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     

Genetic based sensorless hybrid intelligent controller for strip loop formation control between inter-stands in hot steel rolling mills

Safe operating environment is essential for all complex industrial processes. The safety issues in steel rolling mill when the hot strip passes through consecutive mill stands have been considered in this paper. Formation of sag in strip is a common problem in the rolling process. The excessive sag can lead to scrap runs and damage to machinery. Conventional controllers for mill actuation system are based on a rolling model. The factors like rise in temperature, aging, wear and tear are not taken into account while designing a conventional controller. Therefore, the conventional controller cannot yield a requisite controlled output. In this paper, a new Genetic-neuro-fuzzy hybrid controller without tension sensor has been proposed to optimize the quantum of excessive sag and reduce it. The performance of the proposed controller has been compared with the performance of fuzzy logic controller, Neuro-fuzzy controller and conventional controller with the help of data collected from the plant. The simulation results depict that the proposed controller has superior performance than the other controllers.     

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     

Multiwalled carbon nanotubes grafted chitosan nanobiocomposite: A prosperous functional nanomaterials for glucose biosensor application

Multiwalled carbon nanotubes (MWNTs) grafted chitosan (CS) nanowire (NW) was prepared by phase separation method. Glucose oxidase (GOx) was sequentially immobilized into MWNT-CS-NW to obtain MWNT-CS-NW/GOx biosensor. Field emission scanning electron microscopy (FESEM) images of MWNT-CS-NW/GOx reveals the existence of MWNT and CS. Cyclic voltammetry and amperometry were used to evaluate the electrochemical determination of glucose. The MWNT-CS-NW/GOx biosensor shows an excellent performance for glucose at +0.34 V with a high sensitivity (5.03 μA/mM) and lower response time (3 s) in a wide concentration range of 1-10 mM (correlation coefficient of 0.9988). In addition, MWNT-CS-NW/GOx biosensor possesses better reproducibility, storage stability and there is negligible interference from other electroactive components.     

Investigation on microstructure and tensile behaviour of stir cast LM13 aluminium alloy reinforced with copper coated short steel fibers using response surface methodology

Copper coated steel fibers reinforced LM13 aluminium alloy composites have been prepared using stir casting process. Experiments have been designed using response surface methodology by varying wt% of reinforcement (0–10), stirrer speed (350–800 rpm) and pouring temperature (700–800 °C). Microstructure, tensile strength and fracture surface of composites have been investigated. Analysis of variance, significance test and confirmation tests have been performed and regressions models have been developed to predict the tensile strength of composites. Response surface plots reveal that tensile strength of composites increases with increasing wt% of copper coated steel fibers reinforcement up to 6 wt%. Further increase in wt% of steel fibers decreases the tensile strength of composites. However tensile strength of composites increases with increasing stirrer speed due to the uniform and homogeneous dispersion of steel fibers in matrix. Optimum stir cast process parameters for obtaining higher tensile strength are found to be 5.9 wt% of reinforcement, 753 °C pouring temperature and stirrer speed of 633 rpm. Fracture mechanism is dominated by steel fiber pullouts in composites with higher wt% of reinforcement and dimples are observed in the surface of composites containing lower levels of wt% of reinforcement.     

Antlion Algorithm Optimized Fuzzy PID Supervised On-line Recurrent Fuzzy Neural Network Based Controller for Brushless DC Motor

In this paper, Antlion algorithm optimized Fuzzy PID supervised on-line Recurrent Fuzzy Neural Network based controller is proposed for the speed control of Brushless DC motor. Learning parameters of the supervised on-line recurrent fuzzy neural network controller, i.e., learning rate (η), dynamic factor (α), and number nodes (N_i) are optimized using Genetic algorithm, Particle Swarm optimization, Ant colony optimization, Bat algorithm, and Antlion algorithm. The proposed controller is tested with different operating conditions of the Brushless DC motor, such as varying load conditions and varying set speed conditions. The time domain specifications such as rise time, overshoot, undershoot, settling time, recovery time, and steady state error and also integral performance indices such as root mean square error, integral of absolute error, integral of squared error, and integral of time multiplied absolute error are measured and compared for above optimized controller. Simulation results show Antlion algorithm optimized Fuzzy PID supervised on-line recurrent fuzzy neural network based controller has proved to be superior than other considered controllers in all aspects. In addition, the experimental verification of proposed control system is presented to test the effectiveness of the proposed controller with different operating conditions of the Brushless DC motor.