Emerging Science Journal (ESJ)
Not a member yet
960 research outputs found
Sort by
Developing Social Studies Teaching Materials for Conflict Resolution Education Using the Branch's ADDIE Research Method
No full textThis study aims to develop social studies teaching materials focused on conflict resolution education, addressing the unique needs of Class VII students in Pasaman, Indonesia. Conflict resolution is a crucial skill in today's global context, particularly in social studies, which integrates essential principles and competencies. Using Branch’s ADDIE research method, the study highlights the urgent need for a specialized guidebook tailored to Pasaman's local context and cultural nuances. Data collection revealed that 57.6% of students had low comprehension of conflict resolution, 28.2% medium, and 14.1% high. These findings emphasize the importance of differentiated instructional materials that cater to diverse learning needs. The proposed guidebook is designed not only to improve conflict resolution skills but also to incorporate elements of local wisdom, fostering a more relevant and engaging learning experience. The study further underscores the collaborative role of stakeholders in developing materials that align with students' understanding levels and cultural backgrounds. By bridging the gap between theory and practice, this research offers a model for creating inclusive, context-specific teaching strategies. The novelty of this study lies in its focus on local adaptation, contributing to a more effective learning process and supporting efforts to cultivate peace and resilience in educational settings
Alternative Method for Calculating Service Level Agreements for Internet Access Services in Rural Areas
No full textIndonesia faces significant challenges in providing equitable internet access, particularly in remote areas. To address these challenges, the government collaborates with private Internet Service Providers (ISPs) under Service Level Agreements (SLAs) that define key service metrics, such as uptime and response time. Network Management System A (NMS A), managed by the government, handles telecommunications infrastructure, while NMS B, operated by the private ISP, delivers the internet services. A critical challenge in assessing SLA performance is distinguishing between service disruptions caused by network issues and those caused by power outages. This study proposes an SLA calculation method that prioritizes uptime data from NMS A, reflecting the government's perspective. If NMS A’s data is unavailable, UPS data is used as an alternative. NMS B’s data is considered only if its correlation with NMS A is strong (≥0.6). Additionally, the study classifies downtime: link failures exceeding 300 seconds require compensation (restitution). Downtime less than or equal to 300 seconds is considered non-restitution, and no compensation is needed. Power outages are considered non-restitution events, as they stem from external factors beyond the service provider's control. This method has been tested in over 1,000 locations, proving its reliability and adaptability for fair, transparent SLA evaluations
Optimization of nSiO2-Filled RTV Silicone Rubber Coatings for Enhanced High Voltage Outdoor Insulator Performance
No full textThis paper presents the experimental results of leakage current testing on nanosilica (nSiO₂)-filled room-temperature vulcanized (RTV) silicone rubber (SiR) coatings for outdoor insulators. Nanosilica composition is prepared based on the optimization results of surface hydrophobicity, surface resistivity, and relative permittivity of the RTV SiR matrix with varying nSiO2 contents. The study found that the insulator with a 4 wt.% nSiO₂-filled RTV SiR coating had the highest surface resistivity, better hydrophobicity, and higher permittivity compared to the unfilled RTV SiR coating. Leakage current tests are performed under several conditions (dry, clean fog, and salt fog) to evaluate the insulation characteristics of the modified RTV SiR coating applied on an actual high-voltage insulator. The results indicate that the 4 wt.% nSiO2-filled RTV SiR-coated insulator significantly reduces the leakage current magnitude and Total Harmonic Distortion (THD) when compared to that of the uncoated as well as to that of unfilled RTV SiR-coated insulators. Under all fog conditions, the 4 wt.% nSiO2-filled RTV SiR-coated insulator with a polluted surface showed the smallest leakage current THD percentage of all insulator samples. Additionally, the cross product of the leakage current magnitude and THD is also calculated to determine the condition of the insulator. The cross-product results show that the 4 wt.% nSiO₂-filled RTV SiR-coated insulator is more effective at reducing it under dry conditions, with a reduction range of 68%–81% compared to the uncoated isolator and 70%–77% compared to the unfilled RTV SiR-coated isolator. This study shows the effectiveness of RTV silicone rubber coating material with nSiO2 filler particles in reducing the magnitude of leakage current and harmonics in polluted environments
Influencer Credibility, Parasocial Relationships, and Product Involvement in Purchase Intentions
No full textThe increasing influence of social media influencers (SMIs) on consumer purchase intentions has become a crucial topic in marketing research, particularly in understanding the mechanisms that drive this effect. This study examines how SMI credibility—defined by trustworthiness, expertise, and attractiveness—affects consumer purchase intentions through the mediating role of parasocial relationships (PSRs) and the moderating role of product involvement. A survey of 205 Thai social media users was conducted, and data were analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that SMI credibility positively impacts purchase intentions and strengthens PSRs, which partially mediate this relationship. Moreover, the moderating role of product involvement uncovers a conditional mediation effect: PSRs have a stronger influence on purchase intentions for low-involvement products, where emotional appeals are more effective than rational evaluations. In contrast, for high-involvement products, consumers prioritize cognitive processing and influencer expertise, weakening the impact of PSRs. This research enhances influencer marketing literature by incorporating emotional and cognitive pathways within a mediated-moderated framework. Practically, it highlights the importance of aligning influencer strategies with product involvement, recommending emotionally engaging influencers for low-involvement products and credibility-driven influencers for high-involvement products to maximize marketing effectiveness
Enhancing Reading Skills Among the Primary Students for the Mokhlan History Content
Full text linkStudents in Thailand are susceptible to being swayed away from reading. This has been a major challenge for teachers in primary schools, as reading skills are necessary for the development of students. With an innovative approach inclusive of Sustainable Development Goals (SDGs), Creative Base Learning (CBL), and Problem-Based Learning (PBL) along with the theoretical underpinning of Constructivist Theory, this research explores enhancing reading skills among primary school students in Thailand. This research adopts a mixed-method approach with data collection from survey questionnaires, key informant interviews, and focus group discussions. The data analysis involves a t-test and the six hats tool for the qualitative support of the quantitative findings. The findings suggest a lack of interactive creative media tools to inculcate student reading skills. The data depicts a need to repeat the reading test at the primary level and include all the stakeholders, including teachers, students, and parents. The novel contributions of this research include the model emphasizing the role of learning and training among various stakeholders to enhance the reading skills of Mokhlan history. This study paves the way for future research on analyzing the factors responsible for students' lack of 21st-century skills, especially at the primary school level
Optimizing Consensus in Blockchain with Deep and Reinforcement Learning
No full textThis study aims to optimize blockchain consensus mechanisms by integrating artificial intelligence techniques to address critical limitations in latency, scalability, computational efficiency, and security inherent in traditional protocols, such as PoW, PoS, and PBFT. The proposed model combines deep neural networks (DNNs) for feature extraction with deep reinforcement learning (DRL), specifically Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), to enable dynamic validator selection and real-time adjustment of consensus difficulty. The training process utilizes a hybrid dataset of historical blockchain records from Ethereum and Hyperledger networks and synthetic data from simulated attack scenarios involving Sybil, 51%, and DoS threats. Experimental evaluations were conducted in private and permitted environments under varying transactional loads. Results show a 60% reduction in confirmation latency compared to PoW, achieving 320 ms, and a 20% improvement over PBFT. Transaction throughput increased to 22,000 transactions per second (TPS), and computational resource consumption was reduced by 30%. The model achieved an attack tolerance of up to 92%, significantly enhancing network resilience. The novelty of this work lies in its autonomous consensus optimization strategy, which enables adaptive and secure protocol behaviour without manual intervention, representing a scalable and efficient solution for future blockchain infrastructures
Extreme Value Model to Forecast PM2.5 Concentration Through a Non-Stationary Process
Full text linkThe objectives of this research were to develop a model to forecast and estimate the return levels for daily maximum PM2.5 concentrations in Thailand, applying Extreme Value Theory (EVT) with the Generalized Extreme Value (GEV) distribution under eight models for stationary and non-stationary process. This research utilized reanalysis data from the NASA EARTHDATA satellite, represented as grid points with a spatial resolution of 50 × 62.5 km, enabling the analysis of daily maximum PM2.5 concentrations across 176 grid points from January 1, 2009 to October 31, 2024. The analysis revealed that Model 2 (μ(t)=β0+β1t where σ and ξ are constants) is the most suitable model for five grid points, namely Sa Kaeo Province, Uthai Thani Province, Nakhon Ratchasima Province, Bueng Kan Province and Mae Hong Son Province, whereas Model 1 (μ, σ and ξ are constants) is suitable for the remaining 171 grid points. Estimating the return levels for return periods of 5, 10, 25, and 50 years showed that Northern Thailand had the most extreme daily PM2.5 concentrations, for all return periods especially Mae Hong Son Province. The results of this analysis can serve as valuable information to support decision-making for response planning in high-risk areas, aiding in efficient resource allocation and preventive measures
YOLOv10-MsA: Attention-Augmented Real-Time Insulator Defect Detection from UAV Imagery
No full textThe reliable operation of transmission systems depends on early detection of defects within high-voltage insulators because it helps stop power outages. Scalable inspection remains a challenge since UAV-based imagery and deep learning generate recent promising solutions. The goal of this study is to build an accurate and time-efficient insulator defect detection system through the YOLOv10 architecture integration of Manhattan Self-Attention (MsA), which strengthens spatial feature detection and increases robustness during evaluations in complex aerial inspection scenarios. The designers implemented the MsA modules into the backbone and neck sections of YOLOv10 to develop YOLOv10-MsA as their novel detection model. The model relied on 5,000 annotated insulator images acquired by unmanned aerial vehicles throughout various defect classes during training and evaluation. Standard object detection metrics consisting of [email protected], [email protected]:0.95, precision, recall, F1-score, and inference speed evaluated the performance of the model. The YOLOv10-MsA system reached an [email protected] performance of 93.1% and an F1-score of 91.9% at an inference speed of 79 FPS, which surpasses YOLOv8, YOLOv9, and baseline YOLOv10. The model demonstrated its best performance at detecting various small and hard-to-detect defects such as chipping and contamination. The application of MsA in detection systems resulted in better accuracy while preserving real-time operation, according to related model assessment. The proposed YOLOv10-MsA serves as a powerful deployable system for UAV-based insulator inspection because it achieves both high accuracy and fast operation. The method establishes conditions for real-time smart infrastructure observation with attention-augmented frameworks for object detection
Human-Centered Organizational Culture in the Global Workplace: Strategic Approaches, Trends, and Practical Models
No full textAmid accelerating global transformations, the need to reconsider human resource management strategies through the lens of human-centricity is becoming increasingly urgent. This study aims to examine the systemic implementation of human-centered organizational culture within international labor contexts, with a focus on enhancing employee well-being, adaptability, and organizational resilience. A mixed-methods approach was employed, combining comparative policy analysis, content analysis of regulatory documents, and empirical case studies. The empirical sample included 320 employees from multinational companies across four sectors (education, IT, healthcare, and manufacturing). The findings revealed statistically significant improvements following the implementation of the proposed model: autonomy increased from 5.48 to 5.86 (p = 0.012), competence from 5.33 to 5.61 (p = 0.038), and relatedness from 5.07 to 5.58 (p = 0.004). Positive emotion expression scores rose from 3.98 to 4.42 (p = 0.009), while the Human-Centeredness Index increased from 4.18 to 4.71 (p = 0.002). These results underscore the limitations of hierarchical management models and highlight the value of flexible, emotionally supportive systems. The scientific contribution of the study lies in the typologization of human-centric management models and the empirical validation of a scalable integration framework that combines emotional intelligence development, inclusive feedback cycles, and leadership support. This model provides a strategic foundation for building sustainable, inclusive, and ethically grounded organizational environments
New Development Model of the Agro-Industrial Complex Under Green Economy Conditions
No full textThis study empirically examines opportunities and provides a conceptual rationale for developing a green economy model tailored to Kazakhstan’s agro-industrial complex (AIC). The research focuses on identifying effective strategies for sustainable agricultural development by combining environmental and economic approaches. The methodological basis includes the analysis of theoretical concepts in economic and mathematical modeling, identification of key factors influencing the sector, and the construction of a block structure to represent these factors. Forecasting models were developed to evaluate the efficiency of various strategic directions for transitioning to sustainable agriculture. The findings indicate that the most influential drivers of sustainable development include the expansion of green financial instruments, increased public and international investment, and the use of digital technologies for collecting and processing data. The forecast of key indicators of the green economy in Kazakhstan up to 2030 confirms the reliability and applicability of the proposed evaluation method. The main contribution of this research is the creation of an integrated model that connects theoretical frameworks with practical tools for managing ecological and economic performance in agriculture. The results can be applied in strategic planning, decision-making for the AIC, and academic programs focused on the green economy and sustainable development in Kazakhstan