Anna dos 6 aos 18 anos

Pós-graduação em Artes - IA

O papel de Nikita Khrushchev no Complexo Militar e Industrial, no quadro da Guerra Fria

A presente dissertação visa aferir a dimensão e a relevância das alterações operadas por Nikita Khrushchev, enquanto líder da União Soviética, no Complexo Militar e Industrial deste país, no período da Guerra Fria. Neste contexto, as mesmas serão analisadas e proceder-se-á, paralelamente, ao estudo do impacto das mesmas a nível interno, bem como a nível externo, na interacção da União Soviética com os restantes actores da comunidade internacional, nomeadamente os Estados Unidos. Mormente, como forma de contextualizar as referidas alterações, proceder-se-á também ao apuramento dos motivos que estiveram subjacentes à transmutação de uma componente relevante nas Relações Internacionais, o Complexo Militar e Industrial Soviético.

Louise Nikita, Rollenbild, Brustbild

Signatur des Originals: S 36/F11691

Louise Nikita, Brustbild

Signatur des Originals: S 36/G01780

Louise Nikita, Brustbild

Signatur des Originals: S 36/G01781

Louise Nikita, Brustbild

Signatur des Originals: S 36/G01782

Louise Nikita, Brustbild

Signatur des Originals: S 36/G01783

Application of Qualitative Imaging Methods to Electrical Performance-Aware Package Board Design

Package-board co-design plays a crucial role in determining the performance of high-speed systems. Although there exist several commercial solutions for electromagnetic analysis and verification, lack of Computer Aided Design (CAD) tools for SI aware design and synthesis lead to longer design cycles and non-optimal package-board interconnect geometries. In this work, the functional similarities between package-board design and radio-frequency (RF) imaging are explored. Consequently, qualitative methods common to the imaging community, like Tikhonov Regularization (TR) and Landweber method are applied to solve multi-objective, multi-variable package design problems. In addition, a new hierarchical iterative piecewise linear algorithm is developed as a wrapper over LBP for an efficient solution in the design space.

A rapid method to assess reactive oxygen species in yeast using H2DCF-DA

A protocol to efficiently assess Reactive Oxygen Species (ROS) levels in yeast cells using H2DCF-DA is described here. This method employs lithium acetate to permeate the cell wall, and thus, augments the release of the fluorescent product, dichlorofluorescein from the cells. This protocol obviates the need for both physical and enzymatic lysis methods that are arduous and time consuming. This method is simple, less time consuming and reproducible, especially while dealing with a large sample size. The lithium acetate method gave significantly reproducible and linear results (P < 0.0001), as compared with direct measurement (P = 0.0005), sonication (P = 0.1466) and bead beating (P = 0.0028).

Half the fishers in the world

Tracing the road ahead for women in fisheries in Asia, a continent that produces the most fish and supports the largest number of fishers in the world.

Power system parameters forecasting using Hilbert-Huang transform and machine learning

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. A part from introduction and references the paper is organized as follows. The second section presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learningbased algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting.

Modelo telegráfico de valoración de opciones

En este trabajo construimos un modelo de mercado financiero basado en un proceso telegráfico más un proceso de saltos para la valoración de opciones Europeas. Vamos a asumir que el tamaño de los saltos es constante y después que es aleatorio, en ambos casos estos saltos ocurren cuando la tendencia del mercado cambia. Estos modelos capturan la dinámica del mercado en periodos con presencia de ciclos financieros. Mostraremos la estructura del conjunto de medidas neutrales al riesgo, además, de fórmulas explícitas para los precios de las opciones Europeas de venta y compra.