Real-time three-dimensional wind simulation for windmill rig tests

This paper deals with the methods of three-dimensional fixed-point wind speed real-time simulation modelled in large band, in order to use them in test rigs for experimental investigation of the wind energy conversion systems. The medium- and long-term components of the non-stationary wind speed are considered as known, being issued from measured data or by adopting a generic model. The spectral characteristics of three-dimensional turbulence are described either by the Kaimal or the von Karman models. The turbulence intensity and the length scale that take part in these models are calculated by the site parameters, using current standards. The basic idea of the methods for large-band three-dimensional wind simulation is to use rational shaping filters that approximate non-integer orders shaping filters issued from the Kaimal and the von Karman models. All the synthesized rational shaping filters use one time constant, automatically adapted to the medium- and long-term components that pilot the other time constants of the shaping filters by a set of parameters practically constant. Some numerical results concerning time series that simulate the non-stationary wind speed with three-dimensional turbulence components based on Kaimal and von Karman models are presented.     

Experimental Study of Classical and Quantum Internal Friction in Solid 4He

We measured the dissipation resulting from internal friction in hcp solid ⁴He at temperatures between 0.8 K and 2.5 K. Solid ⁴He is contained inside an annular metal cell forming a part of a torsional oscillator. An oscillatory motion of the cell walls applies shear stress on the solid ⁴He. The resulting shear strain within the solid ⁴He generates dissipation because of the internal friction. The experimental sensitivity was high enough to detect dissipation caused by internal friction associated with elementary excitations of the solid. At temperatures below 1.6 K, internal friction is associated with diffusion of single point defects responsible for the climb of dislocations. At higher temperatures, the main mechanism of internal friction appears to be associated with phonon exchange between parts of the solid moving relative to each other under the applied shear stress. This particular dissipative mechanism was called “quantum phonon friction” [Popov in Phys. Rev. Lett. 83:1632–1635, 1999]. The physical mechanism associated with this type of friction involves an irreversible transfer of momentum from the phonons to the lattice via an Umklapp process. Our data are in a very good agreement with this model.

     

Pathways to controlled 3D deformation of graphene: Manipulating the motion of topological defects

Functional properties of 2D materials like graphene can be tailored by designing their 3D structure at the Angstrom to nanometer scale. While there are routes to tailoring 3D structure at larger scales, achieving controllable sub-micron 3D deformations has remained an elusive goal since the original discovery of graphene. In this contribution, we summarize the state-of-the-art in controllable 3D structures, and present our perspective on pathways to realizing atomic-scale control. We propose an approach based on strategic application of mechanical load to precisely relocate and position topological defects that give rise to curvature and corrugation to achieve a desired 3D structure. Realizing this approach requires establishing the detailed nature of defect migration and pathways in response to applied load. From a computational perspective, the key needed advances lie in the identification of defect migration mechanisms. These needed advances define new forward and inverse problems: when a fixed stress or strain field is applied, along which pathways will defects migrate?, and vice versa. We provide a formal statement of these forward and inverse problems, and review recent methods that may enable solving them. The forward problem is addressed by determining the potential energy surface of allowable topological configurations through Monte Carlo and Gaussian process models to determine defect migration paths through dynamic programming algorithms or Monte Carlo tree search. Two inverse models are suggested, one based on genetic algorithms and another on convolutional neural networks, to predict the applied loads that induce migration and position defects to achieve desired curvature and corrugation. The realization of controllable 3D structures enables a vast design space at multiple scales to enable new functionality in flexible electronics, soft robotics, biomimetics, optics, and other application areas.     

3D Face Recognition Using a Fusion of PCA and ICA Convolution Descriptors

Neural Processing Letters - This paper introduces a hybrid filter bank-based convolutional network to develop a 3D face recognition system in different orientations. The filter banks approach has...     

Predicting Acute Renal Failure after Cardiac Surgery: Validation and Re‐definition of a Risk‐Stratification Algorithm

Background:  Acute renal failure (ARF) after cardiac surgery is associated with significant morbidity and mortality, irrespective of the need for dialysis. Previous studies have attempted to identify predictors of ARF and develop risk stratification algorithms. This study aims to validate the algorithm in an independent cohort of patients that includes a significant proportion of female and black patients and compares two different definitions of renal outcome.
Methods:  A large single center cardiac surgery database was examined (n, 24,660; 1993–2000) which included 29.9% females and 3.7% black patients. Post‐operative ARF was defined as: a) ARF requiring dialysis, b) > 50% reduction in creatinine clearance relative to baseline or requiring dialysis. Clinical variables related to baseline renal function and cardiovascular disease were used in recursive partitioning analysis for both outcome definitions. Chi‐square goodness of fit analysis was performed to validate the algorithm.
Results:  The frequency of post‐operative ARF requiring dialysis ranged between 0.5 and 15.5% based on the risk categories with the area under the receiver operating characteristic (ROC) curve of 0.78. Using the more inclusive definition of ARF, the frequency was significantly higher ranging from 2.6 to 25%(P < 0.001) with an area under ROC curve of 0.65.
Conclusions:  The renal risk stratification algorithm is valid in predicting post‐operative ARF in an independent cohort of patients, well represented by differences in gender and race. Since the need for dialysis remains subjective, a more objective and inclusive definition of ARF may help in identifying a larger number of patients 'at‐risk'.     

Low-contrast lesion segmentation in advanced MRI experiments by time-domain Ricker-type wavelets and fuzzy 2-means

Automated suspicious region segmentation has become a crucial need for the experts dealing with numerous images containing contrast-based lesions in MRI. Not all solutions, however, are based on mathematical infrastructure or providing adequate flexibility. On the other hand, segmentation of low-contrast lesions is very challenging for researchers; therefore, advanced magnetic resonance imaging (MRI) experiments are not commonly used in researches. Given the need of repeatability and adaptability, we present an automated framework for intelligent segmentation of brain lesions by wavelet imaging and fuzzy 2-means. Besides the general use of the wavelets in image processing, which is edge detection; we employed the second-order Ricker-type wavelets as the core of our novel imaging framework for low-contrast lesion segmentation. We firstly introduced the mathematical basis of several Ricker wavelet functions, which are in symmetrical form satisfying finite-energy and admissibility conditions of mother wavelets. Afterwards, we investigated three types of Ricker wavelets to apply on our clinical dataset containing susceptibility-weighted (SW) and minimum intensity projection SW (mIP-SW) images with barely-visible lesions. Finally, we adjusted the system parameters of the wavelets for optimization and post-segmentation by fuzzy 2-means. According to the preliminary results of the clinical experiments we conducted, our framework provided 93.53% average dice score (DSC) for SWI by Ricker-3 and 92.56% for mIP-SWI by Ricker-2 wavelet, as the main performance criteria of segmentation. Despite the lack of SWI or mIP-SWI experiments in the public datasets, we tested our framework with BraTS 2012 training sets containing real images with visible lesions and achieved an average of 88.13% DSC with 11.66% standard deviation by re-optimized framework for whole lesion segmentation, which is one of the highest among other relevant researches. In detail, 87.52% DSC for LG datasets with 11.32% standard deviation; while 88.34% DSC for HG datasets with 11.77% standard deviation are calculated.