A method to analyse the alignment of core values in collaborative networked organisations

Since collaborative networked organisations are usually formed by independent and heterogeneous entities, it is natural that each member holds his own set of values, and that conflicts among partners might emerge because of some misalignment of values. In contrast, it is often stated in literature that the alignment between the value systems of members involved in collaborative processes is a prerequisite for successful co-working. As a result, the issue of core value alignment in collaborative networks started to attract attention. However, methods to analyse such alignment are lacking mainly because the concept of 'alignment' in this context is still ill defined and shows a multifaceted nature. As a contribution to the area, this article introduces an approach based on causal models and graph theory for the analysis of core value alignment in collaborative networks. The potential application of the approach is then discussed in the virtual organisations' breeding environment context.

Occurrence of suicide and seasonal variation

OBJECTIVE: To review the estimated suicide rates for the Region Metropolitan, the main socio-political center in Chile, for the period 1979-1994, and to determine whether they follow a seasonal pattern. METHOD: Data available for the period 1979-94 at the Forensic Services in Chile was analyzed using ANOVA. RESULTS: It was register 5.386 suicides. While the "warm" months (October, November, December & January) concentrated 39.0% of cases, the so called "cold" months reported 28,7%. This contrast is made even clearer by the month-to-month analysis, showing the highest suicide rate in December (10.9%) against the lowest rate in June (7.0%). Further statistical analysis revealed these differences to be significant. CONCLUSION: The study shows that in Chile, representing as it does the Southern Hemisphere, the suicide rates tend to present a seasonal variation as has elsewhere been determined for in the North Hemisphere.

Interest rate exposure of Spanish banks: A nonparametric analysis

Interest rate risk is one of the major financial risks faced by banks due to the very nature of the banking business. The most common approach in the literature has been to estimate the impact of interest rate risk on banks using a simple linear regression model. However, the relationship between interest rate changes and bank stock returns does not need to be exclusively linear. This article provides a comprehensive analysis of the interest rate exposure of the Spanish banking industry employing both parametric and non parametric estimation methods. Its main contribution is to use, for the first time in the context of banks’ interest rate risk, a nonparametric regression technique that avoids the assumption of a specific functional form. One the one hand, it is found that the Spanish banking sector exhibits a remarkable degree of interest rate exposure, although the impact of interest rate changes on bank stock returns has significantly declined following the introduction of the euro. Further, a pattern of positive exposure emerges during the post-euro period. On the other hand, the results corresponding to the nonparametric model support the expansion of the conventional linear model in an attempt to gain a greater insight into the actual degree of exposure.

Knowledge extraction from medium voltage load diagrams to support the definition of electrical tariffs

With the electricity market liberalization, distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity customers. In this environment all consumers are free to choose their electricity supplier. A fair insight on the customer´s behaviour will permit the definition of specific contract aspects based on the different consumption patterns. In this paper Data Mining (DM) techniques are applied to electricity consumption data from a utility client’s database. To form the different customer´s classes, and find a set of representative consumption patterns, we have used the Two-Step algorithm which is a hierarchical clustering algorithm. Each consumer class will be represented by its load profile resulting from the clustering operation. Next, to characterize each consumer class a classification model will be constructed with the C5.0 classification algorithm.

Data mining techniques to support the classification of MV electricity customers

This paper describes a methodology that was developed for the classification of Medium Voltage (MV) electricity customers. Starting from a sample of data bases, resulting from a monitoring campaign, Data Mining (DM) techniques are used in order to discover a set of a MV consumer typical load profile and, therefore, to extract knowledge regarding to the electric energy consumption patterns. In first stage, it was applied several hierarchical clustering algorithms and compared the clustering performance among them using adequacy measures. In second stage, a classification model was developed in order to allow classifying new consumers in one of the obtained clusters that had resulted from the previously process. Finally, the interpretation of the discovered knowledge are presented and discussed.

Contributos do coordenador do departamento de ciências experimentais na avaliação e desenvolvimento profissional dos professores

Dissertação de Mestrado, Supervisão Pedagógica (especialidade em Ensino das Ciências), 19 de Outubro de 2015, Universidade dos Açores.

From local to global importance measures of uncertainty propagation in composite structures

The influence of uncertainties of input parameters on output response of composite structures is investigated in this paper. In particular, the effects of deviations in mechanical properties, ply angles, ply thickness and on applied loads are studied. The uncertainty propagation and the importance measure of input parameters are analysed using three different approaches: a first-order local method, a Global Sensitivity Analysis (GSA) supported by a variance-based method and an extension of local variance to estimate the global variance over the domain of inputs. Sample results are shown for a shell composite laminated structure built with different composite systems including multi-materials. The importance measures of input parameters on structural response based on numerical results are established and discussed as a function of the anisotropy of composite materials. Needs for global variance methods are discussed by comparing the results obtained from different proposed methodologies. The objective of this paper is to contribute for the use of GSA techniques together with low expensive local importance measures.

Realization of fractional-order controllers: analysis, synthesis and application to the velocity control of a servo system

The synthesis and application of fractional-order controllers is now an active research field. This article investigates the use of fractional-order PID controllers in the velocity control of an experimental modular servo system. The systern consists of a digital servomechanism and open-architecture software environment for real-time control experiments using MATLAB/Simulink. Different tuning methods will be employed, such as heuristics based on the well-known Ziegler Nichols rules, techniques based on Bode’s ideal transfer function and optimization tuning methods. Experimental responses obtained from the application of the several fractional-order controllers are presented and analyzed. The effectiveness and superior performance of the proposed algorithms are also compared with classical integer-order PID controllers.

Human disease in ribeira valley, brazil caused by caraparu, a group c arbovirus - Report of a case

The clinical and laboratory data of a disease in a resident of Ribeira Valley, São Paulo State, southeastern Brazil, caused by an agent close or identical to Caraparu, a Group C arbovirus, was described. Although there is evidence of an intensive circulation of several arboviruses in the area, no diagnosis of human disease by these agents has been made, except the encephalitis cases caused by Rocio virus during an epidemic in 1975-1977. An antigenic difference between Caraparu strains isolated in São Paulo and in Pará States and a close antigenic relationship between Caraparu strain from São Paulo and Bruconha virus were suggested by the serological tests.

Using mobile device detection approaches to augment the accuracy of web delivery content

Recent studies of mobile Web trends show a continuous explosion of mobile-friendly content. However, the increasing number and heterogeneity of mobile devices poses several challenges for Web programmers who want to automatically get the delivery context and adapt the content to mobile devices. In this process, the devices detection phase assumes an important role where an inaccurate detection could result in a poor mobile experience for the enduser. In this paper we compare the most promising approaches for mobile device detection. Based on this study, we present an architecture for a system to detect and deliver uniform m-Learning content to students in a Higher School. We focus mainly on the devices capabilities repository manageable and accessible through an API. We detail the structure of the capabilities XML Schema that formalizes the data within the devices capabilities XML repository and the REST Web Service API for selecting the correspondent devices capabilities data according to a specific request. Finally, we validate our approach by presenting the access and usage statistics of the mobile web interface of the proposed system such as hits and new visitors, mobile platforms, average time on site and rejection rate.

Applications of Fourier methods to the analysis of some stochastic processes

Dissertação de Doutoramento em Matemática: Processos Estocásticos

Human cutaneous leishmaniasis caused by Leishmania (Viannia) braziliensis in Santiago del Estero, Argentina: identification of parasites by monoclonal antibodies and isoenzymes

Diagnostic and parasite characterization and identification studies were carried out in human patients with cutaneous leishmaniasis lesions in Santiago del Estero, Northern Province of Argentina. Diagnostic procedures were biopsies of lesions for smears and inoculations in hamster, needle aspirations of material from ulcers for "in vitro" cultures. Immunodiagnostic techniques applied were IFAT-IgG and Montenegro skin test. Primary isolation of eight stocks of leishmanial parasites was achieved from patients with active lesions. All stocks were biologically characterized by their behaviour in hamster, measurements of amastigote and promastigotes and growth "in vitro". Eight stocks were characterized and identified at species level by their reactivity to a cross-panel of sub-genus and specie-specific Monoclonal Antibodies through an Indirect Immunofluorescence technique and a Dot-ELISA. We conclude from the serodeme analysis of Argentina stocks that: stocks MHOM/AR/92/SE-1; SE-2; SE-4; SE-8; SE-8-I; SE-30; SE-34 and SE-36 are Leishmania (Viannia) braziliensis. Three Leishmania stocks (SE-1; SE-2 and SE-30) did not react with one highly specie-specific Monoclonal Antibody (Clone: B-18, Leishmania (Viannia) braziliensis marker) disclosing two serodeme group patterns. Five out of eight soluble extracts of leishmanial promastigotes were electrophoresed on thin-layer starch gels and examined for the enzyme MPI, Mannose Phosphate Isomerase; MDH, Malate Dehydrogenase; 6PGD, 6 Phosphogluconate Dehydrogenase; NH, Nucleoside Hydrolase, 2-deoxyinosinc as substrate; SOD, Superoxide Dismutase; GPI, Glucose Phosphate Isomerase and ES, Esterase. From the isoenzyme studies we concluded that stocks: MHOM/AR/92/SE-1; SE-2; SE-4; SE-8 and SE-8-I are isoenzymatically Leishmania (Viannia) braziliensis. We need to analyze more enzymes before assigning them to a braziliensis zymodeme.

Multi-scale vegetation classification using earth observation data of the Sundarban mangrove forest, Bangladesh.

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies.

FROM NON-PLACE TO PLACE: A STUDY OF EUROPEAN PUBLIC SPACE AS A SPACE OF IDENTITY

Master Erasmus Mundus Crossways in European Humanities

Deteção e previsão de falhas em equipamentos de produção industrial

As empresas nacionais deparam-se com a necessidade de responder ao mercado com uma grande variedade de produtos, pequenas séries e prazos de entrega reduzidos. A competitividade das empresas num mercado global depende assim da sua eficiência, da sua flexibilidade, da qualidade dos seus produtos e de custos reduzidos. Para se atingirem estes objetivos é necessário desenvolverem-se estratégias e planos de ação que envolvem os equipamentos produtivos, incluindo: a criação de novos equipamentos complexos e mais fiáveis, alteração dos equipamentos existentes modernizando-os de forma a responderem às necessidades atuais e a aumentar a sua disponibilidade e produtividade; e implementação de políticas de manutenção mais assertiva e focada no objetivo de “zero avarias”, como é o caso da manutenção preditiva. Neste contexto, o objetivo principal deste trabalho consiste na previsão do instante temporal ótimo da manutenção de um equipamento industrial – um refinador da fábrica de Mangualde da empresa Sonae Industria, que se encontra em funcionamento contínuo 24 horas por dia, 365 dias por ano. Para o efeito são utilizadas medidas de sensores que monitorizam continuamente o estado do refinador. A principal operação de manutenção deste equipamento é a substituição de dois discos metálicos do seu principal componente – o desfibrador. Consequentemente, o sensor do refinador analisado com maior detalhe é o sensor que mede a distância entre os dois discos do desfibrador. Os modelos ARIMA consistem numa abordagem estatística avançada para previsão de séries temporais. Baseados na descrição da autocorrelação dos dados, estes modelos descrevem uma série temporal como função dos seus valores passados. Neste trabalho, a metodologia ARIMA é utilizada para determinar um modelo que efetua uma previsão dos valores futuros do sensor que mede a distância entre os dois discos do desfibrador, determinando-se assim o momento ótimo da sua substituição e evitando paragens forçadas de produção por ocorrência de uma falha por desgaste dos discos. Os resultados obtidos neste trabalho constituem uma contribuição científica importante para a área da manutenção preditiva e deteção de falhas em equipamentos industriais.