<p>T his paper investigates the theoretical foundations of polyglot persistence by grounding the discussion in aggregate-oriented database principles and Domain-Driven Design (DDD). It examines mechanisms for integrating heterogeneous data stores-such as MongoDB–Neo4j synchronization pipelines, APOC (Awesome Procedures on Cypher) and Change Data Capture (CDC)and evaluates their implications for consistency and real-time data propagation. The study further analyzes the major challenges associated with polyglot adoption, including data consistency, synchronization overhead and operational complexity. While polyglot persistence offers improved flexibility, scalability and performance, the paper argues that these benefits require careful architectural planning and governance to mitigate inherent trade-offs. Additionally, the paper reviews containerization and Database-as-a-Service (DBaaS) deployment models, highlighting their impact on consistency, security and cost. It concludes with a forward-looking assessment of emerging trends-such as AI-driven orchestration and autonomous data fabrics that are poised to influence future distributed data system architectures.</p>

<p>Existing financial systems are bloated with inefficiencies in their operation, lack of transparency and are characterized by and fallible and fragile accumulation points, whereas emerging decentralized finance (DeFi) platforms lack intelligent risk management, self-adaptive governance and provable security assurances. This paper proposes the Intelligent, Verifiable Financial Ledger (IVFL), a novel framework that harmoniously converts both Artificial Intelligence (AI) and blockchain to counteract their core drawbacks. AI-based smart contracts of a formally verifiable character that allows the intelligent, secure and auditable automated execution of complex financial transactions an agile and informed governance system, which is represented by the use of AI enhancements to the Decentralized Autonomous Organization (DAO). Simulation analysis shows that the IVFL framework enables substantial enhancements compared to baseline models, such as detecting anomalies with over 95% accuracy, decreasing operational overhead by 40 percent and becoming less vulnerable to coordinated network attacks. Coming back to provable security and adaptive intelligence, the IVFL framework represents a credible way of creating financial systems.</p>

The globalization of enterprises has intensified the demand for intelligent, language-agnostic Information Management Systems (IMS) capable of understanding and acting upon documents across diverse linguistic and cultural contexts.Traditional monolingual and rule-based pipelines are increasingly inadequate for managing heterogeneous data landscapes where contracts, reports, and regulatory filings span dozens of languages.This paper proposes a comprehensive architectural framework integrating Multilingual Large Language Models (mLLMs) within IMS to achieve cross-lingual document intelligence.By embedding multilingual comprehension, semantic alignment, and decision orchestration across five layers ingestion, embedding alignment, extraction, orchestration, and feedback the framework enables unified, context-aware document-to-decision workflows.It explores how mLLMs such as XLM-R, mT5, and GPT-based variants empower IMS to transcend translation-based methods, fostering inclusive, adaptive, and explainable enterprise ecosystems.The paper further analyzes key challenges in language bias, semantic drift, computational overhead, and governance and outlines future research directions including federated multilingual processing, low-resource language adaptation, and explainable cross-lingual AI.Ultimately, this work positions multilingual LLM-driven IMS as the foundation of globally coherent, ethically governed enterprise intelligence, where linguistic diversity becomes a catalyst for innovation rather than a barrier to understanding.

In a time of instantaneous decision making, streaming data has become an essential tool for customer analytics.The reality is that organizations can leverage real-time insights if they can make sense of the vast, fast data coming from digital and social platforms, IoT devices, and transactional systems.However, making the best use of this data requires machine learning (ML) models that are both accurate and interpretable.As soon as explainable artificial intelligence (XAI) started to gain traction, demand for models able to provide interpretability and justifiability increased even further, particularly in consumer-facing domains characterized by a high degree of reliance on trust, fairness, and regulation.We present a framework for explainable customer analytics in streaming systems, based on the use of interpretable machine learning models.In contrast to traditional batch-learning techniques, our method focuses on low-latency prediction, model adaptability, and human-understandable insights for streaming data.Chapter 2 explains the following customer analytics tasks: churn prediction, segmentation, personalization, lifetime value estimation, and the challenges associated with real-time data processing.We consider recent interpretable ML approaches, such as decision trees, rule-based classifiers, monotonic gradient boosting, and model-agnostic post-hoc explanations (e.g., LIME and SHAP).These approaches are analysed in terms of their suitability for streaming architectures and computational complexity.The paper concludes with a data engineering pipeline implemented on top of Apache Kafka and Apache Flink, which collects and preprocesses an online training dataset, then serves real-time data to the lightweight, interpretable models.We also use concept drift detection techniques to ensure the model's relevance over time.To verify the proposed method, we conducted experiments using publicly available customer data and a simulation of streaming with augmented customer data.The performance of models is evaluated not only on prediction accuracy, but also on interpretability measures, including model fidelity, coverage, and stability.The findings indicate that tree-based models supplemented with SHAP explanations achieve an acceptable trade-off between (real-time) performance and interpretability.We also discuss case studies where the model recommendations are transformed into business actions, such as delivering retention incentives or updating product recommendations, and demonstrate how interpretability strengthens trust among stakeholders and enhances operational efficiency.Although we leave these studies out of scope, in these works, ethical and regulatory considerations of nontransparent ML (including in the case of customer analytics in GDPR, CCPA, and the future AI Act) are considered in detail.Explainability enables organizations to provide customers with meaningful explanations for automated decisions, thereby mitigating legal risk and enhancing the acceptance of AI-driven insights.A roadmap is developed to describe how to integrate explainable machine learning with enterprise-level customer analytics platforms.We provide guidance on model selection, explanatory tools, streaming processing infrastructure, and organizational governance.Our results suggest that a shift from black-box optimization to a more transparent and responsible AI in customer analytics is warranted when decisions have a significant impact on consumer experience and loyalty.

Vaccines, asymptomatic disease screening and lifestyle recommendations are among the preventive health interventions.These services are advised depending on personal aspects like gender, age and risk factors in order to improve health and welfare.The "U.S. Preventive Services Task Force (USPSTF, 2004)" advises doctors to test those with high syphilis infection risk.Asymptomatic persons not at increased risk for syphilis infection should not be routinely screened.The "United States Preventive Services Task Force (USPSTF)" reiterated in 2009 its advise for all pregnant women to get syphilis testing.Supported by the "American Academy of Family Physicians (AAFP, 2009)" clinical policy statement, the USPSTF has suggested screening all pregnant women and those at increased risk for syphilis.Screening at-risk people for syphilis infection can help to lower morbidity.Preventive therapy includes lifestyle recommendations, vaccinations and asymptomatic sickness detection tests.Every decade adults should have a tetanus toxoid injection, a pneumonia and pneumococcal immunization, an annual influenza booster and a herpes zoster vaccination (regardless of past infection).To help doctors make tailored patient decisions, the United States Preventive Services Task Force has developed an easily navigable website (http://epss.ahrq.gov/ePSS/search.jsp).Considerations of the patient's age, gender, smoking history and present sexual activity, Younger old people should be screened for blood pressure, diabetes, hyperlipidemia, obesity, alcohol abuse, colorectal cancer, osteoporosis and breast cancer in addition to receiving suitable counseling and treatment should these diseases be found.In this group, aspirin might be advised to lower their risk of cardiovascular disease.Any further advice will depend on the risk factors connected to the patient.Reduced life expectancy and increasing comorbidities call for less preventative actions for elderly people.The elderly should so avoid some preventive actions, like cancer screenings or aspirin intake to reduce cardiovascular disease risk.Without proof on preventative care, it is imperative to provide recommendations for every patient depending on their own priorities, values and beliefs.

Artificial intelligence (AI) has emerged as a transformative force across various domains of software infrastructure.From automating software development workflows to optimizing cloud resource allocation, AI-driven advancements promise increased efficiency, scalability and security.However, the extent to which AI genuinely enhances software infrastructure remains an open question.This paper explores the tangible improvements AI has introduced to software infrastructure, examines its limitations and evaluates whether AI is fundamentally reshaping software development or merely augmenting existing processes.

BackgroundCardiovascular disease (CVD) is the leading cause of death in the United States, but the importance of gender differences in the prevalence and burden of various CVD has only been acknowledged since the mid-eighties. 8 Beyond the basic description and quantification of these gender differences, some researchers have attempted to identify bias and develop hypotheses related specifically to gender-specific CVD treatment and outcomes.One of the physicians at the forefront of this research was Dr. Bernadette Healy, who in 1991 was the first to present on Yentl Syndrome, which suggested gender bias in coronary A B S T R A C TCardiovascular disease (CVD) remains the leading cause of death in the United States, with gender disparities in presentation and outcomes increasingly recognized.Women with acute coronary syndrome (ACS) often experience atypical symptoms and are subject to care-seeking delays, potentially contributing to poorer prognoses.This literature review aimed to evaluate the extent of treatment delay in female patients with ACS and to assess its relationship with clinical outcomes.Using a structured search of the PubMed database, 14 articles published in the past decade were initially identified, with 11 meeting inclusion criteria following relevance screening.Among the selected studies, five reported statistically significant delays in treatment for women compared to men, with women more likely to present later after symptom onset and experience additional in-hospital delays.These delays were associated with increased short-term mortality, with one study reporting a twofold increase in 30-day mortality among women.Conversely, five studies did not find statistically significant differences, though several showed trends toward longer delays in women.Variability in findings may be attributed to small sample sizes or improved public awareness of sex-specific ACS symptoms in recent years.Several studies identified atypical symptom perception, lack of symptom recognition, and social factors as key contributors to delayed care-seeking behavior among women.Interviews with female patients revealed that many did not perceive their symptoms as consistent with myocardial infarction, leading to delayed presentation.These findings highlight the continued presence of gender-based disparities in ACS treatment and outcomes.Efforts to educate both patients and healthcare providers about atypical symptomatology in women, as well as system-level interventions to reduce delays, may improve timely care and reduce mortality.Further research is needed to better understand modifiable barriers to prompt treatment in women and to design targeted strategies that address these gaps.

Artificial Intelligence (AI) is revolutionising project management by offering a suite of advanced tools and techniques that optimise planning, execution, and monitoring processes.This study explores the integration of cutting-edge AI technologies, including Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), and Reinforcement Learning (RL), across traditional and hybrid project management methodologies, such as Agile, Waterfall, and Lean.Empirical studies demonstrate AI's transformative impact, highlighting its ability to address inefficiencies inherent in conventional methodologies while equipping organisations with the agility to meet evolving project demands.AI-driven solutions provide actionable insights through predictive analytics, enabling organisations to pre-emptively identify bottlenecks, allocate resources dynamically, and mitigate risks.Sentiment analysis tools facilitate enhanced stakeholder engagement by monitoring team dynamics and communication patterns and fostering timely interventions.CV applications in resource-intensive industries, such as construction and manufacturing, improve the precision of quality control, safety compliance, and progress tracking.RL offers a strategic advantage by simulating various scenarios to enhance risk management and optimise decision-making processes.Despite its transformative capabilities, AI adoption in project management faces challenges such as fragmented data infrastructure, algorithmic biases, and the opacity of advanced models.Addressing these challenges requires robust data governance frameworks, ethical guidelines, and cross disciplinary collaboration to ensure transparency, accountability, and fairness.Innovative technologies, such as federated learning and generative AI, offer significant potential to propel the field forward by ensuring data privacy, streamlining workflow automation, and fostering adaptive project environments.Integrating AI's analytical precision with human creativity enables organisations to develop scalable and adaptive solutions, effectively addressing the complexities of modern project landscapes.This synergy establishes AI as a fundamental pillar of project management excellence, empowering organisations to drive sustainable innovation and efficiency in an ever evolving global environment.Offering actionable insights and recommendations, this study highlights the importance of strategic and ethical AI integration to fully realise its transformative potential in project management. Computer Vision (CV)In industries such as construction and manufacturing, CV technologies monitor physical resources, ensure safety compliance, and track asset utilisation through real-time image and video analyses 10 . Reinforcement Learning (RL)RL algorithms dynamically adjust project schedules and resource allocations based on ongoing performance metrics and are especially useful in highly variable environments, such as software sprints or large-scale engineering projects 3 . 2.