Application of polymeric flocculant for enhancing settling of the pond ash particles and water drainage from hydraulically stowed pond ash

Delayed settling of the ash particles and poor drainage of water from the pond ash are the major problems faced during the hydraulic stowing of pond ash. In this study the effect of polymeric flocculant on settling of the ash particles and drainage of water during pond ash stowing are investigated. In addition, the parameters, viz. drainage and absorption of water during pond ash stowing are quantified by stowing a mine goaf model with pond ash slurries of five different concentrations added with and without flocculant. The study revealed that addition of only 5 × 10^?6 of Sodium Carboxymethyl Cellulose (Na-CMC) flocculant with the pond ash slurries during stowing offers best result in terms of quicker settling of the ash particles and enhanced water drainage from the hydraulically stowed pond ash. Besides, it resulted in drainage of more than 85% of the total water used in the initial 45 min of stowing. The improvement in drainage is caused due to coagulation and flocculation of the pond ash particles because of charge neutralization and particle–particle bridging. This study may provide a basis for estimating the drainage and absorption of water during the real pond ash stowing operation in underground mines.     

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.     

A system for debugging via online tracing and dynamic slicing

Dynamic slicing is a promising trace based technique that helps programmers in the process of debugging. In order to debug a failed run, dynamic slicing requires the dynamic dependence graph (DDG) information for that particular run. The two major challenges involved in utilizing dynamic slicing as a debugging technique are the efficient computation of the DDG and the efficient computation of the dynamic slice, given the DDG. In this paper, we present an efficient debugger, which first computes the DDG efficiently while the program is executing; dynamic slicing is later performed efficiently on the computed DDG, on demand. To minimize program slowdown during the online computation of DDG, we make the design decision of not outputting the computed dependencies to a file, instead, storing them in memory in a specially allocated fixed size circular buffer. The size of the buffer limits the length of the execution history that can be stored. To maximize the execution history that can be maintained, we introduce optimizations to eliminate the storage of most of the generated dependencies, at the same time ensuring that those that are stored are sufficient to capture the bug. Experiments conducted on CPU‐intensive programs show that our optimizations are able to reduce the trace rate from 16 to 0.8 bytes per executed instruction. This enables us to store the dependence trace history for a window of 20 million executed instructions in a 16‐MB buffer. Our debugger is also very efficient, yielding slicing times of around a second, and only slowing down the execution of the program by a factor of 19 during the online tracing step. Using recently proposed architectural support for monitoring, we are also able to handle multithreaded programs running on multicore processors. Copyright © 2011 John Wiley & Sons, Ltd.     

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.     

Two-stage quasi-class-E power amplifier in GaN HEMT technology

This letter presents a two-stage quasi-class-E monolithic microwave integrated circuit power amplifier at 2.0GHz, which is based on field-plated GaN high electron mobility transistor technology. It consists of a driver stage and a power stage. The circuit schematic is described. The amplifier achieves an output power of 37.5dBm into a 50-/spl Omega/ load, a power added efficiency (PAE) of 50%, and a gain of 18.2dB. A power density of 5.6W/mm is achieved.     

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.     

Interagent communication and synchronization support in the DaAgent mobile agent-based computing system

This paper describes the design, implementation, and evaluation of interagent communication and synchronization models in the DaAgent mobile-agent based computing system. Based on the requirements of some sample Internet computing applications, eight system-level models of interagent communication and synchronization are proposed. A new synchronization mechanism called location synchronization that is relevant for interacting mobile agents is also proposed. This paper evaluates the eight models based on their utility, performance, level of communication and synchronization support, and applicability in the Internet computing environment. A prototype implementation and detailed performance evaluation of these models based on two interacting, multiagent applications are also presented.     

Auto-Discovering Configurations for Service Management

Determining service configurations is essential for effective service management. In this paper we describe a model-driven approach for service configuration auto-discovery. We develop metrics for performance and scalability analysis of such auto-discovery mechanisms. Our approach addresses several problems in auto-discovery: specification of what services to discover, how to efficiently distribute service discovery, and how to match instances of services into related groups. We use object-oriented models for discovery specifications, a flexible bus-based architecture for distribution and communication, and a novel multi-phased, instance matching approach. We have applied our approach to typical e-commerce services, Enterprise Resource Planning applications, like SAP, and Microsoft Exchange services running on a mixture of Windows and Unix platforms. The main contribution of this work is the flexibility of our models, architecture and algorithms to address discovery of a multitude of services.     

New Algorithms for Resource Reclaiming from Precedence Constrained Tasks in Multiprocessor Real-Time Systems

The scheduling of tasks in multiprocessor real-time systems has attracted many researchers in the recent past. Tasks in these systems have deadlines to be met, and most of the real-time scheduling algorithms use worst case computation times to schedule these tasks. Many resources will be left unused if the tasks are dispatched purely based on the schedule produced by these scheduling algorithms, since most of the tasks will take less time to execute than their respective worst case computation times. Resource reclaiming refers to the problem of reclaiming the resources left unused by a real-time task when it takes less time to execute than its worst case computation time. In this paper, we propose two algorithms to reclaim these resources from real-time tasks that are constrained by precedence relations and resource requirements, in shared memory multiprocessor systems. We introduce a notion called a restriction vector for each task which captures its resource and precedence constraints with other tasks. This will help not only in the efficient implementation of the algorithms, but also in obtaining an improvement in performance over the reclaiming algorithms proposed in earlier work [[2]]. We compare our resource reclaiming algorithms with the earlier algorithms and, by experimental studies, show that they reclaim more resources, thereby increasing the guarantee ratio (the ratio of the number of tasks guaranteed to meet their deadlines to the number of tasks that have arrived), which is the basic requirement of any resource reclaiming algorithm. From our simulation studies, we demonstrate that complex reclaiming algorithms with high reclaiming overheads do not lead to an improvement in the guarantee ratio.