bluuebunny/crossref_metadata_2025_split huggingface dataset:
Review on big data application of medical system based on fog computing and IoT technology
Authors: Qin Baoling , Tang Huiying , Chen Hongtao , Cui Liangyun , Liu Jianjie , Yu Xueke 
Published: December 2019

Abstract
From the current general situation of big data in medical systems, most of the medical system architectures of comprehensive related literature are based on cloud computing. With the deepening of big data application in the medical system, the drawbacks of medical network systems are apparent, for example, long network time delay, cloud computing overload, information leakage. A new intelligent medical system based on fog computing and IoT technology is proposed to effectively solve the problem of big data application through the comprehensive analysis of many works of literature. Compared with the traditional cloud computing model, the system which adopts a distributed fog computing model based on edge computing has the advantages of reducing network delay and plays its benefits of distributed, edge and decentralization. That the system takes advantage of IoT’s features of multi-node, low-energy consumption and big data technology is a new type of medical system that integrates intelligence and big data application. The system is effective through the application and practice of many medical institutions. In short, this system has several characteristics. For example, it can effectively improve medical intelligence and big data processing ability, significantly reduce network delay, effectively improve data security and effectiveness, largely improve the management level and service quality of medical institutions, play the important role of artificial intelligence and big data in the medical field, and greatly improve disease screening rate and reduce misdiagnosis rate. Ultimately it leads to reducing the contradiction between doctors and patients, providing patients with more convenience, faster and more efficient service quality, and alleviating the current situation of difficult and time-consuming medical treatment.
The application of EDXRF on soil heavy metal analyzation in a planting area
Authors: Li Xiaozhe , Chen Yi , Ge Liangquan , Li Fei , Tan Wei , Xu Jingru 
Published: December 2019

Abstract
With the increase of heavy metal concentration in soil, serious environmental problems have arisen. At present, heavy metals mainly refer to biotoxic heavy metals, including Lead, Cadmium, Mercury and Chromium, etc., which are produced in large quantities by human activities and difficult to be degraded, resulting in their loss of original activity and chronic poisoning because of the reaction with proteins and enzymes in human body. The soil concentration in the planting area exceeds the standard and is enriched in human beings, animals and plants, which directly endangers the living environment and health. In this paper, the soil in the planting area next to Xiangzhang canteens of Chengdu University of Technology was selected for field investigation, and the data analysis and risk assessment were carried out after testing the concentration of heavy metals in the surrounding soil through reasonable sampling, sample treatment and Energy-Dispersive X-ray fluorescence analysis techniques.
Research on the optimization of express delivery route of logistics base under the constraint of carbon quota
Authors: Liao Zhigao , Deng Zongqi , Jian Kerong , Guo Yi 
Published: December 2019

Abstract
This paper considers the route selection of express delivery under the constraint of carbon emission. Two kinds of path optimization models, not con carbon emissions and under the constraint of carbon quota, are constructed respectively to optimize the distribution route of express delivery stations, and this paper further analyses the impact mechanism of carbon quota on the optimization decision-making of distribution route. Finally, carbon emission policy recommendations are obtained.
RETRACTED: Application research of data mining algorithm in big data environment
Authors: Zhao Zenghui 
Published: December 2019

Abstract
The Hadoop platform forms a complete large-scale ecological distribution system, including HDFS, MapReduce, HBase and other subsystems. This paper analyzes the parallel processing of Hadoop platform and applies it in the field of data mining algorithms. In order to obtain better algorithm efficiency, a K-Modes clustering algorithm based on big data platform is proposed. It uses cluster mode to replace the central node. The mining process uses naive Bayes to improve mining efficiency. The experimental results show that it has better adaptability, saves time and improves the efficiency of the algorithm.
Study on application of entropy averaging method in airborne gamma-ray spectrum for local disturbance elimination
Authors: Xiong Chao , Zhao Jiankun , Wu Hexi , Ge Liangquan 
Published: December 2019

Abstract
This paper briefly describes the basic principle and the entropy averaging method for local disturbance elimination in airborne gamma-ray spectrum. This method was employed to process t boundary of different lithologies and the identification of metal ore points. The results showed that applying the entropy averaging method to eliminate the interference of the airborne gamma-ray spectrum data, and selecting a larger window width, achieve more significant results compared with the ground gamma ray spectrum.
Full multi-dimensional dynamic comprehensive optimization stochastic simulation is the sustainable trend of future petroleum reservoir description
Authors: Xiao Xiaonan 
Published: December 2019

Abstract
Being an important and sustainable approach in future petroleum reservoir description as well as a new frontiers subject in information optimization decision making of today’s oil/gas field development, multi-information stochastic dynamic comprehensive optimization simulation bears profound research significance and broad application prospect. The present paper is an introduction to theoretic performance and characteristics of multi-dimensional stochastic dynamic comprehensive optimization simulation, to classification, steps of model establishment, frequently-adopted approaches and problems to be considered in stochastic simulation. The paper, based on the author’s years of profound study and practice, also indicates the trend and latest application progress of stochastic simulation.
Application of geometric flow and bandelet coefficient combining method to image fusion
Authors: Zhong Mantian 
Published: December 2019

Abstract
Based on the theory of Bandelet transformation, we use Beamlab200 toolbox and Matlab software, adopt new construction of geometric flow and Bandelet coefficient combination method, apply new introduction of smaller absolute value rules, and take advantage of usage of computer simulation to experiment image fusion to explore the coefficient combination method and new introduction of smaller absolute value rules for geometric flow and Bandelet and test effect of their application to image fusion. The result after the experiment proves that this method is superior than the Beamlab 200 toolbox method in terms of running time, convergence speed, standard deviation, signal-to-noise ratio, gradient and image effect. The method can be applied to repair damaged images and others according to the results.
Design of human-like behavior learning machine based on neural network FPGA
Authors: Tang Ming , Hao Shengli 
Published: December 2019

Abstract
Neural network has been widely used in image recognition, because it can imitate the behavior of biological visual nerve to obtain high recognition accuracy. Aiming at image recognition, this paper studies the machine self-learning system of image recognition results based on neural network FPGA, designs the data sensing and image recognition processing module based on neural network, and studies the key problems of the system self-recognition and machine self-learning process. The image recognition module uses bionics and parallax principle to get the synchronous exposure image of surrounding scenery, reconstruct the three-dimensional shape and position of surrounding objects, and get the information needed by the system design through image processing algorithm. On the basis of this module, a machine self-learning algorithm based on the result of image recognition is designed to build a knowledge base for the machine and guide the machine by modifying the weight and making decision to realize human-like behavior of machine self-learning. The experimental results show that the recognition accuracy of dynamic image recognition in image recognition network is about 80% compared with the corresponding static image recognition, and the time and power consumption are reduced by 24%, 27.8% and 41% respectively.
Data mining in psychological experiments: a research of the relationship between personality, resilience and investment behavior rigidity
Authors: Gao Kai , Qiao Xiaoqian , Mei Zhipeng 
Published: December 2019

Abstract
Data mining in psychological experiments is to discover the relationship between other data and psychological data, so as to discover the psychological state of the subjects. Based on the psychological assessment data of students from Jiangsu Vocational College of Agriculture and Forestry during 2017-2019, we applied the association rules in big data processing to calculate the scores of personality, resilience and rigidity of investment behavior of the subjects. The following conclusions are drawn through analysis of promotion degree and correlation degree between values of each attribute. Firstly, pruning lift is of great significance to understand the associated relationship among personality, resilience and investment behavior rigidity. Secondly, the more openness individuals in the Big Five personality, the stronger the investment behavior rigidity. The stronger the neuroticism, the weaker the investment behavior rigidity. The stronger the extroversion, the stronger the investment behavior rigidity. Thirdly, psychological resilience as a mediator affects the impact of openness and neuroticism on the rigidity of investment behavior.
Matrix factorization recommendation algorithms based on knowledge map representation learning1
Authors: Huang Xuejian , Min Lu , Wang Gensheng , Gan Tian , Li Zhipeng 
Published: December 2019

Abstract
The matrix factorization recommendation algorithm does not consider characteristics of the recommendation object itself, resulting in poor recommendation results. Therefore, a matrix factorization recommendation algorithm based on knowledge map representation learning is proposed. Firstly, the recommendation object is represented as a low dimensional semantic vector by using the knowledge map distributed representation learning algorithm. Then the semantic similarity between objects is calculated, and the semantic similarity is incorporated into the objective optimization function of matrix factorization, so that the feature vectors obtained by matrix factorization can also contain semantic knowledge, which makes up for the shortcoming of matrix factorization recommendation algorithm that does not consider characteristics of the recommendation object itself from the semantic perspective. The experimental results show that the improved algorithm has higher accuracy, recall and coverage than the traditional matrix factorization recommendation algorithm.
Fixed spray type anti-ice deicing control system design
Authors: Liu Wenjiang , Chen Yunquan , Hou Fujin , Jing Yucai , Chen Jie 
Published: December 2019

Abstract
This paper takes intelligent control technology as the core technology, combines with meteorological and road freezing information collection technology and water supply and drainage technology. Firstly, this paper introduces the design of grading subsystem of fixed spray type anti-ice and snow removal control system, and proposes a new type of nozzle suitable for the viaduct at the same time, which can improve spraying effect of the deicing liquid while improving safety guarantee. Then based on analysis of collected on-site road conditions and meteorological actual data and combined with logic control algorithm, a live ice and snow early warning mathematical model is designed to predict the snowy condition of the road surface, and automatically control equipment to spray deicing fluid before or after road icing.. It uses PID control technology to ensure pressure balance inside the spray pipe. The real-time field data is connected to the cloud server with 4G wireless transmission technology, and the existing data and current data are compared and analyzed through the recursive neural network, so as to further accurately predict the ice and snow situation, classify danger level according to national road safety standards, and release it to other platforms. Based on application of actual field, this paper designs a method that can be combined with prevention in advance and post-disposal to eliminate road icing and reduce risks of vehicle driving safety in severe weather.
The analysis method and application of automobile driving direction deviation
Authors: Chen Peijiang , Zhang Mei , Wu Liying 
Published: December 2019

Abstract
When the automobile body deviates, being one of the hidden dangers of road traffic safety, it affects not only the automobile operation stability, but also the accuracy of headlight detection results. The driving directions analyzing method based on binocular stereo vision was presented. The shape center of the front license plate was extracted as automobile body feature point based on mathematical morphology and Hough transform. The sequence of the feature points was fitted into a straight line, which was the driving direction of the automobile. The measurement error of the headlight beam irradiation was corrected. The experiment results show that that this method can effectively detect the deviation angle of the automobile driving direction, and improve the accuracy of headlight detection.
Investigation on mechanical and tribological behaviour of titanium diboride reinforced martensitic stainless steel
Authors: Sadhasivam M , Mohan N , Sankaranarayanan Sankara Raman , Babu S P Kumaresh 
Published: December 2019

Abstract
The role of metal matrix composites (MMCs) has gained huge momentum in various researches as well as in industrial applications due to their high stiffness and higher strength to weight ratio. It helps in achieving enormous weight reduction without compromising the strength of the material. In this study, a novel attempt was made to fabricate MMCs with martensitic stainless steel as matrix and titanium diboride (TiB2) as reinforcement. The AISI 420 matrix with different weight percentages (1%, 2%, and 4%) of TiB2 reinforcement was fabricated by vacuum induction melting technique (VIM). The aim of this study was to analyze the influence of TiB2 particle addition on the mechanical and tribological properties of AISI 420 composites. X-ray diffraction studies revealed the presence of TiB2 phase in the composite along with peak shifting and broadening of the steel matrix due to induced strain. Optical microscopy showed the distribution of TiB2 particles in the matrix along with a significant amount of grain refinement of the composite due to the addition of TiB2. Microhardness results showed that the presence of TiB2 improved the hardness with AISI 420/4%TiB2 composite possessing the maximum hardness. Tensile test results indicated a significant improvement in the ultimate tensile and yield strength of the composite with the addition of TiB2. The wear tests were carried out on a pin on disc tribometer for various loads at a different sliding distance for steady sliding velocity. The dispersion of TiB2 particle helped in achieving better wear resistance of the composites. Wear surface morphology was carried out to explore the wear mechanisms. Among the various wear mechanisms, abrasion and oxidation phenomena dominated in the composites. Taguchi optimization technique predicted that wt% of TiB2 and applied load as influencing factors on the wear rate of the material.
Effect of texture evolution on corrosion resistance of AZ80 magnesium alloy subjected to applied force in simulated body fluid
Authors: Xiong Ying , Yang Zengyuan , Zhu Tao , Jiang Yanyao 
Published: December 2019

Abstract
The electrochemical corrosion behavior of a rolled AZ80 magnesium alloy after pre-compressive strain along different orientation in a simulated body fluid (SBF) was investigated by immersion test for 48 h. The results showed that the corrosion resistance is related to the crystal texture of the material. The texture evolution induced by plastic deformation changed the corrosion behavior of the magnesium alloy. Slip and twin associated with plastic deformation changed the orientation of crystallographic planes. The surface containing highly concentrated orientation of (0002) basal plane has higher corrosion resistance. The appearance of

10

1
¯

2

tension twinning could improve the corrosion resistance of magnesium alloy with specific orientation. However, the coexistence of

10

1
¯

2

tension twinning with high volume fraction and slip with high density dislocation reduced the corrosion resistance and corrosion anisotropy of the magnesium alloy.
Tailoring IONP shape and designing nanocomposite IONS@GN toward modification of SPCE to enhance electrochemical degradation of organic dye
Authors: Ognjanović Miloš , Stanković Dalibor M , Fabián Martin , Vranješ-Đurić Sanja , Antić Bratislav , Dojčinović Biljana 
Published: December 2019

Abstract
Iron oxide nanoparticles (IONP) with different distinctive morphologies (spherical, cubic, flower-like and needles) were utilized for modification of screen-printed carbon electrodes (SPCE) to be used for synthetic organic dye degradation by an electrochemical approach. This platform was implemented for removal of the synthetic organic dye, Reactive Black 5 (RB5) in aqueous solution. Modified SPCE with spherically shaped IONP (IONS) had the highest dye removal efficiency. Thus, IONS were then used for surface decoration of the most common carbon-based materials (graphene, graphene oxide, carboxylated graphene, graphene nanoribbons, graphene nanoplatelets, single- and multi-wall carbon nanotubes), and the nanocomposites formed were deposited on the electrode surfaces. Using IONS/graphene composite (IONS@GN) for electrode modification resulted in the best effect. Removal of RB5 with this electrode was 51% better in comparison with bare SPCE, reducing the time required for complete dye degradation from 61 to 30 min Using IONS-modified SPCE, total RB5 removal occurred in 51 min, improving the performance by 16% over that of bare SPCE. The effects determined, i.e., the best IONP morphology and best type of carbon-based material for nanocomposite formation to enhance RB5 removal will provide guidelines for further modifications of SPCE with nanomaterials and nanocomposites, for application of this electrochemical approach in the degradation of organic pollutants.
Metal hydride hydrogen compressors for energy storage systems: layout features and results of long-term tests
Authors: Tarasov Boris P , Bocharnikov Mikhail S , Yanenko Yurii B , Fursikov Pavel V , Minko Konstantin B , Lototskyy Mykhaylo V 
Published: December 2019

Abstract
This work summarises the results of development and long-term testing of two prototype models of industrial-scale metal-hydride thermal sorption hydrogen compressors, TSC1-3.5/150 (up to 11 Nm3 h−1; water cooling/steam heating) and TSC2-3.5/150 (up to 15 Nm3 h−1; heating and cooling by circulating oil). Both compressors have a two-stage layout utilising LaNi5 for the stage 1 and La0.5Ce0.5Ni5 for stage 2, thus providing hydrogen compression from 0.35 to 15 MPa at a cooling temperature of about 20 °C and a heating temperature between 150 °C and 160 °C. The compressors were operating for one year each and exhibited a satisfactory performance. The results of this work can be used in various medium-to-large scale hydrogen compression applications, including renewable energy systems.
Smartphone-Based Measurement of Executive Function in Older Adults with and without HIV
Authors: Moore Raeanne C , Campbell Laura M , Delgadillo Jeremy D , Paolillo Emily W , Sundermann Erin E , Holden Jason , Schweitzer Pierre , Heaton Robert K , Swendsen Joel 
Published: December 2019

Abstract

Objective
To examine feasibility, convergent validity and biases associated with a mobile color-word interference test (mCWIT) among older persons living with HIV (PLHIV).

Method
Over a 14-day period, 58 PLHIV and 32 HIV-uninfected individuals (aged 50–74) completed the mCWIT on smartphones once per day in real-world settings. Participants also completed a comprehensive laboratory-based neuropsychological evaluation.

Results
A high rate of compliance was observed (86%) in the repeated administration of the mCWIT. A practice effect was observed in the overall sample concerning mCWIT subscores, and these learning effects were greater for PLHIV. Stabilization of performance was observed after 6 (HIV+) and 7 days (HIV−) for completion time and after 2 (HIV−) and 3 days (HIV+) for mCWIT errors. A minor fatigue effect was observed in the overall sample which was unassociated with group status. Moderate to strong correlations were found between mCWIT completion time and mCWIT errors with global neurocognition and with all of the individual neurocognitive domains. The strongest associations were with mCWIT completion time and laboratory-based global neurocognition, executive function, and working memory scores.

Conclusions
Cognitive testing administered within the context of a person’s daily life provides qualitatively different data than neuropsychological testing completed in clinical settings, and it may constitute a more ecologically valid indicator of cognitive performance than traditional methods. Mobile cognitive testing has potential to help characterize real-time cognitive performance and serve as a complementary assessment tool to traditional methods.
Sounds of senescence: male swamp sparrows respond less aggressively to the songs of older individuals
Authors: Zipple Matthew N , Peters Susan , Searcy William A , Nowicki Stephen 
Published: December 2019

Abstract
Age-related changes in assessment signals occur in a diverse array of animals, including humans. Age-related decline in vocal quality in humans is known to affect perceived attractiveness by potential mates and voters, but whether such changes have functional implications for nonhuman animals is poorly understood. Most studies of age-related change in animal signals focus on increases in signal quality that occur soon after the age of first breeding (“delayed maturation”), but a few have shown that signal quality declines in older individuals after a mid-life peak (“behavioral senescence”). Whether other individuals are able to detect this senescent decline of assessment signals has not previously been tested. Here we use playback experiments to show that wild male swamp sparrows (Melospiza georgiana) respond more aggressively to songs from 2-year-old males as compared with songs from the same males when they are 10 years old. Senescence in signals that, like birdsong, affect reproductive success through intrasexual competition or mate choice may be of evolutionary significance.