Studies have found that shamanic practices are associated with statistically significant reductions in mood disturbance relative to baseline. However, contrary results were obtained for non-shamanic practitioners exposed to shamanic-like techniques. These inconsistent results may be partially due to a personality trait referred to as schizotypy, which has been demonstrated to influence suscepti...

BACKGROUND Disturbance is a key influence on forest carbon dynamics, but the complexity of spatial and temporal patterns in forest disturbance makes it difficult to quantify their impacts on carbon flux over broad spatial domains. Here we used a time series of Landsat remote sensing images and a climate-driven carbon cycle process model to evaluate carbon fluxes at the ecoregion scale in wester...

We took advantage of regional differences in environmental forcing and consumer abundance to examine the relative importance of nutrient availability (bottom-up), grazing pressure (top-down), and storm waves (disturbance) in determining the standing biomass and net primary production (NPP) of the giant kelp Macrocystis pyrifera in central and southern California. Using a nine-year data set coll...

in recent years, emotion dysregulation and anxiety sensitivity have been introduced of the most effective factors in sleep disturbance. the purpose of this study was to examine emotion dysregulation and anxiety sensitivity as potential contributor to sleep problems in students. the statistical population of this research consisted of all the bu-ali sina university’s students in the academic yea...

Disturbances influence community structure and ecosystem functioning. Bacteria are key players in ecosystems and it is therefore crucial to understand the effect of disturbances on bacterial communities and how they respond to them, both compositionally and functionally. The main aim of this study was to test the effect of differences in disturbance strength on bacterial communities. For this, ...

This paper presents a supervisory generalized predictive control (GPC) by combining GPC with statistical process control (SPC) for the control of the thin film deposition process. In the supervised GPC, the deposition process is described as an ARMAX model for each production run and GPC is applied to the in situ thickness-sensing data for thickness control. Supervisory strategies, developed fr...