Potential drought stress in a Swiss mountain catchment-Ensemble forecasting of high mountain soil moisture reveals a drastic decrease, despite major uncertainties

Climate change is expected to profoundly influence the hydrosphere of mountain ecosystems. The focus of current process-based research is centered on the reaction of glaciers and runoff to climate change; spatially explicit impacts on soil moisture remain widely neglected. We spatio-temporally analyzed the impact of the climate on soil moisture in a mesoscale high mountain catchment to facilitate the development of mitigation and adaptation strategies at the level of vegetation patterns. Two regional climate models were downscaled using three different approaches (statistical downscaling, delta change, and direct use) to drive a hydrological model (WaSiM-ETH) for reference and scenario period (1960–1990 and 2070–2100), resulting in an ensemble forecast of six members. For all ensembles members we found large changes in temperature, resulting in decreasing snow and ice storage and earlier runoff, but only small changes in evapotranspiration. The occurrence of downscaled dry spells was found to fluctuate greatly, causing soil moisture depletion and drought stress potential to show high variability in both space and time. In general, the choice of the downscaling approach had a stronger influence on the results than the applied regional climate model. All of the results indicate that summer soil moisture decreases, which leads to more frequent declines below a critical soil moisture level and an advanced evapotranspiration deficit. Forests up to an elevation of 1800 m a.s.l. are likely to be threatened the most, while alpine areas and most pastures remain nearly unaffected. Nevertheless, the ensemble variability was found to be extremely high and should be interpreted as a bandwidth of possible future drought stress situations.

Feelings of revenge, retaliation motive, and posttraumatic stress reactions in crime victims

Individuals with posttraumatic stress disorder (PTSD) are often said to experience strong feelings of revenge. However, there is a need for confirmatory empirical studies. Therefore, in a study of 174 victims of violent crimes, the relation between feelings of revenge and posttraumatic stress reactions was investigated. Feelings of revenge were correlated with intrusion and hyperarousal but not with avoidance. Feelings of revenge explained incremental variance of intrusion and hyperarousalwhen the variance explained by victimological variables was controlled. The retaliation motive implied in feelings of revenge did not account for the relation between feelings of revenge and posttraumatic stress reactions. However, the relation was moderated by the time since victimization. Therefore, feelings of revenge must presumably be regarded as a maladaptive coping reaction to experienced injustice, but not in the first period after victimization.

Anger and posttraumatic stress disorder symptoms in crime victims: A longitudinal analysis

Among trauma-exposed individuals, severity of posttraumatic stress disorder (PTSD) symptoms is strongly correlated with anger. The authors used 2 longitudinal data sets with 282 and 218 crime victims, respectively, to investigate the temporal sequence of anger and PTSD symptoms following the assault. Cross-lagged regression analyses indicated that PTSD symptoms predicted subsequent level of anger, but that anger did not predict subsequent PTSD symptoms. Testing alternative models (common factor model, unmeasured 3rd variable model) that might account for spuriousness of the relation strengthened confidence in the results of the cross-lagged analyses. Further analyses suggested that rumination mediates the effect of PTSD symptoms on anger.

Dynamic SLA management with forecasting using multi-objective optimization

Cost-efficient operation while satisfying performance and availability guarantees in Service Level Agreements (SLAs) is a challenge for Cloud Computing, as these are potentially conflicting objectives. We present a framework for SLA management based on multi-objective optimization. The framework features a forecasting model for determining the best virtual machine-to-host allocation given the need to minimize SLA violations, energy consumption and resource wasting. A comprehensive SLA management solution is proposed that uses event processing for monitoring and enables dynamic provisioning of virtual machines onto the physical infrastructure. We validated our implementation against serveral standard heuristics and were able to show that our approach is significantly better.