Showing papers by "University of Extremadura published in 2013"

Hyperspectral Remote Sensing Data Analysis and Future Challenges

Abstract: Hyperspectral remote sensing technology has advanced significantly in the past two decades. Current sensors onboard airborne and spaceborne platforms cover large areas of the Earth surface with unprecedented spectral, spatial, and temporal resolutions. These characteristics enable a myriad of applications requiring fine identification of materials or estimation of physical parameters. Very often, these applications rely on sophisticated and complex data analysis methods. The sources of difficulties are, namely, the high dimensionality and size of the hyperspectral data, the spectral mixing (linear and nonlinear), and the degradation mechanisms associated to the measurement process such as noise and atmospheric effects. This paper presents a tutorial/overview cross section of some relevant hyperspectral data analysis methods and algorithms, organized in six main topics: data fusion, unmixing, classification, target detection, physical parameter retrieval, and fast computing. In all topics, we describe the state-of-the-art, provide illustrative examples, and point to future challenges and research directions.

Generalized Composite Kernel Framework for Hyperspectral Image Classification

Abstract: This paper presents a new framework for the development of generalized composite kernel machines for hyperspectral image classification. We construct a new family of generalized composite kernels which exhibit great flexibility when combining the spectral and the spatial information contained in the hyperspectral data, without any weight parameters. The classifier adopted in this work is the multinomial logistic regression, and the spatial information is modeled from extended multiattribute profiles. In order to illustrate the good performance of the proposed framework, support vector machines are also used for evaluation purposes. Our experimental results with real hyperspectral images collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory's Airborne Visible/Infrared Imaging Spectrometer and the Reflective Optics Spectrographic Imaging System indicate that the proposed framework leads to state-of-the-art classification performance in complex analysis scenarios.

Biogeographical patterns and determinants of invasion by forest pathogens in Europe

Abstract: A large database of invasive forest pathogens (IFPs) was developed to investigate the patterns and determinants of invasion in Europe. Detailed taxonomic and biological information on the invasive species was combined with country-specific data on land use, climate, and the time since invasion to identify the determinants of invasiveness, and to differentiate the class of environments which share territorial and climate features associated with a susceptibility to invasion. IFPs increased exponentially in the last four decades. Until 1919, IFPs already present moved across Europe. Then, new IFPs were introduced mainly from North America, and recently from Asia. Hybrid pathogens also appeared. Countries with a wider range of environments, higher human impact or international trade hosted more IFPs. Rainfall influenced the diffusion rates. Environmental conditions of the new and original ranges and systematic and ecological attributes affected invasiveness. Further spread of established IFPs is expected in countries that have experienced commercial isolation in the recent past. Densely populated countries with high environmental diversity may be the weakest links in attempts to prevent new arrivals. Tight coordination of actions against new arrivals is needed. Eradication seems impossible, and prevention seems the only reliable measure, although this will be difficult in the face of global mobility.

Grid-Connected Photovoltaic Generation Plants: Components and Operation

Abstract: The main design objective of photovoltaic (PV) systems has been, for a long time, to extract the maximum power from the PV array and inject it into the ac grid. Therefore, the maximum power point tracking (MPPT) of a uniformly irradiated PV array and the maximization of the conversion efficiency have been the main design issues. However, when the PV plant is connected to the grid, special attention has to be paid to the reliability of the system, the power quality, and the implementation of protection and grid synchronization functions. Modern power plants are required to maximize their energy production, requiring suitable control strategies to solve the problems related to the partial shading phenomena and different orientation of the PV modules toward the sun. Moreover, the new policy concerning the injection of reactive power into the grid makes the development of suitable topologies and control algorithms mandatory. A general view of actual solutions for applications of the PV energy systems is presented. This article covers several important issues, including the most reliable models used for simulation, which are useful in the design of control systems, and the MPPT function, particularly in distributed applications. The main topologies used in the PV power processing system and, finally, grid connection aspects are discussed, with emphasis on synchronization, protections, and integration.

Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study

Abstract: Four statistical techniques for modelling landslide susceptibility were compared: multiple logistic regression (MLR), multivariate adaptive regression splines (MARS), classification and regression trees (CART), and maximum entropy (MAXENT). According to the literature, MARS and MAXENT have never been used in landslide susceptibility modelling, and CART has been used only twice. Twenty independent variables were used as predictors, including lithology as a categorical variable. Two sets of random samples were used, for a total of 90 model replicates (with and without lithology, and with different proportions of positive and negative data). The model performance was evaluated using the area under the receiver operating characteristic curve (AUC) statistic. The main results are (a) the inclusion of lithology improves the model performance; (b) the best AUC values for single models are MLR (0.76), MARS (0.76), CART (0.77), and MAXENT (0.78); (c) a smaller amount of negative data provides better results; (d) the models with the highest prediction capability are obtained with MAXENT and CART; and (e) the combination of different models is a way to evaluate the model reliability. We further discuss some key issues in landslide modelling, including the influence of the various methods that we used, the sample size, and the random replicate procedures.

Spectral–Spatial Classification of Hyperspectral Data Using Loopy Belief Propagation and Active Learning

Abstract: In this paper, we propose a new framework for spectral-spatial classification of hyperspectral image data. The proposed approach serves as an engine in the context of which active learning algorithms can exploit both spatial and spectral information simultaneously. An important contribution of our paper is the fact that we exploit the marginal probability distribution which uses the whole information in the hyperspectral data. We learn such distributions from both the spectral and spatial information contained in the original hyperspectral data using loopy belief propagation. The adopted probabilistic model is a discriminative random field in which the association potential is a multinomial logistic regression classifier and the interaction potential is a Markov random field multilevel logistic prior. Our experimental results with hyperspectral data sets collected using the National Aeronautics and Space Administration's Airborne Visible Infrared Imaging Spectrometer and the Reflective Optics System Imaging Spectrometer system indicate that the proposed framework provides state-of-the-art performance when compared to other similar developments.

Analysis and Optimizations of Global and Local Versions of the RX Algorithm for Anomaly Detection in Hyperspectral Data

Abstract: Anomaly detection is an important task for hyperspectral data exploitation. A standard approach for anomaly detection in the literature is the method developed by Reed and Xiaoli, also called RX algorithm. A variation of this algorithm consists of applying the same concept to a local sliding window centered around each image pixel. The computational cost is very high for RX algorithm and it strongly increases for its local versions. However, current advances in high performance computing help to reduce the run-time of these algorithms. So, for the standard RX, it is possible to achieve a processing time similar to the data acquisition time and to increase the practical interest for its local versions. In this paper, we discuss several optimizations which exploit different forms of acceleration for these algorithms. First, we explain how the calculation of the correlation matrix and its inverse can be accelerated through optimization techniques based on the properties of these particular matrices and the efficient use of linear algebra libraries. Second, we describe parallel implementations of the RX algorithm, optimized for multicore platforms. These are well-known, inexpensive and widely available high performance computing platforms. The ability to detect anomalies of the global and local versions of RX is explored using a wide set of experiments, using both synthetic and real data, which are used for comparing the optimized versions of the global and local RX algorithms in terms of anomaly detection accuracy and computational efficiency. The synthetic images have been generated under different noise conditions and anomalous features. The two real scenes used in the experiments are a hyperspectral data set collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) system over the World Trade Center (WTC) in New York, five days after the terrorist attacks, and another data set collected by the HYperspectral Digital Image Collection Experiment (HYDICE). Experimental results indicate that the proposed optimizations can significantly improve the performance of the considered algorithms without reducing their anomaly detection accuracy.

Semisupervised Self-Learning for Hyperspectral Image Classification

Abstract: Remotely sensed hyperspectral imaging allows for the detailed analysis of the surface of the Earth using advanced imaging instruments which can produce high-dimensional images with hundreds of spectral bands. Supervised hyperspectral image classification is a difficult task due to the unbalance between the high dimensionality of the data and the limited availability of labeled training samples in real analysis scenarios. While the collection of labeled samples is generally difficult, expensive, and time-consuming, unlabeled samples can be generated in a much easier way. This observation has fostered the idea of adopting semisupervised learning techniques in hyperspectral image classification. The main assumption of such techniques is that the new (unlabeled) training samples can be obtained from a (limited) set of available labeled samples without significant effort/cost. In this paper, we develop a new approach for semisupervised learning which adapts available active learning methods (in which a trained expert actively selects unlabeled samples) to a self-learning framework in which the machine learning algorithm itself selects the most useful and informative unlabeled samples for classification purposes. In this way, the labels of the selected pixels are estimated by the classifier itself, with the advantage that no extra cost is required for labeling the selected pixels using this machine-machine framework when compared with traditional machine-human active learning. The proposed approach is illustrated with two different classifiers: multinomial logistic regression and a probabilistic pixelwise support vector machine. Our experimental results with real hyperspectral images collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory's Airborne Visible-Infrared Imaging Spectrometer and the Reflective Optics Spectrographic Imaging System indicate that the use of self-learning represents an effective and promising strategy in the context of hyperspectral image classification.

The genetics of eating disorders

Abstract: Over the past decade, considerable advances have been made in understanding genetic influences on eating pathology. Eating disorders aggregate in families, and twin studies reveal that additive genetic factors account for approximately 40% to 60% of liability to anorexia nervosa (AN), bulimia nervosa (BN), and binge eating disorder (BED). Molecular genetics studies have been undertaken to identify alterations in deoxyribonucleic acid sequence and/or gene expression that may be involved in the pathogenesis of disordered eating behaviors, symptoms, and related disorders and to uncover potential genetic variants that may contribute to variability of treatment response. This article provides an in-depth review of the scientific literature on the genetics of AN, BN, and BED including extant studies, emerging hypotheses, future directions, and clinical implications.

Semisupervised Hyperspectral Image Classification Using Soft Sparse Multinomial Logistic Regression

Abstract: In this letter, we propose a new semisupervised learning (SSL) algorithm for remotely sensed hyperspectral image classification. Our main contribution is the development of a new soft sparse multinomial logistic regression model which exploits both hard and soft labels. In our terminology, these labels respectively correspond to labeled and unlabeled training samples. The proposed algorithm represents an innovative contribution with regard to conventional SSL algorithms that only assign hard labels to unlabeled samples. The effectiveness of our proposed method is evaluated via experiments with real hyperspectral images, in which comparisons with conventional semisupervised self-learning algorithms with hard labels are carried out. In such comparisons, our method exhibits state-of-the-art performance.

The LRRK2 G2019S mutant exacerbates basal autophagy through activation of the MEK/ERK pathway.

Abstract: Mutations in leucine-rich repeat kinase 2 (LRRK2) are a major cause of familial Parkinsonism, and the G2019S mutation of LRRK2 is one of the most prevalent mutations. The deregulation of autophagic processes in nerve cells is thought to be a possible cause of Parkinson’s disease (PD). In this study, we observed that G2019S mutant fibroblasts exhibited higher autophagic activity levels than control fibroblasts. Elevated levels of autophagic activity can trigger cell death, and in our study, G2019S mutant cells exhibited increased apoptosis hallmarks compared to control cells. LRRK2 is able to induce the phosphorylation of MAPK/ERK kinases (MEK). The use of 1,4-diamino-2,3-dicyano-1,4-bis[2-aminophenylthio]butadiene (U0126), a highly selective inhibitor of MEK1/2, reduced the enhanced autophagy and sensibility observed in G2019S LRRK2 mutation cells. These data suggest that the G2019S mutation induces autophagy via MEK/ERK pathway and that the inhibition of this exacerbated autophagy reduces the sensitivity observed in G2019S mutant cells.

Rhodamine and BODIPY chemodosimeters and chemosensors for the detection of Hg2+, based on fluorescence enhancement effects

Abstract: Fluorescent sensors for Hg2+ are demonstrating their potential in a variety of fields such as environmental and biological applications. The review focuses on the recent development of rhodamine derivatives in which the spirolactam (non-fluorescent) to ring-opened amide (fluorescent) process was utilized and on the development of BODIPY derivatives in which the photoinduced electron transfer (PET) process was utilized. New trends in the immobilization of the molecular probes on solid supports, as polymers and/or nanostructures, have been emphasized. The different recognition mechanisms used for the signal responses have been analyzed. The spectroscopic properties, reaction media, analytical parameters, interferences by other ions and practical applications have been summarized.

Social Capital and Subjective Wellbeing in Europe: A New Approach on Social Capital

Abstract: This paper aims to analyze the relationship between the various dimensions of social capital and subjective wellbeing. Data used in this study come from the fourth wave of the European Social Survey and different measures of wellbeing are used to take account of both the cognitive and affective processes of individual wellbeing (i.e. life satisfaction, happiness, and subjective wellbeing). A factor analysis is performed to summarize information coming from a large set of variables into different components corresponding to each dimension of social capital (i.e. networks, norms, and trust). Among the results, we find that the impact of social capital on subjective wellbeing differ depending on the component of social capital which is under analysis. In particular, social networks, social trust and institutional trust are the components that show a higher correlation with subjective wellbeing. Furthermore, in addition to the positive effects of the individual variables, our results suggest that social capital at the aggregate level positively correlates with individual wellbeing, thus pointing to an external or environmental effect of social capital.

Tryptophan-enriched cereal intake improves nocturnal sleep, melatonin, serotonin, and total antioxidant capacity levels and mood in elderly humans.

Abstract: Melatonin and serotonin rhythms, which exhibit a close association with the endogenous circadian component of sleep, are attenuated with increasing age. This decrease seems to be linked to sleep alterations in the elderly. Chrononutrition is a field of chronobiology that establishes the principle of consuming foodstuffs at times of the day when they are more useful for health, improving, therefore, biorhythms and physical performance. Our aim was to analyze whether the consumption of cereals enriched with tryptophan, the precursor of both serotonin and melatonin, may help in the reconsolidation of the sleep/wake cycle and counteract depression and anxiety in 35 middle-aged/elderly (aged 55–75 year) volunteers in a simple blind assay. Data were collected for 3 weeks according to the following schedule: The control week participants consumed standard cereals (22.5 mg tryptophan in 30 g cereals per dose) at breakfast and dinner; for the treatment week, cereals enriched with a higher dose of tryptophan (60 mg tryptophan in 30 g cereals per dose) were eaten at both breakfast and dinner; the posttreatment week volunteers consumed their usual diet. Each participant wore a wrist actimeter that logged activity during the whole experiment. Urine was collected to analyze melatonin and serotonin urinary metabolites and to measure total antioxidant capacity. The consumption of cereals containing the higher dose in tryptophan increased sleep efficiency, actual sleep time, immobile time, and decreased total nocturnal activity, sleep fragmentation index, and sleep latency. Urinary 6-sulfatoxymelatonin, 5-hydroxyindoleacetic acid levels, and urinary total antioxidant capacity also increased respectively after tryptophan-enriched cereal ingestion as well as improving anxiety and depression symptoms. Cereals enriched with tryptophan may be useful as a chrononutrition tool for alterations in the sleep/wake cycle due to age.

Quantifying the benefits of international scientific collaboration

Abstract: We analyze the benefits in terms of scientific impact deriving from international collaboration, examining both those for a country when it collaborates and also those for the other countries when they are collaborating with the former. The data show the more countries there are involved in the collaboration, the greater the gain in impact. Contrary to what we expected, the scientific impact of a country does not significantly influence the benefit it derives from collaboration, but does seem to positively influence the benefit obtained by the other countries collaborating with it. Although there was a weak correlation between these two classes of benefit, the countries with the highest impact were clear outliers from this correlation, tending to provide proportionally more benefit to their collaborating countries than they themselves obtained. Two surprising findings were the null benefit resulting from collaboration with Iran, and the small benefit resulting from collaboration with the United States despite its high impact.

Multicomponent Reactions for de Novo Synthesis of BODIPY Probes: In Vivo Imaging of Phagocytic Macrophages

Abstract: Multicomponent reactions are excellent tools to generate complex structures with broad chemical diversity and fluorescent properties. Herein we describe the adaptation of the fluorescent BODIPY scaffold to multicomponent reaction chemistry with the synthesis of BODIPY adducts with high fluorescence quantum yields and good cell permeability. From this library we identified one BODIPY derivative (PhagoGreen) as a low-pH sensing fluorescent probe that enabled imaging of phagosomal acidification in activated macrophages. The fluorescence emission of PhagoGreen was proportional to the degree of activation of macrophages and could be specifically blocked by bafilomycin A, an inhibitor of phagosomal acidification. PhagoGreen does not impair the normal functions of macrophages and can be used to image phagocytic macrophages in vivo.

The Importance of Parents’ Behavior in their Children’s Enjoyment and Amotivation in Sports

Abstract: The main aim of the research was to examine the relationship between motivational orientations and parents’ behavior with regard to the players’ motivational orientation, motivational climate, enjoyment and amotivation. The sample comprised 723 athletes (M = 12.37, SD = 1.48) and 723 parents (M = 46.46, SD = 2.56). Players were male and female who belonged to federative basketball, handball, football and volleyball teams. Parents and athletes completed questionnaires that assessed motivational orientations, parents’ involvement in the practice as well as enjoyment and motivation in the sport. Results showed a positive relationship between parents’ support of the sport and players’ enjoyment and a negative relationship with players’ amotivation. Moreover, in players who perceived more pressure from their parents, there was a positive association with amotivation and a negative one with enjoyment. Lastly, it was emphasized that appropriate parental participation can promote an increase of players’ enjoyment of and motivation for

Sequential induction of auxin efflux and influx carriers regulates lateral root emergence

Abstract: In Arabidopsis, lateral roots originate from pericycle cells deep within the primary root. New lateral root primordia ( LRP) have to emerge through several overlaying tissues. Here, we report that auxin produced in new LRP is transported towards the outer tissues where it triggers cell separation by inducing both the auxin influx carrier LAX3 and cell-wall enzymes. LAX3 is expressed in just two cell files overlaying new LRP. To understand how this striking pattern of LAX3 expression is regulated, we developed a mathematical model that captures the network regulating its expression and auxin transport within realistic three-dimensional cell and tissue geometries. Our model revealed that, for the LAX3 spatial expression to be robust to natural variations in root tissue geometry, an efflux carrier is required-later identified to be PIN3. To prevent LAX3 from being transiently expressed in multiple cell files, PIN3 and LAX3 must be induced consecutively, which we later demonstrated to be the case. Our study exemplifies how mathematical models can be used to direct experiments to elucidate complex developmental processes.

Ball possession effectiveness in men's and women's elite basketball according to situational variables in different game periods.

Abstract: The aim of the present study was to identify the importance of basketball performance indicators in predicting the effectiveness of ball possessions in men's and women's basketball, when controlling for situational variables and game periods. The sample consisted of 7234 ball possessions, corresponding to 40 games from the Spanish professional leagues. The effects of the predictor variables on successful ball possessions according to game period were analysed using binary logistic regressions. Results from men's teams show interactions with number of passes and ending player during the first five minutes, with starting and ending zone, defensive systems, screens used and possession duration during the middle thirty minutes, and there were interactions with passes used, possession duration and players involved during the last five minutes. Results from women's teams show interactions with starting and ending zone, passes used, defensive systems and ending player during the first five minutes, and ...

Identifying Basketball Performance Indicators in Regular Season and Playoff Games

Abstract: The aim of the present study was to identify basketball game performance indicators which best discriminate winners and losers in regular season and playoffs. The sample used was composed by 323 games of ACB Spanish Basketball League from the regular season (n=306) and from the playoffs (n=17). A previous cluster analysis allowed splitting the sample in balanced (equal or below 12 points), unbalanced (between 13 and 28 points) and very unbalanced games (above 28 points). A discriminant analysis was used to identify the performance indicators either in regular season and playoff games. In regular season games, the winning teams dominated in assists, defensive rebounds, successful 2 and 3-point field-goals. However, in playoff games the winning teams' superiority was only in defensive rebounding. In practical applications, these results may help the coaches to accurately design training programs to reflect the importance of having different offensive set plays and also have specific conditioning programs to prepare for defensive rebounding.

Prospective primary teachers’ self-efficacy and emotions in science teaching

Abstract: The self-efficacy of prospective primary teachers was studied, considering in particular the relationship of that construct with the emotions they expect to experience as future science teachers, differentiating between when they will be teaching the content of the ‘nature sciences’ (biology and geology) and that of the ‘hard sciences’ (physics and chemistry). The study instrument was a questionnaire completed by 188 prospective primary school teachers in their initial education at the University of Extremadura during the academic year 2009/2010. The results showed them to mostly have positive emotions towards nature sciences and negative towards the hard sciences. While their beliefs concerning their self-efficacy are significantly related to their emotions about their future teaching of the hard sciences, high self-efficacy was significantly correlated with more positive emotions and fewer negative emotions towards physics and chemistry.

Toughening of super-hard ultra-fine grained B4C densified by spark-plasma sintering via SiC addition

Abstract: Toughening of super-hard B 4 C ceramics with ultra-fine grained microstructures via the addition of SiC (15 wt.%) or the simultaneous addition of SiC (15 wt.%) and graphite (2 wt.%) is reported. The ultra-fine grained B 4 C–SiC and B 4 C–SiC–C composites prepared by spark-plasma sintering from powder mixtures subjected to high-energy co-ball-milling are found to be remarkably tougher (i.e., ∼65% and 50%) than the pure B 4 C ceramic with a coarsened microstructure. Crack bridging by the homogenously dispersed SiC grains can give an explanation for the improvement in toughness. Also, the addition of SiC to the B 4 C matrix was found to change the fracture mode from purely transgranular to a mixture of intergranular and transgranular fracture. This is derived from the weakness of the B 4 C–SiC interfaces due to the existence of residual thermo-elastic stresses. It was also found that despite SiC is softer than B 4 C, the B 4 C–SiC are yet extremely hard if densified appropriately, with the hardness even reaching 36 GPa.

An assessment of innovativeness in KIBS: implications on KIBS' co-creation culture, innovation capability, and performance

Abstract: Purpose – The purpose of this paper is to investigate the relationship between innovative culture, innovation efforts, and their performance among knowledge-intensive business services (KIBS). Innovation intensity is evaluated in the technical and administrative domains. Performance indicators include customer-related outcomes and market and financial results relative to competition. To provide insight into how innovativeness contributes to sustaining a KIBS' competitiveness, the mediating role of its predisposition to involve customers and front-line employees in new service development is also considered. Design/methodology/approach – In accordance with the objectives of the research, and from an extensive review of the literature, the authors develop a conceptual model and test it on a sample of 154 Spanish KIBS using structural equation modelling. Findings – The results show that KIBS' appraisal of customers' and front-line employees' participation in new service co-creation is strongly determined by ...

Pain in the elderly

Abstract: Pain leads to unpleasant sensory or emotional experience for any individual. In the elderly, given their biopsychosocial characteristics, the pain requires a specific approach, different from other age groups: this is the objective of this article, which dealt with the different types of pain, the assessment and treatment of the same.

GPU Implementation of an Automatic Target Detection and Classification Algorithm for Hyperspectral Image Analysis

Abstract: The detection of (moving or static) targets in remotely sensed hyperspectral images often requires real-time responses for swift decisions that depend upon high computing performance of algorithm analysis. The automatic target detection and classification algorithm (ATDCA) has been widely used for this purpose. In this letter, we develop several optimizations for accelerating the computational performance of ATDCA. The first one focuses on the use of the Gram-Schmidt orthogonalization method instead of the orthogonal projection process adopted by the classic algorithm. The second one is focused on the development of a new implementation of the algorithm on commodity graphics processing units (GPUs). The proposed GPU implementation properly exploits the GPU architecture at low level, including shared memory, and provides coalesced accesses to memory that lead to very significant speedup factors, thus taking full advantage of the computational power of GPUs. The GPU implementation is specifically tailored to hyperspectral imagery and the special characteristics of this kind of data, achieving real-time performance of ATDCA for the first time in the literature. The proposed optimizations are evaluated not only in terms of target detection accuracy but also in terms of computational performance using two different GPU architectures by NVIDIA: Tesla C1060 and GeForce GTX 580, taking advantage of the performance of operations in single-precision floating point. Experiments are conducted using hyperspectral data sets collected by three different hyperspectral imaging instruments. These results reveal considerable acceleration factors while retaining the same target detection accuracy for the algorithm.