Journal of Engineering Research and Reports

Real-Time Decision Intelligence in Healthcare Project Delivery Using Adaptive Data Environments

2026-07-01T09:11:10+00:00

Introduction: Healthcare project delivery increasingly relies on data-intensive systems to manage complex infrastructure, multidisciplinary stakeholders and rapidly evolving clinical requirements. Traditional project reporting approaches are often unable to provide the timely insights required for proactive decision-making.

Aim: This review synthesises current evidence on real-time data architectures, performance impacts and governance mechanisms that support decision intelligence in healthcare project delivery.

Methods: A systematic review was conducted in accordance with PRISMA 2020 guidelines. Literature searches were performed in Scopus, Web of Science and IEEE Xplore databases, covering publications from 2015 to 2025. The SPIDER framework guided the search strategy, focusing on decision intelligence, healthcare infrastructure and real-time analytics. Following screening and eligibility assessment, 12 high-quality studies were selected from an initial pool of 400 records. Methodological quality was evaluated using the Mixed Methods Appraisal Tool (MMAT), and the evidence was synthesised through critical analysis of technical architectures, operational outcomes and governance frameworks.

Results: The findings identify Lambda Architecture as the predominant framework for integrating real-time stream processing with long-term data auditability. Adaptive data environments demonstrated substantial operational benefits, including process efficiency improvements of up to 32%, reductions in medication turnaround times by 26% and enhanced stakeholder coordination through centralised decision-support systems. Large-scale implementations reported significant financial savings and reduced infrastructure costs. Automated governance mechanisms, including machine learning-based data quality assurance and AI-driven compliance monitoring, achieved high levels of data integrity and security compliance, supporting reliable decision-making in complex healthcare environments.

Conclusion: Real-time decision intelligence transforms healthcare project management from a reactive reporting function into a proactive decision-support capability. Adaptive data environments provide a robust foundation for improving project performance, governance and organisational resilience. Despite challenges related to interoperability, organisational readiness and regulatory compliance, these technologies represent a critical pathway towards future-ready healthcare project delivery and infrastructure management.

Machine Learning-based Framework for Early Diabetes Risk Classification

2026-06-26T11:15:30+00:00

Aims: This study evaluated the effectiveness of supervised machine learning algorithms for early diabetes risk classification using demographic, behavioural, cardiovascular, and general health-related indicators. It also examined the variables most strongly associated with diabetes occurrence patterns.

Study Design: A quantitative experimental design was used, based on supervised multiclass classification and comparative machine learning evaluation.

Place and Duration of Study: The experimental analysis was conducted using the Diabetes Health Indicators BRFSS2015 dataset between March 2026 and mid-May 2026.

Methodology: The dataset contained demographic, lifestyle, cardiovascular, and general health-related variables associated with diabetes conditions. Before model implementation, duplicate inspection, exploratory data analysis, feature standardisation, and variable consistency evaluation were performed to improve analytical stability. Diabetes status was used as the target variable in a multiclass classification framework. Logistic Regression, K-Nearest Neighbours, Naïve Bayes, and AdaBoost classifiers were implemented using Python-based machine learning libraries. The dataset was divided into training and testing subsets using an 80:20 ratio. Model performance was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC. Feature importance analysis and ROC curve comparison were also performed to assess classification behaviour and variable contribution patterns.

Results: Logistic Regression achieved the highest ROC-AUC value of 0.814 and demonstrated stable discrimination across diabetes categories. AdaBoost achieved the highest accuracy score of 0.847 and produced competitive precision, recall, and F1-score values. K-Nearest Neighbours showed moderate classification capability, whereas Naïve Bayes demonstrated comparatively weaker classification consistency. Feature importance analysis identified HighBP, GenHlth, Age, BMI, CholCheck, and HighChol as influential variables.

Conclusion: The findings indicate that supervised machine learning methods can support early diabetes risk classification. Cardiovascular conditions, obesity-related indicators, and general health variables were important contributors to classification behaviour within the implemented framework.

An Internal Architecture-Based Strategy to Mitigate Delamination and Improve Flexural Capacity of Composite Beam Structures

2026-06-29T12:29:52+00:00

Delamination is an important failure mechanism that can limit the flexural response of laminated composite beam structures, particularly when increased section depth raises the demand on interlaminar load transfer. This study investigated an internal architecture-based strategy for reducing visible delamination at first failure and improving the flexural response of additively manufactured composite beams. Six beam configurations were manufactured using nylon as the matrix material and continuous carbon fibre as the reinforcing phase. Two reference beams, with depths of 20 mm and 30 mm, were produced without internal nylon corridors. Four modified beams were produced by introducing single or multiple nylon corridor arrangements within selected carbon-fibre reinforced regions. All specimens had a width of 19 mm and were tested under three-point bending over a 100 mm span using displacement-controlled loading. The observed first failure load and mode, load-displacement response, and relative manufacturing cost were evaluated. For the 20 mm deep specimens, the modified B3 and B5 configurations increased the first failure load by approximately 12% compared with the corresponding reference beam. The B5 specimen also changed the observed first failure mode from delamination to flexural failure without visible delamination. For the 30 mm deep specimens, the B4 and B6 configurations increased the first failure load by approximately 45% and 42%, respectively, and both showed flexural failure without visible delamination at first failure. The modified configurations also reduced the software-estimated manufacturing cost by decreasing the amount of continuous carbon-fibre reinforcement. The results suggest that internal nylon corridor arrangements may improve material efficiency and influence the failure response of continuous carbon-fibre reinforced composite beams. However, the findings should be interpreted as preliminary because one specimen was tested for each configuration.

Functional Quality Evaluation of an Integrated Hospital Information System: A Case Study of a Private Clinic

2026-06-29T12:34:29+00:00

This study evaluates the functional quality of an integrated hospital information system developed and deployed in a private clinic. The evaluation was based on a multidimensional approach combining functional validation, user satisfaction assessment and operational impact analysis. Functional validation was conducted through twenty-five test scenarios covering the main system modules, namely authentication, patient management, appointment management, consultation management, hospitalisation management and laboratory management. User satisfaction was assessed through a questionnaire administered to fifteen active users representing administrative, clinical and management roles. Operational impact was examined by comparing selected administrative indicators before and after system deployment.

The results showed that twenty-three of the twenty-five functional test scenarios were successfully validated, corresponding to an overall functional compliance rate of 92%. Authentication, patient management, appointment management and hospitalisation management achieved full compliance, whereas consultation management and laboratory management recorded compliance rates of 75%. The user satisfaction assessment involving fifteen active users revealed an overall satisfaction rate of 87%. Operational analysis showed a reduction in average patient record retrieval time from eight minutes to two minutes and a decrease in average monthly report generation time from 180 minutes to 30 minutes.

These findings indicate that the system effectively supports the clinic’s administrative and medical information management activities. However, the study was conducted in a single private clinic with a limited number of participants, which may restrict the generalisability of the results. Future work should include additional healthcare facilities and broader software quality dimensions, such as security, reliability, maintainability and interoperability.

Ethical and Secure Deployment of Generative AI: Balancing Innovation, Data Privacy, and Enterprise Risk Governance

2026-06-30T09:52:18+00:00

This study examines the ethical and secure deployment of generative artificial intelligence in enterprise environments, with emphasis on innovation, data privacy, and risk governance. It addresses the gap between the rapid organisational adoption of generative AI systems and the slower development of institutional mechanisms for managing ethical, privacy, security, and regulatory risks. The study identifies the absence of a validated, integrated governance instrument that combines ethical, privacy, security, and enterprise risk controls for the specific characteristics of generative systems. A desk-based mixed-methods approach was used, combining systematic literature review, document analysis, thematic synthesis, comparative evaluation of governance frameworks, and Analytic Hierarchy Process weighting. Evidence was drawn from peer-reviewed literature and authoritative international governance instruments. Eight governance dimensions were assessed: scope and coverage, risk classification, data privacy, ethics and accountability, security controls, legal enforceability, enterprise applicability, and adaptability to generative AI. The findings show that existing governance instruments provide useful but fragmented coverage when applied independently. Ethics and accountability emerged as the highest-weighted dimension, followed by data privacy, security controls, and enterprise applicability. The proposed framework integrates five pillars: ethics and accountability, privacy and data governance, security governance, enterprise risk management, and innovation and compliance alignment. The study concludes that responsible enterprise deployment of generative AI requires coordinated, multi-layered governance rather than reliance on isolated ethical, technical, or legal controls.

Analyzing the Response Current of a Series RL Circuit Using Graphic

2026-07-02T07:37:09+00:00

This study presents a MATLAB-based graphical approach for analysing the transient current response of a series resistor-inductor (RL) circuit. It focuses on decomposing the total current into natural-response and forced-response components under specified circuit parameters. Resistance, inductance and the electrical angle of the power-supply voltage are treated as key variables. The complete response-current expression for the first-order circuit equation is derived, and the natural, forced and complete response-current values are calculated and plotted in MATLAB. Rather than relying only on algebraic calculation, the work emphasises the simultaneous display of the natural, forced and complete current curves. Several verification cases demonstrate how changes in supply voltage and angular input affect the resulting current waveforms. An additional protection-relay example illustrates the practical relevance of the method for power-system transient analysis. The results indicate that graphical representation can clarify the behaviour of transient current components in a series RL circuit and support comparison between calculated values and plotted waveforms. The proposed MATLAB procedure offers a simple instructional tool for visualising response-current characteristics and examining the effect of circuit parameters on transient behaviour. The study is intended mainly for educational and explanatory purposes, particularly for readers seeking to understand the relationship between analytical calculation and graphical representation in basic RL circuit analysis.

Entropy Generation and Bejan Number Analysis in Micropolar Fluid Flow with Variable Thermal Conductivity

2026-07-03T11:54:21+00:00

This study examines entropy generation and Bejan number behaviour in steady, two-dimensional, incompressible micropolar fluid flow over a linearly stretching sheet with variable thermal conductivity. The flow is considered in the presence of a transverse magnetic field and a homogeneous porous medium, with viscous dissipation and heat generation/absorption included in the thermal formulation. The governing boundary-layer partial differential equations are transformed into coupled nonlinear ordinary differential equations by applying suitable similarity transformations. The resulting boundary value problem is solved numerically using the shooting technique combined with the fourth-order Runge-Kutta scheme, and the numerical formulation is validated against limiting cases reported in earlier studies. The effects of micropolar coupling, magnetic field strength, variable thermal conductivity, Prandtl number, and Eckert number are examined through velocity, temperature, entropy generation, and Bejan number profiles. The results indicate that increasing the magnetic parameter retards the velocity field while increasing the temperature distribution and entropy generation near the stretching surface. The micropolar parameter enhances microrotational effects and influences the balance between thermal and frictional irreversibilities. Variable thermal conductivity modifies heat diffusion and shows a comparatively limited effect on the overall entropy generation within the considered parameter range. The Bejan number analysis indicates that the relative dominance of heat-transfer and fluid-friction irreversibilities depends strongly on the governing thermal and magnetic parameters. These results provide a basis for assessing thermodynamic losses in micropolar fluid transport systems.

Screening-Level Deterministic Estimates of Fault-Controlled Reservoir Prospects in the Mag Field, Niger Delta Basin

2026-07-03T12:05:40+00:00

This study presents an integrated seismic interpretation and deterministic volumetric screening of fault-controlled reservoir prospects in the MAG Field, shallow offshore western Niger Delta Basin, Nigeria. The study reassessed the field’s structural framework, calibrated reservoir markers to seismic reflections, generated time and depth structural maps, and identified additional hydrocarbon opportunities within the limits of the available dataset. The dataset comprised seventeen 2D seismic lines, referenced 3D seismic information, and well data from MAG-01 and MAG-02. Review of the seismic coverage showed that the available 3D data do not extend across the MAG well locations, limiting their direct use for detailed reservoir-scale volumetric evaluation. Consequently, the nearest 3D seismic line was integrated with the 2D seismic framework to provide structural context. Well-to-seismic calibration using MAG-01 produced a fair-to-good tie, with a correlation coefficient of approximately 0.58. A second-order polynomial time-depth relationship derived from MAG-01 was applied for depth conversion. Interpretation indicates that the MAG Field is a NW-SE-trending collapse-crest rollover anticline bounded by a major northern growth fault and a southern antithetic fault. Three reservoir intervals, D-5 sand, E sand, and E-1 sand, were mapped. Deterministic screening identified five reservoir-prospect combinations, with the largest estimates in the E-1 East and E Sand North-East prospects. The results support preliminary reassessment of MAG Field prospectivity but require improved seismic coverage, velocity control, and uncertainty-based volumetric evaluation before prospect maturation.