bluuebunny/crossref_metadata_2025_split huggingface dataset:
Contribution of Ultra-processed Food to the Daily Food Intake of HIV-positive and HIV-Negative Women during Pregnancy
Authors: Agostini Clarissa de Oliveira , Zoche Ester , Corrêa Rafaela da Silveira , Chaves Eunice Beatriz Martin , Corleta Helena von Eye , Bosa Vera Lúcia 
Published: September 2019

Objective To assess the daily dietary intake and energy contribution of ultra-processed foods among women who are positive and negative for the human immunodeficiency virus (HIV) during pregnancy.
Methods This case–control study included 77 HIV-positive and 79 HIV-negative puerperal women between 2015 and 2016. The socioeconomic and maternal demographic data were assessed, and a food frequency questionnaire (FFQ) adapted for pregnant women was applied. The Fisher exact test and the Mann-Whitney test were applied to detect differences between the groups. Linear regression was used to assess the associations between the intake of ultra-processed food and energy, macro- and micronutrients, with values of p < 0.05 considered significant.
Results The HIV-positive group was older (p < 0.001) and had lower income (p = 0.016) and level of schooling (p < 0.001) than the HIV-negative group. Both groups presented similar average food intake: 4,082.99 Kcal/day and 4,369.24 Kcal/day for the HIV-positive and HIV-negative women respectively (p = 0.258).The HIV-positive group consumed less protein (p = 0.048), carbohydrates (p = 0.028) and calcium (p = 0.001), and more total fats (p = 0.003). Ultra-processed foods accounted for 39.80% and 40.10% of the HIV-positive and HIV-negative groups' caloric intake respectively (p = 0.893). The intake of these foods was associated with a higher consumption of carbohydrates (p < 0.001), trans fat (p = 0.013) and sodium (p < 0.001), as well as lower protein (p < 0.001) and fiber intake (p = 0.022).
Conclusion These findings demonstrate that the energy consumption and ultra-processed food intake were similar in both groups, which reinforces the trend toward a high intake of ultra-processed food in the general population. The intake of ultra-processed food was positively associated with the consumption of carbohydrates, trans fat and sodium, and negatively associated with the consumption of protein and fiber.
Report of the Phenotype of a Patient with Roberts Syndrome and a Rare ESCO2 Variant
Authors: da Costa Almeida Carla Bastos , Welter Amanda Thum , Abech Gabriel Dotta , Brandão Gabriela Rangel , Flores José Antônio Monteiro , Schüle Birgitt , Francke Uta , Fiegenbaum Marilu , Zen Paulo Ricardo Gazzola , Rosa Rafael Fabiano Machado 
Published: September 2019

AbstractRoberts syndrome is a rare autosomal recessive genetic disease. In this report, we report a Brazilian patient with a rare ESCO2 variant. The patient manifested a broad range of clinical findings including the significant, bilateral shortening of the extremities. He deteriorated and passed away at 20 days of age. High-resolution GTG-banded karyotype showed lack of centromeric constriction in some chromosomes, premature centromere separation in others, and repulsion of the heterochromatin regions. Molecular analysis of the ESCO2 gene revealed a deletion of 4 bp involving exon 4 in homozygosity (NM_00107420.2:c.875_878delACAG), which causes loss of ESCO2 function. We describe the clinical presentation caused by a rare ESCO2 variant.
Land–atmosphere feedbacks exacerbate concurrent soil drought and atmospheric aridity
Authors: Zhou Sha , Williams A. Park , Berg Alexis M. , Cook Benjamin I. , Zhang Yao , Hagemann Stefan , Lorenz Ruth , Seneviratne Sonia I. , Gentine Pierre 
Published: September 2019

Significance
Soil drought and atmospheric aridity can be disastrous for ecosystems and society. This study demonstrates the critical role of land–atmosphere feedbacks in driving cooccurring soil drought and atmospheric aridity. The frequency and intensity of atmospheric aridity are greatly reduced without the feedback of soil moisture to atmospheric temperature and humidity. Soil moisture can also impact precipitation to amplify soil moisture deficits under dry conditions. These land–atmosphere processes lead to high probability of concurrent soil drought and atmospheric aridity. Compared to the historical period, models project future frequency and intensity of concurrent soil drought and atmospheric aridity to be further enhanced by land–atmosphere feedbacks, which may pose large risks to ecosystem services and human well-being in the future.
VO2 thin films for smart windows: Numerical study of the optical properties and performance improvement
Authors: Derkaoui I , Khenfouch M , Benkhali M , Rezzouk A 
Published: September 2019

Abstract
Vanadium dioxide (VO2) have attracted tremendous interest in recent years due to their semiconductor-metal transition near room temperature. In this work, we report numerical simulation of the optical properties of VO2 thin layers which include the index of refraction, the coefficient of extinction, the reflectance and the transmittance. In this sense, we used the Model of Drude-Lorentz for calculating coefficients at from the optical parameters n and k using the determination of the dielectric constant ε (ω, T). Knowing that the semiconductor-metal transition temperature of VO2 is 68 °C, the thickness effect on the optical properties of VO2 thin films has been studied by transmittance simulations. Our results revealed a significant changes in optical properties of VO2 thin layers, which produces many interesting applications especially for smart windows.
Characterization of ZnO thin films elaborated by cathodic sputtering with different oxygen percentages: investigation of surface energy.
Authors: Najih Y , Adar M , Charafih Y , Rahmani K , Khouch Z , Bengourram J , Kouider N , Mabrouki M 
Published: September 2019

Abstract
In this work, transparent thin films of Zinc Oxide (ZnO) were elaborated by RF sputtering with different oxygen percentages (10%, 20%, 30%, 50%). A structural (X-ray Diffraction) and morphological (Atomic Force Microscopy) study was carried out to investigate the influence of the elaboration conditions. A high roughness was obtained for the ZnO film deposited with 50% of O2 under 100w RF power, and the roughness increased with increasing O2 rate. The physicochemical properties were characterized by the calculation of surface energy, the results showed that the surface of the ZnO thin films are governed by short-range forces, i.e. the dispersive component (LW) is larger than the polar component (AB). The calculation of the interaction energy (ΔG
iwi
) has shown that the surfaces of ZnO films are hydrophobic (ΔG
iwi
<0) and the degree of hydrophobicity increases with increasing oxygen percentage.
The effect of Zn2+ on the anion vacancies in ZnO thin-films grown using chemical bath deposition.
Authors: Molefe F V , Mofokeng S J , Khenfouch M , Achehboune M , Dhlamini M S , Mothudi B M , Koao L F 
Published: September 2019

Abstract
Zinc Oxide (ZnO) thin-films were grown on glass substrate using chemical bath deposition method (CBD) from precursors containing varying molar concentrations of zinc acetate. The structure, morphology, optical and luminescence properties of the films were investigated using X-ray diffraction (XRD), Scanning electron microscopy (SEM), Ultraviolet visible spectroscopy (UV/VIS/NIR) and Photoluminescence spectroscopy (PL). The XRD result showed that at low molar concentrations of zinc acetate the structure consists of Zn(OH)2 and ZnO phases. As the molar concentration of zinc acetate increases the well-known hexagonal wurtzite structure of ZnO was developed. The average crystalite size estimated using Scherrer’s formula was about 41.19 nm. It was found that the average crystallite size increased with an increase in the molar concentration of zinc acetate. SEM observations showed the presence of nanoparticles forming aggregated nanoflakes. The surface morphology was found to be dependent on the concentration of zinc acetate. The UV-Vis spectra showed that the absorption band edge shift to the higher wavelength with an increase in molar concentration of zinc acetate. The band gap energy of ZnO thin-films determined from UV reflectance spectra was found to decrease from 3.17 to 2.73 eV with an increase in the zinc acetate concentration. The PL results showed that the luminescence intensities decrease with an increase in the molar concentration of zinc acetate without any noticeable shift in position.
Research and Development of Network Security and Risk Management
Authors: Zhang Qingfeng , Ma Yaping , Bai Shuya 
Published: September 2019

Abstract
With the advent of the 21st century, China is in a period of social transformation and is in the stage of deepening reform. Social contradictions are complex, hot events are frequent, and public opinion is active. Under the conditions of the Internet, especially in the context of the prosperous media, the public opinion pattern has undergone new changes. With the popularity of the Internet and the increasing number of Internet users, the Internet has become a large social place. After a hot event occurs, it often causes heated discussion, and overheated public opinion or embarrassing grievances will also form corresponding events. Improper handling often results in “secondary disasters”. In recent years, the concept of lyricism has been frequently used, and public opinion analysis has been promoted at the same time in both theoretical research and practical application. The related network risk management has also received increasing attention. Various situations have shown that lyric research is heating up. Based on this status quo, this paper sorts out and comments, summarizes the research status at home and abroad, and focuses on the rational situation of the situation of public opinion research.
Research and Application of Artificial Intelligence Technology in the Field of Risk Perception
Authors: Bai Shuya , Fang Danhui , Zhang Qingfeng 
Published: September 2019

Abstract
In recent years, the breakthrough of data, algorithms and computing power has made artificial intelligence ushered in the third wave. The rapid development of artificial intelligence will profoundly change human society and the world, promote human understanding, perception and the ability of decision-making in the world. Applying artificial intelligence to disaster risk perception research can improve risk perception and accuracy and effectively avoid risks. Using big data technology, artificial intelligence algorithm and threat intelligence technology to study the construction of information security situational awareness system, the model, architecture and concrete implementation of information security situational awareness system are proposed. This study combines artificial intelligence and risk perception to sort out the application and development trend of artificial intelligence in disaster risk perception in recent years, and proposes factors that affect public disaster risk perception and coping behavior.
An Efficient Short Blind Proxy Re-signatures Scheme
Authors: Yang Xiaodong , Ma Tingchun , Chen Chunlin , Li Yutong , Wang Meiding 
Published: September 2019

Abstract
The existing proxy re-signature schemes have drawbacks in terms of privacy protection and signature efficiency. Our scheme is based on the proxy re-signature scheme, which combines the main idea of short blind signature and proposes the definition of short blind proxy re-signature scheme. Then, using the signature framework of the random oracle model, we propose a short blind proxy re-signature scheme based on the random oracle model, and prove the security of the scheme under the adaptive chosen-message attacks. The analysis shows that compared with the existing similar schemes, our scheme does not add additional verification information under the premise of shortening the signature length, and the applicability is better in the interactive transmission of devices with limited calculation and transmission performance.
An Intrusion Detection Method for Industrial Control System Based on Gate Recurrent Unit
Authors: Chen Tusheng , Lin Peng , Ling Jie 
Published: September 2019

Abstract
We proposed an industrial control system intrusion detection method based on gate recurrent unit neural network to handle the problem that the intrusion detection method based on traditional machine learning algorithm such as SVM, decision tree, NN etc., cannot effectively deal with massive, high-dimensional, time related network traffic data in industrial control system. We used the update gate and the reset gate of GRU to save the information of the data in the time dimension. Its deep structure can fully learn the data features, and we used Adam algorithm to optimize the gradient training process of the neural network. Comparison experiments were conducted with the intrusion detection method based on machine learning algorithms such as SVM, decision tree, NN, RNN, LSTM etc. The results show that the proposed method has higher classification accuracy than SVM, decision tree, NN and RNN, and the accuracy is basically the same as LSTM but the training time is greatly reduced.
DBN-Based Fingerspelling Recognition Approach using Feature fusion
Authors: Hu Yong 
Published: September 2019

Abstract
Sign language recognition offers effective and precise approach of recognizing gestures or postures. In this work, a vision-based framework is presented for recognizing fingerspelling alphabets and a comparison is conducted to show the efficiency of feature fusion. Fused features and Deep Belief Network are used in the proposed framework. In the experiments stage, a comparison between the fused features and the individual ones is performed by using two public fingerspelling datasets. Experiment results show the improvement of the feature fusion.
Research on the Educational Mechanism for Cooperative Training of Innovative and Entrepreneurial Talents in University
Authors: Chen Xuegang , Duan Sheng , Mo Dongmin , Wang Luda , Wu Qian , Zhang Meiling 
Published: September 2019

Abstract
From the perspective of the cooperative education mechanism between university and enterprise, and studies the theory of education, the mechanism of university education and the relationship of the talent training mode-the mechanism of education, takes the cooperative training cases of three universities of china as the data source, puts forward a talent training mechanism of cooperative innovation and entrepreneurship. This mechanism clarifies the educational goal, improves the curriculum-teaching method, increases the feedback mechanism of enterprise-university, redefines the evaluation method of education, ensures good communication of university-enterprise-student, and then guarantees the normal operation of the cooperative education mechanism. The mechanism is market demand-oriented, to cultivate talents adapted to social development, to solve the problem of disconnection between theory and practice, students lack of practice, and to provide a strong theoretical basis for promoting the training of national innovative entrepreneurs.
Multi-authority Attribute-Based Encryption with User Revocation and Outsourcing Decryption
Authors: Zhang Xinyue , Chen Yue , Yan Xincheng , Jia Hongyong 
Published: September 2019

Abstract
Attribute based encryption scheme is widely used to share sensitive data in cloud storage environment because it can realize fine-grained access control. Aiming at the problem of user revocation and high cost of decryption in cloud storage environment, a multi-authority attribute based encryption scheme with efficient user revocation and outsourcing decryption was proposed. It supports large universe. Each authority manages a specific set of attributes and distributes attribute keys to users with corresponding attributes, avoiding the problem that a single authority may easily become a system bottleneck. The user revocation algorithm of the scheme can realize the efficient revocation of the authorized users, ensuring the forward security of ciphertext. It’s proved to be statically-secure under random oracle and the efficiency analysis shows that it reduces user decryption cost greatly
Semi-Physical Simulation Design of UAV Simulation Training System
Authors: Zheng Xing , Bao Chuanmei , Lin Dongsheng , Liu Changliang 
Published: September 2019

Abstract
The UAV simulation training system simulates the pre-flight ground commissioning and in-flight operation monitoring of UAV. The UAV semi-physical simulation training system is built by integrating mathematical model, physical model, simulation computer, simulation software and other auxiliary equipment. This paper describes the functions and structure of the UAV simulation training system based on semi-physical simulation technology, and specifies the approach to implementation of simulation module algorithm, design and key technologies of the system.
Research on Rating Error and Quality Metrics for Collaborative Filtering Recommendation Methods
Authors: Zhao Kun , Pi Jiaming 
Published: September 2019

Abstract
The collaborative filtering recommendation system has been widely used in E-commerce as a relatively successful recommendation system. At present, the focus of collaborative filtering recommendation research is mainly on how to improve the accuracy of recommendation by improving the recommendation algorithm. However, in real world, the user’s rating behaviour is not perfectly rational. It is no odd that there is deviation of rating to any a given item for a user in real evaluation. In this case, what it means for the improvement of collaborative filtering recommendation methods, and how they performed when we use the commonly used quality metrics to evaluate the collaborative filtering recommendation methods? In view of these problems, this paper presupposes that the user’s rating behaviour is a bounded rational behaviour, and based on the normality hypothesis of rating error, introduces a simulated rating experiment innovatively to analyse the effect that rating prediction can achieve in sense of the commonly used quality metrics. This study is of significance for the research and application of collaborative filtering recommendation technology.
Public Opinion Analysis of Big Data Based on Machine Learning
Authors: Chen Xuegang , Fan Hongbin , Duan Sheng , Wang Luda , Zhang Jialu 
Published: September 2019

Abstract
Network public opinions can be regarded as a collection of network comments, that is, a network comment can be regarded as the basic unit of network public opinion, based on this, network comment can be divided into spam comment, subjective comment and objective comment, we combine feature extraction method with machine learning method, machine learning method includes naive bayes and threshold delineation, and classify different types of network comments. By a large number of experiments, we have proved that the classifying accuracy of network comments is high, and the machine learning method can reduce the noise of text classification.
Air Combat Command and Guidance Situation Assessment Based on Attack Area
Authors: You Hanghang , Yu Minjian , Han Qisong 
Published: September 2019

Abstract
Aiming at the problem that the situation function in traditional air combat command and guidance situation assessment lacks comprehensive consideration of advantages and threats, an air combat command and guidance situation assessment method based on attack area is proposed. In this method, combined with a certain type of air-to-air missile attack area, aiming at the comprehensive situation of advantages and threats, the best angle, distance, speed and height are proposed, a new situation function is constructed, and the air combat command and guidance situation evaluation model is improved. Through case analysis and comparison with traditional methods, the accuracy and effectiveness of the method are verified.
A Hybrid Search Scheduler for Dynamic Auto-driving Team Scheduling with Time Window under Cloud Plan
Authors: Li Ming 
Published: September 2019

Abstract
The car cloud plan means that the traveller in the cloud plan does not need to buy an automatic driving vehicle, only need to join the cloud plan, send the travel instruction to the cloud scheduler through the mobile device in hand, and then the cloud scheduler provides the travel service. With the development of actual travel, more and more problems are moving towards the direction of dynamic travel. In this paper, a hybrid solver based on ant colony optimization system search architecture is designed. In the ant colony algorithm, a new algorithm is constructed by combining local search heuristic algorithm with path problem. This paper combines the cloud planning platform with automatic driving vehicle scheduling to quickly collect customer orders and have rapid large-scale processing capabilities, using the order information sent by the traveller to the cloud, through an ant colony searcher to achieve rapid processing of orders.
Medical Image Fusion Based on Sparse Representation and Guided Filtering
Authors: Huang Sa , Chu Guangyu , Fei Yifan , Zhang Xiaoli , Wang Hailiang 
Published: September 2019

Abstract
In this paper, we proposed a medical image fusion algorithm based on sparse representation and guided filtering. One of attractive features in the algorithm is that it can preserve the structural information of structural image and color information of functional image. We use a sparse representation in low frequency, initialize the dictionary by using DCT transform, and train the dictionary with each input source image as a training example. It not only ensures time complexity, but also ensures that the low-frequency fusion rules are adaptive. At high frequencies, we use the method of guided filtering to extract structural information from high-frequency images, and use the injection method to fuse high-frequency sub-bands to ensure the validity and richness of structural information. Experimental results show that the proposed fusion algorithm is superior to comparative algorithms in terms of subjective and objective evaluation methods.
Application of Intelligent Traceability Management System in Agriculture—Take Aodong Fruit and Vegetable Planting Cooperative as an Example
Authors: Gao Hongmei , Wang Zhida , Liu Yuchuan 
Published: September 2019

Abstract
At present, strengthening the construction of agricultural product quality and safety traceability system and vigorously developing digital and intelligent agriculture have become the focus of future agricultural development. This project concentrates on the development of Golden Pear planting and processing industry chain in Aodong Village, Shigezhuang Town, Wuqing district, Tianjin. It combines Internet of Things, QR code traceability technology with cloud big data to develop an intelligent management system for Yongbei Golden Pear industry chain. The intelligent platform management makes fine traceability management from production to consumption possible, so as to further enhance the characteristic brand of cooperative agricultural products, create cooperative agricultural products sales channels and new image, and improve quality and efficiency of agriculture.