Generation of SH-type waves in a poroelastic layer sandwiched between prestressed orthotropic and nonhomogeneous mantles

The present work is concerned with a detailed illustration on the study of horizontally polarized shear waves (SH-type) propagation in a prestressed fluid saturated anisotropic porous layer sandwiched by prestressed orthotropic medium and nonhomogeneous mantles. The frequency equation for the assumed model is derived and their medium characteristics, such as porosity, prestress, anisotropy, and nonhomogeneity, are discussed. Numerical treatment is given to analyze these effects on phase velocities of SH-type waves and is plotted in various graphs. The parametric study divulges that the magnitude of wave velocities increases with the increase of nonhomogeneity parameter and prestress parameter.     

Biocompatible antimicrobial cotton fibres for healthcare industries: a biogenic approach for synthesis of bio‐organic‐coated silver nanoparticles

Cotton fibres coated with biogenically fabricated silver nanoparticles (SNPs) are most sought material because of their enhanced activity and biocompatibility. After successful synthesis of SNPs on cotton fibres using leaf extract of Vitex negundo Linn, the fibres were studied using diffuse reflectance spectroscopy, scanning electron microscopy, nanoparticle tracking analysis, energy dispersive X‐ray, and inductively coupled plasma atomic emission spectrometry. The characterisation revealed uniformly distributed spherical agglomerates of SNPs having individual particle size around 50 nm with the deposition load of 423 μg of silver per gram of cotton. Antimicrobial assay of cotton–SNPs fibres showed effective performance against pathogenic bacteria and fungi. The method is biogenic, environmentally benign, rapid, and cost‐effective, producing highly biocompatible antimicrobial coating required for the healthcare industry.Inspec keywords: cotton, health care, nanoparticles, coatings, silver, fibres, nanofabrication, scanning electron microscopy, X‐ray chemical analysis, atomic emission spectroscopy, plasma applications, microorganisms, biotechnologyOther keywords: biocompatible antimicrobial cotton fibre coating, healthcare industry, bioorganic‐coated silver nanoparticle synthesis, biogenically fabricated silver nanoparticle, SNP, leaf extraction, Vitex negundo Linn, diffuse reflectance spectroscopy, scanning electron microscopy, nanoparticle tracking analysis, energy dispersive X‐ray spectrometry, inductively coupled plasma atomic emission spectrometry, uniformly distributed spherical agglomerate, antimicrobial assay, pathogenic bacteria, fungi, Ag     

Manifold Learning Using Linear Local Tangent Space Alignment (LLTSA) Algorithm for Noise Removal in Wavelet Filtered Vibration Signal

A denoising procedure is proposed to remove both out-band and in-band noise for extraction of weak bursts in signal obtained from defective bearing. Energy of continuous wavelet scalogram is computed and the band having higher energy is selected to remove the out-band noise. Signals of selected band are brought together to form a high-dimensional waveform feature space. Further, low dimensional waveform manifold is formed using linear local tangent space alignment (LLTSA) algorithm to remove in-band noise. A criterion, entitled as frequency factor is also proposed to determine the optimum neighbour size of LLTSA. The two complicated conditions are chosen to demonstrate the effectiveness of the technique in the extraction of bursts in the noisy situations. A significant improvement in the signal to noise ratio is observed when in-band noise is removed using manifold learning by LLTSA algorithm. The experimental result reveals the success of the proposed denoising procedure in extraction of defect features, even in the case of noisy condition.     

Context sensitive trust based geographic opportunistic routing in mobile ad hoc networks

Sādhanā - Position based opportunistic routing (POR) is a stateless, robust, and reliable geographic routing protocol in Mobile AdHoc NETwork (MANET). The opportunistic routing embraces...     

Automatic text classification using BPLion-neural network and semantic word processing

Text mining has become a major research topic in which text classification is the important task for finding the relevant information from the new document. Accordingly, this paper presents a semantic word processing technique for text categorization that utilizes semantic keywords, instead of using independent features of the keywords in the documents. Hence, the dimensionality of the search space can be reduced. Here, the Back Propagation Lion algorithm (BP Lion algorithm) is also proposed to overcome the problem in updating the neuron weight. The proposed text classification methodology is experimented over two data sets, namely, 20 Newsgroup and Reuter. The performance of the proposed BPLion is analysed, in terms of sensitivity, specificity, and accuracy, and compared with the performance of the existing works. The result shows that the proposed BPLion algorithm and semantic processing methodology classifies the documents with less training time and more classification accuracy of 90.9%.     

An improved anti-forensics JPEG compression using Least Cuckoo Search algorithm

The lossy nature of the JPEG compression leaves traces which are utilized by the forensic agents to identify the local tampering in the image. In this paper, a tricky anti-forensic method has been proposed to remove the traces left by the JPEG compression in both the spatial domain and discrete cosine transform domain. A novel Least Cuckoo Search algorithm is devised in the proposed anti-forensic compression scheme. Moreover, a new fitness function called histogram deviation is formulated in the optimization algorithm. The experimentation of the proposed anti-forensic compression scheme is performed over uncompressed images from UCID database. The performance of the proposed method is evaluated, and it is compared with the existing methods using PSNR, MSE and classification accuracy as measures. The experimentation ensued with promising results, i.e. accuracy of 0.97, PSNR of 44.34?dB, and MSE of 0.1789 which prove the efficacy of the proposed method.     

Test Case Prioritization in Unit and Integration Testing: A Shuffled-Frog-Leaping Approach

Both unit and integration testing are incredibly crucial for almost any software application because each of them operates a distinct process to examine the product. Due to resource constraints, when software is subjected to modifications, the drastic increase in the count of test cases forces the testers to opt for a test optimization strategy. One such strategy is test case prioritization (TCP). Existing works have propounded various methodologies that re-order the system-level test cases intending to boost either the fault detection capabilities or the coverage efficacy at the earliest. Nonetheless, singularity in objective functions and the lack of dissimilitude among the re-ordered test sequences have degraded the cogency of their approaches. Considering such gaps and scenarios when the meteoric and continuous updations in the software make the intensive unit and integration testing process more fragile, this study has introduced a memetics-inspired methodology for TCP. The proposed structure is first embedded with diverse parameters, and then traditional steps of the shuffled-frog-leaping approach (SFLA) are followed to prioritize the test cases at unit and integration levels. On 5 standard test functions, a comparative analysis is conducted between the established algorithms and the proposed approach, where the latter enhances the coverage rate and fault detection of re-ordered test sets. Investigation results related to the mean average percentage of fault detection (APFD) confirmed that the proposed approach exceeds the memetic, basic multi-walk, PSO, and optimized multi-walk by 21.7%, 13.99%, 12.24%, and 11.51%, respectively.     

PVC-Based 2,2,2-Cryptand Sensor for Zinc Ions

A PVC-based membrane of 2,2,2-cryptand exhibits a very good response for Zn(2+) in a wide concentration range (from 2.06 ppm to 6.54 × 10(3) ppm) with a slope of 22.0 mV/decade of Zn(2+) concentration. The response time of the sensor is <10 s, and the membrane can be used for more than 3 months without any observed divergence in potentials. The proposed sensor exhibits very good selectivity for Zn(2+) over other cations and can be used in a wide pH range (2.8-7.0). It has also been possible to use this assembly as an indicator electrode in potentiometric titrations involving zinc ions.