Betulin-Loaded Topical Gels: A Novel Phytotherapeutic Approach for Arthritis Management.

Abstract

Carcinogen risk exposure within manufacturing environments is a major occupational health risk which could have long-term implications for workers. The basic methods of monitoring carcinogen exposure include periodic inspections and periodic sampling, which cannot ensure real-time detection and prevention risk. This article presents a novel carcinogen risk assessment framework to combine IoT-enabled AI sensors with predictive technology to potentially address this issue. The framework proposed here would continuously monitor environmentally hazardous conditions in manufacturing settings where carcinogens exist. It would reliably detect whether harmful chemicals, particulate matter, or gases which could pose carcinogenic risks are present. The hazard prediction-program would use best practice machine learning (ML) models to predict exposure levels before they reach potentially hazardous levels, enabling workers to take preventative action in order to avoid the hazard altogether. Subscriber data-stream from IoT sensors that can detect hazardous gases or particulate matter will be accessed via edge AI modules for primary analysis, while cloud-based predictive modeling (i.e., long short-term memory (LSTM) networks and randomized forests) will advise them about the nature of future levels of exposure and exposures. This framework has empirical evidence demonstrating reasonable accuracy in detecting hazardous conditions and only takes a matter of seconds to deliver an alert. Upon notification of hazardous exposure, immediate real-time action is deployed, including cutting production, ventilation control, etc. Findings from simulations demonstrate the system’s ability to detect risk at over 96% accuracy and predict exposure events 2 hours in advance. This gives managers not only increased worker safety, but environmental health compliance as well. In the future, utilizing predictive modeling with IoT technology will represent a real leap forward in smart manufacturing, creating a scalable solution for managing carcinogen exposure risks and protecting worker health in industrial settings.