Kinetics of vinyl polymerization initiated by potassium permanganate/β-hydroxybutyric acid redox system

The aqueous polymerization of acrylamide initiated by potassium permanganate/β-hydroxybutyric acid has been studied volumetrically at 35 ± 0.01°C in an inert medium. The rate of polymerization shows nearly square root dependence on β-hydroxybutyric acid at low concentration (3.12 – 12.5 · 10^?3 mol dm^?3). The order with respect to potassium permanganate has been found to be 0.6 indicating thereby a bimolecular mode of termination. The polymerization rate has been varied linearly at low monomer concentrations i.e. from 2.5 –7.0 · 10^?2 mol dm^?3. The dependence of number average degree of polymerization on the initial rate of polymerization and temperature has been determined. The over-all activation energy has been found to be 51.66 kJ mol^?1. A kinetic reaction scheme is proposed on the basis of experimental results.     

A mixed boundary-value problem of elasticity with parabolic boundary

Summary The mixed boundary-values problem of elasticity with parabolic boundary when the prescribed displacement is parabolic has been reduced to the solution of the non-homogeneous Hilbert problem following the method of complex variable. The result has been compared with that obtained byParia [5] for parabolic boundary with prescribed constant displacement as a particular case.

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Zusammenfassung Das gemischte Randwertproblem der Elastizitätstheorie wird für parabolische Berandung auf die Lösung des inhomogenen Hilbert-Problems unter Benützung der komplexen Methode zurückgeführt. Die Randverschiebung wird durch ein Polynom zweiten Grades dargestellt. Das Ergebnis wird mit dem vonParia [5] verglichen, das als Sonderfall konstante Verschiebungen am parabolischen Rand vorschreibt.

     

HSIC-based affinity measure for learning on graphs

Pattern Analysis and Applications - The performance of graph-based learning techniques largely relies on the edges defined between the vertices of the graph. These edges that represent the affinity...     

Determination of Active Slip/Twinning Modes in AZ31 Mg Alloy Near Room Temperature

In order to determine the deformation modes in AZ31 magnesium alloy at room temperature, computer simulations of deformation texture development and calculation of formability have been carried out. The simulation results were compared with the measured texture results. Based on agreement between the experiments and simulations the active deformation modes were determined. A Visco Plastic Self Consistent model was employed for the simulation of plastic deformation. Simulations and experiments were performed for different initial textures. The goal of the study was to develop the understanding of deformation texture evolution and its effects on mechanical properties of magnesium, with an ultimate goal of improving room temperature formability of magnesium alloys. This article was presented at Materials Science & Technology 2006, Innovations in Metal Forming symposium held in Cincinnati, OH, October 15-19, 2006.     

Generalized Bonferroni Mean Operator for Fuzzy Number Intuitionistic Fuzzy Sets and its Application to Multiattribute Decision Making

The Bonferroni mean (BM) was originally introduced by Bonferroni in 1950. A prominent characteristic of BM is its capability to capture the interrelationship between input arguments. This makes BM useful in various application fields, such as decision making, information retrieval, pattern recognition, and data mining. In this paper, we examine the issue of fuzzy number intuitionistic fuzzy information fusion. We first propose a new generalized Bonferroni mean operator called generalized fuzzy number intuitionistic fuzzy weighted Bonferroni mean (GFNIFWBM) operator for aggregating fuzzy number intuitionistic fuzzy information. The properties of the new aggregation operator are studied and their special cases are examined. Furthermore, based on the GFNIFWBM operator, an approach to deal with multiattribute decision‐making problems under fuzzy number intuitionistic fuzzy environment is developed. Finally, a practical example is provided to illustrate the multiattribute decision‐making process.     

RBF neural networks for solving the inverse problem of backscattering spectra

This paper investigates a new method to solve the inverse problem of Rutherford backscattering (RBS) data. The inverse problem is to determine the sample structure information from measured spectra, which can be defined as a function approximation problem. We propose using radial basis function (RBF) neural networks to approximate an inverse function. Each RBS spectrum, which may contain up to 128 data points, is compressed by the principal component analysis, so that the dimensionality of input data and complexity of the network are reduced significantly. Our theoretical consideration is tested by numerical experiments with the example of the SiGe thin film sample and corresponding backscattering spectra. A comparison of the RBF method with multilayer perceptrons reveals that the former has better performance in extracting structural information from spectra. Furthermore, the proposed method can handle redundancies properly, which are caused by the constraint of output variables. This study is the first method based on RBF to deal with the inverse RBS data analysis problem.     

CP2EH: a comprehensive privacy-preserving e-health scheme over cloud

The Journal of Supercomputing - In the Attribute-Based Encryption (ABE) scheme, patients encrypt their electronic health record (EHR), attach the appropriate attributes with it, and outsource them...     

Fuzzy Based Ant Colony Optimization Scheduling in Cloud Computing

Cloud computing is an Information Technology deployment model established on virtualization. Task scheduling states the set of rules for task allocations to an exact virtual machine in the cloud computing environment. However, task scheduling challenges such as optimal task scheduling performance solutions, are addressed in cloud computing. First, the cloud computing performance due to task scheduling is improved by proposing a Dynamic Weighted Round-Robin algorithm. This recommended DWRR algorithm improves the task scheduling performance by considering resource competencies, task priorities, and length. Second, a heuristic algorithm called Hybrid Particle Swarm Parallel Ant Colony Optimization is proposed to solve the task execution delay problem in DWRR based task scheduling. In the end, a fuzzy logic system is designed for HPSPACO that expands task scheduling in the cloud environment. A fuzzy method is proposed for the inertia weight update of the PSO and pheromone trails update of the PACO. Thus, the proposed Fuzzy Hybrid Particle Swarm Parallel Ant Colony Optimization on cloud computing achieves improved task scheduling by minimizing the execution and waiting time, system throughput, and maximizing resource utilization.     

A Two-Step Filtering approach for detecting maize and soybean phenology with time-series MODIS data

The crop developmental stage represents essential information for irrigation scheduling/fertilizer management, understanding seasonal ecosystem carbon dioxide (CO₂) exchange, and evaluating crop productivity. In this study, we devised an approach called the Two-Step Filtering (TSF) for detecting the phenological stages of maize and soybean from time-series Wide Dynamic Range Vegetation Index (WDRVI) data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m observations. The TSF method consists of a Two-Step Filtering scheme that includes: (i) smoothing the temporal WDRVI data with a wavelet-based filter and (ii) deriving the optimum scaling parameters from shape-model fitting procedure. The date of key crop development stages are then estimated by using the optimum scaling parameters and an initial value of the specific phenological date on the shape model, which are preliminary defined in reference to ground-based crop growth stage observations. The shape model is a crop-specific WDRVI curve with typical seasonal features, which were defined by averaging smoothed, multi-year WDRVI profiles from MODIS 250-m data collected over irrigated maize and soybean study sites.In this study, the TSF method was applied to MODIS-derived WDRVI data over a 6-year period (2003 to 2008) for two irrigated sites and one rainfed site planted to either maize or soybean as part of the Carbon Sequestration Program (CSP) at the University of Nebraska-Lincoln. A comparison of satellite-based retrievals with ground-based crop growth stage observations collected by the CSP over the six growing seasons for these three sites showed that the TSF method can accurately estimate the date of four key phenological stages of maize (V2.5: early vegetative stage, R1: silking stage, R5: dent stage and R6: maturity) and soybean (V1: early vegetative stage, R5: beginning seed, R6: full seed and R7: beginning maturity). The root mean square error (RMSE) of phenological-stage estimation for maize ranged from 2.9 [R1] to 7.0 [R5] days and from 3.2 [R6] to 6.9 [R7] days for soybean, respectively. In addition, the TSF method was also applied for two years (2001 and 2002) over eastern Nebraska to test its ability to characterize the spatio-temporal patterns of these key phenological stages over a larger geographic area. The MODIS-derived crop phenological stage dates agreed well with the statistical crop progress data reported by the United State Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) for eastern Nebraska's three crop agricultural statistic districts (ASDs). At the ASD-level, the RMSE of phenological-stage estimation ranged from 1.6 [R1] to 5.6 [R5] days for maize and from 2.5 [R7] to 5.3 [R5] days for soybean.