Effects of growth reducer and nitrogen fertilization on morphological variables, SPAD index, interception of radiation and productivity of wheat

ABSTRACT The objective of this study was to evaluate the effect of growth reducer and nitrogen fertilization on morphological variables, SPAD index, radiation interception, and grain yield of three cultivars of wheat. The experimental design was a randomized block in factorial scheme 3x5x2, with three cultivars (Mestre, Iguaçú and Itaipú), five nitrogen doses (0, 40, 80, 120, 160 Kg ha-1), and application or no application of a growth reducer, with three replications. The following characteristics were evaluated: plant height, SPAD index, leaf area index (LAI), Global Radiation Interception (GRI) and grain yield. The Tukey test (p < 0.05) was used for the comparison between the means of cultivar and growth reducer factors, and for a regression analysis to evaluate N levels. Increasing the dose of nitrogen promotes an increase in LAI of plants of wheat crops differently among cultivars, which leads to a greater degree of global radiation interception. At doses higher or equal to 120 Kg ha-1 of nitrogen, there are significant differences in grain yield between treatments with and without the application of the growth reducer. The significant interaction between growth reducer and nitrogen dose, showed that applications of growth reducer increase the GRI at doses above and below 80 Kg ha-1 of nitrogen. Nitrogen rates of 138 and 109 Kg ha-1 are responsible for maximum grain yields of wheat, which is 4235 and 3787 Kg ha-1 with and without the use of growth reducer, respectively.

ABOUT PSYCHOLOGICAL VARIABLES IN APPLICATION SCORING MODELS

The purpose of this study is to investigate the contribution of psychological variables and scales suggested by Economic Psychology in predicting individuals’ default. Therefore, a sample of 555 individuals completed a self-completion questionnaire, which was composed of psychological variables and scales. By adopting the methodology of the logistic regression, the following psychological and behavioral characteristics were found associated with the group of individuals in default: a) negative dimensions related to money (suffering, inequality and conflict); b) high scores on the self-efficacy scale, probably indicating a greater degree of optimism and over-confidence; c) buyers classified as compulsive; d) individuals who consider it necessary to give gifts to children and friends on special dates, even though many people consider this a luxury; e) problems of self-control identified by individuals who drink an average of more than four glasses of alcoholic beverage a day.

RELATION BETWEEN BACKGROUND VARIABLES, VALUES AND CORPORATE SOCIAL RESPONSIBILITY

ABSTRACT Consumer perception of corporate social responsibility (CSR) can be directly influenced by individual value structures. This research aims to provide new knowledge regarding the relationship between basic human values and the public's perception of CSR. It focuses on the values of higher education students and their views regarding a particular corporate social initiative. The study reveals that social, educational, and economic circumstances influence human values. Those values in turn influence why different students perceive CSR differently. These findings are relevant to companies as they provide a more detailed understanding of why certain consumer groups perceive certain CSR initiatives the way that they do. They also suggest that universities should increase their awareness of the importance of integrating human values and CSR in the curricula of future business managers and social leaders.

Validation of a measurement scale for the relationship between the orientation to corporate social responsibility and other business strategic variables

The importance of Social Responsibility (SR) is higher if this business variable is related with other ones of strategic nature in business activity (competitive success that the company achieved, performance that the firms develop and innovations that they carries out). The hypothesis is that organizations that focus on SR are those who get higher outputs and innovate more, achieving greater competitive success. A scale for measuring the orientation to SR has defined in order to determine the degree of relationship between above elements. This instrument is original because previous scales do not exist in the literature which could measure, on the one hand, the three classics sub-constructs theoretically accepted that SR is made up and, on the other hand, the relationship between SR and the other variables. As a result of causal relationships analysis we conclude with a scale of 21 indicators, validated scale with a sample of firms belonging to the Autonomous Community of Extremadura and it is the first empirical validation of these dimensions we know so far, in this context.

Caraterização dos habitats propícios à conservação da Estrelinha-de-Santa-Maria (Regulus regulus sanctaemariae)

Dissertação de Mestrado, Gestão e Conservação da Natureza, 11 de Junho de 2014, Universidade dos Açores.

Clustering of variables with a three-way approach for health sciences

TPM Vol. 21, No. 4, December 2014, 435-447 – Special Issue © 2014 Cises.

Caracterização e valorização do recreio balnear

Dissertação de Mestrado, Ciências Económicas e Empresariais, 9 Dezembro de 2015 , Universidade dos Açores.

Enhancing the selection of a model-based clustering with external categorical variables

In cluster analysis, it can be useful to interpret the partition built from the data in the light of external categorical variables which are not directly involved to cluster the data. An approach is proposed in the model-based clustering context to select a number of clusters which both fits the data well and takes advantage of the potential illustrative ability of the external variables. This approach makes use of the integrated joint likelihood of the data and the partitions at hand, namely the model-based partition and the partitions associated to the external variables. It is noteworthy that each mixture model is fitted by the maximum likelihood methodology to the data, excluding the external variables which are used to select a relevant mixture model only. Numerical experiments illustrate the promising behaviour of the derived criterion. © 2014 Springer-Verlag Berlin Heidelberg.

Soil sampling design and sampling techniques for soil properties monitoring – common difficulties related to forest soils

Mathematical models and statistical analysis are key instruments in soil science scientific research as they can describe and/or predict the current state of a soil system. These tools allow us to explore the behavior of soil related processes and properties as well as to generate new hypotheses for future experimentation. A good model and analysis of soil properties variations, that permit us to extract suitable conclusions and estimating spatially correlated variables at unsampled locations, is clearly dependent on the amount and quality of data and of the robustness techniques and estimators. On the other hand, the quality of data is obviously dependent from a competent data collection procedure and from a capable laboratory analytical work. Following the standard soil sampling protocols available, soil samples should be collected according to key points such as a convenient spatial scale, landscape homogeneity (or non-homogeneity), land color, soil texture, land slope, land solar exposition. Obtaining good quality data from forest soils is predictably expensive as it is labor intensive and demands many manpower and equipment both in field work and in laboratory analysis. Also, the sampling collection scheme that should be used on a data collection procedure in forest field is not simple to design as the sampling strategies chosen are strongly dependent on soil taxonomy. In fact, a sampling grid will not be able to be followed if rocks at the predicted collecting depth are found, or no soil at all is found, or large trees bar the soil collection. Considering this, a proficient design of a soil data sampling campaign in forest field is not always a simple process and sometimes represents a truly huge challenge. In this work, we present some difficulties that have occurred during two experiments on forest soil that were conducted in order to study the spatial variation of some soil physical-chemical properties. Two different sampling protocols were considered for monitoring two types of forest soils located in NW Portugal: umbric regosol and lithosol. Two different equipments for sampling collection were also used: a manual auger and a shovel. Both scenarios were analyzed and the results achieved have allowed us to consider that monitoring forest soil in order to do some mathematical and statistical investigations needs a sampling procedure to data collection compatible to established protocols but a pre-defined grid assumption often fail when the variability of the soil property is not uniform in space. In this case, sampling grid should be conveniently adapted from one part of the landscape to another and this fact should be taken into consideration of a mathematical procedure.

An uncertainty and sensitivity analysis applied to the prioritisation of pharmaceuticals as surface water contaminants from wastewater treatment plant direct emissions

In this study, the concentration probability distributions of 82 pharmaceutical compounds detected in the effluents of 179 European wastewater treatment plants were computed and inserted into a multimedia fate model. The comparative ecotoxicological impact of the direct emission of these compounds from wastewater treatment plants on freshwater ecosystems, based on a potentially affected fraction (PAF) of species approach, was assessed to rank compounds based on priority. As many pharmaceuticals are acids or bases, the multimedia fate model accounts for regressions to estimate pH-dependent fate parameters. An uncertainty analysis was performed by means of Monte Carlo analysis, which included the uncertainty of fate and ecotoxicity model input variables, as well as the spatial variability of landscape characteristics on the European continental scale. Several pharmaceutical compounds were identified as being of greatest concern, including 7 analgesics/anti-inflammatories, 3 β-blockers, 3 psychiatric drugs, and 1 each of 6 other therapeutic classes. The fate and impact modelling relied extensively on estimated data, given that most of these compounds have little or no experimental fate or ecotoxicity data available, as well as a limited reported occurrence in effluents. The contribution of estimated model input variables to the variance of freshwater ecotoxicity impact, as well as the lack of experimental abiotic degradation data for most compounds, helped in establishing priorities for further testing. Generally, the effluent concentration and the ecotoxicity effect factor were the model input variables with the most significant effect on the uncertainty of output results.

Enhancing the selection of a model-based clustering with external categorical variables

In cluster analysis, it can be useful to interpret the partition built from the data in the light of external categorical variables which are not directly involved to cluster the data. An approach is proposed in the model-based clustering context to select a number of clusters which both fits the data well and takes advantage of the potential illustrative ability of the external variables. This approach makes use of the integrated joint likelihood of the data and the partitions at hand, namely the model-based partition and the partitions associated to the external variables. It is noteworthy that each mixture model is fitted by the maximum likelihood methodology to the data, excluding the external variables which are used to select a relevant mixture model only. Numerical experiments illustrate the promising behaviour of the derived criterion.

Local Fractional Variational Iteration Method for Local Fractional Poisson Equations in Two Independent Variables

The local fractional Poisson equations in two independent variables that appear in mathematical physics involving the local fractional derivatives are investigated in this paper. The approximate solutions with the nondifferentiable functions are obtained by using the local fractional variational iteration method.

Reducing the number of membership functions in linguistic variables

Dissertation presented at Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia in fulfilment of the requirements for the Masters degree in Mathematics and Applications, specialization in Actuarial Sciences, Statistics and Operations Research

QUESTIONNAIRES IN THE SCREENING FOR Schistosoma mansoni INFECTION: A STUDY OF SOCIO DEMOGRAPHIC AND WATER CONTACT VARIABLES IN FOUR COMMUNITIES IN BRAZIL

The use of questionnaires has been recommended for identifying, at a lower cost, individuals at risk for schistosomiasis. In this study, validity of information obtained by questionnaire in the screening for Schistosoma mansoni infection was assessed in four communities in the State of Minas Gerais, Brazil. Explanatory variables were water contact activities, sociodemographic characteristics and previous treatment for schistosomiasis. From 677, 1474, 766 and 3290 individuals eligible for stool examination in the communities, 89 to 97% participated in the study. The estimated probability of individuals to be infected, if they have all characteristics identified as independently associated with S.mansoni infection, varied from 15% in Canabrava, to 42% in Belo Horizonte, 48% in Comercinho and 80% in São José do Acácio. Our results do not support the hypothesis that a same questionnaire on risk factors could be used in screening for S.mansoni infection in different communities.