Modeling and predicting caffeine contamination in surface waters using artificial intelligence and standard statistical methods.

Caffeine, considered an emerging contaminant, serves as an indicator of anthropic influence on water resources. This research employs various modeling techniques, including Artificial Neural Networks (ANN), Random Forest (RF), and more, along with hybrid and ensemble methods, to predict caffeine concentrations (in regression and classification scenarios) using readily available water quality parameters. The results indicate Ensemble-RF as the most effective method for estimating caffeine concentrations, while classification scenarios highlight Ensemble-RF, ANN, and Ensemble-ANN as promising methodologies for predicting contamination levels.

Environmental scanning of cocaine trafficking in Brazil: Evidence from geospatial intelligence and natural language processing methods.

Cocaine trafficking threatens countries' national security and is a major public health challenge. Cocaine is transported from producer countries to consumer markets using various routes, methods, and transportation means. These routes develop in the geographical environment, are carefully planned and are geo-strategic objects that respond to the opportunities that drug trafficking organisations (DTOs) find to reduce the risks of interdiction.