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Over the past eight decades, millions of people have been surveyed on their political opinions. Until recently, however, polls rarely included enough questions in a given domain to apply scaling techniques such as IRT models at the individual level, preventing scholars from taking full advantage of historical survey data. To address this problem, we develop a Bayesian group-level IRT approach that models latent traits at the level of demographic and/or geographic groups rather than individuals. We use a hierarchical model to borrow strength cross-sectionally and dynamic linear models to do so across time. The group-level estimates can be weighted to generate estimates for geographic units. This framework opens up vast new areas of research on historical public opinion, especially at the subnational level. We illustrate this potential by estimating the average policy liberalism of citizens in each U.S. state in each year between 1972 and 2012.

Over the past eight decades, millions of people have been surveyed on their political opinions. Until recently, however, polls rarely included enough questions in a given domain to apply scaling techniques such as IRT models at the individual level, preventing scholars from taking full advantage of historical survey data. To address this problem, we develop a Bayesian group-level IRT approach that models latent traits at the level of demographic and/or geographic groups rather than individuals. We use a hierarchical model to borrow strength cross-sectionally and dynamic linear models to do so across time. The group-level estimates can be weighted to generate estimates for geographic units. This framework opens up vast new areas of research on historical public opinion, especially at the subnational level. We illustrate this potential by estimating the average policy liberalism of citizens in each U.S. state in each year between 1972 and 2012. ; Massachusetts Institute of Technology. School of Humanities, Arts, and Social Sciences

Corrosion is one of the pathologies that most affects the resistance of reinforced concrete. There are numerous ancient structures still in use affected by corrosion that need proper evaluation and remedial treatment for their maintenance. In this sense, there has been an increasing tendency to use nondestructive testing techniques that do not alter the reinforcement elements of such vulnerable structures. This work presents a combined methodology by using ground penetrating radar (GPR) and infrared thermography (IRT) techniques for the detection and evaluation of corrosion. The methodology was applied to the case study of an old construction that belongs to the abandoned military battery of Cabo Udra (Galicia, Spain). The combination of these complementary techniques allowed for the identification of areas with different dielectric and thermal conductivity, as well as different reflection patterns and intensity of the GPR waves. Thus, from the analysis of the GPR signals and IRT images acquired, it was possible to interpret corroded areas and moisture, along with inner damages such as cracking and debonding. These pathologies have a direct effect on the durability and sustentation of a structure, while the knowledge of their existence might be useful for engineers engaged in the design of maintenance works.

The Salatiga City Government through the Health Office organizes a system of services and socialization to the community in a preventive manner. The intended community includes P-IRT (Food and Home Industry) which constitutes the food and beverage industry sector which is included in the small industry. In carrying out the delivery of information, employees must carry out a program of training activities as well as by conducting socialization to the community directly, where it is more time consuming, costly, and energy. This also makes the service and delivery of information less efficient. Therefore a web-based and mobile-based information system design is needed to complement these shortcomings. By using the Laravel Framework and Android Programming as well as a collaborative filter algorithm, a web-based application and an Android phone are generated to address the problem. Sharing information and discussions between the public and related agencies is easier to use this system.

Corrosion is one of the pathologies that most affects the resistance of reinforced concrete. There are numerous ancient structures still in use affected by corrosion that need proper evaluation and remedial treatment for their maintenance. In this sense, there has been an increasing tendency to use nondestructive testing techniques that do not alter the reinforcement elements of such vulnerable structures. This work presents a combined methodology by using ground penetrating radar (GPR) and infrared thermography (IRT) techniques for the detection and evaluation of corrosion. The methodology was applied to the case study of an old construction that belongs to the abandoned military battery of Cabo Udra (Galicia, Spain). The combination of these complementary techniques allowed for the identification of areas with different dielectric and thermal conductivity, as well as different reflection patterns and intensity of the GPR waves. Thus, from the analysis of the GPR signals and IRT images acquired, it was possible to interpret corroded areas and moisture, along with inner damages such as cracking and debonding. These pathologies have a direct effect on the durability and sustentation of a structure, while the knowledge of their existence might be useful for engineers engaged in the design of maintenance works. ; Agencia Estatal de Investigación | Ref. RTI2018-095893-B-C21 ; Universidad de Salamanca. Cátedra Iberdrola VIII Centenario

Corrosion is one of the pathologies that most affects the resistance of reinforced concrete. There are numerous ancient structures still in use affected by corrosion that need proper evaluation and remedial treatment for their maintenance. In this sense, there has been an increasing tendency to use nondestructive testing techniques that do not alter the reinforcement elements of such vulnerable structures. This work presents a combined methodology by using ground penetrating radar (GPR) and infrared thermography (IRT) techniques for the detection and evaluation of corrosion. The methodology was applied to the case study of an old construction that belongs to the abandoned military battery of Cabo Udra (Galicia, Spain). The combination of these complementary techniques allowed for the identification of areas with different dielectric and thermal conductivity, as well as different reflection patterns and intensity of the GPR waves. Thus, from the analysis of the GPR signals and IRT images acquired, it was possible to interpret corroded areas and moisture, along with inner damages such as cracking and debonding. These pathologies have a direct effect on the durability and sustentation of a structure, while the knowledge of their existence might be useful for engineers engaged in the design of maintenance works. ; Agencia Estatal de Investigación | Ref. RTI2018-095893-B-C21 ; Universidad de Salamanca. Cátedra Iberdrola VIII Centenario

AbstractThis study is to evaluate the performance of fully Bayesian information criteria, namely, LOO, WAIC and WBIC in terms of the accuracy in determining the number of latent classes of a mixture IRT model while comparing it to the conventional model via non-random walk MCMC algorithms and to further compare their performance with conventional information criteria including AIC, BIC, CAIC, SABIC, and DIC. Monte Carlo simulations were carried out to evaluate these criteria under different situations. The results indicate that AIC, BIC, and their related CAIC and SABIC tend to select the simpler model and are not recommended when the actual data involve multiple latent classes. For the three fully Bayesian measures, WBIC can be used for detecting the number of latent classes for tests with at least 30 items, while WAIC and LOO are suggested to be used together with their effective number of parameters in choosing the correct number of latent classes.

The investigation of individual and community well-being has acquired a particular relevance over time for governments to develop strategies and identify resources for improving standards of living. To this aim, it is necessary to analyse changes at the overall level and examine how subjective well-being differs between different sub-groups of the population as well as across local areas. Using data measuring the well-being of residents in the Romagna area (Italy), we propose a multidimensional approach within the item response theory (IRT) framework to estimate an overall score of community Subjective Well-Being (SWB) and individual scores reflecting specific dimensions, taking into account for the ordinal polytomous nature of the items. The results show that aspects dealing with Life Evaluation mainly affect the overall SWB, while issues pertaining to Community and Environment are less important. The proposed approach is effective in developing an indicator taking into account the multidimensionality of SWB and estimating individual scores reflecting the heterogeneity among residents.