Elementary Differential Equations By Ricardo Asin Pdf Download !!BETTER!!

In the Aar massif, main foliation and major deformation structures were developed during NW-SE compression associated with the Alpine orogeny (Steck 1968). To be precise, shearing at the brittle to ductile transition may have initiated at different stages between 22-20 Ma and 14-12 Ma, followed by purely brittle deformation at around 10 Ma (Rolland et al. 2009). In light of the onset of dynamic recrystallization in quartz, Bambauer et al. (2009) defined a quartz recrystallization isograd in the northern part of the Aar massif. To the south, the grain size of recrystallized grains increases due to an increase of metamorphic temperatures from N to S. The aim of the current project is to carry out quantitative analysis on changes of the dynamic and static recrystallization behavior of quartz. Across the Aar massif, two general types of microstructures have to be discriminated: (i) weakly to moderately deformed host rocks and (ii) intensely deformed mylonites to ultramylonites out of high strain shear zones. In (i), volume fraction and size of recrystallized quartz grains increase towards the S showing grain size changes from around 5 m up to ca. 200 m. Southern microstructures are characterized by complete recrystallization. In terms of recrystallization processes, a transition from bulging recrystallization in the N to subgrain rotation recrystallization in the S occurs. Such a change in dynamic recrystallization processes combined with a grain size increase points towards reduced differential stresses with increasing temperature. This temperature gradient is also corroborated by a switch in the active glide systems in quartz from basal to rhomb dominated glide. In contrast to the granitic host rocks, the mylonites and ultramylonites (ii) show smaller recrystallized grain sizes due to enhanced strain rates. However, they also reveal a general increase of recrystallized grain sizes from N to S. In the S, microstructures from (i) and (ii) show equidimensional

Accelerometry is increasingly used to quantify physical activity (PA) and related energy expenditure (EE). Linear regression models designed to derive PAEE from accelerometry-counts have shown their limits, mostly due to the lack of consideration of the nature of activities performed. Here we tested whether a model coupling an automatic activity/posture recognition (AAR) algorithm with an activity-specific count-based model, developed in 61 subjects in laboratory conditions, improved PAEE and total EE (TEE) predictions from a hip-worn triaxial-accelerometer (ActigraphGT3X+) in free-living conditions. Data from two independent subject groups of varying body mass index and age were considered: 20 subjects engaged in a 3-h urban-circuit, with activity-by-activity reference PAEE from combined heart-rate and accelerometry monitoring (Actiheart); and 56 subjects involved in a 14-day trial, with PAEE and TEE measured using the doubly-labeled water method. PAEE was estimated from accelerometry using the activity-specific model coupled to the AAR algorithm (AAR model), a simple linear model (SLM), and equations provided by the companion-software of used activity-devices (Freedson and Actiheart models). AAR-model predictions were in closer agreement with selected references than those from other count-based models, both for PAEE during the urban-circuit (RMSE = 6.19 vs 7.90 for SLM and 9.62 kJ/min for Freedson) and for EE over the 14-day trial, reaching Actiheart performances in the latter (PAEE: RMSE = 0.93 vs. 1.53 for SLM, 1.43 for Freedson, 0.91 MJ/day for Actiheart; TEE: RMSE = 1.05 vs. 1.57 for SLM, 1.70 for Freedson, 0.95 MJ/day for Actiheart). Overall, the AAR model resulted in a 43% increase of daily PAEE variance explained by accelerometry predictions. NEW & NOTEWORTHY Although triaxial accelerometry is widely used in free-living conditions to assess the impact of physical activity energy expenditure (PAEE) on health, its precision and accuracy are often debated