Quantifying The Shortage of Mental Health Care in Venezuela Through Media Content Analysis

The aim of this thesis was to assess the gaps and deficits in the mental health care staffing and related prescription drug or therapeutic intervention availability in Venezuela using media content analysis. This thesis also assessed the measures suggested by Venezuelan medical professionals for addressing the population\u27s needs for mental health services amid the nation\u27s crisis. The shortage of mental health care in Venezuela was assessed because various stressors, including life events, chronic stressors, and daily hassles, are substantially less than optimal among Venezuelans. The mental health consequences of these factors, along with the detrimental psychosocial demands commonly faced by Venezuelans, was explored within this study. Such an investigation is critical in light of the poor prioritization of intangible mental health care within the already inadequate health care system existing in Venezuela. The used media content included newspapers and periodicals published in Venezuela and foreign newspapers covering the medical crisis in Venezuela, published or posted interviews with Venezuelan medical personnel describing the health care crisis, social media posts involving requests for or availability of medicine and services, and social media posts of videos or images as visual testimony of the crisis. Coder reliability was assessed, and descriptive and inferential statistical tests were implemented for deductive analysis of the study\u27s results and to find possible answers to the presented research questions

RAVEN: a GUI and an Artificial Intelligence Engine in a Dynamic PRA Framework

Increases in computational power and pressure for more accurate simulations and estimations of accident scenario consequences are driving the need for Dynamic Probabilistic Risk Assessment (PRA) [1] of very complex models. While more sophisticated algorithms and computational power address the back end of this challenge, the front end is still handled by engineers that need to extract meaningful information from the large amount of data and build these complex models. Compounding this problem is the difficulty in knowledge transfer and retention, and the increasing speed of software development. The above-described issues would have negatively impacted deployment of the new high fidelity plant simulator RELAP-7 (Reactor Excursion and Leak Analysis Program) at Idaho National Laboratory. Therefore, RAVEN that was initially focused to be the plant controller for RELAP-7 will help mitigate future RELAP-7 software engineering risks. In order to accomplish such a task Reactor Analysis and V

Hot Zero and Full Power Validation of PHISICS RELAP-5 Coupling

PHISICS is a reactor analysis toolkit developed over the last 3 years at the Idaho National Laboratory. It has been coupled with the reactor safety analysis code RELAP5-3D. PHISICS is aimed at providing an optimal trade off between needed computational resources (in the range of 10~100 computer processors) and accuracy. In fact, this range has been identified as the next 5 to 10 years average computational capability available to nuclear reactor design and optimization nuclear reactor cores. Detailed information about the individual modules of PHISICS can be found in [1]. An overview of the modules used in this study is given in the next subsection. Lately, the Idaho National Laboratory gained access plant data for the first cycle of a PWR, including Hot Zero Power (HZP) and Hot Full Power (HFP). This data provides the opportunity to validate the transport solver, the interpolation capability for mixed macro and micro cross section and the criticality search option of the PHISICS pack

LWR First Recycle of TRU with Thorium Oxide for Transmutation and Cross Sections

Thorium has been considered as an option to uranium-based fuel, based on considerations of resource utilization (thorium is approximately three times more plentiful than uranium) and as a result of concerns about proliferation and waste management (e.g. reduced production of plutonium, etc.). Since the average composition of natural Thorium is dominated (100%) by the fertile isotope Th-232, Thorium is only useful as a resource for breeding new fissile materials, in this case U-233. Consequently a certain amount of fissile material must be present at the start-up of the reactor in order to guarantee its operation. The thorium fuel can be used in both once-through and recycle options, and in both fast and thermal spectrum systems. The present study has been aimed by the necessity of investigating the option of using reprocessed plutonium/TRU, from a once-through reference LEU scenario (50 GWd/ tIHM), mixed with natural thorium and the need of collect data (mass fractions, cross-sections etc.) for this particular fuel cycle scenario. As previously pointed out, the fissile plutonium is needed to guarantee the operation of the reactor. Four different scenarios have been considered: • Thorium – recycled Plutonium; • Thorium – recycled Plutonium/Neptunium; • Thorium – recycled Plutonium/Neptunium/Americium; • Thorium – recycled Transuranic. The calculations have been performed with SCALE6.1-TRITON

DAKOTA reliability methods applied to RAVEN/RELAP-7.

This report summarizes the result of a NEAMS project focused on the use of reliability methods within the RAVEN and RELAP-7 software framework for assessing failure probabilities as part of probabilistic risk assessment for nuclear power plants. RAVEN is a software tool under development at the Idaho National Laboratory that acts as the control logic driver and post-processing tool for the newly developed Thermal-Hydraulic code RELAP-7. Dakota is a software tool developed at Sandia National Laboratories containing optimization, sensitivity analysis, and uncertainty quantification algorithms. Reliability methods are algorithms which transform the uncertainty problem to an optimization problem to solve for the failure probability, given uncertainty on problem inputs and a failure threshold on an output response. The goal of this work is to demonstrate the use of reliability methods in Dakota with RAVEN/RELAP-7. These capabilities are demonstrated on a demonstration of a Station Blackout analysis of a simplified Pressurized Water Reactor (PWR)

IMPACT OF FISSION PRODUCTS IMPURITY ON THE PLUTONIUM CONTENT IN PWR MOX FUELS

This report presents the results of a neutronics analysis done in response to the charter IFCA-SAT-2 entitled 'Fuel impurity physics calculations'. This charter specifies that the separation of the fission products (FP) during the reprocessing of UOX spent nuclear fuel assemblies (UOX SNF) is not perfect and that, consequently, a certain amount of FP goes into the Pu stream used to fabricate PWR MOX fuel assemblies. Only non-gaseous FP have been considered (see the list of 176 isotopes considered in the calculations in Appendix 1). This mixture of Pu and FP is called PuFP. Note that, in this preliminary analysis, the FP losses are considered element-independent, i.e., for example, 1% of FP losses mean that 1% of all non-gaseous FP leak into the Pu stream

An approach based on Support Vector Machines and a K-D Tree search algorithm for identification of the failure domain and safest operating conditions in nuclear systems

The safety of a Nuclear Power Plant (NPP) is verified by analyzing the system responses under normal and accidental conditions. This is done by resorting to a Best-Estimate (BE) Thermal-Hydraulic (TH) code, whose outcomes are compared to given safety thresholds enforced by regulation. This allows identifying the limit-state function that separates the failure domain from the safe domain. In practice, the TH model response is affected by uncertainties (both epistemic and aleatory), which make the limit-state function and the failure domain probabilistic. The present paper sets forth an innovative approach to identify the failure domain together with the safest plant operating conditions. The approach relies on the use of Reduced Order Models (ROMs) and K-D Tree. The model failure boundary is approximated by Support Vector Machines (SVMs) and, then, projected onto the space of the controllable variables (i.e., the model inputs that can be manipulated by the plant operator, such as reactor control-rods position, feed-water flow-rate through the plant primary loops, accumulator water temperature and pressure, repair times, etc.). The farthest point from the failure boundary is, then, computed by means of a K-D Tree-based nearest neighbor algorithm; this point represents the combination of input values corresponding to the safest operating conditions. The approach is shown to give satisfactory results with reference to one analytical example and one real case study regarding the Peak Cladding Temperature (PCT) reached in a Boiling Water Reactor (BWR) during a Station-Black-Out (SBO), simulated using RELAP5-3D

Dynamic PRA: an Overview of New Algorithms to Generate, Analyze and Visualize Data

State of the art PRA methods, i.e. Dynamic PRA (DPRA) methodologies, largely employ system simulator codes to accurately model system dynamics. Typically, these system simulator codes (e.g., RELAP5 ) are coupled with other codes (e.g., ADAPT, RAVEN that monitor and control the simulation. The latter codes, in particular, introduce both deterministic (e.g., system control logic, operating procedures) and stochastic (e.g., component failures, variable uncertainties) elements into the simulation. A typical DPRA analysis is performed by: 1. Sampling values of a set of parameters from the uncertainty space of interest 2. Simulating the system behavior for that specific set of parameter values 3. Analyzing the set of simulation runs 4. Visualizing the correlations between parameter values and simulation outcome Step 1 is typically performed by randomly sampling from a given distribution (i.e., Monte-Carlo) or selecting such parameter values as inputs from the user (i.e., Dynamic Event Tre

文化財における金属周辺木材の白色現象:特徴と発生機構

筑波大学 (University of Tsukuba)201

Distribuição geográfica do período de incubação da ferrugem do cafeeiro em cenários de mudanças climáticas

The objective of this work was to simulate the geographical distribution of the incubation period of coffee leaf rust in Coffea arabica, using data of two regional climate models, Eta-HadGEM2-ES and Eta-MIROC5. The scenario of high greenhouse gas emission (RCP 8.5 W m-2) was used for the states of Minas Gerais and São Paulo, Brazil, for current and future climate scenarios. The behavior of six different regression equations for incubation period (IP), available in the literature, was also analyzed as affected by data from the regional climate models. The results indicate the possibility of an increase in the affected area in the studied region, when the IP is less than 19 days, from 0.5% for Eta-MIROC5 to 14.2% for Eta-HadGEM2-ES. The severity of coffee leaf rust in future scenarios should increase in the hottest and wettest months of the year, extending to the driest and coldest months. The potential of rust infection is estimated differently by the studied equations. In higher temperature scenarios, the Kushalappa & Martins equation indicates a very high severity potential.O objetivo deste trabalho foi simular a distribuição geográfica do período de incubação da ferrugem do cafeeiro Coffea arabica, com uso de dados de dois modelos climáticos regionais, o Eta-HadGEM2-ES e o Eta-MIROC5. O cenário de alta emissão de gases de efeito estufa (RCP 8,5 W m-2) foi utilizado para os estados de Minas Gerais e São Paulo, para os cenários climáticos atual e futuro. O comportamento de seis diferentes equações de regressão do período de incubação (PI), disponíveis na literatura, também foi analisado em função dos dados dos modelos climáticos regionais. Os resultados indicam possibilidade de aumento de área afetada na região estudada, com PI inferior a 19 dias, de 0,5% para Eta-MIROC5 a 14,2% para Eta-HadGEM2-ES. A severidade da ferrugem do cafeeiro em cenários futuros deverá aumentar nos meses mais quentes e úmidos do ano, estendendo-se para os meses mais secos e frios. O potencial de infecção da ferrugem é estimado de forma diferente pelas equações estudadas. Em cenários de temperaturas mais elevadas, a equação de Kushalappa & Martins indica um potencial muito alto de severidade