bluuebunny/crossref_metadata_2025 huggingface dataset:
A Self-Adjusting Search Domain Method-Based Genetic Algorithm for Solving Flexible Job Shop Scheduling Problem
Authors: Li Bin , Xia Xuewen
Published: October 2022

As a nondeterministic polynomial (NP) problem, the flexible job shop scheduling problem (FJSP) is a difficult problem to be solved in terms of finding an acceptable solution. In last decades, genetic algorithm (GA) displays very promising performance in the field. In this article, a hybrid algorithm combining global and local search with reinitialization (GLRe)-based GA is proposed to minimize makespan for FJSP. The solution of FJSP is conveniently represented by a double-layer chromosome representation method, which is convenient for subsequent genetic operations, that is, sorting of operations and selection of machines. Two strategies of choosing the job with the most remaining operations (CRO) and 6-dimensional variable determined search position (6D-VSP) are proposed as two components for GA, which are applied to generate a population with superior quality and reduce the global search space during the initialization stage. At the same time, in order to prevent the loss of diversity during evolution, a reinitialization strategy is introduced in the later stage of evolution to adaptively adjust the search domain of the problem. Finally, two sets of benchmark data are tested. The experimental results demonstrate the accuracy and effectiveness of the GLRe proposed in this article for solving FJSP.
Finite-Element Analysis of the Effect of Utilizing Various Material Assemblies in “All on Four” on the Stresses on Mandible Bone and Prosthetic Parts
Authors: Shash Yomna H. , El-Wakad Mohamed T. , Eldosoky Mohamed A. A. , Dohiem Mohamed M.
Published: October 2022

Background. Fixed prostheses often utilize the “All-on-four” technique, in which four implants are inserted into the jaw bone, and a framework supports them. Titanium is usually used in the fabrication of “All-on-four” parts, due to its superior mechanical properties; however, it has drawbacks such as aesthetic impairment, casting issues, stress shielding, and incompatibility with imaging techniques. These drawbacks have motivated researchers to find alternative materials such as polymers. Recently, the new polymeric material PEEK has a major role in most areas of dentistry, and therefore, it can represent an alternative biomaterial to overcome the drawbacks of titanium. The density of bone is expected to influence the choice of “All-on-four” materials. Purpose. This research applied finite-element investigations to evaluate the stresses on bone tissues and prosthetic parts in “All on four,” utilizing three assemblies of materials, in normal and low bone densities. These assemblies were titanium (Type 1), titanium/PEEK (Type 2), and PEEK (Type 3). Materials and Methods. A 3D Mandibular model was constructed with a fixed prosthesis, and three assemblies of materials were stimulated, under 300 N unilateral force. The von Mises stresses were computed for the prosthetic parts and mucosa, while the maximum and minimum principal stresses/strains were computed for bone tissues due to their brittle and ductile properties. Moreover, the displacements of implants were extracted to check the prosthesis stability. Results. Type 2 and Type 3 minimized the stresses on frameworks, implants, abutments, and bone tissues, however, increased the mucosal stress, in comparison to Type 1. In the low-density model, Type 3 was recommended to reduce the stresses/strains on bone tissues and decrease the implant displacement, avoiding bone failure and increasing prosthesis stability. Conclusions. The bone density influenced the choice of “All-on-four” assembly. Moreover, further research on PEEK implants and abutments is required in the future.
Finite Element Analysis Model Design on the Mechanical Properties of Prefabricated Shear Wall Structure
Authors: Zhang Yuan
Published: October 2022

At present, the mechanical properties analysis method of prefabricated shear wall structure based on static test is low overall simulation precision. In this paper, the finite element method is used to analyze the force performance of the prefabricated shear wall structure. Based on the shear of concrete material, elastic modulus, Poisson ratio, open fracture, the transfer coefficients of open and closed fracture, and tensile strength parameters, the finite element model is established by ANSYS. The vertical load and the horizontal load of the prefabricated shear wall structure are calculated, and the limit load value is obtained. The maximum stress change of the connector, the energy dissipation of the shear wall structure, and the change of the equivalent viscous damping coefficient are obtained by the way of loading and displacement change. The experimental results show that the simulated values of stiffness degradation, maximum displacement, maximum strain, and stress distribution obtained by this method are in good agreement with the actual values and are reliable.
Tripartite Evolutionary Game of Agricultural Service Scale Management and Small Farmers’ Interests under Government Preferential Policies
Authors: Gou Kaomin , Wang Luyun
Published: October 2022

In agricultural production with small farmers as the main body, the service scale operation is one of the ways to obtain scale operation benefits. This paper constructs a tripartite evolutionary game model of agricultural scale service providers (integrators), demand subjects (farmers), and agriculture-related government departments and analyzes the influencing factors, influencing relations, and evolutionary stable equilibrium results of each participant’s strategy choice. The results show that improving rewards and punishments and strengthening supervision by agriculture-related government departments will help integrators provide high-quality services and encourage farmers to actively produce and reduce speculation; when the cost difference between high-quality service and low-quality service is large, the probability of high-quality service provided by integrators will be reduced, and the evolution result of low Pareto efficiency may appear in the system; when the speculative cost of farmers is not high, the lobbying efforts of integrators will increase the speculative behavior of farmers. Finally, MATLAB2018a is used to simulate and analyze the validity of the model results, which provides a theoretical reference for the scale operation of agricultural services.
Quantitative Evaluation of Big Data Development Policy: Text Data Analysis Based on Coword Network and Policy Tools
Authors: Hong Xuefei
Published: October 2022

In the context of the continuous promotion of China’s big data development strategy, this paper quantitively analyses China’s existing national-level big data policies from the perspective of policy instruments and coword networks, discusses the rationality of existing policies, explores ways to improve policies, and provides a reference for the innovation of China’s big data policies. This paper carries out a quantitative textual analysis of China’s national big data policy from the perspective of policy instruments using word frequency analysis to obtain a keyword coword matrix and visualization analysis tools to obtain a coword network. This paper further studies the network node characteristics and structure using social network analysis methods, including degree centrality, clustering analysis, and multidimensional scale analysis, to identify the policy structure and characteristics. Improving big data policy requires improvements in policy instruments on the supply side, resolving existing policy gaps, and strengthening coordination with other policies.
Analysis of Performance Improvement of Real-time Internet of Things Application Data Processing in the Movie Industry Platform
Authors: Meng Yang
Published: October 2022

The goal of this study is to plan and develop complete strategies to improve the performance of film industry. The primary objectives of this study are to investigate a dataset generated by a IoT application and the nature of the data forms obtained, the speed of the data arriving rate, and the required query response time and to list the issues that the current film industry faces when attempting to handle IoT applications in real time. Finally, in film industry platforms, high performance with varied stream circulation levels of real-time IoT application information was realized. In this study, we proposed three alternative methods on top of the Storm platform, nicknamed Re-Storm, to improve the performance of IoT application data. Three different proposed strategies are (1) data stream graph optimization framework, (2) energy-efficient self-scheduling strategy, and (3) real-time data stream computing with memory DVFS. The work proposed a methodology for dealing with heterogeneous traffic-aware incoming rate of data streams Re-Storm at multiple traffic points, resulting in a short response time and great energy efficiency. It is divided into three parts, the first of which is a scientific model for fast response time and great energy efficiency. The distribution of resources is then considered using DVFS approaches, and successful optimum association methods are shown. Third is self-allocation of worker nodes towards optimizing DSG using hot swapping and making the span minimization technique. Furthermore, the testing findings suggest that Re-Storm outperforms Storm by 20–30% for real-time streaming data of IoT applications. This research focuses on high energy efficiency, short reaction time, and managing data stream traffic arrival rate. A model for a specific phase of data coming via IoT and real-time computing devices was built on top of the Storm platform. There is no need to change any software approach or hardware component in this design, but only merely add an energy-efficient and traffic-aware algorithm. The design and development of this algorithm take into account all of the needs of the data produced by IoT applications. It is an open-source platform with less prerequisites for addressing a more sophisticated big data challenge.
Estimation of Finite Population Mean in Simple and Stratified Random Sampling by Utilizing the Auxiliary, Ranks, and Square of the Auxiliary Information
Authors: Ullah Kalim , Hussain Zawar , Hussain Ishfaq , Cheema Salman Arif , Almaspoor Zahra , El-Morshedy M.
Published: October 2022

In this article, estimating the finite population means under simple random and stratified random sampling schemes. Our proposition is based on the notion of using auxiliary information in a more rigorous fashion. Specifically, we use ranks and squared values of the auxiliary information in addition to observed values of the auxiliary variable. The applicability of the proposed family of estimators is demonstrated by considering real data sets coming from diverse fields of applications. Moreover, the performance comparison is conducted with respect to a recently proposed family of estimators. The findings are encouraging and superior performance of the suggested family of estimators is witnessed and documented throughout the article.
Electrical Property of 3D Printed Continuous Fiber Reinforced Thermoplastic Composite Mesh Reflecting Surfaces
Authors: Lin Kunyang , Ma Xiaofei , Cui Zhen , Kang Youwei , Huang Pengfei , Li Huanxiao , Wu Di , Su Guanlong , Tian Xiaoyong
Published: October 2022

Continuous fiber reinforced thermoplastic composites have been widely used in modern aerospace and other high-end manufacturing fields because of their light weight, high strength, fatigue resistance, and corrosion resistance properties. Due to the reinforcement of carbon fiber strands, continuous fiber reinforced thermoplastic composites have good conductivity which makes it a potential material for the preparation of space-borne antennas reflecting surfaces. The reflecting surfaces of common mesh antennas are usually prepared by gold-plated molybdenum wire which is expensive and hard to produce. In this study, the continuous fiber reinforced thermoplastic composites mesh reflecting surfaces are prepared by 3D printing technology. The effect of different mesh shape and mesh size on the electrical properties are investigated systematically. The electrical property of the reflecting surface were tested by waveguide method at the S band with the frequency of 1.9 ~ 2.3GHz. The results show that the reflection loss of the 3D printed continuous fiber reinforced thermoplastic composite mesh reflecting surfaces are lower than 0.25 dB, which can well meet the requirement of space-borne antennas in the S waveband. The reflection loss of the 3D printed continuous fiber reinforced thermoplastic composite mesh reflecting surfaces increases with the increase of mesh size accordingly for both the quadrangular and the triangular mesh reflecting surface. The reflecting property of the mesh reflecting surface tends to be better with a higher surface mass density. The results foresee that the continuous fiber reinforced thermoplastic composites can be used to develop the reflector of large mesh antenna in the future work.
Integrative Analysis Reveals a Nine TP53 Pathway‐Related lncRNA Prognostic Signature in Endometrial Cancer
Authors: Wang Ying , Qu Xiaoling , Li Lisha , He Deng
Published: October 2022

Background. TP53 mutation is a common mutation gene in uterine corpus endometrial carcinoma (UCEC), and the TP53 signaling pathway plays an essential role in the tumorigenesis, progression, and immune infiltration in UCEC. We aimed to discover TP53 pathway‐related lncRNAs in UCEC. Materials and methods. 528 UCEC patients with 587 transcriptional profiles were enrolled in this study. We first investigated the differential status of TP53 signaling pathway between tumor and normal tissues by GSEA analysis, then identified TP53 pathway‐related lncRNAs, accordingly establishing a nine TP53 pathway related to the lncRNA signature in the training set and verified this signature in the test set. Besides, the interaction network was constructed; the immune infiltration, drug response to cisplatin and paclitaxel, and mutation atlas were investigated. Finally, we performed a subgroup analysis to check the universality of this signature. Results. A nine TP53 pathway‐related lncRNA prognostic signature was constructed and verified superior accuracy in predicting the overall survival of UCEC patients. Besides, high‐risk patients showed a poor prognosis, but they were more sensitive to the cisplatin and paclitaxel. Notably, M2 macrophages were higher infiltrated in high‐risk patients, and TP53 showed a significantly higher mutation in high‐risk patients than low‐risk patients. Conclusions. We constructed and verified a nine TP53 pathway‐related lncRNA prognostic signature in UCEC, which also contributes to the decision‐making of the chemotherapy.
[Retracted] The Comparative Pharmaco‐ and Histokinetics of the Therapeutic Dose of Estradiol Valerate and Bromocriptine in Common Quails
Authors: Nadeem Muhammad , Nisa Mehr-un , Bangash Maria Hussain , Abideen Zain Ul , Sattar Rukhsana , Sattar Huma , Ullah Muhammad Saif , Ruby Tahira , Khan Aleem Ahmed , Ahmad Aqeel
Published: October 2022

The current study is aimed at examining the overall effects of steroids on the tissues of organisms and pharmacotherapeutics and pharmaco‐histokinetics of several steroids, including Bromocriptine as mesylate and estradiol valerate in common quails (Coturnix coturnix). A total of 100 birds were used for pharmaco‐histokinetics. The research was carried out in two separate trials, one during the fall season and the other during the spring season. Each experiment lasted for five, ten, fifteen, and twenty days. Each study group used 20 birds while basing their experiments on a control group of 5. At the stretch of five, ten, fifteen, and twenty days in each season, therapeutic dosages were administered to a sum of two groups representing two separate steroid trial groups. Each steroid was administered to each bird in a therapeutic dose, which was three drops administered twice daily. Clinical symptoms include despondency, sluggishness, and variations in weight and temperature that almost all treated birds display. However, only in trials conducted in the fall was a sizable degree of body enlargement in one treated bird noticed. The winter testing showed a mortality rate. Four birds have died in the twenty‐day group. One bird died when treated with estradiol valerate, and three birds died treated with Bromocriptine as mesylate. Both the male and female birds showed signs of having lost some of their body weight. The treated birds’ kidney, stomach, hearts, and livers exhibited some edema. In comparison, almost all birds show enteritis, which indicates that steroids mainly affect the intestine. There were apparent differences in the histological analysis of heart and skeletal muscle and some treated birds with the control group. The kidney, liver, and intestine show the major histopathological change in all treated birds.
Differentiated Speed Planning for Connected and Automated Electric Vehicles at Signalized Intersections considering Dynamic Wireless Power Transfer
Authors: Yang Lan , Han Mengjie , Fang Shan , Wu Guoyuan , Sheng He , Wei Heng , Zhao Xiangmo
Published: October 2022

Deploying the dynamic wireless power transfer (DWPT) equipment at intersections can improve the transportation efficiency and decrease the energy consumption. It can easily turn the disadvantages of waiting time for red phase into the advantages of charging benefits for electric vehicles (EVs). This study develops a multiobjective speed planning model with differentiated charging strategy to optimize the EV’s trajectory at signalized intersections with DWPT. To mitigate the negative impact of DWPT, the proposed model is divided into charging priority driving (C-eco-driving) mode and time priority driving (T-eco-driving) mode for connected and automated electric vehicles (CAEVs) to apply different scenarios. Meanwhile, a widely applied car-following model, i.e., the Intelligent Driver Model (IDM), has been calibrated with the ground-truth dataset to meet the intersection characteristic for EVs in mixed traffic. The efficiency and robustness of two eco-driving modes were validated in single-vehicle and mixed traffic based on MATLAB simulation. For single vehicle, the electricity benefits of the C-eco-driving mode and T-eco-driving mode increase about 0.0867 kWh and 0.0532 kWh, respectively. T-eco-driving mode reduces 2.5 s of travel time. For mixed traffic, the C-eco-driving mode provides more charging benefits about 51~73% than no-control strategy. In contrast, the T-eco-driving mode provides significant time benefits of 2.6 s and slight electricity benefits of 12~30% than no-control strategy. The increment of market penetration rate and wireless charging length can significantly improve the charging benefits. When charging length has the same value, the closer the DWPT facilities to the intersection, the more obvious the charging benefits.
Multifactor Association Analysis Model of Preschool Education Based on Multivariate Nonlinear Matrix Theory
Authors: Tan Yunyun , Zheng Chuanqin
Published: October 2022

This paper uses the multivariate nonlinear matrix theory to conduct an in-depth study and analysis of the multifactor correlation in preschool education, and designs a multifactor correlation analysis model to be applied to the actual preschool education process. A nonlinear approach is used to define the dynamics of a planar segmented smooth system with visible folds on a switching surface, and conditions are given for the system to have a sliding periodic orbit through this visible fold. A curriculum relatedness analysis algorithm based on Euclidean distance is proposed, which takes distance as a perspective and calculates the curriculum relatedness by measuring the difference of the curriculum in different dimensions; a curriculum relatedness analysis algorithm based on cosine similarity is proposed, which takes a vector as a perspective and calculates the curriculum relatedness by measuring the cosine of the angle between different curriculum vectors; a curriculum relatedness analysis algorithm based on correlation coefficient is proposed. The course relevance analysis algorithm based on the correlation coefficient evaluates the relevance of different courses from a centralized perspective. After preprocessing the course results, two different types of datasets were selected for the experiments of the three algorithms. The results show that all three algorithms can calculate the correlation degree of the courses, which can provide scientific data support for the curriculum arrangement of the school so that the school can scientifically verify and optimize the curriculum arrangement. The three algorithms are compared from the calculation results and theories, among which the correlation coefficient correlation analysis algorithm has the best effect. This study proposes suggestions to promote students’ noncognitive ability development from the perspective of preschool education development; grasp the critical period of children’s noncognitive ability development and pay attention to preschool education investment; further develop inclusive preschool education to ensure the educational opportunities of disadvantaged groups; promote regional preschool education reform to ensure the quality of preschool education in rural areas; and guide parents’ participation and cooperate with schools to cultivate children’s noncognitive ability.
Analysis of the Path of Utilizing Big Data to Innovate Archive Management Mode to Enhance Service Capability
Authors: Luo Wenjingling , Hu Haijian
Published: October 2022

With the rapid development of modern science and technology, we are now in an era of big data and digitalization of network, and people’s normal work and life are also implicitly influenced. Archives, as the information of various work examinations, are the imprints of the past and the basis for guiding the future. Compared with the traditional management, the information-based archive management is more efficient and of better quality. The efficiency of all sectors of society in the background of the information age has taken a qualitative leap and improved. In order to develop the archive management business and make the archive management more efficient and secure, a set of scientific and efficient information-based archive management work mode should be worked out on the road of practice in the new era. With the arrival of the era of big data brings fundamental changes to data mining technology, which can analyze and process a large amount of data and dig intelligently, making it possible to dig deeper into the data. Therefore, it is necessary to actively explore archival data mining technology, so that data mining technology can be widely applied to archival management work, and the potential archival information can be excavated and delivered as much as possible to play its proper role and maximize the economic and social benefits of archival data. The article analyzes the characteristics of archive management in the context of big data, proposes a specific path to improve the quality of archive management work in view of the current problems in archive management work, and designs the archive management structure based on big data for improving the quality of archive management work.
Construction and Validation of a Prognostic Model Based on mRNAsi-Related Genes in Breast Cancer
Authors: Zhao Xugui , Lin Jianqing
Published: October 2022

Background. Breast cancer is a big threat to the women across the world with substantial morbidity and mortality. The pressing matter of our study is to establish a prognostic gene model for breast cancer based on mRNAsi for predicting patient’s prognostic survival. Methods. From The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we downloaded the expression profiles of genes in breast cancer. On the basis of one-class logistic regression (OCLR) machine learning algorithm, mRNAsi of samples was calculated. Kaplan-Meier (K-M) and Kruskal-Wallis (K-W) tests were utilized for the assessment of the connection between mRNAsi and clinicopathological variables of the samples. As for the analysis on the correlation between mRNAsi and immune infiltration, ESTIMATE combined with Spearman test was employed. The weighted gene coexpression network analysis (WGCNA) network was established by utilizing the differentially expressed genes in breast cancer, and the target module with the most significant correlation with mRNAsi was screened. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to figure out the biological functions of the target module. As for the construction of the prognostic model, univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were performed on genes in the module. The single sample gene set enrichment analysis (ssGSEA) and tumor mutational burden were employed for the analysis on immune infiltration and gene mutations in the high- and low-risk groups. As for the analysis on whether this model had the prognostic value, the nomogram and calibration curves of risk scores and clinical characteristics were drawn. Results. Nine mRNAsi-related genes (CFB, MAL2, PSME2, MRPL13, HMGB3, DCTPP1, SHCBP1, SLC35A2, and EVA1B) comprised the prognostic model. According to the results of ssGSEA and gene mutation analysis, differences were shown in immune cell infiltration and gene mutation frequency between the high- and low-risk groups. Conclusion. Nine mRNAsi-related genes screened in our research can be considered as the biomarkers to predict breast cancer patients’ prognoses, and this model has a potential relationship with individual somatic gene mutations and immune regulation. This study can offer new insight into the development of diagnostic and clinical treatment strategies for breast cancer.
Economic Policy Uncertainty Shocks and Chinese Stock Market Volatility: An Empirical Analysis with SVAR
Authors: Cai Dongliang , Zhang Tong , Han Kefei , Liang Jingqi
Published: October 2022

This paper proposes a framework for examining the interaction between stock market volatilities and economic uncertainty shocks, aiming to understand better the influence of economic uncertainty shocks on the Chinese stock market. The major empirical results include the followings. First, the economic policy uncertainty shocks push the Chinese stock volatility up, increasing the market risk. A 1‐standard‐deviation shock of economic policy uncertainty will enhance the stock volatility of the two composite indices by approximately 7% in 12 months. Second, the stock volatility reacted more intensely to fiscal and monetary economic policy uncertainty shocks, with a 1‐standard‐deviation shock that can enhance the stock volatility of the two composite indices by more than 10% in 12 months. Third, different stock indices exhibit different patterns of cumulative impulse responses, and the reaction of the volatility of the SSE real estate index to economic policy uncertainty shocks is more significantly intense than other indices. Besides, we have proved the robustness of empirical results by reestimating the models with a lag order of 2. Overall, our research results can provide policy and managerial insights for the sustainable development of the Chinese stock market and beyond.
Designing Sustainable Recovery Network of End‐of‐Life Product during the COVID‐19 Pandemic: A Real and Applied Case Study
Authors: Abbasi Sina , Daneshmand-Mehr Maryam , Ghane Kanafi Armin
Published: October 2022

One of the most important aspects of supply chain management (SCM) is the recovery network (RN), which covers all activities associated with return products (such as collection, recovery, repair, recycling, and waste disposal). Our goal in this paper is to provide a new mathematical model of sustainable end‐of‐life management (SEOLM) during the COVID‐19 pandemic for readers. The suggested recovery network model (RNM) can explain the trade‐offs between economic (minimizing total costs), environmental (minimizing bad environmental impacts), and social (minimizing bad social impacts) aspects during the pandemic and the great lockdown. A new RN can be designed with a sustainable and hygienic design when taking environmental, economic, and social considerations into account. It proposes guidelines for managers and scholars on how to address recovery network design (RND) challenges during the pandemic through a mathematical article with a sustainable approach. The scalarization approach of a multi‐objective mixed‐integer programming (MOMIP) problem in this paper is the weighted sum method. The validation of the presented model and the related Pareto frontier has been illustrated by a case study and numerical example. To perform the optimization process, Lingo software is used.
Visualization Method of Key Knowledge Points of Nursing Teaching Management System Based on SOM Algorithm and Biomedical Diagnosis
Authors: Hu Die , Fang Le
Published: October 2022

The traditional nursing teaching knowledge point recommendation algorithm based on collaborative filtering is difficult to deal with the problem of data sparsity, while the traditional recommendation algorithm based on matrix decomposition has poor scalability in dealing with high-dimensional data, and their recommendation results are only determined according to the prediction score, resulting in low recommendation accuracy. In view of this, a nursing teaching knowledge point recommendation method based on a SOM neural network and ranking factor decomposition machine is proposed. Firstly, the SOM neural network is used to cluster users based on users’ academic background information, then the partial order relationship of nursing teaching knowledge points is constructed by using users’ explicit and implicit web access behavior, and finally, the factor decomposition machine is used as the ranking function to classify users’ academic background web access behavior, borrowing nursing teaching introduction text, and other characteristic information were modeled, and the peer-to-peer ranking learning algorithm was used to accurately recommend nursing teaching knowledge points. Experimental results show that the proposed method can effectively alleviate the problem of data sparsity and improve the accuracy and efficiency of recommendations.
A Deep Ranking Weighted Multihashing Recommender System for Item Recommendation
Authors: Kumar Suresh , Singh Jyoti Prakash , Jain Vinay Kumar , Marahatta Avinab
Published: October 2022

Collaborative filtering (CF) techniques are used in recommender systems to provide users with specialised recommendations on social websites and in e-commerce. But they suffer from sparsity and cold start problems (CSP) and fail to interpret why they recommend a new item. A novel deep ranking weighted multihash recommender (DRWMR) system is designed to suppress sparsity and CSP. The proposed DRWMR system contains two stages: the neighbours’ formation and recommendation phases. Initially, the data is fed to the deep convolutional neural network (CNN). The significant features are extracted from CNN. The CNN contains an additional layer; the hash code is generated by minimising pairwise ranking loss and classification loss. Therefore, a weight is assigned to different hash tables and hash bits for a recommendation. Then, the similarity between users is obtained based on the weighted hammering distance; the similarity between users helps to form the neighbourhood for the active user. Finally, the rating for unknown items can be obtained by taking the weighted average rating of the neighbourhood, and a list of the top n items can be produced. The effectiveness and accuracy of the proposed DRWMR system are tested on the MovieLens 100 K dataset and compared with the existing methods. Based on the evaluation results, the proposed DRWMR system gives precision (0.16), the root mean squared error (RMSE) of 0.73 and the recall (0.08), the mean absolute error (MAE) of 0.57, and the F − 1 measure (0.101).
A Novel Semiautomatic Interpretation Model for Impulse Neutron Oxygen Activation Time Spectrum Data
Authors: Dong Yong , Li Mengxia , Liao Ruiquan
Published: October 2022

The existing interpretation models for the time spectrum of impulse neutron oxygen activation require interpreters to select the peak range or background range manually from the time spectrum curve, and there is no adaptive interpretation model that can determine the peak range or background range. In this paper, an adaptive selection rule for background segment is proposed, and a semiautomatic interpretation model is constructed by combining background segment interpretation model. Firstly, the interpretation operator selects the time spectrum curve, then the algorithm program adaptively determines the background segment according to the rules, and then calculates and displays the transit time and volume flow according to the background segment interpretation model. The processing results of the measured data show that the interpretation model in this paper not only retains the interpretation precision of the background interpretation model, but also reduces the labor intensity of the interpretation operator, realizing the semiautomatic interpretation of the time spectrum.
Modeling Method of Intelligent Scoring for Solfeggio Training Based on Deep Learning
Authors: Lu Ying
Published: October 2022

In the solfeggio training, melody model singing is an important part of auditory training. Reasonable and effective model singing training is of great significance to train the singer’s hearing. So, the level of model singing also reflects the level of the singer’s solfeggio to a certain extent. The correct model singing score will help students to master their own real melody model singing level and help students to better improve their solfeggio skills. However, the melody model singing training scoring system that has been developed has low accuracy and needs to be improved. In order to improve the accuracy of the intelligent scoring results of melody model singing training, this paper used deep learning technology to study the intelligent scoring method of melody model singing training. This paper first extracted the features of songs, then collected a large number of song features, trained the existing feature data, and established an intelligent scoring model for melody model singing training. Then, the students’ model songs and standard songs were used as two inputs, and the similarity of the characteristics of the two songs was analyzed; then, the score results of the model songs were obtained. The validity of this research should be verified by a comparative experiment. The research results showed that the melody model singing training intelligent scoring model, established by deep learning technology, has a higher accuracy rate, and the accuracy rate was increased by 6.82% compared with the original scoring model, indicating the research has practical significance.
An Improved Epidemiological Model for the Underprivileged People in the Contemporary Pandemics
Authors: Uddin Mahtab , Mugdha Shafayat Bin Shabbir , Shermin Tamanna , Chowdhury Kawsar Newaz
Published: October 2022

In this work, we introduce an improved form of the basic SEIRD model based on Python simulation for the troublesome people who are oblivious about the contemporary pandemics due to diverse social impediments, especially those economically underprivileged. In the extant epidemiological models, some unorthodox issues are yet to be considered, such as poverty, illiteracy, and carelessness towards health issues, significantly influencing the data modeling. Our focus is to overcome these issues by adding two more branches, for instance, uncovered and apathetic people, which significantly influence the practical purposes. For the data simulation, we have used the Python‐based algorithm that trains the desired system based on a set of real‐time data with the proposed model and provides predicted data with a certain level of accuracy. Comparative discussions, statistical error analysis, and correlation‐regression analysis have been introduced to validate the proposed epidemiological model. To show the numerical evidence, the investigation comprised the figurative and tabular modes for both real‐time and predicted data. Finally, we discussed some concluding remarks based on our findings.
Big Data-Driven Vocational Undergraduate Talent Training and Social Needs Adaptability Analysis and Collaborative Path Mining
Authors: Lu Qianqian , Zhang Lin , Lin Chunlei
Published: October 2022

Aiming at the problem of mismatch between talent cultivation and social demand in the process of undergraduate education, this paper proposes a big data-driven method of adaptability analysis and collaborative path mining between vocational undergraduate talent cultivation and social demand. Starting from the big data-driven vocational undergraduate talent training and social needs, this paper points out the problems existing in the current social needs and puts forward the basic framework of vocational undergraduate talent training mode. Secondly, the clustering model of talent training and social demand is analyzed, and the clustering mining method is proposed. Finally, the big data-driven personnel training and social adaptation mining analysis, in their own ability and social needs adaptability analysis, the basic adaptation accounted for a higher proportion. Professional competence has a higher trust value in cluster analysis. Today’s social employment situation is becoming more and more severe, and how to enhance the quality of profession undergraduate students has become one of the theoretical and practical issues worthy of attention in China’s colleges at this stage. The talent training model of colleges and universities is closely in route with the demands of society, and the problem of student hire is prominent. Therefore, this paper proposes a student employability training program that combines the elements of student employability through social needs research.
Applications of the Multiattribute Decision-Making for the Development of the Tourism Industry Using Complex Intuitionistic Fuzzy Hamy Mean Operators
Authors: Hussain Abrar , Ullah Kifayat , Ahmad Jihad , Karamti Hanen , Pamucar Dragan , Wang Haolun
Published: October 2022

In the aggregation of uncertain information, it is very important to consider the interrelationship of the input information. Hamy mean (HM) is one of the fine tools to deal with such scenarios. This paper aims to extend the idea of the HM operator and dual HM (DHM) operator in the framework of complex intuitionistic fuzzy sets (CIFSs). The main benefit of using the frame of complex intuitionistic fuzzy CIF information is that it handles two possibilities of the truth degree (TD) and falsity degree (FD) of the uncertain information. We proposed four types of HM operators: CIF Hamy mean (CIFHM), CIF weighted Hamy mean (CIFWHM), CIF dual Hamy mean (CIFDHM), and CIF weighted dual Hamy mean (CIFWDHM) operators. The validity of the proposed HM operators is numerically established. The proposed HM operators are utilized to assess a multiattribute decision-making (MADM) problem where the case study of tourism destination places is discussed. For this purpose, a MADM algorithm involving the proposed HM operators is proposed and applied to the numerical example. The effectiveness and flexibility of the proposed method are also discussed, and the sensitivity of the involved parameters is studied. The conclusive remarks, after a comparative study, show that the results obtained in the frame of CIFSs improve the accuracy of the results by using the proposed HM operators.
Novel Target Study to Cure Cardiovascular Disease regarding Proprotein Converse Subtilisin/Kexin Type 9
Authors: Zhao Yingjing , Li Weihang , Li Weiye , Tao Hong , Li Yuting , Wu Bo , Wang Xinhui , Zhou Huasong , Gao Bo
Published: October 2022

Objective. This study is aimed at screening the potential ideal lead compounds from natural drug library (ZINC database), which had potential inhibition effects against proprotein converse subtilisin/kexin type 9 (PCSK9), and contributing to enrich the practical basis of PCSK9 inhibitor screening. Methods. A series of computer‐aided virtual screening techniques were used to identify potential inhibitors of PCSK9. Structure‐based virtual screening by LibDock was carried out to calculate the LibDock scores, followed by ADME (absorption, distribution, metabolism, and excretion) and toxicity predictions. Molecule docking was next employed to demonstrate the binding affinity and mechanism between the candidate ligands and PCSK9 macromolecule. Finally, molecular dynamics simulation was performed to evaluate the stability of ligand‐PCSK9 complex under natural circumstance. Results. Two novel natural compounds ZINC000004099069 and ZINC000014952116 from the ZINC database were found to bind with PCSK9 with a higher binging affinity together with more favorable interaction energy. Also, they were predicted to be non‐CYP2D6 inhibitors, together with low rodent carcinogenicity and AMES mutagenicity as well as hepatotoxicity. Molecular dynamics simulation analysis demonstrated that these two complex ZINC000004099069‐ and ZINC000014952116‐PCSK9 had more favorable potential energy compared to the reference ligand, which could exist stably whether in vivo or in vitro. Conclusion. This study elucidated that ZINC000004099069 and ZINC000014952116 were finally screened as safe and potential drug candidates, which may have great significance in the development of PCSK9 inhibitor development.
PCBP2 Reduced Oxidative Stress-Induced Apoptosis in Glioma through cGAS/STING Pathway by METTL3-Mediated m6A Modification
Authors: Chen Xiang , Yu Mingchuan , Xu Wei , Kun Peng , Wan Wenbing , Yuhong Xiao , Ye Jing , Liu Yu , Luo Jun
Published: October 2022

Purpose. The most prevalent primary malignant tumor of CNS is glioma, which has a dismal prognosis. The theory of oxidative stress is one of the important theories in the study of its occurrence and development mechanism. In this study, the impacts of PCBP2 on glioma sufferers and the possible mechanisms were examined. Methods. Patients with glioma were obtained from May 2017 to July 2018. Quantitative PCR, microarray analysis, western blot analysis, and immunofluorescence were used in this experiment. Results. PCBP2 mRNA expression level and protein expression in patients with glioma were upregulated compared with paracancerous tissue. OS and DFS of PCBP2 low expression in patients with glioma were higher than those of PCBP2 high expression. PCBP2 promoted the progression and metastasis of glioma. PCBP2 reduced oxidative stress-induced apoptosis of glioma. PCBP2 suppressed the cGAS/STING pathway of glioma. PCBP2 protein interlinked with cGAS and cGAS was one target for PCBP2. METTL3-mediated m6A modification increases PCBP2 stability. Conclusion. Along the cGAS-STING signal pathway, PCBP2 decreased the apoptosis that oxidative stress-induced glioma caused, which might be a potential target to suppress oxidative stress-induced apoptosis of glioma.
[Retracted] Application and Effect Evaluation of Nursing Risk Management in Nursing of Cardiovascular Medicine
Authors: Li Dayan , Liu Yue , Pan Miaodan , Zhao Lili
Published: October 2022

Cardiovascular medicine patients have complex conditions and rapid progress. They will cause a variety of complications during the illness and are difficult to care for. In addition, the above departments have many treatment tools, complex nursing points, and nursing risks. Scientific risk management should be carried out to avoid the occurrence of adverse events. Existing studies incorporating nursing risk management into cardiovascular health care are incomplete. This paper aims to explore the analysis and research methods of nursing cardiovascular medicine applications and effects evaluation based on nursing risk management and deep learning. Through the observation and experiment of grouping 100 cardiovascular medicine patients in a hospital, the total satisfaction degree of the experimental group reached 90%, and the control group was only 48%, which is quite different in comparison. The factors affecting the occurrence of nursing risk have the characteristics of multiplicity, instability, and uncertainty. Therefore, in order to improve the nursing effect and the rescue rate, it is imperative to strengthen the nursing risk management and, at the same time, reduce the patient’s physical pain and nursing risk.
Construction of a Colorectal Cancer Prognostic Risk Model and Screening of Prognostic Risk Genes Using Machine-Learning Algorithms
Authors: Du Xuezhi , Qi Han , Ji Wenbin , Li Peiyuan , Hua Run , Hu Wenliang , Qi Feng
Published: October 2022

This study is aimed at constructing a prognostic risk model for colorectal cancer (CRC) using machine-learning algorithms to provide accurate staging and screening of credible prognostic risk genes. We extracted CRC data from GSE126092 and GSE156355 of the Gene Expression Omnibus (GEO) database and datasets from TCGA to analyze the differentially expressed genes (DEGs) using bioinformatics analysis. Among the 330 shared DEGs related to CRC prognosis, we divided the analysis period into different phases and applied univariate COX regression, LASSO, and multivariate COX regression analysis. GO analysis and KEGG analysis revealed that the functions of these DEGs were primarily focused on cell cycle, DNA replication, cell mitosis, and other related functions, and this confirmed our results from a biological perspective. Finally, a prognostic risk model for CRC based on the CHGA, CLU, PLK1, AXIN2, NR3C2, IL17RB, GCG, and AJUBA genes was constructed, and the risk score enabled us to predict the prognosis for CRC. To obtain a comprehensive and accurate model, we used both internal and external evaluations, and the model was able to correctly differentiate patients with CRC into a high-risk group with poor prognosis and a low-risk group with good prognosis. The AUC values of the 3-, 5-, and 10-year survival ROC curves were 0.715, 0.721, and 0.777, respectively, according to the internal evaluation, and the AUC values were 0.606, 0.698, and 0.608, respectively, for the external evaluation using GSE39582 from the GEO database. We determined that CLU, PLK1, and IL17RB could be considered to be independent prognostic factors for CRC with significantly different expression (

P
<
0.05

). Using machine-learning methods, a prognostic risk model comprised of eight genes was constructed. Not only does this model provide improved treatment guidance, but it also provides a novel perspective for analyzing survival conditions at a deeper biological level.