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In this paper, wave propagation in a poroelastic thick-walled hollow cylinder is investigated in the framework of Biot’s extension theory. Biot’s theory of poroelasticity is valid for isotropic porous solids saturated with non-viscous fluid. The bulk and shear viscosities are not considered in the classical Biot’s theory. Biot’s extension theory takes all these into an account. Biot’s extension theory is applied here to investigate the radial vibrations in thick-walled hollow poroelastic cylinder. The paper aims to discuss these issues.

By considering the stress-free boundaries, the frequency equation is obtained in the presence of dissipation. Limiting case when the ratio between thickness and inner radius is very small is investigated numerically. In the limiting case, the asymptotic expansions of Bessel functions are employed so that frequency equation is separated into two parts which gives attenuation coefficient and phase velocity. If the shear viscosity is neglected, then the problem reduces to that of the classical Biot’s theory.

For the numerical purpose, the solids Berea sandstone and bone are used. The results are presented graphically.

Radial vibrations of thick-walled hollow poroelastic cylinder are investigated in the framework of Biot’s extension theory. Due to the mathematical complexity, limiting case is considered. The complex valued frequency equation is discussed numerically which gives the attenuation coefficient and phase velocity. If shear viscosity is neglected, then the problem reduces to that of the classical Biot’s theory. The comparison has been made between the current results and that of classical results.

The purpose of this paper is to investigate the effect of initial stress and heterogeneity on the propagation of torsional waves in dissipative medium. The problem consists of dry sand poroelastic half-space embedded between heterogeneous self-reinforced half-space and poroelastic medium. The frequency equation is derived in the framework of Biot's theory with some variants.

Torsional wave propagation in dry sand poroelastic half-space embedded between self-reinforced half-space and poroelastic medium. All the constituents here are assumed to be dissipative, heterogeneous and initial stressed.

Phase velocity and attenuation are computed against wavenumber for various values of self-reinforcement parameter, inhomogeneity parameter and initial stress. Particular cases are discussed in absence of dissipation. The numerical results are presented graphically.

Initial stress and heterogeneity effects on torsional waves in dry sand half-space between reinforced half-space and poroelastic medium are investigated. The frequency equation is derived, and which intern gives the phase velocity and attenuation coefficient for various values of initial stress, self-reinforcement parameter and heterogeneity parameter. From the numerical results, it is clear that as wavenumber varies phase velocity and attenuation are periodic in nature for all the cases. Particular cases are discussed in absence of dissipation. This kind of analysis can be extended to any elastic solid by taking magnetic, thermo and piezoelectric effects into account.

The purpose of this paper is to investigate the effect of reinforcement, inhomogeneity and initial stress on the propagation of shear waves. The problem consists of magneto poroelastic medium sandwiched between self-reinforced medium and poroelastic half space. Using Biot’s theory of wave propagation, the frequency equation is obtained.

Shear wave propagation in magneto poroelastic medium embedded between a self-reinforced medium and poroelastic half space is investigated. This particular setup is quite possible in the Earth crust. All the three media are assumed to be inhomogeneous under initial stress. The significant effects of initial stress and inhomogeneity parameters of individual media have been studied.

Phase velocity is computed against wavenumber for various values of self-reinforcement, heterogeneity parameter and initial stress. Classical elasticity results are deduced as a particular case of the present study. Also in the absence of inhomogeneity and initial stress, frequency equation is discussed. Graphical representation is made to exhibit the results.

Shear wave propagation in magneto poroelastic medium embedded between a self-reinforced medium, and poroelastic half space are investigated in presence of initial stress, and inhomogeneity parameter. For heterogeneous poroelastic half space, the Whittaker’s solution is obtained. From the numerical results, it is observed that heterogeneity parameter, inhomogeneity parameter and reinforcement parameter have significant influences on the wave characteristics. In addition, frequency equation is discussed in absence of inhomogeneity and initial stress. For the validation purpose, numerical results are also computed for a particular case.

This paper describes with the help of a case study how vendor managed inventory (VMI) model managers in real time the dynamic needs of supply chain process. Throughout the supply chain, VMI model is a way to cut inventory related costs and keep inventory levels low. VMI model helps companies to reduce the inventory‐associated costs by shifting the responsibility of managing and replenishing inventory to vendors. A study of a leading tyre manufacturer (XYZ) is reported. The supply chain of this company includes raw materials, manufacturing operations and the distribution network. The product range includes truck tyres, front‐end tractor tyres, scooter tyres, LCV tyres, and passenger radial tyres for both domestic and international market from manufacturing plants at different locations in the country. A prototype of VMI model based on simulation approach has been developed to illustrte how VMI model can enhance the performance of this organization by minimizing the inventory associated costs.

This study aims to explore the motivators of mothers’ experience on their engagement behavior in the case of baby care toiletries. Additionally, the role of Brand trust and commitment have also been evaluated.

The conceptual model was empirically tested based on the data collected through a survey using 320 samples from India and 431 samples from Sri Lanka. Data were analyzed using structural equation modeling.

Sensory and behavioral dimensions of brand experience can be considered as key drivers of brand trust and brand commitment among millennial mothers in the context of baby care toiletries. It was observed that brand trust had a significant positive impact on brand commitment. There was a significant relationship between brand trust, brand commitment and customer engagement. It was also inferred that brand loyalty is the consequence of customer engagement.

Marketers should gear up initiatives targeting new mothers through healthy aspects and genuine packaging to strengthen the mother’s trust through periodic uses of the product.

The present study is one of the unique empirical investigations that examine the antecedents of consumer engagement in the less researched context of high inherent risk products like baby toiletries.

Aspect-based sentiment analysis (ASA) is a task of sentiment analysis that requires predicting aspect sentiment polarity for a given sentence. Many traditional techniques use graph-based mechanisms, which reduce prediction accuracy and introduce large amounts of noise. The other problem with graph-based mechanisms is that for some context words, the feelings change depending on the aspect, and therefore it is impossible to draw conclusions on their own. ASA is challenging because a given sentence can reveal complicated feelings about multiple aspects.

This research proposed an optimized attention-based DL model known as optimized aspect and self-attention aware long short-term memory for target-based semantic analysis (OAS-LSTM-TSA). The proposed model goes through three phases: preprocessing, aspect extraction and classification. Aspect extraction is done using a double-layered convolutional neural network (DL-CNN). The optimized aspect and self-attention embedded LSTM (OAS-LSTM) is used to classify aspect sentiment into three classes: positive, neutral and negative.

To detect and classify sentiment polarity of the aspect using the optimized aspect and self-attention embedded LSTM (OAS-LSTM) model. The results of the proposed method revealed that it achieves a high accuracy of 95.3 per cent for the restaurant dataset and 96.7 per cent for the laptop dataset.

The novelty of the research work is the addition of two effective attention layers in the network model, loss function reduction and accuracy enhancement, using a recent efficient optimization algorithm. The loss function in OAS-LSTM is minimized using the adaptive pelican optimization algorithm, thus increasing the accuracy rate. The performance of the proposed method is validated on four real-time datasets, Rest14, Lap14, Rest15 and Rest16, for various performance metrics.

Jamun is an underutilised fruit crop of India whose utilization needs to be considerably increased. Despite its impressive nutritional profile and several health benefits, its cultivation is still very limited. Hence, this study aims to highlight the essential nutrients, health benefits and value-added products of jamun fruit and seed.

Major sources of bibliometric information such as Web of Science, Scopus, PubMed and Google Scholar were extensively searched with keywords such as nutritional composition of jamun, bioactive compounds, health benefits and jamun-based food products to obtain a database of 317 papers. Thirty four publications met the criteria for review.

Jamun fruit (Syzygium cumini L.) is known for its attractive colour, astringent taste, enormous nutrients and nutraceutical properties; nevertheless, this fruit is seasonal, perishable and underutilised. Furthermore, the fruit is used for the treatment of diabetes and also possess anti-cancer, anti-inflammatory, anti-obesity and cardio-protection properties. Investigations were done on the production of value-added food products such as read-to-serve beverages, jam, cookies and cake from jamun due to their several health benefits. The present review was an attempt to provide spotlights on the health benefits and food product applications of jamun fruit and its by-products.

This review systematically collates evidence from various published sources regarding the nutritional profile, development of food products from jamun and their health benefits. The compiled information will help researchers and professional become aware of the significance of jamun fruit and thereby enhances its processing and applications in food systems.

Electronic devices aid communication during new communication phases and the scope of cognitive radio networks has changed communication paradigms through efficient use of spectrums. The communication prototype of cognitive radio networks defines user roles as primary user and secondary user in the context of the spectrum allocation and use. The users who have licensed authority of the spectrum are denoted as primary users, while other eligible users who access the corresponding spectrum are secondary users.

The multiple factors of transmission service quality can have a negative influence due to improper scheduling of spectrum bands between primary users and secondary users. There are considerable contributions in contemporary literature concerning spectrum band scheduling under spectrum sensing. However, the majority of the scheduling models are intended to manage a limited number of transmission service quality factors. Moreover, these service quality factors are functional and derived algorithmically from the current corresponding spectrum. However, there is evidence of credible performance deficiency regarding contemporary spectrum sensing methods

This article intends to portray a fuzzy guided integrated factors-based spectrum band sharing within the spectrum used by secondary users. This study attempts to explain the significance of this proposal compared to other contemporary models.

This article intends to portray a fuzzy guided integrated factors-based spectrum band sharing within the spectrum used by secondary users. This study attempts to explain the significance of this proposal compared to other contemporary models.

With the advancements in photo editing software, it is possible to generate fake images, degrading the trust in digital images. Forged images, which appear like authentic images, can be created without leaving any visual clues about the alteration in the image. Image forensic field has introduced several forgery detection techniques, which effectively distinguish fake images from the original ones, to restore the trust in digital images. Among several forgery images, spliced images involving human faces are more unsafe. Hence, there is a need for a forgery detection approach to detect the spliced images.

This paper proposes a Taylor-rider optimization algorithm-based deep convolutional neural network (Taylor-ROA-based DeepCNN) for detecting spliced images. Initially, the human faces in the spliced images are detected using the Viola–Jones algorithm, from which the 3-dimensional (3D) shape of the face is established using landmark-based 3D morphable model (L3DMM), which estimates the light coefficients. Then, the distance measures, such as Bhattacharya, Seuclidean, Euclidean, Hamming, Chebyshev and correlation coefficients are determined from the light coefficients of the faces. These form the feature vector to the proposed Taylor-ROA-based DeepCNN, which determines the spliced images.

Experimental analysis using DSO-1, DSI-1, real dataset and hybrid dataset reveal that the proposed approach acquired the maximal accuracy, true positive rate (TPR) and true negative rate (TNR) of 99%, 98.88% and 96.03%, respectively, for DSO-1 dataset. The proposed method reached the performance improvement of 24.49%, 8.92%, 6.72%, 4.17%, 0.25%, 0.13%, 0.06%, and 0.06% in comparison to the existing methods, such as Kee and Farid's, shape from shading (SFS), random guess, Bo Peng et al., neural network, FOA-SVNN, CNN-based MBK, and Manoj Kumar et al., respectively, in terms of accuracy.

The Taylor-ROA is developed by integrating the Taylor series in rider optimization algorithm (ROA) for optimally tuning the DeepCNN.

The purpose of this paper is to focus on the cost-effective and environmentally sustainable operation of thermal power systems to allocate optimum active power generation resultant for a feasible solution in diverse load patterns using the grey wolf optimization (GWO) algorithm.

The economic dispatch problem is formulated as a bi-objective optimization subjected to several operational and practical constraints. A normalized price penalty factor approach is used to convert these objectives into a single one. The GWO algorithm is adopted as an optimization tool in which the exploration and exploitation process in search space is carried through encircling, hunting and attacking.

A linear interpolated price penalty model is developed based on simple analytical geometry equations that perfectly blend two non-commensurable objectives. The desired GWO algorithm reports a new optimum thermal generation schedule for a feasible solution for different operational strategies. These are better than the earlier reports regarding solution quality.

The proposed method seems to be a promising optimization tool for the utilities, thereby modifying their operating strategies to generate electricity at minimum energy cost and pollution levels. Thus, a strategic balance is derived among economic development, energy cost and environmental sustainability.

A single optimization tool is used in both quadratic and non-convex cost characteristics thermal modal. The GWO algorithm has discovered the best, cost-effective and environmentally sustainable generation dispatch.