Innovative technological capability in firms of the tourism sector: a study of the hotels in the city of Rio de Janeiro during the 1990-2008 period

This article presents the preliminary report of the research project entitled "Innovative technological capability in firms of the tourism sector: a study of the hotels in the city of Rio de Janeiro during the 1990-2008 period". The objective of this project is to apply and evaluate an analytical model of technological capability and underlying learning processes and examine the accumulation trajectory of innovative technological capability in the firms of tourism service industry, and the impact of learning processes undertaken by these firms on the technological capability levels achieved during the 1990-2008 period.

Proportional cancer incidence according to selected sites: comparison between residents in the City of S. Paulo, Brazil: Japanese and Brazilian/Portuguese descent

The percentual distributions of selected sites of cancer cases according to origin, sex and age are compared. Data were obtained from the Registry of Cancer of S. Paulo (School of Public Health of the University of S. Paulo, Brazil). The reference period for inhabitants of Japanese descent was 1969/78 and for those of Brazilian descent, the period was 1969/75. Standardized Proportionate Incidence Ratios (SPIR) with approximate 95% Confidence Intervals (CI) were evaluated using age specific Incidence Ratios of S. Paulo, 1973, as standards. The results agree with findings of previous works on mortality, but show different patterns according to origin. The well known fact that some sub-groups of a population may be different from the overall group is once again brought to the fore. Attention should be drawn to the differences detected for stomach, skin and prostate, in males, and for stomach, skin, cervix and uterus in females.

Propriedades psicométricas da versão portuguesa do Kansas City Cardiomyopathy Questionnaire na miocardiopatia dilatada com insuficiência cardíaca congestiva

Vários estudos demonstraram que os doentes com insuficiência cardíaca congestiva (ICC) têm um compromisso da qualidade de vida relacionada com a saúde (QVRS), tendo esta, nos últimos anos, vindo a tornar-se um endpoint primário quando se analisa o impacto do tratamento de situações crónicas como a ICC. Objectivos: Avaliar as propriedades psicométricas da versão portuguesa de um novo instrumento específico para medir a QVRS na ICC em doentes hospitalizados: o Kansas City Cardiomyopathy Questionnaire (KCCQ). População e Métodos: O KCCQ foi aplicado a uma amostra consecutiva de 193 doentes internados por ICC. Destes, 105 repetiram esta avaliação 3 meses após admissão hospitalar, não havendo eventos ocorridos durante este período de tempo. A idade era 64,4± 12,4 anos (entre 21 e 88), com 72,5% a pertencer ao sexo masculino, sendo a ICC de etiologia isquémica em 42%. Resultados: Esta versão do KCCQ foi sujeita a validação estatística semelhante à americana com a avaliação da fidelidade e validade. A fidelidade foi avaliada pela consistência interna dos domínios e dos somatórios, apresentando valores Alpha de Cronbach idênticos nos vários domínios e somatórios ( =0,50 a =0,94). A validade foi analisada pela convergência, pela sensibilidade às diferenças entre grupos e pela sensibilidade à alteração da condição clínica. Avaliou-se a validade convergente de todos os domínios relacionados com funcionalidade, pela relação verificada entre estes e uma medida de funcionalidade, a classificação da New York Heart Association (NYHA), tendo-se verificado correlações significativas (p<0,01), como medida para avaliar a funcionalidade em doentes com ICC. Efectuou-se uma análise de variância entre o domínio limitação física, os somatórios e as classes da NYHA, tendo-se encontrado diferenças estatisticamente significativas (F=23,4; F=36,4; F=37,4; p=0,0001), na capacidade de descriminação da gravidade da condição clínica. Foi realizada uma segunda avaliação em 105 doentes na consulta do 3º mês após a intervenção clínica, tendo-se observado alterações significativas nas médias dos domínios avaliados entre o internamento e a consulta (diferenças de 14,9 a 30,6 numa escala de 0-100), indicando que os domínios avaliados são sensíveis à mudança da condição clínica. A correlação interdimensões da qualidade de vida que compõe este instrumento é moderada, sugerindo dimensões independentes, apoiando a sua estrutura multifactorial e a adequabilidade desta medida para a sua avaliação. Conclusão: O KCCQ é um instrumento válido, sensível à mudança e específico para medir a QVRS numa população portuguesa com miocardiopatia dilatada e ICC. ABSTRACT - Several studies have shown that patients with congestive heart failure (CHF) have a compromised health-related quality of life (HRQL), and this, in recent years, has become a primary endpoint when considering the impact of treatment of chronic conditions such as CHF. Objectives: To evaluate the psychometric properties of the Portuguese version of a new specific instrument to measure HRQL in patients hospitalized for CHF: the Kansas City Cardiomyopathy Questionnaire (KCCQ). Methods: The KCCQ was applied to a sample of 193 consecutive patients hospitalized for CHF. Of these, 105 repeated the assessment 3 months after admission, with no events during this period. Mean age was 64.4±12.4 years (21-88), and 72.5% were 72.5% male. CHF was of ischemic etiology in 42% of cases. Results: This version of the KCCQ was subjected to statistical validation, with assessment of reliability and validity, similar to the American version. Reliability was assessed by the internal consistency of the domains and summary scores, which showed similar values of Cronbach alpha (0.50-0.94). Validity was assessed by convergence, sensitivity to differences between groups and sensitivity to changes in clinical condition. We evaluated the convergent validity of all domains related to functionality, through the relationship between them and a measure of functionality, the New York Heart Association (NYHA) classification. Significant correlations were found (p<0.01) for this measure of functionality in patients with CHF. Analysis of variance between the physical limitation domain, the summary scores and NYHA class was performed and statistically significant differences were found (F=23.4; F=36.4; F=37.4, p=0.0001) in the ability to discriminate severity of clinical condition. A second evaluation was performed on 105 patients at the 3-month follow-up outpatient appointment, and significant changes were observed in the mean scores of the domains assessed between hospital admission and the clinic appointment (differences from 14.9 to 30.6 on a scale of 0-100), indicating that the domains assessed are sensitive to changes in clinical condition. The correlation between dimensions of quality of life in the KCCQ is moderate, suggesting that the dimensions are independent, supporting the multifactorial nature of HRQL and the suitability of this measure for its evaluation. Conclusion: The KCCQ is a valid instrument, sensitive to change and a specific measure of HRQL in a population with dilated cardiomyopathy and CHF.

Microscopic analysis of "prato", "mussarela" and "mineiro" cheese sold in street markets of the City of S. Paulo, Southeastern Brazil

INTRODUCTION: Cheese should be produced from ingredients of good quality and processed under hygienic conditions. Further, cheese should be transported, stored and sold in an appropriate manner in order to avoid, among other things, the incorporation of extraneous materials (filth) of biological origin or otherwise, in contravention of the relevant food legislation. The aim of the study was to evaluate the hygienic conditions of "prato", "mussarela", and "mineiro" cheeses sold at the street food markets in the city of S. Paulo, Brazil. MATERIALS AND METHOD: Forty-seven samples of each of the three types of cheese were collected during the period from March, 1993 to February, 1994. The Latin square was used as a statistical model for sampling and random selection of the street markets from which to collect the cheese samples. The samples were analysed for the presence of extraneous matters outside for which purpose the samples were washed and filtered and inside, for which the methodology of enzymathic digestion of the sample with pancreatine, followed by filtering,was used. RESULTS AND CONCLUSION: Of the 141 samples analysed, 75.9% exhibited at least one sort of extraneous matters. For the "prato" and "mussarela" cheeses, the high number of contaminated samples was due mainly to extraneous matters present inside the cheese, whereas in the "mineiro" cheese, besides the internal filth, 100% of the samples had external filth.

Infant mortality rates according to socioeconomic status in a Brazilian city

OBJECTIVE: Data from municipal databases can be used to plan interventions aimed at reducing inequities in health care. The objective of the study was to determine the distribution of infant mortality according to an urban geoeconomic classification using routinely collected municipal data. METHODS: All live births (total of 42,381) and infant deaths (total of 731) that occurred between 1994 and 1998 in Ribeirão Preto, Brazil, were considered. Four different geoeconomic areas were defined according to the family head's income in each administrative urban zone. RESULTS: The trends for infant mortality rate and its different components, neonatal mortality rate and post-neonatal mortality rate, decreased in Ribeirão Preto from 1994 to 1998 (chi-square for trend, p<0.05). These rates were inversely correlated with the distribution of lower salaries in the geoeconomic areas (less than 5 minimum wages per family head), in particular the post-neonatal mortality rate (chi-square for trend, p<0.05). Finally, the poor area showed a steady increase in excess infant mortality. CONCLUSIONS: The results indicate that infant mortality rates are associated with social inequality and can be monitored using municipal databases. The findings also suggest an increase in the impact of social inequality on infant health in Ribeirão Preto, especially in the poor area. The monitoring of health inequalities using municipal databases may be an increasingly more useful tool given the continuous decentralization of health management at the municipal level in Brazil.

An architecture for an effective usage of data mining in business intelligence systems

Business Intelligence (BI) is one emergent area of the Decision Support Systems (DSS) discipline. Over the last years, the evolution in this area has been considerable. Similarly, in the last years, there has been a huge growth and consolidation of the Data Mining (DM) field. DM is being used with success in BI systems, but a truly DM integration with BI is lacking. Therefore, a lack of an effective usage of DM in BI can be found in some BI systems. An architecture that pretends to conduct to an effective usage of DM in BI is presented.

Data mining contributions to characterize MV consumers and to improve the suppliers-consumers settlements

This paper deals with the establishment of a characterization methodology of electric power profiles of medium voltage (MV) consumers. The characterization is supported on the data base knowledge discovery process (KDD). Data Mining techniques are used with the purpose of obtaining typical load profiles of MV customers and specific knowledge of their customers’ consumption habits. In order to form the different customers’ classes and to find a set of representative consumption patterns, a hierarchical clustering algorithm and a clustering ensemble combination approach (WEACS) are used. Taking into account the typical consumption profile of the class to which the customers belong, new tariff options were defined and new energy coefficients prices were proposed. Finally, and with the results obtained, the consequences that these will have in the interaction between customer and electric power suppliers are analyzed.

Data mining techniques contributions to support electrical vehicle demand response

The introduction of Electric Vehicles (EVs) together with the implementation of smart grids will raise new challenges to power system operators. This paper proposes a demand response program for electric vehicle users which provides the network operator with another useful resource that consists in reducing vehicles charging necessities. This demand response program enables vehicle users to get some profit by agreeing to reduce their travel necessities and minimum battery level requirements on a given period. To support network operator actions, the amount of demand response usage can be estimated using data mining techniques applied to a database containing a large set of operation scenarios. The paper includes a case study based on simulated operation scenarios that consider different operation conditions, e.g. available renewable generation, and considering a diversity of distributed resources and electric vehicles with vehicle-to-grid capacity and demand response capacity in a 33 bus distribution network.

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.

Using data mining techniques to support DR programs definition in smart grids

In recent years, Power Systems (PS) have experimented many changes in their operation. The introduction of new players managing Distributed Generation (DG) units, and the existence of new Demand Response (DR) programs make the control of the system a more complex problem and allow a more flexible management. An intelligent resource management in the context of smart grids is of huge important so that smart grids functions are assured. This paper proposes a new methodology to support system operators and/or Virtual Power Players (VPPs) to determine effective and efficient DR programs that can be put into practice. This method is based on the use of data mining techniques applied to a database which is obtained for a large set of operation scenarios. The paper includes a case study based on 27,000 scenarios considering a diversity of distributed resources in a 32 bus distribution network.

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.

Power systems reliability calculation based on fuzzy data mining

This paper presents a methodology supported on the data base knowledge discovery process (KDD), in order to find out the failure probability of electrical equipments’, which belong to a real electrical high voltage network. Data Mining (DM) techniques are used to discover a set of outcome failure probability and, therefore, to extract knowledge concerning to the unavailability of the electrical equipments such us power transformers and high-voltages power lines. The framework includes several steps, following the analysis of the real data base, the pre-processing data, the application of DM algorithms, and finally, the interpretation of the discovered knowledge. To validate the proposed methodology, a case study which includes real databases is used. This data have a heavy uncertainty due to climate conditions for this reason it was used fuzzy logic to determine the set of the electrical components failure probabilities in order to reestablish the service. The results reflect an interesting potential of this approach and encourage further research on the topic.

Data mining applications in power systems - case-studies and future trends

Presently power system operation produces huge volumes of data that is still treated in a very limited way. Knowledge discovery and machine learning can make use of these data resulting in relevant knowledge with very positive impact. In the context of competitive electricity markets these data is of even higher value making clear the trend to make data mining techniques application in power systems more relevant. This paper presents two cases based on real data, showing the importance of the use of data mining for supporting demand response and for supporting player strategic behavior.

Zonal prices analysis supported by a data mining based methodology

A methodology based on data mining techniques to support the analysis of zonal prices in real transmission networks is proposed in this paper. The mentioned methodology uses clustering algorithms to group the buses in typical classes that include a set of buses with similar LMP values. Two different clustering algorithms have been used to determine the LMP clusters: the two-step and K-means algorithms. In order to evaluate the quality of the partition as well as the best performance algorithm adequacy measurements indices are used. The paper includes a case study using a Locational Marginal Prices (LMP) data base from the California ISO (CAISO) in order to identify zonal prices.