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

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

Comportamento meteorológico e sua influência na vindima de 2010 na Serra Gaúcha.

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Early changes in brain structure correlate with language outcomes in children with neonatal encephalopathy.

Global patterns of brain injury correlate with motor, cognitive, and language outcomes in survivors of neonatal encephalopathy (NE). However, it is still unclear whether local changes in brain structure predict specific deficits. We therefore examined whether differences in brain structure at 6 months of age are associated with neurodevelopmental outcomes in this population. We enrolled 32 children with NE, performed structural brain MR imaging at 6 months, and assessed neurodevelopmental outcomes at 30 months. All subjects underwent T1-weighted imaging at 3 T using a 3D IR-SPGR sequence. Images were normalized in intensity and nonlinearly registered to a template constructed specifically for this population, creating a deformation field map. We then used deformation based morphometry (DBM) to correlate variation in the local volume of gray and white matter with composite scores on the Bayley Scales of Infant and Toddler Development (Bayley-III) at 30 months. Our general linear model included gestational age, sex, birth weight, and treatment with hypothermia as covariates. Regional brain volume was significantly associated with language scores, particularly in perisylvian cortical regions including the left supramarginal gyrus, posterior superior and middle temporal gyri, and right insula, as well as inferior frontoparietal subcortical white matter. We did not find significant correlations between regional brain volume and motor or cognitive scale scores. We conclude that, in children with a history of NE, local changes in the volume of perisylvian gray and white matter at 6 months are correlated with language outcome at 30 months. Quantitative measures of brain volume on early MRI may help identify infants at risk for poor language outcomes

Novel LoadProGen procedure for micro-grid design in emerging country scenarios: Application to energy storage sizing

This paper is devoted to describe the development, implementation and application of a novel procedure to properly design the electrification process in rural areas of Emerging Countries (EC). The procedure exploits a bottom-up approach, i.e. target applications are related to micro-grids devoted to satisfy the electrical needs of small communities. The procedure starts from microscopic data (i.e. single electric appliances) to effectively catch the customer needs (i.e. bottom) and it matches them with the available energy sources in the target area. In particular, a tool named LoadProGen developed by the Energy4Growing research group of Politecnico di Milano, is presented: the mathematical approach proposed is detailed and a real field case study relevant to a micro-grid deployed in Tanzania is provided. The tool is based on the gathering of information about the target area, i.e. to get information from interview and field audit, and on a stochastic approach to build up realistic estimation of the electric load profile of the considered uses. The energy needs forecast (cfr. load profile) is then adopted in a second procedure devoted to design a micro-grid capable to properly feed the loads. In this work, for sake of exemplification, this latter is supposed to be a photovoltaic based micro-grid integrated with an electrochemical storage

Monte Belo: características da identidade regional para uma indicação geográfica de vinhos.

bitstream/CNPUV/9754/1/cir076.pdfDisponível também no formato online

Manejo do dossel vegetativo e seu efeito nos componentes de produção da videira Merlot.

A poda verde é uma prática cultural utilizada para melhorar as condições do dossel vegetativo dos vinhedos, visando a favorecer a qualidade da uva e do vinho. Nesse sentido, realizou-se este experimento entre as safras de 1993/1994 e 1996/1997, com diferentes modalidades de poda verde, num vinhedo do cv. Merlot conduzido em latada. Houve 12 tratamentos e três repetições, sendo o delineamento experimental em blocos casualizados. Os tratamentos constituíram-se da testemunha e de 11 diferentes modalidades de poda verde, ou seja, desbrota, desponta e desfolha, algumas delas em diferentes épocas do ciclo vegetativo da videira. O componente principal 1, da análise de componentes principais (ACP) feita em cada ano, separadamente, mostra que o tratamento 10 (desbrota + desponta + desfolha realizada no início da floração, eliminando-se as folhas abaixo dos cachos) discriminou-se nos quatro anos, e os tratamentos 7 (desfolha realizada 21 dias antes da colheita, eliminando-se metade das folhas abaixo dos cachos) e 6 (desfolha realizada 21 dias antes da colheita, eliminando-se as folhas abaixo dos cachos), em três deles; a ACP da média dos quatro anos também evidencia essa discriminação entre eles. Constata-se que o tratamento 10 foi um dos que tiveram intensidade de poda verde mais intensa, caracterizando-se por variáveis indicativas de plantas com vigor e produtividade mais baixos que os demais