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Identifying the flow regime is a prerequisite for accurately modeling two-phase flow. This paper aims to introduce a comprehensive data-driven workflow for flow regime identification.

A numerical two-phase flow model was validated against experimental data and was used to generate dynamic pressure signals for three different flow regimes. First, four distinct methods were used for feature extraction: discrete wavelet transform (DWT), empirical mode decomposition, power spectral density and the time series analysis method. Kernel Fisher discriminant analysis (KFDA) was used to simultaneously perform dimensionality reduction and machine learning (ML) classification for each set of features. Finally, the Shapley additive explanations (SHAP) method was applied to make the workflow explainable.

The results highlighted that the DWT + KFDA method exhibited the highest testing and training accuracy at 95.2% and 88.8%, respectively. Results also include a virtual flow regime map to facilitate the visualization of features in two dimension. Finally, SHAP analysis showed that minimum and maximum values extracted at the fourth and second signal decomposition levels of DWT are the best flow-distinguishing features.

This workflow can be applied to opaque pipes fitted with pressure sensors to achieve flow assurance and automatic monitoring of two-phase flow occurring in many process industries.

This paper presents a novel flow regime identification method by fusing dynamic pressure measurements with ML techniques. The authors’ novel DWT + KFDA method demonstrates superior performance for flow regime identification with explainability.

This study aims to measure the impact of information and communication technology (ICT) infrastructure on divorce for a sample of developed countries for the period (2012-2021).

This paper uses fixed effect and generalized method of moments (GMM) estimation to delve into the impact of eight ICT infrastructure factors on divorce. The analyses are based on data collected from the World Bank Development Indicators.

The fixed effect analysis revealed that an increase in both fixed-telephone subscriptions and population covered by at least a 4G led to an increase in divorce. However, an increase in mobile cellular subscriptions and active mobile broadband subscriptions lead to a decrease in divorce. The results of the GMM estimator showed that fixed broadband internet prices, mobile broadband internet prices, the percentage of the population covered by the 4 G mobile network and fixed-telephone subscriptions had a substantial effect on divorce. While fixed broadband subscriptions, active mobile broadband subscriptions and mobile cellular basket were found to hinder divorce.

The findings of this study contribute to the existing literature by highlighting the impact of ICT on divorce in a sample of developed countries. Furthermore, it helps decision-makers and families to understand the causes of divorce related to ICT infrastructure in these societies, so they can understand this phenomenon and provide proactive policies.

This study used multiple factors to measure ICT infrastructure as well as the GMM estimator to delve into the impact of ICT on divorce.

Literature on entrepreneurial resourcefulness (ER) has grown constantly in the last two decades. ER is a construct that describes the specific behavior of entrepreneurs, focusing on the generation and deployment of resources to pursue an opportunity. Since the ER literature has expanded and diversified, the purpose of this study is to integrate its findings with existing knowledge about the construct.

The study applies a systematic literature review approach, following the methodology of Tranfield et al. (2003). The authors identify and synthesize 31 studies focusing on ER.

The literature on ER can function on four different levels: (1) individual, (2) organizational, (3) contextual, and (4) effectual level. Studies on ER concentrate on either the individual or the organizational level, with the contextual and effectual levels appearing as additional study categories for the studies. Behind this categorization, research views ER either as an antecedent influencing a specific effect or as an outcome resulting from a particular context.

This paper is the first of its nature, structuring the existing ER research and proposing a research agenda on ER with seven concrete research avenues and their research questions. Based on the systematic literature review, the authors develop a framework consolidating the interrelations of the different levels.

The pandemic has necessitated employees to work virtually due to mandatory work-from-home setup. Since every employee is not comfortable working online owing to their individual differences which impact performance, thus, it is essential to identify individual characteristics governing performance. As per conventional theories, cognition and metacognition have a significant impact on employee performance, and the key to performance in a collaborative online environment also is metacognition. However, this has been scarcely explored in the context of virtual workspace. This study, therefore, empirically investigates the influence of metacognition and its sub-domains on employees' virtual performance given the challenges they face in a virtual work environment.

The cross-sectional study used a purposive sampling technique for data collection. Data collected from 534 professionals with high and low levels of metacognitive ability is analysed using univariate analysis to ascertain whether metacognitive ability helps employees deal with challenges associated with virtual work environments and perform better.

Results confirm a significant relationship between the level of metacognitive ability and virtual performance. Further, the findings also confirm the interaction effect of the level of metacognitive ability and challenge of maintaining work and non-work boundaries and the need for the physical presence of team member/s in predicting virtual performance.

This study is the first empirical attempt to examine the linkage between metacognitive ability and performance among professionals in the context of post-pandemic virtual work environment and challenges.

In this paper, we provide new evidence to strengthen the stock market overreaction hypothesis by examining a new context that has not been explored before. Our research is inspired by the widely held belief that investor sentiment experiences abrupt changes from optimism to pessimism as the market switches between bull and bear states.

If the stock market overreaction hypothesis is correct, it implies that investors are inclined to become excessively optimistic during bull markets and overly pessimistic during bear markets, resulting in overreaction and subsequent market correction. Consequently, the study first develops two testable hypotheses that can be used to uncover the presence of stock market overreaction with subsequent correction. These hypotheses are then tested using long-term data from the US market.

The study's findings support the hypothesis while also revealing a significant asymmetry in investor overreaction between bull and bear markets. Specifically, our results indicate that investors tend to overreact towards the end of a bear market, and the subsequent bull market starts with a prompt and robust correction. Conversely, investors appear to overreact only towards the end of a prolonged bull market. The correction during a bear market is not confined to its initial phase but extends across its entire duration.

Our study has some limitations related to its focus on investigating stock market overreaction in the US market and analyzing the pattern of mean returns during bull and bear market states. Expanding our study to different global markets would be necessary to understand whether the same stock market overreaction effect exists universally. Furthermore, exploring the relationship between volatility and overreaction during different market phases would be an exciting direction for future research, as it could provide a more complete picture of market dynamics.

Our study confirms the presence of the stock market overreaction effect, which contradicts the efficient market hypothesis. We have observed specific price patterns during bull and bear markets that investors can potentially exploit. However, successfully capitalizing on these patterns depends on accurately predicting the turning points between bull and bear market states.

The results of our study have significant implications for market regulators. Stock market overreactions resulting in market corrections can severely disrupt the market, leading to significant financial losses for investors and undermining investor confidence in the overall market. Further, the existence of overreactions suggests that the stock market may not always be efficient, raising regulatory concerns. Policymakers and regulators may need to implement policies and regulations to mitigate the effects of overreactions and subsequent market corrections.

This paper aims to provide additional support for the stock market overreaction hypothesis using a new setting in which this hypothesis has not been previously investigated.

This research aimed to fill several gaps in the tipping literature which has overlooked the server's perspective in identifying and understanding variables that influence a tip amount and therefore where they concentrate their efforts during the service encounter. Furthermore, the extant literature has theorized how or why certain variables influence the tip amount, but these studies fail to capture insight from server's which would supplement the theory and provide a more in-depth understanding of the mechanisms at play.

This study adopts a grounded theory approach using semi-structured one-on-one interviews with tipped restaurant employees who were identified and selected using snowball sampling. Content analysis is employed to code and categorize the data.

The content analysis revealed five categories where servers focus their time and effort to earn tips: service quality, connection, personal factors, expertise and food quality. The server's personality was identified as a variable the tipping literature has largely ignored as a determinant of the tip amount. Server's shift their style of service for groups of eight or more people, and for regular customers, who must dine in the restaurant at least once per week. Lastly, despite the many drawbacks associated with working for tips, servers would not want to replace it with any other method of compensation.

This is the first qualitative study focused on understanding the server's role in the service exchange relationship since McCarty et al. (1990) study. The results provide new insights on the often-studied variables from the tipping literature.

The purpose of this study is to use a lifetime income measure to evaluate the long-run housing affordability for an understudied cohort of households in the literature – the millennials. The authors do this in the context of Malaysia, measuring long-run affordability for four housing types across geographic locations and income distributions.

This study calculates a long-run housing affordability index (HAI) using data on house prices and household incomes. Essentially a ratio of predicted lifetime incomes to house prices, the HAI is computed for four common housing types in Malaysia from 2005 to 2016 and for six states in the country. The HAI is also compared across four income percentiles.

The analysis reveals varying patterns of housing affordability among different states in Malaysia. Housing affordability has declined since 2010, with most housing types being unaffordable for millennial-led households with the lowest income. Housing is most affordable for those in the highest income bracket, although even here, there are pockets of unaffordable housing as well.

Based on the findings, this study proposes three targeted interventions to improve housing affordability for Malaysian millennials.

This study fills a gap in the literature by examining the long-run housing affordability of Malaysian millennial-led households based on both geographic location and income distribution. The millennial population is understudied in the housing affordability literature, making this study a valuable contribution to the field.