Emerging Science Journal (ESJ)
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Improving the Theoretical and Methodological Framework for Implementing Digital Twin Technology in Various Sectors of Agriculture
Get PDFThe aim of this study is to systematize and improve the theoretical and methodological framework for implementing digital twin technology. The study focuses on digital twins in agriculture. This paper is designed to solve the scientific problem associated with the development of a methodological framework for the implementation of digital twins in the work of agricultural organizations. Using methods of analysis of socio-economic phenomena and processes on the basis of a set of scientific approaches, economic-statistical analysis, and others, the study considers the importance of digital twins of agricultural machinery and equipment, identifies trends in agriculture determined by digitalization, and suggests promising areas for digital twins of agricultural machinery and equipment. This paper also examines the theoretical basis for the implementation of digital twin technology in the agricultural sector of production. New research results complement the theoretical provisions on the essence of digital twin technology; develop the methodological provisions of digital twin technology, represented by the study of their significance, principles, and features of operation. The study may be seen as academically novel as it reveals the prerequisites for implementing digital technology in agriculture as well as clarifies and improves the theoretical and methodological provisions of the application of digital twin technology in various sectors of agriculture. Doi: 10.28991/ESJ-2023-07-04-05 Full Text: PD
Effect of a Classroom-based Intervention on the Social Skills of Students with Learning Difficulties
Get PDFChildren with learning difficulties often face challenges in social skills, hindering their ability to adjust and interact within society. The present study was designed to evaluate the effectiveness of a training program designed to enhance the social skills of individuals with disabilities. The quasi-experimental study involved 20 primary school students with learning difficulties exhibiting deficits in social skills in the United Arab Emirates. To evaluate the level of social skills of the sample children, a social skills assessment scale was employed, which was developed by the researchers. The assessment scale consisted of 24 statements that were organized into three dimensions based on previous research and theoretical frameworks. The results of the present study showed that the training program significantly and positively impacted the social skills of these children. There were statistically significant disparities between the mean ranks of the experimental group and the control group's scores on the social skills assessment scale after program completion. In conclusion, the study recommends integrating the developed training and similar programs into the public and private education curricula, including both government and private schools, to improve the social communication abilities of children with learning difficulties. Doi: 10.28991/ESJ-2023-SIED2-011 Full Text: PD
Digital Transformation Affecting Sustainable Development: A Case of Small and Medium Enterprises during the Covid-19 Pandemic
Get PDFVietnam's economy faces many difficulties and complicated developments. To create an environment with favorable conditions for sustainable development, it is necessary to have innovative business solutions that not only bring profits for businesses but also solve environmental and social problems. In addition, small and medium enterprises (SMEs) have made many positive contributions to economic restructuring, creating stable jobs for hundreds of thousands of employees and ensuring social security. Besides, SMEs in Vietnam have faced many difficulties and challenges during the COVID-19 pandemic. Thus, the papers' objectives explored critical factors affecting the sustainable development of SMEs in Vietnam. The authors applied two methods, such as qualitative and quantitative, with data obtained from 400 managers of small and medium enterprises and used structural equation modeling and SPSS 20.0, Amos software. The article's findings have the digital transformation factor's most substantial impact on sustainable development. The article's novelty is determined by five factors: market trends, state support policy, social responsibility, quality of human resources, and digital transformation. Finally, the authors recommended guidelines to help businesses be more cohesive in removing difficulties and solving issues related to credit relations to put capital into modern technology investment to ensure business effectiveness and sustainable development for SMEs. Doi: 10.28991/ESJ-2023-SPER-017 Full Text: PD
The Safe Learning Environment in the United Arab Emirates Schools and Its Relationship to the Development of Creative Thinking Among Students
Get PDFThe study aimed to assess the relationship between a safe learning environment in Emirati schools and the development of student's creative thinking. Using a descriptive method with stratified random sampling, the researchers selected a sample of 500 male and female teachers. Two questionnaires were employed: one assessing the safe learning environment (20 items) and another measuring creative thinking (20 items). Results indicated a high teacher perception of a safe learning environment, with statistically significant chi-square values for all items. Similarly, teachers perceived a high level of creative thinking development, with significant chi-square values for all items. Gender and experience did not show statistically significant differences in the perception of a safe learning environment. However, teachers with over 10 years of experience demonstrated higher levels of creative thinking development. Notably, a significant correlation was found between a safe learning environment and the development of students' creative thinking in Emirati schools. This study aligns with the UAE Ministry of Education's mission to create a safe and creative educational system that meets the needs of a globally competitive knowledge society. Doi: 10.28991/ESJ-2023-SIED2-014 Full Text: PD
Determinants of English Language Proficiency: A Multifaceted Analysis
Get PDFThis study investigates the determinants of English language proficiency among students at Panyapiwat Institute of Management (PIM) in accordance with the Common European Framework of Reference for Languages (CEFR) standards. The determinant factors under examination encompass students' attitudes, prior English language knowledge, information-seeking behavior, satisfaction with English language learning, teachers' expertise, teacher readiness, teaching methodologies, familial support, environmental factors, and international exposure. Data were gathered through a survey administered to 469 PIM students, and the analysis employed Partial Least Squares Structural Equation Modelling. The findings revealed that five significant factors influence PIM students' English proficiency, namely their prior English language knowledge, inclination toward seeking knowledge, teachers' expertise, classroom environment, and practical language usage experiences. Additionally, the research demonstrated a noteworthy impact of students' Grade Point Average (GPA) and the time dedicated to learning English on their CEFR scores. This study contributes to the field by shedding light on the multifaceted factors influencing English language proficiency among PIM students, offering insights that can inform language education strategies and policies. It emphasizes the importance of prior knowledge, information-seeking behavior, teacher quality, classroom environment, and practical language application in enhancing English language skills. Doi: 10.28991/ESJ-2023-SIED2-020 Full Text: PD
Batch and Streaming Data Ingestion towards Creating Holistic Health Records
Get PDFThe healthcare sector has been moving toward Electronic Health Record (EHR) systems that produce enormous amounts of healthcare data due to the increased emphasis on getting the appropriate information to the right person, wherever they are, at any time. This highlights the need for a holistic approach to ingest, exploit, and manage these huge amounts of data for achieving better health management and promotion in general. This manuscript proposes such an approach, providing a mechanism allowing all health ecosystem entities to obtain actionable knowledge from heterogeneous data in a multimodal way. The mechanism includes diverse techniques for automatically ingesting healthcare-related information from heterogeneous sources that produce batch/streaming data, managing, fusing, and aggregating this data into new data structures (i.e., Holistic Health Records (HHRs)). The latter enable the aggregation of data coming from different sources, such as Internet of Medical Things (IoMT) devices, online/offline platforms, while to effectively construct the HHRs, the mechanism develops various data management techniques covering the overall data path, from data acquisition and cleaning to data integration, modelling, and interpretation. The mechanism has been evaluated upon different healthcare scenarios, ranging from hospital-retrieved data to patient platforms, combined with data obtained from IoMT devices, having produced useful insights towards its successful and wide adaptation in this domain. In order to implement a paradigm shift from heterogeneous and independent data sources, limited data exploitation, and health records, the mechanism has combined multidisciplinary technologies toward HHRs. Doi: 10.28991/ESJ-2023-07-02-03 Full Text: PD
Utilizing Machine Learning to Reassess the Predictability of Bank Stocks
Get PDFObjectives: Accurate prediction of stock market returns is a very challenging task due to the volatile and non-linear nature of the financial stock markets. In this work, we consider conventional time series analysis techniques with additional information from the Google Trend website to predict stock price returns. We further utilize a machine learning algorithm, namely Random Forest, to predict the next day closing price of four Greek systemic banks. Methods/Analysis: The financial data considered in this work comprise Open, Close prices of stocks and Trading Volume. In the context of our analysis, these data are further used to create new variables that serve as additional inputs to the proposed machine learning based model. Specifically, we consider variables for each of the banks in the dataset, such as 7 DAYS MA,14 DAYS MA, 21 DAYS MA, 7 DAYS STD DEV and Volume. One step ahead out of sample prediction following the rolling window approach has been applied. Performance evaluation of the proposed model has been done using standard strategic indicators: RMSE and MAPE. Findings: Our results depict that the proposed models effectively predict the stock market prices, providing insight about the applicability of the proposed methodology scheme to various stock market price predictions. Novelty /Improvement: The originality of this study is that Machine Learning Methods highlighted by the Random Forest Technique were used to forecast the closing price of each stock in the Banking Sector for the following trading session. Doi: 10.28991/ESJ-2023-07-03-04 Full Text: PD
State of Charge Estimation of Lead Acid Battery using Neural Network for Advanced Renewable Energy Systems
Get PDFThe Solar Dryer Dome (SDD), an independent energy system equipped with Artificial Intelligence to support the drying process, has been developed. However, inaccurate state-of-charge (SOC) predictions in each battery cell resulted in the vulnerability of the battery to over-charging and over-discharging, which accelerated the battery performance degradation. This research aims to develop an accurate neural network model for predicting the SOC of battery-cell level. The model aims to maintain the battery cell balance under dynamic load applications. It is accompanied by a developed dashboard to monitor and provide crucial information for early maintenance of the battery in the SDD. The results show that the neural network estimates the SOC with the lowest MAE of 0.175, followed by the Random Forest and support vector machine methods with MAE of 0.223 and 0.259, respectively. A dashboard was developed to help farmers monitor batteries efficiently. This research contributes to battery-cell level SOC prediction and the dashboard for battery status monitoring. Doi: 10.28991/ESJ-2023-07-03-02 Full Text: PD
Climatic Factor Differences and Mangosteen Fruit Quality between On- and Off-Season Productions
Get PDFThe objective of this study was to investigate the differences in climatic factors and fruit quality between on- and off-season production periods. Climate, soil, and mangosteen measurements were all studied during on- and off-season production. We chose 40 mangosteen trees and observed flowering and fruit set rates over two production periods. The results showed that the number of flowers per branch, the number of fruits per branch, the circumference of fruits, and the fruit weight were higher during the on-season mangosteen production period than during the off-season mangosteen production period. However, the number of edible pulp segments, peel thickness, percentage of translucent flesh, and fruit gumminess were lower in the on-season mangosteen production period than in the off-season mangosteen production period. The percentage of fruit scars did not differ between the on- and off-season mangosteen production periods. When compared to the on-season mangosteen production period, there was lower relative humidity, soil moisture at 120 cm depth, and leaf wetness at 15 cm above ground during the off-season mangosteen production period; however, there was higher air temperature, soil moisture, and soil temperature at all four depth levels. Doi: 10.28991/ESJ-2023-07-02-020 Full Text: PD
Global Metabolic Changes by Bacillus Cyclic Lipopeptide Extracts on Stress Responses of Para Rubber Leaf
Get PDFChanging environmental conditions can generate abiotic stress, such as the scarcity of water and exposure to chemicals. This includes biotic stress like Phytophthora palmivora infection, which causes leaf fall disease and inhibits the growth rate of para rubber seedlings, resulting in economic loss. To prevent abiotic and biotic stresses, biocontrol agents such as cyclic lipopeptides (CLPs) from Bacillus spp. have been introduced to reduce the use of chemically synthesized fungicides and fertilizers. This study aimed to use Bacillus CLP extracts as a biological agent to stimulate the plant growth system in para rubber seedlings under stress conditions compared with the exogenous plant hormone (salicylic acid, SA). CLP extracts obtained from B. subtilis PTKU12 and exogenous SA were applied to the leaves of para rubber seedlings. The extracted metabolites from each treatment were analyzed by untargeted metabolomics for metabolite identification and metabolic networks under stress responses. In both treatments, 1,702 and 979 metabolites were detected in the positive and negative ion modes of electrospray ionization, respectively. The differential analysis revealed that the accumulation of up-regulated metabolites in the treatment of CLP extracts was higher than in the exogenous SA treatment, belonging to 56 metabolic pathways. The analysis of metabolic pathways indicated that CLP extracts employed alanine, aspartate, and glutamate metabolisms for stress responses leading to plant growth promotion. These findings revealed that the metabolic network for plant growth promotion induced by BacillusCLP extracts could be considered a protective option for para rubber plantations. Doi: 10.28991/ESJ-2023-07-03-022 Full Text: PD