A partitioning methodology that optimizes the communication cost for reconfigurable computing systems

This paper focuses on the design process for reconfigurable architecture. Our contribution focuses on introducing a new temporal partitioning algorithm. Our algorithm is based on typical mathematic flow to solve the temporal partitioning problem. This algorithm optimizes the transfer of data required between design partitions and the reconfiguration overhead. Results show that our algorithm considerably decreases the communication cost and the latency compared with other well known algorithms.     

Some transparent semi-conductor metal oxides: Comparative investigations in terms of Wemple–DiDomenico parameters, mechanical performance and Amlouk–Boubaker opto-thermal expansivity

Wemple–DiDomenico model parameters for oxides thin layers (Sb₂O₄, SnO₂, ZnO and WO₃) have been determined from experimental measurement of reflection R(λ) and T(λ) transmission ratios within the Ultraviolet–Visible–Near-Infrared (UV/VIS/NIR) domain (300–1800 nm). This study allowed deducing ε₁(λ) and ε₂(λ) expressions. Exploitation of results concerning their variations enabled estimating the plasma pulse ω_p, the relaxation time τ, the dielectric constant ε_∞ and susceptibility χ_e. Furthermore, opto-thermal and mechanical investigations have been carried out and discussed relatively to the already established optical and thermal characteristics.     

Method to estimate uncertainty associated with parcel size in coarse discrete particle simulation

Coarse grained particle methods significantly reduce the computation cost of large‐scale fluidized bed simulation by lumping many real particles into a computation parcel. This research provides a method to estimate the errors associated with parcel size in large‐scale fluidized bed simulations. This uncertainty is first quantified in small scale domains by comparing results of discrete particle method with that employing coarse parcels of different sizes. Then, this uncertainty is correlated with parcel size and simulation domains consisting of a simple homogeneous cooling system and more complex bubbling and circulating fluidized beds. These correlations allow us to accurately estimate the uncertainty in large‐scale fluidized beds based solely on data obtained in smaller systems. The ability to estimate model‐related uncertainty in larger systems makes this method relevant for industrial applications. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2340–2350, 2018     

Local mechanical behavior mapping of a biopolymer blend using nanoindentation,finite element computation,and simplex optimization strategy

In this study, we suggest a simple scheme to derive interfacial behavior using combination of nanoindentation and finite element computation. The starting point is the experimental generation of a rectangular grid composed of 32 indentations to measure the exact variation of stiffness across the interface of a bio‐based composite. A finite element simulation of nanoindentation is implemented based on elasto‐plastic material model. An optimization strategy is used to identify the behavior of all phases by matching predicted results to observed mechanical response. Results show that extent of interphase layer has a typical dimension of 8.0 ± 4.9 µm. The optimization strategy based on simplex proves to be efficient to derive the elasto‐plastic behavior of the blend across the interface with a residual value of less than 30 µN. The identification procedure demonstrates that the extent of the interfacial region depends on the measured physical quantity. The contrast across the interface for both Young's and the tangent moduli appear to be more effective than the contrast given by the yield stress. Identified Young's moduli for zein, starch, and interfacial zone are 4.78 ± 0.27, 4.13 ± 0.19, and 3.91 ± 0.17 GPa. Plasticity parameter represented by tangent modulus varies in the same order as 1238 ± 120, 847 ± 108, and 976 ± 94 MPa, respectively. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 44891.     

The Signaling Role of CD40 Ligand in Platelet Biology and in Platelet Component Transfusion

The CD40 ligand (CD40L) is a transmembrane molecule of crucial interest in cell signaling in innate and adaptive immunity. It is expressed by a variety of cells, but mainly by activated T-lymphocytes and platelets. CD40L may be cleaved into a soluble form (sCD40L) that has a cytokine-like activity. Both forms bind to several receptors, including CD40. This interaction is necessary for the antigen specific immune response. Furthermore, CD40L and sCD40L are involved in inflammation and a panoply of immune related and vascular pathologies. Soluble CD40L is primarily produced by platelets after activation, degranulation and cleavage, which may present a problem for transfusion. Soluble CD40L is involved in adverse transfusion events including transfusion related acute lung injury (TRALI). Although platelet storage designed for transfusion occurs in sterile conditions, platelets are activated and release sCD40L without known agonists. Recently, proteomic studies identified signaling pathways activated in platelet concentrates. Soluble CD40L is a good candidate for platelet activation in an auto-amplification loop. In this review, we describe the immunomodulatory role of CD40L in physiological and pathological conditions. We will focus on the main signaling pathways activated by CD40L after binding to its different receptors.     

Fuzzy decision support for tools selection in the core front end activities of new product development

The innovation process may be divided into three main parts: the front end (FE), the new product development (NPD) process, and the commercialization. Every NPD process has a FE in which products and projects are defined. However, companies tend to begin the stages of FE without a clear definition or analysis of the process to go from Opportunity Identification to Concept Generation; as a result, the FE process is often aborted or forced to be restarted. Koen’s Model for the FE is composed of five phases. In each of the phases, several tools can be used by designers/managers in order to improve, structure, and organize their work. However, these tools tend to be selected and used in a heuristic manner. Additionally, some tools are more effective during certain phases of the FE than others. Using tools in the FE has a cost to the company, in terms of time, space needed, people involved, etc. Hence, an economic evaluation of the cost of tool usage is critical, and there is furthermore a need to characterize them in terms of their influence on the FE. This paper focuses on decision support for managers/designers in their process of assessing the cost of choosing/using tools in the core front end (CFE) activities identified by Koen, namely Opportunity Identification and Opportunity Analysis. This is achieved by first analyzing the influencing factors (firm context, industry context, macro-environment) along with data collection from managers followed by the automatic construction of fuzzy decision support models (FDSM) of the discovered relationships. The decision support focuses upon the estimated investment needed for the use of tools during the CFE. The generation of FDSMs is carried out automatically using a specialized genetic algorithm, applied to learning data obtained from five experienced managers, working for five different companies. The automatically constructed FDSMs accurately reproduced the managers’ estimations using the learning data sets and were very robust when validated with hidden data sets. The developed models can be easily used for quick financial assessments of tools by the person responsible for the early stage of product development within a design team. The type of assessment proposed in this paper would better suit product development teams in companies that are cost-focused and where the trade-offs between what (material), who (staff), and how long (time) to involve in CFE activities can vary a lot and hence largely influence their financial performances later on in the NPD process.     

Turbulence modulation of dense two-phase flows: Comment on “Modelling of dense gas-particle flow in a circulating fluidized bed by Distinct Cluster Method (DCM)”

In a recent paper, Liu X. and Xu X. [2009. Modelling of dense gas-particle flow in a circulating fluidized bed by Distinct Cluster Method (DCM). Powder Technology 195, 235-244] reported the results of numerical simulations of a circulating fluidized bed using Discrete Cluster Method (DCM). We comment on the veracity of the use of a one-way turbulence model to predict turbulence in the context of dense and moderately dense two-phase flows.     

Monte Carlo simulation to reveal the copper dissolution kinetics of an ion selective electrode based on copper sulfide

The present work aims at studying copper dissolution of a Cu²⁺ ion-selective electrode based on a CuS thin film. The electrode is prepared using electrochemical deposition of CuS on a silicon substrate. The obtained film exhibits an apparent cohesive granular structure with an average grain size of about 33 μm, a small porosity content (<4%) and a thickness of about 7.48 μm. The Cu²⁺ electrochemical response shows a nearly Nernstian behavior in the range of pCu 6–1. The copper dissolution is experimentally studied in a wide pH range. In order to quantitatively predict copper mass dissolution, an original numerical model is developed based on Monte Carlo simulation. Our main hypothesis is based on dissolution probability that triggers the whole dissolution process through solution/electrode surface exchanges. Several probability forms are suggested accounting for the real observed electrochemical kinetics. The experimental results show that, under a low pH, the dissolution process severely leads to the consumption of large material. Moreover, our predictions suggest a dissolution profile as a two-stage process irrespective of pH. Our numerical model is able to fit correctly the observed kinetics considering an exponential probability form under all pH conditions.     

Heterogeneous reaction induced by the piston effect in supercritical binary mixtures

It is now well established that the large compressibility of supercritical fluids is responsible for the strong enhancement of the thermo-acoustic heating, leading to the speeding up of the heat transport thanks to the piston effect instead of the expected slowing down. We show in this paper, through numerical simulations, that the hydrodynamics behavior of supercritical fluids also couples with the critical behavior of the solubility of solids to cause the release of a heterogeneous reaction at solid surfaces in dilute binary supercritical mixtures.     

Cooling enhancement of planar-balanced magnetron cathode

In physical vapor deposition on a magnetron cathode, temperature of sensitive components must be kept under threshold limit, so as to ensure the cathode reliability,the process reproducibility, and the best quality of thin films.This can be achieved by an adequate design to enhance the dissipation of heat generated at the cathode. In this paper,temperature distribution and streamlines velocity of the cathode coolant inside a cathode magnetron are analyzed by using CFD solver ANSYS FLUENT in the single-phase method in combination with k–e standards turbulent model.The results show that the design is appropriate under the calculation parameters, and for high heat densities some improvements are necessary to enhance heat dissipation and keep temperature under the threshold limit.