Response surface methodology applied for Orange II photocatalytic degradation in TiO2 aqueous suspensions

BACKGROUND: Heterogeneous photocatalysis is influenced by a number of parameters involving synergistic effects; hence, an experimental strategy design that considers interactions between the main variables is needed. The response surface methodology was applied for the investigation of photodegradation of 20 mg L^?1 Orange II in aqueous solutions and for optimization of color removal efficiency. Preliminary studies were performed to identify the parameters to be selected for optimization. RESULTS: The input variables considered for experimental design were: solution initial pH, oxidizing agent (H₂O₂) initial concentration and UV‐A irradiation time. The multivariate experimental design allowed the development of a quadratic response surface model to be used for the prediction of color removal efficiency over the full range of the experimental region. Under the optimum conditions established in the region of experimentation (pH = 6.9, [H₂O₂]₀ = 183 mg L^?1 and t = 32 min), a 100% color removal efficiency was obtained in experiments. CONCLUSIONS: It was found that the variables considered have important effects on color removal efficiency. The results demonstrate that the use of experimental design strategy is indispensable for successful investigation and adequate modeling of the process because the interdependence of the reaction parameters cannot be neglected. Copyright © 2008 Society of Chemical Industry     

FE3 EPR investigation of PLZT 1/65/35 and 8/65/35

X-band EPR spectra of PLZT 1/65/35 and PLZT 8/65/35 doped with 1% FE³⁺ were recorded at temperature range -175°C to 200°C. Three types of paramagnetic centers were found. Two of them are in strong axial and rhombic crystal field due to neighboring oxygen and lead vacancies. The other center has symmetry determined by polarization and oxygen octahedra tilt. EPR spectra of PLZT 8/65/35 are in agreement with its glassy behavior.     

Optimal control of nonlinear systems to given orbits

Using optimal control techniques we derive and demonstrate the use of an explicit single-step control method for directing a nonlinear system to a target orbit and keeping it there. We require that control values remain near the uncontrolled settings. The full nonlinearity of the problem in state space variables is retained. The “one-step” of the control is typically a composition of known or learned maps over (a) the time required to learn the state, (b) the time to compute the control and (c) the time to apply the control. No special targeting is required, yet the time to control is quite rapid. Working with the dynamics of a well-studied nonlinear electrical circuit, we show how this method works efficiently and accurately in two situations: when the known circuit equations are used, and when control is performed only on a Poincaré section of the reconstructed phase space. In each case, because the control rule is known analytically, the control strategy is computationally efficient while retaining high accuracy. The target locations on the selected target trajectory at each control stage are determined dynamically by the initial conditions and the system dynamics, and the target trajectory is an approximation to an unstable periodic orbit of the uncontrolled system. A linear stability analysis shows that dissipation in the dynamical system is essential for reaching a controllable state.     

Word sense disambiguation for spam filtering

Spam has become a major issue in computer security because it is a channel for threats such as computer viruses, worms, and phishing. More than 86% of received e-mails are spam. Historical approaches to combating these messages, including simple techniques such as sender blacklisting or the use of e-mail signatures, are no longer completely reliable. Many current solutions feature machine-learning algorithms trained using statistical representations of the terms that most commonly appear in such e-mails. However, these methods are merely syntactic and are unable to account for the underlying semantics of terms within messages. In this paper, we explore the use of semantics in spam filtering by introducing a pre-processing step of Word Sense Disambiguation (WSD). Based upon this disambiguated representation, we apply several well-known machine-learning models and show that the proposed method can detect the internal semantics of spam messages.     

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...     

INSIGMA: an intelligent transportation system for urban mobility enhancement

Intelligent Transportation Systems (ITS) aim to improve safety, mobility and environmental performance of road transport. The INSIGMA project provides a fresh look at the possible innovations in this field, by enhancing the functionality and accuracy of ITS in urban environments. This paper describes the architecture, sensors, processing algorithms, output modules and advantages of the developed system. A comparison of existing ITS systems has been provided as background. Special attention has been given to performance and privacy issues, as the system includes social aspects such as location monitoring.     

The making of the Xian terra‐cotta soldiers

Abstract

This paper describes the making of a short film on the Xian terra‐cotta soldiers using our integrated HU‐MANOID software. The method of creating and animating the soldiers’ faces is first presented. Then, we show how our approach, based on metaballs and spline surfaces, was used for designing and deforming soldiers’ bodies. For the animation of the bodies, we describe the motion control methods. Clothes for the soldiers are then described as well as horses and decor design. For the rendering, we explained our strategy using parallel machines. Finally, problems of integration are addressed.     

A novel electromyographic signal simulator for muscle contraction studies

Mathematical simulation has been widely used in biomedical and biological sciences. In the case of the surface electromyographic (SEMG) activity, some models have been proposed aiming to study muscle contraction strategies that are used during different tasks and conditions. Most of SEMG simulators are based on energy modulation of a Gaussian noise. This work proposes a novel simulator in which the user-defined parameters are associated with the motor units (MUs) recruitment and their firing rate. Comparison between the mean spectrum of real SEMG signals collected in isometric contraction of the muscle biceps brachii and the mean spectrum obtained from simulated SEMG signals showed a good agreement, pointing the proposed simulator seems to be capable to generate consistent electromyographic signals in time and frequency domains and that can be used in many studies, in particular in the evaluation of automatic methods aimed to detect muscular contraction.     

Decomposition Approaches for Constrained Spatial Auction Market Problems

We consider several equilibrium formulations for the problem of managing spatially distributed auction markets of a homogeneous commodity, which are joined by transmission lines in a network. At each market, traders and buyers are determined by their price functions and choose their offer/bid values. We present equivalent variational inequality, optimization, and saddle point formulations of this problem. The corresponding models possess a special structure of constraint and cost functions and lead to different decomposition schemes. We propose proximal and splitting type methods and discuss their properties and preliminary computational results.     

Improving robustness of industrial object detection by automatic generation of synthetic images from CAD models

Object detection (OD) is used for visual quality control in factories. Images that compose training datasets are often collected directly from the production line and labeled with bounding boxes manually. Such data represent well the inference context but might lack diversity, implying a risk of overfitting. To address this issue, we propose a dataset construction method based on an automated pipeline, which receives a CAD model of an object and returns a set of realistic synthetic labeled images (code publicly available). Our approach can be easily used by non-expert users and is relevant for industrial applications, where CAD models are widely available. We performed experiments to compare the use of datasets obtained by the two different ways—collecting and labeling real images or applying the proposed automated pipeline—in the classification of five different industrial parts. To ensure that both approaches can be used without deep learning expertise, all training parameters were kept fixed during these experiments. In our results, both methods were successful for some objects but failed for others. However, we have shown that the combined use of real and synthetic images led to better results. This finding has the potential to make industrial OD models more robust to poor data collection and labeling errors, without increasing the difficulty of the training process.