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Recent public health policy emphasizes the achievement of healthy aging as average life expectancy increases worldwide. Evidence for healthy aging from low- and middle-income countries (LMIC) is limited. The purpose of this paper is to assess the prospects of healthy aging and its associated factors in the Indian context.

The study was based on a national-level panel survey, the Indian Human Development Survey (IHDS) conducted in 2004-05 and 2011-12. The analytical sample consists of 10,218 elderly individuals who were 60 years old and above at the baseline. Change in health status was assessed based on disability and disease incidence at the follow-up. A generalized estimating equation (GEE) model was performed to assess health status change.

Increasing age was a risk factor for all dimensions of health outcomes. Elderly from the lowest wealth quintiles were more likely to lose health due to short-term morbidity, whereas the highest wealth quintiles were more likely to lose health due to long-term and multi-morbidity, indicating evidence for the presence of the “disease of affluence”. Social capital, such as living in a joint family acted as a protective factor against health risks.

With the results showing the evidence of the “disease of affluence” and “disease of poverty” in different health outcomes, there should be a health policy focus that copes with undergoing epidemiological transition. It is also important to pay attention to health-protecting factors such as social and familial support to achieve healthy aging.

The entropy and thermal behavior analyses of non-Newtonian nanofluid double-diffusive natural convection inside complex domains may captivate a bunch of scholars’ attention because of the potential utilizations that they possess in modern industries, for example, heat exchangers, solar energy collectors and cooling of electronic apparatuses. This study aims to investigate the second law and thermal behavior of non-Newtonian double-diffusive natural convection (DDNC) of Al₂O₃-H₂O nanofluid within a C-shaped cavity emplacing two hot baffles and impacted by a magnetic field.

For the governing equations of the complicated and practical system with all considered parameters to be solved via a formidable numerical approach, the finite element method acts as an approach to achieving the desired solution. This method allows us to gain a detailed solution to the studied geometry.

This investigation has been executed for the considered parameters of range, such as power-law index, baffle length, Lewis number, buoyancy ratio, Hartmann number and Rayleigh number. The main results reveal that isothermal and concentration lines are significantly more distorted, indicating intensified concentration and temperature distributions because of the growth of baffle length (L). Nu_ave decreases by 8.4% and 0.8% while it enhances by 49.86% and 33.87%, respectively, because of growth in the L from 0.1 to 0.2 and 0.2 to 0.3.

Such a comprehensive study on the second law and thermal behavior of DDNC of Al₂O₃-H₂O nanofluid within a C-shaped cavity emplacing two hot baffles and impacted by magnetic field has not yet been carried out.

The purpose of this study/paper is to propose a mathematical model that is able to predict the future popularity based on the view count of a particular YouTube video. Since the emergence of video-sharing sites from early 2005, YouTube has been pioneering in its performance and holds the largest share of internet traffic. YouTube plays a significant role in popularizing information on social network. For all social media sites, viewership is an important and vital component to measure diffusion on a video-sharing site, which is defined in terms of the number of view counts. In the era of social media marketing, companies demand an efficient system that can predict the popularity of video in advance. Diffusion prediction of video can help marketing firms and brand companies to inflate traffic and help the firms in generating revenue.

In the present work, viewership is studied as an important diffusion-affecting parameter pertaining to YouTube videos. Primarily, a mathematical diffusion model is proposed to predict YouTube video diffusion based on the varying situations of viewership. The proposal segregates the total number of viewers into two classes – neoterics viewers, i.e. viewers those viewing a video on a direct basis, and followers, i.e. viewers those watching under the influence.

The approach is supplemented with numerical illustration done on the real YouTube data set. Results prove that the proposed approach contributes significantly to predict viewership of video. The proposed model brings predicted viewership and its classification highly close to the true value.

Thereby, a behavioral rationale for the modeling and quantification is offered in terms of the two varied and yet connected classes of viewers – “neoterics” and “followers.”

Switching power converters for photovoltaic (PV) applications with high gain are rapidly expanding. To obtain better voltage gain, low switch stress, low ripple and cost-effective converters, researchers are developing several topologies.

It was decided to use the particle swarm optimization approach for this system in order to compute the precise PI controller gain parameters under steady state and dynamic changing circumstances. A high-gain q- ZS boost converter is used as an intermittent converter between a PV and brushless direct current (BLDC) motor to attain maximum power point tracking, which also reduces the torque ripples. A MATLAB/Simulink environment has been used to build and test the positive output quadratic boost high gain converters (PQBHGC)-1, PQBHGC-8, PQBHGC-4 and PQBHGC-3 topologies to analyse their effectiveness in PV-driven BLDC motor applications. The simulation results show that the PQBHGC-3 topology is effective in comparison with other HG cell DC–DC converters in terms of efficiency, reduced ripples, etc. which is most suitable for PV-driven BLDC applications.

The simulation results have showed that the PQBHGC-3 gives better performance with minimum voltage ripple of 2V and current ripple of 0.4A which eventually reduces the ripples in the torque in a BLDC motor. Also, the efficiency for the suggested PQBHGC-3 for PV-based BLDC applications is the best with 99%.

This study is the first of its kind comparing the different topologies of PQBHGC-1, PQBHGC-8, PQBHGC-4 and PQBHGC-3 topologies to analyse their effectiveness in PV-driven BLDC motor applications. This study suggests that the PQBHGC-3 topology is most suitable in PV-driven BLDC applications.

YouTube allows its users to upload and view videos on its platform. YouTube provides notification to the subscribers whenever a channel uploads a new video thereby making the channel subscribers the potential viewers of the video. And thus, they are the first to come to know about any new offering. But later on, the view count also increases due to virality, that is, mass sharing of the content by the users on different social media platforms similar to word-of-mouth in the field of marketing. Therefore, the purpose of this paper is to examine different diffusion patterns as they can help to inflate traffic and generate revenue.

YouTube's view count grows majorly through virality. The pattern of view count growth has generally been considered unimodal in most of the available research in the field of YouTube. In the present work, the growth process due to views through the subscribers and views due to word-of-mouth (virality) is presented. Considering that the impact of virality in view count growth comes later in the video life cycle; the viewing patterns of both the segments have been mathematically modeled; independently.

Different models have been proposed to capture the view count growth pattern and how the impact of virality changes the view count growth curve and thereby results in a multimodal curve structure. The proposed models have been verified on various view count data sets of YouTube videos using SPSS (Statistical Package for the Social Sciences), and their ranks have been determined using a weighted criteria–based approach. The results obtained clearly depict the presence of many modes in the life cycle of view counts.

Till now, the literature is evident of the video life cycle following a bell shape curve. This study claims that the initial thrust is by subscribers and then the contribution in the view count by people watching via word-of-mouth comes into picture and brings in another hump in the growth curve.

This study aimed to present fabrication of novel poly(lactic acid) (PLA) mediated chitosan nanoparticles (CNPs) and their impregnation on cotton fabric for enhanced antibacterial and physical properties.

The PLA-CNPs were characterized using scanning electron microscopy, energy dispersive x-ray spectroscopy, Fourier transform infrared (FTIR) spectroscopy and zeta size analysis. The prepared PLA-CNPs were impregnated on cotton fabric via pad-dry-cure method. The finished cotton fabric was then characterized for its antibacterial activity, functional and other physical textile properties.

The spectral and optical properties demonstrate that the NPs expressed spherical morphologies with an average particle diameter of 88.02 nm. The antibacterial activity of treated fabrics ranged between 75 and 90 per cent depending on the concentration of PLA-CNPs.

Because of enhanced awareness and desire for ecofriendly products, the use of sustainable and functional textiles is increasing day by day. For the said purpose, industries are using different chemical treatments to achieve desired end functionality. Currently, different synthetic antibacterial agents are in practice, but they lack sustainable approach to save the environment. In this study, the researchers have developed PLA mediated CNPs for sustainable antibacterial and physical properties of treated cotton fabric.

To the best of the authors’ knowledge, this is first attempt to fabricate PLA-incorporated CNPs for application on cotton fabric followed by a detailed characterization.

Based on the foundations of the schema theory, the elaboration likelihood model (ELM) and customer experience literature, this research examines how the interplay between a consumer's previous shopping experience(s) and perceived credibility of negative online word-of-mouth (PCNWOM) leads to improved consumer–firm relationship quality (RQ).

The authors utilised series of scenario-based experiments (N = 918) to test the research hypotheses.

The authors show that a focal customer's previous shopping experiences attenuate the perceived credibility of negative word-of-mouth on social media by other customers, which in turn weakens consumer–firm RQ. The authors also show that positive and negative perceptual experiences are asymmetric.

First, the online shopping experiences described in the experimental scenarios were generic and did not refer to any particular product/service. Thus, calibrating products and services into categories, and studying how product type differences impact online shopping experiences warrant further research.

From a practical perspective, the authors demonstrate that not only does enhancing consumer–firm relationship quality demand meticulous integration of consumers' website and social media experiences but also in positive vs negative perception scenarios, RQ wane as review frequency increases.

The authors contribute significant insight into the existing literature by specifically adopting the premise that consumers' previous online shopping experience(s) will influence how credibly they will perceive negative online WOM posted on social media.

The purpose of this study was to examine the impacts of consumers’ motivations (i.e. remuneration, social, and empowerment) on online purchase intentions mediated through trust towards retailers present on social media.

Data were collected from consumers residing in the three metropolitan cities of Pakistan, and the research model was tested using the covariance-based structural equation modelling in Amos.

The results showed that remuneration and social motivations positively influenced consumers’ online purchase intentions directly, as well as indirectly mediated through trust. However, trust fully mediated the relationship between empowerment motivation and consumers’ online purchase intentions.

The existing literature reveals that only a handful of studies have endeavoured to understand consumers’ trust in the context of social media marketing, and the literature in this field is not matured yet. The novelty of this research lies in its contribution to understanding the impacts of consumers’ motives (i.e. remuneration, social, and empowerment) on trust towards retailers present on social media, which have not been explored before. In addition, it examines trust towards retailers present on social media as an underlying mechanism that affects the relationships between consumers’ motives and online purchase intentions.