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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.

Expanding on the real-world financial market framework and considering the current market turmoil, with cryptocurrencies (where contracts for difference (CFDs) are extremely common) (Hasso et al., 2019) displaying unprecedented volatility, the authors aim to test in an online laboratory setting whether displaying a risk warning message is truly effective in reducing the level of risk taken and whether the placement of this method makes a difference.

To explore the impact of risk disclosure framing on risk-taking behavior, the authors conducted an online pair-wise lottery choice experiment. In addition to manipulating risk awareness through the presence or absence of risk warning messages of varying intensity, the authors also considered dynamic inconsistency, cognitive ability and questionnaire-based financial risk tolerance (FRT) scores. The authors aimed to identify potential relationships between these variables and experimentally elicited risk aversion. The authors' study offers valuable insights into the complex nature of risky decision-making and sheds light on the importance of considering dynamic inconsistency in addition to risk awareness and aversion.

The authors' results provide statistical evidence for the efficacy of informative and very salient messages in mitigating risky decision, hinting at several policy implications. The authors also provide some statistical evidence in support of the relationship between cognitive abilities and risk preferences. The authors detect that individual with low cognitive abilities scores display great risk aversion.

This study investigates the impact of risk warning messages on investment decisions in an online laboratory setting – a unique approach. However, the authors go beyond this and also examine the potential influence of dynamic inconsistency on decision-making, adding further value to the literature on this topic. To ensure a comprehensive understanding of the participants, the authors collect data on cognitive ability and FRT using questionnaires. This study provides a simple and cost-effective framework that can be easily replicated in future research – a valuable contribution to the field.

The study aims to investigate the impact of indirectly evoked incentives, in the form of supervisor’s preferences, on the decision about accounting policy regarding depreciation method selection and to examine subsequent post-decision distortion by evaluating the depreciation method.

The authors conducted two experiments with control and treatment groups, manipulating the supervisor’s indirectly evoked preferences. In Study 2, the authors also measured the evaluation of both depreciation methods to investigate post-decisional distortion regarding the assessment of the depreciation method chosen in a decision task. Study 1 was conducted among 85 accounting students, while Study 2 consisted of 200 accountants.

Both studies revealed the significant impact of supervisor’s indirectly evoked preferences on accounting policy decisions. Participants who were aware of supervisors’ preferences were more likely to choose the depreciation method that was consistent with those preferences. The authors also found that those participants attached a higher value to the depreciation method, providing evidence that adherence to the supervisor’s preferences results in a distorted assessment of the depreciation methods.

First, this study shows that indirectly evoked supervisors’ preferences may lead to a departure from substantive criteria resulting in low-quality accounting outcomes. Second, the assessment of the depreciation method is inseparable from the situational context, as the evaluation of the depreciation method is interdependent upon the preferences of the choice of a depreciation method and the fulfillment of those preferences.

In successfully meeting city and metropolitan growth, sustainable development is compulsory. Sustainability is a must-focus for any project, particularly for large and mega rail infrastructure. This paper aims to investigate to what degree social, environmental and economic factors influence the government when planning sustainable rail infrastructure projects. To respond to such a matter, this paper focuses on two Australian mega-rail projects: the South West Rail Link (SWRL) and the Mernda Rail Extension (MRE).

As the basis of an experimental evaluation framework strengths, weaknesses, opportunities and threats (SWOT) and factor analysis were used. These two methods were specifically selected as comparative tools for SWRL and SWRL projects, to measure their overall sustainability effect.

Using factor analysis, in the MRE, the factors of network capacity, accessibility, employment and urban planning were seen frequently throughout the case study. However, politics and economic growth had lower frequencies throughout this case study. This difference between the high-weighted factors is likely a key element that determined the SWRL to be more sustainable than the MRE. The SWOT analysis showed the strengths the MRE had over the SWRL such as resource use and waste management, and natural habitat preservation. These two analyses have shown that overall, calculating the sustainability levels of a project can be subjective, based on the conditions surrounding various analysis techniques.

This paper first introduces SWRL and MRE projects followed by a discussion about their overall sustainable development. Both projects go beyond the traditional megaprojects' goal of improving economic growth by developing and enhancing infrastructure. Globally, for such projects, sustainability measures are now considered alongside the goal of economic growth. Second, SWOT and factor analysis are undertaken to further evaluate the complexity of such projects. This includes their overall sustainable development vision alignment with environmental, economic and social factors.

The purpose of this paper is to provide a comprehensive review of a non-contact full-field optical measurement technique known as digital image correlation (DIC).

The approach of this review paper is to introduce the research pertaining to DIC. It comprehensively covers crucial facets including its principles, historical development, core challenges, current research status and practical applications. Additionally, it delves into unresolved issues and outlines future research objectives.

The findings of this review encompass essential aspects of DIC, including core issues like the subpixel registration algorithm, camera calibration, measurement of surface deformation in 3D complex structures and applications in ultra-high-temperature settings. Additionally, the review presents the prevailing strategies for addressing these challenges, the most recent advancements in DIC applications across quasi-static, dynamic, ultra-high-temperature, large-scale and micro-scale engineering domains, along with key directions for future research endeavors.

This review holds a substantial value as it furnishes a comprehensive and in-depth introduction to DIC, while also spotlighting its prospective applications.

The purpose of this paper is to present the investigation on contemporary applications of game design in architectural visualization, urban environmental planning and the results to address the problems of traditional methods specifically in terms of interactivity and visualization. The authors present the prototype that incorporates information modeling and virtual reality (VR) into interactive architectural visualization.

The proposed system supports a virtual walkthrough process that allows users to navigate in an architectural space of their imagination and design. The immersion in the design of living space or environment has been made possible through the inclusion of VR through the use of WebVR technology to deploy the design to be experienced.

This study investigates and establishes a framework that explores the intricacies of 3 imensional (3D) Architectural Visualization in a real-time engine by designing a visual experience with a better level of user interaction and then deploying it through VR.

To the best of the authors’ knowledge, this is one of the few experimental frameworks which perfectly integrates VR for visualization in digital home design environments. The prospective applications of this framework are in several fields of construction innovation, architectural design, 3D modeling, virtual and augmented reality.

High-resolution remote sensing images possess a wealth of semantic information. However, these images often contain objects of different sizes and distributions, which make the semantic segmentation task challenging. In this paper, a bidirectional feature fusion network (BFFNet) is designed to address this challenge, which aims at increasing the accurate recognition of surface objects in order to effectively classify special features.

There are two main crucial elements in BFFNet. Firstly, the mean-weighted module (MWM) is used to obtain the key features in the main network. Secondly, the proposed polarization enhanced branch network performs feature extraction simultaneously with the main network to obtain different feature information. The authors then fuse these two features in both directions while applying a cross-entropy loss function to monitor the network training process. Finally, BFFNet is validated on two publicly available datasets, Potsdam and Vaihingen.

In this paper, a quantitative analysis method is used to illustrate that the proposed network achieves superior performance of 2–6%, respectively, compared to other mainstream segmentation networks from experimental results on two datasets. Complete ablation experiments are also conducted to demonstrate the effectiveness of the elements in the network. In summary, BFFNet has proven to be effective in achieving accurate identification of small objects and in reducing the effect of shadows on the segmentation process.

The originality of the paper is the proposal of a BFFNet based on multi-scale and multi-attention strategies to improve the ability to accurately segment high-resolution and complex remote sensing images, especially for small objects and shadow-obscured objects.

The multisource uncertainties, including material dispersion, load fluctuation and geometrical tolerance, have crucial effects on fatigue performance of turbine bladed disks. In view of the aim of this paper, it is essential to develop an advanced approach to efficiently quantify their influences and evaluate the fatigue life of turbine bladed disks.

In this study, a novel combined machine learning strategy is performed to fatigue assessment of turbine bladed disks. Proposed model consists of two modeling phases in terms of response surface method (RSM) and support vector regression (SVR), namely RSM-SVR. Two different input sets obtained from basic variables were used as the inputs of RSM, then the predicted results by RSM in first phase is used as inputs of SVR model by using a group data-handling strategy. By this way, the nonlinear flexibility of SVR inputs is improved and RSM-SVR model presents the high-tendency and efficiency characteristics.

The accuracy and tendency of the RSM-SVR model, applied to the fatigue life estimation of turbine bladed disks, are validated. The results indicate that the proposed model is capable of accurately simulating the nonlinear response of turbine bladed disks under multisource uncertainties, and SVR-RSM model provides an accurate prediction strategy compared to RSM and SVR for fatigue analysis of complex structures.

The results indicate that the proposed model is capable of accurately simulate the nonlinear response of turbine bladed disks under multisource uncertainties, and SVR-RSM model provides an accurate prediction compared to RSM and SVRE for fatigue analysis of turbine bladed disk.

This study aims to progress scholarly understanding of the staycation phenomenon by examining customer segments and documenting local customers’ attribute preferences.

A stated choice experiment is used to examine customer preferences for staycation package attributes. Latent class discrete choice modeling is deployed to classify customers into market segments based on their preferences. The profile of each segment is enhanced by documenting customer characteristics and consumption styles.

Six prominent market segments are identified using a combination of sociodemographics, consumption styles and staycation attribute preferences. The findings draw on consumer experiences during the COVID-19 pandemic to generate theoretical insights into preferred staycation packages. Empirically, the estimation results from the research framework and choice experimental method demonstrate that staycation market segments exhibit distinct preference structures.

Practitioners and policymakers can incorporate the findings of this study in designing and/or assessing staycation packages. This can ensure differentiated products for defined segments that resonate within local communities through positive word of mouth, thus offering prospective spillovers to visiting friends and relatives.

This is a pioneering study on preference heterogeneity from the customer perspective, with a focus on staycation markets. The findings can encourage and assist hotel sector leaders to capitalize on local market developments to achieve a more resilient hospitality business model.

This paper aims to demonstrate that Facebook likes affect outcomes in nonprofit settings. Specifically, Facebook likes influence affinity to nonprofits, which, in turn, affects fundraising outcomes.

The authors report three studies that establish that relationship. To examine social contagion, Study 1 – an auction field study – relies on selling artwork created by underprivileged youth. To isolate signaling, Study 2 manipulates the number of total Facebook likes on a page. To isolate commitment escalation, Study 3 manipulates whether a participant clicks a Facebook like.

The results show that Facebook likes increase willingness to contribute in nonprofit settings and that the process goes through affinity, as well as through Facebook impressions and bidding intensity. The total number of Facebook likes has a direct signaling effect and an indirect social contagion effect.

The effectiveness of the proposed mechanisms is limited to nonprofit settings and only applies to short-term effects.

Facebook likes serve as both a quality signal and a commitment mechanism. The magnitude of commitment escalation is larger, and the relationship is moderated by familiarity with the organization. Managers should target Facebook likes at those less familiar with the organization and should prioritize getting a potential donor to leave a like as a step leading to donation, in essence mapping a donor journey from prospective to active, where Facebook likes play an essential role in the journey. In a charity auction setting, the donor journey involves an additional step of bidder intensity.

The approach the authors study is shown effective in nonprofit settings but does not appear to extend to corporate social responsibility more broadly.

To the best of the authors’ knowledge, this study is the first investigation to map Facebook likes to a seller’s journey through signals and commitment, as well as the only investigation to map Facebook likes to charity auctions and show the effectiveness of this in the field.