European Journal of Science, Innovation and Technology

Contemporary Challenges in IT: AI in Healthcare and Education

Bongs Lainjo — Wed, 20 Aug 2025 00:06:47 +0300

The article examines the modern issues and possibilities of Artificial Intelligence (AI), both in healthcare and education, as two of the most pressing spheres of society that require AI implementation. As countries stay on track to achieve the United Nations Sustainable Development Goals, specifically SDG 3 (Good Health and Well-being) and SDG 4 (Quality Education), AI has become a disruptive tool that could change the way service delivery is done, how people access services, and the outcome. However, the paper stresses that AI could worsen the current inequalities unless close attention is paid to these issues through careful ethical governance, inclusive design, and investment in digital infrastructure.

AI applications in healthcare include various types of diagnostic imaging, telemedicine, clinical decision support systems, and new models of care delivered through technologies such as NLP and predictive analytics. The case studies of the U.S. and Rwanda also represent the opportunities and hazards of AI, such as diagnostic level accuracy, data fragmentation problem, algorithm bias, and minimal clinical validation.

In education, AI is used in the form of intelligent tutoring systems, adaptive assessments, and automation of administration and learning. Such efforts as adaptive learning provided by Arizona State University and the Rori AI tutor project in Ghana show opportunities related to equity and scalability. However, there is still some worry about privacy, algorithm discrimination, and ineffective teacher training.

Through a comparative analysis of the two sectors, the article provides an insightful look into their similarities, including infrastructure requirements and data bias, as well as the differences between user adoption, regulation, and risk tolerance. It proclaims cross-sectoral policy frameworks, capacity-building programs, and an ethics approach to AI design to guarantee sustainable and fair AI adoption.

Machine Learning in Healthcare

Ahmed Karam — Fri, 22 Aug 2025 00:00:00 +0300

Machine learning (ML) has emerged as a center of gravity in the healthcare industry, providing an unequaled capacity to perform prodigious and intricate processes to frame better decisions, diagnoses, and therapies. ML permits early diagnosis of illnesses, predictive analytics of patient outcomes, and individualized treatment planning via utilizing the patterns of the observed data with the implementation of algorithms that can be educated. The paradigm shift is fueled by the speedy expansion of electronic health records (EHRs), medical imaging repositories, wearable device outputs, and genomic datasets. The Healthcare ML applications range widely in scope, with some of their uses being computer vision in treating radiology and pathology, natural language processing to analyze raw clinical notes, population health management, predictive analytics, and others. Next, various operational efficiencies are attained in ML-based scheduling, resource allocation, and fraud detection systems. Nonetheless, implementing ML technology into clinical practice is not problem-free, and the following factors should still be considered: low data quality, bias in the ML model, privacy, and compliance with regulations. Countermeasures against these obstacles in the form of federated learning, explainable AI, and resilient governance systems are on the rise, allowing for more secure and fairer implementation. The paper will summarize principles, essential applications, technical and ethical aspects, and practical case scenarios to comprehensively see ML in the healthcare industry. It also provides an overview of how the intersection of technical innovation and clinical relevance has the potential to transform patient care, as well as amplify the effectiveness of clinical care and have an impact on improving patient health at every level. Finally, achieving this potential ought to necessitate interdisciplinary approaches, critical assessment, and ethical innovation so that ML-based healthcare systems can be precise, responsible, and reflective of patient health.

The TriAxis Culture Index (TAC Index): A New Paradigm for Organizational Culture

Akshay Dipali — Mon, 25 Aug 2025 00:00:00 +0300

For years, companies have measured workplace culture using tools like annual surveys and eNPS scores. These methods were simple, but often too slow, too shallow, and too disconnected from business outcomes to drive real change. This paper introduces the TriAxis Culture Index (TAC Index) as a new approach, one that treats culture not just as an HR concern, but as a strategic system that can be measured, managed, and improved in real time.

The TriAxis Culture Index (TAC Index) is built around three core cultural indicators: Feedback Loop Velocity (FLV), Sentiment Resilience Score (SRS), and the Employee-Generated Revenue Index (EGRI). Each metric captures a distinct dimension of how people experience work from how quickly feedback is acted upon, to how effectively teams recover from stress, to how much employee-driven innovation and effort contribute to revenue. By combining these indicators with AI-powered analysis, the TAC Index delivers an integrated scorecard that connects culture directly to performance, risk, and engagement.

This paper explores how the TAC Index addresses the limitations of traditional surveys, and how it offers both HR and executive leaders a clearer way to understand and act on culture. Drawing from industry case examples and academic insights, we show how organizations using the TAC Index can detect early signs of disengagement, improve decision-making, and align culture with long-term business goals. In a world where workforce sentiment shifts quickly, this framework helps organizations stay adaptive, resilient, and grounded in data.

Construction Safety Management System Implementation: Current Practices and Gaps in the Indonesian Construction Industry

Kaharuddin Kaharuddin, Hasmar Halim, Basyar Bustan, Andi Maal — Mon, 25 Aug 2025 00:00:00 +0300

The implementation of the Construction Safety Management System (CSMS) is a critical requirement for ensuring worker safety, public protection, and project success in the Indonesian construction industry. Despite the existence of regulatory frameworks such as Minister of Public Works and Housing Regulation (Permen PUPR) No. 10 of 2021, the level of CSMS implementation varies significantly across projects. This study evaluates the current practice of CSMS in a road and bridge preservation project: the BTS Kota Palopo – BTS Kabupaten Luwu project in South Sulawesi, executed by PT. Millenium Persada. A mixed-method approach was employed, combining field observations, document reviews, and structured interviews with project personnel, guided by the audit criteria in Permen PUPR No. 10 of 2021. The assessment covered five core elements of CSMS: Leadership and Worker Participation, Hazard Identification and Risk Assessment, Operational Safety Management, Internal Audit, and Management Review. The results indicate an overall implementation level of 80.93%, categorized as "Good" according to Government Regulation No. 50 of 2012. However, several gaps were identified, including inconsistent communication, inadequate supervision, insufficient safety training, and non-compliance in equipment operation procedures. Using Analytic Hierarchy Process (AHP) analysis via Expert Choice software, key improvement strategies were prioritized: strengthening internal supervision, enhancing safety training programs, improving safety signage, and adopting digital monitoring tools. This study provides empirical evidence of CSMS performance in infrastructure preservation projects and offers practical recommendations for contractors and regulatory bodies to improve safety outcomes and move toward zero accident goals.

Development of an Effective and Efficient Low-Cost Bridge Maintenance Information System Integration for the Mamminasata Region

Ela Dwi Hardyanti, Sugiarto, Vita Fajriani Ridwan — Mon, 01 Sep 2025 17:45:44 +0300

Effective and efficient management of bridge infrastructure is crucial for maintaining regional connectivity and economic vitality. However, many local governments face significant challenges in implementing maintenance strategies due to high software licensing costs, limited technical capacity, and fragmented data management. This research addresses these issues by designing and evaluating a low-cost Bridge Maintenance Information System tailored for the Mamminasata region (Gowa and Makassar). The system innovatively integrates Microsoft Excel for data processing and spatial analysis (clustering, hotspot analysis, forecasting) with Google Earth for visualization. Utilizing a mixed-methods approach, the system's effectiveness and efficiency were rigorously evaluated against predefined indicators. Results demonstrate a substantial improvement in operational efficiency, with the time required for identifying maintenance priorities reduced drastically from approximately seven days to just one day (an 85% reduction). Furthermore, the system achieved high accuracy in data integration and spatial representation, supported by a validated forecasting model with an error rate consistently below 5%. The analysis identified critical bridges and high-risk zones, providing an objective, data-driven basis for strategic maintenance planning and budget allocation. Evaluation confirms that the Excel-Google Earth approach is not only significantly more cost-effective (by eliminating expensive software licenses) but also user-friendly and maintainable, effectively meeting the core objectives of effectiveness and efficiency. This study contributes a practical, accessible, and replicable model for bridge asset management in resource-constrained environments, offering a viable alternative to costly proprietary GIS solutions.

Investigating the Multi-Faceted Influences on Academic Outcomes in Early Schooling: Primary Years

Aishah Khan — Mon, 01 Sep 2025 00:00:00 +0300

Academic outcomes in early childhood are shaped by a complex interaction of factors, yet much of the existing literature approaches these influences in isolation—focusing separately on individual traits, family background, or school quality. This fragmented approach creates a gap in understanding how these forces interact dynamically in the formative years of education. This paper addresses that gap by adopting an ecological, systems-based perspective to explore the multifaceted nature of early academic development.

Drawing on Bronfenbrenner’s Ecological Systems Theory, the review critically synthesises evidence from studies related to cognitive traits, socio-emotional skills, parenting practices, school climate, peer relationships, and community conditions. Each layer of influence—ranging from the child’s immediate behavioural patterns to broader societal structures—is evaluated for its individual contribution and its interdependence with other domains. The literature review moves beyond descriptive summaries by identifying methodological limitations, contradictions across findings, and gaps in sample diversity.

The discussion section reframes academic performance as the outcome of interacting systems rather than isolated variables. It also highlights the mediating and moderating pathways through which risk and resilience operate. Based on these findings, the paper proposes practical strategies for educators, school leaders, and policymakers, while also outlining feasible implementation pathways. Finally, recommendations for future research emphasise the need for longitudinal and inclusive studies to address persistent equity gaps and develop more context-sensitive educational policies.

Biodegradation Aromatic Hydrocarbons of Naphthalene by Aspergillus flavus and Penicillium fumigates

Haider Msahir Ateshan, Alyaa Hussein Talib — Wed, 03 Sep 2025 00:00:00 +0300

Filamentous fungi were identified in the soil that had been contaminated by petroleum hydrocarbon and wastewaters released by the oil refinery of Nasiriyah in East part of the Nasiriyah city and Nasiriyah oil field (Al-Kati’a) by using dilution method. The study demonstrated a strong capability of Aspergillus flavus and Penicillium fumigatus to remove aromatic hydrocarbons, specifically Naphthalene. The biodegradation process was carried out under optimized conditions, using various concentrations of Naphthalene. The findings revealed that Aspergillus flavus and Penicillium fumigatus were the highest frequently isolated fungi, with Aspergillus flavus showing a 100% occurrence rate and Penicillium fumigatus 97%. Both fungi were exposed to petroleum hydrocarbons in both solid and liquid media and successfully demonstrated the capacity for degradation Naphthalene. Their colony diameters were measured on solid media, confirming their adaptability and growth in the presence of Naphthalene. Both fungi were capable of degrading Naphthalene at a concentration of 50 ppm. The degradation products and residual Naphthalene levels were identified using Fourier-transform infrared spectroscopy (FTIR) and gas chromatography-mass spectrometry (GC-MS). The results of statistical analysis showed that the two fungi’s performance differed significantly.

A Multiplexed Microfluidic Platform for Precision Single-Particle Loading and ITP-Based DNA Extraction from Individual Cells

Marie Emmanuella Andjui — Thu, 04 Sep 2025 00:00:00 +0300

The analysis of single cells using genomic methods has established itself as a strong method for understanding variations between cells in cancer diagnosis and developing systems and microbial populations. The critical limitation in implementing single-cell workflows is obtaining efficient cell and nucleic acid separation with high precision. This document demonstrates the complete development process of an ITP-based single-particle loader integrated with multiple components for fast DNA extraction from isolated cells with high purity.

The proposed system combines hydrodynamic forces alongside di-electrophoresis to automatically position single cells and beads into well-defined reaction chambers of a microfluidic array. The device has reaction zones with customised ITP buffers and electric field gradients for efficient genomic DNA extraction and clean concentrate acquisition. This platform design enables parallel extraction processes to handle dozens and hundreds of single cells efficiently and well.

This paper outlines how engineers solved fundamental synchronisation obstacles between single-cell retrieval and ITP start-up by developing optimal electrode setups and fluidic gate functionality and controlling electrostatic timing precisely. The evaluated system demonstrates over 90% DNA recovery success performance metrics, combined with minimal lane interference as it operates effectively with subsequent sequencing pipelines. The scalable technology allows essential applications in single-cell omics research, rare cell diagnostic procedures, and real-time cell sorting through molecular evaluations.