Current Concepts in Engineering Research and Technology Vol. 5

Evaluation of Equipment Maintenance Approaches in Roofing Sheet Manufacturing Companies in Kumasi, Ghana

Norbert Johnson Amoanab — 2026-11-10

Background: Effective equipment maintenance is important for sustaining production reliability and managing operational costs in manufacturing firms. This study evaluated the use of in-house and contract maintenance approaches among roofing sheet manufacturing companies in the Kumasi Metropolis, Ghana, and examined factors influencing the choice of maintenance approach.

Methods: A descriptive research design was adopted. The study covered all 13 registered roofing sheet manufacturing companies in the metropolis. Questionnaires were distributed to 52 key staff members involved in maintenance operations, of which 40 were returned, giving a 77% response rate. Survey and interview data were analysed using descriptive and inferential statistical methods.

Results: All respondents reported the use of both in-house and contract maintenance. In-house maintenance was performed regularly by 75% of respondents, whereas contract maintenance was used occasionally by all respondents. Contract maintenance was associated mainly with the need for specialised expertise, inadequate staffing, limited equipment or tools, resource allocation considerations, and cost savings. The regression analysis indicated that the assessed factors explained 44.1% of the variance in the perceived importance of outsourcing, with cost saving identified in the manuscript as the significant predictor.

Conclusion: Roofing sheet manufacturers in Kumasi use a hybrid maintenance approach, combining internal capability with external support according to equipment and operational requirements. The findings provide a basis for maintenance-policy decisions within the study context.

Solar Irradiance Forecasting: Methods, Challenges, and Emerging Trends

Kusum Dalal — 2026-11-10

Solar irradiance forecasting connects atmospheric observations with decisions about photovoltaic generation, storage and electricity-system operation. Yet comparisons between forecasting methods are frequently weakened by differences in radiation components, forecast horizons, spatial support, information availability and verification procedures. This critical narrative review examines physical, statistical and learning-based approaches, with particular attention to the relationship between predictive information and defensible evidence of improvement. Literature published from 2010 to 1 August 2026, supplemented by foundational contributions, was selected through targeted scholarly searches and appraised for measurement quality, chronological integrity, benchmark strength, uncertainty treatment and operational relevance. The synthesis indicates that short-horizon performance depends strongly on observing cloud structures before they affect the target, whereas longer horizons require credible representation of atmospheric evolution. Machine learning can improve feature extraction, model combination and forecast calibration, but architectural complexity alone does not establish transferable skill. Comparative studies provide stronger support for combining complementary observations and forecasts than for a universally superior model family. Probabilistic methods broaden the information available to decision-makers, although calibrated marginal distributions do not guarantee realistic spatial or temporal scenarios. Emerging diffusion models, multimodal networks and physics-informed approaches offer promising capabilities, but their evaluation must distinguish satellite retrieval from forecasting and genuine advance information from future observations used as experimental upper bounds. Persistent challenges include geographically uneven observations, component-specific measurement errors, changing cloud and aerosol regimes, data leakage and inadequate testing of deployment constraints. A decision-oriented research agenda should prioritise independent multi-site benchmarks, availability-aware forecast archives, conditional uncertainty verification and prospective operational trials. Progress is therefore best judged by reproducible gains under realistic information constraints, rather than by isolated improvements in average error on a single dataset.

Securing Autonomous AI Agents: Attack Surfaces, Incident Evidence, and an Identity-Centered Governance Framework

Alex Mathew — 2026-11-10

Autonomous artificial intelligence (AI) agents combine language-model reasoning with memory, external information retrieval, software tools, and delegated execution. These capabilities enable productive multi-step workflows but also convert errors and manipulated content into potentially consequential system actions. This narrative review examines the security boundaries surrounding agent objectives, identities, tools, memory, communications, and human authorisation. Evidence is synthesized from peer-reviewed agent-security studies, established security frameworks, organisational surveys, and three publicly documented cases involving research training, simulated enterprise environments, and controlled cyber evaluation with live-internet access. The cases are treated as distinct evidence types rather than interchangeable measures of incident prevalence. A six-pillar governance framework is developed around accountable non-human identity, trusted context handling, least-privilege tool execution, secure inter-agent delegation, runtime monitoring and containment, and meaningful human oversight. The framework is connected to the OWASP Top 10 for Agentic Applications and zero-trust principles, with implementation measures and testable outcomes. Elementary set, graph, and expected-loss formulations illustrate control objectives under explicitly stated assumptions, without claiming empirically calibrated breach probabilities. The review concludes that safe agent deployment depends on enforceable technical boundaries and accountable organisational processes, and identifies priorities for prospective evaluation.