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A prevention and control tracking system based on three-dimensional (3D) face recognition was designed to improve the target tracking accuracy of the prevention and control tracking system. The ARM control chip of TMS320DM6446 was selected as the control chip of the ARM control module. The CMOS image acquisition sensor of the image acquisition module collected face images. The collected images were transmitted to the 3D face recognition module. The 3D face recognition module used the Gabor wavelet algorithm to extract the 3D face contour features of the face image. Moreover, the LDA algorithm was used to recognize faces based on 3D face contour features. The 3D face recognition results were compared with the faces in the face library to determine whether prevention and control tracking were necessary. When prevention and control tracking was needed, the GPS tracking and positioning module embedded in the mobile device terminal of the target object was started. The GPS tracking and positioning module was used to prevent and control the tracking of the target. The results of prevention and control tracking were displayed to the system users using a VGA display. The experimental results indicated that the designed system could accurately recognize faces and achieve prevention and control tracking of the target based on the face recognition results. [ABSTRACT FROM AUTHOR]

Extended Reality (XR) technology, the umbrella term covering hyper-realistic technologies, is known to enhance consumer experience and is therefore developing rapidly and being utilized across various industries. Growing studies have examined XR technology and consumer experience; however, the literature has failed to fully explore hyper-realistic technology through a holistic perspective. To fill this gap, we analyzed 720 Korean and international articles through semantic network analysis and topic modeling and identified the literature on XR research in consumer experience. As a result, we extracted six main topics: “Tourism,” “Buying Behavior,” “XR Technology Acceptance,” “Virtual Space,” “Game,” and “XR Environment.” The results provide comprehensive insight on XR technology in consumer experience, whereas the literature is bounded on the production side as revealing a lack of academic discourse on consumer rights and responsibilities. Research reflecting the consumer welfare perspective is, therefore, recommended for future studies.

Parkinson's disease is associated with memory loss, anxiety, and depression in the brain. Problems such as poor balance and difficulty during walking can be observed in addition to symptoms of impaired posture and rigidity. The field dedicated to making computers capable of learning autonomously, without having to be explicitly programmed, is known as machine learning. An approach to the diagnosis of Parkinson's disease, which is based on artificial intelligence, is discussed in this article. The input for this system is provided through photographic examples of Parkinson's disease patient handwriting. Received photos are preprocessed using the relief feature option to begin the process. This is helpful in the process of selecting characteristics for the identification of Parkinson's disease. After that, the linear discriminant analysis (LDA) algorithm is employed to reduce the dimensions, bringing down the total number of dimensions that are present in the input data. The photos are then classified via radial basis function-support vector machine (SVM-RBF), k-nearest neighbors (KNN), and naive Bayes algorithms, respectively. [ABSTRACT FROM AUTHOR]

This paper is based on the VAR model, a regression analysis of the correlation between labor education and ideological and political education. Firstly, the LDA algorithm is used to speculate about the document to obtain the probability distribution of the subject, and this is used as the current subject feature. Then use the gray correlation algorithm to measure the distribution characteristics of each different indicator from the object dimension, and measure the correlation size and positive and negative by extracting the relative difference between indicators. Finally, the TF-IDF algorithm is used to determine the word weights, and text clustering is invoked to merge texts with the same theme. After the teaching of the VAR model, the significance of the learning motivation of the students in the experimental group is 0.009, the passing rate of the final grades is around 76%, and the significance of the dimensions of the learning effect is less than 0.05. It shows that the integration of labor education with ideological and political education can lead students to strengthen their concepts of labor so as to realize students’ comprehensive development.

The construction of college students’ career planning and employment guidance system is an important part of the current university talent cultivation system, and the importance of career planning is becoming more and more prominent under the current severe employment situation. This paper firstly constructs the Executive regression model and LDA algorithm to deal with the content and basic data of the questionnaire survey of 600 students from 10 provinces and 30 schools, uses regression to analyze the cognition and form of college students’ career planning and the factors that have an impact on their view of choosing a career, constructs the college students’ employment guidance system at the same time, and puts forward the suggestions on the contemporary college students’ career choice and employment. The results of the study show that professional knowledge and skills, a strong sense of responsibility, optimism and cheerfulness, and good social skills are the main advantages of students. Only 47% of college students have clear career goals, while 21% have no career goals. When choosing a career, the decisive influence factor is “environment,” which accounts for 51.63%, followed by personal preferences, which account for 45.7%. After graduation, 63.72% of college students chose employment, while 17.14% and 10.34% chose to start their own business and go to graduate school, respectively. It can be seen that in order to improve the feasibility and effectiveness of college students’ career planning, colleges, and universities should strengthen the education of college students’ awareness of career planning, establish a sound career planning management and service system, and maximize college students’ employment competitiveness.

The quality of myrrh decoction pieces can be influenced by factors such as origin, source, and processing methods. The quality of myrrh in the market varies greatly, and adulteration is a serious issue, highlighting the urgent need for improved quality control measures. This study explores the integration of GC–MS analysis and sensor selection in electronic nose technology for the improved classification of myrrh decoction pieces. GC–MS analysis revealed the presence of 130 volatile compounds in the six myrrh samples, primarily composed of alkene compounds, and each sample exhibited variations in composition. An electronic nose system was designed using a sensor array consisting of six sensors selected from twelve sensors capable of detecting volatile compounds consistent with myrrh composition, including WO3 quantum dots, Fe2O3 hollow nanorods, ZnFe2O4 nanorods, SnO2 nanowires, and two commercially available sensors. The sensors exhibited distinct response patterns to the myrrh samples, indicating their suitability for myrrh analysis. Various sensor parameters, including response, response and recovery time, integral area, and slope, were computed to characterize the sensors' performance. These parameters provided valuable insight into the sensor–gas interactions and the unique chemical profiles of the myrrh samples. The LDA model demonstrated high accuracy in differentiating between the myrrh types, utilizing the discriminative features captured by the sensor array, with a classification accuracy of 90% on the testing set. This research provides a comprehensive evaluation method for the quality control of myrrh pieces and a scientific basis for the development and utilization of myrrh. [ABSTRACT FROM AUTHOR]

IntroductionThe COVID-19 pandemic could generate a turning point for introducing a new system for sports participation and business. The purpose of this study is to explore trends and topic structures of COVID-19-related sports research by analyzing the relevant literature.MethodsSports studies related to COVID-19 were collected in searching international academic databases. After the pre-processing step using the refinement and morpheme analysis function of the Net Miner program, topic modeling and social network analysis were used to analyze Journal Citation Reports found using the search term ‘COVID-19 sports’.ResultsAs a result, this study used subject modeling to reveal important potential topics in COVID-19-related sports research articles. ‘Sports participation’, ‘elite players’, and ‘sports industry’ were macroscopically classified, and detailed research topics could be identified from each division.ConclusionThis study revealed important latent topics from COVID-19-related sports research articles using topic modeling. The results of the research elucidate the structure of academic knowledge on this topic and provide guidance for future research.

The rapid popularity of government social media has generated huge amounts of text data, and the analysis of these data has gradually become the focus of digital government research. This study uses Python language to analyze the big data of the Chinese provincial government Weibo. First, this study uses a web crawler approach to collect and statistically describe over 360,000 data from 31 provincial government microblogs in China, covering the period from January 2018 to April 2022. Second, a word separation engine is constructed and these text data are analyzed using word cloud word frequencies as well as semantic relationships. Finally, the text data were analyzed for sentiment using natural language processing methods, and the text topics were studied using LDA algorithm. The results of this study show that, first, the number and scale of posts on the Chinese government Weibo have grown rapidly. Second, government Weibo has certain social attributes, and the epidemics, people's livelihood, and services have become the focus of government Weibo. Third, the contents of government Weibo account for more than 30% of negative sentiments. The classified topics show that the epidemics and epidemic prevention and control overshadowed the other topics, which inhibits the diversification of government Weibo. [ABSTRACT FROM AUTHOR]

The quality of myrrh decoction pieces can be influenced by factors such as origin, source, and processing methods. The quality of myrrh in the market varies greatly, and adulteration is a serious issue, highlighting the urgent need for improved quality control measures. This study explores the integration of GC–MS analysis and sensor selection in electronic nose technology for the improved classification of myrrh decoction pieces. GC–MS analysis revealed the presence of 130 volatile compounds in the six myrrh samples, primarily composed of alkene compounds, and each sample exhibited variations in composition. An electronic nose system was designed using a sensor array consisting of six sensors selected from twelve sensors capable of detecting volatile compounds consistent with myrrh composition, including WO3 quantum dots, Fe2O3 hollow nanorods, ZnFe2O4 nanorods, SnO2 nanowires, and two commercially available sensors. The sensors exhibited distinct response patterns to the myrrh samples, indicating their suitability for myrrh analysis. Various sensor parameters, including response, response and recovery time, integral area, and slope, were computed to characterize the sensors’ performance. These parameters provided valuable insight into the sensor–gas interactions and the unique chemical profiles of the myrrh samples. The LDA model demonstrated high accuracy in differentiating between the myrrh types, utilizing the discriminative features captured by the sensor array, with a classification accuracy of 90% on the testing set. This research provides a comprehensive evaluation method for the quality control of myrrh pieces and a scientific basis for the development and utilization of myrrh.

With the recent full-fledged MyData service, interest in the use of personal data is increasing. However, studies on MyData are still in the early stages, focusing on legal and institutional discussions, and studies from a comprehensive perspective are insufficient. Therefore, this study aimed at finding the potential topics formed by social discussions by analyzing news data from 2018 to the present. News data analysis using LDA topic modeling were conducted and 6 potential topics including digital transformation in finance, scope of Mydata business license, amendments and data-related laws, safe use of big data, data economy promotion policy and strategy of the financial industry were derived. This study has significance in that it comprehensively viewed the issues that emerged with the MyData and deriving gaps in previous discussion. Future research is expected to identify changes after the launch of MyData service and provide specific implications through research by specific industries. [ABSTRACT FROM AUTHOR]

Copyright of Umm Al-Qura University Journal for Languages & Literature is the property of Association of Arab Universities and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Oil Drilling & Production Technology / Shiyou Zuancai Gongyi is the property of Shiyou Zuancai Gongyi Bianjibu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The present study determined new value that can be created through the convergence between artificial intelligence technology (AIT) and all industries by deriving and thoroughly analyzing major issues related to artificial intelligence (AI). This study analyzes domestic articles related to AI using topic modeling method based on LDA algorithm. Keywords were extracted from 3,889 articles of eleven metropolitan newspapers, eight business newspapers and major broadcasting companies; articles were selected by searching for the keyword "artificial intelligence". Keywords were extracted by optimizing the relevance parameter  to improve the measure of pointwise mutual information (PMI), which shows the association among the keywords of each topic, and topic names were inferred from keywords based on valid evidence. The extracted topics widely showed changes occurring throughout society, economy, industries, culture, and the support policy and vision of the government. [ABSTRACT FROM AUTHOR]

Myoelectric prosthesis has become an important aid to disabled people. Although it can help people to recover to a nearly normal life, whether they can adapt to severe working conditions is a subject that is yet to be studied. Generally speaking, the working environment is dominated by vibration. This paper takes the gripping action as its research object, and focuses on the identification of grasping intentions under different vibration frequencies in different working conditions. In this way, the possibility of the disabled people who wear myoelectric prosthesis to work in various vibration environment is studied. In this paper, an experimental test platform capable of simulating 0–50 Hz vibration was established, and the Surface Electromyography (sEMG) signals of the human arm in the open and grasping states were obtained through the MP160 physiological record analysis system. Considering the reliability of human intention recognition and the rapidity of algorithm processing, six different time-domain features and the Linear Discriminant Analysis (LDA) classifier were selected as the sEMG signal feature extraction and recognition algorithms in this paper. When two kinds of features, Zero Crossing (ZC) and Root Mean Square (RMS), were used as input, the accuracy of LDA algorithm can reach 96.9%. When three features, RMS, Minimum Value (MIN), and Variance (VAR), were used as inputs, the accuracy of the LDA algorithm can reach 98.0%. When the six features were used as inputs, the accuracy of the LDA algorithm reached 98.4%. In the analysis of different vibration frequencies, it was found that when the vibration frequency reached 20 Hz, the average accuracy of the LDA algorithm in recognizing actions was low, while at 0 Hz, 40 Hz and 50 Hz, the average accuracy was relatively high. This is of great significance in guiding disabled people to work in a vibration environment in the future.

Copyright of SDU Journal of Engineering Sciences & Design / Mühendislik Bilimleri ve Tasarım Dergisi is the property of Journal of Engineering Sciences & Design and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Studies on competency in medical education often explore the acquisition, performance, and evaluation of particular skills, knowledge, or behaviors that constitute physician competency. As physician competency reflects social demands according to changes in the medical environment, analyzing the research trends of physician competency by period is necessary to derive major research topics for future studies. Therefore, a more macroscopic method is required to analyze the core competencies of physicians in this era., Objective: This study aimed to analyze research trends related to physicians' competency in reflecting social needs according to changes in the medical environment., Methods: We used topic modeling to identify potential research topics by analyzing data from studies related to physician competency published between 2011 and 2020. We preprocessed 1354 articles and extracted 272 keywords., Results: The terms that appeared most frequently in the research related to physician competency since 2010 were knowledge, hospital, family, job, guidelines, management, and communication. The terms that appeared in most studies were education, model, knowledge, and hospital. Topic modeling revealed that the main topics about physician competency included Evidence-based clinical practice, Community-based healthcare, Patient care, Career and self-management, Continuous professional development, and Communication and cooperation. We divided the studies into 4 periods (2011-2013, 2014-2016, 2017-2019, and 2020-2021) and performed a linear regression analysis. The results showed a change in topics by period. The hot topics that have shown increased interest among scholars over time include Community-based healthcare, Career and self-management, and Continuous professional development., Conclusions: On the basis of the analysis of research trends, it is predicted that physician professionalism and community-based medicine will continue to be studied in future studies on physician competency., (©So Jung Yune, Youngjon Kim, Jea Woog Lee. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 19.07.2023.)

Introduction: The COVID-19 pandemic could generate a turning point for introducing a new system for sports participation and business. The purpose of this study is to explore trends and topic structures of COVID-19-related sports research by analyzing the relevant literature., Methods: Sports studies related to COVID-19 were collected in searching international academic databases. After the pre-processing step using the refinement and morpheme analysis function of the Net Miner program, topic modeling and social network analysis were used to analyze Journal Citation Reports found using the search term 'COVID-19 sports'., Results: As a result, this study used subject modeling to reveal important potential topics in COVID-19-related sports research articles. 'Sports participation', 'elite players', and 'sports industry' were macroscopically classified, and detailed research topics could be identified from each division., Conclusion: This study revealed important latent topics from COVID-19-related sports research articles using topic modeling. The results of the research elucidate the structure of academic knowledge on this topic and provide guidance for future research., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Lee, Kim and Han.)

Myoelectric prosthesis has become an important aid to disabled people. Although it can help people to recover to a nearly normal life, whether they can adapt to severe working conditions is a subject that is yet to be studied. Generally speaking, the working environment is dominated by vibration. This paper takes the gripping action as its research object, and focuses on the identification of grasping intentions under different vibration frequencies in different working conditions. In this way, the possibility of the disabled people who wear myoelectric prosthesis to work in various vibration environment is studied. In this paper, an experimental test platform capable of simulating 0–50 Hz vibration was established, and the Surface Electromyography (sEMG) signals of the human arm in the open and grasping states were obtained through the MP160 physiological record analysis system. Considering the reliability of human intention recognition and the rapidity of algorithm processing, six different time-domain features and the Linear Discriminant Analysis (LDA) classifier were selected as the sEMG signal feature extraction and recognition algorithms in this paper. When two kinds of features, Zero Crossing (ZC) and Root Mean Square (RMS), were used as input, the accuracy of LDA algorithm can reach 96.9%. When three features, RMS, Minimum Value (MIN), and Variance (VAR), were used as inputs, the accuracy of the LDA algorithm can reach 98.0%. When the six features were used as inputs, the accuracy of the LDA algorithm reached 98.4%. In the analysis of different vibration frequencies, it was found that when the vibration frequency reached 20 Hz, the average accuracy of the LDA algorithm in recognizing actions was low, while at 0 Hz, 40 Hz and 50 Hz, the average accuracy was relatively high. This is of great significance in guiding disabled people to work in a vibration environment in the future. [ABSTRACT FROM AUTHOR]