Emerging Science Journal (ESJ)
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960 research outputs found
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Project-Based Learning With TikTok: A Digital Strategy for OHS Regulatory Training
No full textThis study examines the pedagogical integration of TikTok within a Project-Based Learning (PBL) framework to enhance the teaching and internalization of Occupational Health and Safety (OHS) regulations in higher education, specifically Executive Decree 255. The primary aim was to evaluate the effectiveness of student-created TikTok videos in fostering active learning, regulatory comprehension, and the development of key academic and professional competencies. Seventy-three undergraduate students from the Practicum III course in the OHS program at Universidad Técnica Particular de Loja participated in the initiative. The structured methodology comprised five phases: topic assignment, digital training, guided video production, public dissemination, and multidimensional evaluation. Student outcomes were assessed through rubric-based evaluation, TikTok engagement analytics, and a validated satisfaction questionnaire addressing eight pedagogical constructs (Cronbach’s α=0.989; KMO=0.934). Results indicate enhanced conceptual understanding, increased motivation, and the strengthening of transversal competencies such as digital communication and learner autonomy. The novelty of this approach lies in the purposeful use of a widely adopted social media platform as both a pedagogical tool and a dissemination medium, offering a replicable model for other STEM disciplines that require the translation of complex regulatory or technical content into engaging and accessible formats
Institutional Co-Evolution and Hybrid Regulation in the Digital Economy: A Case Study of BRICS Nations
Full text linkThis study investigates the institutional co-evolution associated with digitalization processes in BRICS countries, emphasizing the development of hybrid regulatory frameworks that integrate state intervention, platform-based self-regulation, and entrepreneurial institutional agency. The primary objective is to analyze how these frameworks operate within heterogeneous governance environments and address the sustainability challenges arising in emerging digital economies. Grounded in the theory of institutional co-evolution, the research applies a mixed-methods design, combining bibliometric mapping, comparative policy analysis, and multiple linear regression on cross-national panel data from Brazil, Russia, India, China, and South Africa (2018 - 2022). The findings demonstrate that increasing levels of digitalization and innovation are significantly correlated with reductions in environmental risks, while GDP growth remains positively associated with CO2 emissions; underscoring a structural tension between economic expansion and ecological resilience. To address this contradiction, the study proposes and empirically validates an Optimized Hybrid Model of institutional regulation, which improves sustainability indicators by 18.5%. The novelty of this research lies in the operationalization of institutional co-evolution within digital governance, offering a transferable policy model for flexible, adaptive regulation in complex, data-intensive economies. These results contribute to the advancement of institutional theory and provide actionable insights for the governance of transitional digital systems
The Impact of Higher Secondary ICT Education on University STEM Student Performance
Full text linkThis study investigates the significant impact of ICT education from the Higher Secondary Certificate (HSC) on Bangladeshi students' progress in tertiary STEM fields. Through utilizing a comprehensive examination of demographic profiles, proficiency assessments, facility rating systems, and satisfaction measures, this study determines the complex relationships between HSC-level ICT education as well as success in STEM areas at the university level. Data were collected through an online survey from 244 students enrolled in Computer Science and Engineering (CSE), Software Engineering (SWE), and Information Technology Management (ITM) departments. The results highlight how many different factors have significant effects on students' first-semester SGPA. Several variables, including prior ICT knowledge on data handling from college, quality of instruction provided by the college ICT teacher, and HSC-level ICT course grade, have strong relationships with student performance at the university level. This study illustrates the positive impact of improved instructional materials and teacher-led projects on strong skill development, a phenomenon that will increase overall satisfaction among learners. Although geographical location, gender, and college type have been explored, it does not appear that they have significantly affected ICT course grades directly. Instead, instructional components and techniques for improving skills become important factors in determining students' academic performance. The study not only finds significant relationships but also promotes curriculum improvements with a focus towards ICT education technique optimization. With an aim of improving instructional methods and curriculum design, these observations provide governments and other individuals within education with suggestions that are applicable. The study highlights how important it is to effectively utilize ICT education in order to encourage overall STEM development in Bangladesh's educational system
A Novel Statistical Process Control Approach for PM2.5 Monitoring Using Time Series Modeling
No full textThis research seeks to create a novel control chart capable of managing autocorrelated time series data by proposing a modified Exponentially Weighted Moving Average (EWMA) approach tailored to processes following the ARMA(p,q) model, which also makes use of exponential white noise. The key methodological contribution involves an explicit formula to compute the Average Run Length (ARL), while the Numerical Integral Equation (NIE) approach is utilized for verification purposes. The proposed formula not only demonstrated 100% agreement with NIE results but also significantly reduced computational time, requiring less than 0.001 seconds per run, compared to the 3–4 seconds typically needed by NIE. To assess the performance, simulation experiments and real-world case studies on PM2.5 air pollution data from Nakhon Phanom, Nan, and Nonthaburi provinces in Thailand were conducted. Our modified control chart was better at identifying minimal changes than a standard EWMA chart, as shown by lower ARL1, SDRL1, AEQL, and optimal PCI values. The one-sided chart structure, designed to monitor upward shifts in pollutant levels, further supports its application in environmental surveillance. Overall, the study introduces a fast, accurate, and practical tool for quality control in autocorrelated environments, offering both analytical and computational advantages over existing methods
Adaptive Segmentation of Information Sequences for Machine Learning Modular Regression Models
Full text linkThe research objective is to construct an adaptive model for modular machine learning structures that improves the processing quality of information sequences. The novelty of the proposed methodology is that it can identify segments of an information sequence obtained using various methods and assign models with the best quality indicator values to subsequences. Classical methods allow tuning of the model to the entire data sample. The improvement consists of the proposed solutions that consider the inverse problem of forming segments of data sequences, such that their properties correspond to the processing model. The proposed methodology was tested on various models and datasets. Segmentation and assignment of regression models with the best characteristics to individual segments allow the reduction of the mean square error (MSE) and mean absolute error (MAE) to 8%. The findings show an opportunity to increase of 5-8% for weak LR, SVM, and GR models, while strong DT, CNN, ANN, ANFIS, and XGBoost models improve by 1-4% in scenarios with limited data. Segmentation enables more efficient training and reduces sensitivity to noise and outliers. The proposed solution allows the selection of segmentation strategies and model combinations considering local data properties. Its application enables the implementation of existing machine learning architectures to improve the quality of training and analysis of information sequences and increase adaptability, scalability, and interpretability
Integrating Social Intelligence Into Character-Based Education: A Contextual Learning Model in Modern Boarding Schools
Full text linkObjectives: This study designs and evaluates a social intelligence-based learning model for social studies in modern Islamic boarding schools (pesantren) in Indonesia, addressing the lack of social-emotional integration in faith-based curricula. Methods/Analysis: A mixed-methods approach involved 150 participants from five pesantren, including students, teachers, and school leaders. Qualitative data were gathered via interviews, observations, documentation, and FGDs, while quantitative data were collected using a validated 25-item Social Skills Questionnaire. Descriptive statistics, paired-sample t-tests, and Cohen’s d assessed effectiveness, with methodological and theoretical triangulation ensuring qualitative validity. Findings: The model—integrating social values, interactive strategies, contextual materials, authentic assessment, and teacher facilitation produced significant gains in social intelligence. Problem-solving and tolerance improved most (+1.7 points each), and student participation rose from 42% to 88%. Both students and teachers reported high satisfaction with the collaborative, contextually relevant approach. The questionnaire demonstrated high reliability (Cronbach’s α = 0.87). Novelty/Improvement: This study introduces a culturally grounded, faith-based pedagogical framework embedding social intelligence into character-based education. By aligning Islamic values and Acehnese wisdom with 21st-century social competencies it provides a replicable model for enhancing social-emotional learning in similar educational contexts
Leaving No One Behind in Access to Higher Education in the Baltic States and Poland
Full text linkThe article presents research on the accessibility of higher education in the Baltic States and Poland and its compliance with the principle of “Leaving No One Behind”. A qualitative approach was used to achieve the research objective using analysis of statistical indicators and the contents of documents. The results of the study reveal that the progress on higher education attainment is not homogeneous and not consistent in the countries analyzed. Document analysis indicates that higher education accessibility differs across Lithuania, Latvia, Estonia, and Poland. Lithuania and Latvia are the most accessible due to unified application systems, while Poland lacks such a system, creating extra costs and delays. A comparison of the mandatory examinations and minimum requirements set by the countries shows that Lithuania has the highest barriers to access to higher education: people with lower results or learning gaps in secondary education are deprived of the opportunity to acquire higher education, and their chances of avoiding falling behind are lower. Lithuania does not provide for exceptions for the admission of people with disabilities: they must meet the same requirements as other applicants. Another group of people who may face exclusion in Lithuania, Latvia, and Poland are members of national minorities. These results suggest that the governments of the countries and universities need to be more inclusive in their admission policies
Influence of Digital Infrastructure on Project Management Success: Readiness, Fitness, and Tools as Moderators
No full textIn an era defined by rapid digital transformation, project-based organizations face increasing pressure to modernize and digitalize their operational frameworks to ensure competitive advantage and Project Management Success. However, only limited research has examined how foundational pillars of digital infrastructure interact and jointly influence project outcomes, leaving a gap in understanding the structural pathways that link digital infrastructure to Project Management Success and informed decision-making. This study investigates how the three pillars of digital infrastructure (Digital Readiness, Digital Fitness, and Digital Tools) jointly and individually influence Project Management Success through applying Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze survey data collected from experienced project management professionals who possess appreciable experience in digital technology systems and digital transformation leadership. The developed model captures both direct effects of the Digital Infrastructure Pillars on Project Management Success and the moderation effects between them. The results show that all three pillars significantly enhance Project Management Success, with Digital Readiness emerging as the most influential strategic enabler. Moreover, Digital Readiness positively moderates the effect of Digital Tools on Project Management Success, indicating that digital technology investments are more effective when supported by foundational digital preparedness. These findings offer a validated framework for assessing digital maturity in project environments and provide strategic and leadership guidance for organizations seeking to enhance project performance and achieve Project Management Success through balanced investment in digital infrastructure, integrated digital transformation strategies, digital technology adoption, and digital competencies
Evaluating Sensitivity of Double EWMA Chart for ARL Under Trend SAR(1) Model and Applications
No full textThe goal of this study is to offer the precise average run length (ARL) on the Double Exponentially Weighted Moving Average (double EWMA) control chart for the data underlying the first-order seasonal autoregressive (SAR(1)L) with trend model. A comparison was made between the explicit formula and the computed ARL obtained using the numerical integral equation (NIE) approach, employing four quadrature methods: the midpoint, Simpson’s, trapezoidal, and Boole’s rules. The comparison was based on accuracy percentage (%Acc) and computation time (in seconds). The results showed that there was not much variation in accuracy between the ARL results of the explicit ARL and ARL via the NIE method. The findings indicate that the explicit ARL and NIE approaches produce very consistent accuracy values; however, the explicit formula is significantly more rapid (instantaneous compared to 1.5–26 seconds). The advantage of the double EWMA chart compared to the extended EWMA chart in identifying process changes is demonstrated, encompassing evaluations under both one-sided and two-sided setups with varied LCL values. The results are additionally corroborated by sensitivity measures (AEQL, PCI, RMI) and checked with actual durian export data, guaranteeing that the conclusions are firmly established in both simulated and empirical evidence
FedBHAD: Energy-Efficient Federated Learning for Black Hole Attack Detection in RPL-Based Low-Power IoT Networks
No full textThe internet of things is a network of connected devices that share and send information over the internet, frequently in resource-constrained situations. These are often built using the routing protocol for low-power and lossy networks (RPL), face significant security problems because of their limited computing power, and have energy constraints. The objective of this study is to design an efficient and lightweight mechanism for detecting black hole attacks on RPL-based internet of things networks. The proposed framework presents a distributed collaborative learning framework to reduce the processing load on central nodes while enhancing real-time threat detection. The novelty of the present work lies in integrating distributed learning with feature-based anomaly detection tailored for RPL environments, thereby improving IoT network security while reducing communication and energy overhead. A customized data retrieval algorithm is developed with the Cooja simulator’s configuration and extracts essential network parameters, including rank, expected transmission count, power consumption, forward count, and reception count. The analysis of this dataset allows the detection of black hole attacks. The research analysis indicates that the proposed framework achieves 99.6% detection accuracy, surpassing existing machine learning and deep learning techniques and offering enhanced security, reduced overhead, and lower computational needs