bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cargo and cell‐specific differences in extracellular vesicle populations identified by multiplexed immunofluorescent analysis
Authors: Burbidge Kevin , Zwikelmaier Virginia , Cook Ben , Long Michael M. , Balva Barak , Lonigro Michael , Ispas Grace , Rademacher David J. , Campbell Edward M. 
Published: July 2020

ABSTRACTExtracellular vesicles (EVs) have been implicated in a wide variety of biological activities, have been implicated in the pathogenesis of numerous diseases, and have been proposed to serve as potential biomarkers of disease in human patients and animal models. However, characterization of EV populations is often performed using methods that do not account for the heterogeneity of EV populations and require comparatively large sample sizes to facilitate analysis. Here, we describe an imaging‐based method that allows for the multiplexed characterization of EV populations at the single EV level following centrifugation of EV populations directly onto cover slips, allowing comprehensive analysis of EV populations with relatively small samples. We observe that canonical EV markers are present on subsets of EVs which differ substantially in a producer cell and cargo specific fashion, including differences in EVs containing different HIV‐1 proteins previously reported to be incorporated into pathogenic EVs. We also describe a lectin binding assay to interrogate EVs based on their glycan content, which we observe to change in response to pharmacological modulation of secretory autophagy pathways. These studies collectively reveal that a multiplexed analysis of EV populations using fluorescent microscopy can reveal differences in specific EV populations that may be used to understand the biogenesis of specific EV populations and/or to interrogate small subsets of EVs of interest within larger EV populations in biological samples.
Humility as a Virtue: Oral and Visual Religious Indoctrination to Purify the Female Gender in Italy in the Early Quattrocento
Authors: Stefanacci Davide 
Published: July 2020

AbstractThe Madonna of Humility has been a subject of great interest in the twentieth century. Scholars such as Millard Meiss and H. W. van Os have long dwelled on the origin and dissemination of this emblematic iconography. Nevertheless, scholars have said little regarding its role and possible reverberation on women of the late Middle Ages. The need to domesticate the female gender inspired great orators like Saint Bernardino of Siena (1380–1444), who used humility as a means to control society. Not only could humility, if perceived as a virtue, keep women within their limited social spaces, it would have allowed them—by following a specific fabricated way of life—to spiritually lead their households, gaining the possibility of eternal salvation. This article explores the faith-based concept of humility to unlock the forces that pushed toward the diffusion of a religious iconography, capable of imposing a modest way of life that was more in keeping with the Catholic teachings of the late Trecento and early Quattrocento. Several portrayals of the Madonna of Humility are proposed to investigate the role these paintings might have played in Italy in exalting humility as a noble virtue.
Facing Another: The Attenuation of Contact as Space in Dhofar, Oman
Authors: Russell Kamala 
Published: July 2020

AbstractThis article considers the properly spatial aspects of communicative practices among speakers of Śħerēt, a Modern South Arabian language, living in Dhofar, Oman. I argue that participants in face-to-face interactions (particularly the domestic hospitality that dominates daily activity) move, speak, and position themselves in ways that attenuate interactional contact itself. This drawing out of contact is a site of normative practice across modalities including body posture, gaze, movement, and seating position in participation frameworks. Not simply creating distance or imposing categorical bounds on relationality, these signs attenuate the intensity of contact as the spatial extent of possible or actual encounters with others by complicating the accessibility of participants. As such, I constitute attenuation as an analytic that registers distortions of contact as manipulations of social space in a way that runs alongside (not counter to) other semiotic functions of gradation and categorization. The role of space as the medium of contact with others and its attenuation points to Dhofari concerns about accessibility that locally structure both interactional performance and understandings of sociality as such. This article in turn indicates new ways we can describe the nonneutrality of the spaces of social life.
Enterprise Investment Value Analysis Based on Machine Learning Model of Rapidminer
Authors: Chong Zhang , Xinrui Zhang , Zipei Yang 
Published: July 2020

Abstract
Whether an enterprise has investment value and how to measure the investment value of an enterprise are the primary concerns of every investor. Effective evaluation of enterprises’ investment values can not only help enterprises to recognize their own problems and values and to achieve faster growth, but also help investors to obtain more investment returns. The investment value of enterprises is reflected both in financial data and non-financial data. This paper is based on data mining and analysis of 3500 listed companies to establish a more comprehensive and objective evaluation model of enterprise investment value. Also, after data cleaning, data are trained by machine learning in RapidMiner software to compare the accuracy of several models, thus key factors affecting enterprise investment value are analyzed and investment advice will be given to investors, and enterprises can strengthen their capabilities in concerning areas to enhance their own value to attract more investment.
Breast Cancer Classification using Deep Convolutional Neural Network
Authors: Aslam Muhammad Aqeel , Aslam  , Cui Daxiang 
Published: July 2020

Abstract
Over the last decade, the demand for early diagnosis of breast cancer has resulted in new research avenues. According to the world health organization (WHO), a successful treatment plan can be provided to individuals suffering from breast cancer once the non-communicable disease is diagnosed at an early stage. An early diagnosis of cure disease can reduce mortality all over the world. Computer-Aided Diagnosis (CAD) tools are widely implemented to diagnose and detect different kinds of abnormalities. In the last few years, the use of the CAD system has become common to increase the accuracy in different research areas. The CAD systems have minimum human intervention and producing accurate results. In this study, we proposed a CAD technique for the diagnosis of breast cancer using a Deep Convolutional Neural Network followed by Softmax classifier. The proposed technique was tested on the Wisconsin Breast Cancer Datasets (WBCD). The proposed classifier produced an accuracy of 100% and 99.1% for two different datasets, which indicates effective diagnostic capabilities and promising results. Moreover, we test our proposed architecture with different train-test partitions.
The Design of Improved Neighborhood Rough Set Algorithm and Its Application in Diabetes Prediction Research
Authors: Gao Jing , Cheng Ming , Shen Ruifang , Hu Wei 
Published: July 2020

Abstract
In order to reduce the importance of neighboring rough set only by single attribute, this paper proposes an improved neighborhood rough set attribute reduction algorithm (INRS), which increases the dependence of conditional attributes based on considering the importance of individual features. Relationship, that is, whether it affects the effect of other conditional attributes on decision attributes after deleting the attribute. The importance of a certain attribute to the decision attribute is divided into two parts: direct influence (after the attribute is deleted, the obtained decision attribute depends on the degree of reduction of the conditional attribute) and indirect influence (relative to the influence of other conditional attributes on the role of the decision attribute when there is no such attribute), so that the importance of each attribute can be clearly identified. When the attribute is reduced, the potential attributes are not reduced. In this paper, the data of diabetes in a hospital of the National Population and Health Science Data Sharing Service Platform was collected. The attribute reduction algorithm was used to attribute the diabetes dataset, and the random forest (RF) was used for classification prediction, which formed high precision. The Diabetes Prediction Model aims to provide support for doctors’ clinical diagnosis and disease research to improve the level of clinical diagnosis and treatment.
Robust Beamforming and Power Minimization Design in MISO Health Monitoring System
Authors: Pei Mingjun , Jia Xuemeng , Fu Rong , Jiao Zhanwei 
Published: July 2020

Abstract
In this paper, we study the power splitting (PS) scheme for each user in a multiple-input single-output (MISO) health monitoring system, while considering simultaneous wireless information and power transmission (SWIPT). We construct an optimization problem with robust power minimization constrained by the signal-to-inference-plus-noise ratio (SINR) and energy harvesting (EH). For channel uncertainty, this problem is a non-convexity and is not easy to solve and it can be translated into semidefinite program (SDP) by utilizing S-Procedure. Then the above optimization problem is combined with different channel uncertainty models, and the Bernstein-type inequality and Gaussian error function are introduced to further simplify the optimization problem. Simulation results prove the performance of the proposed robust scheme and point out its effective help for the development of modern telemedicine systems.
Research on Decision-making Method for Allocation of Product Maintenance Spare Parts
Authors: Qian Wu , Chaowei Wang , Fei Pei , Yingcheng Xu , Xiaowei Lu 
Published: July 2020

Abstract
Under the current market situation, improving the efficiency and quality of after-sales service has become one of the effective means for improving the competitiveness of an enterprise. In this paper, the cost coefficient method is introduced into the research on decision-making method for allocation of product maintenance spare parts. By analyzing the factors influencing the transportation of maintenance spare parts, a model is established for the allocation cost coefficient of maintenance spare parts. The optimal transportation route of maintenance spare parts is calculated by using A-star algorithm. The optimal allocation scheme is obtained according to the table-manipulation method on linear programming. The feasibility of the model is verified by an example. The method is scientific, reasonable, simple and easy to learn. With strong generalization and operability, the method can effectively improve the allocation efficiency of maintenance spare parts and save the cost of after-sales service.
Target Tracking Based on Deep Optimization Features
Authors: Yang Tingting , Wei Haiping , Qi Yuanchen , Cui Naixiao 
Published: July 2020

Abstract
Target tracking algorithm based on deep learning generally adopts offline trained deep network to extract features, but with unsatisfactory separation effect of the target and the background. A target tracking algorithm based on deep optimization features is proposed to solve this problem. First, the Conv3-1 and Conv4-3 layers are selected from the offline trained convolutional neural network model VGGNet16 for basic feature extraction. Then the loss function is used to quantitatively describe the effectiveness of different filter positioning target in the basic feature layer. Finally, the selected high-efficiency filters are fused to extract deep optimization features and achieve effective tracking under the correlation filtering framework. The experimental results show that compared with other related algorithms, this algorithm can better recognize the target and present better results.
Study on Consumer Product Supplier Evaluation Method Based on Actor Analysis Thinking
Authors: Heliang Song , Xiaowei Lu , Qian Wu , Yingcheng Xu , Ya Li 
Published: July 2020

Abstract
This paper, based on the existing results of studying supplier evaluation indexes and Actor Analysis Thinking, and by combining the characteristics of consumer product suppliers, first establishes a consumer product supplier evaluation index system covering 10 indexes including product price, product quality, product performance, product series, timeliness of delivery, response capability, collaborative capacity, technical support capability and service attitude; then, refines the traditional Actor Analysis Method, reasonably converts some non-economic factors into economic factors, and improves the scientificity of the non-economic factor evaluation method by using comprehensive weighted average method; and finally proves the feasibility of the method proposed herein with practical application cases. The consumer product supplier evaluation method based on Actor Analysis Thinking proposed herein is scientific, rational, simple and easy to operate, and can provide technical support for enterprises and research institutions to carry out supplier evaluation, supply chain management, etc.
Stochastic Power Flow Calculation of High-Density Correlation Input Power Grid Based on Hybrid Gaussian Model
Authors: Qing Wang , Li Hongxin , Wei Wang , Rong Xue 
Published: July 2020

Abstract
Traditional stochastic power flow calculation assumes that the input variables are uncorrelated and directly used to calculate high-density correlation input variables has certain errors. To this end, this paper proposes a stochastic power flow algorithm based on mixed Gaussian models that takes into account input correlation. First, a mixed Gaussian model is established based on load data, then uniform sampling and Naf transformation are used to process random variables, and the high-density correlation in the original space is converted into independent random variables, and the piecewise linear Monte Carlo method is used to carry out random power flow. Calculation can greatly reduce the calculation time and reduce the truncation error as much as possible. Finally, combined with the actual calculation examples of IEEE-30, the results show that the algorithm can simplify the actual calculation process, improve the calculation efficiency, and has certain practical value in engineering.
Research on New Media Advertising Marketing Strategy in China under the “Internet +” Background
Authors: Wu Qixiang 
Published: July 2020

Abstract
In the context of the “Internet +” era, new media advertising marketing is essentially different from the original advertising marketing. At the same time, there are also some emerging problems, for which corresponding optimization strategies are proposed. According to the analysis results of new media advertising marketing status under the background of “Internet +”, the development prospects of advertising marketing is analysed. The analysis shows that overall environment restricts the new media advertising marketing, the industry system transformation impacts the new media advertising marketing, and its own business model advertising and marketing development have limitations. To address the above problems, the paper put forward corresponding strategies, including promoting the leading position of communicators, optimizing the internal industrial structure of new media, broadening advertising channels, and determining the cultural characteristics of advertising audiences. The proposed strategies can be deployed to optimize the new media marketing methods, enhance the advertising company’s information dissemination capabilities, enhance its economic returns, and promote the development of China’s new media advertising industry.
Evaluation of Energy Consumption in Block-Chains with Proof of Work and Proof of Stake
Authors: Zhang Rong , Chan Wai Kin (Victor) 
Published: July 2020

Abstract
Although there are several special features in block-chain technology such as machine trust, traceability, and security, high energy consumption remains an issue in broadening the applications of block-chain technology. Some researchers proposed the use of proof of stake (PoS) mechanism rather than proof of work (PoW) mechanism to reduce energy consumption of block-chain. However, because PoS cannot guarantee fairness, mixed consensus mechanisms could be a solution and has been adopted in many studies. This paper aims to evaluate the performances of PoW, PoS and mixed consensus mechanisms from three aspects: energy consumption, fairness, and reliability. An agent-based model of a typical block-chain system equipped with different consensus mechanisms is created in NetLogo. This model simulates and evaluates the performances of different consensus mechanisms in the block-chain system.
Reliability Analysis of Distribution Network Operation Based On Short-Term Future Big Data Technology
Authors: Jing Zhe , Yu Cong , Xi Feiruo , Wu Fengzhi , Tao Zhixiang , Yang Peihao 
Published: July 2020

Abstract
This paper firstly establishes a four-dimensional index system for distribution network operation reliability. Using principal component analysis method, it extracts the main evaluation indicators from a large amount of data, and analyzes the influencing factors of the indicators according to the main evaluation indicators. According to the parallel association rules The method establishes the relevant model, and extracts the main indicators of operational reliability and the strong correlation rules between each influencing factors, so as to obtain the main influencing factors. The artificial neural network is proposed to predict, based on historical data and real-time data. The main influencing factors are used as the input and output of the forecast, and the output is expressed by the main evaluation index, and the operational reliability index for a period of time is judged. The calculation of the proposed strategy can quickly and effectively predict the operational reliability of the distribution network.
Research on Interference Test of 24GHz Millimeter Wave Radar to 5G Equipment
Authors: Xu Y , Zhaojun Q 
Published: July 2020

Abstract
The millimeter wave frequency band tends to be developed with the application of radar and communication systems for civil use. It is necessary to analyze the interference coexistence between millimeter wave communication system and radar system. In order to make good use of frequency planning for the millimeter wave frequency band, a microwave anechoic room experiment is tested in this paper. The experiment includes a 24GHz 5G base station, a 24GHz 5G test terminal and a radar to verify the interference of 24GHz millimeter-wave radar to 5G equipment operating at nearby frequencies. This experiment tests and evaluates the detection capability of a single target in the limit state as much as possible and data of base station sensitivity, 5G uplink throughput and 5G downlink throughput have been tested in multiple situations to verify the accuracy and feasibility to this design of radar system. Therefore, the reference and support for 24GHz spectrum allocation can be provided through this method.
Research on the e2e Latency Calculation Considering the Gate Mechanism in Time-Sensitive Networking
Authors: Cao Zhipeng , Liu Qinrang , Liu Dongpei 
Published: July 2020

Abstract
Time-Sensitive Networking (TSN) is an emerging technology that solves the problem of Ethernet deterministic transmission. As its main idea, TSN is compatible with traditional Ethernet Best Effort transmission, while enabling transmission with bounded delay, low jitter, and low packet loss rate for Time-Sensitive traffic. However, existing methods of solving the problem of delay calculation are either too complex or too impractical. This paper proposed a Time-aware calculation method that can calculate the transmission delay in TSN. Firstly, we analyze the 802.1Qbv Time-Aware shaper for specifying its function; Secondly, the network topology, the network traffic, and especially the function of the switch are modeled. We divide the switch into 3 time-windows. Finally, a Time-aware calculation method is proposed, and the transmission cost is calculated considering the frame size, bandwidth, and the percentage of protected window. The experiments show that the method can calculate the end-to-end translation with different senarios and parameters with low complexity.
Modeling and Solution Algorithm for Fourth-Party Logistics Routing Problem Based on Service Composition
Authors: Min Xiaochuan , Meng Fanchao , Chu Dianhui , Wang Lei 
Published: July 2020

Abstract
Aiming at the problems of limited transportation resources and high transportation costs of a single third-party logistics supplier, our research proposes a route planning model based on service combination from the perspective of fourth-party logistics, which aims to find the route with minimum total cost under the premise of meeting customer needs. In order to improve solution efficiency, a specific genetic algorithm is designed. The algorithm adopts backtracking idea and adaptive operations, which can avoid infeasible solution caused by strict constraints, while reducing the time required for crossover and mutation operations to repair infeasible solutions. In addition, Dijkstra-based heuristic algorithm, traditional genetic algorithm and traditional ant colony algorithm are implemented. The comparison of computational experiments on different scales instances shows that the specific genetic algorithm can obtain the optimal solution in small-scale instances, and the solution quality in large-scale problems is better than other algorithms.
Wireless Sensor Networks Congestion Control Algorithm Based on Fusion Judgment
Authors: Xiang Haiyue 
Published: July 2020

Abstract
With the rapid development of sensor network, the network scale is expanding and nodes are deployed in a large amount. The dynamic change of wireless link quality and sudden traffic of sensor network are easy to cause network congestion, packet loss and waste of energy. In this paper, a congestion avoidance control algorithm (FJCA) for wireless sensor networks was proposed based on the fusion judgment method, and factors such as the distance from node to base station, the residual energy of node and the length of cache queue were considered. And compared it with TADR and MDD algorithms, the results show that the algorithm has higher energy efficiency and balance of nodes, which can improve network throughput more effectively, extended the life cycle of wireless sensor networks, and improve the service quality of network.
Development of a High Precision Multi-channel Electric Control Pump Signal Monitoring System
Authors: Bo Wen , Yi Jiangang , Xu Ping 
Published: July 2020

Abstract
The electric control hydraulic pump, which is the core of the hydraulic system, is widely used in modern industry. Therefore, it is necessary to monitor and control the signals of the electric control hydraulic pump in real time to improve the safety of the whole hydraulic system. In order to solve the safety and reliability problems of the operation of the electric control hydraulic pump equipment, a new high-precision multi-channel electric control pump signal monitoring system is proposed in this paper. The signal monitoring system of the electric control pump integrates high-precision power supply, multi-channel signal monitoring instrument and computer monitoring software as one automatic control and measuring device. The developed multi-channel signal monitor is powered by the power supply with good linearity and adjustable range voltage, and the monitor completes the data acquisition and data transmission task of the electric control pump system. The monitoring software on the remote computer processes and analyzes the real-time data of the equipment obtained from the network. The developed system improves the utilization ratio of electric hydraulic pump and has good application value.