Journal of Artificial Intelligence in Fluid Dynamics

Global vs Local Template Efficiency Tradeoffs in Large APEX Workspaces

2026-01-24T07:54:01+03:00

Large-scale Oracle APEX environments often span multiple workspaces with diverse application
domains, developer teams, and governance models. In such settings, the decision to adopt global shared
templates or local workspace-level template variants directly affects UI consistency, maintenance
overhead, and long-term sustainability of the application portfolio. This article examines the structural
tradeoffs between global and local template strategies, emphasizing how update propagation,
customization flexibility, and design system evolution behave differently in federated enterprise
deployments. Findings show that while global templates maximize consistency and streamline
modernization, they increase the impact radius of UI changes. Conversely, local templates support
domain-specific autonomy but introduce incremental divergence that increases refactoring cost over time.
The study concludes that a layered governance model enforcing global structural framing while enabling
selective local overrides yields the most balanced operational outcome in large APEX workspaces.

Index Selectivity Distortion Under Non-Uniform Data Distributions in Oracle DB

2026-01-24T07:55:59+03:00

Index selectivity plays a central role in Oracle’s cost-based optimization process, yet it becomes highly
unreliable when data distributions exhibit Zipfian or power-law characteristics. In such cases, a small
number of high-frequency values distort the optimizer’s cardinality estimates, leading to inefficient
index range scans, unstable execution plans, and inconsistent query performance. This study examines
how selectivity distortion emerges under non-uniform value frequencies, evaluates the limitations of
histogram-based statistical modeling, and analyzes the conditions under which adaptive cursor sharing
and parameter-sensitivity detection can stabilize plan behavior. The results show that no single tuning
feature is sufficient; performance stability requires coordinated alignment between statistics
maintenance, workload predictability, and physical data organization. When data lifecycle monitoring
and query structure governance are applied consistently, index performance becomes more robust, even
in highly skewed enterprise environments.

Model Degradation Behaviors in Continual Learning Lifecycles

2026-01-24T07:57:56+03:00

This article examines the mechanisms and manifestations of model degradation within continual
learning lifecycles, focusing on the progression of representational drift and catastrophic forgetting
during sequential model updates. A multi-layer analytical methodology was applied to observe how
internal neural representations, gradient interference patterns, and parameter importance distributions
evolve over time. The results demonstrate that degradation often begins in intermediate semantic layers
and can remain undetected at the performance level until later stages. Gradient conflict and task
dissimilarity were found to accelerate deterioration, whereas selective memory replay and dynamically
timed retraining mitigated these effects. The study concludes that continual learning stability requires
adaptive monitoring and intervention strategies to preserve performance integrity in evolving
operational environments.