Liquidity black holes: why modern financial regulation in developed countries is making short-term capital flows to developing countries even more volatile

Includes bibliography

A specialized genetic algorithm to solve the short term transmission network expansion planning

In this paper, the short term transmission network expansion planning (STTNEP) is solved through a specialized genetic algorithm (SGA). A complete AC model of the transmission network is used, which permits the formulation of an integrated power system transmission network expansion planning problem (real and reactive power planning). The characteristics of the proposed SGA to solve the STTNEP problem are detailed and an interior point method is employed to solve nonlinear programming problems during the solution steps of the SGA. Results of tests carried out with two electrical energy systems show the capabilities of the SGA and also the viability of using the AC model to solve the STTNEP problem. © 2009 IEEE.

Biochemical biomarkers in Nile tilapia (Oreochromis niloticus) after short-term exposure to diesel oil, pure biodiesel and biodiesel blends

Fossil fuels such as diesel are being gradually replaced by biodiesel, a renewable energy source, cheaper and less polluting. However, little is known about the toxic effects of this new energy source on aquatic organisms. Thus, we evaluated biochemical biomarkers related to oxidative stress in Nile tilapia (Oreochromis niloticus) after two and seven exposure days to diesel and pure biodiesel (B100) and blends B5 and B20 at concentrations of 0.01 and 0.1mLL -1. The hepatic ethoxyresorufin-O-deethylase activity was highly induced in all groups, except for those animals exposed to B100. There was an increase in lipid peroxidation in liver and gills in the group exposed to the higher concentration of B5. All treatments caused a significant increase in the levels of 1-hydroxypyrene excreted in the bile after 2 and 7d, except for those fish exposed to B100. The hepatic glutathione-S-transferase increased after 7d in animals exposed to the higher concentration of diesel and in the gill of fish exposed to the higher concentration of pure diesel and B5, but decreased for the two tested concentrations of B100. Superoxide dismutase, catalase and glutathione peroxidase also presented significant changes according to the treatments for all groups, including B100. Biodiesel B20 in the conditions tested had fewer adverse effects than diesel and B5 for the Nile tilapia, and can be suggested as a less harmful fuel in substitution to diesel. However, even B100 could activate biochemical responses in fish, at the experimental conditions tested, indicating that this fuel can also represent a risk to the aquatic biota. © 2011 Elsevier Ltd.

An application of CHA to concurrent short-term transmission expansion & reactive power planning

In this paper a heuristic technique for solving simultaneous short-term transmission network expansion and reactive power planning problem (TEPRPP) via an AC model is presented. A constructive heuristic algorithm (CHA) aimed to obtaining a significant quality solution for such problem is employed. An interior point method (IPM) is applied to solve TEPRPP as a nonlinear programming (NLP) during the solution steps of the algorithm. For each proposed network topology, an indicator is deployed to identify the weak buses for reactive power sources placement. The objective function of NLP includes the costs of new transmission lines, real power losses as well as reactive power sources. By allocating reactive power sources at load buses, the circuit capacity may increase while the cost of new lines can be decreased. The proposed methodology is tested on Garver's system and the obtained results shows its capability and the viability of using AC model for solving such non-convex optimization problem. © 2011 IEEE.

Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter

This paper proposes a filter based on a general regression neural network and a moving average filter, for preprocessing half-hourly load data for short-term multinodal load forecasting, discussed in another paper. Tests made with half-hourly load data from nine New Zealand electrical substations demonstrate that this filter is able to handle noise, missing data and abnormal data. © 2011 IEEE.

Short-term multinodal load forecasting in distribution systems using general regression neural networks

Multinodal load forecasting deals with the loads of several interest nodes in an electrical network system, which is also known as bus load forecasting. To perform this demand, it is necessary a technique that is precise, trustable and has a short-time processing. This paper proposes two methodologies based on general regression neural networks for short-term multinodal load forecasting. The first individually forecast the local loads and the second forecast the global load and individually forecast the load participation factors to estimate the local loads. To design the forecasters it wasn't necessary the previous study of the local loads. Tests were made using a New Zealand distribution subsystem and the results obtained are compatible with the ones founded in the specialized literature. © 2011 IEEE.

Short-term planning of electric power distribution networks using multiobjective genetic algorithim

The high active and reactive power level demanded by the distribution systems, the growth of consuming centers, and the long lines of the distribution systems result in voltage variations in the busses compromising the quality of energy supplied. To ensure the energy quality supplied in the distribution system short-term planning, some devices and actions are used to implement an effective control of voltage, reactive power, and power factor of the network. Among these devices and actions are the voltage regulators (VRs) and capacitor banks (CBs), as well as exchanging the conductors sizes of distribution lines. This paper presents a methodology based on the Non-Dominated Sorting Genetic Algorithm (NSGA-II) for optimized allocation of VRs, CBs, and exchange of conductors in radial distribution systems. The Multiobjective Genetic Algorithm (MGA) is aided by an inference process developed using fuzzy logic, which applies specialized knowledge to achieve the reduction of the search space for the allocation of CBs and VRs.

Production strategies for short term grow-out of the Amazon River prawn Macrobrachium amazonicum (Heller 1862) in ponds

The aim of this study was to evaluate the effect of stocking ponds using graded and ungraded juveniles and performing drained and combined harvesting on the production of M. amazonicum. A randomized completed-blocks design with 4 treatments (farming strategies) and 3 replicates was used. Treatments were: Upper size-graded juveniles, Lower size-graded juveniles, Ungraded juveniles, all with total drained harvesting, and Combined Harvesting (ungraded juveniles). Twelve earthen ponds were stocked at 40 juveniles.m -2, according to the treatment. After 3.5 months prawns were completely harvested. Lower size-graded prawns showed smaller average weight (3.37 ± 0.25 g) than upper size-graded (4.03 ± 0.40 g) and ungraded ones (3.80 ± 0.16 g). Survival percentage varied from 68 ± 9 to 76 ± 10, productivity was slightly higher than 1,000 kg.ha -1 and apparent feed conversion rate varied from 3.0 ± 0.7 to 3.7 ± 1.3. These parameters did not differ among the farming strategies. The best strategy for short term grow-out M. amazonicum in earthen ponds is stocking ungraded juveniles and performing total harvesting by draining ponds at the end of rearing cycle. Grading juveniles before stocking and selective-harvesting managements are not advantageous because they increase costs and do not improve any production parameter.

Predicting academic success of engineering students in technical drawing from working memory

Tests on spatial aptitude, in particular Visualization, have been shown to be efficient predictors of the academic performance of Technical Drawing stu-dents. It has recently been found that Spatial Working Memory (a construct defined as the ability to perform tasks with a figurative content that require si-multaneous storage and transformation of information) is strongly associated with Visualization. In the present study we analyze the predictive efficiency of a bat-tery of tests that included tests on Visualization, SpatialWorking Memory, Spatial Short-term Memory and Executive Function on a sample of first year engineering students. The results show that Spatial Working Memory (SWM) is the most important predictor of academic success in Technical Drawing. In our view, SWM tests can be useful for detecting as early as possible those students who will require more attention and support in the teaching-learning process.

The programming of short-term financing

Includes bibliography

Quantification of uncertainties associated with space-time estimates of short-term soil CO2 emissions in a sugar cane area

The characterization of soil CO2 emissions (FCO2) is important for the study of the global carbon cycle. This phenomenon presents great variability in space and time, a characteristic that makes attempts at modeling and forecasting FCO2 challenging. Although spatial estimates have been performed in several studies, the association of these estimates with the uncertainties inherent in the estimation procedures is not considered. This study aimed to evaluate the local, spatial, local-temporal and spatial-temporal uncertainties of short-term FCO2 after harvest period in a sugar cane area. The FCO2 was featured in a sampling grid of 60m×60m containing 127 points with minimum separation distances from 0.5 to 10m between points. The FCO2 was evaluated 7 times within a total period of 10 days. The variability of FCO2 was described by descriptive statistics and variogram modeling. To calculate the uncertainties, 300 realizations made by sequential Gaussian simulation were considered. Local uncertainties were evaluated using the probability values exceeding certain critical thresholds, while the spatial uncertainties considering the probability of regions with high probability values together exceed the adopted limits. Using the daily uncertainties, the local-spatial and spatial-temporal uncertainty (Ftemp) was obtained. The daily and mean emissions showed a variability structure that was described by spherical and Gaussian models. The differences between the daily maps were related to variations in the magnitude of FCO2, covering mean values ranging from 1.28±0.11μmolm-2s-1 (F197) to 1.82±0.07μmolm-2s-1 (F195). The Ftemp showed low spatial uncertainty coupled with high local uncertainty estimates. The average emission showed great spatial uncertainty of the simulated values. The evaluation of uncertainties associated with the knowledge of temporal and spatial variability is an important tool for understanding many phenomena over time, such as the quantification of greenhouse gases or the identification of areas with high crop productivity. © 2013 Elsevier B.V.

Changes during chilled storage of whole tilapia and short-term frozen storage of tilapia fillets

This study evaluated the physicochemical changes in Nile tilapia (n = 82, 373.71 ± 61.91 g) refrigerated for up to 92 h and in the frozen fillets. The tilapias were captured with nets, slaughtered by ice and water shock (1:1) in a temperature of approximately 2°C for 30 min, and stored refrigerated at 4°C in polystyrene boxes containing ice. The fish were filleted, and filets were weighed and frozen. The drip loss and protein were determined after 23 days of frozen storage. After 4 h of storage, all fish were in full rigor mortis. The pH of the muscles decreased for up to 45 h of the storage period. The fillets obtained from tilapia stored for more than 72 h lost more weight and protein. Thus, the filleting or processing of tilapia should be done before 72 h of cold storage, since deterioration of the fish starts to occur after this period. Copyright © Taylor & Francis Group, LLC.

Multiobjective short-term planning of electric power distribution systems using NSGA-II

This paper presents a mixed integer nonlinear programming multiobjective model for short-term planning of distribution networks that considers in an integrated manner the following planning activities: allocation of capacitor banks; voltage regulators; the cable replacement of branches and feeders. The objective functions considered in the proposed model are: to minimize operational and investment costs and minimize the voltage deviations in the the network buses, subject to a set of technical and operational constraints. A multiobjective genetic algorithm based on a Non-Dominated Sorting Genetic Algorithm (NSGA-II) is proposed to solve this model. The proposed mathematical model and solution methodology is validated testing a medium voltage distribution system with 135 buses. © 2013 Brazilian Society for Automatics - SBA.

Orientaciones metodológicas para la programación a corto plazo = Methodological principles for short-term programming

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Evaluación de un plan de desarrollo a corto plazo = Evaluation of a short-term development plan

Incluye Bibliografía