bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on the Construction of a Nursing Education Management Model Based on a Small Data-Driven Model and Its Application
Authors: Lv Qingna , Zhang Yanyun , Li Yanyan , Yu Yang
Published: March 2022

Based on the concept of responsible holistic nursing care, a whole-process dual-tutor nursing practice model is established and its application effects are explored. This paper firstly reviews the research progress of nursing workload prediction methods at home and abroad, in order to provide a reference for clinical nursing workers in China to choose a scientific, reasonable, and easy-to-use nursing workload prediction method. It is proposed to construct a nursing education management model based on small data to provide ideas and references for nursing education management to effectively predict the evolutionary trend of students’ behaviour and improve the level of accurate services. The experimental group adopted a dual-tutor responsibility system for the whole-process nursing practice model, including a complete three-level supervision system: a dual-tutor teaching system, a PDCA responsibility system for continuous improvement, and a multichannel teacher-student interaction platform; the control group adopted the traditional nursing practice model.
A Novel Necroptosis-Related miRNA Signature for Predicting the Prognosis of Breast Cancer Metastasis
Authors: Zheng Lin , Wang Jie , Jiang Hongnan , Dong Honglin
Published: March 2022

Objective. Necroptosis was recently identified as a form of programmed cell death that plays an essential role in breast cancer metastasis. MicroRNAs (miRNAs) have long been recognized to affect cell death and tumor growth. In this study, we aimed to screen for necroptosis-associated miRNAs that predict breast cancer metastasis. Method. This study used The Cancer Genome Atlas (TCGA) public database to obtain miRNA expression data and associated clinical data from breast cancer patients and then retrieved miRNA data related to necrosis and apoptosis. Next, using Cox regression model analysis (univariate or multivariate) as well as a comparison analysis (differential analysis), a prognostic multi-miRNA molecular marker was established. Finally, prognosis-related miRNAs were utilized to identify target genes, and the functions of the target genes were analyzed for enrichment to investigate the probable mechanisms of the miRNAs. Results. Ten miRNAs were screened through differential analysis to build models: hsa-miR-148a-3p, hsa-miR-223-3p, hsa-miR-331-3p, has-miR-181a-5p, hsa-miR-181b-5p, hsa-miR-181c-5p, hsa-miR-181d-5p, hsa-miR-200a-5p, hsa-miR-141-3p, and hsa-miR-425-5p. The multivariate Cox regression model was an independent prognostic factor (univariate Cox regression results:

HR
=
3.2642

,

95
%
CI
=
1.5773
−
6.7554

,

P
=
0.0014

; multivariate Cox regression results:

HR
=
3.1578

,

95
%
CI
=
1.5083
−
6

,

P
=
0.0023

). The survival curve of the risk score also revealed that patients with a high risk score had a poor prognosis (

P
=
2
e
−
04

). The receiver operating characteristic (ROC) curve showed that the model has a certain prediction ability. Batch survival analysis of the miRNAs in the model was conducted and showed that hsa-miR-331-3p (

P
=
0.0182

) was strongly associated with prognosis. Twenty-three predicted target genes were obtained, and Gene Ontology (GO) enrichment analysis showed that these target genes were strongly enriched in transcriptional initiation and cell membrane trafficking. Conclusion. Our research identified a novel miRNA marker for predicting breast cancer patient prognosis and lays the groundwork for future research on necroptosis-related genes.
Application of Big Data Analysis Techniques in Sports Training and Physical Fitness Analysis
Authors: Li Xinwen , Chen Xiaowei , Guo Lihui , Rochester Christine A.
Published: March 2022

With the development of sports and information technology, people use mathematical tools and computer technology to study sports data and mine the intrinsic value of sports data. Statistical methods are the most widely used to achieve this goal. The research purpose of sports effect evaluation research is to understand the impact of sports on physical fitness through mining and analysis of sports data and to provide theoretical guidance for the public to participate in fitness activities scientifically and effectively. At present, in the study of combining individual performance test data, the research on the standardization of physical fitness monitoring data for sports training is relatively scarce. Therefore, based on the background of big data, this paper integrates the existing data standardization work and designs a plan for the standardization of physical fitness monitoring data for sports training. Combined with machine learning, data preprocessing is performed to obtain the data required by the machine model. The comprehensive physical fitness rating model and the recommendation model are established to realize the development of physical fitness monitoring service applications. In the experiment, compared with the three classical methods, the results show that the classification accuracy of this paper is 4% higher than that of other algorithms, which can more intuitively reflect the characteristic samples of sports training. In this paper, the data mining and analysis technology based on feature indicators in the mining and application of sports data has great application value for human fitness guidance and has certain research value and market application prospects.
A Competent MWCNT-Grafted MnOx/Pt Nanoanode for the Direct Formic Acid Fuel Cells
Authors: Al-Akraa Islam M. , Mamdouh Moutaz M. , Asal Yaser M. , Mohammad Ahmad M.
Published: March 2022

A novel “MnOx/Pt/MWCNT-GC” nanocatalyst is recommended for the electrooxidation of formic acid (EOFA), the principal anodic reaction in the direct formic acid fuel cells (DFAFCs). The sequential (layer-by-layer) protocol was employed to prepare the catalyst through the electrodeposition of Pt (nano-Pt) and manganese oxide (nano-MnOx) nanoparticles onto the surface of a glassy carbon (GC) electrode supported with multiwalled carbon nanotubes (MWCNTs). The nano-MnOx could successfully mediate the mechanism of EOFA by accelerating the charge transfer, “electronic effect”. On the other hand, MWCNTs could enhance the catalytic performance by changing the surface geometry that inhibited the adsorption of poisoning CO, which is a typical intermediate in the reaction mechanism of EOFA that is responsible for the potential deterioration of the catalytic performance of DFAFCs. Interestingly with this modification, a significant enhancement in the catalytic activity and stability toward EOFA was achieved. Several techniques will be employed to evaluate the catalyst’s morphology, composition, crystal structure, and activity and further to understand the role of each of the nano-MnOx and MWCNTs in the catalytic enhancement.
Self-Excited Vibration Analysis of Gear-Bearing System with Multipoint Mesh and Variable Bearing Dynamic Coefficients
Authors: Zhang Hao , Cao Shiheng , Li Pengyu , Han Qingkai
Published: March 2022

The gear-bearing system is the most important part of integrally centrifugal compressors. According to statistics, the majority of integrally geared compressor accidents are caused by excessive vibration of the geared rotor. However, its complicated dynamic characteristics and inevitable vibration faults in actual operation present significant challenges throughout the analysis and design stages. In this paper, the coupled self-excited vibration of the gear system characterized by multipoint meshing and oil film bearing supporting is investigated. Firstly, the structure of the gear system in an integrally geared compressor is used as a research object. The modeling approach of meshing excitation, including time-varying mesh stiffness, gear meshing error, and tooth backlash are introduced. However, the variable stiffness and damping coefficient equations of journal bearing and oil film thrust bearing are modeled and utilized to approximate the variable bearing force and simplify the vibration computation under the assumption of Newtonian fluid. Then, a dimensionless modeling method of the gear system considering gyroscopic moment of gear disk, variable meshing force, as well as variable stiffness and damping coefficient is proposed. Based on the dynamic model, the influence of the bearing dynamic coefficients and load on the vibration of the entire gear system is studied. Among which, the vibration displacement and meshing force are examined using frequency-domain and time-domain analysis methods. The results suggest that the flexible support can restrain the system’s nonlinear motion, whilst increasing load on the gear system can improve gear operation stability and reduce load fluctuation.
3D Indoor Scene Reconstruction and Layout Based on Virtual Reality Technology and Few-Shot Learning
Authors: Li Huanmei
Published: March 2022

Indoor three-dimensional layout has a strong application background, such as virtual office three-dimensional layout planning, museum three-dimensional layout planning, and cave scene three-dimensional layout planning, which have been widely used in telecommuting, education, tourism, and other industries. In view of this, this paper proposes an indoor landscape reconstruction method based on VR (virtual reality) and draws indoor landscape information and images by using VR technology to generate an indoor landscape reconstruction panorama. A model is established to correct the distance error and reflectivity error of depth image, improve the accuracy of the depth image, and finally improve the accuracy of three-dimensional indoor scene TDR (three-dimensional reconstruction). In the process of optimizing layout, the Monte Carlo sampling method is used based on the Markov chain, and constraints are used as density functions to guide layout sampling and generate a number of reasonable scene layout suggestions in the iterative process of the sampler. Experiments show that this method can provide scientific and reasonable guidance to users’ scene layout and help them complete the furniture layout quickly.
Digital Twins Model of Industrial Product Management and Control Based on Lightweight Deep Learning
Authors: Huang Zuoyue , Yan Zhitao
Published: March 2022

Digital twins (DTs) can realize the integration of information and entities. It is widely used because of its simulation characteristics and virtual reality (VR) mapping. Its application to industrial product management and control is explored. First, the concept and the functions in different stages of DTs are expounded. Second, the Workench simulation platform and SolidWorks software are applied in the design of the aluminum alloy flange according to DTs in the design stage of industrial product management and control. Third, the role of DTs in industrial product management and control is confirmed through a comparative experiment. Finally, an intelligent algorithm for the automatic identification of internal defects is designed based on lightweight deep learning to improve the efficiency of ultrasonic detection. The results show that the accuracy of the lightweight convolution neural network (CNN) is 94.1%; the model size is 2.9 MB; the network is more lightweight and has an excellent performance in ultrasonic defect detection; the nonlinear finite element analysis results and the test results are consistent. Therefore, it is proved that the finite element analysis method is reliable and helps to improve the efficiency and shorten the design cycle. The emergence of DTs provides a technical scheme for product management and control under the three-dimensional model.
Research on the Nonlinear Characteristics of Acoustic Emission and Damage Mechanism of Gassy Coal under Unloading Conditions
Authors: Liu Jie , Li Qiuping , Zhang Ran , Wang Zaiquan , Lu Shouqing , Sa Zhanyou , Wang Hao
Published: March 2022

Coal mining and production activities lead to static loading and unloading changes of coal stress in front of the working face, and the stress change process has a significant influence on the occurrence and development of coal-rock gas dynamic disasters. In this paper, the macroscopic failure characteristics, acoustic emission timing characteristics, and acoustic emission nonlinear characteristics of coal with different gas pressures under true triaxial loading and unloading conditions were experimentally studied. The results showed that the macroscopic failure form of coal with different gas pressures under unloading conditions was tensile-shear composite failure, and the crack structure was formed near the unloading surface. The fractal dimension

D

and multifractal parameter

Δ
α

of acoustic emission time series both could reflect the complexity of coal fracture process. With the increase of gas pressure, the fractal dimension

D

and multifractal parameter

Δ
α

decreased, which indicated that the greater the gas pressure, the lower the complexity of coal fracture process. Under different gas pressures, the dynamic change trends of multifractal parameter

Δ
α

were similar, taking the beginning of unloading as the dividing point, the change was roughly in the form of “W.” When the stress state and failure form of coal body changed, the multifractal parameter

Δ
α

changed synchronously, indicating that the change of

Δ
α

could reflect the transformation process of failure mechanism of coal body under load to a certain extent, which was of great significance for clarifying the occurrence and development mechanism of coal-rock gas dynamic disasters and ensuring the safe production in underground coal mines.
Experimental Study on the Effect of Grouting Defects on Mechanical Properties of the Rebar Connected by Full‐Grouted Sleeves
Authors: Guo Fei , Li Junhua , He Sicong , Zhou Chunheng
Published: March 2022

In this article, the mechanical properties of the steel bars connected by the full‐grouted sleeve with grouting defects were investigated experimentally. The study focused on two defect factors: the insufficiency of grouting material strength and the lack of grouting fullness. The HRB400 steel bar with a diameter of 20 mm and the full‐grouted sleeve with the model of GTQ4J 16 were applied in the experiment. The mechanical properties of the steel bar connected by grouted sleeve under different conditions were tested under tensile load, and these mechanical parameters were compared with the standard values in the specification. The results show that both kinds of grouting defects have a significant impact on the mechanical properties of reinforcement. The tensile strength and elongation of the joint were decreased with the decrease of the strength of the grouting material. When the water‐binder ratio reaches 0.14, the strength of the grouting material is 66.4 MPa, and the mechanical properties of the joint cannot meet the requirements. At the same time, the mechanical properties of the joints were also reduced with the decrease of the grouting fullness. When the grouting fullness is below 80%, the tensile strength decreased more obviously than before, and the mechanical properties cannot reach the relevant standard.
BSSH: Bluetooth Indoor Positioning Strategy for Ship Cabin Based on Hierarchical Area
Authors: Gao JunBo , Qin ZhenXiao , Fan Tao , Sun Wei
Published: March 2022

In the process of ship's ocean transportation, the monitoring of crew's work completion is a very important part in the ship safety management, and using indoor positioning technology can improve the construction of ship management system. However, the current indoor positioning technology is not ideal in the engine room environment in the face of the complex electromagnetic environment and spatial structure. In order to improve the positioning accuracy, this paper proposes a Bluetooth indoor positioning strategy for ship cabin based on hierarchical area (BSSH) for deploying and dividing the regions requiring indoor positioning and combines Bluetooth Low Energy (BLE) with Bluetooth fingerprint positioning technology. Processing the fingerprint library by using region hierarchy and proximity classification algorithms to obtain reliable results in the ship environment, the results show that better Beacon deployment and hierarchical area algorithms can lead to a localization accuracy of 80.38%.
Glass Refraction Distortion Object Detection via Abstract Features
Authors: Cai Lei , Chen Chuang , Sun Qiankun , Chai Haojie
Published: March 2022

Glass reflection and refraction lead to missing and distorted object feature data, affecting the accuracy of object detection. In order to solve the above problems, this paper proposed a glass refraction distortion object detection via abstract features. The number of parameters of the algorithm is reduced by introducing skip connections and expansion modules with different expansion rates. The abstract feature information of the object is extracted by binary cross-entropy loss. Meanwhile, the abstract feature distance between the object domain and source domain is reduced by a loss function, which improves the accuracy of object detection under glass interference. To verify the effectiveness of the algorithm in this paper, the GRI dataset is produced and made public on GitHub. The algorithm of this paper is compared with the current state-of-the-art Deep Face, VGG Face, TBE-CNN, DA-GAN, PEN-3D, LMZMPM, and the average detection accuracy of our algorithm is 92.57% at the highest, and the number of parameters is only 5.13 M.
Study on Settlement Influence of Newly Excavated Tunnel Undercrossing Large Diameter Pipeline
Authors: Xu Qian , Zhang Wenchao , Chen Cheng , Lu Jun , Tang Peng
Published: March 2022

With the rapid development of tunnel construction, there will be an increasing number of engineering cases about undercrossing existing pipelines. During the undercrossing process, the settlement control of existing pipelines is relatively strict. If the construction is not handled properly, the existing pipelines will cause a larger settlement, which will affect their normal use. This paper takes an existing pipeline project in Nanjing as the research object and uses numerical simulation to explore the influence of different excavation sequences and grouting reinforcement scopes on the existing pipelines above the newly built tunnels when using shallow tunnel excavation. The results show that the sections are constructed first on both sides of the construction, and the middle section is constructed subsequently, which not only increases the excavation speed but also the pipeline deformation is smaller, especially in controlling the differential settlement on both sides of the pipeline. By studying the relationship between the grouting reinforcement range and the vertical distance from the newly built tunnel to the existing pipeline, it is found that the soil engineering effect within 0.3 d above the arch line is more reasonable, and the feasibility of the proposed scheme is verified through actual monitoring data. This research can provide a reference for similar projects in the future.
Feature Selection Model Based on Gorilla Troops Optimizer for Intrusion Detection Systems
Authors: Ahmed Ibrahim , Dahou Abdelghani , Chelloug Samia Allaoua , Al-qaness Mohammed A. A. , Elaziz Mohamed Abd
Published: March 2022

Cyber security is a fundamental challenge to the Internet of things (IoT) and smart home environments .This paper presents a modified method to ystem (IDS).setection dntrusion ienhance the performance of the This modification is achieved by introducing an alternative feature selection (FS) . ptimizer (GTO) algorithm.oroops torilla gmodel based on the Recently, FS has played a significant role in increasing the detection of anomalies in IDSs. To evaluate the efficiency of the developed method, a set of experimental conducted using three datasets, including NSL-KDD, CICIDS2017, and Bot-IoT datasets.asresults w xtraction (FE) model to reduce the dimensions of these datasets as a first step.Teeature f used as a areetworks (CNN) neural nonvolutional cThe hen, the extracted features are passed to the FS model for detection. The results of the developed method are compared with the well-known IDS technique. The results show the superiority of the developed method over all other methods according to the performance metrics.
A Weighted Error Distance Metrics (WEDM) for Performance Evaluation on Multiple Change-Point (MCP) Detection in Synthetic Time Series
Authors: Qi Jin Peng. , Pu Fang. , Zhu Ying. , Zhang Ping.
Published: March 2022

Change-point detection (CPD) is to find abrupt changes in time-series data. Various computational algorithms have been developed for CPD applications. To compare the different CPD models, many performance metrics have been introduced to evaluate the algorithms. Each of the previous evaluation methods measures the different aspects of the methods. Based on the existing weighted error distance (WED) method on single change-point (CP) detection, a novel WED metrics (WEDM) was proposed to evaluate the overall performance of a CPD model across not only repetitive tests on single CP detection, but also successive tests on multiple change-point (MCP) detection on synthetic time series under the random slide window (RSW) and fixed slide window (FSW) frameworks. In the proposed WEDM method, a concept of normalized error distance was introduced that allows comparisons of the distance between the estimated change-point (eCP) position and the target change point (tCP) in the synthetic time series. In the successive MCPs detection, the proposed WEDM method first divides the original time-series sample into a series of data segments in terms of the assigned tCPs set and then calculates a normalized error distance (NED) value for each segment. Next, our WEDM presents the frequency and WED distribution of the resultant eCPs from all data segments in the normalized positive-error distance (NPED) and the normalized negative-error distance (NNED) intervals in the same coordinates. Last, the mean WED (MWED) and MWTD (1-MWED) were obtained and then dealt with as important performance evaluation indexes. Based on the synthetic datasets in the Matlab platform, repetitive tests on single CP detection were executed by using different CPD models, including ternary search tree (TST), binary search tree (BST), Kolmogorov–Smirnov (KS) tests, t-tests (T), and singular spectrum analysis (SSA) algorithms. Meanwhile, successive tests on MCPs detection were implemented under the fixed slide window (FSW) and random slide window (RSW) frameworks. These CPD models mentioned above were evaluated in terms of our WED metrics, together with supplementary indexes for evaluating the convergence of different CPD models, including rates of hit, miss, error, and computing time, respectively. The experimental results showed the value of this WEDM method.
In-Plane Impact Resistance of a Diamond-Shaped Hierarchical Honeycomb
Authors: Song Hang , Zhang Chenyang , Wang Pengyu , Min Benzhi , Wang Zhenqing
Published: March 2022

The introduction of hierarchical structures into conventional honeycomb structure materials can increase the mechanical properties of honeycomb materials. This paper studies the in-plane collapse response of a new second-order diamond-shaped hierarchical honeycomb (DHH) (the cell walls are composed of equilateral triangles) and systematically studies the quasi-static collapse response and dynamic collapse response of the DHH in two directions through the finite element method. The quasi-static collapse response of the DHH is analyzed by a two-scale research method, and combined with momentum conservation law, the study is extended to dynamic collapse response. This paper also studies the collapse response of the DHH with different number of substructures, different relative densities, and different impact speeds, and the results are verified by theoretical analysis and numerical simulation. The results show that adding hierarchical structure can greatly increase the collapse stress of the diamond honeycomb structure and make the structure have better energy absorption effect.
Construction and Optimization of Transboundary Business Financial Credit Network in the Era of 5G Communication
Authors: Zhao Bingqian , Lyu Xiaojing , Qi Ning
Published: March 2022

The e-commerce industry has broken through the original geographical restrictions, successfully integrated with world trade, and evolved new e-commerce model. The e-commerce model, that is, e-commerce transactions across international trade, has been rapidly expanded. However, e-commerce transactions across international trade have innate virtuality and information imbalance. These problems have caused many credibility problems and, at the same time, restrict and hinder the healthy and sustainable evolution of transactions across international trade. Credit evaluation of transaction entities through credit evaluation models is an effective way to restrict the behavior of transaction entities. However, the existing credit evaluation models lack pertinence and effectiveness when applied in the context of cross-border e-commerce. In the era of 5G communication, building a complete credit evaluation system through 5G-related technologies will certainly become a new way for the stable evolution of transactions across international trade. This not only can effectively control the risks of cross-border trade and improve efficiency but also properly resolve the uncertainty caused by information imbalance. In order to better promote the development of e-commerce, this article establishes an e-business evaluation module based on integrated fusion performance rating. The weight and the membership of each factor are determined by AHP. Finally, the model was verified by an example. The results show that the evaluation system and the method proposed in this paper are feasible and effective for solving practical problems and provide a solid foundation for the construction of the network of my country’s e-commerce credit rating system. Establishing a scientific and reasonable e-commerce integrity evaluation system has very good practical significance.
Research on the Millionaires’ Problem under the Malicious Model Based on the Elliptic Curve Cryptography
Authors: Liu Xin , Xu Yang , Xu Gang , Li Baoshan
Published: March 2022

With the rapid development of blockchain, big data, cloud computing, and artificial intelligence, the security of multisource data collaborative computing has become increasingly prominent. Secure multiparty computing has become the core technology of privacy collaborative computing. Millionaires’ problem is the cornerstone of secure multiparty computation. Firstly, this paper proposes a 0-1 coding rule, which is used to solve the millionaires’ problem under the semihonest model. Aiming at the possible malicious behaviors of the protocol under the semihonest model, the millionaires’ problem protocol under the malicious model based on the elliptic curve cryptography is designed by using cryptographic tools such as the zero-knowledge proof and the cut-choose method. This protocol not only can effectively solve the millionaires’ problem but also can safely and effectively prevent malicious behaviors. Meanwhile, the security ordering designed by the protocol can be effectively applied to a quality evaluation in the blockchain.
Performance Evaluation of Boer × Central Highland Crossbred Bucks and Farmers’ Perceptions on Crossbred Goats in Northeastern Ethiopia
Authors: Tesema Zeleke , Alemayehu Kefyalew , Kebede Damitie , Getachew Tesfaye , Deribe Belay , Alebachew Getachew W. , Yizengaw Liuel
Published: March 2022

The study aimed to characterize the production system, to evaluate the genetic merit of Boer × Central Highland crossbred bucks, and to solicit the perception of farmers about crossbred goats and crossbreeding program. Data were collected through a personal interview, focus group discussion, field observation, and measurement of the live animal. Besides, data on growth performance were extracted from available performance records at Sirinka sheep and goats breeding station. Data were analyzed using SAS, and the breeding values for bucks were estimated using WOMBAT software. Goats were the second most important animal species, and income generation, home meat consumption, and saving were found to be the main reasons for keeping goats with index values of 0.484, 0.355, and 0.085, respectively. The production system was characterized as a low-input production system. Feed shortage and disease/poor veterinary service were the most important constraints for the goat crossbreeding program. The overall mean estimated breeding values (EBV) for three- and six-month weight of disseminated crossbred bucks were 0.53 and 0.31 kg, respectively. The three-month weight EBV for crossbred goats disseminated in Amhara Sayint and Habru district was lower than their contemporary group. Likewise, the six-month weight EBV for crossbred goats distributed in Amhara Sayint was lower than the contemporary group mean. These results depict the absence of buck selection based on their genetic merit. As per farmers’ perception, crossbred goats were superior (odds ratio = 3.94 to 20.9,

P
<
0.001

) to indigenous goats in terms of production traits. Besides, the price of the crossbred goat was higher (213 to 372 ETB/head) than indigenous goats with similar management and age. However, poor adaptability and fitness were the major demerits of Boer × Central Highland crossbred goats under a smallholder management system. Therefore, while introducing exotic breeds, it is imperative to give due attention to nutrition and veterinary service.
Complex Traffic Network Analysis Method Based on a Multiscale Aggregation Model
Authors: Pei Yingying , Zhu Xia , Li Guohong , Jin Yongtao , Liu Yuyan , Liu Yuanping , Liu Gang , Fan Jiangxia
Published: March 2022

Identifying the aggregation characteristics of the geospatial network is an important aspect of analyzing highway traffic networks. Based on the complex network theory, this paper studies the block aggregation characteristics of the highway traffic network and proposes an Improved PageRank Spectral Clustering (IPSC) Algorithm to divide the functional blocks of highway traffic networks. Firstly, the theoretical model of a highway traffic network is constructed by adding location attribute weight, geographical distance weight, road grade weight, and dynamic traffic congestion weight. Secondly, the improved PageRank algorithm is used to get the ranking of key nodes of the highway traffic network. The clustering center and the number of clusters are determined by the ranking of key nodes and the shortest path distance. Then the improved spectral clustering algorithm is used to divide the functional blocks of the highway transportation network and identify the special common blocks of the highway transportation network. Finally, the IPSC Algorithm is used to analyze the aggregation mode of complex traffic networks at city and district scales. Crossing the limit of administrative division, the division results of special common blocks of highway transportation networks are obtained. Maintaining the connectivity between blocks can improve the overall efficiency of highway transportation networks.
Analysis of Sports Video Intelligent Classification Technology Based on Neural Network Algorithm and Transfer Learning
Authors: Guangyu Han
Published: March 2022

With the rapid development of information technology, digital content shows an explosive growth trend. Sports video classification is of great significance for digital content archiving in the server. Therefore, the accurate classification of sports video categories is realized by using deep neural network algorithm (DNN), convolutional neural network (CNN), and transfer learning. Block brightness comparison coding (BICC) and block color histogram are proposed, which reflect the brightness relationship between different regions in video and the color information in the region. The maximum mean difference (MMD) algorithm is adopted to achieve the purpose of transfer learning. On the basis of obtaining the features of sports video images, the sports video image classification method based on deep learning coding model is adopted to realize sports video classification. The results show that, for different types of sports videos, the overall classification effect of this method is obviously better than other current sports video classification methods, which greatly improves the classification effect of sports videos.
FoSSA Optimization-Based SVM Classifier for the Recognition of Partial Discharge Patterns in HV Cables
Authors: Sun Kang , Meng Yuxuan , Dong Shuchun
Published: March 2022

In order to enhance the classification accuracy and the generalization performance of the SVM classifier in cable partial discharge (PD) pattern recognition, a firefly optimized sparrow search algorithm (FoSSA) is proposed to optimize its kernel function parameters and penalty factors. First, the Circle-Gauss hybrid mapping model is employed in the population initialization stage of the sparrow search algorithm (SSA) to eliminate the uneven population distribution of random mapping. Sparrows tend to fall into local extremums during the search process, while the firefly algorithm has a fast optimization speed and strong local search ability. Thus, a firefly disturbance is added in the sparrow search process, and the fitness value is recalculated to update the sparrow position to enhance the sparrow’s local optimization ability and accuracy. Finally, based on the SSA, a dynamic step-size strategy is adopted to make the step size dynamically decrease with the number of iterations and improve the accuracy of convergence. Six benchmark functions are employed to evaluate the optimization performance of the FoSSA quantitatively. Experiment results show that the recognition accuracy of the PD patterns using the SVM optimized by the FoSSA could reach 97.5%.
Study on the Mix of Slurry Membrane for Soil Conditioning of EPB Shield and Its Application Effect in Water‐Rich Gravel Strata
Authors: Zhao Jinpeng , Tan Zhongsheng , Yu Rongsen , Jia Aidong , Qu Chao
Published: March 2022

The water inrush and large surface settlement encountered by EPB shield crossing water‐rich gravel layer have attracted much attention. Based on the section tunnel engineering of the Inner Mongolia University South Campus Station‐Xilin Park Station of line 2 of Hohhot Rail Transit, the optimum mix proportion of bentonite slurry and foam diluent for forming slurry membrane in water‐rich gravel stratum was studied in this study at first. The characteristics of conditioned soil were analyzed in combination with slag soil. The water plugging effect and surface settlement with or without slurry membrane were investigated with numerical simulation. The numerical model has been effectively verified by comparing the field monitoring and numerical simulation of surface settlement value. Further, the water shutoff mechanism of the slurry membrane is revealed. The setting of the slurry membrane exhibited a good water plugging effect and effectively decreased the surface settlement. The construction method of “water plugging with a slurry membrane” proposed in this study could powerfully combine the respective advantages of EPB shield and slurry balance shield, ensuring the safety, high quality, and high efficiency of tunnel construction.
Bidirectional Association between Hypertension and NAFLD: A Systematic Review and Meta-Analysis of Observational Studies
Authors: Li Gerui , Peng Yuanyuan , Chen Ze , Li Hang , Liu Danli , Ye Xujun
Published: March 2022

An increasing body of evidence connects non-alcoholic fatty liver disease (NAFLD) to hypertension. The objective of this systematic review and meta-analysis was to estimate the nature and magnitude of the association between NAFLD and hypertension. We systematically searched PubMed, Embase, Cochrane Library, and Web of Science for observational studies published up to May 1, 2021. Cohort studies that reported data on the association between NAFLD and incident hypertension or between hypertension and incident NAFLD were included. We used random-effects models to conduct meta-analysis on the measures of association from individual studies. A total of 11 studies were eligible for inclusion, among which 4 studies including 25,260 participants reported the association between hypertension and new-onset NAFLD. The presence of hypertension was significantly associated with an increased risk of incident NAFLD (HR 1.63, 95% CI: 1.41–1.88; I2 = 37.6%). On the other hand, 9 studies with data on 46,487 participants analyzed the effects of NAFLD on incident hypertension. Pooled analysis showed that the presence of NAFLD was significantly associated with an increased incidence of hypertension (HR 1.55, 95% CI: 1.29–1.87; I2 = 80.5%). There was significant heterogeneity among the studies in this analysis (

p

 < 0.01). Sensitivity analyses showed that the magnitude of the association was significantly different in subgroups stratified by a mean age of participants and geographical location, which explains part of the heterogeneity. In conclusion, this meta-analysis indicates the existence of a bidirectional relationship between NAFLD and hypertension independent of traditional cardiometabolic risk factors.
Prediction Model for Infectious Disease Health Literacy Based on Synthetic Minority Oversampling Technique Algorithm
Authors: Zhou Rongsheng , Yin Weihao , Li Wenjin , Wang Yingchun , Lu Jing , Li Zhong , Hu Xinxin
Published: March 2022

Objective. Improving health literacy in infectious diseases is a direct manifestation of the solid advance in disease control and prevention. Our study is aimed at exploring applying synthetic minority oversampling technique (SMOTE) in the prediction assessment of whether residents and business employees have infectious disease health literacy. Methods. The Chinese resident infectious disease health literacy evaluation scale was used to investigate the associated variables. The screened variables were input variables and the presence or absence of infectious diseases health literacy as outcome variables. Logistic regression, random forest, and support vector machine (SVM) models were built in the data sets before and after treatment by the SMOTE algorithm, respectively, and the performance of the models was evaluated by receiver operating characteristic curves (ROC). Results. Logistic regression, random forest, and SVM achieved accuracies of 0.828, 0.612, and 0.654 before SMOTE algorithm processing, and the areas under the ROC curves (AUCs) of the three models were 0.754, 0.817, and 0.759, respectively. The accuracies were 0.938, 0.911, and 0.894 after SMOTE algorithm processing, and the AUCs of the three models were 0.913, 0.925, and 0.910, respectively. Conclusions. The random forest model based on the SMOTE has high application value in assessing whether residents versus enterprise employees have infectious disease health literacy.
A Novel Approach for Characterizing Solutions of Rough Optimization Problems Based on Boundary Region
Authors: Wahed Khalifa Hamiden Abd El- , Pamucar Dragan , Kacem Amina Hadj , Afifi W. A.
Published: March 2022

Rough set theory, presented by Pawlak in 1981, is one of the most well-known methods for communicating ambiguity by estimating an item based on some knowledge rather than membership. The concept of a rough function and its convexity and differentiability in regard to its boundary region are discussed in this work. The boundary notion is also used to present a new form of rough programming issue and its solutions. Finally, numerical examples are provided to demonstrate the proposed method and emphasize its advantages over other approaches.
Few-Shot Learning-Driven Optimal Allocation Model of University Human Resources Based on the Apriori Algorithm
Authors: Ren Qiufang
Published: March 2022

For universities, HR is the first element and the first resource to build university resources. Systematic construction and management of HR and effective development and utilization are the key factors to promote the development of universities. In this paper, the Apriori correlation rule mining algorithm is studied, the optimal allocation model of university HR based on the Apriori algorithm is studied, and the strategy of optimal allocation of university HR is discussed. DM can discover the talent patterns and laws within the organization, so as to find the talent model within the organization. The results show that the method proposed in this paper can be used to mine the correlation rules of the university HR data set, and good results can be achieved. The system can realize the standardization, safety, and paperless work of university personnel, which has changed the original manual work mode and greatly improved the work efficiency. The method in this paper can be used to determine a reasonable HR structure according to the development goals and plans of our school, establish a scientific talent evaluation mechanism, clarify the age structure of teachers, provide an important scientific method, and provide objective decision support for formulating talent demand planning, talent recruitment, and training.
Seismic Performance Evaluation of Prefabricated Shear Wall Structure with Rabbet‐Unbonded Horizontal Connection
Authors: Sun Chongfang , Zhao Zhiqiang
Published: March 2022

This paper is concerned about seismic performance evaluation of precast shear wall structure with Rabbet‐unbonded Horizontal Connection (RHC). RHC is composed of rabbets and unbonded rebar segments. The rabbets are used to improve the shear capacity and prevent slippage of connection, and the unbonded rebar segments are used to improve the ductility and energy dissipation. The constitutive relation of unbonded region is put forward and verified, which is applied in RHC shear wall structure model. The pushover analysis, capacity spectrum analysis and seismic performance evaluation of RHC shear wall structure are performed, which are compared with that of conventional connection shear wall structure and cast‐in‐place shear wall structure. The result shows that RHC shear wall structure reaches basic seismic fortification target of “no damage in small earthquake, repairable in moderate earthquake, no collapse in severe earthquake,” and basically reaches the seismic performance of cast‐in‐situ shear wall structure. Finally the effect of unbonded length and unbonded level on storey drift and interlayer drift angle are analyzed and the design proposal is put forward.
Economic Crisis Early Warning of Real Estate Companies Based on PSO-Optimized SVM
Authors: Li Xinhui
Published: March 2022

For the possible economic crisis of real estate companies, by analyzing the shortcomings of support vector machine (SVM) model, after optimization with particle swarm optimization (PSO), the PSO-SVM model was established by changing economic condition parameters and using data to warn the real estate economic crisis. Then, this model is used to warn the economic capacity of four real estate companies in Beijing. The results show that this model can further predict and analyze the solvency index, operating ability index, development ability index, profitability index, cash flow index, and comprehensive financial index of real estate companies and come to the conclusion that profitability index is the most important, and higher profitability can resist the harm brought by economic crisis. Finally, it analyzes the economic crisis that real estate companies may encounter and puts forward specific suggestions.
“Best of Both World”: The Amalgamation of Fuzzy Delphi Method with Nominal Group Technique for Dengue Risk Prioritisation Decision-Making
Authors: Ahmad Zamzuri Mohd‘Ammar Ihsan , Naserrudin Nurul Athirah , Ali Noor Khalili Mohd , Aziz Suriyati Abd , Rashid Mohammad Faid Abd. , Dapari Rahmat , Ramdzan Abdul Rahman , Hod Rozita , Hassan Mohd Rohaizat
Published: March 2022

Introduction. Dengue remains a public health threat. Clarifying the characteristics of future threats and prioritising intervention towards the highest risk potential can help to control and prevent dengue outbreaks. However, obtaining a consensus from panels of experts is certainly challenging due to the relative subjectivity of experience. Therefore, this article incorporates the fuzzy Delphi method (FDM) within a nominal group technique (NGT) as a multicriteria decision-making tool for (1) describing the characteristics of socioecological attributes (SEAs) with a high risk of causing dengue outbreak and (2) ranking those SEAs as priorities for intervention. Material and Methods. Experts were recruited using a purposive sampling technique. Informed consent was obtained before the start of the study. The NGT process began with an introductory presentation of dengue SEA by the moderator, followed by “silent generation.” Next, each participant provided information in a round-robin fashion. Ideas were collected by the moderator and displayed publicly. All experts were given ample time and space to contribute and justify their ideas without interruption during the discussion step to yield agreeable SEA characteristics. Ultimately, FDM was incorporated in the voting step to ensure rigorous analysis. The study was approved by an ethical committee before its commencement. Results. A total of 10 field experts participated in the study, with a median experience of 7.5 years working on a dengue team. The common characteristics of SEA prone to cause dengue outbreaks were the presence of human-made containers, in high quantities, left unattended, and covered from direct sunlight. Apart from that, all eight SEAs passed the triangulation of fuzzy numbers and defuzzification processes. The average fuzzy numbers ranged between 0.500 and 0.780, and the threshold value (

d

) ranged from 0.055 to 0.196. Of the potential risk factors identified, experts ranked illegal dumping sites as the most important, followed by old and unused items and construction sites. Conclusion. The NGT process successfully helped to obtain a consensus among the expert panels in describing SEA characteristics. Nevertheless, the integration of FDM offered a robust analysis that validated their ranking in dengue risk prioritisation. Therefore, we strongly recommend the application of FDM to be incorporated in any public health decision-making process.
Analytical and Numerical Study to a Forced Van der Pol Oscillator
Authors: Salas Alvaro , Martínez H Lorenzo J. , Ocampo R David L.
Published: March 2022

In this paper we show the way to apply some and analytical and numerical techniques in order to solve the forced Van der Pol oscillator. We illustrate the obtained results with examples. A comparison with Runge–Kutta numerical method is made in order to see the accuracy of the approximated analytical solution.