Optimal moment to change pressure regulator and sprayer kit on center pivot irrigation machines: a theoretical proposal

A theoretical model was developed in order to determine the optimal moment for substituting the sprayer and pressure regulator kit on a center pivot irrigation machine. The model is based on the hypothesis that pressure regulator and sprayer deterioration decrease irrigation uniformity. To compensate the deficit that happens at under irrigated areas, an increase on irrigation depth is required. The model considers: additional water consumption and energy costs, maintenance and labor costs, as well as yield losses associated with under or over irrigated areas. The sum of all these components is compared to buying and installing a new spray kit cost, allowing the farmer to decide the best moment to renovate the sprayer and pressure regulator kits on a center pivot irrigation machine based on economic criteria.

Optimal moment to change pressure regulator and sprayer kit on center pivot irrigation machines: application to a study case

A theoretical model developed by the authors for determining the optimal moment to substitute sprayer and pressure regulator kit on a center pivot irrigating potatoes and beans has been applied. The methodology compares the sum of the costs due to additional consumption of water and energy, maintenance and labor, as well as yield losses associated to areas with deficit or over irrigation to the costs due to buy and install a new sprinkling set on the pivot. The results showed that for a reduction of 3.07% of the Hermann and Hein’s Uniformity Coefficient (UCh), the substitution of the sprinkling module on the pivot is justified when potatoes and beans are cultivated.

A bayesian analysis for the parameters of the exponential-logarithmic distribution

The exponential-logarithmic is a new lifetime distribution with decreasing failure rate and interesting applications in the biological and engineering sciences. Thus, a Bayesian analysis of the parameters would be desirable. Bayesian estimation requires the selection of prior distributions for all parameters of the model. In this case, researchers usually seek to choose a prior that has little information on the parameters, allowing the data to be very informative relative to the prior information. Assuming some noninformative prior distributions, we present a Bayesian analysis using Markov Chain Monte Carlo (MCMC) methods. Jeffreys prior is derived for the parameters of exponential-logarithmic distribution and compared with other common priors such as beta, gamma, and uniform distributions. In this article, we show through a simulation study that the maximum likelihood estimate may not exist except under restrictive conditions. In addition, the posterior density is sometimes bimodal when an improper prior density is used. © 2013 Copyright Taylor and Francis Group, LLC.

Air flow humidification for air-assisted boom sprayer

The air-assisted ground spray is fairly widespread. However, due to the unpredictable weather conditions, the operational efficiency is impaired by stops on grounds of low humidity and high temperatures. The aim of this work was to assess an air humidification method and evaluate its impact on temperature and air humidity for the air curtain of the air-assisted sprayer. With respect to relative air humidity, it has increased in 6.59%, being the maximum change when inserting 1.92 L min-1. So, it is concluded that the pipeline humidification might significantly reduce temperature and enhance air humidity. The treatments performed in this study consisted of a varied flow of a humidity device, related to weather conditions. Temperature and relative air humidity were measured at 1.0 m height from right to left of middle point of the machine, corresponding to the end of the spray boom, in the middle and end of right spray boom. The readings were also performed at three different distances from the end of the pipeline and at 0.25 and 0.50 m from that to the soil. The results show that 0.48 L min-1 in the humidification system has promoted a better efficiency in reducing air-temperature, on average 2.52 ºC when compared to the non-humidified one.

A modified Primal-Dual Logarithmic-Barrier Method for solving the Optimal Power Flow problem with discrete and continuous control variables

The aim of solving the Optimal Power Flow problem is to determine the optimal state of an electric power transmission system, that is, the voltage magnitude and phase angles and the tap ratios of the transformers that optimize the performance of a given system, while satisfying its physical and operating constraints. The Optimal Power Flow problem is modeled as a large-scale mixed-discrete nonlinear programming problem. This paper proposes a method for handling the discrete variables of the Optimal Power Flow problem. A penalty function is presented. Due to the inclusion of the penalty function into the objective function, a sequence of nonlinear programming problems with only continuous variables is obtained and the solutions of these problems converge to a solution of the mixed problem. The obtained nonlinear programming problems are solved by a Primal-Dual Logarithmic-Barrier Method. Numerical tests using the IEEE 14, 30, 118 and 300-Bus test systems indicate that the method is efficient. (C) 2012 Elsevier B.V. All rights reserved.

Blind Deconvolution via Lower-Bounded Logarithmic Image Priors

In this work we devise two novel algorithms for blind deconvolution based on a family of logarithmic image priors. In contrast to recent approaches, we consider a minimalistic formulation of the blind deconvolution problem where there are only two energy terms: a least-squares term for the data fidelity and an image prior based on a lower-bounded logarithm of the norm of the image gradients. We show that this energy formulation is sufficient to achieve the state of the art in blind deconvolution with a good margin over previous methods. Much of the performance is due to the chosen prior. On the one hand, this prior is very effective in favoring sparsity of the image gradients. On the other hand, this prior is non convex. Therefore, solutions that can deal effectively with local minima of the energy become necessary. We devise two iterative minimization algorithms that at each iteration solve convex problems: one obtained via the primal-dual approach and one via majorization-minimization. While the former is computationally efficient, the latter achieves state-of-the-art performance on a public dataset.

A logarithmic efficient estimator of the probability of ruin with recuperation for spectrally negative Lévy risk processes

This article provides an importance sampling algorithm for computing the probability of ruin with recuperation of a spectrally negative Lévy risk process with light-tailed downwards jumps. Ruin with recuperation corresponds to the following double passage event: for some t∈(0,∞)t∈(0,∞), the risk process starting at level x∈[0,∞)x∈[0,∞) falls below the null level during the period [0,t][0,t] and returns above the null level at the end of the period tt. The proposed Monte Carlo estimator is logarithmic efficient, as t,x→∞t,x→∞, when y=t/xy=t/x is constant and below a certain bound.

Applicative theories for logarithmic complexity classes

We present applicative theories of words corresponding to weak, and especially logarithmic, complexity classes. The theories for the logarithmic hierarchy and alternating logarithmic time formalise function algebras with concatenation recursion as main principle. We present two theories for logarithmic space where the first formalises a new two-sorted algebra which is very similar to Cook and Bellantoni's famous two-sorted algebra B for polynomial time [4]. The second theory describes logarithmic space by formalising concatenation- and sharply bounded recursion. All theories contain the predicates WW representing words, and VV representing temporary inaccessible words. They are inspired by Cantini's theories [6] formalising B.

A Logarithmic Image Prior for Blind Deconvolution

Blind Deconvolution consists in the estimation of a sharp image and a blur kernel from an observed blurry image. Because the blur model admits several solutions it is necessary to devise an image prior that favors the true blur kernel and sharp image. Many successful image priors enforce the sparsity of the sharp image gradients. Ideally the L0 “norm” is the best choice for promoting sparsity, but because it is computationally intractable, some methods have used a logarithmic approximation. In this work we also study a logarithmic image prior. We show empirically how well the prior suits the blind deconvolution problem. Our analysis confirms experimentally the hypothesis that a prior should not necessarily model natural image statistics to correctly estimate the blur kernel. Furthermore, we show that a simple Maximum a Posteriori formulation is enough to achieve state of the art results. To minimize such formulation we devise two iterative minimization algorithms that cope with the non-convexity of the logarithmic prior: one obtained via the primal-dual approach and one via majorization-minimization.

The logarithmic spiral, autoisoptic curve

In the Line of Investigation that in the department of “Technical Drawing” in the School of Agriculture Engineering of Madrid, we carry out on the study of The Technical Curves and his singularities, we demonstrate an interesting property of the Logarithmic Spiral. The demonstrated property consists of which the logarithmic spiral is a autoisoptic curve, that is to say that if from a point P anyone of the spiral tangent straight lines draw up to the previous arc, these form a constant angle α. This demonstration is novel and in addition we get to contribute a method to calculate the angle α given the equation of the spiral.

Dispersive optical bistability in quantum wells with logarithmic gain

We present an analytical model for studying optical bistability in semiconductor lasers that exhibit a logarithmic dependence of the optical gain on carrier concentration. Model results are shown for a Fabry–Pérot quantum-well laser and compared with the predictions of a commercial computer-aided design (CAD) software tool.

Classical solutions for a logarithmic fractional diffusion equation

We prove global existence and uniqueness of strong solutions to the logarithmic porous medium type equation with fractional diffusion ?tu + (?)1/2 log(1 + u) = 0, posed for x ? R, with nonnegative initial data in some function space of LlogL type. The solutions are shown to become bounded and C? smooth in (x, t) for all positive times. We also reformulate this equation as a transport equation with nonlocal velocity and critical viscosity, a topic of current relevance. Interesting functional inequalities are involved.