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Using narratives from leading international academics and commentators, the authors chart four, possible, “universities of the future” models and discuss how current university management issues can enable or hinder them.

Deploying a Gioia methodology analysis of “University of the Future” narratives, the authors derive 12 categories of institutional properties and, ultimately, four distinct models.

The authors identify how current, classic and polytechnic institutions can adapt their operations and service delivery in order to transition into future-ready business models.

The authors interpret the opinions and predictions from world-leading experts in the higher education field in order to present the first, to our knowledge, typology of aspirational university models.

Acquisitions activity in the United States is more intense today than at any time since the 1960s. Among the trends in today's acquisitions market: larger acquisitions, many over $200 million; the changing mix of companies involved; and a switch to cash payment and higher premiums. Cutting through the acquisitions maze requires sound strategic planning.

This chapter examines birth outcomes of patients enrolled in Familias Sanas (Healthy Families), an educational intervention designed to reduce health disadvantages of low-income, immigrant Latina mothers by providing social support during and after pregnancy.

Using a randomized control-group design, the project recruited 440 pregnant Latina women, 88% of whom were first generation. Birth outcomes were collected through medical charts and analyzed using regression analysis to evaluate if there were any differences between patients enrolled in Familias Sanas compared to those patients who followed a typical prenatal course.

Control and intervention groups were found to be similar with regard to demographic characteristics. In addition, we did not observe a decrease in rate of a number of common pregnancy-related complications. Likewise, rates of operative delivery were similar between the two groups as were fetal weight at delivery and use of regional anesthesia at delivery.

The lack of improvements in birth outcomes for this study was perhaps because this social support intervention was not significant enough to override long-standing stressors such as socioeconomic status, poor nutrition, genetics, and other environmental stressors.

This study was set in an inner-city, urban hospital with a large percentage of patients being of Hispanic descent. The study itself is a randomized controlled clinical trial, and data were collected directly from electronic medical records by physicians.

The integration of additive manufacturing (AM) processes into a production environment requires a cost‐model that allows the precise estimation of the total cost per part, although the part might be produced in the same build job together with other parts of different sizes, complexities and quantities. Several cost‐models have been proposed in the past, but most of them are not able to calculate the costs for each single part in a mixed build job or are not suitable for Selective Laser Melting (SLM). The purpose of this paper is to develop a cost model, including all pre‐ and post‐processing steps linked to SLM.

Based on collected data and the generic cost model of Alexander et al., an adapted model was developed for the SLM process including all required pre‐ and post‐processes. Each process was analysed and modelled in detail, allowing an evaluation of the influences of the different geometries on the cost of each part.

By simultaneously building up multiple parts, the manufacturing as well as the set‐up time and therefore the total cost per part can be significantly reduced. In the presented case study a cost reduction of 41 per cent can be achieved in average.

Using different cost allocation algorithms, the developed cost model enables a precise determination of total cost per part avoiding that any geometry is preferred in simultaneous manufacture. This helps to optimize build jobs and to manufacture SLM parts more economically by pooling parts from different projects, whereas the cost per part can still be precisely determined.

Introduction. This study examined the association between self-rated physical and oral health, cigarette smoking, and history of criminal justice contact (i.e., never arrested; arrested, but never incarcerated; or incarcerated in reform school, detention, jail, or prison) among African American men and women. Methods. We conducted descriptive statistical, linear regression, and multinomial regression analyses of the African American subsample (n = 3,570) from the National Survey of American Life (2001–2003). Results. Overall, African American women reported lower arrest rates and histories of incarceration than African American men. Additionally, we found that criminal justice contact was associated with lower self-rated physical health and oral health and higher levels of smoking for both men and women. African American women who had been arrested and detained in facilities other than jail had more chronic health problems than their male counterparts. Furthermore, having been arrested or spent time in a reform school, detention center, jail, or prison significantly increased the odds of African American men being a current smoker. Lastly, among African American women, those who had any level of criminal justice contact were likely to be current smokers and former smokers compared to those without a history of criminal justice contact. Conclusion. Addressing the health of African Americans with criminal justice contact is a critical step in reducing health disparities and improving the overall health and well-being of African American men and women. Furthermore, attention to differences by gender and specific types of criminal justice contact are important for a more precise understanding of these relationships.

The purpose of this paper is to present research work based on the authors’ conceptual framework reported in the VRAP Conference 2013. It is related with an efficient method to obtain an optimal part build orientation for additive manufacturing (AM) by using AM features with associated AM production knowledge and multi-attribute decision-making (MADM). The paper also emphasizes the importance of AM feature and the implied AM knowledge in AM process planning.

To solve the orientation problem in AM, two sub-tasks, the generation of a set of alternative orientations and the identification of an optimal one within the generated list, should be accomplished. In this paper, AM feature is defined and associated with AM production knowledge to be used for generating a set of alternative orientations. Key attributes for the decision-making of the orientation problem are then identified and used to represent those generated orientations. Finally, an integrated MADM model is adopted to find out the optimal orientation among the generated alternative orientations.

The proposed method to find out an optimal part build orientation for those parts with simple or medium complex geometric shapes is reasonable and efficient. It also has the potential to deal with more complex parts with cellular or porous structures in a short time by using high-performance computers.

The proposed method is a proof-of-concept. There is a need to investigate AM feature types and the association with related AM production knowledge further so as to suite the context of orientating parts with more complex geometric features. There are also research opportunities for developing more advanced algorithms to recognize AM features and generate alternative orientations and refine alternative orientations.

AM feature is defined and introduced to the orientation problem in AM for generating the alternative orientations. It is also used as one of the key attributes for decision-making so as to help express production requirements on specific geometric features of a desired part.