Multi-site downscaling of maximum and minimum daily temperature using support vector machine

Climate change impact assessment studies involve downscaling large-scale atmospheric predictor variables (LSAPVs) simulated by general circulation models (GCMs) to site-scale meteorological variables. This article presents a least-square support vector machine (LS-SVM)-based methodology for multi-site downscaling of maximum and minimum daily temperature series. The methodology involves (1) delineation of sites in the study area into clusters based on correlation structure of predictands, (2) downscaling LSAPVs to monthly time series of predictands at a representative site identified in each of the clusters, (3) translation of the downscaled information in each cluster from the representative site to that at other sites using LS-SVM inter-site regression relationships, and (4) disaggregation of the information at each site from monthly to daily time scale using k-nearest neighbour disaggregation methodology. Effectiveness of the methodology is demonstrated by application to data pertaining to four sites in the catchment of Beas river basin, India. Simulations of Canadian coupled global climate model (CGCM3.1/T63) for four IPCC SRES scenarios namely A1B, A2, B1 and COMMIT were downscaled to future projections of the predictands in the study area. Comparison of results with those based on recently proposed multivariate multiple linear regression (MMLR) based downscaling method and multi-site multivariate statistical downscaling (MMSD) method indicate that the proposed method is promising and it can be considered as a feasible choice in statistical downscaling studies. The performance of the method in downscaling daily minimum temperature was found to be better when compared with that in downscaling daily maximum temperature. Results indicate an increase in annual average maximum and minimum temperatures at all the sites for A1B, A2 and B1 scenarios. The projected increment is high for A2 scenario, and it is followed by that for A1B, B1 and COMMIT scenarios. Projections, in general, indicated an increase in mean monthly maximum and minimum temperatures during January to February and October to December.

STAT-1 expression is regulated by IGFBP-3 in malignant glioma cells and is a strong predictor of poor survival in patients with glioblastoma Laboratory investigation

Object. Insulin-like growth factor binding proteins (IGEBPs) have been implicated in the pathogenesis of glioma. In a previous study the authors demonstrated that IGFBP-3 is a novel glioblastoma biomarker associated with poor survival. Since signal transducer and activator of transcription 1 (STAT-1) has been shown to be regulated by IGFBP-3 during chondrogenesis and is a prosurvival and radioresistant molecule in different tumors, the aim in the present study was to explore the functional significance of IGFBP-3 in malignant glioma cells, to determine if STAT-1 is indeed regulated by IGFBP-3, and to study the potential of STAT-1 as a biomarker in glioblastoma. Methods. The functional significance of IGFBP-3 was investigated using the short hairpin (sh)RNA gene knockdown approach on U251MG cells. STAT-1 regulation by IGFBP-3 was tested on U251MG and U87MG cells by shRNA gene knockdown and exogenous treatment with recombinant IGFBP-3 protein. Subsequently, the expression of STAT-1 was analyzed with real-time reverse transcription polymerase chain reaction (RT-PCR) and immunohistochemistry (IHC) in glioblastoma and control brain tissues. Survival analyses were done on a uniformly treated prospective cohort of adults with newly diagnosed glioblastoma (136 patients) using Kaplan-Meier and Cox regression models. Results. IGFBP-3 knockdown significantly impaired proliferation, motility, migration, and invasive capacity of U251MG cells in vitro (p < 0.005). Exogenous overexpression of IGFBP-3 in U251MG and U87MG cells demonstrated STAT-1 regulation. The mean transcript levels (by real-time RT-PCR) and the mean labeling index of STAT-1 (by IHC) were significantly higher in glioblastoma than in control brain tissues (p = 0.0239 and p < 0.001, respectively). Multivariate survival analysis revealed that STAT-1 protein expression (HR 1.015, p = 0.033, 95% CI 1.001-1.029) along with patient age (HR 1.025, p = 0.005, 95% CI 1.008-1.042) were significant predictors of shorter survival in patients with glioblastoma. Conclusions. IGFBP-3 influences tumor cell proliferation, migration, and invasion and regulates STAT-1 expression in malignant glioma cells. STAT-1 is overexpressed in human glioblastoma tissues and emerges as a novel prognostic biomarker.

Phytoplankton size structure in the southern Bay of Bengal modified by the Summer Monsoon Current and associated eddies: Implications on the vertical biogenic flux

The present study combines field and satellite observations to investigate how hydrographical transformations influence phytoplankton size structure in the southern Bay of Bengal during the peak Southwest Monsoon/Summer Monsoon (July-August). The intrusion of the Summer Monsoon Current (SMC) into the Bay of Bengal and associated changes in sea surface chemistry, traceable eastward up to 90 degrees E along 8 degrees N, seems to influence biology of the region significantly. Both in situ and satellite (MODIS) data revealed low surface chlorophyll except in the area influenced by the SMC During the study period, two well-developed cydonic eddies (north) and an anti-cyclonic eddy (south), closely linked to the main eastward flow of the SMC, were sampled. Considering the capping effect of the low-saline surface water that is characteristic of the Bay of Bengal, the impact of the cyclonic eddy, estimated in terms of enhanced nutrients and chlorophyll, was mostly restricted to the subsurface waters (below 20 m depth). Conversely, the anti-cyclonic eddy aided by the SMC was characterized by considerably higher nutrient concentration and chlorophyll in the upper water column (upper 60 m), which was contrary to the general characteristic of such eddies. Albeit smaller phytoplankton predominated the southern Bay of Bengal (60-95% of the total chlorophyll), the contribution of large phytoplankton was double in the regions influenced by the SMC and associated eddies. Multivariate analysis revealed the extent to which SMC-associated eddies spatially influence phytoplankton community structure. The study presents the first direct quantification of the size structure of phytoplankton from the southern Bay of Bengal and demonstrates that the SMC-associated hydrographical ramifications significantly increase the phytoplankton biomass contributed by larger phytoplankton and thereby influence the vertical opal and organic carbon flux in the region. (C) 2014 Elsevier B.V. All rights reserved.

An Algorithmic Characterization of Polynomial Functions over Z(pn)

In this paper we consider polynomial representability of functions defined over , where p is a prime and n is a positive integer. Our aim is to provide an algorithmic characterization that (i) answers the decision problem: to determine whether a given function over is polynomially representable or not, and (ii) finds the polynomial if it is polynomially representable. The previous characterizations given by Kempner (Trans. Am. Math. Soc. 22(2):240-266, 1921) and Carlitz (Acta Arith. 9(1), 67-78, 1964) are existential in nature and only lead to an exhaustive search method, i.e. algorithm with complexity exponential in size of the input. Our characterization leads to an algorithm whose running time is linear in size of input. We also extend our result to the multivariate case.

Associated jet and subjet rates in light-quark and gluon jet discrimination

We show that in studies of light quark- and gluon-initiated jet discrimination, it is important to include the information on softer reconstructed jets (associated jets) around a primary hard jet. This is particularly relevant while adopting a small radius parameter for reconstructing hadronic jets. The probability of having an associated jet as a function of the primary jet transverse momentum (PT) and radius, the minimum associated jet pi, and the association radius is computed up to next-to-double logarithmic accuracy (NDLA), and the predictions are compared with results from Herwig++, Pythia6 and Pythia8 Monte Carlos (MC). We demonstrate the improvement in quark-gluon discrimination on using the associated jet rate variable with the help of a multivariate analysis. The associated jet rates are found to be only mildly sensitive to the choice of parton shower and hadronization algorithms, as well as to the effects of initial state radiation and underlying event. In addition, the number of k(t) subjets of an anti-k(t) jet is found to be an observable that leads to a rather uniform prediction across different MC's, broadly being in agreement with predictions in NDLA, as compared to the often used number of charged tracks observable.

On the detection of human influence in extreme precipitation over India

Climate change is expected to influence extreme precipitation which in turn might affect risks of pluvial flooding. Recent studies on extreme rainfall over India vary in their definition of extremes, scales of analyses and conclusions about nature of changes in such extremes. Fingerprint-based detection and attribution (D&A) offer a formal way of investigating the presence of anthropogenic signals in hydroclimatic observations. There have been recent efforts to quantify human effects in the components of the hydrologic cycle at large scales, including precipitation extremes. This study conducts a D&A analysis on precipitation extremes over India, considering both univariate and multivariate fingerprints, using a standardized probability-based index (SPI) from annual maximum one-day (RX1D) and five-day accumulated (RX5D) rainfall. The pattern-correlation based fingerprint method is used for the D&A analysis. Transformation of annual extreme values to SPI and subsequent interpolation to coarser grids are carried out to facilitate comparison between observations and model simulations. Our results show that in spite of employing these methods to address scale and physical processes mismatch between observed and model simulated extremes, attributing changes in regional extreme precipitation to anthropogenic climate change is difficult. At very high (95%) confidence, no signals are detected for RX1D, while for the RX5D and multivariate cases only the anthropogenic (ANT) signal is detected, though the fingerprints are in general found to be noisy. The findings indicate that model simulations may underestimate regional climate system responses to increasing human forcings for extremes, and though anthropogenic factors may have a role to play in causing changes in extreme precipitation, their detection is difficult at regional scales and not statistically significant. (C) 2015 Elsevier B.V. All rights reserved.

Delineation of homogeneous hydrometeorological regions using wavelet-based global fuzzy cluster analysis

Identification of homogeneous hydrometeorological regions (HMRs) is necessary for various applications. Such regions are delineated by various approaches considering rainfall and temperature as two key variables. In conventional approaches, formation of regions is based on principal components (PCs)/statistics/indices determined from time series of the key variables at monthly and seasonal scales. An issue with use of PCs for regionalization is that they have to be extracted from contemporaneous records of hydrometeorological variables. Therefore, delineated regions may not be effective when the available records are limited over contemporaneous time period. A drawback associated with the use of statistics/indices is that they do not provide effective representation of the key variables when the records exhibit non-stationarity. Consequently, the resulting regions may not be effective for the desired purpose. To address these issues, a new approach is proposed in this article. The approach considers information extracted from wavelet transformations of the observed multivariate hydrometeorological time series as the basis for regionalization by global fuzzy c-means clustering procedure. The approach can account for dynamic variability in the time series and its non-stationarity (if any). Effectiveness of the proposed approach in forming HMRs is demonstrated by application to India, as there are no prior attempts to form such regions over the country. Drought severity-area-frequency (SAF) curves are constructed corresponding to each of the newly formed regions for the use in regional drought analysis, by considering standardized precipitation evapotranspiration index (SPEI) as the drought indicator.

Elliptical slice sampling

Many probabilistic models introduce strong dependencies between variables using a latent multivariate Gaussian distribution or a Gaussian process. We present a new Markov chain Monte Carlo algorithm for performing inference in models with multivariate Gaussian priors. Its key properties are: 1) it has simple, generic code applicable to many models, 2) it has no free parameters, 3) it works well for a variety of Gaussian process based models. These properties make our method ideal for use while model building, removing the need to spend time deriving and tuning updates for more complex algorithms.

Simpatía y conducta prosocial en niños de 6 y 7 años

Resumen: El presente estudio analiza el rol de la simpatía en la conducta prosocial infantil, determinando posibles diferencias en función del sexo y la edad. La muestra estuvo constituida por 275 niños de ambos sexos, escolarizados, de 6 y 7 años de edad de las provincias de Chaco y Corrientes, Argentina. Previo consentimiento informado de los padres, se administró la Escala de simpatía para niños de 6 y 7 años de edad, de Oros (2006), el Prosocial BehaviorScale de Caprara y Pastorelli (1993) traducido y adaptado al español por Del Barrio, Moreno y López, (2001), y el Cuestionario de Conducta Prosocial (PBQ) de Weiner y Duveen (1981).En función de los objetivos propuestos, se realizó un análisis univariado de varianza (ANOVA) y análisis multivariados de variancia (MANOVAs). Los diferentes resultados obtenidos, se discuten en función de los desarrollos teóricos y empíricos encontrados hasta el momento, hallando una consistencia general entre los mismos

Bienestar subjetivo en la Argentina durante el período 2005-2007 : determinantes de la felicidad, según la Encuesta de la Deuda Social Argentina (EDSA)

Este artículo tiene como principal objetivo estudiar los determinantes de la autopercepción de felicidad en la Argentina entre 2005 y 2007, utilizando información de la Encuesta de la Deuda Social Argentina (EDSA) relevada por la Universidad Católica Argentina (UCA). El estudio se lleva a cabo mediante un análisis estadístico descriptivo y una serie de modelos econométricos multivariados de tipo logit ordenado que permitieron identificar la percepción de suficientes determinantes que afectan la autopercepción de felicidad de manera positiva y estadísticamente significativa: el ingreso; el estado de salud autopercibido; el empleo y su calidad; el estado civil; la cantidad de hijos en el hogar; la menor discriminación percibida; estar en comunión con Dios; y el tiempo libre.

Las tecnologías de la información y comunicación en la enseñanza universitaria : influencia sobre la motivación, el autoaprendizaje y la participación activa del alumno

[Es] Este estudio analiza la opinión de los alumnos de diferentes licenciaturas sobre la utilidad didáctica de las tecnologías de la información y comunicación (TICs) en la universidad. Se utilizaron páginas web y el correo electrónico para facilitar diferentes herramientas educativas (guiones docentes, artículos, páginas web, trabajos prácticos y bibliografía). Los análisis univariante y multivariante de los datos obtenidos de las encuestas realizadas a los estudiantes al inicio y final de la asignatura, demuestran que, con independencia de la titulación, el 64% del alumnado considera que la utilización de las TICs mejora la comunicación alumno – profesor, e incrementa la motivación y la participación activa del estudiante.

Patrones de innovación en las empresas hosteleras: el caso español

[ES] El objetivo de este trabajo es contribuir a la mejora del conocimiento existente sobre los patrones de innovación en las empresas hosteleras. Para ello, se ha utilizado una muestra representativa de 443 empresas hosteleras españolas pertenecientes al CNAE-55, que forman parte de la Tercera Encuesta de Innovación Tecnológica elaborada por la Comisión Europea (CIS-3) la cual se basa en una versión revisada del Manual de Oslo.

Contribution of Resource-Based View and Entrepreneurial Orientation on Small Firm Growth

[EN] This research provides a useful framework for identifying a small firms’ propensity to engage in entrepreneurial orientation. We examine the impact of the Entrepreneurial Orientation (EO) as a main resource and capability on small firm’ growth. The growth seems to come out as an important demonstration of the entrepreneurial orientation of small firms (Davidsson, 1989; Green and Brown, 1997; Janney and Gregory, 2006). Thus, this research builds on prior conceptual research that suggests a positive integration between entrepreneurial orientation and resource-based view. In the first instance, the research will focus on reviewing literature in the emerging area of entrepreneurial orientation as it applies to growth oriented small firms and resource-based view of the firm. Secondly, an empirical study was developed based on a stratified sample of small firms of manufacturing industry. Data were submitted to a multivariate statistical analysis and a linear regression model was performed in order to predict the influence of the resources and capabilities on small firms’ growth. In this sense, we consider the construct growth as a dependent variable and the ones relates with resources and capabilities (entrepreneur resources, firm resources, networks and EO) as independent variables. The research results suggest a set of resources and capabilities that promote the growth of the small firms. Also, the EO seems to have a predictive value on growth. Explaining variables related with resources and capabilities and EO were identified as essential in growth oriented small firms. It was still possible to conclude that the entrepreneurial firms which grew seem to have resources and develop more capabilities and take advantage in the search for those competences. This attitude reflects on the EO of the firm. This study has important implication for both researchers and practitioners. It highlights the necessity of firms to develop superior EO of all their members and also to invest on better resources and consequently superior capabilities as a way of reaching higher levels of growth. While previous authors have attempted to analyse certain aspects of this process (linkage between entrepreneurial orientation and growth), this research developed a framework that combines these and others factors (resource-based view) pertinent to growth oriented small firms. The results support the necessity to identify explicative variables of multiple levels to explain the growth of small firms. The adoption of an entrepreneurial orientation as an indispensable variable to the growth oriented small firms seems pertinent.

Uncovering Ecosystem Service Bundles through Social Preferences

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Analysis of volatility transmissions in integrated and interconnected markets: The case of the Iberian and French markets

This paper models the mean and volatility spillovers of prices within the integrated Iberian and the interconnected Spanish and French electricity markets. Using the constant (CCC) and dynamic conditional correlation (DCC) bivariate models with three different specifications of the univariate variance processes, we study the extent to which increasing interconnection and harmonization in regulation have favoured price convergence. The data consist of daily prices calculated as the arithmetic mean of the hourly prices over a span from July 1st 2007 until February 29th 2012. The DCC model in which the variances of the univariate processes are specified with a VARMA(1,1) fits the data best for the integrated MIBEL whereas a CCC model with a GARCH(1,1) specification for the univariate variance processes is selected to model the price series in Spain and France. Results show that there are significant mean and volatility spillovers in the MIBEL, indicating strong interdependence between the two markets, while there is a weaker evidence of integration between the Spanish and French markets. We provide new evidence that the EU target of achieving a single electricity market largely depends on increasing trade between countries and homogeneous rules of market functioning.