LLM Multimodal Traffic Accident Forecasting.

With the rise in traffic congestion in urban centers, predicting accidents has become paramount for city planning and public safety. This work comprehensively studied the efficacy of modern deep learning (DL) methods in forecasting traffic accidents and enhancing Level-4 and Level-5 (L-4 and L-5) driving assistants with actionable visual and language cues. Using a rich dataset detailing accident occurrences, we juxtaposed the Transformer model against traditional time series models like ARIMA and the more recent Prophet model.

LLM Adaptive PID Control for B5G Truck Platooning Systems.

This paper presents an exploration into the capabilities of an adaptive PID controller within the realm of truck platooning operations, situating the inquiry within the context of Cognitive Radio and AI-enhanced 5G and Beyond 5G (B5G) networks. We developed a Deep Learning (DL) model that emulates an adaptive PID controller, taking into account the implications of factors such as communication latency, packet loss, and communication range, alongside considerations of reliability, robustness, and security. Furthermore, we harnessed a Large Language Model (LLM), GPT-3.

Occupational exposure to natural radioactivity in a zircon sand milling plant.

Raw zirconium sand is one of the substances (naturally occurring radioactive material, NORM) which is widely used in the ceramic industry. This sand contains varying concentrations of natural radionuclides: mostly U-238 but also Th-232 and U-235, together with their daughters, and therefore may need to be regulated by Directive 96/29/EURATOM. This paper describes the method used to perform the radiological study on a zircon sand milling plant and presents the results obtained.

Simulation of germanium detector calibration using the Monte Carlo method: comparison between point and surface source models.

Simulation of detector calibration using the Monte Carlo method is very convenient. The computational calibration procedure using the MCNP code was validated by comparing results of the simulation with laboratory measurements. The standard source used for this validation was a disc-shaped filter where fission and activation products were deposited.

Developing a virtual reality application for training nuclear power plant operators: setting up a database containing dose rates in the refuelling plant.

Operators in Nuclear Power Plants can receive high doses during refuelling operations. A training programme for simulating refuelling operations will be useful in reducing the doses received by workers as well as minimising operation time. With this goal in mind, a virtual reality application is developed within the framework of the CIPRES project.