Evaluation of the potential production of 2nd generation ethanol from waste pruning of the olive grove

Avaliação do potencial de produção de etanol de 2.ª geração a partir dos resíduos das podas do olival

Alkaline pretreatments of agricultural residues for ethanol production

Residues of olive trees pruning need to be removed every year so as not to compromise the health of the fruit (olives). However, at present, despite disposal costs, there is no monetization this waste. Therefore, the use of this waste for the production of cellulosic ethanol could be quite promising. In the present work, the optimization of dilute acid pretreatment conditions of olive tree pruning was studied. A fundamental step in the production of ethanol from lignocellulosic materials is the improvement of the carbohydrates accessibility to enzymatic hydrolysis, which can be performed with physic-chemical pretreatments.Fundação para a Ciência e a Tecnologia (Reference: SFRH/ BD/ 49683/ 2009

An Efficient Mixed Integer Programming Algorithm for Minimizing the Training Sample Misclassification Cost in Two-group Classification

In this paper, we introduce the Divide and Conquer (D&C) algorithm, a computationally efficient algorithm for determining classification rules which minimize the training sample misclassification cost in two-group classification. This classification rule can be derived using mixed integer programming (MIP) techniques. However, it is well-documented that the complexity of MIP-based classification problems grows exponentially as a function of the size of the training sample and the number of attributes describing the observations, requiring special-purpose algorithms to solve even small size problems within a reasonable computational time. The D&C algorithm derives its name from the fact that it relies, a.o., on partitioning the problem in smaller, more easily handled subproblems, rendering it substantially faster than previously proposed algorithms. The D&C algorithm solves the problem to the exact optimal solution (i.e., it is not a heuristic that approximates the solution), and allows for the analysis of much larger training samples than previous methods. For instance, our computational experiments indicate that, on average, the D&C algorithm solves problems with 2 attributes and 500 observations more than 3 times faster, and problems with 5 attributes and 100 observations over 50 times faster than Soltysik and Yarnold's software, which may be the fastest existing algorithm. We believe that the D&C algorithm contributes significantly to the field of classification analysis, because it substantially widens the array of data sets that can be analyzed meaningfully using methods which require MIP techniques, in particular methods which seek to minimize the misclassification cost in the training sample. The programs implementing the D&C algorithm are available from the authors upon request

Nonparametric Two-Group Classification: Concepts and a SAS-Based Software Package

In this paper, we introduce BestClass, a set of SAS macros, available in the mainframe and workstation environment, designed for solving two-group classification problems using a class of recently developed nonparametric classification methods. The criteria used to estimate the classification function are based on either minimizing a function of the absolute deviations from the surface which separates the groups, or directly minimizing a function of the number of misclassified entities in the training sample. The solution techniques used by BestClass to estimate the classification rule utilize the mathematical programming routines of the SAS/OR@ software. Recently, a number of research studies have reported that under certain data conditions this class of classification methods can provide more accurate classification results than existing methods, such as Fisher's linear discriminant function and logistic regression. However, these robust classification methods have not yet been implemented in the major statistical packages, and hence are beyond the reach of those statistical analysts who are unfamiliar with mathematical programming techniques. We use a limited simulation experiment and an example to compare and contrast properties of the methods included in BestClass with existing parametric and nonparametric methods. We believe that BestClass contributes significantly to the field of nonparametric classification analysis, in that it provides the statistical community with convenient access to this recently developed class of methods. BestClass is available from the authors

Second Order Mathematical Formulations Programming for Discriminant Analysis

This paper introduces a nonparametric formulation-based mathematical programming (MP) for solving the classification problem in discriminant analysis. This method differs from previously proposed MP-based models in that, even though the final discriminant function is linear in terms of the parameters to be estimated, the formulation is quadratic in terms of the predictor (attribute) variables. By including second order (i.e., quadratic and cross-product) terms of the attribute variables, the model is similar in concept to the usual treatment of multiple predictor variables in statistical methods such as Fisher's linear discriminant analysis, and allows an analysis of how including nonlinear terms and interaction affect the predictive ability of the estimated classification function. Using simulation experiments involving data conditions for which nonlinear classifiers are appropriate, the classificatory performance of this class of second order MP models is compared with that of existing statistical (linear and quadratic) and first order MP-based formulations. The results of these experiments show that the proposed formulation appears to be a very attractive alternative to previously introduced linear and quadratic statistical and linear MP-based classification methods

Stochastic Judgments in the AHP: The Measurement of Rank Reversal Probabilities

Recently, the issue of rank reversal of alternatives in the Analytic Hierarchy Process (AHP) has captured the attention of a number of researchers. Most of the research on rank reversal has addressed the case where the pairwise comparisons of the alternatives are represented by single values, focusing on mathematical properties inherent to the AHP methodology that can lead to rank reversal if a new alternative is added or an existing one is deleted. A second situation, completely unrelated to the mathematical foundations of the AHP, in which rank reversal can occur is the case where the pairwise judgments are stochastic, rather than single values. If the relative preference ratings are uncertain, one has judgment intervals, and as a consequence there is a possibility that the rankings resulting from an AHP analysis are reversed, i.e., incorrect. It is important for modeler and decision maker alike to be aware of the likelihood that this situation of rank reversal will occur. In this paper, we introduce methods for assessing the relative preference of the alternatives in terms of their rankings, if the pairwise comparisons of the alternatives are stochastic. We develop multivariate statistical techniques to obtain point estimates and confidence intervals of the rank reversal probabilities, and show how simulation experiments can be used as an effective and accurate tool for analyzing the stability of the preference rankings under uncertainty. This information about the extent to which the ranking of the alternatives is sensitive to the stochastic nature of the pairwise judgments should be valuable information into the decision making process, much like variability and confidence intervals are crucial tools for statistical inference. Although the focus of our analysis is on stochastic preference judgments, our sampling method for estimating rank reversal probabilities can be extended to the case of non-stochastic imprecise fuzzy judgments. We provide simulation experiments and numerical examples comparing our method with that proposed previously by Saaty and Vargas (1987) for imprecise interval judgments

Finding the jigsaw piece for our jigsaw puzzle with corporate social responsibility: the impact of CSR on prospective applicants’ responses

Purpose – This study aims to examine the influence of different corporate social responsibility (CSR) dimensions on prospective applicants’ responses, namely, organizational attractiveness and intention to apply for a job vacancy (IAJV). Design/methodology/approach – Using an experimental 2 × 3 crossed factorial design (n = 195), the level of engagement of a hypothetical company in socially responsible practices (high vs low) was manipulated concerning three dimensions of CSR (employees, community and environment and economic level). Participants were randomly assigned to one of the six conditions and, after reading the corresponding scenario, were asked to evaluate the extent to which the company was considered a good place to work and their IAJV in it. Findings – The level of engagement in socially responsible practices had a positive effect both on the degree to which participants favorably perceived the organization as a place to work and on their IAJV. Furthermore, the level of engagement in practices toward employees and in the economic domain had a stronger effect on participants’ responses than the engagement in practices that benefit community and environment. Research limitations/implications – Data were obtained in a laboratory setting, so the generalization of results to actual job search settings must be made with caution. Practical implications – CSR can be a source of competitive advantage in the recruitment of new employees. Because not all CSR dimensions have the same effect on applicants’ responses, companies should take into account the CSR dimensions in which they are engaged and communicate them to the public. Originality/value – As far as we know, this is the first study to examine the impact of different CSR dimensions both on organizational attractiveness and IAJV

How socially responsible human resource management fosters work engagement: The role of perceived organizational support and affective organizational commitment

Purpose – In recent years, efforts to reinforce the links between corporate social responsibility and human resource management have highlighted employees’ role as crucial organizational stakeholders. This study aims to investigate whether workers’ perception of socially responsible human resource management (SR-HRM) based on employee-focused practices is related to work engagement (WE). This research also explored whether perceived organizational support (POS) and affective commitment (AC) can contribute to explaining this relationship. Social exchange theory and job demands-resources model were used to theoretically frame the research. Design/methodology/approach – Data were collected from a sample of 222 employees working in diverse organizations, using individual online surveys. Several analyses were conducted to assure data robustness to common method bias. Findings – The results confirm that SR-HRM fosters WE and that this effect is subject to sequential mediation by POS and AC. Accordingly, SR-HRM practices contribute to higher level of POS, which then foster stronger affective bonds with employers and, in turn, higher levels of vigor, absorption and dedication among workers. Originality/value – The findings contribute to the expansion of the SR-HRM literature by providing a deeper understanding of how this management strategy affects employees’ job-related attitudes, particularly WE a much-overlooked variable in this realm.info:eu-repo/semantics/publishedVersio

OrientAÇÕES, AÇÕES e reAÇÕES do Ensino das Ciências no 1º Ciclo

Relatório apresentado para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Educação Pré Escolar e Ensino do 1º ciclo do Ensino Básico

Evaluation of composts and liming materials in the phytostabilization of a mine soil using perennial ryegrass

A microcosm experiment was carried out to evaluate the effects of municipal solid waste compost (MSWC) or garden waste compost (GWC), and liming materials in the rehabilitation of a soil affected by mining activities, and to study the use of perennial ryegrass (Lolium perenne L.) for phystostabilization. The performance of the amendments was assessed by soil chemical parameters, total and bioavailable metals (Cu, Pb and Zn), soil enzymatic activities, and plant relative growth and mineral composition. In general, both composts corrected soil acidity and increased the total organic matter content of the soil, although with a better performance in the case of MSWC, especially when considering total N and available P and K levels in the amended soil. The application of both composts and liming materials led to a decrease in the mobile fractions of Cu, Pb and Zn, but mobilisable fractions of Cu and Zn increased with MSWC application. Plant biomass increased more than three times in the presence of 50 Mg MSWC ha−1 and with the combined use of 25 or 50 Mg MSWC ha−1 and CaO, but no significant differences were observed when GWC was applied. Plant tissue analysis showed that the treatments did not significantly reduce Cu, Pb and Zn uptake by the plant. Dehydrogenase, and the enzymes related to the N-cycle, urease and protease, had increased activities with increasing MSWC application rate. Conversely, the enzymatic activities of both enzymes related to the C-cycle, cellulase and β-glucosidase, were only positively affected by GWC application, a compost obtained from raw materials rich in C. Principal component analyses evidenced this clear separation between the effect of MSWC on soil enzymes related to the N-cycle and of GWC on soil enzymes related to the C-cycle. This study indicates that MSWC (50 Mg ha−1, limed or unlimed) can be used successfully in the remediation of a highly acidic metal-contaminated soil, allowing the establishment of perennial ryegrass