Principle Component Analysis in Conjuction with Data Driven Methods for Sediment Load Prediction

This study investigates sediment load prediction and generalization from laboratory scale to field scale using principle component analysis (PCA) in conjunction with data driven methods of artificial neural networks (ANNs) and genetic algorithms (GAs). Five main dimensionless parameters for total load are identified by using PCA. These parameters are used in the input vector of ANN for predicting total sediment loads. In addition, nonlinear equations are constructed, based upon the same identified dimensionless parameters. The optimal values of exponents and constants of the equations are obtained by the GA method. The performance of the so-developed ANN and GA based methods is evaluated using laboratory and field data. Results show that the expert methods (ANN and GA), calibrated with laboratory data, are capable of predicting total sediment load in field, thus showing their transferability. In addition, this study shows that the expert methods are not transferable for suspended load, perhaps due to insufficient laboratory data. Yet, these methods are able to predict suspended load in field, when trained with respective field data.     

Uncertainty visualization using HDR volume rendering

In this paper, we explore a novel idea of using high dynamic range (HDR) technology for uncertainty visualization. We focus on scalar volumetric data sets where every data point is associated with scalar uncertainty. We design a transfer function that maps each data point to a color in HDR space. The luminance component of the color is exploited to capture uncertainty. We modify existing tone mapping techniques and suitably integrate them with volume ray casting to obtain a low dynamic range (LDR) image. The resulting image is displayed on a conventional 8-bits-per-channel display device. The usage of HDR mapping reveals fine details in uncertainty distribution and enables the users to interactively study the data in the context of corresponding uncertainty information. We demonstrate the utility of our method and evaluate the results using data sets from ocean modeling.     

Effect of temperature and α-irradiation on gas permeability for polymeric membrane

In the present study the polyethersulphone (PES) membranes of thickness (35 ±2) μm were prepared by solution cast method. The permeability of these membranes was calculated by varying the temperature and by irradiation of α ions. For the variation of temperature, the gas permeation cell was dipped in a constant temperature water bath in the temperature range from 303–373 K, which is well below the glass transition temperature (498 K). The permeability of H₂ and CO₂ increased with increasing temperature. The PES membrane was exposed by a-source (₉₅Am₂₄₁) of strength (1 μ Ci) in vacuum of the order of 10⁻⁶ torr, with fluence 2.7 × 10⁷ ions/cm². The permeability of H₂ and CO₂ has been observed for irradiated membrane with increasing etching time. The permeability increases with increasing etching time for both gases. There was a sudden change in permeability for both the gases when observed at 18 min etching. At this stage the tracks are visible with optical instrument, which confirms that the pores are generated. Most of pores seen in the micrograph are circular cross-section ones.     

Linear stochastic approximation driven by slowly varying Markov chains

We study a linear stochastic approximation algorithm that arises in the context of reinforcement learning. The algorithm employs a decreasing step-size, and is driven by Markov noise with time-varying statistics. We show that under suitable conditions, the algorithm can track the changes in the statistics of the Markov noise, as long as these changes are slower than the rate at which the step-size of the algorithm goes to zero.     

A flexible and recoverable client/server database event notification system

A software architecture is presented that allows client application programs to interact with a DBMS server in a flexible and powerful way, using either direct, volatile messages, or messages sent via recoverable queues. Normal requests from clients to the server and replies from the server to clients can be transmitted using direct or recoverable messages. In addition, an application event notification mechanism is provided, whereby client applications running anywhere on the network can register for events, and when those events are raised, the clients are notified. A novel parameter passing mechanism allows a set of tuples to be included in an event notification. The event mechanism is particularly useful in an active DBMS, where events can be raised by triggers to signal running application programs. Received July 21, 1995 / Accepted May 30, 1996     

Look Left, Look Right, Look Left Again: An Application of Fractal Symbolic Analysis to Linear Algebra Code Restructuring

Fractal symbolic analysis is a symbolic analysis technique for verifying the legality of program transformations. It is strictly more powerful than dependence analysis; for example, it can be used to verify the legality of blocking LU factorization with pivoting, a task for which dependence analysis is inadequate. In this paper, we show how fractal symbolic analysis can be used to convert between left- and right-looking versions of three kernels of central importance in computational science: triangular solve, Cholesky factorization, and LU factorization with pivoting.     

Numerical Solution of Fractional Advection-Dispersion Equation

Numerical schemes and stability criteria are developed for solution of the one-dimensional fractional advection-dispersion equation (FRADE) derived by revising Fick’s first law. Employing 74 sets of dye test data measured on natural streams, it is found that the fractional order F of the partial differential operator acting on the dispersion term varies around the most frequently occurring value of F = 1.65 in the range of 1.4 to 2.0. Two series expansions are proposed for approximation of the limit definitions of fractional derivatives. On this ground, two three-term finite-difference schemes?“1.3 Backward Scheme” having the first-order accuracy and “F.3 Central Scheme” possessing the F-th order accuracy?are presented for fractional order derivatives. The F.3 scheme is found to perform better than does the 1.3 scheme in terms of error and stability analyses and is thus recommended for numerical solution of FRADE. The fractional dispersion model characterized by the FRADE and the F.3 scheme can accurately simulate the long-tailed dispersion processes in natural rivers.     

Nanoindentation measurement of core–skin interphase viscoelastic properties in a sandwich glass composite

Mechanics of Time-Dependent Materials - Debonding at the core–skin interphase region is one of the primary failure modes in core sandwich composites under shear loads. As a result, the...     

Design and development of bioinspired calcium phosphate nanoparticles of MTX: pharmacodynamic and pharmacokinetic evaluation

The aim of this investigation is the management of rheumatoid arthritis (RA) by developing methotrexate-loaded calcium phosphate nanoparticles (MTX-CAP-NP) and to evaluate pharmacokinetic and pharmacodynamic behavior in adjuvant induced arthritis model. The nanoparticles were synthesized by wet precipitation method and optimized by Box-Behnken experimental design. MTX-CAP-NPs were characterized by TEM, FTIR, DSC and XRD studies. The particle size, zeta potential and entrapment efficiency of the optimized nanoparticles were found to be 204.90?±?64?nm, ?11.58?±?4.80?mV, and 88.33?±?3.74%, respectively. TEM, FTIR, DSC and XRD studies revealed that the developed nanoparticles were nearly spherical in shape and the crystalline structure of CAP-NP was not changed after MTX loading. The pharmacokinetic studies revealed that MTX-CAP-NP enhanced bioavailability of MTX by 2.6-fold when compared to marketed formulation (FOLITRAX-10). Under pharmacodynamic evaluation, arthritic assessment, radiography and histopathology studies revealed that CAP has ability to regenerate cartilage and bone therefore, together with MTX, MTX-CAP-NPs have shown significant reduction in disease progression. The overall work demonstrated that the developed nanodelivery system was well tolerated and more effective than the marketed formulation.     

On a stochastic sensor selection algorithm with applications in sensor scheduling and sensor coverage

In this note we consider the following problem. Suppose a set of sensors is jointly trying to estimate a process. One sensor takes a measurement at every time step and the measurements are then exchanged among all the sensors. What is the sensor schedule that results in the minimum error covariance? We describe a stochastic sensor selection strategy that is easy to implement and is computationally tractable. The problem described above comes up in many domains out of which we discuss two. In the sensor selection problem, there are multiple sensors that cannot operate simultaneously (e.g., sonars in the same frequency band). Thus measurements need to be scheduled. In the sensor coverage problem, a geographical area needs to be covered by mobile sensors each with limited range. Thus from every position, the sensors obtain a different view-point of the area and the sensors need to optimize their trajectories. The algorithm is applied to these problems and illustrated through simple examples.