Direct Research Journal of Engineering and Information Technology

Awareness of Mobile Health (mHealth) Interventions Among Antenatal Clinic Attendees in Primary Health Care Facilities in Ilorin South, Kwara State, Nigeria

2026-08-18T11:10:41+00:00

The successful implementation of mobile health (mHealth) interventions in antenatal care depends not only on provider-side factors but equally on whether pregnant women, as the intended beneficiaries of such interventions, are themselves aware that mobile phones can serve as a channel for receiving maternal health information, reminders, and support. This study examined the level of awareness of mHealth interventions among antenatal clinic attendees in Primary Health Care (PHC) facilities in Ilorin South Local Government Area, Kwara State, Nigeria. A descriptive cross-sectional survey design was adopted, with a sample of 325 antenatal clinic attendees selected through a multistage sampling technique combining random selection of facilities, proportionate allocation, and systematic selection of respondents. Data were analysed using descriptive statistics and one-sample t-tests against a criterion mean of 3.00, at the 0.05 level of significance. Findings revealed a generally high overall level of awareness (grand mean = 3.51, SD = 1.01), significantly above the criterion mean, t (324) = 9.10, p < .001. At the item level, awareness of SMS reminders recorded the highest mean (3.84) and was significantly above the criterion (p < .001), while awareness of pregnancy-related mobile applications recorded the lowest mean (3.11) and, notably, did not differ significantly from the criterion mean (p = .088), indicating that application-specific awareness in this sample is not statistically distinguishable from a neutral midpoint. This pattern is consistent with, though not a direct test of, the digital divide literature documenting lower reach for smartphone-dependent technologies among lower-resource populations. The study concludes that antenatal clinic attendees in Ilorin South report considerably stronger, statistically confirmed awareness of SMS-based mHealth tools than of application-based tools, and recommends that mHealth implementation strategies in the study area prioritise SMS-based approaches as an immediate entry point, while further, targeted research directly measures smartphone ownership and digital literacy before application-based strategies are pursued.

Autonomous Cyber Defense Learning Using Reinforcement and Threat Intelligence

2026-08-28T13:27:54+00:00

The increasing sophistication, frequency, and scale of cyberattacks have created significant challenges for conventional cybersecurity systems. Traditional security solutions such as firewalls, signature-based intrusion detection systems, and antivirus software are largely reactive and depend on predefined rules and known attack patterns. Consequently, these systems often struggle to detect and respond effectively to emerging threats such as Advanced Persistent Threats (APTs), zero-day attacks, ransomware, botnets, and insider attacks. Recent advancements in Artificial Intelligence (AI), particularly Reinforcement Learning (RL), have demonstrated the potential to create autonomous systems capable of learning and adapting to dynamic environments. Simultaneously, Cyber Threat Intelligence (CTI) provides valuable contextual information regarding threat actors, attack techniques, vulnerabilities, and indicators of compromise. This study proposes an Autonomous Cyber Defense Framework that integrates Reinforcement Learning and Threat Intelligence to enhance threat detection, decision-making, and automated response capabilities. The framework employs a Deep Q-Network (DQN) agent that continuously learns optimal defense actions through interaction with network environments while utilizing threat intelligence feeds to improve situational awareness. Experimental evaluation was conducted using benchmark cybersecurity datasets, including CICIDS2017 for Intrusion Detection, UNSW-NB15 for attack classification, CTU-13 for botnet detection and Custom Threat Feeds for threat intelligence. The results indicate that the proposed framework achieved a precision rate of 98.4%, a recall rate of 98.2%, an F1-score of 98.3%, and a threat mitigation rate of 96.8%. False positive rate of 1.9, False negative rate of 1.5 and Response rate of 41%, significantly outperforming traditional machine learning and signature-based security approaches. The findings demonstrate that integrating reinforcement learning with threat intelligence can provide a highly adaptive and proactive cyber defense mechanism suitable for modern network environments.

Numerical Code-Based Assessment of Live Load Patterning Effect on Bending Moment Envelopes in Reinforced Concrete Beam-Column Frames

2026-09-02T18:39:26+00:00

This study evaluates the influence of slab idealisation and live-load pattern on the bending-moment response of a continuous reinforced-concrete subframe using Robot Structural Analysis Professional (RSAP), using manual analysis to BS 8110 as the reference. The objectives were to establish mesh convergence for the analytical shell model, compare the internal continuous beam and column responses of the manual, cladding and analytical shell models, and quantify their differences. Mesh refinement showed that the shell solution was practically converged at 0.25 m; refinement from 0.25 to 0.125 m changed the span 1–2 and interior-support moments by only 0.10% and 0.13%, respectively. The cladding model closely reproduced the manual beam results, with differences of 2.76% for span 1–2, 5.35% for span 2–3 and 0.48% at the interior support. In contrast, the shell model produced reductions of 18.49%, 28.59% and 28.02%, respectively. The shell model also increased the interior-column axial load by 23.06%, while reducing the column top moment by 16.90%. The findings indicate that the observed differences are associated with different structural load-transfer mechanisms rather than mesh refinement alone. The study recommends converged FE models with independent equilibrium and benchmark checks and selection of slab idealisation according to the structural behaviour being represented.

Data Ethics and Responsible AI Use: A Scoping Review of Governance Principles, Implementation Challenges and Future Directions

2026-09-12T20:56:04+00:00

Artificial intelligence is increasingly embedded in education, healthcare, finance, public administration, scientific research and media systems. These applications create efficiency and innovation but intensify concerns about privacy, bias, opacity, accountability, consent, surveillance, intellectual property, misinformation and the concentration of technological power. Data ethics is central to this debate because AI systems inherit the legal, social and epistemic properties of the data on which they are trained, evaluated and deployed. This paper examines the relationship between data ethics and responsible AI use by synthesising recent literature on ethical principles, AI governance frameworks, data stewardship, implementation barriers and sector-specific risks, and proposes an integrated framework for developing, deploying and monitoring AI systems in ways that are lawful, transparent, accountable, inclusive and human-centred. A scoping review and evidence-mapping design was adopted. Note for readers: the evidence base was developed as a structured evidence map rather than a fully registered systematic review with dual independent database screening; a reproducible search rerun using Scopus or Web of Science is recommended before final journal publication, and this limitation is discussed fully in the Limitations section. Traceable scholarly articles, open books, standards, legal instruments and official policy documents published between 2016 and 2026 were identified through targeted searches of institutional portals, citation chaining and open scholarly platforms. Evidence was charted and synthesised thematically across six literature clusters. Twenty-eight high-relevance sources were retained for primary synthesis, supplemented by additional methodological and comparative sources. The evidence shows strong convergence around fairness, transparency, privacy, accountability, human oversight, safety and inclusiveness, but weaker agreement on implementation. The most persistent gap is not lack of principles; it is the difficulty of converting principles into lifecycle controls, institutional accountability, enforceable standards and measurable outcomes. Recent evidence from 2025 and 2026 corroborates this finding: even as AI regulation has expanded globally, the proportion of organisations with formally implemented AI governance frameworks remains critically low. Developing countries face additional constraints related to regulatory capacity, digital infrastructure, local datasets, talent, procurement dependence and unequal bargaining power with global technology providers. Responsible AI cannot be achieved by technical optimisation alone. It requires ethically sourced and well-governed data, risk-based governance, documentation, participatory oversight, continuous monitoring, enforceable accountability and context-sensitive capacity building. The proposed six-layer integrated framework ethical leadership, data ethics, model governance, deployment controls, stakeholder assurance and continuous learning provides a conceptual pathway for researchers, policymakers, developers and organisational leaders seeking trustworthy AI systems. Empirical validation of this framework across sectors and regional contexts is an identified priority for future research.