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Subcooled flow boiling phenomenon is characterized by coolant phase change in the vicinity of the heated wall. Although coolant phase change from liquid to vapour phase significantly enhances the heat transfer coefficient due to latent heat of vaporization, eventually the formed vapor bubbles may coalesce and deteriorate the heat transfer from the heated wall to the liquid phase. Due to the poor heat transfer characteristics of the vapour phase, the heat transfer rate drastically reduces when it reaches a specific value of wall heat flux. Such a threshold value is identified as critical heat flux (CHF), and the phenomenon is known as departure from nucleate boiling (DNB). An accurate prediction of CHF and its location is critical to the safe operation of nuclear reactors. Therefore, the present study aims at the prediction of DNB type CHF in a hexagonal sub-assembly.

Computational fluid dynamics (CFD) simulations are performed to predict DNB in a hexagonal sub-assembly. The methodology uses an Eulerian–Eulerian multiphase flow (EEMF) model in conjunction with multiple size group (MuSiG) model. The breakup and coalescence of vapour bubbles are accounted using a population balance approach.

Bubble departure diameter parameters in EEMF framework are recalibrated to simulate the near atmospheric pressure conditions. The predictions from the modified correlation for bubble departure diameter are found to be in good agreement against the experimental data. The simulations are further extended to investigate the influence of blockage (b) on DNB type CHF at low operating pressure conditions. Larger size vapour bubbles are observed to move away from the corner sub-channel region due to the presence of blockage. Corner sub-channels were found to be more prone to experience DNB type CHF compared to the interior and edge sub-channels.

An accurate prediction of CHF and its location is critical to the safe operation of nuclear reactors. Moreover, a wide spectrum of heat transfer equipment of engineering interest will be benefited by an accurate prediction of wall characteristics using breakup and coalescence-based models as described in the present study.

Simulations are performed to predict DNB type CHF. The EEMF and wall heat flux partition model framework coupled with the MuSiG model is novel, and a detailed variation of the coolant velocity, temperature and vapour volume fraction in a hexagonal sub-assembly was obtained. The present CFD model framework was observed to predict the onset of vapour volume fraction and DNB type CHF. Simulations are further extended to predict CHF in a hexagonal sub-assembly under the influence of blockage. For all the values of blockage, the vapour volume fraction is found to be higher in the corner region, and thus the corner sub-channel experiences CHF. Although DNB type CHF is observed in corner sub-channel, it is noticed that the presence of blockage in the interior sub-channel promotes the coolant mixing and results in higher values of CHF in the corner sub-channel.

This paper aims to present a geometrical void model in conjunction with a multiscale method to evaluate the effect of interraster distance, bead (raster) width and layer height, on the voids concentration (volume) and subsequently calculate the final mechanical properties of the fused deposition modeling parts at constant infill.

A geometric model of the voids inside the representative volume element (RVE) is combined with a two-scale asymptotic homogenization method. The RVEs are subjected to periodic boundary conditions solved by finite element (FE) to calculate the effective mechanical properties of the corresponding RVEs. The results are validated with literature and experiments.

Bead width from 0.2 to 0.3 mm, reported a decrease of 25% and 24% void volume for a constant layer height (0.1 and 0.2 mm – 75% infill). It is reported that the void’s volume increased up to 14%, 32% and 36% for 75%, 50% and 25% infill by varying layer height (0.1–0.2  and 0.3 mm), respectively. For elastic modulus, 14%, 9% and 10% increase is reported when the void’s volume is decreased from 0.3 to 0.1 mm at a constant 75% infill density. The bead width and layer height have an inverse effect on voids volume.

This work brings values: a multiscale-geometric model capable of predicting the voids controllability by varying interraster distance, layer height and bead width. The idealized RVE generation slicer software and Solidworks save time and cost (<10 min, $0). The proposed model can effectively compute the mechanical properties together with the voids analysis.

This paper studies the dynamic effects of selected macroeconomic factors on the performance of the housing market in Kenya using Autoregressive Distributed Lag (ARDL) Models. This study aims to explain the dynamic effects of the macroeconomic factors on the three indicators of the housing market performance: housing prices growth, sales index and rent index.

This study used ARDL Models on time series data from 1975 to 2020 of the selected macroeconomic factors sourced from Kenya National Bureau of Statistics, Central Bank of Kenya and Hass Consult Limited.

The results indicate that household income, gross domestic product (GDP), inflation rates and exchange rates have both short-run and long-run effects on housing prices while interest rates, diaspora remittance, construction output and urban population have no significant effects on housing prices both in the short and long run. However, only household income, interest rates, private capital inflows and exchange rates have a significant effect on housing sales both in the short and long run. Furthermore, household income, GDP, interest rates and exchange rates significantly affect housing rental growth in the short and long run. The findings are key for policymaking, especially at the appraisal stages of real estate investments by the developers.

The authors recommend the use of both the traditional hedonic models in conjunction with the dynamic models during real estate project appraisals as this would ensure that developers only invest in the right projects in the right economic situations.

The imbalance between housing demand and supply has prompted an investigation into the role of macroeconomic variables on the housing market in Kenya. Although the effects of the variables have been documented, there is a need to document the short-run and long-term effects of the factors to precisely understand the behavior of the housing market as a way of shielding developers from economic losses.

This paper aims to discuss the suitability of topic modeling as a review method, identifies and compares the machine learning (ML) research trends in five primary business organization verticals.

This study presents a review framework of published research about adopting ML techniques in a business organization context. It identifies research trends and issues using topic modeling through the Latent Dirichlet allocation technique in conjunction with other text analysis techniques in five primary business verticals – human resources (HR), marketing, operations, strategy and finance.

The results identify that the ML adoption is maximum in the marketing domain and minimum in the HR domain. The operations domain witnesses the application of ML to the maximum number of distinct research areas. The results also help to identify the potential areas of ML applications in future.

This paper contributes to the existing literature by finding trends of ML applications in the business domain through the review of published research. Although there is a growth of research publications in ML in the business domain, literature review papers are scarce. Therefore, the endeavor of this study is to do a thorough review of the current status of ML applications in business by analyzing research articles published in the past ten years in various journals.

The primary aim of this study is to identify and analyze the key factors that impact the intentions of Jordanian tourists to visit and revisit destinations using the Jannah Jo app.

A self-administered questionnaires via Google Forms was employed comprising a sample of 401 Jordanian tourists who have the Jannah Jo app. Partial least squares-structural equation modeling approach was applied for hypotheses testing.

The present investigation has revealed that the constructs of perceived ease of use (PEU), perceived usefulness (PU) and perceived value (PV) exerted a significant and positive impact on electronic word of mouth (e-WOM). Additionally, e-WOM was observed to wield a positive and significant influence on the attitudes of consumers' decision-making, thereby ultimately affecting the intentions of Jordanian tourists with regard to their decisions to visit and revisit destinations. Nevertheless, it is noteworthy that the results indicated that neither augmented reality nor content quality exhibited any statistically significant positive relationship with e-WOM.

Tourism agencies striving to encourage the adoption of smart applications must grasp the relevance of e-WOM within the contemporary digital milieu. Additionally, they should acknowledge the significance of tourists' intentions concerning both revisiting and initial visits. This research contends that such agencies ought to take into account the substantial influence exerted by PEU, PU and PV in shaping the favorable e-WOM discourse.

By integrating the technology acceptance model in conjunction with other relevant variables, this research strives to develop a comprehensive model that advances the comprehension of the intricate determinants affecting tourists' engagements with mobile applications. Furthermore, it is noteworthy that this study represents the initial investigation conducted in the Middle East, specifically in Jordan, on this subject matter.

Present study aimed to integrate Geographic Information Systems (GIS) and Building Information Modeling (BIM) in conjunction with multicriteria decision-making (MCDM) to enhance infrastructure investment planning.

This analysis combines GIS databases with BIM simulations for a novel highway project. Around 150 potential alternatives were simulated, narrowed to 25 more effective routes and 3 options underwent in-depth analysis using PROMETHEE method for decision-making, based on environmental, cost and safety criteria, allowing for comprehensive cross-perspective comparisons.

A comprehensive framework proposed was validated through a case study. Demonstrating its adaptability with customizable parameters. It aids decision-making, cost estimation, environmental impact analysis and outcome prediction. Considering these critical factors, this study holds the potential to advance new techniques for assessment and planning railways, power lines, gas and water.

The study acknowledges limitations in GIS data quality, particularly in underdeveloped areas or regions with limited technology access. It also overlooks other pertinent variables, like social, economic, political and cultural issues. Thus, conclusions from these simulations may not entirely represent reality or diverse potential scenarios.

The proposed method automates decision-making, reducing subjectivity, aids in selecting effective alternatives and considers environmental criteria to mitigate negative impacts. Additionally, it minimizes costs and risks while demonstrating adaptability for assessing diverse infrastructures.

By integrating GIS and BIM data to support a MCDM workflow, this study proposes to fill the existing research gap in decision-making prioritization and mitigate subjective biases.

The purpose of this study is to investigate whether the corporate environmental, social and governance (ESG) performance of enterprise is influenced by the enterprise digital transformation. In addition, this study explains how enterprise digital transformation affects ESG performance.

The sample covers 4,646 nonfinancial companies listed on China’s A-share market from 2009 to 2021. The study adopts the fixed-effects multiple linear regression to perform the data analysis.

The study finds that enterprise digital transformation has a significant inverted U-shaped impact on ESG performance. Moderate digital transformation can improve enterprise ESG performance, whereas excessive digital transformation will bring new organizational conflicts and increase enterprise costs, which is detrimental to ESG performance. This inverted U-shaped effect is more pronounced in industrial cities, manufacturing industries and enterprises with less financing constraints and executives with financial backgrounds. Enterprise digital transformation mainly affects ESG performance by affecting the level of internal information communication and disclosure, the level of internal control and the principal-agent cost.

The government should take multiple measures to encourage enterprises to choose appropriate digital transformation based on their own production behaviors and development strategies, encourage them to innovate and upgrade their organizational management and development models in conjunction with digital transformation and guide them to use digital technology to improve ESG performance.

This study shows that irrational digital transformation cannot effectively improve the ESG performance of enterprises and promote the sustainable development of the country. Enterprises should carry out reasonable digital transformation according to their own development needs and finally improve the green and sustainable development ability of enterprises and promote the sustainable development of society.

This study examines the relationship between enterprise digital transformation and ESG performance. Different from the linear relationship between the two in previous major studies, this study proves the inverse U-shaped relationship between enterprise digital transformation and ESG performance through mathematical theoretical model derivation and empirical test. This study also explores in detail how corporate digital transformation affects ESG performance, as well as discusses heterogeneity at the city, industry and firm levels. It is proposed that enterprises should take into account their own characteristics and carry out reasonable digital transformation according to their development needs.

Gillette’s historically successful “razor and blade” business model (RBM) has been a promising benchmark for multiple businesses across diverse industries worldwide in the past several decades. The extant literature deals with very few nuances of this business model notwithstanding the fact that there are several variants of this business model being put to practical use by firms in diverse industries in grossly metaphorically equivalent situations.

This study adopts the 2 × 2 truth table framework from the domains of mathematical logic and combinatorics in fleshing out all possible (four logical possibilities) variants of the razor and blade business model for further analysis. This application presents four mutually exclusive yet collectively exhaustive possibilities on any chosen dimension. Two major dimensions (viz., provision of subsidy and intra- or extra-firm involvement in the making of razors or blades or both) form part of the discussion in this paper. In addition, this study synthesizes and streamlines entrepreneurial wisdom from multiple intra-industry and inter-industry benchmarks in terms of real-time firms explicitly or implicitly adopting several variants of the RBM that suit their unique context and idiosyncratic trajectory of evolution in situations that are grossly reflective of the metaphorically equivalent scenario of razor and recurrent blades. Inductive method of research is carried out with real-time cases from diverse industries with a pivotally common pattern of razor and blade model in some form or the other.

Several new variants of the razor and blade model (much beyond what the extant literature explicitly projects) have been discovered from the multiple metaphorically equivalent cases of RBM across industries. All of these expand the portfolio of options that relevant entrepreneurial firms can explore and exploit the best possible option chosen from them, given their unique context and idiosyncratic trajectory of growth.

This study has enriched the literature by presenting and analyzing a more inclusive or perhaps comprehensive palette of explicit choices in the form of several variants of the RBM for the relevant entrepreneurial firms to choose from. Future research can undertake the task of comparing these variants of RBM with those of upcoming servitization business models such as guaranteed availability, subscription and performance-based contracting and exploring the prospects of diverse combinations.

Smart entrepreneurial firms identify and adopt inspiring benchmarks (like razor and blade model whenever appropriate) duly tweaked and blended into a gestalt benchmark for optimal profits and attractive market shares. They target diverse market segments for tied-goods with different variants or combinations of the relevant benchmarks in the form of variegated customer value propositions (CVPs) that have unique and enticing appeal to the respective market segments.

Value-sensitive customers on the rise globally choose the option that best suits them from among multiple alternatives offered by competing firms in the market. As long as the ratio of utility to price of such an offer is among the highest, even a no-frills CVP may be most appealing to one market segment while a plush CVP may be tempting to yet another market segment simultaneously. While professional business firms embrace resource leverage practices consciously, amateur customers do so subconsciously. Each party subliminally desires to have the maximum bang-to-buck ratio as the optimal return on investment, given their priorities ceteris paribus.

Prior studies on the RBM have explicitly captured only a few variants of the razor and blade model. This study is perhaps the first of its kind that ferrets out many other variants (more than ten) of the razor and blade model with due simplification and exemplification, justification and demystification.

Based on the systematic literature review, this paper aims to propose a framework of Construction 4.0 (C4.0) scenarios, identifying Industry 4.0 (I4.0) enabling technologies and their applications in the construction industry. The paper reviews C4.0 trends and potential areas for development.

In this research, a systematic literature review (SLR) methodology has been applied, including bibliographic coupling analysis (BCA), co-citation network analysis of keywords, the content analysis with the visualisation of similarities (VOSviewer) software and aggregative thematic analysis (ATA). In total, 170 articles from the top 22 top construction journals in the Scopus database between 2013 and 2021 were analysed.

Six C4.0 scenarios of applications were identified. Out of nine I4.0 technology domains, Industrial Internet of Things (IIoT), Cloud Computing, Big Data and Analytics had the most references in C4.0 research, while applications of augmented/virtual reality, vertical and horizontal integration and autonomous robotics yet provide ample avenues for the future applied research. The C4.0 application scenarios include efficient energy usage, prefabricated construction, sustainability, safety and environmental management, indoor occupant comfort and efficient asset utilisation.

This research contributes to the body of knowledge by offering a framework of C4.0 scenarios revealing the status quo of research published in the top construction journals into I4.0 technology applications in the sector. The framework evaluates current C4.0 research trends and gaps in relation to nine I4.0 technology domains as compared with more advanced industry sectors and informs academic community, practitioners and strategic policymakers with interest in C4.0 trends.

With the advent of ChatGPT, a sophisticated generative artificial intelligence (AI) tool, maintaining academic integrity in all educational settings has recently become a challenge for educators. This paper discusses a method and necessary strategies to confront this challenge.

In this study, a language model was defined to achieve high accuracy in distinguishing ChatGPT-generated essays from human written essays with a particular focus on “not falsely” classifying genuinely human-written essays as AI-generated (Negative).

Via support vector machine (SVM) algorithm 100% accuracy was recorded for identifying human generated essays. The author discussed the key use of Recall and F2 score for measuring classification performance and the importance of eliminating False Negatives and making sure that no actual human generated essays are incorrectly classified as AI generated. The results of the proposed model's classification algorithms were compared to those of AI-generated text detection software developed by OpenAI, GPTZero and Copyleaks.

AI-generated essays submitted by students can be detected by teachers and educational designers using the proposed language model and machine learning (ML) classifier at a high accuracy. Human (student)-generated essays can and must be correctly identified with 100% accuracy even if the overall classification accuracy performance is slightly reduced.

This is the first and only study that used an n-gram bag-of-words (BOWs) discrepancy language model as input for a classifier to make such prediction and compared the classification results of other AI-generated text detection software in an empirical way.