Employee training is expensive and time-consuming as evidenced by the fact that teaching new employees how to operate factory equipment is usually done in groups to save time and resources. For some industrial facilities, training often requires shutting down operations and putting employees and trainees in potentially dangerous situations. Flour mill operators are typically exposed to work hazards such as dust, excessive noise, explosions, electrocutions, and injuries from improperly guarded machinery. Thus, this paper addressed the issues previously mentioned by developing a virtual reality training software mainly focused on the procedures involved during break release measurement. It is the process wherein a mill operator takes samples from specific roller mills and measures the percentage of flour extracted from a flour milling machine. With this software, the flour mill machine is modeled under simulated conditions giving the user a fully immersive training experience without the fear or danger of experiencing real-life errors and accidents. Twenty-five flour mill operators participated in testing and evaluating the VR trainer software. The results indicated that 92% were satisfied with their training experience, 88% were pleased with the software’s functionality, and 98.67% gave positive ratings in the user satisfaction category of the VR training environment.

Finding available parking spots in urban areas is a challenging and time-consuming task due to inefficient traditional methods. This paper presents a smart parking solution that leverages You Only Look Once (YOLOv5) for car detection and body type classification, coupled with Simple Online Real-time Tracking (SORT) for continuous tracking of vehicle bounding boxes. This study introduces a straightforward 2D environment modeling to improve parking management. The proposed system demonstrates performance with a top precision of 0.992, a top recall of 1.0, a mean Average Precision (mAP) of 0.995 at IoU threshold 0.50, and a mAP of 0.953 across IoU thresholds 0.50–0.95. The color detection accuracy and parking occupancy detection also achieved a top score of 100% in the 30-day test experiment. Future work involves deploying the system in various sites to evaluate its robustness and effectiveness in diverse real-world conditions.

Abstract The development of complex software requires tools promoting fail-fast approaches, so that bugs and unexpected behavior can be quickly identified and fixed. Tools for data validation may save the day of computer programmers. In fact, processing invalid data is a waste of resources at best, and a drama at worst if the problem remains unnoticed and wrong results are used for business. Answer Set Programming (ASP) is not an exception, but the quest for better and better performance resulted in systems that essentially do not validate data. Even under the simplistic assumption that input/output data are eventually validated by external tools, invalid data may appear in other portions of the program, and go undetected until some other module of the designed software suddenly breaks. This paper formalizes the problem of data validation for ASP programs, introduces a language to specify data validation, and presents valasp, a tool to inject data validation in ordinary programs. The proposed approach promotes fail-fast techniques at coding time without imposing any lag on the deployed system if data are pretended to be valid. Validation can be specified in terms of statements using YAML, ASP and Python. Additionally, the proposed approach opens the possibility to use ASP for validating data of imperative programming languages.