Recovery and Recycling Silica Flux in Submerged Arc Welding – Acceptable Properties and Economical Correlation

Silicon - This research aims at optimum utilization of waste recycled submerged arc welding slag along with virgin flux in place of pure virgin flux without compromising the quality of welding and...     

Ubiquitin-Specific Protease 29 Regulates Cdc25A-Mediated Tumorigenesis

Cell division cycle 25A (Cdc25A) is a dual-specificity phosphatase that is overexpressed in several cancer cells and promotes tumorigenesis. In normal cells, Cdc25A expression is regulated tightly, but the changes in expression patterns in cancer cells that lead to tumorigenesis are unknown. In this study, we showed that ubiquitin-specific protease 29 (USP29) stabilized Cdc25A protein expression in cancer cell lines by protecting it from ubiquitin-mediated proteasomal degradation. The presence of USP29 effectively blocked polyubiquitination of Cdc25A and extended its half-life. CRISPR-Cas9-mediated knockdown of USP29 in HeLa cells resulted in cell cycle arrest at the G0/G1 phase. We also showed that USP29 knockdown hampered Cdc25A-mediated cell proliferation, migration, and invasion of cancer cells in vitro. Moreover, NSG nude mice transplanted with USP29-depleted cells significantly reduced the size of the tumors, whereas the reconstitution of Cdc25A in USP29-depleted cells significantly increased the tumor size. Altogether, our results implied that USP29 promoted cell cycle progression and oncogenic transformation by regulating protein turnover of Cdc25A.     

Evaluation of wear of turning carbide inserts using neural networks

Recent trends, being towards mostly unmanned automated machining systems and consistent system operations, need reliable on-line monitoring processes. A proper on-line cutting tool condition monitoring system is essential for deciding when to change the tool. Many methods have been attempted in this connection.Recently, artificial neural networks have been tried for this purpose because of its inherent simplicity and reasonably quick data-processing capability. The present work uses the back propagation algorithm for training the neural network of 5-3-1 structure. The technique shows close matching of estimation of average flank wear and directly measured wear value. Thus the system developed demonstrates the possibility of successful tool wear monitoring on-line.     

Study of polyaniline polyacrylamide composites by positron annihilation technique

Polyaniline doped with nonoxidizing Bronsted acids is recognized as a conducting material, as its electrical conductivity changes with percentage of doping. In the present work, different percentages of doped polyaniline were blended with polyacrylamide and their electrical conductivities as well as the positron annihilation lifetimes were measured. Analysis of data yielded three lifetime components. It was observed that the value of the short lifetime component remained constant for doping concentration, whereas that of the intermediate component τ₂ decreased. The relative intensity pertaining to τ₂, however, increased with the increase in doped PANI concentration. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 89: 930–933, 2003     

GPU accelerated novel particle filtering method

In this paper, a graphics processor unit (GPU) accelerated particle filtering algorithm is presented with an introduction to a novel resampling technique. The aim remains in the mitigation of particle impoverishment as well as computational burden, problems which are commonly associated with classical (systematic) resampled particle filtering. The proposed algorithm employs a priori-space dependent distribution in addition to the likelihood, and hence is christened as dual distribution dependent (D3) resampling method. Simulation results exhibit lesser values for root mean square error (RMSE) in comparison to that for systematic resampling. D3 resampling is shown to improve particle diversity after each iteration, thereby affecting the overall quality of estimation. However, computational burden is significantly increased owing to few excessive computations within the newly formulated resampling framework. With a view to obtaining parallel speedup we introduce a CUDA version of the proposed method for necessary acceleration by GPU. The GPU programming model is detailed in the context of this paper. Implementation issues are discussed along with illustration of empirical computational efficiency, as obtained by executing the CUDA code on Quadro 2000 GPU. The GPU enabled code has a speedup of 3 and 4 over the sequential executions of systematic and D3 resampling methods respectively. Performance both in terms of RMSE and running time have been elaborated with respect to different selections for threads per block towards effective implementations. It is in this context that, we further introduce a cost to performance metric (CPM) for assessing the algorithmic efficiency of the estimator, involving both quality of estimation and running time as comparative factors, transformed into a unified parameter for assessment. CPM values for estimators obtained from all such different choices for threads per block have been determined and a final value for the chosen parameter is resolved for generation of a holistic effective estimator.     

Approximation schemes for load balanced clustering in wireless sensor networks

Clustering sensor nodes is an efficient technique to improve scalability and life time of a wireless sensor network (WSN). However, in a cluster based WSN, the leaders (cluster heads) consume more energy due to some extra load for various activities such as data collection, data aggregation, and communication of the aggregated data to the base station. Therefore, balancing the load of the cluster heads is a crucial issue for the long run operation of the WSNs. In this paper, we first present a load balanced clustering scheme for wireless sensor networks. We show that the algorithm runs in O(nlogn) time for n sensor nodes. We prove that the algorithm is optimal for the case in which the sensor nodes have equal load. We also show that it is a polynomial time 2-approximation algorithm for the general case, i.e., when the sensor nodes have variable load. We finally improve this algorithm and propose a 1.5-approximation algorithm for the general case. The experimental results show the efficiency of the proposed algorithm in terms of the load balancing of the cluster heads, execution time, and the network life.     

Al2O3─SiC Composites from Aluminosilicate Precursors

Al₂O₃ and SiC composite materials have been produced from mixtures of aluminosilicates (both natural minerals and synthetic) and carbon as precursor materials. These composites are produced by heating a mixture of kaolinite (or synthetic aluminosilicates) and carbon in stoichiometric proportion above 1550°C, so that only Al₂O₃ and SiC remain as the major phases. A similar process has also been used for synthesizing other composite powders having mixtures of Al₂O₃, SiC, TiC, and ZrO₂ in different proportions (all compounds together or selective mixtures of some of them), as desired. The microstructure of hot-pressed dense compacts, produced from these powders, revealed that the SiC phase is distributed very homogeneously, even occasionally within Al₂O₃ grains on a nanosize scale. The homogeneous distribution of SiC particles within the system produced high fracture toughness of the hot-pressed material (K_IC∼ 7.0 MPa · m^1/2) and having Vicker's hardness values greater than 2000 kgf/mm².     

The effect of feedstock and process conditions on the synthesis of high purity CNTs from aromatic hydrocarbons

High purity multi-walled carbon nanotubes were synthesized from aromatic hydrocarbons (benzene, toluene, xylene and trimethyl benzene) using ferrocene as the source of Fe catalyst. Screening studies of aromatic feeds at 675 °C, residence time of 14 s and Fe/C atom ratio of 1.07%, resulted in feedstock carbon conversion of 20-31%, CNT yield of 19.8-30.5%, and catalyst yield of 5.3-8.3 (g CNT/g catalyst). While the quality of the CNTs as determined by TGA, SEM, TEM and Raman spectroscopy, were high and comparable for different feedstocks; their carbon conversion, CNT yield and catalyst yield differed noticeably. A process optimization study for toluene feed showed that carbon conversion of more than 39%, CNT yield of 38.7% and catalyst yield of 18.3 can be achieved at temperature of 800 °C, Fe/C atom ratio of 0.47%, and residence time of 10-20 s.