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The study attempts to investigate the role of social media in spreading awareness regarding ayurvedic immunity boosters (AIB) and changes in diet. Further, the study examines the factors affecting the willingness to pay for ayurvedic immunity boosters (WPIB) during the pandemic and new normal situation with the moderating effect of the “fear of COVID-19 infection.”

The data were collected from millennials in two phases, i.e. the first phase (1 July–August 2021) with 300 respondents and a second phase with (June–August 2022) 257 respondents. An online questionnaire was shared with millennials using the snowball sampling technique. Descriptive statistics with SPSS and SmartPLS 4.0 software were applied to analyze the data.

The results found a variation in AIB content sharing on social media during 2021 and 2022. Results found that respondents reported significant changes in their lifestyle and diet, like consuming honey, khada, tulsi tea, etc. In 2021, health consciousness and trust significantly affected WPIB, whereas in 2022, only health consciousness was substantially affected. Fear of COVID-19 infection moderates the relationship between health consciousness, perceived fear and willingness to pay for ayurvedic products, whereas the effect on consumer preference and trust remains insignificant.

Results could help ayurvedic product manufacturing companies understand the consumers' mindset and the factors that stimulate consumers to buy these immunity boosters. Ayurvedic advertisers should design unambiguous messages that focus on health consciousness and have trustable components to encourage consumers to adopt a healthy lifestyle.

This is one of its kinds of studies that presents the contrasts of how the COVID-19 crisis has significantly changed individuals' dietary intake and affected lifestyle patterns.

In this paper we investigate why the process of structural change in Brazil was growth accelerating before 1980 and why it was growth reducing after this year.

We investigate the causes of this change in behavior using the shift-share decomposition method.

The results indicate that in the first period there were high productivity gains as result of improvement in economic fundamentals such as the quality of capital and of labor and innovations. In this way, reallocation of workers between sectors, that is part of the process of structural change, was an inducer of economic growth. However, after 1980, mainly between 1991 and 2011, sectors that achieved productivity gains did so by reducing labor, which was absorbed by sectors with poor performance in terms of productivity growth. Furthermore, factors such as the deindustrialization that developed countries have been undergoing, the international situation, the stage of Brazilian economic development and its possible premature deindustrialization contributed to a growth reducing structural change.

Our differential to the matter is applying the shift-share methodology without combining any of the ten sectors analyzed, adopting a slightly different time frame than similar studies and presenting the shift-share results in a graphically manner in addition to the traditional numbers. By representing graphically how much each of the ten sectors is contributing to the structural change in the economy we are emphasizing the specificities of each of these sectors instead of just considering the aggregated view like manufacturing industry versus other industries or modern services versus traditional services.

Economic evaluations often utilise individual-patient data (IPD) to calculate probabilities of events based on observed proportions. However, this approach is limited when interest is in the likelihood of extreme biomarker values that vary by observable characteristics such as blood glucose in gestational diabetes mellitus (GDM). Here, instead of directly calculating probabilities using the IPD, we utilised flexible parametric models that estimate the full conditional distribution, capturing the non-normal characteristics of biomarkers and enabling the derivation of tail probabilities for specific populations. In the case study, we used data from the Born in Bradford study (N = 10,353) to model two non-normally distributed GDM biomarkers (2-hours post-load and fasting glucose). First, we applied fully parametric maximum likelihood to estimate alternative flexible models and information criteria for model selection. We then integrated the chosen distributions in a probabilistic decision model that estimates the cost-effective diagnostic thresholds and the expected costs and quality-adjusted life years (QALYs) of the alternative strategies (‘Testing and Treating’, ‘Treat all’, ‘Do Nothing’). The model adopts the ‘payer’ perspective and expresses results in net monetary benefits (NMB). The log-logistic and Singh-Maddala distributions offered the optimal fit for the 2-hours post-load and fasting glucose biomarkers, respectively. At £13,000 per QALY, maximum NMB with ‘Test and Treat’ (−£330) was achieved for a diagnostic threshold of fasting glucose >6.6 mmol/L, 2-hours post-load glucose >9 mmol/L, identifying 2.9% of women as GDM positive. The case study demonstrated that fully parametric approaches can be implemented in healthcare modelling when interest lies in extreme biomarker values.

This study aims to examine the plane wave reflection problem in micropolar orthotropic magneto-thermoelastic half space, considering the influence of impedance as a boundary in a nonlocal elasticity.

This study presents the novel formulation of governing partial differential equations for micropolar orthotropic medium with impact of nonlocal thermo-elasticity under magnetic field.

This study provides the numerical results validation for a particular numerical data and expression for the amplitude ratios of reflected waves and identifies the existence of four different waves, namely, quasi longitudinal displacement qCLD-wave, quasi thermal wave qCT-wave, quasi transverse displacement qCTD-wave and quasi-transverse micro-rotational qCTM-wave. The study derives the velocity equation giving the speed and phase velocity of these waves. The study also shows that the small-scale size effect gives significant impact on phase velocity.

The graphical analysis examines the variation of speeds and coefficients of attenuation of these waves due to frequency, magnetic field and nonlocal parameters. Also, significant conclusions on the variation of reflection coefficient against nonlocal parameter, frequency, impedance parameter and angle of incidence are provided graphically.

The creation of more effective micropolar orthotropic anisotropic materials which are very useful in the daily life and their applications in earth science are greatly impacted by the findings of this study.

The authors of the submitted document initiated and produced it collectively, with equal contributions from all members.

Goat milk yoghurt differs from cow milk yoghurt in that it has a different casein composition and content, which presents several technical challenges, including consistency with an appropriate flavor.

In this study, the antioxidant potential and phytochemical profiling of the fruits (pineapple and papaya) and vegetable (carrot) extracts was evaluated and the effect of their purees on the quality and stability of stirred goat milk yoghurt (GMY) were investigated. The qualities of stirred GMY with carrot (CrY), pineapple (PaY) and papaya (PpY) purees were assessed against the product without puree (CY).

The carrot puree had the highest moisture, ash contents and pH value. The carrot extract had the highest DPPH radical scavenging activity, while the pineapple extract had the highest total phenolic value (1.59 µg GAE/g) and flavonoids content (0.203 µg CE/g). The scanning of all the puree extracts in GC-MS indicated that 5-hydroxymethylfurfural was a major component. The phytochemical quantification of the extracts through multiple reaction monitoring (MRM) against 16 compounds showed the presence of sinapic acid, cinnamic acid, pthalic acid, ferulic acid, 4-OH-benzoic acid, 3-OH-benzoic acid, p-coumaric acid, caffeic acid and vanillic acid in different quantities. The addition of purees and storage period had a significant (p < 0.05) effect on the moisture, pH, titratable acidity, syneresis, viscosity, color values and sensory properties of the products. In all the samples after 15 days of storage, Streptococcus thermophilus and Lactobacillus bulgaricus counts remained above the recommended level of 106CFU/g. Stirred GMY sample produced with pineapple puree showed a higher syneresis and viscosity, but the CrY sample demonstrated the highest antioxidant activity. The developed formulations remained stable with minimum changes in quality and sensory attribute during refrigerated storage for 10 days.

This study suggests that addition of fruit and vegetable improve the viscosity and sensory perception of the product with minimal use of synthetic flavor and preservatives.

The importance of developing and implementing sustainable business practices has never been greater. Business schools are increasingly tasked with preparing students to contribute to this imperative and although progress is being made, the impact of integrating sustainability into business school curriculum has remained uncertain as studies exploring the impact have been lacking. The purpose of this multi-case study was to examine the impact of integration efforts in two distinct undergraduate business programs at Royal Roads University. The research focused on how students' understanding of sustainability and their associated attitudes and behaviors changed as they progressed throughout their programs. In addition to considering the impact of a sustainability-infused curriculum, other factors affecting sustainability orientations were also explored. The study was unique in both its comparative nature and in its investigation of the various contextual factors shaping sustainability orientations. Data were collected through semi-structured interviews and through document analysis. Findings suggest a combination of approaches to integration is most effective in impacting sustainability perspectives. While sustainability was generally understood in a multidimensional manner, there was a noticeable environmental bias and a tendency to view it within the business framework. A need for stronger and more comprehensive conceptualizations was identified. Recommendations include: (a) embed sustainability in a comprehensive manner across the curriculum, (b) move beyond a disciplinary conceptualization of sustainability and introduce stronger sustainability discourse, (c) utilize powerful experiential and place-based pedagogies, (d) pay attention to context and ensure both the formal and the informal curriculum mutually reinforce a pro-sustainability agenda.

Customer lifetime value (CLV) is one of the key indicators to measure the success or health of an organization. How can an organization assess the organization's customers' lifetime value (LTV) and offer relevant strategies to retain prospective and profitable customers? This study offers an integrated view of different methods for calculating CLVs for both loyalty members and non-membership customers.

This study outlines eleven methods for calculating CLV considering (1) the deterministic aspect of NPV (Net present value) models in both finite and infinite timespans, (2) the geometric pattern and (3) the probabilistic aspect of parameter estimates through simulation modeling along with (4) the migration models for including “the probability that customers will return in the future” as a key input for CLV calculation.

The CLV models are validated in the context of complementary and alternative medicine (CAM)in the healthcare industry. The results show that understanding CLV can help the organization develop strategies to retain valuable customers while maintaining profit margins.

The integrated CLV models provide an overview of the mathematical estimation of LTVs depending on the nature of the customers and the business circumstances and can be applied to other business settings.

Throughout human history, the occurrence of disasters has been inevitable, leading to significant human, financial and emotional consequences. Therefore, it is crucial to establish a well-designed plan to efficiently manage such situations when disaster strikes. The purpose of this study is to develop a comprehensive program that encompasses multiple aspects of postdisaster relief.

A multiobjective model has been developed for postdisaster relief, with the aim of minimizing social dissatisfaction, economic costs and environmental damage. The model has been solved using exact methods for different scenarios. The objective is to achieve the most optimal outcomes in the context of postdisaster relief operations.

A real case study of an earthquake in Haiti has been conducted. The acquired results and subsequent management analysis have effectively assessed the logic of the model. As a result, the model’s performance has been validated and deemed reliable based on the findings and insights obtained.

Ultimately, the model provides the optimal quantities of each product to be shipped and determines the appropriate mode of transportation. Additionally, the application of the epsilon constraint method results in a set of Pareto optimal solutions. Through a comprehensive examination of the presented solutions, valuable insights and analyses can be obtained, contributing to a better understanding of the model’s effectiveness.

Unlike many existing methods that are primarily focused on two-dimensional localization, this research paper extended the scope to three-dimensional localization. This enhancement is particularly significant for unmanned aerial vehicle (UAV) applications that demand precise altitude information, such as infrastructure inspection and aerial surveillance, thereby broadening the applicability of UAV-assisted wireless networks.

The paper introduced a novel method that employs recurrent neural networks (RNNs) for node localization in three-dimensional space within UAV-assisted wireless networks. It presented an optimization perspective to the node localization problem, aiming to balance localization accuracy with computational efficiency. By formulating the localization task as an optimization challenge, the study proposed strategies to minimize errors while ensuring manageable computational overhead, which are crucial for real-time deployment in dynamic UAV environments.

Simulation results demonstrated significant improvements, including a channel capacity of 99.95%, energy savings of 89.42%, reduced latency by 99.88% and notable data rates for UAV-based communication with an average localization error of 0.8462. Hence, the proposed model can be used to enhance the capacity of UAVs to work effectively in diverse environmental conditions, offering a reliable solution for maintaining connectivity during critical scenarios such as terrestrial environmental crises when traditional infrastructure is unavailable.

Conventional localization methods in wireless sensor networks (WSNs), such as received signal strength (RSS), often entail manual configuration and are beset by limitations in terms of capacity, scalability and efficiency. It is not considered for 3-D localization. In this paper, machine learning such as multi-layer perceptrons (MLP) and RNN are employed to facilitate the capture of intricate spatial relationships and patterns (3-D), resulting in enhanced localization precision and also improved in channel capacity, energy savings and reduced latency of UAVs for wireless communication.