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The aim of this study is to apply an enthalpy-based lattice Boltzmann method with multi distribution function model, to investigate melting process with natural convection inside a cavity with an obstacle. The cavity is filled with water (ice)-based nanofluid containing copper nanoparticles.

This methodology eliminates the requirement of satisfying conditions at the phase change front. The combination of lattice D2Q9 and D2Q5 models is implemented to determine the density, velocity and temperature fields. The simulations are carried out for Rayleigh number of 105, various volume fractions of the nanoparticles and various positions of the cubic obstacle.

The predicated results demonstrate that the use of nanoparticles leads to enhancement of thermal conductivity of nano-enhanced phase change materials in comparison with conventional PCMs. When the position of the obstacle changes from the top to the bottom of the cavity the melting rate increases 75 percent. The numerical study indicates that by increasing the solid concentration from 0 to 0.04, the heat release enhances 52.7, 41.2 and 30 percent when the obstacle is located on the top, middle and bottom sections of the cavity, respectively. It is also observed that, the employment of nanoparticles is more effective when the heat conduction dominates.

The main unacceptable property of most PCMs is their low thermal conductivity, and hence, heat transfer enhancement using nanofluid will be useful for thermal energy storage applications.

The purpose of this paper is to present a practical way to create three kinds of double emulsions such as double emulsion, double-component double emulsion and viscoelastic double emulsion.

A hierarchical T-junction microfluidic device is selected to simulate this phenomenon. A system of the three-phase flows consists of the inner, middle and outer phases were simulated by the direct numerical simulation (DNS) method. The dripping regime is considered for the droplet formation in both T-junctions. The adaptive mesh refinement technique is used to simulate the droplet formation and determine the interface rupture.

The one-step and two-step encapsulation are used to create the double emulsion and the viscoelastic double emulsion, respectively. In both T-junctions, droplets are created by the balance of three parameters which are instability, viscous drag and pressure buildup. The one-step formation of double emulsion is presented for encapsulates the viscoelastic fluid.

The simulated hierarchical microchannel shows some desirable features for creating the complex compounds. The encapsulation process is simulated in micro-scale that is useful for drug delivery applications.

This research aims to examine the impact of RPTs and board of directors' characteristics on the market value of Indian listed banks. Further, this study evaluates the moderation effect of board composition on the association between RPTs banks’ market value.

The sample size consists of 38 banks listed on Bombay stock exchange. The current study is based on secondary data for ten years from 2010 to 2019. Generalized Method of Moment (GMM) was used for estimating the results.

Subsidiary transactions, board of directors' size, composition, diligence, promoters, remuneration and banks' size and leverage have a significant impact on the market value of Indian listed banks. Further, board of directors' composition positively moderates the association between RPTs and banks value measured by Tobin's. Furthermore, corporate governance characteristics have a significant impact on RPTs measured by total RPTs and all subsidiary transactions.

This research is limited only to listed banks whose data are available in the ProwessIQ database, which makes it difficult to generalize the findings on other unlisted banks. This research helps policymakers, investors and creditors to categorize RPTs into different groups to identify the harmful and beneficial once to the bank. The findings suggest that policymakers, investors and creditors should not consider all key personal transactions as harmful transactions; instead, the policymakers, investors and creditors should consider all subsidiary transactions as harmful in the absence of independent directors.

The present study contributes to the existing literature on RPTs by evaluating the interaction effect of board composition on the association between related party transactions and banks' value. Further, this research focuses on the financing industry; Indian banks, which has not been sufficiently researched in comparison to the non-financing industries.

This study aims to investigate the accounting role’s deficiencies in managers’ decision-making processes.

The current research applies a critical review method, which along with a deductive approach – based on a library review of existing sources – examines the underlying causes for the deficiencies of accounting role in the decision-making process of managers; moreover, based on the results obtained, the current study proposes a structural model to explain the issue.

The results exhibit the inadequacies of the accounting role in the decision-making process of managers into three sections: “dilution of financial reporting information content,” “malpractice of accounting information providers” and “managers’ unwillingness to use accounting information.”

This research provides a new perspective on critical accounting studies for the accounting profession, policymakers and managers and invites them to examine the roles of accounting information in more depth and breadth.

This article is the first study that critically expounds upon the literature on the deficiencies of accounting role in the decision-making process of managers and presents these deficiencies in the form of a structural model from three different perspectives.

This study aims to provide a systematic review of the existing literature on the applications of deep learning (DL) in hospitality, tourism and travel as well as an agenda for future research.

Covering a five-year time span (2017–2021), this study systematically reviews journal articles archived in four academic databases: Emerald Insight, Springer, Wiley Online Library and ScienceDirect. All 159 articles reviewed were characterised using six attributes: publisher, year of publication, country studied, type of value created, application area and future suggestions (and/or limitations).

Five application areas and six challenge areas are identified, which characterise the application of DL in hospitality, tourism and travel. In addition, it is observed that DL is mainly used to develop novel models that are creating business value by forecasting (or projecting) some parameter(s) and promoting better offerings to tourists.

Although a few prior papers have provided a literature review of artificial intelligence in tourism and hospitality, none have drilled-down to the specific area of DL applications within the context of hospitality, tourism and travel.

To the best of the authors’ knowledge, this paper represents the first theoretical review of academic research on DL applications in hospitality, tourism and travel. An integrated framework is proposed to expose future research trajectories wherein scholars can contribute significant value. The exploration of the DL literature has significant implications for industry and practice, given that this, as far as the authors know, is the first systematic review of existing literature in this research area.