The integration of Artificial Intelligence (AI) in public health surveillance is transforming the identification and management of outbreaks. Traditional systems typically suffer from delay, isolated data, and reactive designs, which hamper their potential for timely detection of emerging threats. Recent developments in machine learning, natural language processing, and real-time data fusion are shifting the paradigm to proactive and programmatic surveillance. This article analyzes the technical basis, current application, and strategic implications of AI-based surveillance for outbreak identification. A modular approach is proposed to guide implementation across contexts. Drawing on international case studies, from COVID-19 early detection onwards, the article lays out the promise and risks of embedding AI in modern public health systems.
public health surveillance, artificial intelligence, outbreak detection, digital epidemiology, early warning systems
Erick Sewando Mkuchu, Mary Magdalene Yeboah, Solomon Doe Adjaottor
This review discusses cyber resilience in the context of U.S. repurchase agreement (repo) and short-term funding markets set against a backdrop of liquidity stress scenarios affecting their capital adequacy implications amid an evolving digital financial landscape. The research examines the potential of cyber threats directed at several payment systems, FMIs, and settlement networks to disrupt flows of liquidity and how that can drive systemic risk and undermine the credibility of existing regulatory frameworks. It looks at operational resilience, the use of stress-testing methodologies and prudential standards to enhance financial institutions' capacity to absorb shocks stemming from cyber events. The review underscores the desirability of comprehensive approaches that integrate cybersecurity, liquidity risk management and capital planning for stability in wholesale funding markets.
Cyber resilience, Repo markets, Liquidity stress, Capital adequacy, Operational risk.
Gabriel Acheampong, Adwoa Agyeiwaah Ampomah-Britwum
The idea of using after-school mathematics and robotics competitions to boost participation in STEM has become a popular policy and educational measure among policymakers and educators. This approach is based on the idea that early, low-stakes experience with competitive problem solving instills the identity and self-efficacy needed to sustain a path in STEM disciplines. This review compiles existing peer-reviewed research on the link between competitive STEM enrichment and persistence among women, racial/ethnic minority students, and low-income students in the United States. Instead of systematically exploring one type of intervention, we combined results from three related literatures (mathematics contests, educational robotics leagues, and science fairs) to empirically describe what each of these literatures can teach about persistence among students. The more evidence is found and the stronger the causal connection, the more effective the comprehensive STEM summer enrichment programs are in leading to measurable increases in college STEM persistence. In contrast, the results of mathematics competitions are descriptive and frequently show a significant and pervasive demographic gap. Evidence on the impact of robotics leagues is mixed in terms of the nature of the data found and the statistical power behind the findings. Evidence shows a consistent positive short-term impact on student attitudes, while evidence of long-term impact on identity and demographic outcomes is less consistent and less statistically powerful. There is an ongoing debate on the role of competitive framing in supporting or impeding persistence among students who may be at risk for math anxiety and stereotype threat. The field has invested heavily in scaling competitive STEM programs without first determining if competition is the "active ingredient. There is a great deal of investment in scaling up competitive STEM programs without establishing either if there is or isn't a competition effect as the "active ingredient", which has serious implications for the allocation of limited enrichment resources.
STEM persistence, math competitions, educational robotics, underrepresented minorities, after-school enrichment…
Gabriel Acheampong, Adwoa Agyeiwaah Ampomah-Britwum
As the need for data-rich industries continues to grow and the nation has more citizens who require quantitative literacy, the demand for secondary mathematics to go beyond procedural learning has been increasing in the United States. Algebra II is an important gateway course that connects basic algebra with precalculus, is an under-used opportunity to integrate data science skills into the architecture of algebra and is a place of significant opportunity within the K–12 continuum. This narrative review aims to consolidate recent peer-reviewed scholarship (2020–present) to explore the potential for sensibly embedding data science foundations including statistical reasoning, data visualization, exploratory data analysis and computational thinking in the US Algebra II curriculum using evidence-informed pedagogical approaches. This review first maps the pedagogical landscape by interpreting the existing literature on mathematics education, educational technology, equity research, and instructional design through an expert-driven interpretive synthesis and then identifies pedagogical actionable insights. The review brings to light five interrelated themes: transition to a data-focused approach to algebra instruction, using computational thinking to expand access to data literacy, embedded systemic inequities as a result of unequal technology distribution, assessment reform as a leverage point for challenges, and the importance of teacher readiness as a support for fidelity in implementing. There is a conflict between breadth demands of a compressed curriculum and depth needed for authentic data inquiries. Research also suggests that if no effort is made to achieve this equitable scaffolding, integration can widen inequities. Data science can have great promise for improving mathematical literacy in the 21st century when integrated into Algebra II. It is, however, necessary to align policies, provide in-depth professional development for teachers, make digital infrastructure accessible and redesign assessment systems to make them successful. More research is needed to understand how specific pedagogies relate to measurable student learning, especially among historically marginalized student groups.
Data Science Education, Algebra II Pedagogy, Computational Thinking, Mathematical Literacy, K–12 Curriculum Integration
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…
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
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…
