Robust regression applied to fractal/multifractal analysis.

Fractal and multifractal are concepts that have grown increasingly popular in recent years in the soil analysis, along with the development of fractal models. One of the common steps is to calculate the slope of a linear fit commonly using least squares method. This shouldn?t be a special problem, however, in many situations using experimental data the researcher has to select the range of scales at which is going to work neglecting the rest of points to achieve the best linearity that in this type of analysis is necessary. Robust regression is a form of regression analysis designed to circumvent some limitations of traditional parametric and non-parametric methods. In this method we don?t have to assume that the outlier point is simply an extreme observation drawn from the tail of a normal distribution not compromising the validity of the regression results. In this work we have evaluated the capacity of robust regression to select the points in the experimental data used trying to avoid subjective choices. Based on this analysis we have developed a new work methodology that implies two basic steps: ? Evaluation of the improvement of linear fitting when consecutive points are eliminated based on R pvalue. In this way we consider the implications of reducing the number of points. ? Evaluation of the significance of slope difference between fitting with the two extremes points and fitted with the available points. We compare the results applying this methodology and the common used least squares one. The data selected for these comparisons are coming from experimental soil roughness transect and simulated based on middle point displacement method adding tendencies and noise. The results are discussed indicating the advantages and disadvantages of each methodology.

Biokerosene from coconut, babassu, camelina and palm kernel oils: production and properties of their blends with fossil kerosene

On December 20th 2006 the European Commission approved a law proposal to include the civil aviation sector in the European market of carbon dioxide emission rights [European Union Emissions Trading System, EUETS). On July 8th 2009, the European Parliament and Conseil agreed that all flights leaving or landing in the EU airports starting from January 1st 2012 should be included in the EUETS. On November 19th 2008, the EU Directive 2008/101/CE [1] included the civil aviation activities in the EUETS, and this directive was transposed by the Spanish law 13/2010 of July 5th 2010 [2]. Thus, in 2012 the aviation sector should reduce their emissions to 97 % of the mean values registered in the period 2004-2006, and for 2013 these emission reductions should reach 95 % of the mean values for that same period. Trying to face this situation, the aviation companies are planning seriously the use of alternative jet fuels to reduce their greenhouse gas emissions and to lower their costs. However, some US airlines have issued a lawsuit before the European Court of Justice based in that this EU action violates a long standing worldwide aviation treaty, the Chicago convention of 1944, and also the Chinese aviation companies have rejected to pay any EU carbon dioxide tax [3]. Moreover, the USA Departments of Agriculture and Energy and the Navy will invest a total of up to $150 million over three years to spur production of aviation and marine biofuels for commercial and military applications [4]. However, the jet fuels should fulfill a set of extraordinarily sensitive properties to guarantee the safety of planes and passengers during all the flights.

Seismic hazards of the Iberian Peninsula - evaluation with Kernel functions

The seismic hazard of the Iberian Peninsula is analysed using a nonparametric methodology based on statistical kernel functions; the activity rate is derived from the catalogue data, both its spatial dependence (without a seismogenetic zonation) and its magnitude dependence (without using Gutenberg–Richter's law). The catalogue is that of the Instituto Geográfico Nacional, supplemented with other catalogues around the periphery; the quantification of events has been homogenised and spatially or temporally interrelated events have been suppressed to assume a Poisson process. The activity rate is determined by the kernel function, the bandwidth and the effective periods. The resulting rate is compared with that produced using Gutenberg–Richter statistics and a zoned approach. Three attenuation laws have been employed, one for deep sources and two for shallower events, depending on whether their magnitude was above or below 5. The results are presented as seismic hazard maps for different spectral frequencies and for return periods of 475 and 2475 yr, which allows constructing uniform hazard spectra.

Regression analysis of top of descent location for idle-thrust descents

In this paper, multiple regression analysis is used to model the top of descent (TOD) location of user-preferred descent trajectories computed by the flight management system (FMS) on over 1000 commercial flights into Melbourne, Australia. In addition to recording TOD, the cruise altitude, final altitude, cruise Mach, descent speed, wind, and engine type were also identified for use as the independent variables in the regression analysis. Both first-order and second-order models are considered, where cross-validation, hypothesis testing, and additional analysis are used to compare models. This identifies the models that should give the smallest errors if used to predict TOD location for new data in the future. A model that is linear in TOD altitude, final altitude, descent speed, and wind gives an estimated standard deviation of 3.9 nmi for TOD location given the trajectory parame- ters, which means about 80% of predictions would have error less than 5 nmi in absolute value. This accuracy is better than demonstrated by other ground automation predictions using kinetic models. Furthermore, this approach would enable online learning of the model. Additional data or further knowledge of algorithms is necessary to conclude definitively that no second-order terms are appropriate. Possible applications of the linear model are described, including enabling arriving aircraft to fly optimized descents computed by the FMS even in congested airspace.

Airplane materials compatibility with blends of fossil kerosene JET A1 with biokerosenes from babassu, palm kernel and coconut oils

The aviation companies are facing some problems that argue in favor of biofuels: Rising cost of traditional fuel: from 0.71 USD/gallon in May 2003 to 3.09 USD/gallon in January 2012. Environmental concerns: direct emissions from aviation account for about 3 % of the EU’s total greenhouse gas emissions. The International Civil Aviation Organization (ICAO) forecasts that by 2050 they could grow by a further 300-700 %. On December 20th 2006 the European Commission approved a law proposal to include the civil aviation sector in the European market of carbon dioxide emission rights (European Union Emissions Trading System, EUETS)

Blends of babassu, palm kernel and coconut fame with fossil kerosene. Technical aspects of low carbon number methyl esters as a possible source for renewable jet fuel

State of the Art. Process and Distillation. Fuel Characterization. Fuel Compatibility Tests

Regression models with MoPs Bayesian networks

We present a model of Bayesian network for continuous variables, where densities and conditional densities are estimated with B-spline MoPs. We use a novel approach to directly obtain conditional densities estimation using B-spline properties. In particular we implement naive Bayes and wrapper variables selection. Finally we apply our techniques to the problem of predicting neurons morphological variables from electrophysiological ones.

Airplane materials compatibility with blends of fossil kerosene jet a1 with biokerosenes from babassu, palm kernel and coconut oils

El desarrollo de bioqueroseno de diferentes orígenes y su uso creciente, hacen necesario el estudio de la compatibilidad estos nuevos combustibles con los materiales y recubrimientos con los que se encuentra en contacto. Por tanto, el presente proyecto estudia la compatibilidad de los bioquerosenos mezclados en diferentes proporciones con queroseno mineral, para evaluar posteriormente su compatibilidad con diferentes polímeros y composites presentes en la estructura de un avión.Currently there is a big interest to increase the sources of alternative fuels for aviation to get a reduction of their carbon footprint and the deep energetic dependence from fossil fuels of different countries. Although there are studies about how to produce this alternative fuel and how to accomplish the standards for a good performance in the aircraft turbines, there are no studies about how these fuels could affect the different materials of airplanes. In this context this work describes the compatibility of biokerosene blends of coconut, babassu and palm kernel with commercial Jet A-1 testing airplane polymeric materials, metals and composites. As a conclusion, all material samples show a good compatibility with the fuel blends tested.

Enhancing regression models for complex systems using evolutionary techniques for feature engineering

This work proposes an automatic methodology for modeling complex systems. Our methodology is based on the combination of Grammatical Evolution and classical regression to obtain an optimal set of features that take part of a linear and convex model. This technique provides both Feature Engineering and Symbolic Regression in order to infer accurate models with no effort or designer's expertise requirements. As advanced Cloud services are becoming mainstream, the contribution of data centers in the overall power consumption of modern cities is growing dramatically. These facilities consume from 10 to 100 times more power per square foot than typical office buildings. Modeling the power consumption for these infrastructures is crucial to anticipate the effects of aggressive optimization policies, but accurate and fast power modeling is a complex challenge for high-end servers not yet satisfied by analytical approaches. For this case study, our methodology minimizes error in power prediction. This work has been tested using real Cloud applications resulting on an average error in power estimation of 3.98%. Our work improves the possibilities of deriving Cloud energy efficient policies in Cloud data centers being applicable to other computing environments with similar characteristics.

Seismic hazard of the Iberian Peninsula: evaluation with Kernel functions

The seismic hazard of the Iberian Peninsula is analysed using a nonparametric methodology based on statistical kernel functions; the activity rate is derived from the catalogue data, both its spatial dependence (without a seismogenic zonation) and its magnitude dependence (without using Gutenberg–Richter's relationship). The catalogue is that of the Instituto Geográfico Nacional, supplemented with other catalogues around the periphery; the quantification of events has been homogenised and spatially or temporally interrelated events have been suppressed to assume a Poisson process. The activity rate is determined by the kernel function, the bandwidth and the effective periods. The resulting rate is compared with that produced using Gutenberg–Richter statistics and a zoned approach. Three attenuation relationships have been employed, one for deep sources and two for shallower events, depending on whether their magnitude was above or below 5. The results are presented as seismic hazard maps for different spectral frequencies and for return periods of 475 and 2475 yr, which allows constructing uniform hazard spectra

System failure prediction through rare-events elastic-net logistic regression

Predicting failures in a distributed system based on previous events through logistic regression is a standard approach in literature. This technique is not reliable, though, in two situations: in the prediction of rare events, which do not appear in enough proportion for the algorithm to capture, and in environments where there are too many variables, as logistic regression tends to overfit on this situations; while manually selecting a subset of variables to create the model is error- prone. On this paper, we solve an industrial research case that presented this situation with a combination of elastic net logistic regression, a method that allows us to automatically select useful variables, a process of cross-validation on top of it and the application of a rare events prediction technique to reduce computation time. This process provides two layers of cross- validation that automatically obtain the optimal model complexity and the optimal mode l parameters values, while ensuring even rare events will be correctly predicted with a low amount of training instances. We tested this method against real industrial data, obtaining a total of 60 out of 80 possible models with a 90% average model accuracy.

Polynomial regression using a perceptron with axo-axonic connections

Social behavior is mainly based on swarm colonies, in which each individual shares its knowledge about the environment with other individuals to get optimal solutions. Such co-operative model differs from competitive models in the way that individuals die and are born by combining information of alive ones. This paper presents the particle swarm optimization with differential evolution algorithm in order to train a neural network instead the classic back propagation algorithm. The performance of a neural network for particular problems is critically dependant on the choice of the processing elements, the net architecture and the learning algorithm. This work is focused in the development of methods for the evolutionary design of artificial neural networks. This paper focuses in optimizing the topology and structure of connectivity for these networks

Age regression from soft aligned face images using low computational resources

The initial step in most facial age estimation systems consists of accurately aligning a model to the output of a face detector (e.g. an Active Appearance Model). This fitting process is very expensive in terms of computational resources and prone to get stuck in local minima. This makes it impractical for analysing faces in resource limited computing devices. In this paper we build a face age regressor that is able to work directly on faces cropped using a state-of-the-art face detector. Our procedure uses K nearest neighbours (K-NN) regression with a metric based on a properly tuned Fisher Linear Discriminant Analysis (LDA) projection matrix. On FG-NET we achieve a state-of-the-art Mean Absolute Error (MAE) of 5.72 years with manually aligned faces. Using face images cropped by a face detector we get a MAE of 6.87 years in the same database. Moreover, most of the algorithms presented in the literature have been evaluated on single database experiments and therefore, they report optimistically biased results. In our cross-database experiments we get a MAE of roughly 12 years, which would be the expected performance in a real world application.

Conditional switching of vascular endothelial growth factor (VEGF) expression in tumors: Induction of endothelial cell shedding and regression of hemangioblastoma-like vessels by VEGF withdrawal

We have recently shown that VEGF functions as a survival factor for newly formed vessels during developmental neovascularization, but is not required for maintenance of mature vessels. Reasoning that expanding tumors contain a significant fraction of newly formed and remodeling vessels, we examined whether abrupt withdrawal of VEGF will result in regression of preformed tumor vessels. Using a tetracycline-regulated VEGF expression system in xenografted C6 glioma cells, we showed that shutting off VEGF production leads to detachment of endothelial cells from the walls of preformed vessels and their subsequent death by apoptosis. Vascular collapse then leads to hemorrhages and extensive tumor necrosis. These results suggest that enforced withdrawal of vascular survival factors can be applied to target preformed tumor vasculature in established tumors. The system was also used to examine phenotypes resulting from over-expression of VEGF. When expression of the transfected VEGF cDNA was continuously “on,” tumors became hyper-vascularized with abnormally large vessels, presumably arising from excessive fusions. Tumors were significantly less necrotic, suggesting that necrosis in these tumors is the result of insufficient angiogenesis.

Interleukin 12 and B7-1 costimulatory molecule expressed by an adenovirus vector act synergistically to facilitate tumor regression

Stimulation of antitumor immune mechanisms is the primary goal of cancer immunotherapy, and accumulating evidence suggests that effective alteration of the host–tumor relationship involves immunomodulating cytokines and also the presence of costimulatory molecules. To examine the antitumor effect of direct in vivo gene transfer of murine interleukin 12 (IL-12) and B7-1 into tumors, we developed an adenovirus (Ad) vector, AdIL12–B7-1, that encodes the two IL-12 subunits in early region 1 (E1) and the B7-1 gene in E3 under control of the murine cytomegalovirus promoter. This vector expressed high levels of IL-12 and B7-1 in infected murine and human cell lines and in primary murine tumor cells. In mice bearing tumors derived from a transgenic mouse mammary adenocarcinoma, a single intratumoral injection with a low dose (2.5 × 107 pfu/mouse) of AdIL12–B7-1 mediated complete regression in 70% of treated animals. By contrast, administration of a similar dose of recombinant virus encoding IL-12 or B7-1 alone resulted in only a delay in tumor growth. Interestingly, coinjection of two different viruses expressing either IL-12 or B7-1 induced complete tumor regression in only 30% of animals treated at this dose. Significantly, cured animals remained tumor free after rechallenge with fresh tumor cells, suggesting that protective immunity had been induced by treatment with AdIL12–B7-1. These results support the use of Ad vectors as a highly efficient delivery system for synergistically acting molecules and show that the combination of IL-12 and B7-1 within a single Ad vector might be a promising approach for in vivo cancer therapy.