Analytical and numerical study of the densification of carbon/carbon composites by a film-boiling chemical vapor infiltration process

The film-boiling chemical vapor infiltration (CVI) process is a fast process developed for composite material fabrication, and especially carbon/carbon composites. In order to help define optimal conditions, a local 1D model has been developed to study the densification front which establishes itself during the processing of a carbon/carbon fibrous preform. The model features heat conduction, precursor gas diffusion, densification reactions and structural evolution of the porous medium. The effects of total mass flux, Thiele modulus, porous medium geometry on front behavior parameters such as width, velocity and residual porosity are presented as semi-analytical correlations. An existence criterion is produced, which involves a minimal heat flux. Comparison between process-scale experiments and simulation is then possible by inserting the semi-analytical results achieved in the local study of the front into a light numerical model involving the entire preform. The model has been validated with respect to previous experimental and numerical data.     

Kinetics of the selective catalytic reduction of NO by NH3 on a Cu-faujasite catalyst

The kinetics of the selective catalytic reduction (SCR) of NO by NH₃ in the presence of O₂ has been studied on a 5.5% Cu-faujasite (Cu-FAU) catalyst. Cu-FAU was composed of cationic and oxocationic Cu species. The SCR was studied in a gas phase-flowing reactor operating at atmospheric pressure. The reaction conditions explored were: 458<T_R<513 K, 2503 (ppm) < 4000, 12 (%) < 4. The kinetic orders were 0.8–1 with respect to NO, 0.5–1 with respect to O₂, and essentially 0 with respect to NH₃. Based on these kinetic partial orders of reactions and elementary chemistry, a wide variety of mechanisms were explored, and different rate laws were derived. The best fit between the measured and calculated rates for the SCR of NO by NH₃ was obtained with a rate law derived from a redox Mars and van Krevelen mechanism. The catalytic cycle is described by a sequence of three reactions: (i) Cu^I is oxidized by O₂ to “Cu^II-oxo”, (ii) “Cu^II-oxo” reacts with NO to yield “Cu^II-N_xO_y”, and (iii) finally “Cu^II-N_xO_y” is reduced by NH₃ to give N₂, H₂O, and the regeneration of Cu^I (closing of the catalytic cycle). The rate constants of the three steps have been determined at 458, 483, and 513 K. It is shown that Cu^I or “Cu^II-oxo” species constitute the rate-determining active center.     

Verification of randomized consensus algorithms under round-rigid adversaries

International Journal on Software Tools for Technology Transfer - Randomized fault-tolerant distributed algorithms pose a number of challenges for automated verification: (i) parameterization in...     

Sinusoidal disturbance induced topology identification of Hindmarsh-Rose neural networks正弦扰动诱导复杂网络拓扑识别

Topology identification of complex networks is an important problem. Existing research shows that the synchronization of network nodes is an obstacle in the identification of network topology. Identification of the structure of the network presents an interesting challenge during the synchronization of complex networks. We developed a new method using the sinusoidal disturbance to identify the topology when the complex network achieves synchronization. Compared with the disturbance of all the nodes, the disturbance of the key nodes alone can achieve a very good effect. Finally, numerical simulation data are provided to validate our hypothesis.     

“Area Without Numbers”: Using Touchscreen Dynamic Geometry to Reason About Shape

Abstract

In this article, we report on two lessons aimed at introducing junior high school students to the idea of shearing in a touchscreen dynamic geometry environment. By using shearing, we hoped to shift students’ attention away from a formula-driven, computational conception of area toward a more geometric one. We found that the students were able to solve several problems involving the comparison of polygons based on verbal and diagrammatic explanations. We describe the features of the touchscreen dynamic geometry technology that supported their learning, as well as the specific role that the teacher played in modeling students’ reasoning about area.     

Ferritin Protein Regulates the Degradation of Iron Oxide Nanoparticles

Proteins implicated in iron homeostasis are assumed to be also involved in the cellular processing of iron oxide nanoparticles. In this work, the role of an endogenous iron storage protein—namely the ferritin—is examined in the remediation and biodegradation of magnetic iron oxide nanoparticles. Previous in vivo studies suggest the intracellular transfer of the iron ions released during the degradation of nanoparticles to endogenous protein cages within lysosomal compartments. Here, the capacity of ferritin cages to accommodate and store the degradation products of nanoparticles is investigated in vitro in the physiological acidic environment of the lysosomes. Moreover, it is questioned whether ferritin proteins can play an active role in the degradation of the nanoparticles. The magnetic, colloidal, and structural follow‐up of iron oxide nanoparticles and proteins in lysosome‐like medium confirms the efficient remediation of potentially harmful iron ions generated by nanoparticles within ferritins. The presence of ferritins, however, delays the degradation of particles due to a complex colloidal behavior of the mixture in acidic medium. This study exemplifies the important implications of intracellular proteins in processes of degradation and metabolization of iron oxide nanoparticles.     

Surrogate modeling approximation using a mixture of experts based on EM joint estimation

An automatic method to combine several local surrogate models is presented. This method is intended to build accurate and smooth approximation of discontinuous functions that are to be used in structural optimization problems. It strongly relies on the Expectation−Maximization (EM) algorithm for Gaussian mixture models (GMM). To the end of regression, the inputs are clustered together with their output values by means of parameter estimation of the joint distribution. A local expert is then built (linear, quadratic, artificial neural network, moving least squares) on each cluster. Lastly, the local experts are combined using the Gaussian mixture model parameters found by the EM algorithm to obtain a global model. This method is tested over both mathematical test cases and an engineering optimization problem from aeronautics and is found to improve the accuracy of the approximation.     

Boosting support vector machines for imbalanced data sets

Real world data mining applications must address the issue of learning from imbalanced data sets. The problem occurs when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed vector spaces or lack of information. Common approaches for dealing with the class imbalance problem involve modifying the data distribution or modifying the classifier. In this work, we choose to use a combination of both approaches. We use support vector machines with soft margins as the base classifier to solve the skewed vector spaces problem. We then counter the excessive bias introduced by this approach with a boosting algorithm. We found that this ensemble of SVMs makes an impressive improvement in prediction performance, not only for the majority class, but also for the minority class.     

Characterization of renal tumours based on Raman spectra classification

In this study, we propose to evaluate the potential of Raman spectroscopy (RS) to assess renal tumours at surgery. Different classes of Raman renal spectra acquired during a clinical protocol are discriminated using support vector machines classifiers. The influence on the classification scores of various preprocessing steps generally involved in RS are also investigated and evaluated in the particular context of renal tumour characterization. Encouraging results show the interest of RS to evaluate kidney cancer and suggest the potential of this technique as a surgical assistance during partial nephrectomy.