There is no anaerobic work capacity replenishment at critical power intensity: An indirect evidence

Aim. - This study aimed to test if investigate whether the anaerobic work capacity is replenished while exercising at critical power intensity. Then, a known exercise duration, which demands high anaerobic energy contribution, was compared to intermittent exercise duration with passive and active (cycling at critical power intensity) rest periods.Methods. - Nine participants performed five sessions of testing. From the 1st to the 3rd sessions, individuals cycled continuously at different workloads (P-high, P-intermediate and P-low) in order to estimate the critical power and the anaerobic work capacity. The 4th and 5th sessions were performed in order to determine the influence of anaerobic work capacity replenishment oil exercise duration. They consisted of manipulating the resting type (passive or active) between two cycling efforts. The total exercise duration was determined by the sum of the two cycling efforts duration.Results. - The exercise duration under passive resting condition (408.0 +/- 42.0 s) was longer (p<0.05) than known exercise duration at P-intermediate (T-intermediate = 305.8 +/- 30.5 s) and than exercise duration performed under active resting conditions (T-active = 304.4 +/- 30.7s). However, there was no significant difference between T-intermediate and T-active.Conclusion. - These results demonstrated indirect evidence that the anaerobic work capacity is not replenished while exercising at critical power intensity. (C) 2008 Elsevier Masson SAS. All rights reserved.

Spatial electric load forecasting using an evolutionary heuristic

A method for spatial electric load forecasting using elements from evolutionary algorithms is presented. The method uses concepts from knowledge extraction algorithms and linguistic rules' representation to characterize the preferences for land use into a spatial database. The future land use preferences in undeveloped zones in the electrical utility service area are determined using an evolutionary heuristic, which considers a stochastic behavior by crossing over similar rules. The method considers development of new zones and also redevelopment of existing ones. The results are presented in future preference maps. The tests in a real system from a midsized city show a high rate of success when results are compared with information gathered from the utility planning department. The most important features of this method are the need for few data and the simplicity of the algorithm, allowing for future scalability.

Sea waves generated by extratropical cyclones in the South Atlantic Ocean: Hindcast and validation against altimeter data

This work presents analyses of the atmospheric conditions and the hindcast of the surface wave field when six extratropical cyclones formed and displaced over the South Atlantic Ocean (10degreesN, 60degreesS; 75degreesW, 15degreesE) between April and September 1999. These events caused high sea waves associated with hazardous conditions along the south and southeast coast of Brazil. The meteorological composite fields for these cyclones show a strong near-surface wind velocity (up to 14 m s(-1)) during its mature phase. The sea-state wave hindcast was obtained using a third-generation wave model forced by the 10-m above ground level wind field from the National Centers for Environmental Prediction-National Center for Atmospheric Research reanalysis dataset. Closer to the south and southeast Brazilian coast, the hindcast results showed significant wave heights of up to 5 m in some of the events. The wave hindcast results for the significant wave height were compared against satellite altimeter data at 6 h intervals. The statistical index showed a systematic underestimation of the significant wave height by 0.5 m. The correlation between wave hindcast and altimeter measurements was greater than 90%, showing a good phase reproduction by the wave model.

The use of simulation for forecasting the number of docks necessary in an expedition department

The simulation is a very powerful tool to develop more efficient systems, hence it is been widely used with the goal of productivity improvement. Its results, if compared with other methods, are not always optimum; however, if the experiment is rightly elaborated, its results will represent the real situation, enabling its use with a good level of reliability. This work used the simulation (through the ProModel (R) software) in order to study, understand, model and improve the expenditure system of an enterprise, with a premise of keeping the production-delivery flow considering quick, controlled and reliable conditions.

Effect of irrigation level on yield and bioactive amine content of American lettuce

The influence of four levels (25, 50, 75 and 100%) of Class A pan evaporation replenishment (PER) on the marketable yield and bioactive amine content of American lettuce (Lactuca sativa cv Lucy Brown) grown under greenhouse conditions and drip irrigation was investigated. Lettuce was planted in 1.20 m x 2.10 m plots in a completely randomised block design with three replications. Lowest fresh head weights and diameters were obtained at 25% PER. Highest marketable yields and fresh head weights were obtained at 100% PER; however, no significant difference was observed when using 75% PER. The fresh head diameter was smaller only when using 25% PER. Four amines were detected in lettuce grown under 100% PER, with a total content of 7.60 mg kg(-1). Spermidine was the prevalent amine, followed by putrescine, cadaverine and agmatine. Higher spermidine and cadaverine levels were observed in the outer layers of leaves than in the intermediate and inner leaves. The contents of every amine except agmatine increased with water stress; however, a significant difference was observed only between 100 and 25% PER. The concentrations of accumulated putrescine were not capable of negatively affecting the sensory quality of the lettuce. (c) 2005 Society of Chemical Industry.

Multi-Agent Framework for Spatial Load Forecasting

A multi-agent framework for spatial electric load forecasting, especially suited to simulate the different dynamics involved on distribution systems, is presented. The service zone is divided into several sub-zones, each subzone is considered as an independent agent identified with a corresponding load level, and their relationships with the neighbor zones are represented as development probabilities. With this setting, different kind of agents can be developed to simulate the growth pattern of the loads in distribution systems. This paper presents two different kinds of agents to simulate different situations, presenting some promissory results.

A fast electric load forecasting using neural networks

The objective of this work is the development of a methodology for electric load forecasting based on a neural network. Here, it is used Backpropagation algorithm with an adaptive process based on fuzzy logic. This methodology results in fast training, when compared to the conventional formulation of Backpropagation algorithm. Results are presented using data from a Brazilian Electric Company and the performance is very good for the proposal objective.

Feeding activity of Callinectes ornatus Ordway, 1863 and Callinectes danae Smith, 1869 (Crustacea, Brachyura, Portunidae) in Ubatuba, SP, Brazil

The feeding activity along the day cycle and the time consumed for extracellular digestion were evaluated in the portunids C. ornatus and C. danae. Swimming crabs were obtained from trawling in Ubatuba bay, São Paulo, Brazil, during both the rainy and dry seasons. In each season, daily scheduled samples were taken at dawn (±6 h), noon (±12 h), dusk (±18 h) and midnight (±24 h). All individuals were dissected and the degree of stomach replenishment was recorded. In order to estimate the time elapsed for extracellular digestion, crabs were fed, and groups were dissected at 30 min intervals to check the conditions of their stomachs. In general, both species show a higher feeding activity during periods of lower light intensity, as evidenced by an increased percentage of full stomachs in dusk and midnight samples. The obtained results support higher feeding activity at night in these species and indicate short time for extracellular digestion, not exceeding 8 h. Nevertheless, full stomachs were recorded in all sampling schedules. In this case, it should be considered that elimination of certain food items such as fish bones, mollusk shells and carapace fragments of crustaceans could take more time than other items. Additionally, some crab species could require a cycle of cell replacement in the midgut gland epithelium until they can take their next meal.

Local dimension and finite time prediction in spatiotemporal chaotic systems

Predictability is related to the uncertainty in the outcome of future events during the evolution of the state of a system. The cluster weighted modeling (CWM) is interpreted as a tool to detect such an uncertainty and used it in spatially distributed systems. As such, the simple prediction algorithm in conjunction with the CWM forms a powerful set of methods to relate predictability and dimension.

A fast electric load forecasting using adaptive neural networks

This work presents a procedure for electric load forecasting based on adaptive multilayer feedforward neural networks trained by the Backpropagation algorithm. The neural network architecture is formulated by two parameters, the scaling and translation of the postsynaptic functions at each node, and the use of the gradient-descendent method for the adjustment in an iterative way. Besides, the neural network also uses an adaptive process based on fuzzy logic to adjust the network training rate. This methodology provides an efficient modification of the neural network that results in faster convergence and more precise results, in comparison to the conventional formulation Backpropagation algorithm. The adapting of the training rate is effectuated using the information of the global error and global error variation. After finishing the training, the neural network is capable to forecast the electric load of 24 hours ahead. To illustrate the proposed methodology it is used data from a Brazilian Electric Company. © 2003 IEEE.

Local dimension and finite time prediction in coupled map lattices

Forecasting, for obvious reasons, often become the most important goal to be achieved. For spatially extended systems (e.g. atmospheric system) where the local nonlinearities lead to the most unpredictable chaotic evolution, it is highly desirable to have a simple diagnostic tool to identify regions of predictable behaviour. In this paper, we discuss the use of the bred vector (BV) dimension, a recently introduced statistics, to identify the regimes where a finite time forecast is feasible. Using the tools from dynamical systems theory and Bayesian modelling, we show the finite time predictability in two-dimensional coupled map lattices in the regions of low BV dimension. © Indian Academy of Sciences.

Toxoplasma gondii: Comparison of a rhoptry-ELISA with IFAT and MAT for antibody detection in sera of experimentally infected pigs

Indirect ELISA and IFAT have been reported to be more sensitive and specific than agglutination tests. However, MAT is cheaper, easier than the others and does not need special equipment. The purpose of this study was to compare an enzyme linked immunosorbent assay using crude rhoptries of Toxoplasma gondii as coating wells (r-ELISA) with indirect fluorescence antibody test (IFAT) and modified agglutination test (MAT) to detect anti-T. gondii antibodies in sera of experimentally infected pigs. Ten mixed breed pigs between 6.5 and 7.5 weeks old were used. All pigs were negative for the presence of T. gondii antibodies by IFAT (titre < 16), r-ELISA (OD < 0.295) and MAT (titre < 16). Animals received 7 × 107 viable tachyzoites of the RH strain by intramuscular (IM) route at day 0. Serum samples were collected at days -6, 0, 7, 14, 21, 28, 35, 42, 50, and 57. IFAT detected anti-T. gondii antibodies earlier than r-ELISA and MAT. The average of antibody levels was higher at day 35 in IFAT (Log10 = 2.9) and in MAT (Log10 = 3.5), and at day 42 in r-ELISA (OD = 0.797). The antibody levels remained high through the 57th day after inoculation in MAT, and there was a decrease tendency in r-ELISA and IFAT. IFAT was used as gold standard and r-ELISA demonstrated a higher prevalence (73.3%), sensitivity (94.3%), negative predictive value (83.3%), and accuracy (95.6%) than MAT. Kappa agreements among tests were calculated, and the best results were shown by r-ELISA × IFAT (κ = 0.88, p < 0.001). Cross-reaction with Sarcocystis miescheriana was investigated in r-ELISA and OD mean was 0.163 ± 0.035 (n = 65). Additionally, none of the animals inoculated with Sarcocystis reacted positively in r-ELISA. Our results indicate that r-ELISA could be a good method for serological detection of T. gondii infection in pigs. © 2005 Elsevier Inc. All rights reserved.

Usage of enterprise modeling processes and information systems design to forecast demand

Managing the great complexity of enterprise system, due to entities numbers, decision and process varieties involved to be controlled results in a very hard task because deals with the integration of its operations and its information systems. Moreover, the enterprises find themselves in a constant changing process, reacting in a dynamic and competitive environment where their business processes are constantly altered. The transformation of business processes into models allows to analyze and redefine them. Through computing tools usage it is possible to minimize the cost and risks of an enterprise integration design. This article claims for the necessity of modeling the processes in order to define more precisely the enterprise business requirements and the adequate usage of the modeling methodologies. Following these patterns, the paper concerns the process modeling relative to the domain of demand forecasting as a practical example. The domain of demand forecasting was built based on a theoretical review. The resulting models considered as reference model are transformed into information systems and have the aim to introduce a generic solution and be start point of better practical forecasting. The proposal is to promote the adequacy of the information system to the real needs of an enterprise in order to enable it to obtain and accompany better results, minimizing design errors, time, money and effort. The enterprise processes modeling are obtained with the usage of CIMOSA language and to the support information system it was used the UML language.

GDP 4.0 transfer to SGP 3.0 for SCIAMACHY no2 column processing: Verification with SDOAS / GDOAS prototype algorithms and delta-validation with NDACC / UV-visible network data

Until mid 2006, SCIAMACHY data processors for the operational retrieval of nitrogen dioxide (NO2) column data were based on the historical version 2 of the GOME Data Processor (GDP). On top of known problems inherent to GDP 2, ground-based validations of SCIAMACHY NO2 data revealed issues specific to SCIAMACHY, like a large cloud-dependent offset occurring at Northern latitudes. In 2006, the GDOAS prototype algorithm of the improved GDP version 4 was transferred to the off-line SCIAMACHY Ground Processor (SGP) version 3.0. In parallel, the calibration of SCIAMACHY radiometric data was upgraded. Before operational switch-on of SGP 3.0 and public release of upgraded SCIAMACHY NO2 data, we have investigated the accuracy of the algorithm transfer: (a) by checking the consistency of SGP 3.0 with prototype algorithms; and (b) by comparing SGP 3.0 NO2 data with ground-based observations reported by the WMO/GAW NDACC network of UV-visible DOAS/SAOZ spectrometers. This delta-validation study concludes that SGP 3.0 is a significant improvement with respect to the previous processor IPF 5.04. For three particular SCIAMACHY states, the study reveals unexplained features in the slant columns and air mass factors, although the quantitative impact on SGP 3.0 vertical columns is not significant.

Developed and studies on technical and economical viability of an environmental efficient urinal

This work introduces an innovative urinal for public convenience, that promotes at the same time water reuse and personal higiene, in a safe and economical way . Furthermore it demonstrates the latest technology and its technical and economical viabillity of utilization in new and already existing buildings facilities. This new model of personal higiene equipment offers as main benefits the improved economy with subsequent decrease in drinkable water consumption, sanitary safety, low cost and easy installation due to its simplicity and to the fact that it can be installed in already existing facilities. The proposal is constituted by a higienic, ecological and smart system for flushing of public urinals. It is a conjugated system of lavatory and urinal that reuses hands higienization water from the lavatory for flushing purpose. The proposed urinal can be operated manually or automatically by means of a presential sensor. The system promotes drinkable water economy by a rational utilization by avoiding the use of waste water from hand washing in place of clean water for flushing. The proposed equipment increases the economy of clean water in a simple and economical way and it can be installed in any type of public lavatory facilitie such as schools, public buildings, hospitals, commercial buildings, bus terminals, airports, stadiums, parking buildings and shopping centers. Additional benefits of the proposed system is the suggestion of hands washing before and after the use of the urinal without contamination risks from focet handling.and render more attractive the installation for a rational use of clean water in commercial and industrial buildings. Pay-back has shown to be very attractive for a number of internal return rates and also very attractive from the point of view of environmental protection.