Articles

Oluwafemi Oluwagboyega Fabiyi, Mary Magdalene Yeboah

Artificial intelligence (AI)-enabled cloud systems increasingly support critical digital infrastructure in finance, healthcare, government, telecommunications, energy, transportation, and large-scale digital services. Their growing interdependence creates failure modes that can propagate across infrastructure, platform, application, model, cybersecurity, and governance boundaries. Existing resilience approaches address important parts of this problem - including cloud reliability, cyber resilience, AI risk management, operational continuity, and regulatory compliance - but generally treat them as separate domains. This fragmentation limits the ability of organizations to anticipate cross-layer disruptions, coordinate preventive and recovery controls, and convert operational experience into continuously improved resilience. This study develops the Unified Adaptive Cloud Resilience Framework (UACRF), a multi-layer, closed-loop framework for governing resilience in AI-cloud systems that support critical digital infrastructure. UACRF integrates four interdependent layers - infrastructure; platform and operations; application and AI lifecycle; and governance and compliance - with an eight-stage resilience lifecycle: Observe, Detect, Predict, Prevent, Withstand, Recover, Learn, and Adapt. The framework is grounded in continuous observability, predictive intelligence, cross-layer coordination, governed autonomy, closed-loop learning, and measurable resilience. It further specifies a cross-layer risk-propagation model, core constructs, and theoretical propositions that explain how integrated resilience capabilities can reduce detection delay, constrain failure propagation, improve recovery effectiveness, and strengthen long-term adaptive capacity. The paper contributes a common conceptual and operational language for connecting engineering controls, AI lifecycle governance, cybersecurity, and organizational decision-making. A cloud-agnostic reference architecture and an illustrative Amazon Web Services (AWS) mapping demonstrate how the theory can inform implementation without making the framework dependent on a single technology provider. UACRF therefore advances resilience from a collection of isolated controls toward a governed, measurable, and adaptive system capability. The proposed constructs and propositions provide a foundation for subsequent prototype development, fault-injection experiments, comparative evaluation, and sector-specific validation.

UACRF, AI-cloud resilience, critical digital infrastructure, adaptive governance, systemic risk…

Abena Pinamang, Nunana Klenam Djokoto

Drug courts have emerged as treatment-oriented alternatives to conventional criminal justice processing for individuals with substance use disorders. By combining judicial supervision with substance use treatment and behavioral health services, these programs seek to address factors associated with substance-related offending and recidivism. However, evidence concerning their reported outcomes, implementation, and associated challenges remains dispersed across disciplines. This scoping review mapped the literature on drug courts as an alternative approach to reducing recidivism. It examined the characteristics of drug court interventions, reported recidivism and rehabilitation outcomes, implementation strategies and challenges, and gaps in the existing evidence. The review was conducted using a scoping review methodology guided by the JBI approach and reported in accordance with PRISMA-ScR. Literature published in English between 2002 and 2026 was searched across major electronic databases. Eligible evidence addressing adult or juvenile drug courts, treatment courts, or related diversion interventions were studied and synthesized thematically. The literature indicates that drug courts are associated with a range of reported outcomes involving recidivism, treatment engagement, substance use, and rehabilitation. However, the evidence is not uniformly positive: while several studies report reductions in recidivism and improved treatment outcomes, other studies have found no significant differences between drug court participants and comparison groups, and some research has identified disparities in outcomes across participant subgroups and program contexts. Outcomes vary according to program characteristics, treatment quality, participant characteristics, implementation context, and study methodology. The findings highlight the need for consistent evidence-based practices, equitable service delivery, and further longitudinal and multisite research to clarify long-term outcomes.

Drug Courts, Alternative Sentencing, Recidivism, Substance Use Disorders, Criminal Justice

Felix Owusu

The emergence of Large Language Models (LLMs) has substantially expanded the potential role of artificial intelligence in scientific research. Beyond applications in text generation, summarization, and question answering, LLMs are increasingly being investigated for scientific knowledge synthesis, reasoning, hypothesis generation, and research automation. Their ability to process large volumes of scientific information and connect knowledge across domains offers new opportunities for identifying potential relationships, generating candidate hypotheses, and supporting the exploration of complex research problems. However, their application to scientific discovery remains constrained by hallucinations, factual inaccuracies, limited transparency, reproducibility challenges, bias, and uncertainty regarding the scientific validity and novelty of generated hypotheses. This review examines the evolving role of LLMs in scientific discovery, with particular emphasis on scientific hypothesis generation. We synthesize current approaches to LLM-based hypothesis generation, including prompt-based, iterative, retrieval-augmented, multi-agent, and tool-augmented methods, and examine their applications across scientific domains. We further discuss approaches for evaluating generated hypotheses, major technical and methodological challenges, and emerging directions involving human AI collaboration and autonomous scientific discovery. Overall, the review highlights the potential of LLMs to expand the space of scientific hypotheses while emphasizing that their contribution to scientific knowledge ultimately depends on rigorous evaluation, computational or experimental validation, and responsible integration into scientific workflows

Large Language Models, Scientific Discovery, Hypothesis Generation, Artificial Intelligence, Scientific Reasoning…