Experimental study on the effects of big particles physical characteristics on the hydraulic transport inside a horizontal pipe

This paper presents an experimental study of the physical characteristic effects of large particles on hydraulic transport in a horizontal pipe. The particles are spherical and are large with respect to the diameter of the pipe (8%, 10%, 16%and 25%). Experiments were done to test the important parameters in solid transport (pressure, velocity, etc.). As a result, the relationship between the pressure gradient forces and the mixture velocity was sub-stantially different from the pure liquid flow. However, in a single-phase flow a monotonous behavior of the pres-sure drop curve is observed, and the curve of the solid particle flow attains its minimum at the critical velocity. The regimes are characterized with differential pressure measurements and visualizations.     

Electrical and morphological properties of microinjection molded polystyrene/multiwalled carbon nanotubes nanocomposites

Masterbatch dilution was utilized to prepare polystyrene/carbon nanotubes (PS/CNT) nanocomposites for microinjection molding (µIM). The effect of processing parameters, such as injection velocity and melt temperature, on the microstructure and electrical conductivity of injection molded microparts was systematically investigated. The electrical conductivity of the microparts was measured in three perpendicular directions to determine anisotropy. Results showed that the measured conductivity is process‐dependent and melt temperature is the main factor that affects the electrical conductivity of the resultant samples. Electrical conductivity increased with an incremental loading fraction of CNT, and the percolation threshold shifted to higher filler loading concentration which was ascribed to the very high shear rate in µIM. In addition, Raman analysis, SEM observations, and simulation results indicated that CNT is preferentially oriented along the flow direction arising from the high shearing effect induced by µIM. POLYM. ENG. SCI., 56:1182–1190, 2016. © 2016 Society of Plastics Engineers     

Modelling of Gangotri glacier thickness and volume using an artificial neural network

The volume of glaciers in a glacierized basin is an important characteristic for the existence of the glaciers and their evolution. Knowledge of glacier volume motivates scientific interest for two main reasons. First, the volumes of individual glaciers are monitored to estimate future water and sea level rises. Second, glaciers in the Indian Himalayas have been recognized as important water storage systems for municipal, industrial, and hydroelectric power generation purposes. Therefore, estimation of glacier volume is desired to estimate sea level rise accurately. The problem of deriving volume and glacier ice thickness is solved by developing an artificial neural network (ANN) approach that requires glacier boundaries, central branch lines, width-wise lines, digital elevation model (DEM), and slope information. Two geomorphic assumptions were taken in this investigation after testing, and strong relationships were found between elevation values of the frontal ice-denuded area of the Gangotri glacier and ice thickness derived from an ANN.     

Utilizing various sparsity measures for enhancing accuracy of collaborative recommender systems based on local and global similarities

Collaborative filtering is a popular recommendation technique, which suggests items to users by exploiting past user-item interactions involving affinities between pairs of users or items. In spite of their huge success they suffer from a range of problems, the most fundamental being that of data sparsity. When the rating matrix is sparse, local similarity measures yield a poor neighborhood set thus affecting the recommendation quality. In such cases global similarity measures can be used to enrich the neighborhood set by considering transitive relationships among users even in the absence of any common experiences. In this work we propose a recommender system framework utilizing both local and global similarities, taking into account not only the overall sparsity in the rating data, but also sparsity at the user-item level. Several schemes are proposed, based on various sparsity measures pertaining to the active user, for the estimation of the parameter α, that allows the variation of the importance given to the global user similarity with regards to local user similarity. Furthermore, we propose an automatic scheme for weighting the various sparsity measures, through evolutionary approach, to obtain a unified measure of sparsity (UMS). In order to take maximum possible advantage of the various sparsity measures relating to an active user, a scheme based on the UMS is suggested for estimating α. Experimental results demonstrate that the proposed estimates of α, markedly, outperform the schemes for which α is kept constant across all predictions (fixed-α schemes), on accuracy of predicted ratings.     

Model Reference Adaptive Control Design for a Teleoperation System with Output Prediction

In this paper, a new control scheme is proposed to ensure stability and good performance of the teleoperation systems while a wide range of time delays in transmission line is allowed. For this purpose, a new algorithm is recommended for time delay estimation and plant output prediction in the presence of white and color noise. A model reference adaptive controller (MRAC) is designed for the master site using the predicted output of the plant. An independent MRAC is also designed and integrated for the slave site. The proposed control system indicates good stability and tracking performance.     

BusSEngine: a business search engine

With the emergence of World Wide Web, business’ databases are increasingly being queried directly by customers. The customers may not be aware of the underlying data and its structure, and might have never learned a query language that enables them to issue structured queries. Some of the business’ employees who query the databases may also not be aware of the structure of the data, but they are likely to be aware of some labels of elements containing data. We propose in this article: (1) an XML Keyword-Based search engine for answering business’ customers called BusSEngine-K, and (2) an XML loosely Structured-Based search engine for answering business’ employees called BusSEngine-L. The two engines employ novel context-driven search techniques and are built on top of XQuery search engine. The two engines were evaluated experimentally and compared with three recently proposed XML search engines. The results showed marked improvement.     

A comparative study of various meta-heuristic techniques applied to the multilevel thresholding problem

The multilevel thresholding problem is often treated as a problem of optimization of an objective function. This paper presents both adaptation and comparison of six meta-heuristic techniques to solve the multilevel thresholding problem: a genetic algorithm, particle swarm optimization, differential evolution, ant colony, simulated annealing and tabu search. Experiments results show that the genetic algorithm, the particle swarm optimization and the differential evolution are much better in terms of precision, robustness and time convergence than the ant colony, simulated annealing and tabu search. Among the first three algorithms, the differential evolution is the most efficient with respect to the quality of the solution and the particle swarm optimization converges the most quickly.     

A memetic algorithm for the cyclic antibandwidth maximization problem

The cyclic antibandwidth problem is to embed the vertices of a graph G of n vertices on a cycle C _n such that the minimum distance (measured in the cycle) of adjacent vertices is maximized. Exact results/conjectures for this problem exist in the literature for some standard graphs, such as paths, cycles, two-dimensional meshes, and tori, but no algorithm has been proposed for the general graphs in the literature reviewed by us so far. In this paper, we propose a memetic algorithm for the cyclic antibandwidth problem (MACAB) that can be applied on arbitrary graphs. An important feature of this algorithm is the use of breadth first search generated level structures of a graph to explore a variety of solutions. A novel greedy heuristic is designed which explores these level structures to label the vertices of the graph. The algorithm achieves the exact cyclic antibandwidth of all the standard graphs with known optimal values. Based on our experiments we conjecture the cyclic antibandwidth of three-dimensional meshes, hypercubes, and double stars. Experiments show that results obtained by MACAB are substantially better than those given by genetic algorithm.     

Protecting multicast services in optical internet backbones

Many applications in the future Internet will use the multicasting service mode. Since many of these applications will generate large amounts of traffic, and since users expect a high level of service availability, it is important to provision multicasting sessions in the future Internet while also providing protection for multicast sessions against network component failures. In this paper we address the multicast survivability problem of using minimum resources to provision a multicast session and its protection paths (trees) against any single-link failure. We propose a new, and a resource efficient, protection scheme, namely, Segment-based Protection Tree (SPT). In SPT scheme, a given multicast session is first provisioned as a primary multicast tree, and then each segment on the primary tree is protected by a multicast tree instead of a path, as in most existing approaches. We also analyze the recovery performance of SPT and design a reconfiguration calculation algorithm to compute the average number of reconfigurations upon any link failure. By extending SPT to address dynamic traffic scenarios, we also propose two heuristic algorithms, Cost-based SPT (CB_SPT) and Wavelength-based SPT (WB_SPT). We study the performance of the SPT scheme in different traffic scenarios. The numerical results show that SPT outperforms the best existing approaches, optimal path-pair-based shared disjoint paths (OPP_SDPs). SPT uses less than 10% extra resources to provision a survivable multicast session over the optimal solution and up to 4% lower than existing approaches under various traffic scenarios and has an average number of reconfigurations 10–86% less than the best cost efficient approach. Moreover, in dynamic traffic cases, both CB_SPT and WB_SPT achieves overall blocking probability with 20% lower than OPP_SDP in most network scenarios.