Articles

Conventional cyclone risk assessments in Small Island Developing States (SIDS) systematically fail to predict food system collapse locations, leading to misallocated adaptation investments. This study quantifies a fundamental exposure-impact disconnect across Vanuatu's 71 area councils: cyclone exposure explains only 14% of food security impact variance (R² = 0.14, r = 0.38), with topographic, agro-ecological, and infrastructure mediators determining the remaining 86%. Using data from five Category 4–5 cyclones (2015–2023), we constructed a Hazard Exposure Index (HEI; proximity 40%, intensity 35%, frequency 25%) and an Impact Severity Index (ISI; four equally weighted dimensions: agriculture, infrastructure, food insecurity, recovery). Regression with interaction terms reveals three mediating mechanisms. First, topographic elevation reduces ISI by 40–60% at highland area councils (>400 m) relative to coastal equivalents at comparable HEI (r = −0.54, p < 0.001). Second, crop diversity buffers medium-exposure zones (HEI 0.3–0.6; β = −0.082, p = 0.018) but provides no significant protection under extreme cyclone loading (HEI > 0.7), consistent with resilience theory stability thresholds. Third, infrastructure capacity generates "hidden hotspots": Torba province (low exposure, ISI = 0.34) required a median 18 days to reach 50% of affected households versus 6 days in Shefa (high exposure, ISI = 0.61) — a threefold differential driven solely by maritime connectivity deficits. High-exposure provinces with strong infrastructure systematically outperform low-exposure provinces with distribution network weaknesses. A Food Security Resilience Index (FSRe; range: 46–61 on a 0–100 scale) reveals stark provincial heterogeneity — Sanma as buffered regional hub (FSRe = 61) versus Tafea and Shefa as critical/fragile (FSRe 46–47) — enabling a four-category spatial typology for targeted adaptation investments under SIDS fiscal constraints. Findings demonstrate that spatially explicit integration of topography, agro-ecology, and infrastructure is essential for efficient climate adaptation planning in archipelago SIDS, with direct implications for Sendai Framework Target E national DRR strategy development and Green Climate Fund adaptation investment prioritization.

Background:</b> Within contemporary medical education systems dominated by the biomedical paradigm, medical students commonly experience a decline in empathy alongside uncertainty in professional identity formation. Narrative medicine, as an educational approach designed to bridge objective technical reasoning and subjective human experience, has been proposed as a response to this humanistic crisis. However, the psychological mechanisms underlying its effects, the boundaries of existing empirical evidence, and strategies for systematic implementation remain insufficiently clarified. <b>Content:</b> Drawing on a comprehensive review of the literature, this article elucidates two core theoretical mechanisms through which narrative medicine operates. First, through the cycle of attention, representation, and affiliation combined with reflective practice, narrative medicine facilitates the coordinated development of cognitive empathy and emotional resonance. Second, through narrative integration, it supports learners in transforming fragmented clinical experiences into coherent professional identities. A synthesis of empirical studies published over the past decade indicates that well designed narrative medicine curricula can enhance reflective capacity, communication skills, and a sense of professional meaning, although their effectiveness is substantially moderated by curricular intensity, pedagogical modality including digital and multimodal approaches, and cultural context. <b>Conclusion:</b> The integration of narrative medicine currently faces structural barriers, including limited curricular space, the scarcity of standardized assessment tools, and insufficient faculty development. Future efforts should adopt implementation science frameworks to shift from isolated elective offerings toward longitudinal spiral curricula, develop multidimensional evaluation strategies, and explore the use of emerging technologies such as artificial intelligence and virtual reality in narrative pedagogy. These efforts may establish narrative medicine as a foundational pathway for cultivating physicians with core humanistic competencies.

<b>Background: </b>Socioeconomic status (SES) is widely associated with improved health, behavioral, and educational outcomes. However, emerging research suggests that these benefits are not uniformly experienced across populations or contexts. The theory of Marginalization-related Diminished Returns (MDRs) has primarily focused on racial and ethnic disparities, showing that individuals from racially marginalized groups often experience weaker protective effects of SES. There is a lack of evidence on geography—particularly rural residence—as a moderator of SES effects. <b>Objective:</b> This review explores how place, especially rural contexts in the U.S., shapes the extent to which SES translates into improved outcomes. We extend the MDRs framework to include place-based and geography-based marginalization, arguing that even among non-Hispanic White populations, rural residence can lead to diminished returns on education, income, and other forms of capital. <b>Content:</b> Drawing on theoretical models such as Fundamental Cause Theory and Bronfenbrenner’s Ecological Systems Theory, and synthesizing empirical findings from studies of academic achievement, substance use, and educational aspirations, this review highlights how structural disadvantages in rural areas weaken the effectiveness of individual and family-level resources. <b>Conclusion: </b>Rural health and educational disparities are not solely due to a lack of resources but may also reflect systemic conditions that erode the value of existing resources. Policy interventions must be place-aware and address the contextual constraints that limit opportunity. Future research should more explicitly test how geography moderates the effects of SES across a range of outcomes and populations.

<b>Background:</b> Tobacco endgame, defined as elimination of commercial tobacco sales The U.S. tobacco control landscape is a complex, adaptive system shaped by diverse stakeholders, evolving products and regulations, shifting social norms, and the strategic countermeasures of a powerful industry. Managing such complexity requires more than isolated interventions—it demands a coordinated, enterprise-wide approach that accounts for dynamic interactions, feedback loops, and emergent risks. <b>Objective:</b> Drawing on complex systems thinking, Zachman enterprise architecture model, and public health best practices, we conceptualize tobacco control as an evolving enterprise progressing through six interconnected phases: (1) Conception &#x26; Initiation, (2) Policy &#x26; System Design, (3) Implementation &#x26; Operation, (4) Evaluation &#x26; Adaptation, (5) Consolidation &#x26; Endgame Transition, and (6) Sustainment or Sunset. Each phase incorporates governance structures, performance benchmarks, and transition criteria designed to manage interdependence and reduce systemic vulnerabilities. <b>Results:</b> The lifecycle framing emphasizes how tobacco control in the U.S. can evolve as a complex, adaptive enterprise—integrating public health objectives with legal, operational, and cultural change processes. This model supports strategic sequencing, cross-sector alignment, and risk mitigation against emergent industry tactics, enabling a resilient and measurable pathway to the endgame. <b>Conclusions:</b> Seeing tobacco control as a complex enterprise that operates under a lifecycle model may offer a roadmap for achieving and sustaining the tobacco endgame. Using this approach may enhance policy coherence, resource efficiency, and adaptability, ensuring tobacco endgame is achieved.

Machine learning operations (MLOps) comprises the practices, methods, and tooling that facilitate the deployment of reliable ML models in production environments. While many aspects of cloud data platforms are designed to enable reliability, only some managed ML services support the MLOps goals of continuous integration, continuous delivery, data lineage tracking, associated reproducibility, governance, and security. Furthermore, reliability encompasses not only the fulfillment of service-level objectives, but also systematic monitoring, alerting, and incident response automation. Architectural patterns are proposed to enable reliable deployment in cloud data platforms, focusing on the implementation of continuous integration and testing pipelines for ML models and the formulation of continuous delivery and rollout strategies. Continuous integration pipelines reduce the risk of regressions and ensure sufficient model performance at the time of deployment, while continuous delivery pipelines enable rapid updates to production models within acceptable risk profiles. The landscape of publicly available MLOps frameworks, tools, and services is also examined, emphasizing the pros and cons of established and rising solutions in containerization, orchestration, model serving, and inference. Containerization and orchestration contributes to the building of reliable deployment pipelines in cloud data platforms, whether general-purpose tools (e.g. Docker and Kubernetes) or solutions tailored for ML workloads. Containerized serving frameworks designed for high-throughput, low-latency inference can benefit a wide range of business applications, while auto-scaling and model versioning capabilities enhance the ease of use of cloud-native ML services.

In today’s world of a globalized economy and ubiquitous digital transactions, businesses are hungry for ways to increase transaction efficiency and security. In the real economy, solutions that scale to fit transaction volume or velocity are equally valuable. This is true for clearing and settlement and for the day-to-day needs of buyers and sellers alike. Clever observers of both cash and digital transactions can spot cases where technology that supports transaction security or safety can strengthen consumer-borrower ties, mitigate default risks, and reduce recidivism. In general, a cloud solution for payment processing and merchant services solves two major barriers to optimum business technology: lack of scalability and lack of security [1]. The extension of current practice has its advantages, but new solutions unlock significant opportunities for both consumers and financial institutions [2]. The focus of this work is on the provisioning of cloud-based payment processing and merchant services to financial institutions and established global organizations, although the options available with these services mean they are potentially applicable to a wide range of group entities, including non-trading organizations, pension administrators, and group treasurers. With the increased attention to cybersecurity, a mass of data is available to assist the IT departments of the major payment processors, merchants, and acquirers to get cybersecurity on the radar of C-level executives [3]. The case is put forward for the increased targeting of and reporting to the Board’s Audit, Risk, and Liability Committees of publicly held payment processors and merchants to reduce fraud losses and mitigate the reputation and class action lawsuit risk due to data breaches. The progress of technology in the payment sector requires all stakeholders to have a collective approach in order to mitigate fraud and cybersecurity-related risks in new products and services to enhance consumer confidence and the proportion of retail cashless transactions [4].

There have been various concerns about the environmental impact of construction works. This generates a need to take a more proactive approach in evaluating the environmental impacts of construction operations and further explore ways to reduce the environmental impacts. Enormous opportunities exist within the building industry to achieve a reduction in greenhouse gas emissions. The aim of the study is to develop a framework for the evaluation of improvement opportunities for environmental impact for onsite and offsite building construction works using life cycle assessment (LCA) and value-stream-mapping concepts. Various tools for LCA exist; however, there is a need for the development of an LCA framework and improvement opportunities that can be localized to various communities to evaluate improvement opportunities for building construction. This study conducts a review of methods to evaluate the LCA of buildings on local construction sites. A procedure for establishing improvement opportunities is also developed. Based on the author’s knowledge and experience, including site visits, using value stream mapping (VSM) techniques, a conceptual framework of the present state map and future state map of residential construction works was developed. The study presents a procedure for the evaluation of improvement opportunities for the environmental impacts of construction operations.

In the constantly evolving world of cloud computing, appropriate resource allocation is essential for both keeping costs down and ensuring an ongoing flow of apps and services. Because of its adaptability to specific tasks and human behavior, machine learning (ML) is a desirable choice for fulfilling those needs. This study Efficient cloud resource allocation is critical for optimizing performance and cost in cloud computing environments. In order to improve the precision of resource allocation, this study investigates the use of Long Short-Term Memory (LSTM). The LSTM model achieved 97% accuracy, 97.5% precision, 98% recall, and a 97.8% F1-score (F1-score: harmonic mean of precision and recall), according to experimental data. The confusion matrix demonstrates strong classification performance across several resource classes, while the accuracy and loss curves verify steady learning with minimal overfitting. The suggested LSTM model performs better than more conventional ML (machine learning) models like Gradient Boosting (GB) and Logistic Regression (LR), according to a comparative study. These findings underscore the LSTM (Long Short-Term Memory) model’s robustness and suitability for dynamic cloud environments, enabling more accurate forecasting and efficient resource management.

Automated testing and software quality assurance (SQA) practices are essential for ensuring the reliability, scalability, and maintainability of modern software systems. This paper presents a review of widely used automated testing frameworks, including Driven, Data-Driven, Behavior-Driven Development (BDD), and Record/Playback approaches, outlining their methodologies, benefits, and limitations in different development contexts. In parallel, it examines established SQA techniques such as Test-Driven Development, static analysis, and white-box testing, which provide systematic methods for defect detection and quality improvement. The study further examines the role of practical tools, such as Selenium, TestNG, and JUnit, in supporting test automation and validation activities. In addition to highlighting technical capabilities, the paper identifies common challenges faced in automation, including incomplete requirements, integration complexities, and maintaining evolving test suites. Recommended best practices are provided to address these issues, offering guidance for organizations seeking to strengthen their software testing processes through structured frameworks, adaptive techniques, and reliable automation tools.

Advances in web-centric cloud computing have facilitated the establishment of an integrated cloud environment connecting a wide variety of clinical trial stakeholders. A web-centric cloud framework is proposed for real-time monitoring and risk prediction during clinical trials. The framework focuses on identifying relevant datasets, developing a data-management interface, and implementing machine-learning algorithms for data analysis. Detailed descriptions of the data-management interface and the machine-learning processes are provided, targeting active clinical trials with therapeutic uses in cancer. Demonstrations utilize publicly available clinical-trial data from the ClinicalTrials.gov repository. The real-time monitoring and risk prediction systems were assessed by developing five supervised-classification-machine-learning models for trial-status prediction and six unsupervised models for patient-safety-profile assessment, each representing a different phase of the clinical-trial process. All supervised models yielded high accuracy and area-under-the-curve values at the testing stage, while the unsupervised models demonstrated practical applicability. The results underscore the advantages of using the trial-status algorithm, the patient-safety-profile model, and the proposed framework for performing real-time monitoring and risk prediction of clinical trials.

The building construction industry holds a crucial role in the reduction of greenhouse gas emissions globally. The targets for greenhouse gas emissions may not be achieved without a defined strategic plan to meet up with the set targets from various sectors of the economy. Recognizing the enormous potential that the building industry holds in contributing to global greenhouse gas GHG emission reduction, this study describes a framework on how optimization techniques can be used as a guide for emission reduction targets for the housing sector using illustrations of the onsite and offsite building construction industry. Given that some of the GHG gases are also sources of air pollution, this study includes a discussion on how the effort to address air pollution can be used to find a consensus towards addressing the concern about GHG emissions. This study presents procedures for simplified methods of estimation of GHG emissions that various municipalities around the globe can use to estimate and report the emissions from the building construction industry. The study presents a unifying strategy for emission management. The study also demonstrates how programming methods can be applied to GHG emissions management. The approach used in this study is transferable to other industries. The study recommends a unifying strategy for the management and control of emissions in the building construction industry. The study also recommends a coordinated effort in sharing best practices for emission control and management from all jurisdictions globally. In the effort to reduce global emission targets, further studies like this and its expansion is recommended for all sectors of the global economy. It is recommended that these studies should be followed by a concrete effort to achieve good implementation of sustainable emission reduction targets globally.

We investigate and analyze the trends and behaviors in credit card fraud attacks and transactions. First, we perform logical analysis to find hidden patterns and trends, then we leverage game-theoretical models to illustrate the potential strategies of both the attackers and defenders. Next, we demonstrate the strength of industry-scale, privacy-preserving artificial intelligence solutions by presenting the results from our recent exploratory study in this respect. Furthermore, we describe the intrinsic challenges in the context of developing reliable predictive models using more stringent protocols, and hence the need for sector-specific benchmark datasets, and provide potential solutions based on state-of-the-art privacy models. Finally, we conclude the paper by discussing future research lines on the topic, and also the possible real-life implications. The paper underscores the challenges in creating robust AI models for the banking sector. The results also showcase that privacy-preserving AI models can potentially augment sharing capabilities while mitigating liability issues of public-private sector partnerships [1].

This paper includes the impacts of the Internet of Things (IoT), Big Data, and other emerging technologies in the vehicle production sector, digital connectivity, and sustainable transport system. Automated and intelligent transportation for safe, efficient, and sustainable transport systems will be stressed. Key factors to promote automated or connected vehicles including connected environment, integration of all transport modes, advanced cooperative systems, and policy enforcement will be discussed. This paper contains the Axiomatic Categorisation Framework (AFS) for the dynamic alignment in a collection of disparate functions in cyber-physical systems (CPS). Developed system is enhanced for breaking the rules within autonomous vehicles (AV). It means the human personal injury is inevitable while the vehicle does not do any rules. Especially in complicated traffic situations, many of the constraints are mutually exclusive, and there is no way to obey all of them at a time. Also, there is no way to ensure that the self-driving vehicle has priority in all situations [1]. Public distrust in AV systems has to be increased and the investment in this technology has to slow down. Instead, a human driver should be partially responsible for operation. The development of a driver-behavior assistant (DBA) system is proposed, which should be able to break the rules for the distances of such slow development. It is intended to be effective in non-deterministic situations while maintaining the safety of the AV and those involved in the event. A driver's actions would not only be acceptable as a driving strategy but also would be predictable, and therefore other road users could unambiguously react.

For years, risk assessment and financial calculations have been based on mathematical, statistical, and actuarial studies of existing and historical data. The manual process of building datasets, processing data, deriving trends, identifying periodicities, and analyzing diagnostics is extremely expensive and time-consuming. With the automation and evolution of data science technologies, organizations are now bringing in niche data, such as unstructured data, which contain more disruptive and precise signals for decision-making—thereby making predictions and derivative valuations more robust. This discussion highlights how investment decision-making and financial ecosystem activities are set to be transformed with the power of technical automation, data, and artificial intelligence. A noted trend in the financial investment sector is that financial valuations are highly predictive and highly non-linear in long-term occurrences. To understand these robust evolving signals and execute profitable strategies upon them, the investment management process needs to be very dynamic, open, smart, and technically deep. However, with current manual processes, reaching a high-end asset prediction still seems like a shot in the dark. In parallel, open and democratically developed financial ecosystems query relatively riskless premium opportunities in high-finance valuation and perception. The process of evolving financial ecosystems or the use of automated tools and data to move to unique frontiers could make high-yield profiting opportunities very safe and entirely riskless. Financial economic theories and realistic approximation models support this.

Architectural frameworks for large-scale Electronic Health Record (EHR) data platforms are described. Existing EHR data platform architectures often leverage multiple cloud-based solutions blended with institutional infrastructures to manage and analyze clinical data at scale. Key design principles governing the scale of existing EHR data architecture include model design, governance structure, data access management, data security/policy/protection, data-information-language-based standardization, and analytics tool alignment, among others. The rapidly evolving technology landscape and the unprecedented volume of incident and retrospective clinical data being collected and generated within healthcare organizations have led to the emergent need for a dedicated architectural framework to support large-scale computing in the health informatics domain. The application areas of large-scale computing in health informatics include real-time predictive analytics, risk stratification, patient cohort analytics, development of predictive models for specific institutions or population groups, and many more. The use of EHR data for a multitude of decision-making processes in both clinical and non-clinical settings has prompted the establishment of policies prescribing the conditions of access and use of EHR data for non-employed individuals in the organization. Consequently, the demand for accessing, using, and managing EHR data at scale has impacted the over.

Data warehousing is a technique for collecting, managing, and presenting data to help people analyze and use that data effectively. It involves a large database designed to support management-level staff by providing all the relevant historical data for analysis. This chapter begins with a definition of data warehousing, followed by an overview of large-scale policy management to highlight the need for data warehousing. Next, an overview of an ETL framework is presented, along with a discussion of advanced ETL techniques. The chapter concludes with an outline of performance optimization techniques for data warehousing. Data warehousing is considered a key enabler for efficient reporting and analysis, with implementation choices ranging from cost-effective desktop systems to large-scale, mission-critical data marts and warehouses containing petabytes of data. Extract, transform, and load (ETL) systems remain one of the largest cost and effort areas within data warehouse development projects, requiring significant planning and resources to build, manage, and monitor the flow of data from source systems into the data warehouse. The technology and techniques used for ETL can greatly influence the success or failure of a data warehouse. Complex business requirements for data cleansing, loading, transformation, and integration have intensified, while operational plans for real-time and near-real-time reporting add additional challenges. Parallel loading mechanisms, incremental data loading, and runtime update and insert strategies not only improve ETL performance but also optimize data warehousing performance, particularly for large-scale policy management.

Integrated Risk and Compliance Frameworks for Global Data Center Operations: A Governance-Centric Approach presents an objective, evidence-based analysis through a governance-focused lens, emphasizing strategic alignment, stakeholder engagement, and measurable outcomes. Specific risk and compliance paradigms governing data center operations are identified, and the impact of these paradigms on oversight, control, and reporting is assessed. A comparative study of traditional operational controls and a governance-led compliance-centric methodology demonstrates the latter's potential to become the primary driver of data center risk and compliance. Governance encompasses the continued alignment of business goals with risk appetite, regulatory requirements, and operational capabilities; a defined structure for accountability, including oversight committees and established roles and responsibilities for risk and compliance decision rights; and a mechanism for stakeholder engagement that enables periodic and continuous validation of strategic decisions. Integrated risk management frameworks leveraging these principles are a prerequisite for effective risk and compliance management in the context of global data center operations.

The evolution of supply chains (SC) from a linear to a network structure created an opportunity for new processes, product/service offerings, and provider-business. Rising customer service expectations have led to the need for innovative SC designs to develop and sustain competitive performance globally. Firms are forced to respond and adapt accordingly, thereby leading to design, network, operational, and performance dynamics. Traditionally, SCs are treated as static structures, focusing solely on design and/or operational optimization. Such perspectives are not viable options for SC domains, as they address only a portion of the dynamic problem space, use a deterministic assumption of dominant design variables, capitalize on past data to predict future decisions, and offer pre-classified forecasting options complemented with a limited comprehension of systemic SC elasticity. Novel self-learning agentic systems are proposed that blend the sciencematics of SC decisions and dynamics. The designs guide firms seeking to build adaptive SCs using operational decision processes. The designs address the agentic nature of SC, embedding computational interaction models of firm SC networks. The designs contrast the stochastic action-taking and thereby the performance outcomes, discovering opportunities for adaptive operational designs of SC tasks. Fine-tuning and meta-learning are new design capabilities that adapt to evolving dynamic environments. Frameworks for behavioral customization and systematic exploration of the design space are provided as user guides. Exemplar designs are also provided to serve as a translation template for users to express operational models of their own contexts. To account for the dynamics of supply chains (SC), agent-based models are increasingly adopted. Such models exhibit SC structure and/or formulation dynamics. Though existing efforts commence adjacent-only structural changes, dynamism with respect to tasks is crucial for SC design and operational strategy development. Proposed is a process modeling library and workflow for discovering intricate designs of adaptive agentic systems. The library revises Dataflow and Structure, concealing sequencing and context designs of processes. Prompted specifications describe and enact designs. Applications in SC formulation discovery are provided.

Data quality and data lineage are critical concerns for organizations mandated to comply with stringent regulatory regimes. This paper analyses the latest developments in the governance of data quality and data lineage within a regulated financial services organisation. It sets out the underlying regulatory context, describes the concepts employed in the business environment, summarizes how data quality is captured and monitored, examines the artefacts that record data lineage, reviews the roles and responsibilities of staff who implement the necessary processes, and maps areas where improvements are possible. The internal organization and processes of regulated data platforms are shaped not only by the capabilities prescribed by their technical architecture but also by the regulatory regimes under which they operate. These mandates, in particular, require rigorous examination of four aspects of data quality — accuracy, completeness, consistency, and timeliness — and detailed documentation of how data arrives in its final form in the repository. Although data monitoring, alerting, assessment, and remediation are well established, provenance capture remains an area ripe for further investment.

Enterprise databases are the heart of applications and contain the most sensitive and critical information of organizations. While there have been significant advances in the security of databases, vulnerabilities still exist due to mistakes made by application developers, database administrators, and users. Manual detection and patching of such vulnerabilities typically take months, but an automated detection and remediation framework is proposed to fill the gap and eliminate a significant number of these vulnerabilities in near-real time. This framework comprises two key components: a detection engine that leverages static analysis to identify potential patches, coupled with query dynamic testing and fuzzing to identify exploitable misconfigurations; and an orchestration engine that applies detected patches on the database, validates the accuracy of the fix, and rolls back changes if the problem is not resolved. A prototype of this framework has been implemented and validated on a real-time database deployed in an enterprise environment. Because of the complexity of the problem landscape, the research focus is on static vulnerability detection and automated corrective actions. These two capabilities can greatly reduce the manual workload associated with vulnerability detection and significantly enhance the assurance that the granted privileges validate the least privilege principle. The proposed architecture aims to enable the deployment of a detection-and-remediation solution that minimizes human effort, reduces the enterprise-at-risk window, and maximizes the volume of detected vulnerabilities.