Emerging Science Journal (ESJ)
Not a member yet
960 research outputs found
Sort by
Genetic Links Between Common Lung Diseases and Lung Cancer Progression: Bioinformatics and Machine Learning Insights
Full text linkLung cancer (LC) is one of the most frequently diagnosed cancers and remains the leading cause of cancer-related mortality worldwide, representing a significant global health challenge. While numerous common lung diseases (CLDs) are implicated in LC development, the underlying causes of LC originating from CLDs remain inadequately elucidated. A thorough exploration of LC's progression from CLDs is essential; our approach integrated bioinformatics and machine learning, utilizing data from GEO and TCGA databases. We began by identifying differentially expressed genes (DEGs) in LC and CLDs, and our gene-disease network revealed for the first time shared DEGs (LC shares significant genes with TB (36), asthma (10), pneumonia (17), COPD (18), and Idiopathic Pulmonary Fibrosis (IPF) (78)), providing insights into potential connections of LC with CLDs. This analysis not only broadened our understanding of their associations but also identified significant pathways and hub proteins (SPTBN1, KCNA4, SCN7A, KCNQ3, GRIA1, and SDC1) through a protein-protein interaction network (PPI). Furthermore, RNA-seq and clinical data were obtained from the cBioPortal portal for shared DEGs of LC and CLDs, assessing their impact on LC patient survival. Integrated mRNA-Seq and clinical data were analyzed via univariate and multivariate Cox Proportional Hazard models to elucidate the influence of significant genes on survival. Furthermore, we developed and deployed a predictive model leveraging the identified hub genes, which demonstrated high accuracy in predicting LC progression. The identified biomarkers and pathways hold promise for further translational research and potential therapeutic targets, advancing understanding of LC development from CLDs. Additionally, co-expression networks among common genes were explored using the Weighted Gene Co-expression Network Analysis (WGCNA). Finally, the hub genes were validated using the Human Protein Atlas (HPA) database and evaluated through various classification algorithms to ascertain their predictive power and diagnostic potential. Doi: 10.28991/ESJ-2025-09-02-021 Full Text: PD
Pioneering the Security of EHRs Using an Immersive Blockchain Conceptual Framework
Full text linkThis study develops a conceptual framework to enhance the security and functionality of Electronic Health Records (EHRs) in response to advancing healthcare needs. Objectives include strengthening data protection against both traditional and quantum cyber threats, increasing system resilience, and improving user experience and operational efficiency. Methods/Analysis involve a novel combination of Advanced Encryption Standard (AES) and quantum cryptographic algorithms CRYSTALS-Dilithium and CRYSTALS-Kyber within a hybrid blockchain architecture to secure EHRs. Decentralized Autonomous Organizations (DAOs) are incorporated to decentralize control and reinforce security, while artificial intelligence and metaverse integration facilitate user engagement and streamline operations. Findings indicate that this hybrid blockchain model, enhanced with quantum-resistant cryptography and decentralized governance, significantly improves EHR security. AI and the metaverse contribute to user interaction and operational flow. Novelty/Improvement lies in integrating hybrid blockchain, quantum cryptography, AI, and the metaverse into a unified framework, effectively addressing current and future healthcare data management challenges. This multi-layered approach represents a significant advancement over existing systems by bolstering EHR security, user engagement, and operational capabilities. Doi: 10.28991/ESJ-2025-09-01-010 Full Text: PD
Stochastic Diffusive Modeling of CO2 Emissions with Population and Energy Dynamics
Full text linkClimate change, primarily driven by CO2 emissions from energy and non-energy sectors, necessitates effective mitigation strategies. This study develops a stochastic diffusive model to capture the complex dynamics of CO2 concentration, human population growth, and energy production. The objectives are to enhance the predictive accuracy of existing models by incorporating diffusion effects and stochastic variability, offering insights for sustainable environmental policies. A novel numerical scheme, an extension of the Euler-Maruyama algorithm, is proposed to solve stochastic time-dependent partial differential equations governing the model. The scheme's consistency and stability are rigorously analyzed in the mean square sense. Findings reveal that increasing emission rate coefficients in energy and non-energy sectors exacerbates CO2 levels, emphasizing the need for stringent controls. The proposed scheme demonstrates superior accuracy to the non-standard finite difference method, establishing its efficacy in modeling complex environmental processes. This research contributes a robust computational tool to improve existing predictive models, aiding decision-making for long-term ecological sustainability. By addressing uncertainties in the environmental process, the work advances the understanding of interactions between population growth, energy production, and CO2 emissions, offering a significant improvement over the traditional modeling approach. The novelty lies in integrating stochastic dynamics with diffusion to better inform CO2reduction strategies. Doi: 10.28991/ESJ-2025-09-01-012 Full Text: PD
Usability Evaluation of a Mobile Augmented Reality App for PC Hardware Training: A Comparative Study in Three Countries
Full text linkThe proliferation of mobile applications for educational purposes has highlighted the need to evaluate their usability, especially in diverse international contexts. This study addresses the problem of insufficient engagement and effectiveness of educational tools related to PC hardware training, a problem exacerbated by cultural and contextual differences between regions. Understanding the importance of this issue is crucial, as effective educational tools can improve learning outcomes on a global scale. Previous research has explored various educational technologies but often failed to comprehensively address usability across different cultural contexts, limiting the generalization and impact of the results. This gap underscores the need for robust evaluation of educational applications in diverse populations. In this context, our research proposes the analysis of Build_PC, a mobile augmented reality (MAR) application designed to teach PC hardware, using the IBM Computer System Usability Questionnaire (CSUQ) to assess user satisfaction. This study was conducted in three universities from three countries”Ecuador, Indonesia, and Lebanon”covering a variety of cultural and educational settings. The results indicate remarkably high levels of user satisfaction with the augmented reality (AR) application across the three participating universities. Positive feedback suggests that the application effectively engages students and improves their understanding of PC hardware training, regardless of regional differences. The implications of these findings are significant, as they suggest that augmented reality applications may be a viable solution for overcoming educational barriers related to PC hardware training on an international scale. This study highlights the potential of such technology to enhance educational outcomes and provides a framework for future research in the global deployment of educational technologies. Doi: 10.28991/ESJ-2025-09-02-024 Full Text: PD
Gum Rosin Characteristics as Alternative Coating Material to Improve High Voltage Outdoor Insulator Performance
No full textNumerous attempts have been made to enhance the performance of ceramic insulators, including insulators' design modification, surface coating application, and regular maintenance improvement. Room Temperature Vulcanized (RTV) silicone rubber, frequently employed as a coating for outdoor ceramic insulators, may deteriorate due to continuous exposure to ozone and ultraviolet (UV) light, resulting in a loss of insulating properties and potential surface cracking. This research aims to investigate the characteristics of new materials intended as additional coating materials for high voltage insulators to improve the performance of ceramic insulators. The proposed material, gum rosin (C20H30O2), is derived from the distillation of pine tree sap and possesses excellent hydrophobicity properties, meeting one of the requirements for an insulator. This research was carried out in two stages, which are characteristic tests of gum rosin as an additional coating material on RTV silicone rubber consisting of hydrophobicity, surface resistivity, relative permittivity (er), and tan delta, followed by a leakage current test of gum rosin and RTV silicone rubber-coated ceramic insulators to validate the insulation performance improvement. The results show that the addition of 5 wt.% gum rosin to the RTV silicone rubber can improve the characteristics of insulator coating material indicated by an increased contact angle of 7.85° and reduced leakage current magnitude up to 9.42% at a relative humidity of 70%, 7.1% at a relative humidity of 80%, and 10.02% at a relative humidity of 90%. These results proved that gum rosin can be used in addition to the conventional RTV silicone rubber coating material to improve the insulation characteristics of outdoor ceramic insulators
Effective Model of Knowledge-Based Transformation and Sustainable Development in BRICS-T Countries
No full textThis article presents a novel empirical framework for evaluating knowledge-based transformation and sustainable development in BRICS-T countries. The framework is based on an integrated Triple Helix assessment model that quantifies the interrelationships between research output, educational innovation, industry engagement, market alignment and policy support. Using a comprehensive dataset from 60 universities across BRICS-T countries, combined with an AHP-based weighting system derived from 24 cross-sector experts, this study reveals previously unidentified patterns in innovation and educational outcomes. Our method demonstrates that only research output (β = 0.375, p < 0.001) and industry engagement (β = 0.418, p < 0.001) consistently predict innovation output across all BRICS-T countries, while market alignment influences educational quality in only four out of the six nations. The analytical framework successfully quantifies significant performance variations across countries, with innovation output scores ranging from 2.89 to 4.23 and educational quality scores ranging from 3.08 to 4.15. The findings contribute to Triple Helix theory through country-specific decomposition of relationships, supplementing existing knowledge-based economy theories with quantitative evidence of differential effectiveness across emerging economies. This methodology can be implemented for strategic planning in higher education systems transitioning from resource-based to knowledge-based economies
Sustainability Transformation: Leadership, Innovation, and Strategic Flexibility Improve Sustainability Performance
Full text linkThis study investigates how sustainability leadership impacts sustainability performance through strategic flexibility and sustainability innovation, which function as the mediators. This study collected data from 200 respondents using purposive sampling and 6-point Likert scale questionnaires. The data were subsequently analyzed using PLS-SEM. The findings confirm that sustainability leadership significantly and positively impacts sustainability performance through strategic flexibility and innovation. Additionally, data analysis revealed that sustainability leadership has a positive and significant impact on strategic flexibility and sustainability innovation. This study highlights the unique aspect of the Indonesian mining sector. It has not received extensive examination regarding sustainability and sustainability leadership as systems thinking that requires the perspective of interdependent structures and adaptability to changes. Strategic flexibility, as an organization's capability to adapt to changes reactively and proactively, is crucial to the organization's effort to create sustainability innovation to ensure sustainability performance
The Impact of Innovation-Driven Digital Transformation on Export Performance of SMEs
No full textThis study uses quantitative research methods to analyze the impact of innovation-driven digital transformation on the export performance of small and medium-sized enterprises in Vietnam. Data was collected from 403 valid responses from exporting enterprises and analyzed using the partial least squares structural equation model (PLS-SEM). The factors considered include management capacity, corporate culture, competitive pressure, enterprise resources, and government support. Digital transformation was identified as an intermediary variable between these factors and export performance. The research results indicate that digital transformation directly and significantly impacts export performance (β = 0.523, p < 0.01). At the same time, management capacity (β = 0.296, p < 0.01) and competitive pressure (β = 0.295, p <0.01) are important factors promoting the digital transformation process, while enterprise resources (β = 0.274, p < 0.01) and enterprise culture (β = 0.145, p < 0.01) also positively support it. Despite having a lower impact, government support (β = 0.118, p < 0.01) still encourages enterprises to apply digital technology. The study provides practical insights for policymakers and businesses to optimize digital transformation strategies, emphasizing the need for more substantial managerial capabilities and competitive adaptability. Unlike previous research, this study highlights corporate culture and competitive pressure as crucial factors shaping digital transformation in Vietnam’s export sector. Based on the results, the study proposes solutions to enhance competitiveness and export efficiency for small and medium-sized enterprises in Vietnam
Factors Affecting Green Performance of Food Supply Chain Firms: A Parallel Mediation Model
Full text linkObjectives: The objective of this study was to examine the impact of organizational green culture (OGC) on green innovation (GI) and sustainable entrepreneurship practices (SEP), which collectively enhance green performance (GP) in Pakistani food chain sector small and medium enterprises (SMEs). This research investigates how green innovation and sustainable entrepreneurship practices mediate each other towards achieving better green performance. Method: The authors chose deductive quantitative research along with Google Forms-based online surveys to gather data from 239 SMEs using convenience sampling. Structural equation modeling through SmartPLS detected all relationship effects between constructs within the research model. Findings: The study confirms that organizational green culture leads to increased GI and SEP, which in turn contributes to enhanced GP, while SEP operates as the essential mediator between OGC and GP in establishing how cultural values become sustainable practices and environmental improvements. The research merges OGC and innovation aspects with sustainability practices and demonstrates their effects on SMEs through empirical research. Novelty: The research uncovers SEP as a key connection between green culture and performance, which provides business solutions for SMEs that want to merge cultural elements with innovative approaches for sustainability. The research explores green entrepreneurship within emerging markets by demonstrating that developing an organizational green culture leads to creative processes that create sustainable outcomes that enhance environmental results. The paper makes an exceptional contribution by examining two distinct mediators: green innovation and sustainable entrepreneurship practices
The Occupational Indemnity Insurance Modelling: Brighton Mahohoho XGBoost Probabilistic Automated Actuarial Reserving-Pricing-Underwriting
Full text linkThis paper introduces the IFRS 17-Compliant Brighton Mahohoho Probabilistic Framework for Inflation-Adjusted Frequency-Severity Modeling in Occupational Indemnity Insurance, integrating AI-driven actuarial methodologies for loss reserving, risk pricing, and underwriting. Objectives: The framework ensures IFRS 17 compliance while enhancing actuarial accuracy and operational efficiency. Methods/Analysis: A simulation-based dataset of policy, claims, premiums, inflation adjustments, and underwriting data is generated. Claim frequencies and severities are modeled using Poisson and Gamma distributions, with inflation adjustments incorporated into reserves. XGBoost is applied for Automated Actuarial Loss Reserving (ALR) and Automated Actuarial Risk Pricing (ARP), while a weighted average approach estimates Automated Actuarial Loss Reserve Risk Premiums (AALRRP). Findings: Model accuracy is validated through MAE, MSE, RMSE, residual analysis, and scatter plots. IFRS 17 metrics—Contractual Service Margin (CSM), Fulfillment Cash Flows (FCF), Risk Adjustments, and Liabilities—are simulated, with sensitivity analysis ensuring robustness. Policyholders are segmented into underwriting clusters, incorporating expenses, outgo, and revenue to derive the Automated Net Actuarial Underwriting Balance (ANAUB). Novelty/Improvement: This integrated AI-driven actuarial framework significantly advances IFRS 17-compliant pricing and reserving, offering enhanced predictive accuracy, regulatory alignment, and improved risk assessment in occupational indemnity insurance