Emerging Science Journal (ESJ)
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New Economic Model and Conceptual Directions for Increasing Russia's Regional Budgets Sustainability
Get PDFObjectives: This research aims to develop a new economic model and conceptual directions for increasing the sustainability of regional budgets in the Russian Federation. Methods: The research methodology is based on empirical methods (data collection, study, and comparison), methods of synthesizing theoretical and practical material, and mathematical and statistical analyses. When processing information, methods of systematization and grouping were used. The budgets of the constituent entities of the Russian Federation (BCERF) are the object of this study. The authors proposed grouping and revealed the consequences of the factor actions for the regions, leading to a reduction in income, an increase in the expenses of the BSRF, the need for government borrowing, etc. Findings: Conceptual directions for increasing the sustainability of regional budgets in the Russian Federation were proposed, such as eliminating the practice of subsidies in areas not defined by regulatory legal acts and improving the methodology for distributing subsidies for fiscal equalization, aimed at reducing the risks of underfinancing the expenditure obligations of a constituent entity of the Russian Federation (CERF). Novelty: The scientific novelty of this research includes the presentation of more effective mechanisms for controlling budgetary reserves and increasing the sustainability of regional budgets. Doi: 10.28991/ESJ-2024-08-02-015 Full Text: PD
Factors Influencing Employee Retirement Financial Planning: Evidence from Thai Higher Education Institutions
Get PDFThis research aimed to examine the factors that impact the financial planning for retirement among employees in Thai Rajabhat universities. To validate the theoretical framework and the collected empirical data, we utilized the confirmation factor analysis method, which allowed us to assess the relationship between the factors and examine how well the data fits the proposed model. We collected data from a sample of 433 employees by administering a 5-point Likert scale questionnaire. The collected data was subsequently analyzed using the Lavaan package in R Studio software. The research findings revealed that knowledge and understanding, financial status assessment, expected return or investment strategy, risk acceptance or risk tolerance, setting goals in life or goals' clarity, and alternative sources of income or other income played important roles in shaping retirement financial planning among our study participants. To evaluate the theoretical structural model, we conducted statistical analyses and found that it fitted the empirical data at a significance level of 0.05. The statistical results of CMIN/df = 11, GFI = 0.941, AGFI = 0.848, FI = 0.946, and RMSEA = 0.000 provided evidence for the validity and reliability of the proposed model. Going forward, the resulting model will serve as a guideline to evaluate the efficiency of financial planning for the retirement of employees, provide solutions to identified problems, and inform policies and programs that aim to improve retirement financial planning for employees in the higher education sector. Doi: 10.28991/ESJ-2024-08-04-08 Full Text: PD
Impact of Digital Transformation on Mental Healthcare: Opportunities, Challenges, and Role of AI Chat-bots in Symptom Management
Get PDFMental health disorders are a significant global health burden, and access to mental healthcare services remains a challenge. Digital transformation has emerged as a promising solution, but it also presents its own set of challenges. Objectives: This study aims to investigate the impact of digital transformation on mental healthcare, identify the opportunities and challenges it presents, and to examine the role of AI chat-bots in mental health symptom management. Drawing on a comprehensive literature review, a theoretical framework is developed, and five hypotheses are proposed. Methods: This study employs a cross-sectional survey design, collecting data from mental healthcare professionals in three countries. Structural equation modeling was used to test the hypotheses and examine the relationships among digital transformation, opportunities, challenges, AI chat-bot effectiveness, and mental health symptom management. Findings: The results provide support for the hypothesized relationships, highlighting the significant influence of digital transformation on opportunities and challenges, the impact of opportunities on AI chat-bot effectiveness, and the role of AI chat-bots in mental health symptom management. Novelties: This study contributes to the theoretical understanding of digital transformation in mental healthcare and offers practical implications for the development and implementation of effective digital mental health interventions. Doi: 10.28991/ESJ-2024-08-04-012 Full Text: PD
Tribological Performance of Polymer Composite Modified with Calcined Eggshell Particles Post High-Temperature Exposure
Get PDFDuring operation, brake lining material rubs against the disc to generate heat. This heat could decrease the brake lining performance, such as the friction coefficient, specific wear rate, and interface temperature of the rubbing surfaces. The resulting wear debris is environmentally harmful and poses risks to human health. Therefore, this study aimed to replace the harmful material using eggshell particles as a filler in brake lining composite and enhance tribological properties. The brake lining samples were manufactured through three stages: cold compaction, hot compaction, and post-curing. The next step is the samples were subjected to a one-hour high-temperature exposure at 200°C, 300°C, 400°C, and 500°C. The results showed that the high-temperature exposure significantly affected the specific wear rate, friction coefficient, and interface temperature between the brake lining and disc. An interesting finding was that adding calcined eggshell particles in composite could improve the tribological properties up to 400°C. However, the best material's performance resulted when the samples got an exposure temperature of 200°C. Doi: 10.28991/ESJ-2024-08-04-03 Full Text: PD
Corporate Donations in the Context of Covid-19: Insights on Trust and Policy Innovation Opportunities
Get PDFThis study aims to investigate the determinants of corporate donations during the initial phase of the Covid-19 pandemic, focusing on the Portuguese context. It explores the interplay between pandemic-related factors, corporate structures, recipient profiles, and media coverage on the levels of corporate donations. In the absence of publicly available data, a comprehensive database of corporate donations was constructed by analyzing over six thousand news pieces from various media sources between March and May 2020. The database comprises 1171 donations from 709 different institutions. The relationship between corporate donations and multiple variables was examined, including the epidemiological progression of the pandemic, corporate ownership structures, recipient characteristics, and media coverage. Our analysis reveals that during the initial wave of the Covid-19 pandemic in Portugal, corporate donations were predominantly made by large companies, primarily directed toward their local regions. Notably, nearly 93% of all donations were allocated to the National Health System. PPEs and hospital equipment were the preferred donation items among the contributing companies. These findings shed light on the factors influencing corporate donation behavior during emergency situations and provide valuable insights into trust levels within the healthcare system. This study contributes to the existing literature by offering a unique exploration of corporate donation behavior during the Covid-19 pandemic, specifically in Portugal. The comprehensive dataset and findings provide novel insights into the factors shaping corporate donation decisions during crises. Doi: 10.28991/ESJ-2024-08-05-010 Full Text: PD
Breast Cancer Prediction Using Transfer Learning-Based Classification Model
Get PDFBreast cancer is currently the most prevalent type of cancer in women, with a growing number of fatalities worldwide. Different imaging methods like mammography, computed tomography, Magnetic Resonance Imaging, ultrasound, and biopsies assist in detecting breast cancer. Recent developments in deep learning have revolutionized breast cancer pathology by facilitating accurate image categorization. This study introduces a novel approach to enhance detection and classification using the Convolutional Neural Network Deep Learning method and Transfer Learning to create a high-speed, accurate image classification model. The model is trained on pre-processed data subjected to thorough analysis and augmentation to ensure the quality of inputs. The experimental results from the Breast Ultrasound Image dataset indicate that our model, with a 0.1 test size ratio, outperforms its counterparts. It achieved an accuracy of 90.12%, with a loss of 0.2641, validation accuracy of 90.15%, and validation loss of 0.31, evidencing its superior classification capability. This research introduces an innovative approach to the automated diagnosis of breast cancer. By combining CNN, Transfer Learning, and data augmentation, we have developed a desktop application that expedites the classification process and significantly improves accuracy. This advancement represents a key development in machine learning applications for breast cancer prognostics and diagnostics. Doi: 10.28991/ESJ-2024-08-06-014 Full Text: PD
Integrated Learning Models for Micro-Teaching Course
Get PDFThe progressive world of education needs to be accelerated by fulfilling the competencies of prospective teachers who are also progressive through a series of performance tasks that are relevant to learning needs in the 21st century. This research used Analysis, Design, Development, Implementation, and Evaluation (ADDIE) to innovate an integrated learning model for a micro-teaching course. A needs analysis was conducted on 75 students, two lecturers, and 30 teachers to assess actual performance, confirm desired performance, and identify causes of performance gaps. Researchers then designed performance tasks and validated them by 10 raters, tested them on 337 students to test the outer and inner models, and tested them on 30 students, 28 lecturers, and 49 teachers to test differences. Test content validity using the Aiken-V formula and test inter-rater reliability using ICC. Meanwhile, testing the validity and reliability of the construct uses outer and inner model analysis (CB-SEM), and the difference test uses ANOVA. The content validity results prove that all task performance meets the Aiken parameters (0.75-1.00), the interrater reliability value is 0.573, and the Cronbach alpha value is 0.931. Testing the outer model proves that the loading factor task performance value ranges from 0.709-0.874, the Cronbach alpha value ranges from 0.768-0.880, the composite reliability value ranges from 0.768-0.879, the AVE value ranges from 0.580-0.649, and the discriminant validity value ranges from 0.761-0.806. The inner model test proves that the Chi-Square/df value = 2.254, RMSEA value = 0.061, SRMR value = 0.036, NFI value = 0.910, TLI value = 0.936, and CFI value = 0.948. Meanwhile, the results of the ANOVA test confirm that the Sig value = 0.098, so it can be concluded that there are no significant differences between the three sample groups regarding the model innovation results. Thus, the 25-task performance in the integrated learning model has a significant psychometric function relative to the actual situation, so it becomes one of the references that lecturers can use to improve the competency of prospective teachers in micro-teaching courses (not limited to teaching skills, analytical thinking skills, academic integrity, and transformational leadership). Doi: 10.28991/ESJ-2024-08-06-020 Full Text: PD
Augmented Reality in Natural Sciences and Biology Teaching: Systematic Literature Review and Meta-Analysis
Get PDFThis article presents the results of a systematic literature review followed by a meta-analysis of studies on the use of Augmented Reality (AR) in the teaching and learning of Natural Sciences and Biology, among primary and secondary school students. The variables considered were the effects on student learning and motivation, as well as other variables like students understanding and students' perception of the cognitive load. The teaching contexts and strategies used in association with AR were also considered. The PRISMA methodology was used in articles published between 2010 and 2023, in EBSCO, Science Direct, Scopus, Springer Link, Taylor & Francis and Web of Science databases. Seven hundred and twenty-one articles (721) were found, which, after applying the inclusion and exclusion criteria, were reduced to 15. The results showed that, in most studies, AR associated with certain teaching strategies and using a quasi-experimental research methodology produced better results in learning and student motivation and other variables such as student understanding and memorization (from Bloom's taxonomy), and perception of cognitive load. The overall analysis of the data allowed us to observe a strong effect size value (d = 1.13 [0.39;1.86]) in favour of the experimental group regarding learning and a moderate effect on motivation when using AR (d = 0.52 [0.30;0.74]). The same occurred with other variables studied where students obtained better results, which translated into a small or medium effect size. For example, in the perception of cognitive load, the effect size was d= 0.73. Doi: 10.28991/ESJ-2024-08-04-025 Full Text: PD
Comparative Analysis of ARIMA, Prophet, and Glmnet for Long Term Evolution (LTE) Base Station Traffic Forecasting
Get PDFThis study evaluates the performance of three forecasting models”ARIMA, Prophet, and Glmnet”with the primary objective of equipping the telecommunication industry with effective tools for cellular traffic forecasting. These tools lay the foundation for efficient resource management, cost optimization, and enhanced service delivery. The study begins with dataset description and preparation, followed by the selection of traffic forecasting models, and concludes with performance evaluation based on metrics such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Symmetric Mean Absolute Percentage Error (SMAPE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²). The main contribution of this research is a comprehensive comparison of the three forecasting methods, aiding practitioners and researchers in identifying the best prediction model for specific contexts. The findings reveal that Glmnet consistently outperforms ARIMA and Prophet across all categories of traffic forecasting on the selected performance metrics. Its ability to handle complex data structures, manage multicollinearity, and deliver robust and accurate predictions makes it the preferred choice for forecasting cellular network traffic in the telecommunications domain. Doi: 10.28991/ESJ-2024-08-06-04 Full Text: PD
Influence of Non-Economic Factors on the Formation and Development of the Design of Financial Systems
Get PDFThe purpose of this scientific work is to investigate the impact of non-economic factors on the design of financial systems, focusing on the concept of institutional quality, which is measured using six indices according to the World Bank's methodology. To assess this impact, we utilized data from 1996 to 2022 for a wide range of countries, grouped into five clusters based on per capita income. The comparative analysis of these country clusters revealed a direct and consistent relationship between per capita income dynamics and financial development with changes in institutional quality. It also highlighted the significant influence of this relationship on the structural features of national financial systems. The study demonstrates that institutional quality is the starting point of this entire process, determining the effectiveness of the link between financial development and economic growth through changes in the financial structure. The findings confirm the convergence of financial development levels among countries with different financial system structures and legal traditions, provided they maintain high-quality institutions. The study underscores the importance of institutional quality in minimizing the consequences of structural distortions in the financial system and addressing gaps in financial and economic development. These results are crucial for economic policymakers in developing countries and those with low per capita incomes. Doi: 10.28991/ESJ-2024-08-05-08 Full Text: PD