Background: 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. Objective: 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 & Initiation, (2) Policy & System Design, (3) Implementation & Operation, (4) Evaluation & Adaptation, (5) Consolidation & 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. Results: 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. Conclusions: 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.

The world of the stock market is an intricately complex financial ecosystem that demands years of dedicated study to comprehend fully. It relies on risk mitigation practices and fundamental theoretical techniques to engage in speculation regarding stock and cryptocurrency fluctuations. However, this realm is progressively becoming more inclusive, with accessibility expanding beyond traditional educational barriers. Technological advancements, coupled with the ease of entry into this domain and the information-disseminating power of social networks, contribute to a rising number of individuals participating in this financial movement. What makes this evolution disruptive is that the same tools facilitating accessibility also exert influence on the way market trends unfold. This paper delves into the escalating impact of social media within the financial sphere, emphasizing the heightened accessibility to information and market involvement facilitated by platforms like Twitter and Reddit. It sheds light on how social media plays a pivotal role in market manipulation, as evidenced by phenomena such as the r/wallstreetbets subreddit, where meme-based strategies were employed to inflate the prices of stocks like GameStop. The study explores the utilization of social media by influential figures, exemplified by Elon Musk, who leverage their platforms to sway market movements. Additionally, this paper addresses instances of misinformation, such as the confusion surrounding Virgin Galactic's shares following a SpaceX failure and the introduction of "AGUA" in the Mexican stock market, leading to widespread misunderstandings. The paper extends its examination to the effects of social media on cryptocurrencies, highlighting how comments from public figures can significantly impact the prices of Bitcoin and Dogecoin. Overall, it underscores the imperative need for adaptation to these changes in the digital financial paradigm.

The unrelenting proliferation of data, entwined with the prevalence of mobile devices, has given birth to an unprecedented growth of information obscured by noise. With the Internet of Things and myriad endpoint devices generating vast volumes of sensitive and critical data, organizations are tasked with extracting actionable intelligence from this deluge. Governments and enterprises alike, even under pressure from regulatory boards, have strived to harness the power of data and leverage it to enhance safety and security, maximize performance, and mitigate risks. However, the adversaries themselves have capitalized on the unequal battle of big data and artificial intelligence to inflict widespread chaos. Therefore, the demand for big data analytics and AI/ML for high-fidelity intelligence, surveillance, and reconnaissance is at its highest. Today, in the cybersecurity realm, the detection of adverse incidents poses substantial challenges due to the sheer variety, volume, and velocity of deep packet inspection data. State-of-the-art detection techniques have fallen short of detecting the latest attacks after a big data breach incident. On the other hand, computational intelligence techniques such as machine learning have reignited the search for solutions for diverse monitoring problems. Recent advancements in AI/ML frameworks have the potential to analyze IoT/edge-generated big data in near real-time and assist risk assessment and mitigation through automated threat detection and modeling in the big data and AI/ML domain. Industry best practices and case studies are examined that endeavor to showcase how big data coupled with AI/ML unlocks new dimensions and capabilities in improved vigilance and monitoring, prediction of adverse incidents, intelligent modeling, and future uncertainty quantification by data resampling correction. All of these avenues lead to enhanced robustness, security, safety, and performance of industrial processes, computing, and infrastructures. A view of the future and how the potential threats due to the misuse of new technologies from bandwidth to IoT/edge, blockchain, AI, quantum, and autonomous fields is discussed. Cybersecurity is again playing out at a pace set by adversaries with low entry barriers and debilitating tools. The need for innovative solutions for defense from the emerging threat landscape, harnessing the power of new technologies and collaboration, is emphasized.

The pharmaceutical industry relies extensively on Active Pharmaceutical Ingredients (APIs) as essential components in the production of drugs. The supply chain supporting these APIs is complex, encompassing multiple stages from raw material sourcing to distribution to pharmaceutical manufacturers worldwide. This manuscript explores the intricate challenges encountered within the pharmaceutical API supply chain, focusing on regulatory compliance, quality control, supply chain disruptions, and global dependencies. Regulatory compliance poses a significant hurdle, with varying standards across regions necessitating meticulous adherence to ensure market access and product safety. Quality control and assurance are paramount to maintaining consistency and purity in APIs, yet they present ongoing challenges such as batch variability and contamination risks. Supply chain disruptions, ranging from natural disasters to geopolitical tensions, highlight vulnerabilities in global sourcing strategies, underscoring the need for resilient supply chain management practices. Global dependencies on a limited number of suppliers or regions expose the industry to supply shortages and pricing pressures, exacerbated by geopolitical events and trade policies. These dependencies necessitate strategic diversification and risk mitigation efforts to ensure continuity in API availability and affordability. By addressing these challenges collaboratively, stakeholders can enhance the resilience and reliability of the pharmaceutical API supply chain, thereby ensuring uninterrupted access to essential medications and improving global healthcare outcomes.

The management of supply chains is increasingly complex. This study provides a comparative analysis of the cost-benefit analysis for managing various risks. It identifies the financial implications of leveraging artificial intelligence in supply chains to better address risk. Empirical results show a business case for managing some sources of risk more proactively facilitated through predictive modeling techniques offered by AI. Across investigation streams, the use of AI results in an average total cost saving ranging from 41,254 to 4,099,617. Findings from our research can be used to inform managers and theorists about the implications of integrating AI technologies to manage risks in the supply chain. Our work also highlights areas for future research. Given the growing interest in studying sub-second forecasting, our research could be a point of departure for future investigations aimed at considering the impact of forecasting horizons such as an intra-day basis. We formulate a conceptual framework that considers how and to what extent performance evaluation metrics vary according to differences in the fidelity of predictive models and factor importance for identifying risks. We also utilize a mixed-method approach to demonstrate the applicability of our ideas in practice. Our results illustrate the financial implications of integrating AI predictive tools with business processes. Results suggest that real-world companies can circumvent inefficiencies associated with trying to manage many classes of risk via the use of AI-enhanced predictive analytics. As managers need to justify investment to top management, our work supports decision-making by providing a means of conducting a trade-off analysis at the tactical level.

Migrating enterprise policy systems to .NET Core is a key objective of digital transformation in the Insurance IT ecosystem. This change directly addresses strategic drivers: enabling adoption of cloud-first development, resisting market pressure for more flexible and usable enterprise solutions, and preparing for changing demands from regulation and compliance. Phases of operational benefit aligned with risk mitigation form the basis of the migration roadmap, with a strong focus on engaging all relevant stakeholders. Market pressure for a SEAMLESS user experience across ALL applications is a fundamental driver for Investment in digital transformation. Gaps remain in enterprise Operations, where Legislative and regulatory accountability Demand rigid and complex solutions that Liberty has not yet been able to provide. New risk-based capital requirements, Data-Sovereignty controls, Controls for sensitive Data in the Cloud, and new Audit requirements create a long list of challenges for the ecosystem that can no longer be Deferred. At the same time, Cross-organisational integration is becoming more important and integrating partners from the insurance supply-chain requires a much more flexible approach to development and Deployment. These factors combine to generate a credible case for accelerated digital investment with a focus on Migration to Cloud Platforms, with related Risk mitigation, Quality Improvements, and flexibility benefits that close Industry gaps.

The banking sector has shown a strong interest in scaling out and utilizing the microservices architectural pattern within their payments domain, not only to manage increased transaction volumes, but also for compliance and risk-related control. Financial organizations are adopting containerization technologies like Kubernetes and Docker to align with the microservices paradigm. Containerization provides the foundation for automation and operational excellence of microservice-based applications by enabling continuous deployment and automated build-test-release cycles. However, deploying a Kubernetes cluster and the services it hosts in production is not sufficient to guarantee a secure and compliant operating environment. Kubernetes itself should be secured to protect workloads, and risks associated with the services being deployed must be managed continuously.