appears in a smaller region inside of the tumor. Simulations show that if the aggressive strain focuses its efforts on proliferating and does not contribute to angiogenesis signaling when in a hypoxic state, a hypertumor will form. More importantly, this resultant aggressive tumor is paradoxically prone to extinction and hypothesize is the cause of necrosis in many vascularized tumors.
In this study, WRF-Chem is utilized at high resolution (1.333 km grid spacing for the innermost domain) to investigate impacts of southern California anthropogenic emissions (SoCal) on Phoenix ground-level ozone concentrations ([O3]) for a pair of recent exceedance episodes. First, WRF-Chem control simulations, based on the US Environmental Protection Agency (EPA) 2005 National Emissions Inventories (NEI05), are conducted to evaluate model performance. Compared with surface observations of hourly ozone, CO, NOX, and wind fields, the control simulations reproduce observed variability well. Simulated [O3] are comparable with the previous studies in this region. Next, the relative contribution of SoCal and Arizona local anthropogenic emissions (AZ) to ozone exceedances within the Phoenix metropolitan area is investigated via a trio of sensitivity simulations: (1) SoCal emissions are excluded, with all other emissions as in Control; (2) AZ emissions are excluded with all other emissions as in Control; and (3) SoCal and AZ emissions are excluded (i.e., all anthropogenic emissions are eliminated) to account only for Biogenic emissions and lateral boundary inflow (BILB). Based on the USEPA NEI05, results for the selected events indicate the impacts of AZ emissions are dominant on daily maximum 8 h average (DMA8) [O3] in Phoenix. SoCal contributions to DMA8 [O3] for the Phoenix metropolitan area range from a few ppbv to over 30 ppbv (10–30 % relative to Control experiments). [O3] from SoCal and AZ emissions exhibit the expected diurnal characteristics that are determined by physical and photochemical processes, while BILB contributions to DMA8 [O3] in Phoenix also play a key role.
This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.
Methods: This study was a randomized, wait-list, control trial with assessments at baseline and post-intervention (week 4). Participants were asked to meditate using Calm for 10 minutes per day. A p value ≤0.05 was considered statistically significant.
Results: The majority of participants (n=19) stated using Calm helped them cope with the stress of PA school. The intervention group participated in meditation for an average of 76 minutes/week. There were significant differences in all outcomes for the intervention group (all p ≤0.06). There was a significant interaction between group and time factors in emotional exhaustion (p=.016) and depersonalization (p=.025).
Conclusions: Calm is a feasible way to reduce burnout in PA students. Our findings provide information that can be applied to the design of future studies.