The Body Image Virtual Reality Assessment (BIVRA): Measuring the body representation through virtual reality

Our physical and psychological well-being is significantly influenced by how we perceive our body, in addition to our thoughts and emotions associated with it. Dysfunctional body perceptions and attitudes play a key role in the development and maintenance of severe conditions such as eating disorders in both males and females. Given its relevance, some attempts have been made to improve body image assessment methods in terms of perceptual accuracy and body satisfaction taking advantage of technological advances such as virtual reality. However, existing applications have mainly focused on women and clinical conditions. In this study, we presented the Body Image Virtual Reality Assessment (BIVRA), a virtual reality figure rating scale to assess body image in both male and female subjects. We tested BIVRA's ability to measure perceptual accuracy and compared its results with a standardized body satisfaction questionnaire. Additionally, we investigated gender differences. BIVRA was found to be effective in assessing body image. We observed that a perceptually based task successfully captured both low and high levels of body representations, shedding light on the significant gender differences. The association between BIVRA and the body satisfaction questionnaires was moderated by gender, with a stronger association for women. While further validation of BIVRA is needed to fully exploit its potential, our results suggest that the integration of virtual reality into the assessment of body image and related disorders may significantly enhance our understanding of individuals struggling with body image issues and has the potential to advance current methods and techniques.

Managing academic performance by optimal resource allocation

In this paper, we develop and study a complex data-driven framework for human resource management enabling (i) academic talent recognition, (ii) researcher performance measurement, and (iii) renewable resource allocation maximizing the total output of a research unit. Suggested resource allocation guarantees the optimal output under strong economic assumptions: the agents are rational, collaborative and have no incentives to behave selfishly. In reality, however, agents often play strategically maximizing their own utilities, e.g., maximizing the resources assigned to them. This strategic behavior is typically mitigated by implementation of performance-driven or uniform resource allocation schemes. Next to the framework presentation, we address the cost of such mitigation.