Effect of heat treatment and zircon addition on the properties of low-cement castables based on recycled alumina grog

In this investigation, low-cement castables were prepared using 70% alumina grog aggregates obtained from crushed alumina brick waste. The aggregates were thermally treated at 1550 °C for 3 h. Four types of low-cement castables were prepared with various types of aggregates (alumina grog with or without thermal treatment) and fillers (with or without zircon addition), and they were evaluated in terms of their physical, thermal, and chemical properties. Microstructural analysis via scanning electron microscopy (SEM) was performed on the castables before and after slag attack. Compared to the other fabricated castables, the thermally treated alumina grog castables with zircon showed better physical properties, such as a higher bulk density, cold crushing strength, and modulus of rupture and a lower apparent porosity and water absorption. In addition, they had a higher positive linear thermal expansion, refractoriness under load, permanent linear change, and hot modulus of rupture. The results of the SEM with energy dispersive X-ray analysis of the prepared castables confirmed that the mullite and anorthite phases were predominant when zircon was not added and the zircon–mullite phase additionally appeared upon the incorporation of zircon. A quantitative elemental analysis via X-ray fluorescence spectroscopy was employed to determine the composition of the castables. X-ray diffraction analysis showed that the alumina grog castables had a high mullite and low anorthite content, and the thermally treated alumina grog had a high anorthite, low mullite, and high zircon content. The improvement in the mechanical and thermo-mechanical properties of the castables with thermally treated alumina grog and added zircon can be attributed to the formation of the zircon–mullite phase with a low mullite phase content.     

Ageing studies of pineapple leaf fiber–reinforced polyester composites

This experimental study evaluated the water absorption characteristics of pineapple leaf fiber (PALF)–polyester composites of different fiber content. The degree of water absorption was found to increase with fiber loading. The mechanism of diffusion was analyzed and the effect of fiber loading on the sorption kinetics was studied. The diffusion coefficient was calculated and found to increase with fiber content. Studies were also made to correlate water absorption with the cross‐sectional areas of the specimens. The effects of ageing on the tensile properties and dimensional stability of PALF polyester composites were studied under two different ageing conditions. Ageing studies showed a decrease in tensile strength of the composites. The composite specimens subjected to thermal ageing showed only a slight deterioration in strength. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 94: 503–510, 2004     

Design and analysis of text document clustering using salp swarm algorithm

The Journal of Supercomputing - In the technological era, exponential increase of unorganized text documents offers increased difficulties retrieving the most relevant data. The document clustering...     

Cost Effective Optimal Task Scheduling Model in Hybrid Cloud Environment

In today’s world, Cloud Computing (CC) enables the users to access computing resources and services over cloud without any need to own the infrastructure. Cloud Computing is a concept in which a network of devices, located in remote locations, is integrated to perform operations like data collection, processing, data profiling and data storage. In this context, resource allocation and task scheduling are important processes which must be managed based on the requirements of a user. In order to allocate the resources effectively, hybrid cloud is employed since it is a capable solution to process large-scale consumer applications in a pay-by-use manner. Hence, the model is to be designed as a profit-driven framework to reduce cost and make span. With this motivation, the current research work develops a Cost-Effective Optimal Task Scheduling Model (CEOTS). A novel algorithm called Target-based Cost Derivation (TCD) model is used in the proposed work for hybrid clouds. Moreover, the algorithm works on the basis of multi-intentional task completion process with optimal resource allocation. The model was successfully simulated to validate its effectiveness based on factors such as processing time, make span and efficient utilization of virtual machines. The results infer that the proposed model outperformed the existing works and can be relied in future for real-time applications.     

Efficacy of biogenic silver nanoparticles against clinical isolates of fungi causing mycotic keratitis in humans

Mycotic keratitis is mainly responsible for vision loss caused by various fungi. Sometimes, proper treatment of such infection is not possible due to unavailability of effective antifungal agents and development of resistance of such fungi to antimycotic drugs. Hence, it is necessary to search for potential antifungal agents, which can effectively eradicate fungal infection of eyes. Nanoparticles‐based antifungal drugs overcome this problem by increasing permeability and properties of drug molecules. In the present study, silver nanoparticles were synthesised by using Helminthosporium sp. and Chaetomium sp. following sequential reduction technique. The synthesised silver nanoparticles were detected primarily by UV‐visible spectrophotometer showing absorption spectra at 424 and 433 nm, respectively. Nanoparticles tracking analysis confirmed the mean particle size of silver nanoparticles as 45 and 55 nm. The synthesised AgNPs showed significant antifungal activity against fungi causing mycotic keratitis, when used alone and in combination with ketoconazole and amphotericin B in the range of 30–70 microgram per millilitre of minimum inhibitory concentration. Thus, the synthesised AgNPs can be used to enhance the activities of ketoconazole and amphotericin B.Inspec keywords: silver, nanoparticles, nanomedicine, antibacterial activity, diseases, permeability, ultraviolet spectra, visible spectra, particle sizeOther keywords: biogenic silver nanoparticles, fungi, mycotitic keratitis, vision loss, infection, antifungal agents, antimycotic drugs, antifungal drugs, permeability, Helminthosporium sp, Chaetomium sp, sequential reduction technique, UV‐visible spectrophotometer, mean particle size, ketoconazole, amphotericin B, wavelength 424 nm, wavelength 433 nm, size 45 nm, size 55 nm, Ag     

Application of extreme learning machine for series compensated transmission line protection

This paper proposes a new approach based on combined Wavelet Transform-Extreme Learning Machine (WT-ELM) technique for fault section identification (whether the fault is before or after the series capacitor as observed from the relay point), classification and location in a series compensated transmission line. This method uses the samples of fault currents for half cycle duration from the inception of fault. The features of fault currents are extracted by first level decomposition of the current samples using discrete wavelet transform (DWT) and the extracted features are applied as inputs to ELMs for fault section identification, classification and location. The feasibility of the proposed method has been tested on a 400 kV, 300 km series compensated transmission line for all the ten types of faults using MATLAB simulink. On testing 28,800 fault cases with varying fault resistance, fault inception angle, fault distance, load angle, percentage compensation level and source impedance, the performance of the proposed method has been found to be quite promising. The results also indicate that the proposed method is robust to wide variation in system and operating conditions.     

Impact of applying pre-processing techniques for improving classification accuracy

Image denoising is a procedure aimed at removing noise from images while retaining as many important signal features as possible. Many images suffer from poor contrast due to inadequate illumination or finite sensitivity of the imaging device, electronic sensor noise or atmospheric disturbances. This paper proposes a hybrid directional lifting technique for image denoising to retain the original information present in the images. The primary objective of this paper is to show the impact of applying preprocessing techniques for improving classification accuracy. In order to classify the image accurately, effective preservation of edges and contour details of an image is essential. The discrete wavelet transform-based interpolation technique is developed for resolution enhancement. The image is then classified using support vector machine classifier, which is well suitable for image classification. The efficiency of the classifier is analyzed based on receiver operating characteristic (ROC) curves. The quantitative performance measures peak signal to noise ratio and ROC analysis show the significance of the proposed techniques.