Current smoke opacity certification methods are environmentally harmful and logistically inefficient, requiring real emissions, travel, and time off work. In this study, we present a Virtual Reality (VR) training and assessment system that simulates black and white smoke emissions in an immersive, controlled environment, designed to mirror real-world training and testing standards. Participants in the VR group completed two training sessions and a standardized test adapted from an official certification exam. Their performance was compared to a control group that received traditional lecture-based training and paper-based testing. Certification required a test score below 37 for both smoke colors. The VR group achieved mean scores of 33.44 (black smoke) and 57.75 (white smoke), outperforming the control group, which scored 46.00 and 72.00 respectively. The VR group significantly outperformed the control on black smoke$(p<0.05)$achieving passing scores on nearly all tested opacities ($<3$points (15%) deviation per reading). Subjectively, VR participants reported feeling more confident, better prepared, and found the application enjoyable. These results suggest VR is an effective, scalable alternative to traditional training, enhancing performance, increasing user satisfaction, and reducing environmental impact.

Access control policies (ACPs) are natural language statements that describe criteria under which users can access resources. We focus on constructing NIST Next Generation Access Control (NGAC) ABAC model from ACP statements. NGAC is more complex than RBAC or XACML ABAC as it supports dynamic, event-based policies, as well as prohibitions. We provide algorithms that use spaCy, a NLP library, to extract entities and relations from ACP sentences and convert them into the NGAC model. We then convert this NGAC model into Neo4j representation for the purpose of analysis. We apply the approach to various real-world ACP datasets to demonstrate the feasibility and assess scalability. We demonstrate that the approach is scalable and effectively extracts the NGAC ABAC model from large ACP datasets. We also show that redundancies and inconsistencies of ACP sentences are often found in unclean datasets.