Improving confidence in tree species selection for challenging urban sites: a role for leaf turgor loss

High species diversity is argued to be the most important requisite for a resilient urban forest. In spite of this, there are many cities in the northern hemisphere that have very limited species diversity within their tree population. Consequently, there is an immense risk to urban canopy cover, if these over-used species succumb to serious pests or pathogens. Recognition of this should motivate the use of less commonly used species. Analysis of plant traits, such as the leaf water potential at turgor loss (Ψ_P0), can provide useful insights into a species’ capacity to grow in warm and dry urban environments. Therefore, the aim of this study was to evaluate Ψ_P0 of 45 tree species, the majority of which are rare in urban environments. To help evaluate the potential for using Ψ_P0 data to support future decision-making, a survey of professionals engaged with establishing trees in urban environments was also used to assess the relationship between the measured Ψ_P0 and the perceived drought tolerance of selected species. This study demonstrates that Ψ_P0 gives strong evidence for a species’ capacity to tolerate dry growing conditions and is a trait that varies substantially across species. Furthermore, Ψ_P0 was shown to closely relate to the experience of professionals involved in establishing trees in urban environments, thus providing evidence of its practical significance. Use of plant traits, such as Ψ_P0, should, therefore, give those specifying trees confidence to recommend non-traditional species for challenging urban environments.     

Mixtures of modified t-factor analyzers for model-based clustering,classification, and discriminant analysis

A novel family of mixture models is introduced based on modified t-factor analyzers. Modified factor analyzers were recently introduced within the Gaussian context and our work presents a more flexible and robust alternative. We introduce a family of mixtures of modified t-factor analyzers that uses this generalized version of the factor analysis covariance structure. We apply this family within three paradigms: model-based clustering; model-based classification; and model-based discriminant analysis. In addition, we apply the recently published Gaussian analogue to this family under the model-based classification and discriminant analysis paradigms for the first time. Parameter estimation is carried out within the alternating expectation-conditional maximization framework and the Bayesian information criterion is used for model selection. Two real data sets are used to compare our approach to other popular model-based approaches; in these comparisons, the chosen mixtures of modified t-factor analyzers model performs favourably. We conclude with a summary and suggestions for future work.     

You Wouldn't Like Me When I'm Angry: List Experiment Misreporting*

Objective. List experiment respondents may misreport the number of list items that they associate with in order to associate themselves with a socially desirable test item or to disassociate themselves from a socially undesirable test item. Tests for such misreporting were conducted. Methods. List experiments from the 1991 National Race and Politics Survey, the 2006 Cooperative Congressional Election Study, and the 2008 Cooperative Campaign Analysis Project were analyzed or reviewed. Results. Evidence suggested that some respondents deflated their report more than necessary to avoid association with a socially undesirable test item. Conclusions. List experiments may provide inaccurate estimates of the percentage of the population to which the test item applies, but the direction of bias is predictable.     

Doing Good,Public Good,and Public Value

In this article, using multiple illustrative case examples, we demonstrate that philanthropic institutions are in the business of creating public value. In framing the work of philanthropy more broadly to include the process of public value creation, philanthropic institutions and leaders are challenged to be more strategic not only in their mission‐fulfillment grant‐making with nonprofit organizations but also in the way they stimulate and encourage collaboration, create the “third space” necessary to incubate ideas to transform society, and leverage resources to increase the return on their investments toward system‐wide change. The implications for philanthropic actors and institutions suggest that the strategic contributions they make toward creation of public value are those that go beyond transactional performance measures, such as number of dollars spent or clients receiving services, to include ways that their investments are amplified by meaningful partnerships with nonprofit and other organizations, changed behaviors of institutions and individuals, and transformative public policies.     

Compensation or Reinforcement? The Stratification of Parental Responses to Children’s Early Ability

Theory and empirical evidence suggest that parents allocate their investments unequally among their children, thus inducing within-family inequality. We investigate whether parents reinforce or compensate for initial ability differences between their children as well as whether these parental responses vary by family socioeconomic status (SES). Using the Early Childhood Longitudinal Study, Birth Cohort (ECLS-B) and a twin fixed-effects approach to address unobserved heterogeneity, we find that parental responses to early ability differences between their children do vary by family SES. Contrary to prior findings, we find that advantaged parents provide more cognitive stimulation to higher-ability children, and lower-class parents do not respond to ability differences. No analogous stratification in parental responses to birth weight is found, suggesting that parents’ responses vary across domains of child endowments. The reinforcing responses to early ability by high-SES parents do not, however, led to increases in ability differences among children because parental responses have little effect on children’s later cognitive performance in this twin sample.     

Dirichlet models for square contingency tables

We consider the square contingency tables which arise when the same method of classification is applied twice. The hypothesis of marginal homogeneity is then relevant! and can be tested by various methods Models are discussed which contain marginal homogeneity as a special case. They include a class based on univariate and bivariate Dirichlet distributions. The question of ordered categories is briefly discussed. Applications are made to data on unaided distance vision.     

The Missing Link: Deficits of Country-Level Studies. A Review of 22 Articles Explaining Life Satisfaction

To explain country differences in an analytical or structural dependent variable, the application of a macro–micro-model containing contextual hypotheses is necessary. Our methodological study examines whether empirical studies apply such a model. We propose that a theoretical base for country differences is well described in multilevel studies, but aggregate and individual data analyses fail to specify contextual hypotheses ex ante (in the theory section of an article) and instead elaborate on macro–micro explanations ex post (in the discussion section). To test our assumptions, we analyzed 22 studies published in journals cited in the Social Science Citation Index between 2007 and 2010, which compare countries with respect to life satisfaction. Results are in accordance with our expectations. We conclude that cross-country comparisons should apply a macro–micro-model theoretically and empirically, if possible, and include the meso level, if appropriate. In case of insufficient data (i. e. only individual level or aggregate level data), applying a macro–micro-model theoretically may prevent premature conclusions.     

A new prototype system for optimized job shop scheduling

Optimal scheduling of shopfloor activities in an environment of discrete part manufacturing is discussed. The scheduling problem is a well known NP complete one. The main part, the sequencing problem, has been tackled using two techniques: virtual resources identification and taboo search heuristics. The first approach allowed the authors to reduce the complexity of the sequencing from a job shop to a general flow shop problem. On the other hand, the search for an optimal solution, with respect to a fixed strategy, has been achieved via the taboo search. A synthesis of the results of a large number of tests is presented as well as the results of an application to a real case. The latter is shown in comparison with the output of the system being presently used in the examined factory.     

Decentralized WIP-oriented manufacturing control (DEWIP)

DEWIP is a manufacturing control system for job shop environments aiming at achieving short and reliable lead times by establishing WIP control loops between the manufacturing work centres. The paper describes the mode of function, the setting of parameters and simulation results of the new manufacturing control system. The setting of parameters is done with the aid of the funnel model and the theory of logistic operating curves, both developed at the Institute of Production Systems at the University of Hanover. The simulation is conducted using industrial data and makes it possible to assess DEWIP with regard to lead times, WIP level, performance and schedule reliability. DEWIP is compared both with an uncontrolled process and with the manufacturing control systems Load oriented order release (LOOR), Conwip and Polca. The results suggest that DEWIP and the models employed for the setting of parameters are suitable for job shop production and therefore offer a valuable alternative to prevailing centralized manufacturing control systems.