This paper presents a systematic methodology for characterizing AI workload performance on CPU architectures through profiling, optimization, and analysis. We outline a complete framework encompassing workload selection, performance profiling using Linux perf tools, targeted optimization, multi-core scaling analysis, and system monitoring. This methodology provides a reusable framework for performance engineers across different architectures and deployment scenarios.

Securing REST APIs has become a critical engineering priority as modern applications increasingly rely on distributed microservices and cloud-native architectures. Spring Boot, with its opinionated design philosophy and seamless integration with the Spring Security ecosystem, provides a versatile platform for implementing advanced authentication, authorization, and threat-mitigation strategies. This paper examines the essential security challenges associated with public-facing APIs, including identity verification, token management, transport security, and endpoint hardening. It synthesizes established best practices with emerging techniques such as OAuth2 Resource Servers, JWT-based access control, zero-trust patterns, and API-gateway-augmented threat filtering. Through an in-depth architectural analysis and a practical case study, the work demonstrates how secure design principles can be translated into robust and scalable enterprise-grade API systems. The results highlight measurable improvements in integrity, confidentiality, and resilience under load, offering a reference blueprint for practitioners building secure REST APIs in Spring Boot.

The fundamental architecture of credit risk assessment is currently transitioning from traditional, human-centric judgmental processes to highly sophisticated, data-driven underwriting models. This evolution is necessitated by the persistence of a massive "credit invisible" population—individuals who, despite financial stability, lack the historical transactional data required by traditional credit reporting agencies. This report provides an exhaustive analysis of the emergence of alternative data as a primary tool for enhancing predictive accuracy and financial inclusion. It categorizes alternative data into financial, non-financial, behavioral, and psychometric dimensions, examining the mechanism through which each category informs creditworthiness. The study further explores the technological underpinnings of this shift, including Big Data analytics platforms like Hadoop and Spark, and supervised machine learning algorithms such as XGBoost and Random Forest. Central to the narrative is the challenge of algorithmic bias and the theoretical phenomenon of "breaking causation," where predictive correlations defy human intuition and normative legitimacy. The report details a comprehensive governance framework involving data stewardship, end-to-end lineage, and multi-layered bias mitigation strategies. By synthesizing regulatory trends across the U.S. and Europe, including the Fair Credit Reporting Act and the EU AI Act, the research concludes that responsible data-driven underwriting requires a paradigm shift from simple accuracy toward holistic transparency and socio-economic equity.

In large-scale enterprise environments, onboarding disparate asset teams to a centralized Customer Data Lake (CDL) or similar enterprise data repository is often bottlenecked by manual Gap Analysis. This process involves comparing legacy asset tables with the central lake to identify schema overlaps and missing information, a task complicated by non-standardized column naming and vast data volumes. This paper proposes an automated framework for schema matching that utilizes a hybrid approach. We employ a metadata module using Jaro-Winkler string similarity with a domain-specific abbreviation dictionary, alongside a data content module utilizing K-Nearest Neighbors (KNN) classification on feature-engineered embeddings. A unique feedback loop allows the data model to iteratively improve the metadata dictionary. Experimental results demonstrate an F1 score of approximately 90.7%, significantly reducing manual mapping efforts and streamlining the onboarding process.

Automation plays a pivotal role in modern IT ecosystems, facilitating scalable deployment, consistent configuration, and rapid delivery across complex environments. However, the very scale and speed it enables can also exacerbate failures when misconfigured. This study investigates three enterprise-scale automation incidents across industries e-commerce, software-as-a-service (SaaS), and fintech and synthesizes cross-case insights to guide future implementation. We analyze the sociotechnical root causes, including human oversight, environment misconfiguration, and insufficient guardrails, and propose a set of resilience strategies backed by both qualitative case data and academic literature. The study underscores the importance of environmental isolation, access control, observability, and recovery planning in preventing and containing failures. Findings are intended to support IT professionals, site reliability engineers, and organizational leaders in building more fault-tolerant automation systems.

As enterprise data lakes continue to scale to petabyte ranges, the limitations of traditional data layout strategies namely static Hive-style partitioning and Z-Ordering, have become increasingly pronounced. These strategies suffer from data skew, partition explosion, rigid schema dependencies, and costly write amplification, all of which degrade query performance and inflate total cost of ownership (TCO). This paper presents a comprehensive investigation into Liquid Clustering, Databricks' next-generation adaptive data layout framework built atop the Delta Lake protocol. We examine the foundational architecture of Liquid Clustering, including its integration with the Delta Lake transaction log, its incremental write model, and the Predictive Optimization engine powering Automatic Liquid Clustering. We analyze performance benchmarks demonstrating up to 10× query acceleration and 90% data-skipping improvement over traditional methods (per Databricks production benchmarks [4]). Four industry-specific case studies are presented spanning e-commerce, financial services, IoT telemetry, and digital media advertising to illustrate real-world deployment patterns, observed gains, and implementation challenges. We further discuss data volume thresholds, multi-dataset governance strategies across the Medallion Architecture, and cost verification methodologies. The paper concludes with an outlook on emerging trends, including AI-driven autonomous data layout management, integration with serverless Lakehouse platforms, and cross-engine interoperability.

The modern clinical diagnosis and disease monitoring have become impossible without medical imaging techniques like Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and X-ray. The recent developments in Artificial Intelligence (AI) and especially deep learning have contributed greatly to the automated interpretation of complicated medical images. Although such developments have been made, current AI-based diagnostic systems have been characterized by crucial weaknesses, such as low cross-modality generalization, susceptibility to imaging noise and imaging artifact, as well as little insight into diagnostic judgments. These issues decrease the clinical trust and limit the wide adoption of AI systems in the practical healthcare setting. The rationale behind this study is to come up with a more dependable and smarter diagnostic model that can enhance the precision, strength, and elucidations of AI-run medical imaging models. This paper presents a new Cognitive-Aware Medical Imaging Architecture (CAMIA) that combines the dynamic context-aware learning of features with adaptive diagnostic reasoning. In contrast to traditional methods, the suggested framework proposes Self-Evolving Diagnostic Embedding’s (SEDE) which continually updates the feature representations with the help of multi-scale anatomical patterns. Also, the Hierarchical Cross-Modality Attention Mechanism (HCAM) is introduced to support the simultaneous analysis of heterogeneous imaging modalities. The main contributions of the study are the architecture of a fourth generation smart imaging system, the presentation of self-evolving feature models to adaptive diagnosis and effective cross-modality inference model that provides increased diagnostic accuracy. In experimental assessments, the suggested framework has been shown to have a substantial impact on the reliability of detection and the visibility of decisions made in multidimensional clinical imaging conditions.

This analysis investigates the implementation of the SAP Warehouse Management System (WMS) in a manufacturing environment, specifically assessing its effects on internal material replenishment, inbound receiving, and inventory management procedures. Prior to the WMS's introduction, the facility's operations were characterized by manual item transfers between storage bins, the utilization of unstructured pallet storage, and the need for redundant data entry. This led to inaccurate inventory counts, longer search times, and more frequent production interruptions. The new system, however, included specific storage areas for each stock-keeping unit (SKU), simplified the goods receipt process in SAP, and set minimum and maximum inventory levels. The implementation, which lasted over six and a half months and was tested across three busy production lines, led to significant improvements. Inventory accuracy surged from approximately 60% to 98%, while material-related downtime was reduced from 32% to a range of 2–3%. Consequently, the amount of transactional labor needed in the warehouses diminished by a quarter. These findings suggest that enhancements in layout optimization, the application of ABC-based slotting techniques, and the refinement of ERP configurations can substantially elevate operational efficiency, even in the absence of sophisticated automation technologies.

Chronic diseases remain among the leading causes of preventable illness, disability, mortality, and healthcare expenditure in many national health systems. Traditional public health approaches have contributed significantly to disease prevention, but they often rely on broad population-level strategies, delayed surveillance, and reactive intervention models that may fail to identify high-risk individuals and communities early enough. This paper proposes an AI-powered precision public health framework for targeting chronic disease prevention and early intervention at national level. The framework integrates electronic health records, public health surveillance data, social determinants of health, genomic risk information, and predictive analytics to support earlier risk detection, population segmentation, and tailored prevention strategies. It emphasizes the use of artificial intelligence not as a replacement for public health expertise, but as a decision-support mechanism for improving resource allocation, screening, care coordination, and intervention timing. The paper also highlights the ethical, governance, and equity challenges associated with AI implementation, including algorithmic bias, privacy risks, data quality limitations, transparency, and public trust. It argues that AI-powered precision public health can strengthen chronic disease prevention when implemented through responsible governance, human oversight, equity-focused design, and continuous learning health systems. The proposed framework provides a national policy direction for improving early detection, reducing preventable complications, and promoting more targeted and equitable health outcomes.

This study aims to explore the connection between educational leadership, technology use, and students' academic achievement in an electronic-based learning environment. The focus on how the functions of leadership behavior and leadership support in technology adoption affect learners. A quantitative approach was used in this research involving 100 participants, including students, educators, and educational leaders. Descriptive analysis, Spearman correlation, ordinal regression, and multiple regression were used as statistical tools for testing relationships among the examined variables. The results show that technological integration and strategic educational leadership have a somewhat favorable link (ρ = 0.538, p < 0.001). This suggests that more successful technology integration results from greater levels of strategic educational leadership. In a similar vein, a strong positive connection (ρ = 0.627, p < 0.001) was found between student accomplishment and technological incorporation. It implies that technology-integrated learning environments improve the performance of students. Moreover, the findings indicate that leadership support is a strong predictor of technology adoption (β = 2.380, p < 0.001) accounting for 55.8% variance in adoption levels (Nagelkerke R² = 0.558). In the combined regression analysis, both strategic educational leadership (β = 0.781, p = 0.021) and technology integration (β = 0.865, p = 0.006) were found to predict student performance accounting for 36.8% variance (R² = 0.368). The research has indicated that performance can be considerably enhanced through a combination of educational leadership and technology utilization. However, challenges such as inadequate infrastructure, digital divide, and digital illiteracy keep hindering its effective implementation, particularly in nations like Zimbabwe.