α‐Alumina nanospheres from nano‐dispersed boehmite synthesized by a wet chemical route

In this work, stable aqueous suspensions of nano‐boehmite were developed through a hybrid wet‐chemical route that uses hyperbranched dendritic poly(ethylene)imine (PEI) as template material for boehmite formation aiming at the development of a deagglomerated α‐alumina nanopowder after calcination. The method involves firstly the interaction between the Al precursor and PEI followed by the hydrolysis and polycondensation reactions. The study was aiming to investigate the effect of solids content and pH during reactions on both the stability of the final suspension and the morphology of the resulting nanocrystals. For this purpose, the suspensions were evaluated through viscosity measurements, zeta potential analyses, FT‐IR, DLS and sedimentation studies, whereas after the proper centrifugation, drying, and calcination steps, the as‐received nanocrystals were evaluated through SEM, TEM and XRD studies. In addition, the boehmite nanopowder was studied using Thermogravimetric and Differential Thermal Analysis, whereas its sinterability was evaluated by dilatometric measurements. The investigation showed that the conditions employed affect greatly both the morphology of nanocrystals as well as the dispersion and the stability of the suspensions. The boehmite suspension with the optimum dispersion and stability can lead, after calcination at 1050°C, to a fine deagglomerated α‐alumina nanopowder with a mean size at about 10 nm.     

Design space exploration of an optimized compiler approach for a generic reconfigurable array architecture

Several mesh-like coarse-grained reconfigurable architectures have been devised in the last few years accompanied with their corresponding mapping flows. One of the major bottlenecks in mapping algorithms on these architectures is the limited memory access bandwidth. Only a few mapping methodologies encountered the problem of the limited bandwidth while none has explored how the performance improvements are affected, from the architectural characteristics. We study in this paper the impact that the architectural parameters have on performance speedups achieved when the PEs’ local RAMs are used for storing the variables with data reuse opportunities. The data reuse values are transferred in the internal interconnection network instead of being fetched, from external memories, in order to reduce the data transfer burden on the bus network. A novel mapping algorithm is also proposed that uses a list scheduling technique. The experimental results quantified the trade-offs that exist between the performance improvements and the memory access latency, the interconnection network and the processing element’s local RAM size. For this reason, our mapping methodology targets on a flexible architecture template, which permits such an exploration. More specifically, the experiments showed that the improvements increase with the memory access latency, while a richer interconnection topology can improve the operation parallelism by a factor of 1.4 on average. Finally, for the considered set of benchmarks, the operation parallelism has been improved from 8.6% to 85.1% from the application of our methodology, and by having each PE’s Local RAM a size of 8 words.

A Statistical Metadata Model for Simultaneous Manipulation of both Data and Metadata

There is a growing demand for more cost-efficient production processes in Statistical Institutes. One way to address this need is to equip Statistical Information Systems (SIS) with the ability to automatically produce statistical data and metadata of high quality and deliver them to the user via the Internet. Current approaches, although provide for the storage of appropriate metadata, do not use process metadata for guiding the production process. In this paper we present an approach on creating SISs that permits metadata-guided statistical processing based on an object-based, statistical metadata model. The model is not domain specific and can accommodate both microdata and macrodata. We have equipped the model with a set of transformations that can be used to automatically manipulate data and metadata. We show the applicability of transformations with some examples using actual statistical data for R&D expenditures. Finally, we demonstrate how the presented framework can be exploited for the construction of a web site that offers ad hoc query capabilities to the users of statistical data.     

Penetration and distribution of α-tocopherol, α- or γ-tocotrienols applied individually onto murine skin

To evaluate skin penetration of various vitamin E homologs, a 5% solution of either α-tocopherol, α-tocotrienol, or γ-tocotrienol in polyethylene glycol was topically applied to SKH-1 hairless mice. After 0.5, 1, 2, or 4 h (n=four per time point and four per vitamin E homolog), the skin was washed, the animals killed, the skin rapidlly removed, frozen on dry ice, and a biopsy taken and sectioned: stratum corneum (two uppermost, 5-μm sections—SC1 and SC2), epidermis (next two 10μm sections—E1 and E2), papillary dermis (next 100μ, PD), dermis (next 400 μm, D), and subcutaneous fat (next 100 μm, SF). SC1 contained the highest vitamin E concentrations per μ thickness. To compare the distribution of the various vitamin E forms into the skin layers, the percentage of each form was expressed per its respective total. Most surprising was that the largest fraction of skin vitamin E following topical application was found in the deeper subcutaneous layers—the lowest layers, PD (40±15%) and D (36±15%), contained the major portion of the applied vitamin E forms. Although PD only represents about 16% of the total skin thickness, it contains sebaceous glands—lipid secretory organs, and, thus, may account for the vitamin E affinity for this layer. Hence, applied vitamin E penetrates rapidly through the skin, but the highest concentrations are found in the uppermost 5 microns.     

Pulsatile cerebrospinal fluid dynamics in the human brain

Disturbances of the cerebrospinal fluid (CSF) flow in the brain can lead to hydrocephalus, a condition affecting thousands of people annually in the US. Considerable controversy exists about fluid and pressure dynamics, and about how the brain responds to changes in flow patterns and compression in hydrocephalus. This paper presents a new model based on the first principles of fluid mechanics. This model of fluid-structure interactions predicts flows and pressures throughout the brain's ventricular pathways consistent with both animal intracranial pressure (ICP) measurements and human CINE phase-contrast magnetic resonance imaging data. The computations provide approximations of the tissue deformations of the brain parenchyma. The model also quantifies the pulsatile CSF motion including flow reversal in the aqueduct as well as the changes in ICPs due to brain tissue compression. It does not require the existence of large transmural pressure differences as the force for ventricular expansion. Finally, the new model gives an explanation of communicating hydrocephalus and the phenomenon of asymmetric hydrocephalus.     

Greedy learning of multiple objects in images using robust statistics and factorial learning

We consider data that are images containing views of multiple objects. Our task is to learn about each of the objects present in the images. This task can be approached as a factorial learning problem, where each image must be explained by instantiating a model for each of the objects present with the correct instantiation parameters. A major problem with learning a factorial model is that as the number of objects increases, there is a combinatorial explosion of the number of configurations that need to be considered. We develop a method to extract object models sequentially from the data by making use of a robust statistical method, thus avoiding the combinatorial explosion, and present results showing successful extraction of objects from real images.     

Partitioning Methodology for Heterogeneous Reconfigurable Functional Units

A partitioning methodology between the reconfigurable hardware blocks of different granularity, which are embedded in a generic heterogeneous architecture, is presented. The fine-grain reconfigurable logic is realized by an FPGA unit, while the coarse-grain reconfigurable hardware by a 2-Dimensional Array of Processing Elements. Critical parts, called kernels, are mapped on the coarse-grain reconfigurable logic for improving performance. The partitioning method is mainly composed by three steps: the analysis of the input code, the mapping onto the Coarse-Grain Reconfigurable Array and the mapping onto the FPGA. The partitioning flow is implemented by a prototype software framework. Analytical partitioning experiments, using five real-world applications, show that the execution time speedup relative to an all-FPGA solution ranges from 1.4 to 5.0.     

Managing design-time uncertainty

Software and Systems Modeling - Managing design-time uncertainty, i.e., uncertainty that developers have about making design decisions, requires creation of “uncertainty-aware” software...