The Charging Structure for the Great Barrier Reef - A review of willingness to pay

The Great Barrier Reef Marine Park Authority (GBRMPA or the Reef Authority) is currently conducting a comprehensive review of the charging structure for the use of the Great Barrier Reef (GBR) Marine Park with a view to implement potential changes from 1 July 2023

A mobile dose prediction system based on artificial neural networks for NPP emergencies with radioactive material releases

This work presents the approach of a mobile dose prediction system for NPP emergencies with nuclear material release. The objective is to provide extra support to field teams decisions when plant information systems are not available. However, predicting doses due to atmospheric dispersion of radionuclide generally requires execution of complex and computationally intensive physical models. In order to allow such predictions to be made by using limited computational resources such as mobile phones, it is proposed the use of artificial neural networks (ANN) previously trained (offline) with data generated by precise simulations using the NPP atmospheric dispersion system. Typical situations for each postulated accident and respective source terms, as well as a wide range of meteorological conditions have been considered. As a first step, several ANN architectures have been investigated in order to evaluate their ability for dose prediction in hypothetical scenarios in the vicinity of CNAAA Brazilian NPP, in Angra dos Reis, Brazil. As a result, good generalization and a correlation coefficient of 0.99 was achieved for a validation data set (untrained patterns). Then, selected ANNs have been coded in Java programming language to run as an Android application aimed to plot the spatial dose distribution into a map.In this paper, the general architecture of the proposed system is described; numerical results and comparisons between investigated ANN architectures are discussed; performance and limitations of running the Application into a commercial mobile phone are evaluated and possible improvements and future works are pointed

GPU-BASED PARALLEL COMPUTING IN REAL-TIME MODELING OF ATMOSPHERIC TRANSPORT AND DIFFUSION OF RADIOACTIVE MATERIAL

Atmospheric radionuclide dispersion systems (ARDS) are essential mechanisms to predict the consequences of unexpected radioactive releases from nuclear power plants. Considering, that during an eventuality of an accident with a radioactive material release, an accurate forecast is vital to guide the evacuation plan of the possible affected areas. However, in order to predict the dispersion of the radioactive material and its impact on the environment, the model must process information about source term (radioactive materials released, activities and location), weather condition (wind, humidity and precipitation) and geographical characteristics (topography). Furthermore, ARDS is basically composed of 4 main modules: Source Term, Wind Field, Plume Dispersion and Doses Calculations. The Wind Field and Plume Dispersion modules are the ones that require a high computational performance to achieve accurate results within an acceptable time. Taking this into account, this work focuses on the development of a GPU-based parallel Plume Dispersion module, focusing on the radionuclide transport and diffusion calculations, which use a given wind field and a released source term as parameters. The program is being developed using the C ++ programming language, allied with CUDA libraries. In comparative case study between a parallel and sequential version of the slower function of the Plume Dispersion module, a speedup of 11.63 times could be observed

Space Debris Detection in Low Earth Orbit with the Sardinia Radio Telescope

Space debris are orbiting objects that represent a major threat for space operations. The most used countermeasure to face this threat is, by far, collision avoidance, namely the set of maneuvers that allow to avoid a collision with the space debris. Since collision avoidance is tightly related to the knowledge of the debris state (position and speed), the observation of the orbital debris is the key of the problem. In this work a bistatic radar configuration named BIRALET (BIstatic RAdar for LEO Tracking) is used to detect a set of space debris at 410 MHz, using the Sardinia Radio Telescope as the receiver antenna. The signal-to-noise ratio, the Doppler shift and the frequency spectrum for each debris are reported

A PROTOTYPE OF A ROBOTIC RESEARCH FACILITY FOR NUCLEAR APPLICATIONS

This work presents the development of the prototype of a robotic nuclear monitoring facility aimed to support technological and scientific research. It is a terrestrial robot in which nuclear and conventional instrumentation are available and easy-to-use through a user-friendly library for Python programming. The facility may be teleoperated (by mobile devices, notebook or desktop) or operate in autonomous mode,  in which a user-defined program run on robot CPU

Predictive value of VEGF gene polymorphisms for metastatic colorectal cancer patients receiving first-line treatment including fluorouracil, irinotecan, and bevacizumab

The aim of this study is to evaluate the influence of germline vascular endothelial growth factor (VEGF) gene polymorphisms (VGPs) on the efficacy of the anti-VEGF antibody bevacizumab (Bev) in metastatic colorectal cancer (MCRC) patients

Application of simulation modeling for wildfire exposure and transmission assessment in Sardinia, Italy

Abstract The development of comprehensive fire management and risk assessment strategies is of prominent concern in Southern Europe, due to the expanding scale of wildfire risk. In this work, we applied simulation modeling to analyze fine-scale (100-m resolution) wildfire exposure and risk transmission in the 24,000 km2 island of Sardinia (Italy). Sardinia contains a variety of ecological, cultural, anthropic and touristic resources that each summer are threatened by wildfires, and represents well the Mediterranean Basin environments and conditions. Wildfire simulations based on the minimum travel time algorithm were used to characterize wildfire exposure and risk transmission in terms of annual burn probability, flame length, structures exposed and type and amount of transmission. We focused on the historical conditions associated with large (>50 ha) and very large (>200 ha) wildfires that occurred in Sardinia in the period 1998–2016, and combined outputs from wildfire simulation modeling with land uses, building footprint locations, weather, and historical ignition data. The outputs were summarized for weather zones, main wind scenarios and land uses. Our study characterized spatial variations in wildfire spread, exposure and risk transmission among and within weather zones and the main winds associated with large events. This work provides a novel quantitative approach to inform wildfire risk management and planning in Mediterranean areas. The proposed methodology can serve as reference for wildfire risk assessment and can be replicated elsewhere. Findings can be used to better understand the spatial dynamics and patterns of wildfire risk and evaluate expected wildfire behavior or transmission potential in Sardinia and neighboring regions