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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.

Although studies have shown that electronic media exposure can negatively affect infants’ and young children’s language development, exposure to these forms of media is increasing in North America. To better understand the types of electronic media exposure and their potential effects, we utilized naturalistic daylong recordings collected in the homes of bilingual Spanish–English infants of Latinx descent (n = 37). The present study examines contextual aspects of electronic media exposure, and the effects of electronic media on two types of parent–infant social interactions associated with child language development: parentese (a style of infant-directed speech distinguished by its higher pitch, slower tempo, and exaggerated intonation) and parent–infant turn-taking. Using Language ENvironment Analysis (LENA), two daylong audio recordings were collected from each family. These recordings were manually annotated for electronic media type, directedness, language, parental support, parentese, and turn-taking. Our results showed that the infants in our study experienced exposure to many different forms of electronic media, in both English and Spanish, and that the programming was predominantly adult-directed rather than child-directed. While both parentese and turn-taking were reduced in the presence of electronic media, the strength of these effects was modulated by electronic media sources, demonstrating that various devices differentially affect parental language input. These results provide a glimpse into what types of media young bilingual Spanish–English learning infants are experiencing and can help researchers design language interventions that are inclusive and relevant for families from these populations.

The tourism sector continues to be an engine for economic growth for communities (Jenkins, 1980), and in post-disaster scenarios, destination can be overwhelmed with challenges, particularly for the local community. However, it is essential to seek the most effective path of recovery for individuals and communities. Gaps in contemporary post-disaster literature remain, as the focus often centres on infrastructure and economic developments (Cox and Perry, 2011). This paper aims to address such gaps by offering new ideas around the value of recovery focusing more on the victims, their stories and the role of tourism.

As a theoretical paper, the applied method is described as a convenient approach to data collection and analysis. The methodological reasoning is down to the selection and examination of sources used to establish new theoretical understandings. The study applies a pragmatic research approach to embrace the spectrum of views that are present within the constructivist and positivist. The pragmatic philosophical approach supports multi-disciplinary studies and ensures a more holistic consideration of social conditions is embraced.

This paper offers new knowledge and ideas for post-disaster recovery scenarios. It presents a model that incorporates a range of (traditional, digital and immersive) methods and platforms in which stories can be shared and presented. It argues that allowing victims to share stories in different formats could support rehabilitation while also providing stronger, powerful narratives for tourists, leading to more effective tourism experiences.

Disasters leave individuals and communities in devastation and with potentially lifelong trauma. In rehabilitation of victims is a vital ingredient to the recovery of a destination and its social fabric. This research offers new ideas around the use of digital technologies to generate stories of value that not only support victims of disasters but also offer visitors the opportunity to gain a deeper understanding of challenges and difficulties of dark places, often seen as a key part of the visitor experience and motive to places of tragedy.

Though originality is a requirement for copyright protection, the term is not defined, leaving room for differing interpretations. Over the years, there has been a shift in the yardstick for determining originality. The lack of definition gives room for ambiguity and creates hurdle for protection. The difficulty in assessment of originality is more felt in certain forms of work like music, which has its own theories and limitations. Absence of determining tests creates uncertainty for the authors to draw the line between inspiration and infringement. This paper aims to define originality as applicable to each subject matter.

The methodology used is a qualitative analytical approach and draws from theories and relevant case laws.

The requirement of “originality” as laid down in the Copyright Act has to be defined with precision. The requirement of originality for different categories of subject matter encompassed within copyright law will differ and so the term should be redefined with respect to each subject matter.

This paper is an original work and canvasses for a definition of the term originality in the Copyright Act with reference to the various subject matter entitled to protection.

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.

This study aims to propose a risk-induced game theoretic forecasting model to predict average daily rate (ADR) under COVID-19, using an advanced recurrent neural network.

Using three data sets from upper-midscale hotels in three locations (i.e. urban, interstate and suburb), from January 1, 2018, to August 31, 2020, three long-term, short-term memory (LSTM) models were evaluated against five traditional forecasting models.

The models proposed in this study outperform traditional methods, such that the simplest LSTM model is more accurate than most of the benchmark models in two of the three tested hotels. In particular, the results show that traditional methods are inefficient in hotels with rapid fluctuations of demand and ADR, as observed during the pandemic. In contrast, LSTM models perform more accurately for these hotels.

This study is limited by its use of American data and data from midscale hotels as well as only predicting ADR.

This study produced a reliable, accurate forecasting model considering risk and competitor behavior.

This paper extends the application of game theory principles to ADR forecasting and combines it with the concept of risk for forecasting during uncertain times.

This study is the first study, to the best of the authors’ knowledge, to use actual hotel data from the COVID-19 pandemic to determine an appropriate neural network forecasting method for times of uncertainty. The application of Shapley value and operational risk obtained a game-theoretic property-level model, which fits best.