Elements of a methodology to assess the alignment of core-values in collaborative networks

Collaborative networks are typically formed by heterogeneous and autonomous entities, and thus it is natural that each member has its own set of core-values. Since these values somehow drive the behaviour of the involved entities, the ability to quickly identify partners with compatible or common core-values represents an important element for the success of collaborative networks. However, tools to assess or measure the level of alignment of core-values are lacking. Since the concept of 'alignment' in this context is still ill-defined and shows a multifaceted nature, three perspectives are discussed. The first one uses a causal maps approach in order to capture, structure, and represent the influence relationships among core-values. This representation provides the basis to measure the alignment in terms of the structural similarity and influence among value systems. The second perspective considers the compatibility and incompatibility among core-values in order to define the alignment level. Under this perspective we propose a fuzzy inference system to estimate the alignment level, since this approach allows dealing with variables that are vaguely defined, and whose inter-relationships are difficult to define. Another advantage provided by this method is the possibility to incorporate expert human judgment in the definition of the alignment level. The last perspective uses a belief Bayesian network method, and was selected in order to assess the alignment level based on members' past behaviour. An example of application is presented where the details of each method are discussed.

Two-year follow-up study of elderly residents in S. Paulo, Brazil: methodology and preliminary results

INTRODUCTION: Previous cross-sectional studies have shown a high prevalence of chronic disease and disability among the elderly. Given Brazil’s rapid aging process and the obvious consequences of the growing number of old people with chronic diseases and associated disabilities for the provision of health services, a need was felt for a study that would overcome the limitations of cross-sectional data and shed some light on the main factors determining whether a person will live longer and free of disabling diseases, the so-called successful aging. The methodology of the first follow-up study of elderly residents in Brazil is presented. METHOD: The profile of the initial cohort is compared with previous cross-sectional data and an in-depth analysis of nonresponse is carried out in order to assess the validity of future longitudinal analysis. The EPIDOSO (‘Epidemiologia do Idoso’) Study conducted a two-year follow-up of 1,667 elderly people (65+), living in S. Paulo. The study consisted of two waves, each consisting of household, clinical, and biochemical surveys. RESULTS AND CONCLUSIONS: In general, the initial cohort showed a similar profile to previous cross-sectional samples in S. Paulo. There was a majority of women, mostly widows, living in multigenerational households, and a high prevalence of chronic illnesses, psychiatric disturbances, and physical disabilities. Despite all the difficulties inherent in follow-up studies, there was a fairly low rate of nonresponse to the household survey after two years, which did not actually affect the representation of the cohort at the final household assessment, making unbiased longitudinal analysis possible. Concerning the clinical and blood sampling surveys, the respondents tended to be younger and less disabled than the nonrespondents, limiting the use of the clinical and laboratory data to longitudinal analysis aimed at a healthier cohort. It is worth mentioning that gender, education, family support, and socioeconomic status were not important determinants of nonresponse, as is often the case.

Methodology in the epidemiological research of respiratory diseases and environmental pollution

There are complex and diverse methodological problems involved in the clinical and epidemiological study of respiratory diseases and their etiological factors. The association of urban growth, industrialization and environmental deterioration with respiratory diseases makes it necessary to pay more attention to this research area with a multidisciplinary approach. Appropriate study designs and statistical techniques to analyze and improve our understanding of the pathological events and their causes must be implemented to reduce the growing morbidity and mortality through better preventive actions and health programs. The objective of the article is to review the most common methodological problems in this research area and to present the most available statistical tools used.

Culture, politics and identity: critical readings on gender in southeast Asia

Indian Journal of Gender Studies October 2012 vol. 19 no. 3 437-467

The expression of tubulin cofactor A (TBCA) is regulated by a noncoding antisense Tbca RNA during testis maturation

Abstract - Recently, long noncoding RNAs have emerged as pivotal molecules for the regulation of coding genes' expression. These molecules might result from antisense transcription of functional genes originating natural antisense transcripts (NATs) or from transcriptional active pseudogenes. TBCA interacts with β-tubulin and is involved in the folding and dimerization of new tubulin heterodimers, the building blocks of microtubules. Methodology/Principal findings: We found that the mouse genome contains two structurally distinct Tbca genes located in chromosomes 13 (Tbca13) and 16 (Tbca16). Interestingly, the two Tbca genes albeit ubiquitously expressed, present differential expression during mouse testis maturation. In fact, as testis maturation progresses Tbca13 mRNA levels increase progressively, while Tbca16 mRNA levels decrease. This suggests a regulatory mechanism between the two genes and prompted us to investigate the presence of the two proteins. However, using tandem mass spectrometry we were unable to identify the TBCA16 protein in testis extracts even in those corresponding to the maturation step with the highest levels of Tbca16 transcripts. These puzzling results led us to re-analyze the expression of Tbca16. We then detected that Tbca16 transcription produces sense and natural antisense transcripts. Strikingly, the specific depletion by RNAi of these transcripts leads to an increase of Tbca13 transcript levels in a mouse spermatocyte cell line. Conclusions/Significance: Our results demonstrate that Tbca13 mRNA levels are post-transcriptionally regulated by the sense and natural antisense Tbca16 mRNA levels. We propose that this regulatory mechanism operates during spermatogenesis, a process that involves microtubule rearrangements, the assembly of specific microtubule structures and requires critical TBCA levels.

Balanced scorecard: estudo de caso de uma empresa de serviços de gestão

Dissertação para a obtenção do Grau de Mestre em Contabilidade e Finanças Orientadora: Doutora Cláudia Lopes

Construção de vantagens competitivas nas estratégias de internacionalização: estudo do caso da EFACEC

Dissertação apresentada ao Instituto Politécnico do Porto para obtenção do Grau de Mestre em Gestão das Organizações, Ramo de Gestão de Empresas Orientador: Professor Doutor Orlando Manuel Martins Marques de Lima Rua

Fatores influenciadores da gestão da qualidade e independência do auditor: um estudo de caso

Dissertação apresentada ao Instituto Superior de Contabilidade para obtenção do Grau de Mestre em Auditoria Orientada por: Doutora Alcina Dias

Adaptive learning in multiagent systems: a forecasting methodology based on error analysis

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simu-lator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM pro-vides several dynamic strategies for agents’ behaviour. This paper presents a method that aims to provide market players strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses an auxiliary forecasting tool, e.g. an Artificial Neural Net-work, to predict the electricity market prices, and analyses its forecasting error patterns. Through the recognition of such patterns occurrence, the method predicts the expected error for the next forecast, and uses it to adapt the actual forecast. The goal is to approximate the forecast to the real value, reducing the forecasting error.

Comportamentos de interação em alunos com perturbações do espectro do autismo: o contributo de histórias sociais na hora do conto

Dissertação apresentada à Escol a Superior de Educação de Lisboa para obtenção de grau de mestre em Ciências da Educação, especialidade Educação Especial: Problemas Graves de Cognição e Multideficiência

Genetic algorithm methodology applied to intelligent house control

In recent years the use of several new resources in power systems, such as distributed generation, demand response and more recently electric vehicles, has significantly increased. Power systems aim at lowering operational costs, requiring an adequate energy resources management. In this context, load consumption management plays an important role, being necessary to use optimization strategies to adjust the consumption to the supply profile. These optimization strategies can be integrated in demand response programs. The control of the energy consumption of an intelligent house has the objective of optimizing the load consumption. This paper presents a genetic algorithm approach to manage the consumption of a residential house making use of a SCADA system developed by the authors. Consumption management is done reducing or curtailing loads to keep the power consumption in, or below, a specified energy consumption limit. This limit is determined according to the consumer strategy and taking into account the renewable based micro generation, energy price, supplier solicitations, and consumers’ preferences. The proposed approach is compared with a mixed integer non-linear approach.

O fim de um ciclo? A coconstrução da articulação curricular entre o 1.º e o 2.º Ciclos do Ensino Básico

Dissertação Apresentada à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Ciências da Educação - Especialidade Supervisão em Educação

A data-mining based methodology for wind forecasting

In many countries the use of renewable energy is increasing due to the introduction of new energy and environmental policies. Thus, the focus on the efficient integration of renewable energy into electric power systems is becoming extremely important. Several European countries have already achieved high penetration of wind based electricity generation and are gradually evolving towards intensive use of this generation technology. The introduction of wind based generation in power systems poses new challenges for the power system operators. This is mainly due to the variability and uncertainty in weather conditions and, consequently, in the wind based generation. In order to deal with this uncertainty and to improve the power system efficiency, adequate wind forecasting tools must be used. This paper proposes a data-mining-based methodology for very short-term wind forecasting, which is suitable to deal with large real databases. The paper includes a case study based on a real database regarding the last three years of wind speed, and results for wind speed forecasting at 5 minutes intervals.

A data-mining-based methodology for transmission expansion planning

In recent decades, all over the world, competition in the electric power sector has deeply changed the way this sector’s agents play their roles. In most countries, electric process deregulation was conducted in stages, beginning with the clients of higher voltage levels and with larger electricity consumption, and later extended to all electrical consumers. The sector liberalization and the operation of competitive electricity markets were expected to lower prices and improve quality of service, leading to greater consumer satisfaction. Transmission and distribution remain noncompetitive business areas, due to the large infrastructure investments required. However, the industry has yet to clearly establish the best business model for transmission in a competitive environment. After generation, the electricity needs to be delivered to the electrical system nodes where demand requires it, taking into consideration transmission constraints and electrical losses. If the amount of power flowing through a certain line is close to or surpasses the safety limits, then cheap but distant generation might have to be replaced by more expensive closer generation to reduce the exceeded power flows. In a congested area, the optimal price of electricity rises to the marginal cost of the local generation or to the level needed to ration demand to the amount of available electricity. Even without congestion, some power will be lost in the transmission system through heat dissipation, so prices reflect that it is more expensive to supply electricity at the far end of a heavily loaded line than close to an electric power generation. Locational marginal pricing (LMP), resulting from bidding competition, represents electrical and economical values at nodes or in areas that may provide economical indicator signals to the market agents. This article proposes a data-mining-based methodology that helps characterize zonal prices in real power transmission networks. To test our methodology, we used an LMP database from the California Independent System Operator for 2009 to identify economical zones. (CAISO is a nonprofit public benefit corporation charged with operating the majority of California’s high-voltage wholesale power grid.) To group the buses into typical classes that represent a set of buses with the approximate LMP value, we used two-step and k-means clustering algorithms. By analyzing the various LMP components, our goal was to extract knowledge to support the ISO in investment and network-expansion planning.

Strategic bidding methodology for electricity markets using adaptive learning

The very particular characteristics of electricity markets, require deep studies of the interactions between the involved players. MASCEM is a market simulator developed to allow studying electricity market negotiations. This paper presents a new proposal for the definition of MASCEM players’ strategies to negotiate in the market. The proposed methodology is implemented as a multiagent system, using reinforcement learning algorithms to provide players with the capabilities to perceive the changes in the environment, while adapting their bids formulation according to their needs, using a set of different techniques that are at their disposal. This paper also presents a methodology to define players’ models based on the historic of their past actions, interpreting how their choices are affected by past experience, and competition.