Journal of Green Energy and Transition to Sustainability

Bias Amplification Dynamics in Generative Policy Expression Models

2026-01-23T08:01:21+03:00

Generative policy expression models are increasingly embedded in enterprise workflows to guide
decision routing, compliance enforcement, and advisory recommendations. However, their integration
introduces a risk of bias amplification, where model-generated outputs gradually influence operational
norms and shift organizational behavior patterns over time. This study examines how generative
models interact with workflow sequencing, user interpretation, and system state propagation,
demonstrating that even minimal representational skew can accumulate into structural bias within
enterprise processes. The results show that generative advisory systems tend to reinforce historically
dominant procedural pathways, compress the diversity of available decision alternatives, and shape
user expectations toward narrower interpretations of policy logic. These effects are often subtle,
distributed, and long-term, making bias difficult to detect without systemic analysis. The findings
underscore the need for governance-aware model deployment strategies and corrective oversight
mechanisms to prevent the institutionalization of unintended bias dynamics in enterprise
environments.

Causal Inference Layer Integration in Hybrid AI Reasoning Engines

2026-01-23T08:03:30+03:00

Hybrid AI reasoning engines are increasingly used in scientific computing to support model
interpretation, hypothesis testing, and exploratory analysis. However, without an explicit causal
inference layer, these systems tend to rely on correlation-based patterns that do not reliably generalize
across perturbations, parameter shifts, or evolving system conditions. This study evaluates the
integration of a causal reasoning layer into a hybrid inference architecture combining symbolic rules,
predictive models, and structured knowledge representations. Results show that the causal layer
improves interpretability, stabilizes reasoning under noisy or high-dimensional scientific data, and
produces more coherent backward-inference explanations while introducing only moderate
computational overhead. The findings demonstrate that causal inference is not merely an enhancement
but a foundational component for trustworthy scientific AI reasoning.

Constraint-Bounded Logical Inference in Hybrid Neuro-Symbolic AI

2026-01-23T08:05:13+03:00

Hybrid neuro-symbolic reasoning introduces the ability to combine adaptive neural policy learning
with explicit logical constraints required in industrial robotic environments. This article presents a
constraint-bounded inference framework that positions a symbolic rule layer between neural action
proposals and the robotic actuation pipeline, ensuring that decision-making remains interpretable,
safe, and operationally feasible during continuous production workflows. The results show that the
system maintains task continuity under constraint pressure, prevents unsafe behavior in dynamic or
partially observable conditions, and enables rapid updates to operational rules without retraining
neural models. The architecture also improves transparency by producing human-readable rationale
for action arbitration, supporting industrial audit and supervisory requirements. The findings
demonstrate that constraint-bounded logical inference provides a scalable foundation for safe and
reliable autonomous robotics in dynamic manufacturing scenarios.

Custom JavaScript Injection Boundaries within APEX Protected Components

2026-01-23T08:08:06+03:00

Internal enterprise APEX dashboards frequently incorporate custom JavaScript to enhance
interactivity and support dynamic visualization, but uncontrolled script placement can cause execution
behavior to drift across page refresh cycles and undermine session state protections. This article
examines how JavaScript interacts with APEX’s rendering architecture and identifies boundaries that
determine whether script logic executes within controlled or unprotected contexts. A structured
injection boundary model was developed using execution, declaration, and state interaction layers to
guide where and how JavaScript should be introduced. The results demonstrate improved application
consistency, reduced risk of unnoticed logic overrides, and simplified maintainability when scripts are
centralized, lifecycle-aware, and separated from workflow state manipulation. This boundary-based
approach ensures secure and predictable interface customization in internal enterprise environments.