AI-driven predictive analytics framework for proactive supply chain disruption management and contingency planning

Supply chain disruptions ranging from geopolitical instability and pandemics to natural disasters and cyberattacks have intensified the need for advanced disruption management strategies. Traditional risk management approaches relying on reactive responses are increasingly inadequate in volatile, uncertain, complex, and ambiguous (VUCA) environments. Recent developments in artificial intelligence (AI)-driven predictive analytics present new opportunities for enabling proactive disruption identification, real-time monitoring, and contingency planning in global supply chains. This paper reviews existing literature and proposes a comprehensive conceptual framework integrating machine learning models, big data analytics, digital twins, and scenario-based planning to manage disruptions effectively. By synthesizing over 100 scholarly contributions, we highlight how predictive analytics enables early warning detection, decision-support systems, and automated mitigation strategies. The paper further discusses implementation challenges, including data quality, algorithmic bias, ethical considerations, and interoperability, while offering recommendations for practitioners and researchers. Keywords: AI Predictive Analytics, Supply Chain Disruption, Contingency Planning Framework, Machine Learning Resilience, Digital Twin Integration, Proactive Risk Management

Life cycle assessment (LCA) and supply chain network optimization for sustainable integration of bio-based polymers (PLA/PHA) in regional packaging systems

The promise of bio-based polymers like polylactic acid (PLA) and polyhydroxyalkanoates (PHA) as sustainable packaging solutions is tempered by significant real-world challenges: persistent cost premiums, complex supply chain logistics, and geographically variable environmental footprints that diminish their theoretical benefits. This study directly addresses these complexities by combining cradle-to-grave Life Cycle Assessment (ISO 14040/44) with a multi-objective Mixed-Integer Linear Programming (MILP) model to identify economically feasible and environmentally superior configurations for regional PLA and PHA packaging systems across Western Europe. Our innovative approach goes beyond traditional methods by optimizing facility locations, transportation networks, feedstock sourcing, and end-of-life options simultaneously while assessing trade-offs between total costs and Global Warming Potential (GWP). The analysis offers valuable insights: PHA consistently outperforms PLA in net energy demand (-6.5 to +9.6 MJ/kg) and achieves true circularity within effective composting systems, yet requires significant policy support to overcome its 20% capital cost disadvantage. While localized production significantly reduces emissions by 15–30% in regions rich in renewable energy, it also increases costs by 10–15%—a paradox that can only be managed through strategically designed policies. Targeted incentives for composting infrastructure further boost circularity metrics by 40%, turning waste streams into valuable resources. These findings provide policymakers with a rigorous, data-driven framework to guide regional bio-polymer transitions, shifting from generic mandates to precise strategies that promote sustainability. Ultimately, this research shows that the sustainable packaging revolution depends not just on materials innovation but also on strategically coordinating regional advantages and technological potential. Keywords: Life Cycle Assessment, Supply Chain Optimization, Bio-based Polymers, Polyhydroxyalkanoates (PHA), Polylactic Acid (PLA), Circular Economy, Regional Sustainability, Territorial Symbiosis, Packaging Systems, Policy Integration

Causal simulation models for targeting and participation in social protection programs

Social protection programs have emerged as critical policy instruments for poverty alleviation and social welfare enhancement in both developed and developing economies. The effectiveness of these programs fundamentally depends on accurate targeting mechanisms and sustained participation rates among eligible beneficiaries. Traditional approaches to program design and implementation have relied heavily on static eligibility criteria and retrospective evaluation methods, which often fail to capture the dynamic nature of poverty and vulnerability. This study introduces a comprehensive framework for implementing causal simulation models to optimize targeting strategies and enhance participation rates in social protection programs. The research examines how advanced computational modeling techniques, including agent-based modeling, discrete event simulation, and machine learning algorithms, can be integrated to create more responsive and effective social protection systems. The causal simulation approach addresses fundamental challenges in program design by incorporating real-time data analytics, predictive modeling, and scenario planning capabilities. Through systematic analysis of beneficiary characteristics, behavioral patterns, and external socioeconomic factors, these models enable policymakers to identify optimal intervention points and resource allocation strategies. The framework emphasizes the importance of understanding causal relationships between program design features and beneficiary outcomes, moving beyond correlation-based analyses to establish robust cause-and-effect relationships. This methodological advancement is particularly crucial given the increasing complexity of modern social protection landscapes and the growing demand for evidence-based policy formulation. The study presents evidence from multiple jurisdictions demonstrating how causal simulation models can significantly improve targeting accuracy while reducing administrative costs and program leakage. Key findings indicate that simulation-based approaches can enhance beneficiary identification by up to 35% compared to traditional methods, while simultaneously reducing exclusion errors by approximately 28%. The models also demonstrate superior performance in predicting program participation rates, with accuracy improvements ranging from 22% to 41% across different program types. Furthermore, the research reveals that dynamic modeling approaches enable more effective resource planning and budget optimization, leading to improved program sustainability and expanded coverage. The implications of this research extend beyond technical improvements to encompass broader considerations of social equity, program accessibility, and institutional capacity building. The study emphasizes that successful implementation of causal simulation models requires comprehensive stakeholder engagement, robust data infrastructure, and continuous model validation processes. The findings suggest that organizations adopting these advanced modeling approaches must invest in both technological capabilities and human capital development to realize the full potential of simulation-based program design. Keywords: Causal Simulation Models, Social Protection Programs, Targeting Mechanisms, Participation Rates, Agent-Based Modeling, Policy Optimization, Poverty Alleviation, Beneficiary Identification, Predictive Analytics, Program Evaluation

Privacy-First security models for AI-integrated identity governance in multi-access cloud and edge environments

The convergence of artificial intelligence (AI), multi-access edge computing (MEC), and cloud environments has transformed identity governance by enabling real-time decision-making and seamless access control across decentralized infrastructures. However, this evolution has also introduced complex challenges concerning data privacy, identity trust, and security. This review explores privacy-first security models that integrate AI for identity governance in hybrid cloud-edge architectures. It evaluates privacy-preserving techniques such as homomorphic encryption, federated learning, and zero-knowledge proofs, emphasizing their role in ensuring secure identity authentication, authorization, and auditability. The paper critically analyzes the limitations of conventional identity and access management (IAM) frameworks in dynamic, resource-constrained edge environments and proposes adaptive models that embed privacy by design. Furthermore, the review investigates the interplay between explainable AI (XAI) and policy enforcement for transparent and compliant identity governance. By synthesizing advancements in cryptographic methods, AI reasoning engines, and decentralized identity (DID) systems, the paper outlines a roadmap for building secure, scalable, and privacy-compliant identity infrastructures in the era of pervasive computing. Keywords: Privacy-Preserving Identity Governance, AI-Driven Access Control, Multi-Access Edge Computing (MEC). Federated Identity Management, Explainable AI (XAI), Zero-Knowledge Proofs

Ethical Challenges in AI-Powered Supply Chains: A U.S.-Nigeria Policy Perspective

The incorporation of artificial intelligence (AI) into global supply chains is revolutionizing industries by increasing productivity, cutting costs, and improving decision-making. However, the adoption of AI in supply chains also presents significant ethical challenges, especially when comparing advanced economies like the United States and developing economies like Nigeria. This study examines the ethical issues that arise from AI-powered supply chains through a comparative policy lens, focusing on the U.S. and Nigeria. In the U.S., ethical concerns center on privacy, data security, algorithmic transparency, and the possibility of job displacement, while in Nigeria, additional challenges include infrastructure constraints, a lack of regulatory frameworks, and a digital divide that exacerbates the ethical implications of AI adoption in supply chains. This looks at how the policies of the two nations handle these problems, with the US highlighting the necessity of precise laws, moral standards, and corporate accountability in the application of AI. Nigeria's new AI laws, on the other hand, emphasize data governance, capacity building, and the development of an inclusive digital environment. The significance of customized policy solutions that take into account the distinct economic, social, and technical circumstances of each country is highlighted by examining these divergent approaches. The report also emphasizes the necessity of international collaboration in creating uniform ethical guidelines for artificial intelligence in global supply chains. The findings suggest that while AI holds the potential to revolutionize supply chains, it also necessitates careful policy planning and ethical oversight to ensure that its benefits are realized equitably and sustainably across different regions. Keywords: Ethical, Challenges, AI-powered, Supply chains, U.S, Nigeria, Policy perspective

Modelling the impact of climate change on Nigeria’s agricultural sector: A nonlinear modelling approach

Climate change is posing increasing concerns to agricultural output and food security worldwide. This study explores the asymmetric influences of climatic conditions on Nigeria's agricultural output using yearly time series data from 1986 to 2022. This is based on the fact that climate change manifestations in the agriculture sector come in the form of an increase or decrease in climatic variables such as rainfall or temperature. Therefore, the use of an autoregressive distributed lag (ARDL) approach models the nonlinear relationships between temperature, rainfall and agricultural output. Rainfall and temperature indices capture climate variability relative to baseline levels. The model quantifies both immediate and lagged effects of increasing/decreasing rainfall and temperatures on sectoral performance. Cointegration tests confirm long-run equilibrium associations. The estimated asymmetric error correction model reveals rising temperatures and declining rainfall significantly hamper agricultural GDP in the short run. A 1% temperature increase reduces output by N5.1 billion whereas a 1% fall in rainfall lowers it by N9.7 billion. Long-run climate sensitivities also indicate rainfall variability critically constrains productivity. The negative rainfall coefficients agree with agronomic evidence that water stress and droughts diminish yields. Contrastingly, temperature impacts fade over time. Based on the findings of the study, it therefore recommends the development and promotion of heat and drought-resistant crop and livestock varieties to counter the negative impacts of rising temperatures and declining rainfall on agricultural productivity. Keywords: Climate Change, Agricultural Sector, Nonlinear, Autoregressive Distributed Lag (ARDL) Model

The role of fiber-reinforced and self-healing concrete in enhancing U.S. infrastructure durability

The durability of U.S. infrastructure has become a critical concern, as aging systems and increasing environmental stresses continue to strain the nation’s roads, bridges, and public structures. Over 40% of U.S. roadways are in poor or mediocre condition, while more than 46,000 bridges are structurally deficient. Conventional concrete, though widely used, is prone to cracking, corrosion, and structural fatigue, contributing to mounting maintenance costs and safety risks. This review paper explores the role of fiber-reinforced concrete (FRC) and self-healing concrete (SHC) as innovative solutions for enhancing the durability and resilience of U.S. infrastructure. The scope of the review encompasses recent advancements in FRC and SHC technologies, their mechanical and durability characteristics, and real-world applications in various infrastructure sectors. FRC integrates synthetic or natural fibers such as steel, glass, or polypropylene into the concrete mix to improve tensile strength, crack resistance, and impact durability. Meanwhile, SHC leverages biological or chemical agents such as bacterial spores or encapsulated healing agents that autonomously repair microcracks when exposed to water or environmental stimuli. Key findings highlight that FRC significantly enhances structural performance under cyclic loading and extreme environmental conditions, thereby extending service life and reducing maintenance frequency. SHC, on the other hand, shows promise in prolonging infrastructure lifespan by restoring structural integrity autonomously without human intervention. Together, these technologies present a sustainable and cost-effective approach to addressing the infrastructure durability crisis in the U.S. This paper recommends broader adoption of fiber-reinforced and self-healing concrete in federal and state infrastructure projects, particularly in high-stress applications such as highways, tunnels, and marine structures. Future research should focus on improving cost-efficiency, scalability, and the long-term performance of SHC systems in diverse climates. Integrating smart sensing and AI-based monitoring tools with these advanced materials may further revolutionize infrastructure maintenance and durability strategies. Keywords: Fiber-Reinforced Concrete, Self-Healing Concrete, Infrastructure Durability, Concrete Innovation, Structural Performance, Sustainable Construction Materials, Smart Infrastructure

A systematic review of the rolling shear modulus of timber

The rolling shear modulus of Cross-Laminated Timber (CLT) and other engineered timber governs the performance and stability. The study was systematically analyzed using rolling shear modulus tests on hardwoods and softwoods. Literature was searched in Scopus, Google Scholar, and Web of Science for papers on rolling shear modulus, timber, and structural applications. Articles were chosen based on their relevance, high methodological quality, and outcome clarity. The review reveals that values of rolling shear modulus were different between planar, short-span bending, and modified planar shear tests. Spruce and pine have a smaller value of rolling shear modulus as compared to beech and oak. This disparity emphasizes the need for standardized testing for reliable evaluations. An analysis of the results with respect to materials selection, and species-specific structural design in timber engineering is made presenting insights into rolling shear modulus. Findings reveal that the Iosipescu shear test to be the most recommended method for determining the rolling shear modulus of timber. This test directly measures the shear stress-strain response and provides more consistent and reliable results compared to other methods, such as the torsion test or the panel shear test.  Keywords: Rolling Shear Modulus, Timber Engineering, Cross-Laminated Timber, Hardwood, Softwood

Influence of Early Life Experiences, and Religious Beliefs on Positive Mental Health among Undergraduates

Mental health among undergraduate students has become a global concern, with traditional research focusing primarily on psychopathological symptoms rather than positive mental health indicators. This study examined the relationship between early life experiences, religious beliefs, and positive mental health among Nigerian undergraduates, addressing a gap in non-Western mental health research. A descriptive cross-sectional study was conducted with 200 undergraduate students selected through convenience sampling. Data were collected using three validated instruments: the Early Life Experiences Scale (ELES), the Centrality of Religiosity Scale (CRS-5), and the Positive Mental Health Scale (PMH). Simple linear regression analyses were performed to test the study hypotheses. The sample comprised 66% females and 34% males, with 99% identifying as Christian. Early life experiences significantly predicted positive mental health, explaining 2.3% of the variance, with negative early experiences associated with poorer mental health outcomes. Religious beliefs also significantly predicted positive mental health, accounting for 7.3% of the variance, with stronger religious beliefs associated with better mental health. The findings demonstrate that adverse early life experiences negatively impact positive mental health, while religious beliefs serve as a protective factor, enhancing mental well-being among Nigerian undergraduates. These results support the integration of early life history and religious considerations in mental health interventions for university students in collectivistic, religious contexts.  Keywords: Positive Mental Health, Early Life Experiences, Religious Beliefs, Undergraduate Students, Nigeria

Improving team productivity and financial services efficiency with agile story points

This paper explores using Agile story points to enhance team productivity and efficiency in financial services, highlighting their benefits, challenges, and future implications. Story points are essential for estimating task complexity, risk, and effort, offering a flexible approach to sprint planning and resource allocation. The paper discusses the role of story points in improving communication among teams, increasing predictability, and ensuring timely customer delivery. Additionally, it examines the challenges associated with subjectivity in estimations, the influence of team dynamics, and the difficulties of scaling Agile practices in large financial institutions. Recommendations are provided for optimizing story point usage, scaling Agile across teams, and ensuring the necessary technological and organizational support for maximizing productivity. The findings emphasize that, with the right framework and leadership buy-in, Agile story points can drive significant improvements in efficiency within the highly regulated financial services sector. Keywords: Agile Story Points, Team Productivity, Financial Services, Resource Allocation, Sprint Planning, Scaling Agil