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The escalating global mortality rates attributed to cardiovascular diseases (CVDs) have drawn the attention of the World Health Organization (WHO), prompting researchers worldwide to address this pressing health concern actively. This study aims to unravel insights into the relationship between specific diets and CVDs by examining authors, countries, articles, journal productivity and their impact.

Diet patterns are recognised as contributing to the rise of CVDs, prompting a comprehensive analysis of relevant literature from Scopus, Web of Science and PubMed databases using the Biblioshiny software.

The analysis delves into cluster development and major themes within the literature, encompassing holistic approaches to cardiovascular health, the nexus between diet, nutrition and cardiovascular health, the impact of plant-based diets on diverse populations, the role of the Mediterranean diet in cardiovascular health and the influence of dietary diversity on cardiovascular health across cultures.

Noteworthy developments in emerging areas like dietary history records, NutriOptimisation and MediCulinary Sensitivity are identified, providing a foundation for future researchers to contribute to achieving Sustainable Development Goals (SDG) 3.

The study aims to identify Green Lean Six Sigma (GLSS) barriers in the context of Higher Education Institutions (HEIs) and prioritize them for executing the GLSS approach.

A systematic literature review (SLR) was used to identify a total of 14 barriers, which were then verified for greater relevance by the professional judgments of industrial personnel. Moreover, many removal measures strategies are also recommended in this study. Furthermore, this work also utilizes Gray Relational Analysis (GRA) to prioritize the identified GLSS barriers.

The study reveals that training and education, continuous assessment of SDG, organizational culture, resources and skills to facilitate implementation, and assessment of satisfaction and welfare of the employee are the most significant barriers to implementing this approach.

The present study provides an impetus for practitioners and managers to embrace the GLSS strategy through a wide-ranging understanding and exploring these barriers. In this case, the outcomes of this research, and in particular the GRA technique presented by this work, can be used by managers and professionals to rank the GLSS barriers and take appropriate action to eliminate them.

The ranking of GLSS barriers gives top officials of HEIs a very clear view to effectively and efficiently implementing GLSS initiatives. The outcomes also show training and education, sustainable development goals and organizational culture as critical barriers. The findings of this study provide an impetus for managers, policymakers and consultants to embrace the GLSS strategy through a wide-ranging understanding and exploring these barriers.

The GLSS barriers in HEIs may significantly affect the society. HEIs can lessen their environmental effect by using GLSS practices, which can support sustainability initiatives and foster social responsibility. Taking steps to reduce environmental effect can benefit society as a whole. GLSS techniques in HEIs can also result in increased operational effectiveness and cost savings, which can free up resources to be employed in other areas, like boosting student services and improving educational programs. However, failing to implement GLSS procedures in HEIs could have societal repercussions as well. As a result, it is critical for HEIs to identify and remove GLSS barriers in order to advance sustainability, social responsibility and operational effectiveness.

GLSS is a comprehensive methodology that facilitates the optimum utilization of resources, reduces waste and provides the pathway for sustainable development so, the novelty of this study stands in the inclusion of its barriers and HEIs to prioritize them for effective implementation.

The purpose of this paper is to examine the influence of ownership structure and dividend payouts over firm’s profitability, valuation and idiosyncratic risk. The authors further investigate if corporate performance is sector dependent.

The study employs signaling and bankruptcy theories to evaluate the influence of ownership structure and dividend payout over a firm’s corporate performance. The authors use a panel regression approach to measure the performance of family owned firms against that of widely held firms.

The study confines to firms operating out of emerging markets. The results show that family owned firms are dominant with concentrated ownership. The management pays lower dividend leading to lower valuation and higher idiosyncratic risk. The study further illustrates that family ownership concentration and family control both influence firm performance and level of risk. The findings indicate that information asymmetry and under diversification lead to increased idiosyncratic risk, resulting in the erosion of firm’s value. Results also confirm that firms paying regular dividends are less risky and, hence, command a valuation premium.

The evidence supports the proposition that information asymmetry plays a significant role in explaining dividend payouts pattern and related impacts on corporate performance. The originality of the paper lies in factoring idiosyncratic risk while explaining profitability and related valuation among emerging market firms.

The primary objective of this study is to propose a robust multiobjective solution search approach for a mean-variance multiple correlated quality characteristics optimisation problem, so-called “multiple response optimisation (MRO) problem”. The solution approach needs to consider response surface (RS) model parameter uncertainties, response uncertainties, process setting sensitivity and response correlation strength to derive the robust solutions iteratively.

This study adopts a new multiobjective solution search approach to determine robust solutions for a typical mean-variance MRO formulation. A fine-tuned, non-dominated sorting genetic algorithm-II (NSGA-II) is used to derive efficient multiobjective solutions for varied mean-variance MRO problems. The iterative search considers RS model uncertainties, process setting uncertainties and response correlation structure to derive efficient fronts. The final solutions are ranked based on two different multi-criteria decision-making (MCDM) techniques.

Five different mean-variance MRO cases are selected from the literature to verify the efficacy of the proposed solution approach. Results derived from the proposed solution approach are compared and contrasted with the best solution(s) derived from other approaches suggested in the literature. Comparative results indicate significant superiorities of the top-ranked predicted robust solutions in nondominated frequency, closeness-to-target and response variabilities.

The solution approach depends on RS modelling and considers continuous search space.

In this study, promising robust solutions are expected to be more suitable for implementation than point estimate-based MOO solutions for a real-life MRO problem.

No evidence of earlier research demonstrates the superiority of a MOO-based iterative solution search approach for mean-variance MRO problems by simultaneously considering model uncertainties, response correlation and process setting sensitivity.

Recent trials have found that propolis supplementation can beneficially reduce blood pressure (BP) in adults, but there is no definitive consensus on this topic. The purpose of this study is to provide an overview and update the current documents regarding the effects of propolis supplementation on BP by presenting a systematic review and meta-analysis.

The systematic search was conducted, considering all studies published up to July 2024, in the following databases: PubMed, Scopus, Cochrane Library and ISI Web of Science. Data were pooled by using the random-effects model, and weighted mean difference (WMD) was considered as the summary effect size.

In this systematic review and meta-analysis, eight clinical trials were included. The obtained results show that propolis supplementation caused a significant decrease in systolic BP (WMD = −3.93 mmHg, 95% CI = −7.05 to −0.82, p = 0.01 and I2 = 45.2%). However, the meta-analysis results showed that propolis supplementation did not significantly change the levels of diastolic BP (WMD = −1.64 mmHg, 95% CI = −4.60 to 1.32, p = 0.27 and I2 = 74.0%).

The findings of this study suggest that propolis supplementation may be used as a dietary supplement to improve systolic BP, but further studies are needed to confirm these results.

The purpose of this paper is to address three key objectives. The first is the proposal of an enhanced multiobjective optimisation (MOO) solution approach for the mean and mean-variance optimisation of multiple “quality characteristics” (or “responses”), considering predictive uncertainties. The second objective is comparing the solution qualities of the proposed approach with those of existing approaches. The third objective is the proposal of a modified non-dominated sorting genetic algorithm-II (NSGA-II), which improves the solution quality for multiple response optimisation (MRO) problems.

The proposed solution approach integrates empirical response surface (RS) models, a simultaneous prediction interval-based MOO iterative search, and the multi-criteria decision-making (MCDM) technique to select the best implementable efficient solutions.

Implementation of the proposed approach in varied MRO problems demonstrates a significant improvement in the solution quality in worst-case scenarios. Moreover, the results indicate that the solution quality of the modified NSGA-II largely outperforms those of two existing MOO solution strategies.

The enhanced MOO solution approach is limited to parametric RS prediction models and continuous search spaces.

The best-ranked solutions according to the proposed approach are derived considering the model predictive uncertainties and MCDM technique. These solutions (or process setting conditions) are expected to be more reliable for satisfying customer specification compared to point estimate-based MOO solutions in real-life implementation.

No evidence exists of earlier research that has demonstrated the suitability and superiority of an MOO solution approach for both mean and mean-variance MRO problems, considering RS uncertainties. Furthermore, this work illustrates the step-by-step implementation results of the proposed approach for the six selected MRO problems.

Ostensibly the case is about an employee resigning from the organization due to lack of support, job clarity, and information about reporting structure. It addresses issues of socialization process, performance appraisal, and communication issue between colleagues in a consulting organization. The case serves as medium for diagnosis and action planning around integration of new employees into the organization, effective performance appraisal, and resolving communication barriers.

Matter is an important topic of science as a discipline since its inception. Nevertheless, along with the evolution of semantic web, matter has got equal importance among the ontology developers. The current work describes an ontology of matter that the authors developed in the lab. The purpose of this paper is to come up with an exhaustive list of concepts and relations to cover matter domain under one umbrella, after identifying the gaps in the present ontologies.

Ontology was developed following faceted analytico-synthetic approach of knowledge organization. The authors followed hybrid developmental approach which includes top-down as well as bottom-up development strategy, for creating classes and subclasses. The authors modelled matter domain comprehensively considering different aspects of matter. The theories behind the modelling approach helps to maintain the consistency of further extensions.

Final ontology has around 280 concepts and as many as 60 properties which include both object property and datatype property.

There exists very vague definition of concepts in different subject areas, as matter is a domain of study in physics, chemistry, material science, metallurgy, etc. Same material has been adopted differently depending upon purpose of its study/use in that field. For example aspirin is simply a chemical compound in chemistry, whereas in medicine it is also an agent.

Present work claims to influence the ontology engineers to develop more extension to this core ontology of matter (COMAT). Also this will find its use in information retrieval, semantic annotations and in several other semantic knowledge-based systems.

COMAT is the most recent work of the domain. Originality lies in the way matter domain has been looked up, from a very wide perspective, as well as in the approach of modelling the domain.

This study aims to analyse the systemic risk emitted by all publicly listed commercial banks in a key emerging economy, India.

The study makes use of the Tobias and Brunnermeier (2016) estimator to quantify the systemic risk (ΔCoVaR) that banks contribute to the system. The methodology addresses a classification problem based on the probability that a particular bank will emit high systemic risk or moderate systemic risk. The study applies machine learning models such as logistic regression, random forest (RF), neural networks and gradient boosting machine (GBM) and addresses the issue of imbalanced data sets to investigate bank’s balance sheet features and bank’s stock features which may potentially determine the factors of systemic risk emission.

The study reports that across various performance matrices, the authors find that two specifications are preferred: RF and GBM. The study identifies lag of the estimator of systemic risk, stock beta, stock volatility and return on equity as important features to explain emission of systemic risk.

The findings will help banks and regulators with the key features that can be used to formulate the policy decisions.

This study contributes to the existing literature by suggesting classification algorithms that can be used to model the probability of systemic risk emission in a classification problem setting. Further, the study identifies the features responsible for the likelihood of systemic risk.

This paper aims to explain how consumer persuasion knowledge and perceived deception in advertisements can influence consumers’ future evaluation of fake news about a brand.

This research develops a conceptual model using widely used persuasion knowledge theory and confirmation bias theory. A questionnaire-based online survey (n = 410) was conducted by displaying an advertisement stimulus followed by a fake news stimulus to test the model. Covariance-based structural equation modeling was used to analyze the hypothesized research model.

The results demonstrate that consumers with high persuasion knowledge are more likely to trust and adopt fake news about an advertised brand through the mediation of perceived deception in the advertisement. Additionally, perceived deception indirectly affects information adoption through the mediation of news credibility.

Theoretically, this study contributes to the existing body of literature on advertising deception and fake news. This research also extends theory of persuasion knowledge in understanding adoption of fake news. Practically, this study has significant implications for various stakeholders, including brands, social media corporations and consumers.

This research adds novel insights in the relationship of consumers’ persuasion knowledge and credibility and adoption of fake news. Furthermore, the investigation of the relationship between the perceived deception in advertising and the adoption of fake news has not been explored, which is also novel.