatal asphyxia. Probably, the synergistic use of hypothermia associated with melatonin treatment may improve the neurological outcome in infants with HIE. In the past 20 years, the efficacy of melatonin in reducing oxidative stress has been demonstrated in animals; however, clinical trials with sufficient sample size of newborns are lacking to date. Since in 2000 we were among the first to study the neuroprotective properties of melatonin on infants, in this review, we want to summarize the advantages and limitations of the investigations conducted to date.
Key Points
Frequency Hopping Signal Sorting Algorithm Based on Global Feature of Ambiguity Function
Authors: Wu Jixiang , Wang Shaobo , Yang Yinsong , Li Hongguang , Yu Xinyong 
Published: October 2020

Abstract
Aiming at the problem that the traditional feature parameters are difficult to meet the fastness and effectiveness of frequency-hopping signal sorting recognition, a feature extraction method based on ambiguity function theory is proposed. The algorithm applies the ambiguity function theory to the frequency hopping signal sorting and identification. Firstly, the ambiguity function of the single-hop signal is calculated, then the AFMR slice of the signal is extracted by the PSO algorithm, and the global features of the AFMR slice are extracted to form the feature vector. The results of FCM clustering experiments show that the extracted feature parameters have higher sorting accuracy and can adapt to the larger dynamic signal-to-noise ratio.
Review of Web Authentication
Authors: Wang Zikai , Sun Wei 
Published: October 2020

Abstract
In the server situation of web security, a large number of Web sites and users are under the threat of network attacks. Fortunately, a complete authentication system can effectively protect them from some attacks. With the help of the authentication system, web sites can identify their users correctly and then decide whether to provide the corresponding services. The web authentication system is mainly constructed by the credential and the authentication strategy, the credential symbolizes identity and the authentication strategy judges the authenticity of identity. This paper will analyse and discuss some related literature and research findings of credentials and authentication strategies in the world, grasp overall direction, and look forward to the development of web authentication.
Research on Attack Identification Method and Device Method Based on Random Forest Algorithm
Authors: Yu Yang , Xiao Shengyuan , Chen Rui , Du Yu , Ding Kai , Zhu Xiaofan , Zhou Guoqi , He Shengguo , He Hongyan , Huang Zhiwei , Gao Xiaoquan , Yao Hao , Xi Wei , Xian Qing , Fu Yang , Ding Qing 
Published: October 2020

Abstract
Because the energy storage system has the function of stabilizing the voltage and frequency of the grid, when the energy storage system is connected to the grid, it is necessary to obtain its operating status information in a timely and accurate manner. The operating status data of the energy storage system is uploaded to the higher-level dispatch center via the energy storage coordination control device. However, with the intelligence and informationization of the power grid, various cyber attack methods have emerged endlessly. There are various security risks in the way of information interaction based on the network. Attackers can analyze the communication protocols within them, or implement Eavesdropping attacks, Dos attacks, and tampering with sensitive data, etc., causing the energy storage coordination control device to obtain the wrong data sent by the dispatch center. The wrong data causes the energy storage system to output the wrong power, which may have the opposite effect when adjusting the voltage and frequency of the power grid, causing an imbalance in the voltage and frequency of the power grid, resulting in great economic losses, and even causing casualties due to equipment failure. Attackers can also gradually invade the upper-level dispatch center by using the energy storage coordination control device as a springboard. In view of the above problems, this paper uses a random forest-based network attack detection module is used to make real-time judgments on all data flows of the energy storage coordination control device. That is, when the energy storage coordination control device is subjected to a network attack, all data flows of the energy storage coordination control device pass a trained random forest attack detection model, thereby implementing attack detection on real-time data flows.
Transmission Efficiency of Licklider Transmission Protocol (LTP) and Bundle Protocol (BP) in DTN Architecture
Authors: Wang Ding , Yu Hongzhi , Ma Jun 
Published: October 2020

Abstract
From Near-Earth orbit (NEO) to Earth-Sun Lagrangian points will continue to be a majority of Near Earth missions ranging from Near-Earth orbit (NEO) to Earth-Sun Lagrangian points will continue to be a majority of future space missions. A few works have been done with delay/disruption tolerant networking (DTN) technology for Near-Earth orbit (NEO)-space center and deep space communications and provided feasibility for its adoption in NEO-space and deep space missions. [1] However, no much work has been done to fully evaluate the transmission efficiency of DTN in such an environment, especially in the presence of long link disruption, data corruption and lost rate, and link asymmetry. In this paper, we present an experimental performance evaluation of DTN architecture and protocol stack, with Licklider transmission protocol (LTP) serving as a convergence layer adapter (CLA) underneath bundle protocol (BP), in typical NEO-satellite system and deep space communication infrastructures accompanied by a very long link delay, various packet corruption and lost rates, and channel rate symmetry and asymmetry. In addition, we present an experimental research of the transmission efficiency of DTN custody transfer with long link delay. The experiment was conducted by performing realistic file transfers over a virtual server-based test-bed.
Application of BP Neural Network Algorithm in Classification of Red Wine Grades
Authors: Gao Sheng , Fang Jie , Miao Ning 
Published: October 2020

Abstract
The rapid development of society promotes the improvement of people’s living standard. Red wine has entered thousands of households, at the same time, high-quality red wine is more and more loved by people. As an important standard to distinguish the quality of red wine, the grade of red wine plays an important reference role in the evaluation of red wine. Therefore, accurate and efficient classification of red wine grade is particularly important. In this study, the BP neural network model is established, and the MIV algorithm is introduced to screen the chemical properties of red wine, further optimizing the BP neural network. The optimized BP neural network is applied to the classification of red wine grades to complete the efficient classification of red wine grades. The experimental results show that the correct rate of red wine classification can be effectively improved by using the red wine data screened through MIV algorithm. This meets the requirements of accuracy in classification of red wine grades.
Transformation from AADL to Colored Petri Nets for Dynamic Reconfiguration Process
Authors: Liu Lei , Zhao Wei , Jiang Zeyong , Wang Shihai 
Published: October 2020

Abstract
Although integrated modular avionics (IMA) provides many advantages such as the reduced weight and higher efficiency for system operations, safety problems in IMA dynamic reconfiguration process come up due to its complexity to analyze. Architecture Analysis and Design Language (AADL) has advantage to model for embedded systems. However, it is quite limited to employ AADL for analyzing system safety. This paper, seeks to translate the AADL models of IMA dynamic reconfiguration process into colored petri net (CPN), which have advantages to simulate and evaluate embedded systems. Then, the colored petri net (CPN) is proposed for the analysis for AADL models, because CPN can describe the properties and behaviors of dynamic reconfiguration process well. Moreover, it is essential to make a transformation from the AADL models to the CPN models. This work will benefit the subsequent analysis of IMA dynamic reconfiguration process. Finally, a case study is provided for effectiveness the transformation rules.
Research on the Classification of Aviation Safety Reports Based on Text and Knowledge Graph
Authors: Zhang Wenfang , Shi Hongwei , Yang Yang , Luo Yonghui 
Published: October 2020

Abstract
The existing automatic classification methods of aviation safety reports are mainly traditional machine learning classification algorithms, which have the disadvantages of relying on manual feature extraction and selection, unable to consider domain information and dealing with complex dependencies, which limits the improvement of classification accuracy. Therefore, this paper proposes an automatic classification algorithm for aviation safety reports that combines text and knowledge graph. The algorithm uses the knowledge triples with rich relationships as the background knowledge in the aviation safety field into the word2vec word vector training process so that the words can not only learn context information in training but also learn the complex semantics between words in the aviation field information, so that the trained word vectors can express richer semantic features, thereby improving the classification effect. Through a series of comparative experiments, the results show that the algorithm has the highest F1-score value and has a classification accuracy rate of up to 91.4%.
Research on Attack Detection Method of Photovoltaic Grid-connected Interface Device Based on Gradient Boosting Decision Tree
Authors: Yao Hao , He Shengguo , Chen Rui , Chen Yuansheng , Zheng Yucheng , Ding Kai , Zhu Xiaofan , Zhou Guoqi , Huang Zhiwei , Xiao Shengyuan , He Hongyan , Pan Junjun , Xi Wei , Yu Yang , Ning Baifeng 
Published: October 2020

Abstract
Photovoltaic grid-connected interface devices are an important class of smart devices in microgrids. The authenticity and reliability of the data they acquire, as well as the safety and stability of operation, are related to the safe and reliable operation of the entire microgrid system. However, in the context of microgrid intelligence and informatization, information / network attacks will become the norm, making network-dependent information interaction methods subject to various security risks. The photovoltaic grid-connected interface device involves an open operating environment and is extremely vulnerable to network attacks. The attack information will occupy the space or resources of the photovoltaic grid-connected interface device, making the photovoltaic grid-connected interface device unable to respond to other important requests or instructions in a timely manner, and in severe cases, will cause the device to be paralyzed and affect the normal system operation. Aiming at the above problems, this paper presents an attack detection method based on the gradient-upgraded decision tree model, and gives a detailed design process of attack detection model of the photovoltaic grid-connected interface device. That is, the important data flow in the photovoltaic grid-connected interface device is used as the input of the gradient-upgraded decision tree model, and then the gradient-upgraded decision tree model detect or classify flows, finally, intercept the data flow with attack behavior and give a warning prompt, and forward data without attack behaviors normally.
An Automatic Optical Inspection Algorithm of Capacitor Based on Multi-angle Classification and Recognition
Authors: Fang Jian , Shang Lina , Xiong Kai , Gao Guangchun , Zhang Cui 
Published: October 2020

Abstract
There are many plug-in components in PCB circuit production. The AOI of these components has always been a difficult problem because of the large deviation of manual installation position of these components. In this paper, an AOI algorithm based on multi-angle classification and recognition is proposed for the plug-in polar capacitors. The algorithm combines traditional image comparison method with feature recognition method, and uses AdaBoost classifier based on Haar-like feature to recognize and classify multi-angle polar capacitors. Experimental results show that the algorithm proposed in this paper can effectively detect three kinds of defects: capacitor missing, capacitor polarity error and capacitor size mismatch.
Automatic Lung Segmentation in CT Images Using Dilated Convolution Based Weighted Fully Convolutional Network
Authors: Wu Yuchao , Lin Lan 
Published: October 2020

Abstract
Lung cancer is one of the primary lung malignant tumour with the fastest increasing morbidity and mortality and the greatest threat to people’s health and life. Early detection of lung cancer can significantly increase patients’ chance of survival. Lung parenchymal segmentation is an essential pre-processing step for analysing thoracic computed tomography(CT) images. Conventional methods for lung segmentation rely on user generated features, and do not segment lung parenchymal with juxta-pleural nodules accurately. Deep learning has outperformed other methods in image classification and target recognition tasks. In this study, a new dilated convolutional based weighted fully convolutional network (FCN) has been proposed for the segmentation of lung parenchyma to minimize the juxta-pleural nodule issue. The effectiveness of this method was verified by experiments on 173,694 diagnosis CT images of lungs and their corresponding segmentation maps. The Dice similarity coefficient and pixel accuracy achieved are 0.9702 and 0.9833 respectively. The experiment results show that the proposed method can provide more accurate and robust results than traditional FCN.
The Summary of Deep Learning in the Field of Weather Forecast Research
Authors: Bai Yanrong , Wang Juan , Wang Xiaoting , Li Hui 
Published: October 2020

Abstract
This article investigates application of deep learning in the field of meteorology at home and abroad. summarized of deep learning and several deep learning network models widely used in meteorological field, It focuses on research and summary application of deep learning in Meteorological Department of China, and also summarized the future development trend of deep learning in weather forecast application.
A Novel Image Feature Extraction Algorithm Based on the Fusion AutoEncoder and CNN
Authors: Xu Ke , Long Wenhan , Sun Yuan , Lin Yichao 
Published: October 2020

Abstract
Deep learning method has very excellent ability of image feature extraction. In order to get rid of disadvantages of traditional methods require a priori knowledge, this paper proposed an image feature extraction algorithm based on the fusion AutoEncoder and convolutional neural networks (CNN). The method introduces a fast sparsity control technique to AutoEncoder and utilizes AutoEncoder to train the basic elements of image and initialize the convolution kernel of CNN. Meanwhile, the algorithm adds filtering mechanism to the CNN network to keep the sparsity of output characteristics. The results of experiments point out that this method has achieved good performance on the Minist handwritten digital library and the Yale face database. Furthermore, the advanced experimental outcomes indicate that the feature extraction model included the filtering technique is more effective than the model without filtering mechanism by using cross-validation with T Test.
Magnetic Anomaly Feature Extraction Using the Tunable Q-factor Wavelet Transform Based on Non-convex Overlapping Group Shrinkage
Authors: Hu Jie , Guo Baolong , Liao Nannan , Yi Zhong , Zhang Shaohua , He Wangpeng 
Published: October 2020

Abstract
Extraction of the magnetic anomaly signal is one of the difficulties in the magnetic anomaly detection as the weak features extracted are easily disturbed by strong background noise. To address this problem, a sparse feature extraction method based on the tunable Q-factor wavelet transform and overlapping group shrinkage is developed in this paper. Compared with the traditional wavelet transform, the proposed method has excellent characteristics, which can flexibly tune the Q-factor according to the oscillation characteristics of the useful features. In this way, the sparsity of extracted features can be induced more effectively. In addition, the non-convex overlapping group shrinkage can effectively extract weak features from signals with group property, enhancing the extraction accuracy of features. The practical experiment verifies the effectiveness of the proposed method in the magnetic anomaly detection.
Research on the Method of Transforming Vague to Fuzzy Value
Authors: Li Yan , Cheng Bihong 
Published: October 2020

Abstract
In the study of the transformation of Vague sets to Fuzzy sets, the key is to judge the tendency of the Vague degree. To determine the tendency of Vague degree, it is necessary to make decision on the information of the uncertainty. Therefore, the transformation of the Vague set to the Fuzzy set can be regarded as the decision maker’s risk decision under the uncertain conditions. Risk preference of decision-makers is a key decision parameter when decision makers make risk decision. In this paper, the weight of risk - income weighed weight is introduced into the transformation model. Risk preference of decision-makers is well reflected in the transformation results. The membership degree of the Fuzzy set is decomposed into two parts: the true membership value of the Vague set, the value of the risk and the value of the tendencies. The results show that it is necessary and meaningful to consider the risk preference of the decision-makers. The thinking mode of dealing with this kind of problem can be changed from “point” thinking to “face” thinking. The fuzziness and risk preference of decision-makers are presented to the decision-makers in the form of a two-dimensional decision map. The transformation model is to help decision makers to fully understand the facts. The role of the transformation model is to assist decision makers in making scientific decisions rather than making decisions for decision makers.
Electric Vehicle Charging Fault Diagnosis Based on BP Neural Network
Authors: Zhang Yuanxing , Li Taoyong , Zhao Shuaishuai , Yan Xiangwu , Zhang Jing , Diao Xiaohong , Li Bin 
Published: October 2020

Abstract
With the popularization and application of electric vehicles, the problem of charging failure is exposed frequently. In order to effectively and accurately determine the type of charging failure and find out the cause of the failure, a method for identifying the charging failure using neural network is proposed. By collecting charging data and using neural network for training, the failure results are obtained through continuous optimization, and compared with the results of preliminary judgment, the superiority and feasibility of BP neural network are verified.
Research on KNN Algorithm of Charging Device Based on Attack Recognition Method
Authors: Huang Zhiwei , Yan Xichao , Wang Qiangang , Chen Yuansheng , Wu Wenju , Zhan Jiewen , Chen Rui , Ding Kai , Zu Lianxing , Xu Aijun , Su Zongzhou , Chen Dianjun , Lin Sajia , Xu Peng , Wang Chong 
Published: October 2020

Abstract
With the continuous improvement of the level of economic development and the increasingly serious environmental problems, new energy is gradually being respected by people, and more and more electric vehicles have been put on the market and gradually used widely. The development of electric vehicles has led to the development of charging devices. However, traditional charging devices operate in an open environment, and communication with other devices depends on information interaction with the network. With the development of network information today, network attacks are ubiquitous, and there are various risks in network information interaction. As long as there is a security loophole in the charging device, an attacker can use various means to attack the device and even invade the inside of the power grid, which directly affects the safe and stable operation of the power grid. Aiming at the potential threats in operation, a research on the nearest neighbor algorithm of charging device K based on attack recognition method is proposed here to improve the information security and operation reliability of charging device.
Research on Land Battlefield Situation Distribution Network Based on BRN
Authors: Chen Juan , Zheng Xueqiang , Peng Laixian , Xu Zhengqin , Li Aijing 
Published: October 2020

Abstract
In order to meet the requirements of battlefield complex electromagnetic environment and situation distribution instantaneity, the Communication network protocol in land battlefield needs to achieve low delay, high scalability and link robustness. This paper analyzes the development and problems of the situation distribution network in the land battlefield. Based on the analysis of the autonomous cooperative communication mechanism, the basic principle of the BRN protocol based on the communication mechanism is introduced, and the research fields based on this technology in the situation distribution network are proposed, so as to further improve the performance of the situation distribution network in the land battlefield.