A Review of Element-Based Galerkin Methods for Numerical Weather Prediction: Finite Elements,Spectral Elements,and Discontinuous Galerkin

Numerical weather prediction (NWP) is in a period of transition. As resolutions increase, global models are moving towards fully nonhydrostatic dynamical cores, with the local and global models using the same governing equations; therefore we have reached a point where it will be necessary to use a single model for both applications. The new dynamical cores at the heart of these unified models are designed to scale efficiently on clusters with hundreds of thousands or even millions of CPU cores and GPUs. Operational and research NWP codes currently use a wide range of numerical methods: finite differences, spectral transform, finite volumes and, increasingly, finite/spectral elements and discontinuous Galerkin, which constitute element-based Galerkin (EBG) methods. Due to their important role in this transition, will EBGs be the dominant power behind NWP in the next 10 years, or will they just be one of many methods to choose from? One decade after the review of numerical methods for atmospheric modeling by Steppeler et al. (Meteorol Atmos Phys 82:287–301, 2003), this review discusses EBG methods as a viable numerical approach for the next-generation NWP models. One well-known weakness of EBG methods is the generation of unphysical oscillations in advection-dominated flows; special attention is hence devoted to dissipation-based stabilization methods. Since EBGs are geometrically flexible and allow both conforming and non-conforming meshes, as well as grid adaptivity, this review is concluded with a short overview of how mesh generation and dynamic mesh refinement are becoming as important for atmospheric modeling as they have been for engineering applications for many years.     

Matrix-free aerostructural optimization of aircraft wings

In structural optimization subject to failure constraints, computing the gradients of a large number of functions with respect to a large number of design variables may not be computationally practical. Often, the number of constraints in these optimization problems is reduced using constraint aggregation at the expense of a higher mass of the optimal structural design. This work presents results of structural and coupled aerodynamic and structural design optimization of aircraft wings using a novel matrix-free augmented Lagrangian optimizer. By using a matrix-free optimizer, the computation of the full constraint Jacobian at each iteration is replaced by the computation of a small number of Jacobian-vector products. The low cost of the Jacobian-vector products allows optimization problems with thousands of failure constraints to be solved directly, mitigating the effects of constraint aggregation. The results indicate that the matrix-free optimizer reduces the computational work of solving the optimization problem by an order of magnitude compared to a traditional sequential quadratic programming optimizer. Furthermore, the use of a matrix-free optimizer makes the solution of large multidisciplinary design problems, in which gradient information must be obtained through iterative methods, computationally tractable.     

A Strategy of Density-Based Partitioning for Scalability Problem in Network Self-management

A density-based partitioning strategy is proposed for large domain networks in order to deal with the scalability issue found in autonomic networks considering, as a scenario, the autonomic Quality of Service （QoS） management context. The approach adopted focus as on obtaining dense network partitions having more paths for a given vertices set in the domain. It is demonstrated that dense partitions improve autonomic processing scalability, for instance, reducing routing process complexity. The solution looks for a significant trade-off between partition autonomic algorithm execution time and path selection quality in large domains. Simulation scenarios for path selection execution time are presented and discussed. Authors argue that autonomic networks may benefit from the dense partition approach proposed by achieving scalable, efficient and near real-time support for autonomic management systems.     

Generative face alignment through 2.5D active appearance models

This work addresses the matching of a 3D deformable face model to 2D images through a 2.5D Active Appearance Models (AAM). We propose a 2.5D AAM that combines a 3D metric Point Distribution Model (PDM) and a 2D appearance model whose control points are defined by a full perspective projection of the PDM. The advantage is that, assuming a calibrated camera, 3D metric shapes can be retrieved from single view images. Two model fitting algorithms and their computational efficient approximations are proposed: the Simultaneous Forwards Additive (SFA) and the Normalization Forwards Additive (NFA), both based on the Lucas–Kanade framework. The SFA algorithm searches for shape and appearance parameters simultaneously whereas the NFA projects out the appearance from the error image and searches only for the shape parameters. SFA is therefore more accurate. Robust solutions for the SFA and NFA are also proposed in order to take into account the self-occlusion or partial occlusion of the face. Several performance evaluations for the SFA, NFA and theirs efficient approximations were performed. The experiments include evaluating the frequency of converge, the fitting performance in unseen data and the tracking performance in the FGNET Talking Face sequence. All results show that the 2.5D AAM can outperform both the 2D + 3D combined models and the 2D standard methods. The robust extensions to occlusion were tested on a synthetic sequence showing that the model can deal efficiently with large head rotation.     

Investigation on the styrene-butadiene rubber cleavage with periodic acid under the influence of ultrasonic radiation

Summary The cleavage of styrene-butadiene rubber (SBR) with periodic acid (H₅IO₆) and simultaneous injection of ultrasonic radiation has been performed. Two frequencies, 25 and 40 kHz, respectively, have been used. The results demonstrate clearly that 40 kHz ultrasound radiation accelerates significantly the cleavage reaction. The Pearl String Model theory had been used to elucidate this process.     

Impact of white-matter mask selection on DTI histogram-based metrics as potential biomarkers in cerebral small vessel disease

Magnetic Resonance Materials in Physics, Biology and Medicine - Histogram-based metrics extracted from diffusion-tensor imaging (DTI) have been suggested as potential biomarkers for cerebral small...     

Heavy metal removal from simulated wastewater using electrochemical technology: optimization of copper electrodeposition in a membraneless fluidized bed electrode

Heavy metal removal from industrial wastewaters has been intensively studied, since it is well known that they can cause severe problems to human health and aquatic life, even at very low concentrations. In this work, it was demonstrated that electrodeposition in fluidized bed electrode (FBE) can be efficiently employed to remove metal ions from solution, avoid contamination, and recover the metal. Copper electrodeposition from diluted solutions was efficiently performed using a membraneless FBE. The average current efficiency (ACE), average energy consumption (AEC), and space–time yield (AY) was optimized taking into account the operational and process variables. It was noted that for all response variables studied, the raise of supporting electrolyte concentration (C _s) contributed to improvements in the process. The operational conditions current (I) and bed expansion (E) determined the values of CE, Y, and EC under activated control, but the initial copper concentration (C ₀) determined how long the electrodeposition process will work under activated or mass transfer control, thus affecting the average values of CE, Y, and EC. Considering C ₀ of 500 mg L^?1, copper can be optimally recovered with ACE >60 %, AY >38 kg h^?1 m^?3, and AEC <4.0 kWh kg^?1 by applying the lowest I and the highest levels of E and C _s. It was concluded that the electrochemical technology using a membraneless FBE reactor is economically competitive and be applied for the treatment of wastewaters contaminated with copper or other metals.     

Engineered Multifunctional Albumin‐Decorated Porous Silicon Nanoparticles for FcRn Translocation of Insulin

The last decade has seen remarkable advances in the development of drug delivery systems as alternative to parenteral injection‐based delivery of insulin. Neonatal Fc receptor (FcRn)‐mediated transcytosis has been recently proposed as a strategy to increase the transport of drugs across the intestinal epithelium. FcRn‐targeted nanoparticles (NPs) could hijack the FcRn transcytotic pathway and cross the epithelial cell layer. In this study, a novel nanoparticulate system for insulin delivery based on porous silicon NPs is proposed. After surface conjugation with albumin and loading with insulin, the NPs are encapsulated into a pH‐responsive polymeric particle by nanoprecipitation. The developed NP formulation shows controlled size and homogeneous size distribution. Transmission electron microscopy (TEM) images show successful encapsulation of the NPs into pH‐sensitive polymeric particles. No insulin release is detected at acidic conditions, but a controlled release profile is observed at intestinal pH. Toxicity studies show high compatibility of the NPs with intestinal cells. In vitro insulin permeation across the intestinal epithelium shows approximately fivefold increase when insulin is loaded into FcRn‐targeted NPs. Overall, these FcRn‐targeted NPs offer a toolbox in the development of targeted therapies for oral delivery of insulin.     

Addressing a state''s physician workforce priorities through the funding of graduate medical education: the TennCare model

OBJECTIVE: To investigate differences in second-, third-, and fourth-year medical students' knowledge of bloodborne pathogen exposure risks, as well as their attitudes toward, and intentions to comply with, Universal Precautions (UP). DESIGN: Cross-sectional survey. PARTICIPANTS AND SETTING: Surveys about students' knowledge, attitudes, and intentions to comply with UP were completed by 111 second-year (preclinical), 80 third-year, and 60 fourth-year medical students at Washington University School of Medicine in the spring of 1996. RESULTS: Preclinical students knew more than clinical students about the efficacy of hepatitis B vaccine, use of antiretroviral therapy after occupational exposure to human immunodeficiency virus, and nonvaccinated healthcare workers' risk of infection from needlestick injuries (P<.001). Students' perceived risk of occupational exposure to bloodborne pathogens and attitudes toward hepatitis B vaccine did not differ, but preclinical students agreed more strongly that they should double glove for all invasive procedures with sharps (P<.001). Clinical students agreed more strongly with reporting only high-risk needlestick injuries (P=.057) and with rationalizations against using UP (P=.008). Preclinical students more frequently reported contemplating or preparing to comply with double gloving, wearing protective eyewear, reporting all exposures, and safely disposing of sharps, whereas students with clinical experience were more likely to report compliance. Clinical students also were more likely to report having "no plans" to practice the first three of these precautions (P<.001). CONCLUSIONS: Differences in knowledge, attitudes, and intentions to comply with UP between students with and without clinical experience may have important implications for the timing and content of interventions designed to improve compliance with UP.