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Colleges and universities increasingly have the mandate and motivation to integrate sustainability into their curricula. The purpose of this paper is to share the strategy used at the University of Pennsylvania (Penn) and provide an evaluation of its success and guidance to others creating similar programs.

This article summarizes Penn’s Integrating Sustainability Across the Curriculum (ISAC) program. ISAC pairs Penn undergraduate research assistants with instructors in a collaborative effort to incorporate sustainability into courses.

In concert with other Penn initiatives (a course inventory, faculty discussion groups and a research network), ISAC increases Penn’s sustainability-related courses and creates dialogue regarding how various disciplines contribute to sustainability.

The program described in this article is replicable at other institutions. The authors demonstrate that the logistics of recruiting students and establishing the program are straightforward. Undergraduate students are on campus; their pay requirements are modest; and they are desirous of such research experiences.

The ISAC program inculcates a cultural and behavioral shift as students and faculty approach sustainability issues collaboratively, and it facilitates the development of a shared language of environmental sustainability. Such social implications are difficult to quantify, but are nonetheless valuable outcomes.

The faculty–student partnership used to facilitate the integration of sustainability into courses at Penn is original. The ISAC program provides a framework for engaging students and faculty in curriculum development around sustainability in a manner that benefits the student research assistants, the participating faculty and future students.

From December, 1969 to March, 1970 a sample survey was conducted throughout Chile to determine individual utilization and expenditures on health services. As a by‐product of this survey, information was obtained on individual schooling and income by age, occupation, and region of residence. This paper analyzes the latter information. The sample consists of 3,839 Chilean males who were heads of households and who were employed during 1969.

A program of shipment consolidation (SCL) is the purposeful intervention by management to regularly combine several small shipments so that a larger load may be dispatched on the same vehicle. SCL decisions traditionally have been based upon minimization of total logistics costs (inventory plus transportation). The paper aims to answer the following research question: given the environmental implications of vehicle emissions as a function of load weight, are the familiar SCL policies still optimal?

Nonlinear expressions relating carbon dioxide (CO₂) emissions to vehicle weight, and parameterized by trip length and average travel speed, were derived from published experimental data. Those expressions were included in a simulation model that assessed the environmental impact, in addition to the logistics cost, of the policies concerning when to release a consolidated load.

For short holding times, the quantity policy performs best in terms of both logistics cost and pollution reduction. In the case of low‐order arrival rates and long holding times, the time policy is best at reducing emissions and logistics costs. However, the best dispatch policy conflicts in terms of pollution reduction and logistics cost minimization for the following cases: moderate holding times and long holding times combined with high‐order arrival rates. In these cases, it is necessary to consider the speed of travel, trip length and unit cost of emissions when evaluating the policies.

A carbon trading market appears to be on the horizon in several industries, which will establish a price per unit weight for CO₂ emissions and make it beneficial to minimize the total cost (including emissions) of the network. This research only considers CO₂ pollution, but future investigations could also consider other pollutants such as nitrogen oxides, carbon monoxide and volatile organic carbons.

SCL policies can include a “green logistics” component that is based on empirical data.

One undesirable consequence of transportation by truck is CO₂ emissions. However, the impact can be lessened, while still emphasizing total logistics cost per load, with our simulation‐based results for shipment release policies.

Vladimir Nabokov's 1954 novel Lolita is one of the most frequently mentioned works in discussions of censorship, probably because of its undeniable literary merit and the enthusiasm with which its detractors and defenders have condemned and praised it. It has been condemned as pornography for its sexual content and as depravity for its unusual and even shocking subject matter, and has been praised as one of the greatest novels of the twentieth century.

The paper aims to investigate the application of particle swarm optimisation and back propagation in weights optimisation and training of artificial neural networks within the mass appraisal industry and to compare the performance with standalone back propagation, genetic algorithm with back propagation and regression models.

The study utilised linear regression modelling before the semi-log and log-log models with a sample of 3,242 single-family dwellings. This was followed by the hybrid systems in the selection of optimal attribute weights and training of the artificial neural networks. Also, the standalone back propagation algorithm was used for the network training, and finally, the performance of each model was evaluated using accuracy test statistics.

The study found that combining particle swarm optimisation with back propagation in global and local search for attribute weights enhances the predictive accuracy of artificial neural networks. This also enhances transparency of the process, because it shows relative importance of attributes.

A robust assessment of the models’ predictive accuracy was inhibited by fewer accuracy test statistics found in the software. The research demonstrates the efficacy of combining two models in the assessment of property values.

This work demonstrated the practicability of combining particle swarm optimisation with back propagation algorithms in finding optimal weights and training of the artificial neural networks within the mass appraisal environment.

The paper introduced the use of a hybrid system of neural networks support vector machines (NNSVMs) consisting of artificial neural networks (ANNs) and support vector machines (SVMs) to price single-family properties.

The mechanism of the hybrid system is such that its output is given by the SVMs which utilise the results of the ANNs as their input. The results are compared to other property pricing modelling techniques including the standalone ANNs, SVMs, geographically weighted regression (GWR), spatial error model (SEM), spatial lag model (SLM) and the ordinary least squares (OLS). The techniques were applied to a dataset of 3,225 properties sold during the period, January 2012 to May 2014 in Cape Town, South Africa.

The results demonstrate that the hybrid system performed better than ANNs, SVMs and the OLS. However, in comparison to the spatial models (GWR, SEM and SLM) the hybrid system performed abysmally under with SEM favoured as the best pricing technique.

The findings extend the debate in the body of knowledge that the results of the OLS can significantly be improved through the use of spatial models that correct bias estimates and vary prices across the different property locations. Additionally, utilising the result of the hybrid system is thus affected by the black-box nature of the ANNs and SVMs limiting its use to purposes of checks on estimates predicted by the regression-based models.

This paper sets a case study of missing children in the Republic of Ireland against a review of international research to explore broader understandings and responses to the problem.

The study begins by reviewing the literature on pioneering American initiatives dating back to the 1970s and more recent literature from Great Britain where a series of high-profile scandals involving sexual exploitation of teenage girls provoked a number of controversial inquiries into the police and social work professions. The present study was prompted by an evaluation of the 116 000 Missing Children Hotline which was introduced to Ireland in 2012 under the auspices of the European Union (EU) Daphne III Programme by the Irish Society for the Prevention of Cruelty to Children (ISPCC).

The central conclusion emerging from analysis of the evidence is that Missing Children Hotlines remain rooted in representations of ‘stranger danger’ and disconnected from repeat runaway children who feature prominently in police reports from formal care settings or family homes and who are actively targeted by sexual predators and criminal gangs. The implications are that systemic change requires grounding in research strategies which combine police data with anthropological studies to give legitimacy to the voices of runway and sexually exploited children.

The study offers original international perspectives on missing children to epistemological research communities in the fields of social work, criminology and policing with recommendations that Missing Children and Runaway Safe-lines are targeted systemically at keeping runaway children, homeless children and at-risk-youth safe and off the streets.

The authors examine whether or not applicants and recipients of federal disability insurance (DI) inflate their self-assessed health (SAH) problems relative to others. To do this, the authors employ a technique which uses anchoring vignettes. This approach allows them to examine how various cohorts of the population interpret survey questions associated with subjective self-assessments of health. The results of the analysis suggest that DI participants do inflate the severity of a given health problem, but by a small but significant degree. This tendency to exaggerate the severity of disability problems is much more apparent among those with more education (especially those with a college degree). In contrast, racial minorities tend to underestimate severity ratings for a given disability vignette when compared to their white peers.