Experimental validation of some basic assumptions used in physically based soil

In spring 2009, four rill experiments were accomplished on a fallow land. Most external factors as well as discharge quantity (9 L min-1) were held constant or at least in the same range. Following most process based soil erosion models, detachment or runoff values should therefore be similar, but the experimental results show clear differences in sediment concentration, runoff and other measured and calculated values. This fact underlines the problems of process based models: concerning rill erosion, different processes take part and the process described by the models is only responsible for a part of the eroded material

Fluctuation dynamics of a single magnetic chain

"Tunable" fluids such as magnetorheological "MR" and electrorheological "ER" fluids are comprised of paramagnetic or dielectric particles suspended in a low-viscosity liquid. Upon the application of a magnetic or electric field, these fluids display a dramatic, reversible, and rapid increase of the viscosity. This change in viscosity can, in fact, be tuned by varying the applied field, hence the name "tunable fluids". This effect is due to longitudinal aggregation of the particles into chains in the direction of the applied field and the subsequent lateral aggregation into larger semisolid domains. A recent theoretical model by Halsey and Toor "HT" explains chain aggregation in dipolar fluids by a fluctuation-mediated long-range interaction between chains and predicts that this interaction will be equally efficient at all applied fields. This paper describes video-microscopy observations of long, isolated magnetic chains that test HT theory. The measurements show that, in contrast to the HT theory, chain aggregation occurs more efficiently at higher magnetic field strength (H0) and that this efficiency scales as H0½. Our experiments also yield the steady-state and time-dependent fluctuation spectra C(x,x')≡ [h(x)-h(x')]²>½ and C(x,x',t,t')≡ ½ for the instantaneous deviation h(x,t) from an axis parallel to the field direction to a point x on the chain. Results show that the steady-state fluctuation growth is similar to a biased random walk with respect to the interspacing ͉ |x-x'| along the chain, C(x,x')≈|x-x'| α, with a roughness exponent α =0.53±0.02. This result is partially confirmed by Monte Carlo simulations. Time-dependent results also show that chain relaxation is slowed down with respect to classical Brownian diffusion due to the magnetic chain connectivity, C(x,x',t,t')≈|t-t'|β, with a growth exponent β=0.35±0.05<½. All data can be collapsed onto a single curve according to C(x,x',t,t')≈|x-x'| α ψ (|t-t'| / |x-x'| z ), with a dynamic exponent z= α /β≅ 1.42

Energy Efficient Data-Intensive Computing With Mapreduce

Power and energy consumption are critical constraints in data center design and operation. In data centers, MapReduce data-intensive applications demand significant resources and energy. Recognizing the importance and urgency of optimizing energy usage of MapReduce applications, this work aims to provide instrumental tools to measure and evaluate MapReduce energy efficiency and techniques to conserve energy without impacting performance. Energy conservation for data-intensive computing requires enabling technology to provide detailed and systemic energy information and to identify in the underlying system hardware and software. To address this need, we present eTune, a fine-grained, scalable energy profiling framework for data-intensive computing on large-scale distributed systems. eTune leverages performance monitoring counters (PMCs) on modern computer components and statistically builds power-performance correlation models. Using learned models, eTune augments direct measurement with a software-based power estimator that runs on compute nodes and reports power at multiple levels including node, core, memory, and disks with high accuracy. Data-intensive computing differs from traditional high performance computing as most execution time is spent in moving data between storage devices, nodes, and components. Since data movements are potential performance and energy bottlenecks, we propose an analysis framework with methods and metrics for evaluating and characterizing costly built-in MapReduce data movements. The revealed data movement energy characteristics can be exploited in system design and resource allocation to improve data-intensive computing energy efficiency. Finally, we present an optimization technique that targets inefficient built-in MapReduce data movements to conserve energy without impacting performance. The optimization technique allocates the optimal number of compute nodes to applications and dynamically schedules processor frequency during its execution based on data movement characteristics. Experimental results show significant energy savings, though improvements depend on both workload characteristics and policies of resource and dynamic voltage and frequency scheduling. As data volume doubles every two years and more data centers are put into production, energy consumption is expected to grow further. We expect these studies provide direction and insight in building more energy efficient data-intensive systems and applications, and the tools and techniques are adopted by other researchers for their energy efficient studies

Intra- and inter-examiner Reliability of Direct Facial Soft Tissue Measurements Using Digital Calipers

Background: The objective of this study is to determine if facial soft tissue measurements using digital calipers can be reliably taken by the same examiner and by a large group of examiners. Materials and Methods: Ten examiners performed a set of 18 in-clinic measurements on 10 female and 10 male dental students using a digital caliper twice over a 3-week period. The intra-class correlation coefficient and the Shrout-Fleiss method were used for the statistical analysis. Results: Anthropometric intra-examiner reliability was high for all measurements (none fell below R = 0.934). However, inter-examiner reliability exhibited a wide range of values, some reliable (nasal width at widest nostrils [R = 0.922] and subnasale to upper lip [R = 0.926]), and others unreliable [base of nose (R = 0.590), mouth height (R = 0.585), and soft tissue B point to gnathion (R = 0.623)]. Conclusions: Soft tissue measurements of clearly identifiable points measured by the same examiner produced highly consistent, accurate and reliable measurements. Soft tissue points with poor definition resulted in average-to-poor reliabilities measurements

Greedy kernel methods for accelerating implicit integrators for parametric ODEs

We present a novel acceleration method for the solution of parametric ODEs by single-step implicit solvers by means of greedy kernel-based surrogate models. In an offline phase, a set of trajectories is precomputed with a high-accuracy ODE solver for a selected set of parameter samples, and used to train a kernel model which predicts the next point in the trajectory as a function of the last one. This model is cheap to evaluate, and it is used in an online phase for new parameter samples to provide a good initialization point for the nonlinear solver of the implicit integrator. The accuracy of the surrogate reflects into a reduction of the number of iterations until convergence of the solver, thus providing an overall speedup of the full simulation. Interestingly, in addition to providing an acceleration, the accuracy of the solution is maintained, since the ODE solver is still used to guarantee the required precision. Although the method can be applied to a large variety of solvers and different ODEs, we will present in details its use with the Implicit Euler method for the solution of the Burgers equation, which results to be a meaningful test case to demonstrate the method's features