RESUMO
The Ordered Weighted Averaging (OWA) operator is a multicriteria method that has conquered space among researchers in the composite indicators field. Typically, OWA operator weights are defined by the decision maker. This type of weighting is highly criticized, as decision-makers are susceptible to errors and bias in judgment. Some methods have been used to define OWA operator weights objectively. However, none of them is concerned about the quality of the composite indicator. This paper introduces a method that defines the weights of the OWA operator based on two quality parameters of the composite indicator: the ability to capture the concept of the multidimensional phenomenon and the informational loss. The method can be implemented in Microsoft Excel Solver and has a high degree of flexibility and applicability in problems of a multidimensional nature and a high degree of appropriation by researchers and practitioners in the area.â¢Defines weights that maximize the ability of the composite indicator to capture the concept of the multidimensional phenomenon.â¢Considers restrictions to limit the informational loss of the composite indicator or emphasize positive or negative aspects of the multidimensional phenomenon.â¢Offers flexibility in setting the objective and constraints of the optimization algorithm.
RESUMO
This research offers a solution to a highly recognized and controversial problem within the composite indicator literature: sub-indicators weighting. The research proposes a novel hybrid weighting method that maximizes the discriminating power of the composite indicator with objectively defined weights. It considers the experts' uncertainty concerning the conceptual importance of sub-indicators in the multidimensional phenomenon, setting maximum and minimum weights (constraints) in the optimization function. The hybrid weighting scheme, known as the SAW-Max-Entropy method, avoids attributing weights that are incompatible with the multidimensional phenomenon's theoretical framework. At the same time, it reduces the influence of assessment errors and judgment biases on composite indicator scores. The research results show that the SAW-Max-Entropy weighting scheme achieves greater discriminating power than weighting schemes based on the Entropy Index, Expert Opinion, and Equal Weights. The SAW-Max-Entropy method has high application potential due to the increasing use of composite indicators across diverse areas of knowledge. Additionally, the method represents a robust response to the challenge of constructing composite indicators with superior discriminating power.
RESUMO
Alternative data are now widely used in economic analyses worldwide but still infrequent in studies on the Brazilian economy. This research demonstrates how alternative data extracted from Google Trends and Google Mobility contribute to innovative economic analysis. First, it demonstrates that the search for the future on the internet is correlated (R = 0.62) with the average household income in Brazilian states. The three Brazilian states with the most people looking for the future on the internet have an average household income 1.6 times higher than people from states that do not have this behavior. The search for the future represents 10.9% of the economic development potential of the states, while the proportion of people with university degrees, scientific publications, and researchers represents another 60.4%. The reduction in mobility in retail/recreation locations averaged 34.28% in Brazil, Ecuador, Paraguay, and Uruguay. This group of countries had COVID-19 infection and death rates 1.25 and 1.74 times higher than in countries that reduced their mobility in retail/recreation locations by 45.03%. The impact of reduced mobility in retail/recreation locations on the unemployment rate, gross domestic product degrowth, and inflation in countries such as Brazil was 1.1, 2.2, and 2.6 times lower than in countries that reduced mobility more of people. The research contributions are associated with identifying new indicators extracted from alternative data and their application to carry out innovative economic analyses.
RESUMO
Oral Mucositis (OM) is a common adverse effect of head and neck squamous cell carcinoma (HNSCC) treatment. The purpose of this study was to investigate the significance of early changes in tissue electrical parameters (TEPs) in predicting the development of OM in HNSCC patients receiving radiation therapy (RT). The current study combined two study designs. The first was a case-control study. The control group comprised of RT patients who did not receive head and neck RT, and patients with HNSCC who received RT comprised the case group. In the second part of the study, the case group was included in a parallel cohort. A total of 320 patients were assessed for eligibility, and 135 patients were enrolled. Double blinding was performed, and neither the patients nor the care providers knew the measured parameters. The primary outcome was the detection of between-group changes in local TEPs over the follow-up period. The secondary outcome was the appearance of OM grades II, III, or IV and the predictive value of local TEPs in determining the incidence of OM after RT. The variables, impedance module, resistance, reactance, phase angle, and capacitance, were analyzed by the receiver operator curves (ROC). The case and control groups did not differ in demographic and clinical characteristics. Radiation therapy increased the local impedance module, resistance, reactance, and phase angle and reduced the local tissue capacitance in both groups. Evaluation of TEPs in the first week of RT correlated with the development of OM lesions during cancer therapy. ROC analysis showed that local impedance module and resistance presented higher specificity than did other parameters in predicting OM. In conclusion, local tissue electrical parameters measured at the first RT week can be useful tools to predict oral mucositis.