bluuebunny/crossref_metadata_2025 huggingface dataset:
An Efficient Interpretable Visualization Method of Multidimensional Structural Data Matching Based on Job Seekers and Positions
Authors: Si Guoliang , Lv Hengyi , Yuan Hangfei , Xie Dan , Peng Ce
Published: December 2021

With the rapid development of Internet technology, millions of small, medium, and microenterprises are using Internet recruitment platforms to host their recruitment information. They have different job requirements and benefits positions. It is important to understand them for job seekers when choosing a position. Existing Internet recruitment platforms do not provide a detailed analysis of positions and visual methods for multidimensional matching of positions and job applicants. Candidates need to spend a lot of energy to screen out suitable positions. In this paper, we propose an efficient interpretable visualization method of multidimensional structural data matching based on job seekers and positions. First, we extract the keywords of the job seeker’s ability and benefits based on personal information, and we generate a job seeker ability table and a job seeker demand table. After that, we calculate the degree of the support, confidence, and promotion of each rule through the association rules generated by each frequent itemset of recruitment data to obtain the association rule table. We further explore the relationship between the skills required for the three types of positions based on the association rule. Finally, we use the regression method to build a salary forecasting model. On this basis, we predict the salary of job seekers based on the work experience, education, and work city provided by the job seeker. Simulation results show that our method has better performance on the job analysis and recommendation.
Comprehensive Indicator Bank for Resilience of Water Supply Systems
Authors: Baghersad Mostafa , Wilkinson Suzanne , Khatibi Hamed
Published: December 2021

Appropriate indicators are required to measure the resilience of water supply systems (WSSs). However, it is challenging to identify appropriate indicators since there is no comprehensive database of indicators to measure its resiliency. This study will establish a comprehensive bank of indicators to assist water corporations and decision‐makers in selecting appropriate indicators for their particular system. The suggested indicator bank is comprised of three layers such as dimension, attributes, and the number of indicators resulting from 12 different indicator codes that the study has analysed. In addition, this paper presents instructions on how the indicator bank can be used and integrated with water enterprises, enabling decision‐makers to pick the relevant indicators. The proposed indicator bank is an exploratory approach that should be validated in a real work setting since resilience is a challenging concept, and WSSs are complex due to their dependencies to other lifelines such as power networks with too many variables that may affect the actual outcomes.
Identification of Differentially Expressed Genes in COVID-19 and Integrated Bioinformatics Analysis of Signaling Pathways
Authors: Fang Linjie , Tang Tingyu , Hu Mengqi
Published: December 2021

Coronavirus disease 2019 (COVID-19) is acutely infectious pneumonia. Currently, the specific causes and treatment targets of COVID-19 are still unclear. Herein, comprehensive bioinformatics methods were employed to analyze the hub genes in COVID-19 and tried to reveal its potential mechanisms. First of all, 34 groups of COVID-19 lung tissues and 17 other diseases’ lung tissues were selected from the GSE151764 gene expression profile for research. According to the analysis of the DEGs (differentially expressed genes) in the samples using the limma software package, 84 upregulated DEGs and 46 downregulated DEGs were obtained. Later, by the Database for Annotation, Visualization, and Integrated Discovery (DAVID), they were enriched in the Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. It was found that the upregulated DEGs were enriched in the type I interferon signaling pathway, AGE-RAGE signaling pathway in diabetic complications, coronavirus disease, etc. Downregulated DEGs were in cellular response to cytokine stimulus, IL-17 signaling pathway, FoxO signaling pathway, etc. Then, based on GSEA, the enrichment of the gene set in the sample was analyzed in the GO terms, and the gene set was enriched in the positive regulation of myeloid leukocyte cytokine production involved in immune response, programmed necrotic cell death, translesion synthesis, necroptotic process, and condensed nuclear chromosome. Finally, with the help of STRING tools, the PPI (protein-protein interaction) network diagrams of DEGs were constructed. With degree ≥13 as the cutoff degree, 3 upregulated hub genes (ISG15, FN1, and HLA-G) and 4 downregulated hub genes (FOXP3, CXCR4, MMP9, and CD69) were screened out for high degree. All these findings will help us to understand the potential molecular mechanisms of COVID-19, which is also of great significance for its diagnosis and prevention.
A Personalized Recommendation Algorithm Based on Weighted Information Entropy and Particle Swarm Optimization
Authors: Jiang Shuhao , Ding Jincheng , Zhang Liyi
Published: December 2021

Similarity calculation is the most important basic algorithm in collaborative filtering recommendation. It plays an important role in calculating the similarity between users (items), finding nearest neighbors, and predicting scores. However, the existing similarity calculation is affected by over reliance on item scores and data sparsity, resulting in low accuracy of recommendation results. This paper proposes a personalized recommendation algorithm based on information entropy and particle swarm optimization, which takes into account the similarity of users’ score and preference characteristics. It uses random particle swarm optimization to optimize their weights to obtain the comprehensive similarity value. Experimental results on public data sets show that the proposed method can effectively improve the accuracy of recommendation results on the premise of ensuring recommendation coverage.
Ground Risk Assessment of UAV Operations Based on Horizontal Distance Estimation under Uncertain Conditions
Authors: Liu Yang , Zhang Xuejun , Wang Zhi , Gao Ziang , Liu Chang
Published: December 2021

In this paper a ground safety assessment model is introduced based on the probability estimation of possible impact positions when unmanned aerial vehicle (UAV) crashes on the ground. By incorporating the random uncertainties during the descending process, risks associated with UAV’s ground crash are estimated accurately. The number of victims on the ground per flight hour is selected as the indicative index to evaluate the risk levels of the corresponding ground area. We mainly focus on the analysis of uncertainties that usually appear in drag coefficient which would generate a great amount of effects on the travelled horizontal distance from the failure point to the impact point on the ground, which further influences the possible impact positions. The drag force in the air, failure velocity of a UAV, and wind effects in the local area are all considered in the proposed model, as well as ground features, including sheltering effects on the ground, UAV parameter settings, and distribution of local population. Uncertainties in drag force when a UAV descends, UAV’s initial horizontal and vertical speeds at failure point, and local wind patterns are all considered as the indispensable factors in the proposed model. Especially the probability of fatality once hit by the UAV’s debris is explored to make the safety assessment more reliable and valuable. In the end, the actual UAV parameters and official historical weather data are used to estimate the risks in a real operation environment when a failure event happens at a legal flying height. Experimental results are given based on different types of UAVs and random effects in the descent. The results show that all the operations of all kinds of UAVs selected in the validation are so dangerous that the safety of people on the ground cannot be guaranteed, whose value is much bigger than the manned aircraft safety criterion 10−7.
Effects of Diode, CO2, Er : YAG, and Er and Cr : YSGG on Titanium Implant Surfaces by Scanning Electron Microscopy
Authors: Moeintaghavi Amir , Bagheri Hossein , Pour Mahdie Yavari , Shafiei Shervin , Moslemi Hamidreza , Abbasi Kamyar , Nobar Behrad Rahbani , Mehdizadeh Amirreza , Ahrari Farzaneh , Alam Mostafa
Published: December 2021

This study aimed to determine the effects of various lasers on dental implants’ surface characteristics. Nine explanted dental implants were included. Two implants were randomly allocated to four intervention groups, namely, diode (2 W, 810 nm, 10 s), CO2 (2 W, 10600 nm, 10 s), Er : YAG (200 mJ/20 Hz, 2940 nm, 10 s), and Er, Cr : YSGG (200 mJ/20 Hz, 2780 nm, 10 s) groups and one control group. After laser irradiation, all implants were imaged with scanning electron microscopy. Qualitative changes on the surface of implants were evaluated. Quantitative surface changes at the threads and between the threads were assessed by software using depression and prominence plots. The paired t‐test was used for statistical analysis. Diode laser irradiation showed the least surface changes while the Er : YAG group showed the greatest surface changes. Furthermore, CO2 and Er : YAG laser irradiation significantly altered the mean profile area at the threads (p < 0.05), while CO2 and Er, Cr : YSGG laser irradiation significantly altered the mean profile area between the threads (p < 0.05). Diode laser irradiation does not alter the implant surface characteristics. However, the use of CO2, Er : YAG, and Er, Cr : YSGG lasers on titanium implant surfaces is discouraged as they damage the titanium implant surfaces.
Selection of the Key Segment Position for Trapezoidal Tapered Rings and Calculation of the Range of Jack Stroke Differences with a Predetermined Key Segment Position
Authors: Zhang Wencui , Zhang Jian , Yan Jingru , Zhu Yaohong
Published: December 2021

It is generally accepted that selecting the key segment position for trapezoidal tapered rings and controlling the shield machine advancement are challenging tasks for shield tunneling projects. In this work, we propose a method for calculating the key segment position based on the shield tail gap, jack stroke difference, and lining trend. To calculate all possible key segment positions other than that corresponding to the straight joint configuration, the shield tail gap that remains after segment assembly and the jack stroke difference corresponding to the advancement of the segmental lining and lining trend were computed; then, values and importance coefficients were assigned to these factors according to current operating conditions. To ensure that the segmental lining can be assembled successfully with the calculated key position, we established a model to calculate the change in the shield tail gap before and after shield machine advancement based on the spatial relationships of the shield machine, the currently installed segmental rings, and the segment to be installed. Further, we propose a method for calculating the range of jack stroke differences when the predetermined “permitted shield tail gap” and key position are provided. The method is based on the change in the shield tail gap calculated with the above model and the positional relationship between the shield machine’s actual axis and the designed tunnel axis after the current segmental ring has been assembled. The calculated range of jack stroke differences may then be used to control the advancement of the shield machine. We validated the viability of our methods by using the data of Phase 1 works on Line 2 of the Ningbo Rail Transit system.
Construction of the “Internet Plus” Community Smart Elderly Care Service Platform
Authors: Wang Biying , Xu Liangpeng
Published: December 2021

With the rapid development of China’s market economy and the increasing trend of population aging, the traditional community elderly care service model has exposed more and more problems, such as the imbalance between supply and demand, single service, and lack of flexibility. In response to these issues, this research attempts to explore the possible paths and practical challenges of applying the Internet, Internet of Things, mobile networks, big data, and cloud computing to community elderly care services. This research believes that the construction of the “Internet Plus” community smart elderly care services platform is a general trend. Innovating the traditional community elderly care service model is conducive to fully integrating elderly care resources and improving the quality of elderly care services.
Students’ Satisfaction towards E-Assessment for Academic Achievement in ESL at Public Schools and Colleges
Authors: Asad Muhammad Mujtaba , Khan Soomro Raja Bahar , Shamsy Atefeh , Churi Prathamesh
Published: December 2021

The core aim of this research study is to assess, analyze, and evaluate students’ perception of traditional and electronic assessment, which may affect their academic performance. This research study focuses on students’ perceptions about English as a second language in the context of formal assessment and electronic assessment, which is taken as a problem of this study, i.e., students’ perception of traditional and electronic assessment. This study uses a quantitative research approach, and the data have been obtained through a survey questionnaire from 100 students of SSC and HSC Part- I and II. The research results served descriptively and analytically regarding the two significant types of assessment applied in their classrooms; one is the traditional assessment, which is further subdivided into multiple-choice questions (including fill in the blanks and true and false statements), constructed response questions, and extended response questions. At the same time, the second form of assessment is the electronic assessment which is a computer-based assisted assessment. Electronic assessment is the modern technology that includes all forms of assessments, including collaborative approach-based assessment, interactive assessment, portfolios, and group projects. The result of this study reflects exciting findings. The results for the closed-ended questions (survey questionnaire) bend towards the null hypothesis (Ho), which states that there is no significant difference among students of GSL towards traditional and electronic assessment. However, the results of the two open-ended questions bend towards the alternative hypothesis (Hα), which states that there is a significant difference for the level of difference among students of GSL towards traditional and electronic assessment. Most importantly, teachers are obligated to select an assessment tool with careful consideration to ensure the appropriateness of each assessment tool for the learning objectives. Based on the research findings, some valuable recommendations for the policymakers, curriculum developers, students, and teachers are finally presented.
Calcium‐/Calmodulin‐Dependent Protein Kinase II (CaMKII) Inhibition Induces Learning and Memory Impairment and Apoptosis
Authors: Wang Jialu , Xu Xiaoxue , Jia Wanying , Zhao Dongyi , Boczek Tomasz , Gao Qinghua , Wang Qianhui , Fu Yu , He Miao , Shi Ruixue , Tong Xin , Li Meixuan , Tong Yu , Min Dongyu , Wang Wuyang , Guo Feng
Published: December 2021

Objectives. Inhibition of calcium‐/calmodulin‐ (CaM‐) dependent kinase II (CaMKII) is correlated with epilepsy. However, the specific mechanism that underlies learning and memory impairment and neuronal death by CaMKII inhibition remains unclear. Materials and Methods. In this study, KN93, a CaMKII inhibitor, was used to investigate the role of CaMKII during epileptogenesis. We first identified differentially expressed genes (DEGs) in primary cultured hippocampal neurons with or without KN93 treatment using RNA‐sequencing. Then, the impairment of learning and memory by KN93‐induced CaMKII inhibition was assessed using the Morris water maze test. In addition, Western blotting, immunohistochemistry, and TUNEL staining were performed to determine neuronal death, apoptosis, and the relative signaling pathway. Results. KN93‐induced CaMKII inhibition decreased cAMP response element‐binding (CREB) protein activity and impaired learning and memory in Wistar and tremor (TRM) rats, an animal model of genetic epilepsy. CaMKII inhibition also induced neuronal death and reactive astrocyte activation in both the Wistar and TRM hippocampi, deregulating mitogen‐activated protein kinases. Meanwhile, neuronal death and neuron apoptosis were observed in PC12 and primary cultured hippocampal neurons after exposure to KN93, which was reversed by SP600125, an inhibitor of c‐Jun N‐terminal kinase (JNK). Conclusions. CaMKII inhibition caused learning and memory impairment and apoptosis, which might be related to dysregulated JNK signaling.
[Retracted] Sports Action Recognition Based on Image Processing Technology and Analysis of the Development of Sports Industry Pattern
Authors: Jiang Zhaoyin , Zhang Fuyou , Sun Laishuang
Published: December 2021

The current era is an information age, and society is turning to the information age. The image processing technology is also widely used in various fields, and the technology of sports action recognition based on image processing technology can also be said to be appropriate. This article uses a spatial visual feature analysis algorithm to implement it. To implement this algorithm, a series of work such as image collection, feature extraction, and action recognition must be completed first and then implemented through texture functions and other related functions. This algorithm can be used to complete the image‐based sports action recognition technology at the minimum time cost. This algorithm can help sportsmen better complete training and standardize movements to a certain extent. As for the development of China’s current sports industry structure, it is also steadily improving. The people’s love for sports is getting stronger and stronger, which also makes the development of China’s sports industry still benefit a lot.
The Oxidative Stress and Chronic Inflammatory Process in Chagas Disease: Role of Exosomes and Contributing Genetic Factors
Authors: Maldonado Edio , Rojas Diego A. , Urbina Fabiola , Solari Aldo
Published: December 2021

Chagas disease is a neglected tropical disease caused by the flagellated protozoa Trypanosoma cruzi that affects several million people mainly in Latin American countries. Chagas disease has two phases, which are acute and chronic, both separated by an indeterminate time period in which the infected individual is relatively asymptomatic. The acute phase extends for 40‐60 days with atypical and mild symptoms; however, about 30% of the infected patients will develop a symptomatic chronic phase, which is characterized by either cardiac, digestive, neurological, or endocrine problems. Cardiomyopathy is the most important and severe result of Chagas disease, which leads to left ventricular systolic dysfunction, heart failure, and sudden cardiac death. Most deaths are due to heart failure (70%) and sudden death (30%) resulting from cardiomyopathy. During the chronic phase, T. cruzi‐infected macrophages respond with the production of proinflammatory cytokines and production of superoxide and nitric oxide by the NADPH oxidase 2 (NOX2) and inducible nitric oxide synthase (iNOS) enzymes, respectively. During the chronic phase, myocardial changes are produced as a result of chronic inflammation, oxidative stress, fibrosis, and cell death. The cellular inflammatory response is mainly the result of activation of the NF‐κB‐dependent pathway, which activates gene expression of inflammatory cytokines, leading to progressive tissue damage. The persisting production of reactive oxygen species (ROS) is the result of mitochondrial dysfunction in the cardiomyocytes. In this review, we will discuss inflammation and oxidative damage which is produced in the heart during the chronic phase of Chagas disease and recent evidence on the role of macrophages and the production of proinflammatory cytokines during the acute phase and the origin of macrophages/monocytes during the chronic phase of Chagas disease. We will also discuss the contributing factors and mechanisms leading to the chronic inflammation of the cardiac tissue during the chronic phase of the disease as well as the innate and adaptive host immune response. The contribution of genetic factors to the progression of the chronic inflammatory cardiomyopathy of chronic Chagas disease is also discussed. The secreted extracellular vesicles (exosomes) produced for both T. cruzi and infected host cells can play key roles in the host immune response, and those roles are described. Lastly, we describe potential treatments to attenuate the chronic inflammation of the cardiac tissue, designed to improve heart function in chagasic patients.
Relationship between Insulin Secretion and Arterial Stiffness in Essential Hypertension
Authors: Sun Yancui , Zhu Yanqiu , Zhang Lu , Lu Yan , Liu Yan , Zhang Ying , Song Wei , Jiang Yinong , Cheng Yunpeng
Published: December 2021

The study aims to explore the relationship between plasma insulin secretion and arterial stiffness in nondiabetic essential hypertensive patients. A total of 730 nondiabetic essential hypertensive patients registered between January 2016 and October 2020 were enrolled. A two-hour oral glucose tolerance test (OGTT) was performed to detect the levels of C-peptide and blood glucose at 0 hours and 2 hours, as well as the difference between C-peptide (Δ C-peptide) and blood glucose (Δ blood glucose) over the same period. Patients were divided into two groups: the normal glucose tolerance (NGT) group (n = 322) and the impaired glucose tolerance (IGT) group (n = 408). A multiple linear regression analysis was used to evaluate the association between brachial-ankle pulse wave velocity (baPWV) and the other factors. 0 h C-peptide, 2 h C-peptide, and Δ C-peptide were found to be higher in the IGT group. baPWV was positively linear correlated with 2 h C-peptide (r = 0.086,

p
=
0.020

) and Δ C-peptide (r = 0.115,

p
=
0.002

). baPWV remained independently associated with 0 h C-peptide, 2 h C-peptide, and Δ C-peptide, after adjusting by age, gender, smoking, body mass index (BMI), high-density lipoprotein (HDL), cholesterol, systolic blood pressure (SBP), and triglycerides (TG). Our data shows that higher endogenous insulin secretion might play an important role in the progression of arterial stiffness in nondiabetic essential hypertensive patients.
Employing Artificial Neural Networks to Predict the Performance of Domestic Sewage Treatment Terminals in the Rural Region
Authors: Lin Qiang , Luo Ancheng , Zhang Yan , Wang Yunlong , Liang Zhiwei , Yuan Ping
Published: December 2021

Domestic sewage in rural regions is mainly treated by small-scale treatment terminals in China. The large quantities and high dispersion of these terminals render the chemical measurement of effluent to be a time and energy intensive work and further hinder the efficient surveillance of terminals’ performance. After a thorough investigation of 136 operating terminals, this study successfully employs two artificial neural network (ANN) models to predict effluent total nitrogen (TN) and COD (R2 both higher than 0.8) by setting some easily detectable parameters, e.g., pH and conductivity, as inputs. To prevent ANN models getting stuck on local optima and enhance the model performance, genetic algorithm (GA) and particle swarm optimization (PSO) are introduced into ANN, respectively. By comparison, ANN-PSO excels in modelling both TN and COD. The root mean square error (RMSE) and R2 of ANN-PSO in modelling TN are 9.14 and 0.90, respectively, in the training stage, and 11.54 and 0.90, respectively, in the validation stage. The RMSE and R2 of ANN-PSO in modelling COD are 22.10 and 0.90, respectively, in the training stage, and 26.57 and 0.85, respectively, in the validation stage. This is the first study to provide performance prediction models that are available for different terminals. Two established ANN-PSO models show great practical significance in monitoring huge amounts of terminals despite the slight sacrifice of models’ accuracy caused by the great heterogeneity of different terminals.
Optimization Problem of Pricing and Seat Allocation Based on Bilevel Multifollower Programming in High-Speed Railway
Authors: Deng Lianbo , Xu Jing , Zeng Ningxin , Hu Xinlei
Published: December 2021

This paper studies the multistage pricing and seat allocation problems for multiple train services in a high-speed railway (HSR) with multiple origins and destinations (ODs). Taking the maximum total revenue of all trains as the objective function, a joint optimization model of multistage pricing and seat allocation is established. The actual operation constraints, including train seat capacity constraints, price time constraints in each period, and price space constraints among products, are fully considered. We reformulate the optimization model as a bilevel multifollower programming model in which the upper-level model solves the seat allocation problem for all trains serving multiple ODs in the whole booking horizon and the lower optimizes the pricing decisions for each train serving each OD in different decision periods. The upper and lower are a large-scale static seat allocation programming and many small-scale multistage dynamic pricing programming which can be solved independently, respectively. The solving difficulty can be significantly reduced by decomposing. Then, we design an effective solution method based on divide-and-conquer strategy. A real instance of the China’s Wuhan-Guangzhou high-speed railway is employed to validate the advantages of the proposed model and the solution method.
Silica Coating of Metal-Loaded H-ZSM-22 to Form the Core-Shell Nanostructures: Characterization, Textural Properties, and Catalytic Potency in the Esterification of Oleic Acid
Authors: Haghighi Maryam , Fereidooni Mehranoosh
Published: December 2021

In this study, ZSM-22 was synthesized using N,N-diethylaniline as a template through a hydrothermal method. The proton and various metals such as zirconium, strontium, and iron were immobilized on the surface of obtained zeolites through the ion exchange method. The catalysts were studied by Fourier-Transform Infrared Spectroscopy (FT-IR), X-Ray Diffraction (XRD), Brunauer–Emmett–Teller (BET) adsorption isotherms, Transmission Electron Microscope (TEM), Scanning Electron Microscope (SEM), Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES) elemental analysis, and Temperature-Programmed Desorption of ammonia (TPD-NH3) technique for determining the number of acid sites. In the esterification reaction of oleic acid, the operating conditions such as catalyst dosage, temperature, molar ratio of methanol to oil, and reaction time were optimized and adjusted at 11 wt%, 70°C, 10 : 1, and 48 h subsequently. The maximum yield% of 48.07% was achieved in the presence of Zr-H-ZSM-22 at optimum conditions. In order to improve the efficiency of three zeolites Zr-H-ZSM-22, Fe-H-ZSM-22, and Sr-H-ZSM-22, the core-shell structures with SiO2 coating were prepared. Zr-H-ZSM-22@SiO2 was less active than Zr-H-ZSM-22 due to the SiO2 coverage of Lewis active sites.
Application of Visual Sensing Image Processing Technology under Digital Twins to the Intelligent Logistics System
Authors: Miao Jianjun , Lan Shundong
Published: December 2021

Since the founding of the People’s Republic of China, the advantages of logistics are neglected, and the scale operation and the welfare in the industry are difficult to achieve due to the influence of the economic system and social environment. Therefore, a new intelligent logistics distribution management system based on machine vision and visual sensor image processing technology is constructed to respond to the shortcomings of the traditional system, including slow efficiency, huge cost, complex data, and low degree of informatization. Through the analysis and research on the visual sensor image processing technology and the order processing, receipt management, distribution management, scheduling management, and return management that affect logistics distribution, a simulation experiment is used to verify that the visual sensor image processing technology is rigorous, intelligent, and efficient and has high precision. The intelligent logistics distribution management system can effectively solve the problems existing in the traditional logistics distribution management. The experimental results show that the visual sensor image processing technology can collect and analyze the target image and effectively track and monitor it in the logistics distribution process. The average distribution precision of the intelligent logistics distribution management system reaches more than 99.5%, which is greatly improved compared with 90% of the traditional logistics distribution. And it can greatly improve the distribution efficiency, which increases by about 26.5%. The study realizes the information management of the logistics system and automatically completes all the work according to the designed program, so that the real‐time dynamic distribution can be transmitted to the urban logistics distribution at any time.
An Empirical Study on the Relationship between Education and Economic Development Based on PVAR Model
Authors: Sun Zhenzi
Published: December 2021

In order to effectively analyze the dynamic relationship between education and economic development, an empirical study on the relationship between education and economic development based on the PVAR model is proposed. This article expounds on the principles, assumptions, identification, and estimation methods of the PVAR model, takes the education level and economic development level as the research object, explains the corresponding variables, and selects the indicators. Using Cobb–Douglas production function as a theoretical model, this article analyzes the theoretical relationship between the education level and economic development level. Based on this theoretical relationship, this article makes an empirical analysis on the relationship between education level and economic development level using the PVAR model. The results show that, on the whole, economic development can drive the development of education, but there are obvious regional differences in the impact of economic growth on the development of education. The impact of economic growth in the eastern region on education is significantly higher than that in the central and western regions. There is an interactive relationship between education level and economic development level, and there is a certain incubation period for education level and economic development level to play their role.
Mechanical Properties of Titanium Diboride Particles Reinforced Aluminum Alloy Matrix Composites: A Comprehensive Review
Authors: Mohanavel V. , Ravichandran M. , Anandakrishnan V. , Pramanik Alokesh , Meignanamoorthy M. , Karthick Alagar , Muhibbullah M.
Published: December 2021

Aluminum alloys with silicon, magnesium, and copper were extensively used alloying elements in various applications because of their excellent properties. In recent decades, aluminum matrix composites (AMCs) are an advanced engineering material widely utilized in diverse engineering applications, including aircraft, automobile, marine, and shipbuilding, owing to their low density, lightweight, good stiffness, superior strength, and good tribological properties. Aluminum is abundant and its use is as vast as the ocean. It is also the most used matrix material in the composite arena. Therefore, incorporating a ceramic particle into a relatively soft aluminum matrix improves hardness, strength, stiffness, creep, fatigue, and wear properties instead of the conventional materials. This article is an assay to review and spotlight some recent works on the mechanical behaviors of aluminum‐based titanium diboride reinforced metal matrix composite. This review article concentrates on the mechanical properties and the fabrication processes of Al‐TiB2 composites to provide a valuable reference to nurture future research precisely.
A Highly Sensitive Electrochemical Sensor Based on Electrocatalytic Reduction Effect of Cu2+ on Trace Determination of Malathion in Soil and Other Complex Matrices
Authors: Alshareef F. M. , Orif M. I. , Al-Harbi E. A. , El-Shahawi M. S.
Published: December 2021

The current strategy reports a highly sensitive and selective square wave-cathodic stripping voltammetric protocol for malathion determination. The established method was based on the controlled adsorptive accumulation of malathion in the presence of Cu2+ ions in an aqueous solution of pH 2 onto the hanging mercury dropping electrode (HMDE) and measuring the resulting cathodic peak current of the adsorbed species at −0.42 V versus Ag/AgCl electrode. The low limits of detection (LOD) and quantification (LOQ) of malathion of the assay were estimated to be 3.1 × 10−10 and 1.03 × 10−9 M with a linear dynamic range of 1.03×10−9 – 2.0 × 10−7 M, respectively. The method was satisfactorily applied and validated for malathion determination in environmental samples. The experimental Student texp and Fexp values did not exceed the tabulated ttab (2.78) and Ftab (6.39) at 95% (P = 0.05) confidence (n = 5), confirming the precision and independence on the matrix. The developed sensing platform for the detection of malathion shows superior performance to conventional electrochemical methods. The proposed sensor offered simple, economical, reproducible, and applicable approach for the determination of malathion in environmental samples.
A Probabilistic Model of the Unidirectional Tensile Strength of Fiber‐Reinforced Polymers for Structural Design
Authors: Zhang Jianqing , Zhang Ruikun , Zeng Yihua
Published: December 2021

In this paper, a statistical analysis of the tensile strength of FRP composites is conducted. A relatively large experimental database including 58 datasets is first constructed, and the Normal, Lognormal, and Weibull distributions are fitted to the data using a tail‐sensitive Anderson–Darling statistic as the measure of goodness of fit. Fitting results show that the Normal, Lognormal, and Weibull distributions can be used to model the tensile strength of FRP composites. Then, the characteristic value for the tensile strength of FRP composites at a fixed percentile is analyzed. It is found that the Weibull distribution results in a higher safety margin in comparison to either the Normal or the Lognormal distribution. When the experimental justification, the theoretical justification, as well as the design conservativeness are taken into consideration, the Weibull distribution is the most recommended distribution to model the tensile strength of FRP composites. Furthermore, a probabilistic model considering the statistical uncertainty for the tensile strength for FRP composites is proposed. It is believed that the statistical uncertainty can be modeled as a reduction factor, and the recommended value of such factor for engineering design practices is provided based on regression analysis.
Combining Intravoxel Incoherent Motion Diffusion Weighted Imaging and Texture Analysis for a Nomogram to Predict Early Treatment Response to Concurrent Chemoradiotherapy in Cervical Cancer Patients
Authors: Zheng Xiaomin , Li Cuiping , Zhang Lufeng , Cao Feng , Fang Xin , Qian Liting , Dong Jiangning
Published: December 2021

This study aimed to predict early treatment response to concurrent chemoradiotherapy (CCRT) by combining intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) with texture analysis (TA) for cervical cancer patients and to develop a nomogram for estimating the risk of residual tumor. Ninty-three cervical cancer patients underwent conventional MRI and IVIM-DWI before CCRT. We conducted TA using T2WI. The patients were allocated to partial response (PR) and complete response (CR) groups on the basis of posttreatment MRI. Multivariate logistic regression analysis on IVIM-DWI parameters and texture features was employed to filter the independent predictors and construct the predictive nomogram. Its discrimination and calibration performances were estimated. Multivariate analysis on the IVIM-DWI parameters showed that D and f were independent predictors (OR = 4.029 and 0.889, resp.;

p
<
0.05

). However, the multivariate analysis on the texture features indicated that GLCM-correlation, GLRLM-LRE, and GLSZM-ZE were independent predictors (OR = 43.789, 9.774, and 23.738, resp.;

p
<
0.05

). The combination of IVIM-DWI parameters and texture features exhibited the highest predictive performance (AUC = 0.975). The nomogram to identify the patients with high-risk residual tumors exhibited an acceptable predictive performance and stability with a C-index of 0.953. Decision curve analysis demonstrated the clinical use of the nomogram. The results demonstrate that D, f, GLCM-correlation, GLRLM-LRE, and GLSZM-ZE were independent predictors for cervical cancer. The nomogram combining IVIM-DWI parameters and texture features makes it possible to identify cervical cancer patients at a high risk of residual tumor after CCRT.
The Prediction of Multistep Traffic Flow Based on AST-GCN-LSTM
Authors: Hou Fan , Zhang Yue , Fu Xinli , Jiao Lele , Zheng Wen
Published: December 2021

Aiming at the traffic flow prediction problem of the traffic network, this paper proposes a multistep traffic flow prediction model based on attention-based spatial-temporal-graph neural network-long short-term memory neural network (AST-GCN-LSTM). The model can capture the complex spatial dependence of road nodes on the road network and use LSGC (local spectrogram convolution) to capture spatial correlation features from the K-order local neighbors of the road segment nodes in the road network. It is more accurate to extract the information of neighbor nodes by replacing the single-hop neighborhood matrix with K-order local neighborhoods to expand the receptive field of graph convolution. The high-order neighborhood of road nodes is also fully considered instead of only extracting features from first-order neighbor nodes. In addition, an external attribute enhancement unit is designed to extract external factors (weather, point of interest, time, etc.) that affect traffic flow in order to improve the accuracy of the model’s traffic flow prediction. The experimental results show that when considering the static, dynamic, and static and dynamic combination, the model has excellent performance: RMSE (4.0406, 4.0362, 4.0234), MAE (2.7184, 2.7044, 2.7030), accuracy (0.7132, 0.7190, 0.7223).
Construction of Multimedia Assisted Legal Classroom Teaching Model Based on Data Mining Algorithm
Authors: Lu Yu , Lizhi Wang
Published: December 2021

In order to quickly and accurately retrieve a required part from massive multimedia educational resources and improve the utilization of educational resources, a multimedia assisted legal classroom teaching model based on data mining algorithm is designed. Firstly, the attributes of multimedia assisted legal classroom teaching resources are judged, and the numerical resources are standardized and discretized. Then, the B+ tree is used to establish the model’s indexes and index library, and the corresponding retrieval algorithm is designed to complete the resource search, establish the data distribution structure model of the multimedia assisted legal classroom teaching system, mine the data, reconstruct the phase space of the fused data information flow, extract the high-order moment features of the specific data in the multimedia assisted legal classroom teaching system in the reconstructed high-dimensional phase space, and realize the accurate mining of the feature data. The experimental results show that the teaching effect of the designed model has more advantages and can promote the improvement of students’ performance.
The effect of birth month and seasons on athlete aggression
Authors: Gülle Mahmut , Bolat Yavuz
Published: December 2021

In this study, there were 3,335 football players from four continents from around the world formed a study universe in which the causes of athlete aggression. Also, the factors affecting athlete aggression were investigated with different variables. It has been observed that the levels of aggression of footballers are different according to the positions they play. As the number of matches that players participate in increases, aggression levels increase among the important results. A correlation was found between the ages of the footballers and their levels of aggression, but it was concluded that athletes born in December were more aggressive. Those born in January and those born in winter have shown minimal levels of aggression compared to other footballers. It has been interpreted as having higher levels of aggression, with football players born in December and winter receiving more penalties than other participants. In the continents where football players were born, the highest number of athlete aggression was observed in the participating football players from the continent of Europe, while the aggression of athletes born in the continent of Africa and Asia was found to be lower. Taking into account the data obtained from the research, it was concluded that the athlete's position, age, month, and season of birth and continent of birth are factors affecting athlete aggression.
Progress testing and its effect on medical student motivation
Authors: Hermasari Bulan Kakanita , Yudistyaningrum Dimar
Published: December 2021

Assessment has a pivotal role in medical education, as it can direct student learning. Motivation is an essential factor that determines learning assessment results. One of the aims of assessment is to determine the level of student knowledge, one of which can be measured by a progress test. This study aimed to determine the relationship between progress test and student motivation. This study used a cross-sectional observational analytic method. It was conducted in 2017 at Faculty of Medicine Universitas Sebelas Maret (FM UNS), Indonesia. The respondents consisted of 253 students from batch 2014, 2015, and 2016. The sample was selected by stratified random sampling. The instrument used to assess academic motivation was the academic motivation scale (AMS). The research data were analyzed using the Pearson correlation test. There is a significant positive correlation between progress test results and academic motivation (r=0.500; p=0.000). However, there is no significant difference in motivation level based on gender (p=0.889) and student cohort (p=0.533). In the progress test score, there are significant differences based on gender (p=0.014) and grade-point average (p=0.000). However, there is no significant difference in the progress test scores based on the student batch (p=0.212). The results support that progress test is useful assessment method to support medical student’s motivation.
Online mathematics learning experiences of the colleges of education students in Ghana
Authors: Taley Isaac Bengre , Mensah Farouq Sessah , Adjei Philip Acheampong
Published: December 2021

The dominant mode of instructional delivery in Ghanaian Colleges of Education has been the conventional face-to-face. However, the second semester of the 2019/2020 academic year teaching had to be done via an emergency remote online teaching mode due to the novel covid-19 pandemic. In a cross-sectional survey, the online mathematics learning experienced of 497 students sampled from three Colleges of education in Ghana were explored using the adapted community of inquiry survey instrument. It was observed from the study that students’ online mathematics learning experiences were low. Further observation showed that while class cohesion and resolution dimensions were absent, teaching presence, exploration, affective expression, and triggering event dimensions of students’ online mathematics learning experiences were present. Additionally, the results showed that the difference in the magnitude of means in gender was partly dependent on the category of internet use before the remote online mathematics teaching was observed in the dimension of lack of class cohesion. Besides, the students were generally indifferent in their learning experiences regarding genders but significantly in terms of their internet use before the remote online mathematics teaching. Based on the results, implications of the state of the college of education (CoE) students’ online mathematics learning experiences and suggestions for improvement have been proposed.
Dynamic voltage restorer using sliding mode controller: Experimental studies
Authors: Toufik Toumi , Abdelkader Benabedellah , Othmane Abdelkhalek , Ahmed Allali , Aissa Benhamou , Oussama Hafsi
Published: December 2021

The simulation and implementation of a sliding mode control strategy for a single-phase dynamic voltage restorer (DVR) to mitigate load voltage sag swell and harmonics is presented in this work. The control strategy's goal is to compensate for the required voltage by regulating the DVR's voltage via an injection transformer while keeping the load voltage constant. The ability of the DVR to achieve a good performance greatly depends on its control strategy. The controller used in this work is based on SMC theory, which consists of creating a passivation output and a storage function to use as a function of Lyapunov. The proposed control scheme of the DVR is initially evaluated in simulations using MATLAB and validated using a laboratory-scale prototype of the entire system, including a source, the DVR circuit and a load. The control scheme is implemented on a dSPACE 1104 board and the MATLAB real-time toolbox. Both the experimental results have demonstrated the effectiveness the proposed control strategy of DVR in mitigating power qualities issues and therefore enhancing the performance of the network.
Comparative analysis of fuzzy and ANFIS based MPPT controller for wind power generation system
Authors: Muthu Annamalai
Published: December 2021

In recent years, huge developments in wind energy production and meet consumer demand. Numerous researchers have focused on maximum energy generation techniques for the wind system. The main reason for this work is to compare the different smart controllers for the maximum power generation techniques in the wind system. In this article, we developed and modeled a 250-watt wind power system in a MATLAB environment and simulated it in different weather conditions. Based on the simulation results, two intelligent controllers, such as fuzzy and ANFIS, were proposed and compared to obtain the maximum energy generation techniques in the wind system. Finally, the optimal smart controller was chosen based on performance.