https://journaljerr.com/index.php/JERR <p style="text-align: justify;"><strong>Journal of Engineering Research and Reports</strong> <strong>(ISSN: <a href="https://nsl.niscpr.res.in/ISSNPROCESS/issnassignedinfo.jsp">2582-2926</a>) </strong>aims to publish high-quality papers in all areas of engineering. By not excluding papers based on novelty, this journal facilitates the research and wishes to publish papers as long as they are technically correct and scientifically motivated. The journal also encourages the submission of useful reports of negative results. This is a quality controlled, OPEN peer-reviewed, open-access INTERNATIONAL journal.</p> SCIENCEDOMAIN international en-US Journal of Engineering Research and Reports 2582-2926 https://journaljerr.com/index.php/JERR/article/view/2036 <p>Enterprise adoption of artificial intelligence (AI) creates sustainability, social and security risks that firms typically govern through separate ESG and cybersecurity structures. This study asks whether, and under what conditions, large U.S. firms disclose an integrated approach to governing AI across the three domains. It uses a configurational, text-as-data design: the latest Form 10-K of each S&amp;P 500 registrant (N = 500; analysis sample, n = 488) was converted into dictionary-based measures of AI, AI-governance, environmental, social and cybersecurity disclosure, sentence-level co-mention of AI with each risk domain, and an eleven-element score of the Form 10-K Item 1C cybersecurity-governance disclosure. Latent Dirichlet allocation of 9,444 AI-context sentences recovered six themes. Latent profile analysis identified four overlapping disclosure profiles (sustainability-led and AI-light, social-led with weak cyber governance, AI-intensive integrators, and security-anchored moderates) that differ by sector and by year-on-year growth in AI-governance language. Fuzzy-set qualitative comparative analysis showed two equifinal paths to integrated AI-governance disclosure: social-risk framing combined with intensive AI use, which was retained in every robustness specification, and a narrower path combining environmental framing, AI use and large size. Item 1C cybersecurity governance did not enter either main path, but its role is uncertain because the score varies little across firms. AI was mentioned within Item 1C by 24.8% of firms, and 8-K Item 1.05 incident disclosures did not distinguish profiles. Because all measures come from automated coding of disclosure, the findings describe reported rather than verified governance, and the cross-sectional design does not support causal claims. The findings motivate a Unified ESG–Cybersecurity Framework for AI Governance that applies the four NIST AI RMF functions jointly across the three domains through one AI inventory, one risk taxonomy, one reporting line and one disclosure narrative.</p> Pelumi Damola Adeyinka Oluwadayo Mafolasere Olaniyi Valerie Ojinika Ejiofor Busola Motunrayo Olawale Fidelia I. Ahiante Copyright (c) 2026 Author(s). The licensee is the journal publisher. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0 2026-10-31 2026-10-31 28 11 1 20 10.9734/jerr/2026/v28i112036 https://journaljerr.com/index.php/JERR/article/view/2038 <p><strong>Background:</strong> Fibre-reinforced polymer composites offer opportunities to reduce the mass of automotive suspension components while maintaining specified structural performance. However, optimum leaf-spring design requires simultaneous consideration of material properties, geometric parameters and mechanical constraints.</p> <p><strong>Objective:</strong> This study aimed to minimise the mass of laminated semi-elliptic leaf springs manufactured from glass fibre-reinforced polyester (GRP) and carbon fibre-reinforced polyester (CRP), with conventional carbon steel used for comparison.</p> <p><strong>Methods:</strong> A constrained nonlinear optimisation problem was formulated using the number of leaves, normalised leaf width and thickness, fibre volume fraction and fibre orientation angle as design variables. The constraints included strength, deflection, geometry and natural frequency. A load of 1000 N, an eye-to-eye length of 1100 mm and a maximum permitted deflection of 150 mm were adopted. The optimisation was performed using MATLAB, and the resulting solutions were assessed against the Lagrange and Karush–Kuhn–Tucker optimality conditions.</p> <p><strong>Results:</strong> The reported optimum designs comprised four leaves with a thickness of 5.5 mm. The GRP spring had a width of 35 mm and a mass of 1.8 kg, while the tabulated CRP design had a width of 33 mm and a mass of 1.3 kg. Both composite designs had a fibre volume fraction of 0.65 and a fibre orientation angle of 0°. Relative to the 8.3 kg steel spring, the calculated mass reductions were approximately 78% and 84%, respectively.</p> <p><strong>Conclusion:</strong> Under the adopted assumptions and constraints, both composite materials demonstrated substantial potential for leaf-spring mass reduction.</p> Sultan Alqash Badr Bedairi Badr Azzam Copyright (c) 2026 Author(s). The licensee is the journal publisher. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0 2026-10-31 2026-10-31 28 11 302 313 10.9734/jerr/2026/v28i112038