Emerging Science Journal (ESJ)
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A Socio-Legal Study on Vaccine Tourism in the Context of Covid-19 Travel Restrictions
Get PDFThe COVID-19 pandemic has affected the tourism industry harshly. The most effective way to steer clear of the virus is global vaccination. A novel concept of vaccine tourism arises from vaccine manufacturing corporations' limited stock and production capacity. The current paper aims to demystify the socio-legal and ethical underpinnings of vaccine tourism as well as analyze the restrictions on international travel imposed by major countries. The research critically examines key issues considering the literature's current arguments and integrates the current developments and challenges in the field of vaccine tourism. The paper addresses the fact that, in the current circumstances of travel restrictions, insufficiency of raw materials, ambiguous policies, vaccine passport authenticity, skewed distribution, and scarcity of vaccines around the world, the implementation of vaccine tourism is a big challenge. The study tries to understand the emerging concept of vaccine tourism and the major challenges to its growth. Vaccine tourism may be an instrument to revive the tourism sector post-COVID; therefore, understanding the current emerging issues around it would be significant for tourism literature. Doi: 10.28991/ESJ-2023-07-05-012 Full Text: PD
Implementation of Takagi Sugeno Kang Fuzzy with Rough Set Theory and Mini-Batch Gradient Descent Uniform Regularization
Get PDFThe Takagi Sugeno Kang (TSK) fuzzy approach is popular since its output is either a constant or a function. Parameter identification and structure identification are the two key requirements for building the TSK fuzzy system. The input utilized in fuzzy TSK can have an impact on the number of rules produced in such a way that employing more data dimensions typically results in more rules, which causes rule complexity. This issue can be solved by employing a dimension reduction technique that reduces the number of dimensions in the data. After that, the resulting rules are improved with MBGD (Mini-Batch Gradient Descent), which is then altered with uniform regularization (UR). UR can enhance the classifier's fuzzy TSK generalization performance. This study looks at how the rough sets method can be used to reduce data dimensions and use Mini Batch Gradient Descent Uniform Regularization (MBGD-UR) to optimize the rules that come from TSK. 252 respondents' body fat data were utilized as the input, and the mean absolute percentage error (MAPE) was used to analyze the results. Jupyter Notebook software and the Python programming language are used for data processing. The analysis revealed that the MAPE value was 37%, falling into the moderate area. Doi: 10.28991/ESJ-2023-07-03-09 Full Text: PD
An Empirical Analysis of Fintech's Impacts on the Financial Performance of Banks in Kosovo
Get PDFThis analysis aims to empirically investigate the impact of different forms of Fintech on the financial performance of banks in Kosovo from 2010 to 2021. The research is based on secondary data, accounting for 48 observations at quarterly frequencies. The model treats bank performance (i.e., net profits of the bank sector) as an endogenous variable of ATMs, POS, and e-payments. The methodology applied in the research is based on the OLS technique and diagnostic tests for evaluating the normality of distribution, multicollinearity, autocorrelation, specification error, and heteroscedasticity. Results show that the variability of ATMs and e-payments determines bank performance variability. In particular, e-payments show a significant positive impact on bank profitability, whereas ATM payments display a negative impact on bank profitability. In addition, an increase in ATM payments by 1% decreases bank profitability by 0.367%. While an increase in e-payments by 1% increases bank profitability by 0.11%. The POS payments were found to have no significant relationship with bank profitability. Doi: 10.28991/ESJ-2023-07-03-016 Full Text: PD
STEM Talent: A Game Changer in Organizational Digital Transformation
Get PDFAlthough organizational digital transformation (ODT) is implemented globally, Thailand and the Lao People's Democratic Republic do not possess the right factors for success under the tech-no-socio-economic paradigm. Organizations must modernize their capital resources, particularly their talent, in order to become agile, competitive, and resilient in the digital era. In this research, we identify and validate by proposing talent success factors and a framework for enabling and promoting ODT in Thailand and the Lao People's Democratic Republic. The statistical population consisted of 410 individuals who were observed in their digital businesses. Confirmatory factor analysis (CFA) shows that a four-factor model fits. The most influential factor for ODT was found to be transdisciplinary ontology talent (TOT), followed by mental model talent (MMT), enterprise architecture talent (EAT), and strategic agile talent (SAT). The findings demystified the four factors, entitled "STEM talent," in a comprehensive framework and its artifacts while explaining their respective influences. The article proposes a STEM talent and its framework for ODT with high potential, including but not limited to Thailand and the Lao People's Democratic Republic. Doi: 10.28991/ESJ-2023-07-03-020 Full Text: PD
Contributions of Neuroscience to Educational Praxis: A Systematic Review
Get PDFObjectives: In education, neuroscience is an interdisciplinary research field. It seeks to improve educational practice by applying brain research findings. Additional findings from the scientific fields of education, psychology, and neurophysiology aim to enhance the learning process and improve educational practices. The application of neuroscience to education involves neuroscientific and psychological knowledge. Methods/Analysis: In this systematic literature review, the final studies included in the analysis table are decided by searching databases according to predefined inclusion criteria. The PRISMA approach was utilized to study the relationship between neuroscience and the educational process and to optimize the educational process based on the relevant data. Findings: The review's findings emphasize the significance of integrating neuroscience into educational praxis and challenges and raise ethical concerns regarding its implementation in educational contexts. Novelty /Improvement: The discipline of educational neuroscience is associated with education, research, and the cognitive neuroscience of learning. Neuroscience can serve as the basis for education in a similar direction that biology serves as the basis for medicine, meaning that each field retains its innovation but cannot contravene the rules of the other. This study examines the relationship between neuroscience and educational praxis as well as how the educational community might bridge this gap to include prospective findings from neuroscientific research. Doi: 10.28991/ESJ-2023-SIED2-012 Full Text: PD
Development of the "1+2+X” Modular Course System for Information Technology Majors from the Perspective of Dual-Mode IT
Get PDFAs the third generation of IT is developing rapidly, higher education institutions in China are looking to produce innovation-minded talent who can adapt to the dual-mode IT work environments of modern enterprises to meet the demands of the intelligent manufacturing national development strategy. So, this research aims to specify the hierarchical talent training system and mechanism for information technology majors in the higher education system. A 1+2+X modular curriculum system was proposed for the information technology majors based on the group-chain development model that focuses on combining discipline and industry (also known as vertical and horizontal integration). The data analysis was performed through a comparative analysis of the talent training objectives of the Chinese institutes and course systems' national development strategies. The results support the idea that the 1+2+X modular curriculum system can help universities produce innovation-minded talent by designing their curriculum based on the industry and trends rather than just focusing on specialization training. The novelty of this research is that it promotes the idea of professional development along with course training. This paper recommends that future researchers implement the concept in vocational institutes. Doi: 10.28991/ESJ-2023-07-03-024 Full Text: PD
Mixed Tukey Exponentially Weighted Moving Average-Modified Exponentially Weighted Moving Average Control Chart for Process Monitoring
Get PDFThe goal of this study is to present the mixed Tukey exponentially weighted moving average-modified exponentially weighted moving average control chart (MEME-TCC) for monitoring process location with symmetric and skewed distributions in an attempt to significantly improve detection ability. With the benefits of nonparametric assumption robustness. The average and median run lengths are supporting measurements for assessing the performance of a monitoring scheme using Monte Carlo simulation. Furthermore, the average extra quadratic loss (AEQL), relative mean index (RMI), and performance comparison index (PCI) can all be used to evaluate overall performance criteria. The proposed chart is compared with existing charts such as; EWMA, MEWMA, TCC, MEME, MMEE, and MMEE-TCC. The comparison result shows that the proposed chart is the best control chart for detecting small to moderate shifts among all distributional settings. Nevertheless, the EWMA chart detects large shifts more effectively than other charts, except in the case of the gamma distribution, where MEWMA performs best. The results of adapting the proposed control chart to two sets of real data corresponded to the research findings. Doi: 10.28991/ESJ-2023-07-03-014 Full Text: PD
Using PPO Models to Predict the Value of the BNB Cryptocurrency
Get PDFThis paper identifies hidden patterns between trading volumes and the market value of an asset. Based on open market data, we try to improve the existing corpus of research using new, innovative neural network training methods. Dividing into two independent models, we conducted a comparative analysis between two methods of training Proximal Policy Optimization (PPO) models. The primary difference between the two PPO models is the data. To showcase the drastic differences the PPO model makes in market conditions, one model uses historical data from Binance trading history as a data sample and the trading pair BNB/USDT as a predicted asset. Another model, apart from purely price fluctuations, also draws data on trading volume. That way, we can clearly illustrate what the difference can be if we add additional markers for model training. Using PPO models, the authors conduct a comparative analysis of prediction accuracy, taking the sequence of BNB token values and trading volumes on 15-minute candles as variables. The main research question of this paper is to identify an increase in the accuracy of the PPO model when adding additional variables. The primary research gap that we explore is whether PPO models specifically trained on highly volatile assets can be improved by adding additional markers that are closely linked. In our study, we identified the closest marker, which is a trading volume. The study results show that including additional parameters in the form of trading volume significantly reduces the model's accuracy. The scientific contribution of this research is that it shows in practice that the PPO model does not require additional parameters to form accurately predicting models within the framework of market forecasting. Doi: 10.28991/ESJ-2023-07-04-012 Full Text: PD
Program-Target Mechanisms to Ensure the Fiscal Balance of the Federal Constituent
Get PDFThe purpose of this research is to study the possible impact of program costs associated with the development of the real sector of the regional economy on the fiscal balances of the constituent entities of the Russian Federation based on the wavelet analysis method. To achieve this purpose, we conducted a correlation analysis of the time-frequency dependence between the variables of the empirical model: the shares of program costs, the shares of non-repayable receipts, and the share of business taxes in the revenues of the consolidated budgets of constituent entities of the Russian Federation such as the Republic of Mordovia, the Udmurt Republic, Trans-Baikal Territory, and Kaliningrad Region for the period from 2001 to 2021. The research results indicate a significant impact exerted by the program costs of the regional budgets on the development of the real sector to ensure fiscal balance in the Republic of Mordovia, the Udmurt Republic, and the Trans-Baikal Territory on certain time scales. The novelty of this research lies in demonstrating the wavelet analysis effectiveness applied when conducting correlation analysis in cases where the relationships between the analyzed variables follow different patterns at different time horizons, and precisely wavelet analysis makes it possible to reveal the most significant characteristics of the relationship of variables. Earlier studies based on traditional methods ignored the time-frequency dependence between the variables of the empirical model. The practical significance of the research results lies in the fact that they determine the time scale on which the most effective measures and budgetary policy instruments applied within the framework of program-target mechanisms are provided to ensure fiscal balances in the regions. Doi: 10.28991/ESJ-2023-07-05-05 Full Text: PD
Wind Energy Assessment Using Weibull Distribution with Different Numerical Estimation Methods: A Case Study
Get PDFThe demand for electrical energy is increasing every day, which is one of the critical challenges facing the world today. Hence, the necessity of turning to clean renewable energy sources that are not harmful to the environment as an alternative to the traditional generation based on fossil fuels has become more important than ever before. Wind power is one of the renewable sources that provides a clean solution to generate electricity. In this context, the Kingdom of Saudi Arabia announces renewable energy projects to generate 9 GW from wind in 2032. Hence, the aim of this paper is to investigate the most suitable method of Weibull parameter estimation in order to predict wind characteristics and employ it for wind energy assessment in the Qassim region located in the center of the country. In this study, wind data is collected from NASA's forecasts of global energy resources for 2010–2015 based on their availability at altitudes of 10m and 50m and analyzed by using six different methods for Weibull parameter estimation: the graphical method (GM), standard deviation method (SDM), energy pattern factor method (EPF), moment method (MM), alternative maximum likelihood method (AMLM), and novel energy pattern factor method (NEPF). The efficiency of each method is tested by calculating the root mean square error (RMSE) and the relative wind power density error (RPDE). The comparison shows that the most appropriate method for estimating wind power density in the country is the Moment Method (MM), with the lowest RPDE ratio equal to 0.2018%. It has been found that the wind power density in the Qassim region falls into the class 1 category, as it is less than 100 W/m2 at a height of 10m and less than 200 W/m2at an altitude of 50m. The results show the region is only suitable for small off-grid projects. Doi: 10.28991/ESJ-2023-07-06-024 Full Text: PD