• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.613-624
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• 2021

The use of social networking sites (SNS) for e-recruitment has shifted the focus away from traditional hiring and selection processes. They are commonly used in the search and acquisition of new employees and are projected to expand in the near future as an e-recruitment tool. However, there is a lack of material on SNS and their impact on an employers' intention to use these sites for e-recruitment, in the context of Malaysia. Hence, there is an acute necessity for research on the extent that the features of SNS can influence the employers' intention to use SNS for e-recruitment and to know how to keep utilizing the platform for future e-recruitment. This study aims to identify the key features of SNS that lead to employers' intention to use SNS for e-recruitment in Malaysia. In this cross-sectional study, random sampling was utilized to obtain data from 198 recruitment professionals using online survey. The findings show that data quality, reliability, networking spectrum and simplicity of navigation of SNS are the key predicting factors for intention to use SNS for e-recruitment. Therefore, employers should acknowledge these key features of SNS to achieve their e-recruitment goals.

• The Journal of Information Systems
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• v.31 no.1
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• pp.115-146
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• 2022

Purpose The purpose of this study is to find meaningful insights using regression analysis to resolve the digital divide between generations. In the analysis process of this study, social network analysis was applied to approach it with a perspective differentiated from the existing statistical techniques. Design/methodology/approach This study used a social network analysis methodology that transforms and analyzes government-led survey data into relational data. First, the cross-sectional data were converted into relational data, and a continuous core-periphery model and multidimensional scaling method were applied. Afterwards, the relationship between various factors affecting the digital divide and the difference in influence were analyzed by generation. Findings According to the network analysis results, it can be seen that all generations commonly use 'information and news search' and 'living information service'. However, it can be seen that the centrally used services of each generation are clearly different from each other, and the degree of linkage between the services is also clearly different. In addition, it can be seen that the relationship between factors influencing the digital divide by generation is also different.

• Journal of Distribution Science
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• v.20 no.7
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• pp.47-56
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• 2022

Purpose: As the need to transition into the distribution of cleaner energy has garnered corporate and scholarly interests, this study aims to track the research trends in sustainable energy supply chains for five years before 2021. Research methodology: This study was conducted by a bibliometric literature review and analysis to map the field's evolution between 2016 and 2020. Out of an initial title search result of 2,484 papers from the Scopus engine, filtering led to 180 documents obtained. The data was exported in excel format (CSV) to VOSviewer software to generate and analyze network visualization of sustainable energy supply chain trends. Results: The results revealed China's the highest publishing country, with 36 research papers. The Journal of Cleaner Production was the top publishing source, with 22 papers per year. These findings showed five clusters formed in the bibliographic coupling of countries. Circular Economy and Green Supply Chain Management represent the current hot topics. Research gaps identified in the field included limited cross-industry testing and modifying sustainable supply chain models. Conclusion: This paper contributes to the sustainability literature on supply chains by providing an overview of trends and research directions for scholars' and practitioners' consideration in future research.

• Physical Therapy Rehabilitation Science
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• v.11 no.2
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• pp.224-234
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• 2022

Objective: The purpose of this study was to identify the practitioner and organizational characteristics that either detracted from or encouraged the use of evidence-based practice (EBP) by physical therapists. Design: A cross-sectional survey study Methods: Participants were 260 physical therapists currently practicing in South Korea. They completed a questionnaire designed to determine attitudes, beliefs, interest, self-efficacy and barriers to EBP, as well as demographic information about themselves and their practice settings. Logistic regression was used to examine relationships between socio-demographic and work environment characteristics and each practitioner factor. Results: Respondents agreed that the use of evidence in practice was necessary. Although 80% of them agreed that research findings are useful, 71% felt that a divide exists between research and practice. In terms of confidence in their skills, the ability to interpret results of statistical procedures ranked lowest. Despite internet access at work for 63% of respondents, only 14% were given protected work time to search and appraise the literature. Only 2% of respondents stated that their organization had a written requirement to use current evidence in their practice. The primary barrier to implementing EBP was a reported lack of time. Conclusions: In conclusion, most physical therapists stated they had a positive attitude toward EBP and were interested in learning or improving the skills necessary for implementation. Most recognized a need to increase the use of evidence in their daily practice, but a lack of ability to understand the results of research represents a significant barrier to implementing EBP.

• The Journal of Korean Physical Therapy
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• v.33 no.6
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• pp.297-303
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• 2021

Purpose: The mass of facial muscles can be increased through exercise, as is also the case for muscles in the extremities. This systematic review was conducted to investigate the effect of facial exercises on facial muscle strengthening and facial rejuvenation, focusing on recent studies. Methods: A literature search was performed using the PubMed, ScienceDirect, and Web of Science databases. The quality of the trials was evaluated according to the PEDro scale. In total, 11 studies were included in this review: four studies on facial exercise for facial rejuvenation and seven studies on strengthening the muscles of the face. Results: Facial exercises for facial rejuvenation increased the mechanical properties and elasticity of the skin of the face and neck, the thickness and cross-sectional area of the facial muscles, and the fullness of the upper and lower cheeks. Conclusion: A study aimed at strengthening facial muscles showed improvements in labial closure strength and tongue elevation strength. Despite the positive results for facial rejuvenation and muscle strengthening, the level of evidence was low. Therefore, in future research, it will be necessary to investigate the effects of facial exercise in a thoroughly controlled experiment with a sufficient sample size to increase the level of evidence.

• Structural Engineering and Mechanics
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• v.84 no.1
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• pp.1-16
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• 2022

An efficient and accurate classification method for failure modes of reinforced concrete (RC) columns was proposed based on key characteristic parameters. The weight coefficients of seven characteristic parameters for failure modes of RC columns were determined first based on the support vector machine-recursive feature elimination. Then key characteristic parameters for classifying flexure, flexure-shear and shear failure modes of RC columns were selected respectively. Subsequently, a support vector machine with key characteristic parameters (SVM-K) was proposed to classify three types of failure modes of RC columns. The optimal parameters of SVM-K were determined by using the ten-fold cross-validation and the grid-search algorithm based on 270 sets of available experimental data. Results indicate that the proposed SVM-K has high overall accuracy, recall and precision (e.g., accuracy>95%, recall>90%, precision>90%), which means that the proposed SVM-K has superior performance for classification of failure modes of RC columns. Based on the selected key characteristic parameters for different types of failure modes of RC columns, the accuracy of SVM-K is improved and the decision function of SVM-K is simplified by reducing the dimensions and number of support vectors.

• Structural Engineering and Mechanics
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• v.84 no.5
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• pp.605-618
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• 2022

Glass fiber reinforced polymer (GFRP) elastic gridshells consist of long continuous GFRP tubes that form elastic deformations. In this paper, a method for the form-finding of gridshell structures is presented based on the interpretable machine learning (ML) approaches. A comparative study is conducted on several ML algorithms, including support vector regression (SVR), K-nearest neighbors (KNN), decision tree (DT), random forest (RF), AdaBoost, XGBoost, category boosting (CatBoost), and light gradient boosting machine (LightGBM). A numerical example is presented using a standard double-hump gridshell considering two characteristics of deformation as objective functions. The combination of the grid search approach and k-fold cross-validation (CV) is implemented for fine-tuning the parameters of ML models. The results of the comparative study indicate that the LightGBM model presents the highest prediction accuracy. Finally, interpretable ML approaches, including Shapely additive explanations (SHAP), partial dependence plot (PDP), and accumulated local effects (ALE), are applied to explain the predictions of the ML model since it is essential to understand the effect of various values of input parameters on objective functions. As a result of interpretability approaches, an optimum gridshell structure is obtained and new opportunities are verified for form-finding investigation of GFRP elastic gridshells during lifting construction.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.137-144
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• 2023

The purpose of this study is to examine consumers' perceptions of domestic infertility support policies based on infertility-related keywords and the trends of their changes. To this end, Momsholic, a mom cafe which has the most active infertility-related bulletin boards on Naver, was selected as the analysis target, and 'infertility' was selected as a keyword for data search. The data was collected for three months. In addition, network analysis and visualization were performed using R for data collection and analysis, and cross-validation was attempted using the NetDraw function of 'textom 1.0' and the UCINET6 program. As a result of the analysis, the main keywords were cost, artificial insemination, in vitro fertilization, freezing, harvest, ovulation, and how much. Next, looking at the central value of the degree of connection, it was found that the degree of connection between the words cost, cost, how much, problem, public health center, and artificial insemination was high. According to the results of this study, women who visit mom cafes due to infertility in Korea are more interested in the cost. It is believed to be closely related to infertility treatment as well as in vitro fertilization and egg freezing. Therefore, by examining keywords related toinfertility, it has academic significance in that it is possible to identify major factors that end users are interested in. Furthermore, it is possible to redefine the guidelines for domestic infertility support policies by presenting infertility support policies that reflect the factors of interest of end consumers.

• Journal of Korean Association for Spatial Structures
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• v.23 no.3
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• pp.87-94
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• 2023

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure's safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.

• Restorative Dentistry and Endodontics
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• v.46 no.3
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• pp.40.1-40.12
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• 2021

Objectives: The purpose of this study was to critically evaluate the quality, reliability and educational content of the information of vital pulp treatment videos available on YouTube. Materials and Methods: The keywords "pulpotomy" and "pulp capping" were searched on YouTube on 5th July 2020, until 60 English language videos of each search term with a duration shorter than 15 minutes were acquired. Video characteristics were recorded and Video Power Index (VPI) was calculated. Reliability and educational quality of videos were evaluated using the Modified DISCERN score, the Journal of American Medical Association (JAMA) benchmark criteria and Global Quality Scores (GQS). Videos were categorized by uploading source. Results: Regarding pulpotomy, 31.7% of the videos were uploaded by specialists and 68.3% were directed by non-specialists. In the case of pulp capping, the corresponding percentages were 45% and 55%, respectively. Videos uploaded by specialists had significantly higher modified DISCERN, JAMA and GQS scores compared to those uploaded by non-specialists. Endodontists tended to have the highest reliability and VPI scores. Conclusions: YouTube videos on vital pulp treatment contain low educational quality or incomplete information. Low popularity of dental pulp capping and pulpotomy videos may be attributed to the specialized nature of these procedures. As YouTube represents an important source for patient information about different health topics, reliable informative videos should be uploaded by specialized dental professionals.