Green composites based on high‐density polyethylene and Saccharum spontaneum: Effect of filler content on morphology,thermal, and mechanical properties

Here we report the preparation and characterization of a green composite based on high‐density polyethylene and Kaans grass (Saccharum spontaneum). The composites were prepared by conventional melt‐mixing method, using maximum loading of Kaans grass in powder form (KG‐filler) to achieve acceptable range of required properties. Maleic anhydride grafted polyethylene was used as compatibilizer to achieve effective interaction for improved surface adhesion which was confirmed by FT‐IR spectroscopy. Morphological studies revealed good interaction between the base polymer matrices and the KG‐fillers that improved the mechanical and thermal properties of the composites up to certain (10 phr) KG‐filler loading. Study on water absorption property revealed moderate increase in weight at higher KG‐filler loadings. Thermogravimetric analysis (TGA) and melt flow index (MFI) studies indicated retention of thermal stability and flow property of the HDPE/KG‐filler composite at lower filler loadings. POLYM. COMPOS., 36:2157–2166, 2015. © 2014 Society of Plastics Engineers     

Genome-Wide Identification and Characterization of the Brassinazole-resistant (BZR) Gene Family and Its Expression in the Various Developmental Stage and Stress Conditions in Wheat (Triticum aestivum L.)

Brassinosteroids (BRs) play crucial roles in various biological processes, including plant developmental processes and response to diverse biotic and abiotic stresses. However, no information is currently available about this gene family in wheat (Triticum aestivum L.). In the present investigation, we identified the BZR gene family in wheat to understand the evolution and their role in diverse developmental processes and under different stress conditions. In this study, we performed the genome-wide analysis of the BZR gene family in the bread wheat and identified 20 TaBZR genes through a homology search and further characterized them to understand their structure, function, and distribution across various tissues. Phylogenetic analyses lead to the classification of TaBZR genes into five different groups or subfamilies, providing evidence of evolutionary relationship with Arabidopsis thaliana, Zea mays, Glycine max, and Oryza sativa. A gene exon/intron structure analysis showed a distinct evolutionary path and predicted the possible gene duplication events. Further, the physical and biochemical properties, conserved motifs, chromosomal, subcellular localization, and cis-acting regulatory elements were also examined using various computational approaches. In addition, an analysis of public RNA-seq data also shows that TaBZR genes may be involved in diverse developmental processes and stress tolerance mechanisms. Moreover, qRT-PCR results also showed similar expression with slight variation. Collectively, these results suggest that TaBZR genes might play an important role in plant developmental processes and various stress conditions. Therefore, this work provides valuable information for further elucidate the precise role of BZR family members in wheat.     

Ensemble Support Vector Machine Algorithm for Reliability Estimation of a Mining Machine

In this study, a support vector machine (SVM)‐based ensemble model was developed for reliability forecasting. The hyperparameters of the SVM were selected by applying a genetic algorithm. Input variables of the SVM model were selected by maximizing the mean entropy value. The diverse members of the ensemble model were obtained by a k‐means clustering algorithm, and one ensemble member was selected from each cluster by choosing the closest from the cluster center. The optimum cluster number was selected using the Davies–Bouldin index. The developed model was validated by a benchmark turbocharger data set. A comparative study reveals that the proposed method performs better than existing methods on benchmark data sets. A case study was conducted investigating a dumper operated at a coal mine in India. Time‐to‐failure historical data for the dumper were collected, and cumulative time to failure was calculated for reliability forecasting. Study results demonstrate that the developed model performs well with high accuracy (R² = 0.97) in the prediction of dumper failure, and a comparison with other methods demonstrates the superiority of the proposed ensemble SVM model. Copyright © 2014 John Wiley & Sons, Ltd.     

Multiplier‐less VLSI architectures for radix‐22 folded pipelined complex FFT core

The advantages of a multiplier‐less architecture are reduction in hardware and latency. This paper proposes multiplier‐less architectures for the implementation of radix‐2² folded pipelined complex FFT core based on coordinate rotation digital computer (CORDIC) and new distributed arithmetic (NEDA). The number of points considered in the work is sixteen and the folding is done by a factor of four. The proposed designs have been implemented on Xilinx XC5VSX240T‐2FF1738 FPGA and also have been synthesized using the Synopsys design compiler. Proposed designs based on NEDA have reduced area over 83% and based on CORDIC have a reduced area over 78%. The observed slice‐delay product for NEDA based designs are 2.196 and 5.735, and for CORDIC based design is 2.369. Copyright © 2014 John Wiley & Sons, Ltd.     

Effect of Filler Content on the Properties of Polypropylene/Saccharum spontaneum Green Composite

Current study focuses on the exploration of Saccharum spontaneum to fabricate polymer green composite in polypropylene matrix with malic anhydride-grafted compatibilizer. Analysis by thermogravimetric showed slight deviation in thermal stability only at higher kans grass-filler loading, but significant improvement was observed in tensile and impact strength. Storage modulus and loss modulus were reasonably increased and damping factor (tan δ) had no major deviation from base polymer. Fourier transform infrared spectroscopy peaks and morphological data suggest strong polymer-filler interfacial adhesion in the fabricated composite. Melt flow properties confirmed the processability with kans grass-filler content at 60 phr.     

Customer on-boarding strategies for cloud computing services with dynamic service-level agreements

A multi-tenant software as a service (SaaS) provider has to meet the needs of several tenants which adopt its services with diverse business requirements. The tenant needs vary widely with time, and the provider has to account for such fluctuations by suitable provisioning at its end. Handling this elasticity arising out of the tenant base is one of the key challenges for the SaaS provider. In this paper, we study the problem specifically in the SaaS context with the idea built around license provisioning in a tenant–provider perspective. For a given set of tenants with diverse license requirements, it is important to analyze whether there is any way to on-board them such that all constraints laid out as part of the service-level agreement can be honored. The total number of licenses available with the provider plays a crucial role in answering this question. We propose an intuitive model of elasticity that can capture anticipated license need variations at the tenant end. We propose an ILP-based approach for solving this schedulability problem for a collection of tenants. We also propose a simple-minded greedy heuristic to solve the on-boarding problem with elasticity constraints. Results show that our approach gives acceptable performance.     

The variable step-size LMS/F algorithm using nonparametric method for adaptive system identification

A fundamental challenge affecting the performance of a system is the undesired effect of noise on the system. Practically, real-time systems are influenced by Gaussian noise and impulsive noise. Identification of these nonlinear physical systems in the presence of noise offers broader applications than linear system identification. Hence, this article introduces a variable step-size technique to solve the conflicting requirement of rapid convergence and low mean square error (MSE) in the presence of both Gaussian and impulsive noise. Moreover, to avoid over parameterized equations existing in the variable step-size equation, this article proposes the nonparametric variable step-size (NPVSS), which depends on error estimates at instants of time and is used with the least mean square/fourth (LMS/F) algorithm. The computational complexity analysis, computer simulations, and implementation in real-time setup validate that the proposed NPVSS-LMS/F algorithm provides superior performance in terms of convergence time and MSE compared to the existing algorithms for both linear and nonlinear system identification in the presence of noise.