A graphical user interface for Monte Carlo dose calculations for brachytherapy with egs_brachy.

Background: Recent advancements in brachytherapy necessitate precise dose calculations, transitioning from the traditional TG43 planning methods to the more sophisticated TG186 recommendations. However, the availability of accessible and efficient Monte Carlo (MC) codes capable of interfacing with clinical data for these advanced calculations remains limited.

Purpose: This study presents and validates eb_gui, a graphical user interface designed to seamlessly integrate DICOM clinical data with egs_brachy, a fast MC dose calculation algorithm tailored for brachytherapy applications.

Assessing predictability of environmental time series with statistical and machine learning models.

The ever increasing popularity of machine learning methods in virtually all areas of science, engineering and beyond is poised to put established statistical modeling approaches into question. Environmental statistics is no exception, as popular constructs such as neural networks and decision trees are now routinely used to provide forecasts of physical processes ranging from air pollution to meteorology. This presents both challenges and opportunities to the statistical community, which could contribute to the machine learning literature with a model-based approach with formal uncertainty quantification.