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INDONESIA
Emerging Science Journal
Published by Ital Publication
ISSN : 26109182     EISSN : -     DOI : -
Core Subject : Social,
Environmental Science
Emerging Science Journal is not limited to a specific aspect of science and engineering but is instead devoted to a wide range of subfields in the engineering and sciences. While it encourages a broad spectrum of contribution in the engineering and sciences. Articles of interdisciplinary nature are particularly welcome.
Arjuna Subject : -
Articles 903 Documents
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Evaluation of the Lean Approach Implementation in Engineering Education Amine Hadek; Soumia Bakkali; Souad Ajana
Emerging Science Journal Vol 7, No 4 (2023): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-04-07

Abstract

This study aims to study the benefits of lean approach implementation in engineering education in Morocco by using three concrete case studies to present a comparative analysis between situations with and without Lean approach deployment. The first project focuses on improving internet access. The second project aims to improve the conduct of practicum sessions. The third project aims to identify and improve the performance of the processes implemented in the student affairs department. In this research study, deductive methodology is followed, also called the "hypothetical-deductive approach", First, we presented a synthetic analysis of Lean implementation studies existing in the literature between 2000-2023, then we presented three Lean implementation projects in the engineering school in Morocco. This research work allowed us to present, with concrete cases, the contribution of the approach to engineering education in Morocco, whether it is in the teaching processes or in the support and management processes within an engineering school. The involvement and sensibilization of the resources represent key success factors of this Lean transformation. This research work can serve as a baseline reference for other Lean implementation initiatives in the field of education in Morocco. On the one hand, while examining the literature, we found a scarcity of studies that have presented a comparison between the situation without and with the Lean approach. On the other hand, the Lean approach is a new concept that has never been discussed concretely in the context of Moroccan engineering; the present research fills that gap. Doi: 10.28991/ESJ-2023-07-04-07 Full Text: PDF
The Effect of Sodium Humate Feed Additives in Diets for Holstein Breed Heifers Daina Kairiša; Anda Valdovska; Ilze Vircava; Irina Pilvere; Liga Proskina; Daiga Gāliņa; Guntis Gutmanis; Sandijs Mešķis
Emerging Science Journal Vol 7, No 4 (2023): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-04-023

Abstract

The research aim is to examine the possibilities of including the sodium humate (NaHum) additive derived from freshwater sapropel in feed to identify its effects on growth performance, promote haematopoiesis, and modulate the microbiota of the intestinal tract. The research was done under production conditions, complying with Latvian and European Union legislation on the keeping, feeding, and welfare of farm animals. The research had three replications, and for each of them, two groups of Holstein breeding heifers were established: control (3xn=7) and research (3xn=7). The duration of each replication was 9 days in the adaptation period and 105 days in the research period. The heifers of the research group received the NaHum solution additive with feed from the 1st to 35th day (stage 1) at an intake rate of 0.4 mL/kg of live weight, from the 36th to 70th day (stage 2) at an intake rate of 0.5 mL/kg of live weight, and from the 71st to 105th day (stage 3) at an intake rate of 0.6 mL/kg of live weight. The breeding heifers of the research group, receiving NaHum at an intake rate of 0.6 mL/kg of live weight, achieved a significantly higher live weight gain at stage 3 and an overall numerically higher live weight gain (by 4.8 kg) than the control group during the research period. Consequently, a significantly higher relative growth ratio (0.334) was found in the research group at stage 3, which was 0.028 higher than that in the control group. The Lactobacillus spp. count in faecal samples was steady at the end of the research; a significant difference was found between groups, with the average ranging between 6.95 (control group) and 8.49 log CFU/g (research (NaHum) group). The novelty of the research is that it was scientifically proven that feeding the NaHum additive derived from freshwater lake sapropel to the breeding Holstein heifers up to 5 months of age increased their feed intake and live weight gain, as well as activity and health. Doi: 10.28991/ESJ-2023-07-04-023 Full Text: PDF
Forecasting Solar Power Generation Utilizing Machine Learning Models in Lubbock Afshin Balal; Yaser Pakzad Jafarabadi; Ayda Demir; Morris Igene; Michael Giesselmann; Stephen Bayne
Emerging Science Journal Vol 7, No 4 (2023): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-04-02

Abstract

Solar energy is a widely accessible, clean, and sustainable energy source. Solar power harvesting in order to generate electricity on smart grids is essential in light of the present global energy crisis. However, the highly variable nature of solar radiation poses unique challenges for accurately predicting solar photovoltaic (PV) power generation. Factors such as cloud cover, atmospheric conditions, and seasonal variations significantly impact the amount of solar energy available for conversion into electricity. Therefore, it is essential to precisely estimate the output of solar power in order to assess the potential of smart grids. This paper presents a study that utilizes various machine learning models to predict solar photovoltaic (PV) power generation in Lubbock, Texas. Mean Squared Error (MSE) and R² metrics are utilized to demonstrate the performance of each model. The results show that the Random Forest Regression (RFR) and Long Short-Term Memory (LSTM) models outperformed the other models, with a MSE of 2.06% and 2.23% and R² values of 0.977 and 0.975, respectively. In addition, RFR and LSTM demonstrate their capability to capture the intricate patterns and complex relationships inherent in solar power generation data. The developed machine learning models can aid solar PV investors in streamlining their processes and improving their planning for the production of solar energy. Doi: 10.28991/ESJ-2023-07-04-02 Full Text: PDF
A Binary Survivability Prediction Classification Model towards Understanding of Osteosarcoma Prognosis Saravanan Muthaiyah; Vivek Ajit Singh; Thein Oak Kyaw Zaw; Kalaiarasi S. M. Anbananthen; Byeonghwa Park; Myung Joon Kim
Emerging Science Journal Vol 7, No 4 (2023): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-04-018

Abstract

The objective of this study is to explore effective and innovative machine learning techniques that can assist medical professionals in developing more accurate prognoses that can enhance the survivability of osteosarcoma patients by investigating potential prognostic factors and identifying novel therapeutic approaches. A comprehensive analysis was conducted using a dataset of 128 osteosarcoma patients between 1997 to 2011. The dataset included 52 attributes in total that covered a wide range of demographics, together with information on clinical records, treatment protocols, and survival outcomes. Data was obtained from NOCERAL (National Orthopaedic Centre of Excellence in Research and Learning), Kuala Lumpur. Three distinct binary classification methods (i.e., random forest, support vector machine (SVM), and artificial neural network (ANN)) were employed to identify the prognostic factors that are associated with improved survival efficacy measures. The results of this study revealed that both SVM and ANN outperformed random forests in predicting survivability for both the 2-year and 5-year time frames. These findings indicate the potential of SVM and ANN as effective tools for predicting osteosarcoma survivability. The study signifies a significant step towards integrating machine learning techniques into the existing toolkit available to medical practitioners. This study contributes to the medical field by providing a comparative analysis of three prominent machine learning techniques for predicting osteosarcoma survivability. The superior performance of SVM and ANN over random forests highlights the potential of these methods in generating more accurate survivability predictions. Further development and refinement of these machine learning techniques hold promise for enhancing their effectiveness and instilling greater confidence among medical professionals and patients in the predictive capabilities of machine learning and artificial intelligence models for osteosarcoma survivability. Doi: 10.28991/ESJ-2023-07-04-018 Full Text: PDF
Brand Experience on Brand Attachment: The Role of Interpersonal Interaction, Feedback, and Advocacy Tanaporn Hongsuchon; Untung Rahardja; Asif Khan; Tsung-Hao Wu; Chung-Wen Hung; Ruey-Hsing Chang; Chung-Hao Hsu; Shih-Chih Chen
Emerging Science Journal Vol 7, No 4 (2023): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-04-014

Abstract

Value co-creation profoundly affects brand experience, human interaction rules, and customer behavior performance and is a recurring theme in research in the field of virtual brand communities. Studies have determined an influence model for value co-creation, but there is a dearth of research on the impacts of value co-creation activities on brand attachment. This study built a mediation theoretical research framework on value co-creation theory, brand experience, and brand attachment. The purpose of this study is to explore the significance of value co-creation activities on brand experience. Furthermore, this study intends to measure the impact of brand experience on brand attachment. Additionally, this study intends to investigate the mediation role of brand experience in the relationship between value co-creation and brand attachment. This study analyzed 512 data collected by structural equation modeling using registered users of the OPPO community as participants. The results of this empirical test show that the three dimensions of value co-creation (interpersonal interaction, feedback, and advocacy) have a positive effect on brand experience and that brand experience has a positive and significant impact on brand attachment and mediates the relationship between value co-creation and brand attachment. The findings of significance for management are the identification of factors that enhance value co-creation in virtual brand communities. Doi: 10.28991/ESJ-2023-07-04-014 Full Text: PDF
Educational Data Mining to Predict Bachelors Students’ Success David Jacob; Roberto Henriques
Emerging Science Journal Vol 7 (2023): Special Issue "Current Issues, Trends, and New Ideas in Education"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-SIED2-013

Abstract

Predicting academic success is essential in higher education because it is perceived as a critical driver for scientific and technological advancement and countries’ economic and social development. This paper aims to retrieve the most relevant attributes for academic success by applying educational data mining (EDM) techniques to a Portuguese business school bachelor’s historical data. We propose two predictive models to classify each student regarding academic success at enrolment and the end of the first academic year. We implemented a SEMMA methodology and tried several machine learning algorithms, including decision trees, KNN, neural networks, and SVM. The best classifier for academic success at the entry-level reached is a random forest with an accuracy of 69%. At the end of the first academic year, an MLP artificial neural network’s best performance was achieved with an accuracy of 85%. The main findings show that at enrolment or the end of the first year, the grades and, thus, the student’s previous education and engagement with the school environment are decisive in achieving academic success. Doi: 10.28991/ESJ-2023-SIED2-013 Full Text: PDF
Job Performance Evaluation of Medical Social Workers during Covid-19 Crisis: Tasks, Attitudes, and Difficulties Walaa Elsayed
Emerging Science Journal Vol 7, No 4 (2023): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-04-08

Abstract

Objectives: The objective of this study is to evaluate the job performance efficiency of social workers in medical institutions during the COVID-19 pandemic crisis, based on three factors "job tasks, attitudes of co-workers, and difficulties" faced by social workers in performing their duties. Methods/Analysis: This study is a descriptive-analytical study. A comprehensive social survey approach was used to collect data from 54 social workers in isolation hospitals for coronavirus patients. A questionnaire was employed to gather the required data. The collected data were analyzed using weight analysis to determine the value and weighted relative weight of job performance efficiency. Findings: The results of the study showed that the job performance efficiency of social workers in medical institutions during the COVID-19 pandemic crisis was at a middle level, with a total weight of 3611 and a weighted relative weight of 55.7%. Moreover, the study found statistically significant differences in the degree of job performance efficiency according to gender, age, educational qualification, number of experience years, and number of training courses at a 5% significance level. Novelty/Improvement:The study recommends the development of the knowledge and skills of medical social workers through training courses on how to deal with global health crises and disasters. Additionally, reducing the administrative burdens on medical social workers is important, as these burdens restrict their role and limit their ability to perform their tasks with the medical team. Doi: 10.28991/ESJ-2023-07-04-08 Full Text: PDF
The Impact of the COVID-19 Epidemic on Gambling Behavior Intention: The Moderating Effect of Anti-Epidemic Measure Jinquan Zhou; Wenjin He; Susana M. B. Mieiro
Emerging Science Journal Vol 7, No 4 (2023): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-04-024

Abstract

This study focuses on how the COVID-19 epidemic affects gambling motivation and behavior. This research also analyzes the behavioral intervention effects of the anti-epidemic measures on the COVID-19 epidemic and the relationship between the epidemic impact and gambling motivation and behavior. To investigate these connections, this research used Structural Equation Modeling to analyze 334 valid questionnaires collected during COVID-19 from gamblers from mainland China who visited the Macao Special Administrative Region. The results showed that the epidemic impact negatively affected gambling motivation and behavior, and gambling motivation partially mediated the relationship between epidemic impact and gambling behavior. Anti-epidemic measures positively moderated the epidemic’s impact on gambling motivation and behavior. This paper offers a theoretical contribution by proving the influence of the social environment on human motivational behavior, especially the effect of the COVID-19 crisis, and the support of government and enterprise anti-epidemic measures for behavior intervention theory. The practicality of this study consists of behavioral interventions from anti-epidemic efforts by regional government and industry to cope with the epidemic. These measures should influence the gamblers’ behavior intentions by considering the health and safety strategies that may reduce the impact of the COVID-19 epidemic on mainland Chinese gamblers. Doi: 10.28991/ESJ-2023-07-04-024 Full Text: PDF
Desktop vs. Headset: A Comparative Study of User Experience and Engagement for Flexibility Exercise in Virtual Reality Pornpon Thamrongrat; Chaowanan Khundam; Pornpitak Pakdeebun; Dinna Nina Mohd Nizam
Emerging Science Journal Vol 7, No 4 (2023): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-04-03

Abstract

This study aimed to investigate the effectiveness of Virtual Reality (VR) technology for flexibility exercise and compare the physical outcomes, user experience, and engagement of VR desktops and VR headsets. The VR exercise application was designed using motion capture technology and exported to different VR devices. Each of the devices was used by 30 participants to perform a flexibility exercise in VR. Physical outcomes were measured using the sit-and-reach test, and user experience and engagement were evaluated using questionnaires and group discussions. The results showed that VR desktop participants had higher sit-and-reach scores. However, VR headset participants reported a more immersive experience (reality judgment) and motivation (value and usefulness). They also had higher engagement (focused attention and reward) levels than VR desktop participants. There were no significant differences between the two approaches in terms of enjoyment, effort, pressure, choice, correspondence, absorption, perceived usability, and aesthetic appeal. The study highlights the importance of considering physical outcomes, user experience, and engagement by comparing two different VR approaches for flexibility exercise. Further research is needed to explore the limitations and potential benefits of VR technology for physical activity. Doi: 10.28991/ESJ-2023-07-04-03 Full Text: PDF
Exploring Personality Traits in Elite Sport Players and Associate with a Good Project Managers Kateřina Bočková; Slávka Čepelová
Emerging Science Journal Vol 7, No 4 (2023): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-04-019

Abstract

The paper deals with the issue of personality in the context of the professions of an elite tennis player and a project manager. The objective of the conducted research is to find a set of personality characteristics typical for tennis and project management and to find out which personality characteristics of the analyzed professions are similar, or different. To confirm the hypothesis H: "The personality characteristics of an elite tennis player correspond to the demands of his profession, just as the personality characteristics of a project manager correspond to the demands of his profession, and there is demonstrably a correlation in the personality characteristics of elite tennis players and project managers, which corresponds to a correlation in the demands of these two professions," we will use the Cattell's 16 Personality Factor Test, the Spearman correlation analysis, the evaluation of the relevant test indicators difference in statistical significance using the Student t-test, and the comparative analysis. A comparison of the demands of the analyzed professions on a person and a comparison of the personality characteristics of the elite tennis players and the project managers results in the conclusion that the personalities of an elite tennis player and a project manager are somewhat similar, as well as a striking difference between them in the emotional area. The hypothesis was confirmed. An elite tennis player can be a good project manager. Doi: 10.28991/ESJ-2023-07-04-019 Full Text: PDF

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