Dynamic temporary blood facility location-allocation during and post-disaster periods

The key objective of this study is to develop a tool (hybridization or integration of different techniques) for locating the temporary blood banks during and post-disaster conditions that could serve the hospitals with minimum response time. We have used temporary blood centers, which must be located in such a way that it is able to serve the demand of hospitals in nearby region within a shorter duration. We are locating the temporary blood centres for which we are minimizing the maximum distance with hospitals. We have used Tabu search heuristic method to calculate the optimal number of temporary blood centres considering cost components. In addition, we employ Bayesian belief network to prioritize the factors for locating the temporary blood facilities. Workability of our model and methodology is illustrated using a case study including blood centres and hospitals surrounding Jamshedpur city. Our results shows that at-least 6 temporary blood facilities are required to satisfy the demand of blood during and post-disaster periods in Jamshedpur. The results also show that that past disaster conditions, response time and convenience for access are the most important factors for locating the temporary blood facilities during and post-disaster periods

Consequences of unexplainable machine learning for the notions of a trusted doctor and patient autonomy

This paper provides an analysis of the way in which two foundational principles of medical ethics-the trusted doctor and patient autonomy-can be undermined by the use of machine learning (ML) algorithms and addresses its legal significance. This paper can be a guide to both health care providers and other stakeholders about how anticipate and in some cases mitigate ethical conflicts caused by the use of ML in healthcare. It can also be read as a road map as to what needs to be done to achieve an acceptable level of explainability in an ML algorithm when it is used in a healthcare context

Cognitive load, fatigue and aversive simulator symptoms but not manipulated zeitgebers affect duration perception in virtual reality

The perceived duration of an interval depends on numerous aspects of the passed event both endogenous, including physiological arousal, level of wakefulness, attention, and surprise, as well as exogenous such as valence, salience, or context in the environment. There is some evidence that "time-giving" cues from the environment (zeitgebers) are coupled with time perception. The movement of the sun on the horizon was demonstrated to affect interval perception in a study conducted by Schatzschneider et al. (2016) claiming that the sun’s motion is a zeitgeber that influences time perception. In the present study, we undertake the first to our knowledge replication of this effect, extending the analysis to confounding aspects of the used paradigm. We aimed to test the effect of immersion, cognitive load, and changes in the speed of the sun on the horizon of the virtual environment on the perceived interval duration. We did not replicate the original effect, as reported by Schatzschneider et al., however, we did find that the perceived duration of an interval was affected by cognitive load, fatigue, and unpleasant symptoms caused by VR. In our analysis, we used Bayesian statistics to support our conclusion and offer its results as having some important consequences for the field

Traffic matrix estimation method and apparatus

A method and apparatus for the estimation of traffic matrices in a network are disclosed. Mechanisms are disclosed for measuring traffic volume from a plurality of ingress points to a plurality of egress points in a large scanl network, such as an IP backbone network. The traffic matrix is advantageously inferred from widely available link load measurements such as SNMP data