Dynamic Bayesian smooth transition autoregressive models applied to hourly electricity load in southern Brazil

Dynamic Bayesian Smooth Transition Autoregressive (DBSTAR) models are proposed for nonlinear autoregressive time series processes as alternative to both the classical Smooth Transition Autoregressive (STAR) models of Chan and Tong (1986) and the Bayesian Simulation STAR (BSTAR) models of Lopes and Salazar (2005). Unlike those, DBSTAR models are sequential polynomial dynamic analytical models suitable for inherently non-stationary time series with non-linear characteristics such as asymmetric cycles. As they are analytical, they also avoid potential computational problems associated with BSTAR models and allow fast sequential estimation of parameters. Two types of DBSTAR models are defined here based on the method adopted to approximate the transition function of their autoregressive components, namely the Taylor and the B-splines DBSTAR models. A harmonic version of those models, that accounted for the cyclical component explicitly in a flexible yet parsimonious way, were applied to the well-known series of annual Canadian lynx trappings and showed improved fitting when compared to both the classical STAR and the BSTAR models. Another application to a long series of hourly electricity loading in southern Brazil, covering the period of the South-African Football World Cup in June 2010, illustrates the short-term forecasting accuracy of fast computing harmonic DBSTAR models that account for various characteristics such as periodic behaviour (both within-the-day and within-the-week) and average temperature

Effect of N and P Fertilizers on Nutritive Value of Dwarf Elephantgrass (Pennisetum Purpureum) CV N-75 Mott

Crude protein (CP), Ca and P concentrations and in vitro dry matter digestibility (IVDMD) of irrigated dwarf elephantgrass (Pennisetum purpureum cv. N-75 Mott) as affected by the factorial combination of N (0, 150, 300 and 450 kg N/ha/year) and P (0, 50 and 100 kg P205/ha/year) fertilization was evaluated in a tropical dry forest in Zulia State, western region of Venezuela. A randomized complete block design with three replications was used. Soil was a low-fertility sandy-loam Udic Paleustalf (pH 6.1). Nitrogen fertilization did not influence (P\u3e0.05) any of the variables. Mean CP, IVDMD, Ca and P were: 8.13, 61.3, 0.26 and 0.33 %. Phosphorus fertilization only influenced forage Ca content, which decreased (P\u3c0.05) from 0.30 to 0.23 % as applied P rate was increased. The NxP interaction was not significant (P\u3e0.05)

Local dynamics in high-order harmonic generation using Bohmian trajectories

We investigate high-order harmonic generation from a Bohmian-mechanical perspective, and find that the innermost part of the core, represented by a single Bohmian trajectory, leads to the main contributions to the high-harmonic spectra. Using time-frequency analysis, we associate this central Bohmian trajectory to an ensemble of unbound classical trajectories leaving and returning to the core, in agreement with the three step model. In the Bohmian scenario, this physical picture builds up non-locally near the core via the quantum mechanical phase of the wavefunction. This implies that the flow of the wavefunction far from the core alters the central Bohmian trajectory. We also show how this phase degrades in time for the peripheral Bohmian trajectories as they leave the core region.Comment: 7 pages, 3 figures; the manuscript has been considerably extended and modified with regard to the previous version

Time series forecasting with the WARIMAX-GARCH method

It is well-known that causal forecasting methods that include appropriately chosen Exogenous Variables (EVs) very often present improved forecasting performances over univariate methods. However, in practice, EVs are usually difficult to obtain and in many cases are not available at all. In this paper, a new causal forecasting approach, called Wavelet Auto-Regressive Integrated Moving Average with eXogenous variables and Generalized Auto-Regressive Conditional Heteroscedasticity (WARIMAX-GARCH) method, is proposed to improve predictive performance and accuracy but also to address, at least in part, the problem of unavailable EVs. Basically, the WARIMAX-GARCH method obtains Wavelet “EVs” (WEVs) from Auto-Regressive Integrated Moving Average with eXogenous variables and Generalized Auto-Regressive Conditional Heteroscedasticity (ARIMAX-GARCH) models applied to Wavelet Components (WCs) that are initially determined from the underlying time series. The WEVs are, in fact, treated by the WARIMAX-GARCH method as if they were conventional EVs. Similarly to GARCH and ARIMA-GARCH models, the WARIMAX-GARCH method is suitable for time series exhibiting non-linear characteristics such as conditional variance that depends on past values of observed data. However, unlike those, it can explicitly model frequency domain patterns in the series to help improve predictive performance. An application to a daily time series of dam displacement in Brazil shows the WARIMAX-GARCH method to remarkably outperform the ARIMA-GARCH method, as well as the (multi-layer perceptron) Artificial Neural Network (ANN) and its wavelet version referred to as Wavelet Artificial Neural Network (WANN) as in [1], on statistical measures for both in-sample and out-of-sample forecasting

Runoff at the micro-plot and slope scale following wildfire, central Portugal

Through their effects on soil properties and vegetation/litter cover, wildfires can strongly enhance overland flow generation and accelerate soil erosion [1] and, thereby, negatively affect land-use sustainability as well as downstream aquatic and flood zones. Wildfires are a common phenomenon in present-day Portugal, devastating in an average year some 100.000 ha of forest and woodlands and in an exceptional year like 2003 over 400.000 ha. There therefore exists a clear need in Portugal for a tool that can provide guidance to post-fire land management by predicting soil erosion risk, on the one hand, and, on the other, the mitigation effectiveness of soil conservation measures. Such a tool has recently been developed for the Western U.S.A. [3: ERMiT] but its suitability for Portuguese forests will need to be corroborated by field observations. Testing the suitability of existing erosion models in recently burned forest areas in Portugal is, in a nutshell, the aim of the EROSFIRE projects. In the first EROSFIRE project the emphasis was on the prediction of erosion at the scale of individual hill slopes. In the ongoing EROSFIRE-II project the spatial scope is extended to include the catchment scale, so that also the connectivity between hill slopes as well as channel and road processes are being addressed. Besides ERMiT, the principal models under evaluation for slope-scale erosion prediction are: (i) the variant of USLE [4] applied by the Portuguese Water Institute after the wildfires of 2003; (ii) the Morgan–Morgan–Finney model (MMF) [5]; (iii) MEFIDIS [6]. From these models, MEFIDIS and perhaps MMF will, after successful calibration at the slope scale, also be applied for predicting catchment-scale sediment yields of extreme events

Runoff and erosion at the micro-plot and slope scale in a small burnt catchment, central Portugal

Wildfires can have important impacts on hydrological processes and soil erosion in forest catchments, due to the destruction of vegetation cover and changes to soil properties. However, the processes involved are non-linear and not fully understood. This has severely limited the understanding on the impacts of wildfires, especially in the up-scaling from hillslopes to catchments; in consequence, current models are poorly adapted for burnt forest conditions. The objective of this presentation is to give an overview of the hydrological response and sediment yield from the micro-plot to slope scale, in the first year following a wildfire (2008/2009) that burnt an entire catchment nearby the Colmeal village, central Portugal. The overview will focus on three slopes inside the catchment, with samples including: • Runoff at micro-plot scale (12 bounded plots) and slope scale (12 open plots); • Sediments and Organic Matter loss at micro-plot scale (12 bounded plots) and slope scale (12 open plots plus 3 Sediment fences); • Rainfall and Soil moisture data; • Soil Water Repellency and Ground Cover data. The analysis of the first year following the wildfire clearly shows the complexity of runoff generation and the associated sediment transport in recently burnt areas, with pronounced differences between hillslopes and across spatial scales as well as with marked variations through time. This work was performed in the framework of the EROSFIRE-II project (PTDC/AGR-CFL/70968/2006) which has as overall aim to predict soil erosion risk in recently burnt forest areas, including common post-fire forest management practices; the project focuses on the simultaneous measurement of runoff and soil erosion at multiple spatial scales.The results to be presented in this session are expected to show how sediment is generated, transported and exported in the Colmeal watershed; and contribute to understand and simulate erosion processes in burnt catchments, including for model development and evaluation

Effect of the Cutting Frequency on Yield and Crude Protein and Mineral Contents of Panicum Maximum

Dry matter yield per cutting and total production, crude protein content and mineral composition of Guineagrass (Panicum maximum Jacq.) as affected by three cutting frequencies (28, 42 and 56 days) were evaluated in a wet/dry tropical area in Venezuela. Only CP was affected (P\u3c0.05) by the cutting frequency, decreasing (P\u3c0.05) from 11.9 to 9.2 % as the harvest interval increased. Mean DM yield per cutting and accumulated over the 336-day experiment (661 and 5140 kg/ha) were low, probably due to low rainfall (600 mm) during the trial. Mean macro (%) and microelement (ppm) concentrations were: Ca, 0.9; P, 0.31; Mg, 0.45; K, 2,17; Na, 0.09; Cu, 11; Fe, 133 and Mn, 269, which were regarded as nutritionally adequate for grazing ruminants

Surface wave, skin effect, and per unit length parameters of the single-wire transmission line at low frequency, for nonmagnetic and magnetic wires

Surface wave technology for high-speed communications is a current research topic aimed to respond to increasing data rate demands on existing copper infrastructures. Also, the topic of surface waves has recently gained importance in the modeling of transmission line towers hit by lightning strikes with spectral content in the megahertz band. The single-wire transmission line structure (return conductor absent) cannot support TEM waves; it supports a TM Sommerfeld wave fully described by two propagation constants and two characteristic impedances. Nonetheless, the literature on single-wire transmission-line structures has been employing quasi-static per unit length constitutive parameters, inductance and capacitance, borrowed from ordinary two-conductor transmission line TEM analysis. This work develops, discusses, and compares various possible definitions of these constitutive parameters using different physical approaches: TEM-approach, circuit-approach, and energy-approach. Numerical results for nonmagnetic and magnetic wires, copper and steel wires, respectively, are obtained in the range 1 Hz to 1 GHz. Our analysis shows that in some circumstances the TEM and circuit approaches may lead to nonphysical results, but, remarkably, all the approaches seem to converge to a common dominant term either in the per unit length capacitance or in the per unit length inductance, whose product is frequency-invariant. Considering the different approaches under discussion, the differences among the observed results for the per unit length constitutive parameters are negligibly small, of second-order importance