bluuebunny/crossref_metadata_2025_split huggingface dataset:
Menu scheduling for high blood pressure patient with optimization method through Integer Programming
Authors: Hui Liew Su , Sufahani Suliadi Firdaus , Khalid Kamil , Wahab Mohd Helmy Abd , Idrus Syed Zulkarnain Syed , Ahmad Asmala , Subramaniam Tamil Selvan 
Published: June 2021

Abstract
High blood pressure or hypertension is a global public health issue. High blood pressure can lead to cardiovascular disease, stroke, diabetes and others. The diet problem of high blood pressure patients has always been concerned by the field of operational research. Some previous and recent research have proposed that diet plays an important role in affecting the condition of high blood pressure. A balanced diet will provide essential nutrients for high blood pressure patients that help to control and lower blood pressure. The purpose of this study is to construct a diet menu that meet the nutritional requirements of high blood pressure patient as well as minimize the food cost by developing a mathematical model using linear and integer programming approaches. The model of this study involved 10 type of food groups with 413 types of food variables based on the Malaysian recipes. The findings show that integer programming is a good alternative for linear programming. Integer programming leads to an optimal and effective solutions for the diet problem of high blood pressure patients.
Relationship between Leg Dynamometer with Squat and Deadlift 1RM Score among University Athletes
Authors: Najiah Wan Nur , Lee Ernie Leong Yen , Malek Nor Fazila Abd , Malik Zulezwan Ab , Chan Ebby Waqqash Mohamad , Ghazali Novri , Nadzalan Ali Md 
Published: June 2021

Abstract
This study aimed to investigate the relationship between Leg Dynamometer (LD) with squat and deadlift one repetition maximum (1RM) test scores. Participant of this study involved 50 male university athletes who were recreationally active (aged between 21-25 years old). Participants performed LD and one repetition maximum for squat and deadlift test. The score of LD were tested for relationship with the 1RM squat and deadlift. Pearson correlation were conducted to test the relationship. Results showed that there was a significant correlation at high level (r = 0.88, p = 0.000, p <0.001) between the LD test score and the squat while a very high correlation was obtained between the LD test score and the deadlift (r = 0.98, p = 0.000, p <0.001). As the conclusion, LD test that is simple and easy to be conducted produce similar result as the squat and deadlift 1RM test and can be used as a simple assessment of muscular strength among university athletes.
Studies Suspension Prevention System of Distance University using Analysis of Learning Activity and Learner’s Big Data
Authors: Chung Kwang Sik , Kuo Tony Chiu-Tien 
Published: June 2021

Abstract
Due to the increase in withdrawals and temporary absences of students from changes in the external environment, distance learning universities are trying to establish various policies and increase the number of enrolled students, and at the same time, are trying to establish various policies and efforts to increase the enrollment. However, it is difficult to systematically diagnose the main cause of the interruption of students, and prior research efforts related to the problem of student suspension in distance learning universities have accumulated, since there are various reasons for the student suspensions. In the proposed distance university Studies Suspension Prevention System (SSPS), distance university students can use two types of learning analytics services. In order to analyze learning activities, we propose the asynchronous learning activity analysis module, and the synchronous learning activity analysis module. In the asynchronous analysis module and symchronous analysis module, quiz, LINE group chatting & discussion forum communication, and online lecture has a learning state score according to the lecturer’s directions. Learning activities in the learning management system have three kinds of learning states, passive activity state, negative activity state, and medium activity state. Learning activity states are used to predict the student learning state. In the proposed Student Support System, there are two types of learning support services connected to the smart learning portal server. One is the intelligent distance university chatbot service for personalized chatting and caring services. The other is push message services for alarm, warning, notices, and alerts, such as dashboard service.
Design of Blockchain based e-Voting System for Vote Requirements
Authors: Choi Seiwoong , Kang Jihun , Chung Kwang Sik 
Published: June 2021

Abstract
Voting refers to the submission of an election or an opinion on a specific matter by expressing an intention on the ballot to a certain place. The existing voting method has the burden of counting time and cost. For this reason, research has been conducted to introduce an e-voting system. However, despite the advantages of e-voting, it is not widely used due to the risk of manipulation of voting results and various requirements. Recently, in order to reduce the risk of data manipulation, research is being conducted to apply the blockchain, a technology that guarantees data integrity, to e-voting. Blockchain guarantees the integrity of data, but has a weakness in secrecy. This paper applies the critical encryption technique to the blockchain and satisfies the requirements for voting such as verifiability, anonymity, fairness, non-reusability, competence, safety, transparency, and non-ticketing. We propose a system design and implementation method. The proposed blockchain-based e-voting system provides voter anonymity by issuing a voter certificate based on a blockchain address. The e-voting election monitoring committee generates a threshold group encryption key, and the proposed blockchain-based e-voting system guarantees confidentiality by a threshold group encryption algorithm during the voting process. The voting result is encrypted through a homomorphic encryption algorithm and stored in the blockchain. Thus, the released voting results ensure safety, confidentiality, transparency, and non-vote ticketing. In addition, the proposed blockchain-based e-voting system guarantees the unity and competence of voting through the blockchain’s smart contract.
Comparative Analysis of Graph Databases for Git Data
Authors: Falcão Thiago A. , Furtado Phillip M. , Queiroz Jordan S. , Matos Paulo J. O. , Antunes Tiago F. , Carvalho Frederico S. , Fonseca Paulo C. R. , Giuntini Felipe T. 
Published: June 2021

Abstract
In past decades, the requirements that database management systems (DBMSs) must achieve have become increasingly stringent (speed, data volume). This increase in complexity led to the development of a wide range of non-relational databases strategies, each one suited for specific scenarios. In this context, Graph Database Management Systems (GDBMSs) became popular to represent social networks and other domains that can be intuitively represented as graph-like structures. In this paper, we represent Version Control System data, specifically Git, from a large software project in a graph structure and compared three popular GDBMSs: Neo4j, Janus Graph and Dgraph. We evaluated read/write operations performance for common activities, such as inserting new commits into the graph and retrieving the complete commit history of a specific project. With this contribution, researches and engineers may choose, assertively, the better solution for their needs.
Construction of University Intelligent Manufacturing Learning Factory Laboratory for Emerging Engineering
Authors: Xu Ping , Yi Jiangang 
Published: June 2021

Abstract
In order to meet the needs of intelligent manufacturing professional personnel training, from the new perspective of cultivating the ability of engineering project, combined with the present situation of laboratory construction in colleges and universities, through the analysis of the present stage laboratory experiment process, aiming at the present stage in the process of practical teaching of experiment course in the theory and the actual production contact less problems, put forward a kind of combining teaching with enterprise production line of intelligent manufacturing factory laboratory construction planning study. Taking the customization of personalized pen holder as an example, this paper expounds the process of laboratory construction and experimental work, the machining, visual identification, intelligent logistics, MES and organic combination of experimental projects, such as through a multidisciplinary integration of laboratory construction, lead the students to combining theory with practice, apply theory to the purpose of production, improve the innovative ability of undergraduate talents for colleges and universities.
A Novel Improved Brain Tumor Segmentation Method Using Deep Learning Network
Authors: Yan Haoyuan , Chen Aiguo 
Published: June 2021

Abstract
Aiming at the problems of uneven brain tumor data classification and insufficient feature extraction, an improved brain tumor segmentation (BTS) method using deep learning network is proposed in this study. Here, we use U-net as to be the main network architecture, combined with the advantages of the residual network Resnet, which uses skip connections in each layer of encoding and decoding to form a residual module to avoid the disappearance of the gradient. Data enhancement is applied in data processing. To further improve the processing performance, we add a learning mechanism to the network and incorporates the compression and excitation module scSE on both space and channel to extract more useful features. This article is verified on the BraTS 2018 public brain data set. On the 66 officially provided verification sets, the network after adding the scSE module has obtained better segmentation results for the entire tumor, tumor core and enhanced tumor.
An Auto-Contouring Method for Kidney based on SVM
Authors: Li Bo , Xue Jing , Wang Ping , Gu Saikun , Chen Wei , Qian Pengjiang , Jiang Yizhang 
Published: June 2021

Abstract
In adaptive radiotherapy planning, the contour map of the treatment target area is still the most difficult problem. Usually this work is done by a professional radiologist oncologist, which is very time-consuming and labor-intensive. In order to solve this problem, this paper proposes an automatic contour method based on support vector machine. Experiments were conducted on the lower abdomen MR data set of eight patients. In the experiment, the simulated data was only used to train the classifier, and the treatment day images were used to evaluate the performance of the classifier. DSI was used to compare the manual contour and automatic contour of the kidney. The experiment showed that, The automatic contour method based on support vector machine has better classification performance than most classification algorithms. Among the eight sets of results, the DSI value of the six sets of results is 1, and the smallest DSI value is also greater than 9.423.
Research on Pipeline Defect Classification Based on SVM
Authors: Liu Peisheng , Han Yunping , Dong Chengshun 
Published: June 2021

Abstract
The defect identification and evaluation of buried steel pipeline is a long-term challenge in the field of pipeline detection, and the prerequisite for efficient identification of defects is the accurate extraction of pipeline damage signals. Aiming at the characteristics of buried pipeline defect signals, a method of pipeline defect signal extraction and recognition is proposed, which is based on support vector machine (SVM). A dictionary is obtained by learning from the original signal, the dictionary is used to construct a sparse model of the defect signal using a regularized orthogonal matching pursuit algorithm, and the feature vector of the signal is obtained according to the compressed sensing theory. Furthermore, multi-classification SVM is used to establish a mapping relationship between the feature vector of the defect signal and the actual defect type of the pipeline, and Genetic Algorithm-Particle Swarm Optimization is used to guide the selection of SVM parameters. The results showed that the proposed classification method can realize the accurate division of the damage degree of pipeline defects.
Extraction of Space Domain Entity and Relation via Word Vector Representation and Clustering Method
Authors: Wei Zhanji , Wan Gang , Huang Lingyong , Mu Yao , Yin Yunxia 
Published: June 2021

Abstract
Knowledge graph has shown great value in search engine, natural language Q&A, recommendation system and other application scenarios in recent years. The basic elements of a knowledge graph are entities and relations therein, so how to automatically extract entities and relations from natural language texts becomes a key issue in knowledge graph construction. In this paper, we propose an unsupervised method to extract space domain entities and relations with the goal of building a space knowledge graph. Firstly, a neural network model is used to extract implicit semantic features of domain words represented by dense vectors from original space domain corpus, and then new entities are discovered by clustering in vector space through a small number of labeled data. By concatenating space domain-specific word vectors and general domain word vectors, universal vector representations of entities are obtained, which include general features and domain features as well. On this basis, semantic vectors of relations between entities are calculated, and more new entity relations can be extracted from the corpus by using semantic vectors of relations. Compared with supervised method, the entity and relation extraction method proposed in this paper only needs a small amount of labeled data, thus is quite suitable for the construction of knowledge graph in space domain where labeled data is rather rare and expensive.
The Influence of Electronic Word-of-Mouth Dispersion on Order Decision from the Perspective of Self-Construal
Authors: Wu W L , Song M M , Duan Y C , Hong Y , Sui W J 
Published: June 2021

Abstract
Based on attribution theory, this paper explores the mechanism behind the influence of hotel electronic word-of-mouth dispersion on consumer order decision. Results show that (1) discrete electronic word-of-mouth negatively impact the order decision; (2) attribution selection could mediate the effect of electronic word-of-mouth dispersion on order decision of consumers; (3) independent self-construal weakens the negative effect of electronic word-of-mouth dispersion on order decision of consumers; and (4) consumers with high endowment reduce the tendency of electronic word-of-mouth dispersion due to online supporter reviews. Findings not only contribute to electronic word-of-mouth dispersion studies in the field of consumer behaviour, but also provide theoretical guidance and reference for hotel order management based on electronic word-of-mouth.
Research on Partial Pressure of Oilfield Water Injection Pipeline Network System Based on Fuzzy Logic
Authors: Zhang Ruijie , Wu Zhenming , Wang Mengxiao , Guo Yapeng , Mao Guoan 
Published: June 2021

Abstract
Aiming at the middle and late stages of oilfield development, the retirement of water injection wells in old areas of the water injection system, the increase of water injection wells in new areas, and the incorporation of tertiary injection wells into the subsequent water flooding have caused great changes in the layout of the water injection pipeline network, causing the mixed injection of high and low pressure wells, a fuzzy logic method is proposed to divide the pressure of the water injection pipeline network, establishing a partial pressure reasoning model, setting the fuzzy domain and reasoning rules, and finally realize the division of high and low pressure areas of a large number of water injection wells. aking an oilfield water injection pipe network system as an example, the effectiveness of the method is proved through Matlab simulation and application research.
Mathematical Modelling of Spatial Position of 4-High Mill Housing
Authors: Li Yang-Long , Lei Zhen-Yao , Wang Yong-Qiang , Wang Feng-Qin 
Published: June 2021

Abstract
Nowadays with the rise of laser measure technique, the laser tracker is applied to measure and calibrate the spatial position of mill equipment in cold and hot rolling mills. How to analyze the spatial state of mill housing with the amount measured data via laser tracker is the key to the problem. However, there is still no effective model to study the spatial position of mill housing. In this paper, a comprehensive mathematical model of the spatial position of 4-high mill housing was established. The least square method was used to fit the liner plates. It can be used to calculate the spatial distance and inclination relationship of each liner plate. The model was validated by the measured data of the 4-high mill housing via laser tracker. The calculated results of the model were consistent with those of the spatial analysis software, and the relative deviation was less than 0.01%. The median and opening value and inclination state of the mill liner plates were further studied. The ability and efficiency of data analysis could be improved based on this model, which has good prospect in on-site application.
Optimization Analysis of Suction Probe for Formation Sampling While Drilling
Authors: Jiang Chuanlong , Zhang Yang , Yan Tingjun , Chen Zhongshuai , Zhang Hui 
Published: June 2021

Abstract
Formation sampling while drilling (FSWD) is one of the most advanced formation sampling technologies in the world. During the operation, a small probe with a cushion is pressed against the wellbore to reduce the pressure in the probe enough to break the cake seal and extract the fluid from the formation. The content of drilling mud filtrate (drilling filtrate or filtrate) in the extracted formation fluid is determined by pollution rate. When the pollution rate of the extracted formation fluid drops to the specified index, the valve in the sample chamber is opened for sampling. When the sampling volume is reached, the pump and valve are closed. The time from the pump opening to the pump closing is called sampling time. The shorter the sampling time is, the shorter the circulation time of drilling fluid will be, so as to reduce the sticking accidents. Due to the influence of wellbore radial space, the size of the suction probe is limited. In order to avoid phase change of the pumped formation fluid during the operation process, the suction pressure difference should not be too large, etc. In this paper, the finite element simulation method is used to analyze the relationship between the above changes of the suction probe and the sampling time in the simulation results from the aspects of changing the size, shape, structure and arrangement of the suction probe in the formation fluid sampling tool while drilling, so as to realize the optimal selection of the suction probe. The research results of this method have important guiding significance to the design of fluid sampling tool while drilling.
Finite Element Simulation and Shape Optimization of Acoustic Horn
Authors: Wang Kaidong , Guo Sanxue , Wang Mingyong 
Published: June 2021

Abstract
Horn refers to the tube with continuous change of cross-sectional area, which can improve the matching of diaphragm and air load, so as to improve the electro acoustic conversion efficiency. For long-distance strong sound equipment, the purpose of using horn is to pursue higher sound pressure level, and control directivity and smoothness of sound pressure level response. In order to obtain higher sound pressure level and sharper directivity, parametric curve expressions of conical, exponential, hyperbolic and parabolic horn were derived. By using COMSOL multiphysics software, two-dimensional axisymmetric finite element simulation models of conical, exponential, hyperbolic and parabolic horn were established. Combined with if + end if programming function of the software, the effects of different cross-section shapes of horn on far-field sound pressure level and directivity were analyzed. The simulation results show that the parabolic horn can achieve a higher sound pressure level, and the sound pressure level is more than 3dB higher than the other three in the frequency band above 2000Hz. In the aspect of directivity, the beam width of parabolic horn is the smallest and more stable, and the overall directivity is the strongest. By using the shape optimization function of the software and constructing 8-order Bernstein polynomials, based on the parabolic horn, the shape of the horn was optimized for the most sensitive frequency range of 2000-4000hz, and the sound pressure level of the optimized horn was significantly improved in a large frequency range. This paper can provide technical reference for the design and development of horn loudspeaker.
An Improved Gray Wolf Optimization Algorithm for Solving Disassembly Sequencing Problems
Authors: Guo Laide , Peng Fangjie , Song Dapeng , Hu Chengbo , Dou Ling 
Published: June 2021

Abstract
In recent years, the utilization of end-of-life products which may cause serious environmental pollution has received much attention from both industrial and academia. The recycling and reusing of waste products have an essential step that is disassembly. A disassembly sequencing planning problem is studied with minimizing disassembly time to get the near-optimal solution. This work uses an improved algorithm for solving disassembly sequencing problems based on an improved gray wolf optimization algorithm, which has a group optimization options that can simulate the gray wolfs’ predation behaviours. An initial solution generator, a new solution generator, and a random mutation operator are adopted to improve the proposed algorithm, which can promote its efficiency and effectiveness. Experimental data show its superiority to solve this work’s problem comparing with the classical genetic algorithm.
Early Identification of Rice Leaf Blast Based on Hyperspectral Imaging
Authors: Yuan Jian Qing , Li Lei , Yan Wei 
Published: June 2021

Abstract
Rice blast is one of the three major rice diseases recognized in the world, which greatly harms the quality and the yield of rice. In order to distinguish rice leaf blast disease from nutrient deficiency and diagnose early the leaf blast disease, this study was based on the natural incidence of rice and field experiments, hyperspectral imagers were used to obtain the imaging spectrum of health, nitrogen deficiency, mild disease and severe disease. Spectra of 4 types of leaves were extracted, and three kinds of different data pretreatment methods were used, and the SPA feature extraction method was combined with the support vector machine(SVM) and the linear discriminant analysis(LDA) to construct the rice leaf blast identification model. The experimental results show that, after preprocessing by the Savitzky-Golay method, 9 characteristic wavelengths were extracted by SPA for modeling, and the models had the best recognition effect. The prediction accuracy of the SG-SPA-SVM model and the SG-SPA-LDA model were both 98.7%.