Surface profile prediction and analysis applied to turning process

An approach for the prediction of surface profile in turning process using Radial Basis Function (RBF) neural networks is presented. The input parameters of the RBF networks are cutting speed, depth of cut and feed rate. The output parameters are Fast Fourier Transform (FFT) vector of surface profile for the prediction of surface profile. The RBF networks are trained with adaptive optimal training parameters related to cutting parameters and predict surface profile using the corresponding optimal network topology for each new cutting condition. A very good performance of surface profile prediction, in terms of agreement with experimental data, was achieved with high accuracy, low cost and high speed. It is found that the RBF networks have the advantage over Back Propagation (BP) neural networks. Furthermore, a new group of training and testing data were also used to analyse the influence of tool wear and chip formation on prediction accuracy using RBF neural networks

Fredholm Transform and Local Rapid Stabilization for a Kuramoto-Sivashinsky Equation

This paper is devoted to the study of the local rapid exponential stabilization problem for a controlled Kuramoto-Sivashinsky equation on a bounded interval. We build a feedback control law to force the solution of the closed-loop system to decay exponentially to zero with arbitrarily prescribed decay rates, provided that the initial datum is small enough. Our approach uses a method we introduced for the rapid stabilization of a Korteweg-de Vries equation. It relies on the construction of a suitable integral transform and can be applied to many other equations

Development of polymeric hollow fiber membranes containing catalytic metal nanoparticules.

Metal nanoparticles (MNPs) have unique physico-chemical properties advantageous for catalytic applications which differ from bulk material. However, the main drawback of MNPs is their insufficient stability due to a high trend for aggregation. To cope with this inconvenience, the stabilization of MNPs in polymeric matrices has been tested. This procedure is a promising strategy to maintain catalytic properties. The aim of this work is the synthesis of polymer-stabilized MNPs inside functionalized polymeric membranes in order to build catalytic membrane reactors. First, the polymeric support must have functional groups capable to retain nanoparticle precursors (i.e. sulfonic), then, nanoparticles can grow inside the polymeric matrix by chemical reduction of metal ions. Two different strategies have been used in this work. Firstly, polyethersulfone microfiltration hollow fibers have been modified by applying polyelectrolyte multilayers. Secondly, polysulfone ultrafiltration membranes were modified by UV-photografting using sodium p-styrene sulfonate as a vinyl monomer. The catalytic performance of developed hollow fibers has been evaluated by using the reduction of nitrophenol to aminophenol by sodium borohydride. Hollow fiber modules with Pd MNPs have been tested in dead-end and cross-flow filtration. Complete nitrophenol degradation is possible depending on operation parameters such as applied pressure and permeate flux

Gender-Identity Protection, Trade, and the Trump Administration: A Tale of Reluctant Progressivism

The Trump Administration has been hostile to transgender people, stripping away many protections from discrimination established by the prior administration. It is therefore striking that President Trump’s signature international agreement to date—the “new NAFTA” recently negotiated with Canada and Mexico—includes a provision requiring all three countries to implement appropriate policies to protect workers against discrimination based on gender identity. This provision has a similar requirement with respect to discrimination on the basis of sexual orientation, notwithstanding the fact that the Trump Administration’s domestic policies have also shown hostility to such protections. How did this provision come to be included in the trade agreement? How powerful is it in practice? And what lessons does its inclusion have for international trade law more generally? Drawing on subtle changes in the wording of the initial and revised texts of the trade agreement, this Essay hypothesizes that the initial inclusion of gender-identity and sexual-orientation protections took place with little to no interagency consultation with the Department of Justice, which has taken a strong position against such workplace protections. Once these protections made it into the initial public draft, the Trump Administration could—and did—water down the protections in subsequent negotiations, but the Administration could not remove the protections entirely. The net effect is an international commitment to the protection of gender identity and sexual orientation that is substantively weak but still meaningful—and that carries considerable expressive force. The inclusion of the protections shows that trade agreements can lead even powerful governments to make value-laden commitments at odds with their own domestic agendas

Full wave propagation modelling in view to integrated ICRH wave coupling/RF sheaths modelling

RF sheaths rectification can be the reason for operational limits for Ion Cyclotron Range of Frequencies (ICRF) heating systems via impurity production or excessive heat loads. To simulate this process in realistic geometry, the Self-consistent Sheaths and Waves for Ion Cyclotron Heating (SSWICH) code is a minimal set of coupled equations that computes self-consistently wave propagation and DC plasma biasing. The present version of its wave propagation module only deals with the Slow Wave assumed to be the source of RF sheath oscillations. However the ICRF power coupling to the plasma is due to the fast wave (FW). This paper proposes to replace this one wave equation module by a full wave module in either 2D or 3D as a first step towards integrated modelling of RF sheaths and wave coupling. Since the FW is propagative in the main plasma, Perfectly Matched Layers (PMLs) adapted for plasmas were implemented at the inner side of the simulation domain to absorb outgoing waves and tested numerically with tilted BD in Cartesian geometry, by either rotating the cold magnetized plasma dielectric tensors in 2D or rotating the coordinate vector basis in 3D. The PML was further formulated in cylindrical coordinates to account for for the toroidal curvature of the plasma. Toroidal curvature itself does not seem to change much the coupling. A detailed 3D geometrical description of Tore Supra and ASDEX Upgrade (AUG) antennas was included in the coupling code. The full antenna structure was introduced, since its toroidal symmetry with respect to the septum plane is broken (FS bars, toroidal phasing, non-symmetrical structure). Reliable convergence has been obtained with the density profile up to the leading edge of antenna limiters. Parallel electric field maps have been obtained as an input for the present version of SSWICH

Catalytic hollow fiber membranes prepared using layer-by-layer adsorption of polyelectrolytes and metal nanoparticles

Immobilization of metalnanoparticles in hollowfibermembranes via alternating adsorption of polyelectrolytes and negatively charged Au nanoparticles yields catalytic reactors with high surface areas. SEM images show that this technique deposits a high density of unaggregated metalnanoparticles both on the surfaces and in the pores of the hollowfibers. Catalytic reduction of 4-nitrophenol with NaBH4, which can be easily monitored by UV–vis spectrophotometry, demonstrates that the nanoparticles in the hollowfibermembrane are highly catalytically active. In a single pass through the membrane, >99% of the 4-nitrophenol is reduced to 4-aminophenol, but this conversion decreases over time. The conversion decline may stem from catalyst fouling caused by by-products of 4-aminophenol oxidation

Agriculture in the Guan Zhong Area of China From the Late Warring States to the End of Western Han.

The goal of my research was to depict the state of agriculture in the Guan Zhong area of China from approximately the mid-third century through the mid-first century B.C. Various kinds of source materials, most of which have an implicit or explicit relationship to the Guan Zhong area enabled me to research different aspects of contemporary agriculture. These included the administration of agriculture, agricultural dwelling places and other buildings, and agricultural activities. Available written materials provided valuable information on the farmers' obligations to the government and government attempts to promote agricultural production via government policies and designated officials. They also contained many references to how and when agricultural activities were conducted. Information on the farmers' dwelling places and surroundings, as well as some of the activities in which they engaged, was obtained from Han tomb models of farmhouses, farming machinery, and village sites. I concluded that it was possible to make an in-depth study of the state of agriculture during the Qin/Han period if one focused on the Guan Zhong region

Surface profile prediction and analysis applied to turning process

An approach for the prediction of surface profile in turning process using Radial Basis Function (RBF) neural networks is presented. The input parameters of the RBF networks are cutting speed, depth of cut and feed rate. The output parameters are Fast Fourier Transform (FFT) vector of surface profile for the prediction of surface profile. The RBF networks are trained with adaptive optimal training parameters related to cutting parameters and predict surface profile using the corresponding optimal network topology for each new cutting condition. A very good performance of surface profile prediction, in terms of agreement with experimental data, was achieved with high accuracy, low cost and high speed. It is found that the RBF networks have the advantage over Back Propagation (BP) neural networks. Furthermore, a new group of training and testing data were also used to analyse the influence of tool wear and chip formation on prediction accuracy using RBF neural networks