Why do they separate it or not? Attitudes and behaviors towards organic waste separation

This article shows the results of an exploratory study related to the separation of organic waste in order to offer suggestions for the improvement of waste disposal communication campaigns. The overall objective is to analyze attitude and behavior of those who do and those who do not separate organic waste, related to a specific promotional campaign carried out in two neighborhoods, in the municipality of Badalona (Spain), within the framework of the study of proenvironmental attitudes and behaviors and based on the Psychosocial Four Spheres Model. 1,010 interviews were conducted and data was analyzed using Chi-Squared Automatic Interaction Detector (CHAID). Waste separation behavior was used as a dependent variable. The reasons given to explain why people do or do not separate organic waste and sociodemographic variables, have been introduced as independent variables. In accordance with the Four Spheres Model, results show significant differences in waste separation. Based on the profiles obtained, we find some predictive variables that facilitate the development of communication campaigns according to the requirements of each community.

Why do they separate it or not? Attitudes and behaviors towards organic waste separation

This article shows the results of an exploratory study related to the separation of organic waste in order to offer suggestions for the improvement of waste disposal communication campaigns. The overall objective is to analyze attitude and behavior of those who do and those who do not separate organic waste, related to a specific promotional campaign carried out in two neighborhoods, in the municipality of Badalona (Spain), within the framework of the study of proenvironmental attitudes and behaviors and based on the Psychosocial Four Spheres Model. 1,010 interviews were conducted and data was analyzed using Chi-Squared Automatic Interaction Detector (CHAID). Waste separation behavior was used as a dependent variable. The reasons given to explain why people do or do not separate organic waste and sociodemographic variables, have been introduced as independent variables. In accordance with the Four Spheres Model, results show significant differences in waste separation. Based on the profiles obtained, we find some predictive variables that facilitate the development of communication campaigns according to the requirements of each community.

Why do they separate it or not? Attitudes and behaviors towards organic waste separation

This article shows the results of an exploratory study related to the separation of organic waste in order to offer suggestions for the improvement of waste disposal communication campaigns. The overall objective is to analyze attitude and behavior of those who do and those who do not separate organic waste, related to a specific promotional campaign carried out in two neighborhoods, in the municipality of Badalona (Spain), within the framework of the study of proenvironmental attitudes and behaviors and based on the Psychosocial Four Spheres Model. 1,010 interviews were conducted and data was analyzed using Chi-Squared Automatic Interaction Detector (CHAID). Waste separation behavior was used as a dependent variable. The reasons given to explain why people do or do not separate organic waste and sociodemographic variables, have been introduced as independent variables. In accordance with the Four Spheres Model, results show significant differences in waste separation. Based on the profiles obtained, we find some predictive variables that facilitate the development of communication campaigns according to the requirements of each community.

Calculation of the variance in surveys of the economic climate

Public opinion surveys have become progressively incorporated into systems of official statistics. Surveys of the economic climate are usually qualitative because they collect opinions of businesspeople and/or experts about the long-term indicators described by a number of variables. In such cases the responses are expressed in ordinal numbers, that is, the respondents verbally report, for example, whether during a given trimester the sales or the new orders have increased, decreased or remained the same as in the previous trimester. These data allow to calculate the percent of respondents in the total population (results are extrapolated), who select every one of the three options. Data are often presented in the form of an index calculated as the difference between the percent of those who claim that a given variable has improved in value and of those who claim that it has deteriorated. As in any survey conducted on a sample the question of the measurement of the sample error of the results has to be addressed, since the error influences both the reliability of the results and the calculation of the sample size adequate for a desired confidence interval. The results presented here are based on data from the Survey of the Business Climate (Encuesta de Clima Empresarial) developed through the collaboration of the Statistical Institute of Catalonia (Institut d’Estadística de Catalunya) with the Chambers of Commerce (Cámaras de Comercio) of Sabadell and Terrassa.

State-dependent threshold STAR models

In this paper we consider extensions of smooth transition autoregressive (STAR) models to situations where the threshold is a time-varying function of variables that affect the separation of regimes of the time series under consideration. Our specification is motivated by the observation that unusually high/low values for an economic variable may sometimes be best thought of in relative terms. State-dependent logistic STAR and contemporaneous-threshold STAR models are introduced and discussed. These models are also used to investigate the dynamics of U.S. short-term interest rates, where the threshold is allowed to be a function of past output growth and inflation.

On the stability of the optimal value and the optimal set in optimization problems

The paper develops a stability theory for the optimal value and the optimal set mapping of optimization problems posed in a Banach space. The problems considered in this paper have an arbitrary number of inequality constraints involving lower semicontinuous (not necessarily convex) functions and one closed abstract constraint set. The considered perturbations lead to problems of the same type as the nominal one (with the same space of variables and the same number of constraints), where the abstract constraint set can also be perturbed. The spaces of functions involved in the problems (objective and constraints) are equipped with the metric of the uniform convergence on the bounded sets, meanwhile in the space of closed sets we consider, coherently, the Attouch-Wets topology. The paper examines, in a unified way, the lower and upper semicontinuity of the optimal value function, and the closedness, lower and upper semicontinuity (in the sense of Berge) of the optimal set mapping. This paper can be seen as a second part of the stability theory presented in [17], where we studied the stability of the feasible set mapping (completed here with the analysis of the Lipschitz-like property).

Interacting software-agents to support a kind of expert supervision systems

Expert supervision systems are software applications specially designed to automate process monitoring. The goal is to reduce the dependency on human operators to assure the correct operation of a process including faulty situations. Construction of this kind of application involves an important task of design and development in order to represent and to manipulate process data and behaviour at different degrees of abstraction for interfacing with data acquisition systems connected to the process. This is an open problem that becomes more complex with the number of variables, parameters and relations to account for the complexity of the process. Multiple specialised modules tuned to solve simpler tasks that operate under a co-ordination provide a solution. A modular architecture based on concepts of software agents, taking advantage of the integration of diverse knowledge-based techniques, is proposed for this purpose. The components (software agents, communication mechanisms and perception/action mechanisms) are based on ICa (Intelligent Control architecture), software middleware supporting the build-up of applications with software agent features

Indicator taxa of benthic diatom communities: a case study in Mediterranean streams

A key issue in the implementation of the Water Framework Directive is the classification of streams and rivers using biological quality parameters and type-specific reference conditions. Four groups of stream types were defined in NE Spain on the basis of 152 diatom samples by means of detrended correspondence analysis and classification techniques. Diatom analysis was restricted to epilithic taxa, and the sites included gradients ranging from near-natural streams to sites with poor ecological quality. The main gradient shows a clear separation of sites in relation to the degree of human influence: polluted streams (mainly located in the lowlands) differ from streams in mountainous areas and in the Pyrenees. A second gradient is related to physiographical features. Headwater streams can be distinguished by their catchment geology. The type-specific diatom taxa for the stream types studied were determined by using indicator species analysis (IndVal). The type-specific taxa from near-natural streams are coincident with the indicator taxa for high ecological status. Human impact reduced the typological heterogeneity of the diatom community composition. Overall, the diatom communities in NE Spain exhibit a regional distribution pattern that closely corresponds with that observed in river systems elsewhere. Physiographical differences are only evident in undisturbed sites, while nutrient enrichment and other human disturbances may mask the regional differences in the distribution of diatom communities

Development and validation of AMANDA, a new algorithm for selecting highly relevant regions in Molecular Interaction Fields

Descriptors based on Molecular Interaction Fields (MIF) are highly suitable for drug discovery, but their size (thousands of variables) often limits their application in practice. Here we describe a simple and fast computational method that extracts from a MIF a handful of highly informative points (hot spots) which summarize the most relevant information. The method was specifically developed for drug discovery, is fast, and does not require human supervision, being suitable for its application on very large series of compounds. The quality of the results has been tested by running the method on the ligand structure of a large number of ligand-receptor complexes and then comparing the position of the selected hot spots with actual atoms of the receptor. As an additional test, the hot spots obtained with the novel method were used to obtain GRIND-like molecular descriptors which were compared with the original GRIND. In both cases the results show that the novel method is highly suitable for describing ligand-receptor interactions and compares favorably with other state-of-the-art methods.

Location of hubs in a competitive environment

We offer a formulation that locates hubs on a network in a competitiveenvironment; that is, customer capture is sought, which happenswhenever the location of a new hub results in a reduction of thecurrent cost (time, distance) needed by the traffic that goes from thespecified origin to the specified destination.The formulation presented here reduces the number of variables andconstraints as compared to existing covering models. This model issuited for both air passenger and cargo transportation.In this model, each origin-destination flow can go through either oneor two hubs, and each demand point can be assigned to more than a hub,depending on the different destinations of its traffic. Links(``spokes'' have no capacity limit. Computational experience is provided.

Relying on the agent in charge of production for project evaluation

I study the optimal project choice when the principal relies on the agent in charge of production for project evaluation. The principal has to choose between a safe project generating a fixed revenue and a risky project generating an uncertain revenue. The agent has private information about the production cost under each project but also about the signal regarding the profitability of the risky project. If the signal favoring the adoption of the risky project is goods news to the agent, integrating production and project evaluation tasks does not generate any loss compared to the benchmark in which the principal herself receives the signal. By contrast, if it is bad news, task integration creates an endogenous reservation utility which is type-dependent and thereby generates countervailing incentives, which can make a bias toward either project optimal. Our results can offer an explanation for why good firms can go bad and a rationale for the separation of day-to-day operating decisions from long-term strategic decisions stressed by Williamson.

Computation of multiple correspondence analysis, with code in R

The generalization of simple correspondence analysis, for two categorical variables, to multiple correspondence analysis where they may be three or more variables, is not straighforward, both from a mathematical and computational point of view. In this paper we detail the exact computational steps involved in performing a multiple correspondence analysis, including the special aspects of adjusting the principal inertias to correct the percentages of inertia, supplementary points and subset analysis. Furthermore, we give the algorithm for joint correspondence analysis where the cross-tabulations of all unique pairs of variables are analysed jointly. The code in the R language for every step of the computations is given, as well as the results of each computation.

Immigration and the survival of the welfare state

This paper analyzes the political sustainability of the welfare state in a model where immigration policy is also endogenous. In the model, the skills of the native population are affected by immigration and skill accumulation. Moreover, immigrants affect future policies, once they gain the right to vote. The main finding is that the long-run survival of redistributive policies is linked to an immigration policy specifying both skill and quantity restrictions. In particular, in steady state the unskilled majority admits a limited inflow of unskilled immigrants in order to offset growth in the fraction of skilled voters and maintain a high degree of income redistribution.Interestingly, equilibrium immigration policy shifts from unrestricted skilled immigration,when the country is skill-scarce, to restricted unskilled immigration, as the fraction of native skilled workers increases. The analysis also suggests a new set of variables that may help explain international differences in immigration restrictions.

A BPE model for the Burgers' equation

We study the BPE (Brownian particle equation) model of the Burgers equationpresented in the preceeding article [6]. More precisely, we are interestedin establishing the existence and uniqueness properties of solutions usingprobabilistic techniques.

Prediction of the bulking phenomenon in wastewater treatment plants

The control and prediction of wastewater treatment plants poses an important goal: to avoid breaking the environmental balance by always keeping the system in stable operating conditions. It is known that qualitative information — coming from microscopic examinations and subjective remarks — has a deep influence on the activated sludge process. In particular, on the total amount of effluent suspended solids, one of the measures of overall plant performance. The search for an input–output model of this variable and the prediction of sudden increases (bulking episodes) is thus a central concern to ensure the fulfillment of current discharge limitations. Unfortunately, the strong interrelationbetween variables, their heterogeneity and the very high amount of missing information makes the use of traditional techniques difficult, or even impossible. Through the combined use of several methods — rough set theory and artificial neural networks, mainly — reasonable prediction models are found, which also serve to show the different importance of variables and provide insight into the process dynamics