Identifying Mental Disorder from the Faces of Women with Borderline Personality Disorder

Although appearance-based cues can help to diagnose physical illness, visual manifestations of mental disorder may be more elusive. Here, we investigated whether individuals could distinguish women with a serious mental disorder (borderline personality disorder) from demographically- and IQ-matched non-psychiatric controls. Participants rated mentally ill targets as more likely to have a mental disorder from photos more accurately than chance, despite not believing that such judgments were possible. The configuration of facial cues played an important role in these judgments, as interfering with the spatial relationships between facial features reduced participants’ accuracy to chance guessing. Further investigation showed similar results when participants rated the targets for specific mental disorders (borderline personality disorder, major depressive disorder) and rated the mentally ill targets as more depressed, angry, anxious, disgusted, emotionally unstable, distressed, and less happy. Moreover, the depression ratings significantly correlated with the targets’ actual depressive symptoms. Thus, individuals may be able to infer aspects of mental disorder from minimal facial cues.     

100 key research questions for the post‐2015 development agenda

The Sustainable Development Goals (SDGs) herald a new phase for international development. This article presents the results of a consultative exercise to collaboratively identify 100 research questions of critical importance for the post‐2015 international development agenda. The final shortlist is grouped into nine thematic areas and was selected by 21 representatives of international and non‐governmental organisations and consultancies, and 14 academics with diverse disciplinary expertise from an initial pool of 704 questions submitted by 110 organisations based in 34 countries. The shortlist includes questions addressing long‐standing problems, new challenges and broader issues related to development policies, practices and institutions. Collectively, these questions are relevant for future development‐related research priorities of governmental and non‐governmental organisations worldwide and could act as focal points for transdisciplinary research collaborations.     

Doubling up when times are tough: A study of obligations to share a home in response to economic hardship

This study uses a factorial vignette design embedded in an Internet survey to investigate attitudes toward an adult child and parent living together in response to economic hardship. Over half of Americans said the desirability of intergenerational co-residence depends on particularistic aspects of the family, notably the quality of family relationships. Support for co-residence is greatest when the adult child is single rather than partnered. Support is weaker if the adult child is cohabiting rather than married to the partner, although groups with greater exposure to cohabitation make less of a distinction between cohabitation and marriage. Presence of a grandchild does not affect views about co-residence. There is more support for sharing a home when a mother needs a place to live than when the adult child does. Responses to open-ended questions show that individuals invoke both universalistic family obligations and particularistic qualities of family relationships to explain their attitudes.     

Bivariate Birnbaum-Saunders accelerated lifetime model: estimation and diagnostic analysis

In this paper, we discuss the bivariate Birnbaum-Saunders accelerated lifetime model, in which we have modeled the dependence structure of bivariate survival data through the use of frailty models. Specifically, we propose the bivariate model Birnbaum-Saunders with the following frailty distributions: gamma, positive stable and logarithmic series. We present a study of inference and diagnostic analysis for the proposed model, more concisely, are proposed a diagnostic analysis based in local influence and residual analysis to assess the fit model, as well as, to detect influential observations. In this regard, we derived the normal curvatures of local influence under different perturbation schemes and we performed some simulation studies for assessing the potential of residuals to detect misspecification in the systematic component, the presence in the stochastic component of the model and to detect outliers. Finally, we apply the methodology studied to real data set from recurrence in times of infections of 38 kidney patients using a portable dialysis machine, we analyzed these data considering independence within the pairs and using the bivariate Birnbaum-Saunders accelerated lifetime model, so that we could make a comparison and verify the importance of modeling dependence within the times of infection associated with the same patient.     

COST INCREASES DUE TO DEMAND UNCERTAINTY IN MRP LOT SIZING

This paper analyzes the cost increases due to demand uncertainty in single-level MRP lot sizing on a rolling horizon. It is shown that forecast errors have a tremendous effect on the cost effectiveness of lot-sizing techniques even when these forecast errors are small. Moreover, the cost differences between different techniques become rather insignificant in the presence of forecast errors. Since most industrial firms face demand uncertainty to some extent, our findings may have important managerial implications. Various simulation experiments give insight into both the nature and the magnitude of the cost increases for different heuristics. Analytical results are developed for the constant-demand case with random noise and forecasting by exponential smoothing. It is also shown how optimal buffers can be obtained by use of a simple model. Although the analysis in this paper is restricted to simplified cases, the results merit further consideration and study. This paper is one of the first to inject forecast errors into MRP lot-sizing research. As such it attempts to deal with one of the major objections against the practical relevance of previous research in this area.     

AN APPROXIMATION TO THE HYPERCUBE MODEL WITH PATROL-INITIATED ACTIVITIES: AN APPLICATION TO POLICE

This paper applies the patrol-initiated-activity hypercube queuing model to historical data from a police agency. The model allows servers to handle both calls assigned by a central dispatcher and activities initiated by the servers. By duplicating a fairly complex dispatch strategy, the model was found to predict both assigned and server-initiated work loads accurately for the overall system and individual servers. The model is apparently ineffective in predicting small unit-travel-time differences for this police agency.     

STOCHASTIC COST-VOLUME-PROFIT ANALYSIS WITH A LINEAR DEMAND FUNCTION

In this paper we incorporate a linear demand function to model the price-volume causal relationship into stochastic cost-volume-profit (CVP) analysis. We assume that the objective function is to maximize expected profit; other objective functions are also discussed and compared. A linear stochastic model follows from which probabilistic statements can be easily obtained if the random variables are assumed to be multivariate normal. The basic framework is shown to be a special case of project value maximization where project value is the cash flow of the project discounted for time and risk according to the capital asset pricing model. Moreover, an intertemporal extension that considers inventory is developed. In summary, a new approach to stochastic CVP analysis that incorporates the management decision process in an uncertain environment is developed.