Roof–envelope ratio impact on green roof energy performance

This paper addresses the impact of roof-to-envelope ratio on overall energy savings of a green roof design over conventional roof designs. Simulations were performed using a modified version of the Environmental System Performance program simulator, developed at the University of Strathclyde. The modified design employed a model developed by Columbia University and the Goddard Institute of Space Science which models the evapotranspiritive effect of a green roof calculated using the Bowen ratio; that is, the ratio of sensible heat flux to the surrounding air to the latent heat flux resulting from evapourative energy losses. The resulting heat flux term is proportional to the external surface convection, but inversely proportional to the surface Bowen ratio, which is held constant and chosen to match experimental results obtained for a given roof design. The present study performed simulations for the month of July in a Toronto climate on square warehouse style one, two, and three-story buildings, with windows occupying 10% of the area of each wall. For the first set of simulations, the internal building load of each story was set to zero, and the roof–envelope ratio was increased by increasing the building width and length. For the final simulations, several roof–envelope ratios were chosen, and the internal load of each story was increased from 0 to 50,000 W. As the roof–envelope ratio increases, the cooling load of the upper floor for multi-story designs approaches the entire building cooling load. This indicates the importance of upper zone cooling in total building energy reductions. Furthermore, the total energy savings of a green-roofed building over a conventional roofed building were far more significant for single-story structures. A 250 × 250 m green-roof design with 50,000 W internal loading was found to have percentage energy savings of 73%, 29%, and 18%, for a one, two, and three-story design, respectively.     

Multiple Imputation of Missing or Faulty Values Under Linear Constraints

Many statistical agencies, survey organizations, and research centers collect data that suffer from item nonresponse and erroneous or inconsistent values. These data may be required to satisfy linear constraints, for example, bounds on individual variables and inequalities for ratios or sums of variables. Often these constraints are designed to identify faulty values, which then are blanked and imputed. The data also may exhibit complex distributional features, including nonlinear relationships and highly nonnormal distributions. We present a fully Bayesian, joint model for modeling or imputing data with missing/blanked values under linear constraints that (i) automatically incorporates the constraints in inferences and imputations, and (ii) uses a flexible Dirichlet process mixture of multivariate normal distributions to reflect complex distributional features. Our strategy for estimation is to augment the observed data with draws from a hypothetical population in which the constraints are not present, thereby taking advantage of computationally expedient methods for fitting mixture models. Missing/blanked items are sampled from their posterior distribution using the Hit-and-Run sampler, which guarantees that all imputations satisfy the constraints. We illustrate the approach using manufacturing data from Colombia, examining the potential to preserve joint distributions and a regression from the plant productivity literature. Supplementary materials for this article are available online.     

Orthogonal Stiefel manifold optimization for eigen-decomposed covariance parameter estimation in mixture models

Within the mixture model-based clustering literature, parsimonious models with eigen-decomposed component covariance matrices have dominated for over a decade. Although originally introduced as a fourteen-member family of models, the current state-of-the-art is to utilize just ten of these models; the rationale for not using the other four models usually centers around parameter estimation difficulties. Following close examination of these four models, we find that two are actually easily implemented using existing algorithms but that two benefit from a novel approach. We present and implement algorithms that use an accelerated line search for optimization on the orthogonal Stiefel manifold. Furthermore, we show that the ‘extra’ models that these decompositions facilitate outperform the current state-of-the art when applied to two benchmark data sets.     

Graduate education in an age of decline

In the 1980s, sociology departments in a number of universities have been caught up in a deteriorating educational environment. Many have experienced reduced numbers of faculty and students, often becoming little more than service departments. This article looks at this process as it occurred at a midwestern university noted for its strong theoretical tradition in graduate education. How this happened and the effects it had on graduate students is the focus of this work. Barbara Ryan graduated from Washington University in St. Louis in 1986 and presently is teaching in the Department of Sociology at Northern Illinois University. Her specialty area is social movements and her dissertation was an analysis of change in the contemporary women’s movement.     

Privatization and Public Opinion in Germany

Privatization has been a major issue around the world, but research on public opinion about it has been scarce. The German Social Survey provides an opportunity to compare citizen opinions from a formerly socialist-authoritarian regime with those from a democratic regime, in their opinions about privatizing banks, electrical power, and hospitals. As do citizens in surveys in other nations, Germans support privatization of the services in the order just given. Citizens from the east, where privatization led to sharp increases in unemployment, oppose privatization much more than do westerners. A LISREL analysis indicates that their opposition is not due to their concerns about its economic effects on themselves or the nation (economic pessimism), but more due to perception of the proper role of government (opposition to government spending), and sense of political efficacy. The analysis also reflects on the roles of other variables such as ideology, partisanship, gender, being unemployed, education, and preference for taxes versus public services. We discuss implications for theory and research on public opinion about government policies and services, such as the role of direct economic self-interest versus more symbolic and ideological orientations.     

A new approach to assessing ethical conduct in scientific work

The intent of the current article is to describe the development of a new approach to the study of ethical conduct in scientific research settings. The approach presented in this article has two main components. The first component entails the development of a taxonomy of ethical events as they occur across a broad range of scientific disciplines. The second involves the identification of proximate criteria that will allow systematic and objective evaluation of ethical behaviors through low-fidelity performance simulations. Two proposed measures based on the new approach are intended to identify and measure variations in the scientific environment that might predispose certain individuals to make unethical decisions.     

Latent Variable Models for Mixed Discrete and Continuous Outcomes

We propose a latent variable model for mixed discrete and continuous outcomes. The model accommodates any mixture of outcomes from an exponential family and allows for arbitrary covariate effects, as well as direct modelling of covariates on the latent variable. An EM algorithm is proposed for parameter estimation and estimates of the latent variables are produced as a by-product of the analysis. A generalized likelihood ratio test can be used to test the significance of covariates affecting the latent outcomes. This method is applied to birth defects data, where the outcomes of interest are continuous measures of size and binary indicators of minor physical anomalies. Infants who were exposed in utero to anticonvulsant medications are compared with controls.