A radiotracer study of the adsorption behavior of aqueous Ba(2+) ions on nanoparticles of zero-valent iron

Recently, iron nanoparticles are increasingly being tested as adsorbents for various types of organic and inorganic pollutants. In this study, nanoparticles of zero-valent iron (NZVI) synthesized under atmospheric conditions were employed for the removal of Ba(2+) ions in a concentration range 10(-3) to 10(-6) M. Throughout the study, (133)Ba was used as a tracer to study the effects of time, concentration, and temperature. The obtained data was analyzed using various kinetic models and adsorption isotherms. Pseudo-second-order kinetics and Dubinin-Radushkevich isotherm model provided the best correlation with the obtained data. Observed thermodynamic parameters showed that the process is exothermic and hence enthalpy-driven.     

Exact analysis of hydrogen induced stress in metal solid sphere

The effect of hydrogen in metals requires a detailed study involving an analysis of the stress-strain tensor field, stemming from the introduction of hydrogen atoms to the system. A model is created, utilizing the strain-stress state generated by the hydrogen atom and the coupling effect between the hydrogen and metal atom. A set of equations are established for a spherical body in the context of theory of elasticity and stress components within the system are determined. The effect of hydrogen concentration along with hydrogenation and dehydrogenation are investigated involving theoretical analysis and analytical ratios. Calculations are obtained for spherical solid systems. The effects of hydrogenation was resulted in intensive compressive stresses near the surface of the sphere, whereas tensile stresses were occurred near its surface during dehydrogenation.     

Radon concentration measurements in bituminous coal mines

Radon measurements were carried out in Kozlu, Karadon and Uzülmez underground coal mines of Zonguldak bituminous coal basin in Turkey. Passive-time integrating method, which is the most widely used technique for the measurement of radon concentration in air, was applied by using nuclear etched track detectors (CR-39) in the study area. The radon concentration measurements were performed on a total of 42 points in those three mines. The annual exposure, the annual effective dose and lifetime fatality risk, which are the important parameters for the health of workers, were estimated based on chronic occupational exposure to the radon gas, which is calculated using UNCEAR-2000 and ICRP-65 models. The radon concentrations at several coal production faces are higher than the action level of 1000 Bq m(-3). It is suggested that the ventilation rates should be rearranged to reduce the radon concentration.     

Generalized algorithm for parallel sorting on product networks

We generalize the well-known odd-even merge sorting algorithm, originally due to Batcher (1968), and show how this generalized algorithm can be applied to sorting on product networks. If G is an arbitrary factor graph with N nodes, its r-dimensional product contains N^r nodes. Our algorithm sorts N^r keys stored in the r-dimensional product of G in O(r^rF(N)) time, where F(N) depends on G. We show that, for any factor graph G, F(N) is, at most, O(N), establishing an upper bound of O(r² N) for the time complexity of sorting N^r keys on any product network. For product networks with bounded r(e.g. for grids), this leads to the asymptotic complexity of O(N) to sort N^r keys, which is optimal for several instances of product networks. There are factor graphs for which F(N)=O(log² N), which leads to the asymptotic running time of O(log² N) to sort N^r keys. For networks with bounded N (e.g. in the hypercube N=2, fixed), the asymptotic complexity becomes O(r²). We show how to apply the algorithm to several cases of well-known product networks, as well as others introduced recently. We compare the performance of our algorithm to well-known algorithms developed specifically for these networks, as well as others. The result of these comparisons led us to conjecture that the proposed algorithm is probably the best deterministic algorithm that can be found in terms of the low asymptotic complexity with a small constant     

Robust gesture recognition using feature pre-processing and weighted dynamic time warping

Gesture recognition is a technology often used in human-computer interaction applications. Dynamic time warping (DTW) is one of the techniques used in gesture recognition to find an optimal alignment between two sequences. Oftentimes a pre-processing of sequences is required to remove variations due to different camera or body orientations or due to different skeleton sizes between the reference gesture sequences and the test gesture sequences. We discuss a set of pre-processing methods to make the gesture recognition mechanism robust to these variations. DTW computes a dissimilarity measure by time-warping the sequences on a per sample basis by using the distance between the current reference and test sequences. However, all body joints involved in a gesture are not equally important in computing the distance between two sequence samples. We propose a weighted DTW method that weights joints by optimizing a discriminant ratio. Finally, we demonstrate the performance of our pre-processing and the weighted DTW method and compare our results with the conventional DTW and state-of-the-art.     

A general backpropagation algorithm for feedforward neural networks learning

A general backpropagation algorithm is proposed for feedforward neural network learning with time varying inputs. The Lyapunov function approach is used to rigorously analyze the convergence of weights, with the use of the algorithm, toward minima of the error function. Sufficient conditions to guarantee the convergence of weights for time varying inputs are derived. It is shown that most commonly used backpropagation learning algorithms are special cases of the developed general algorithm.     

Mean shift based gradient vector flow for image segmentation

In recent years, gradient vector flow (GVF) based algorithms have been successfully used to segment a variety of 2-D and 3-D imagery. However, due to the compromise of internal and external energy forces within the resulting partial differential equations, these methods may lead to biased segmentation results. In this paper, we propose MSGVF, a mean shift based GVF segmentation algorithm that can successfully locate the correct borders. MSGVF is developed so that when the contour reaches equilibrium, the various forces resulting from the different energy terms are balanced. In addition, the smoothness constraint of image pixels is kept so that over- or under-segmentation can be reduced. Experimental results on publicly accessible datasets of dermoscopic and optic disc images demonstrate that the proposed method effectively detects the borders of the objects of interest.     

A versatile software tool making best use of sparse data for closed loop process control

This paper presents the design of a software supported sliding mode controller for a biochemical process. The state of the process is characterized by cell mass and nutrient amount. The controller is designed for tracking of a desired profile in cell mass and it is shown that the nutrient amount in the controlled bioreactor evolves bounded. A smart software tool named Support Vector Machine (SVM), which minimizes the upper bound of an empirical risk function, is proposed to approximate the nonlinear function seen in the control law by using very limited number of numerical data. This removes the necessity of knowing the functional form of the nominal nonlinearity in the control law. It is shown that the controller is robust against noisy measurements, considerable amount of parameter variations, discontinuities in the command signal and large initial errors. The contribution of the present work is the achievement of robustness and tracking performance on a benchmarking process, under the presence of limited prior knowledge.