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The collaborative filtering algorithm is a classical and widely used approach in product recommendation systems. However, the existing algorithms rely mostly on common ratings of items and do not consider temporal information about items or user interests. To solve this problem, this study proposes a new user-item composite filtering (UICF) recommendation framework by leveraging temporal semantics.

The UICF framework fully utilizes the time information of item ratings for measuring the similarity of items and takes into account the short-term and long-term interest decay for computing users’ latest interest degrees. For an item to be probably recommended to a user, the interest degrees of the user on all the historically rated items are weighted by their similarities with the item to be recommended and then added up to predict the recommendation degree.

Comprehensive experiments on the MovieLens and KuaiRec datasets for user movie recommendation were conducted to evaluate the performance of the proposed UICF framework. Experimental results show that the UICF outperformed three well-known recommendation algorithms Item-Based Collaborative Filtering (IBCF), User-Based Collaborative Filtering (UBCF) and User-Popularity Composite Filtering (UPCF) in the root mean square error (RMSE), mean absolute error (MAE) and F1 metrics, especially yielding an average decrease of 11.9% in MAE.

A UICF recommendation framework is proposed that combines a time-aware item similarity model and a time-wise user interest degree model. It overcomes the limitations of common rating items and utilizes temporal information in item ratings and user interests effectively, resulting in more accurate and personalized recommendations.

The insidious proliferation of online misinformation represents a significant societal problem. With a wealth of research dedicated to the topic, it is still unclear what determines fake news sharing. This paper comparatively examines fake and accurate news sharing in a novel experimental setting that manipulates news about terrorism.

The authors follow an extended version of the uses-and-gratification framework for news sharing, complemented by variables commonly employed in fake news rebuttal studies.

Logistic regression and classification trees revealed worry about the topic, media literacy, information-seeking and conservatism as significant predictors of willingness to share news online. No significant association was found for general analytical thinking, journalism skepticism, conspiracy ideation, uses-and-gratification motives or pass-time coping strategies.

The current results broaden and expand the literature examining beliefs in and sharing of misinformation, highlighting the role of media literacy in protecting the public against the spread of fake news.

This is, to the authors’ knowledge, the first study to integrate a breadth of theoretically and empirically driven predictors of fake news sharing within a single experimental framework.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-12-2022-0693

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.

The purpose of this paper is to enhance the performance of robots in peg-in-hole assembly tasks, enabling them to swiftly and robustly accomplish the task. It also focuses on the robot’s ability to generalize across assemblies with different hole sizes.

Human behavior in peg-in-hole assembly serves as inspiration, where individuals visually locate the hole firstly and then continuously adjust the peg pose based on force/torque feedback during the insertion process. This paper proposes a novel framework that integrate visual servo and adjustment based on force/torque feedback, the authors use deep neural network (DNN) and image processing techniques to determine the pose of hole, then an incremental learning approach based on a broad learning system (BLS) is used to simulate human learning ability, the number of adjustments required for insertion process is continuously reduced.

The author conducted experiments on visual servo, adjustment based on force/torque feedback, and the proposed framework. Visual servo inferred the pixel position and orientation of the target hole in only about 0.12 s, and the robot achieved peg insertion with 1–3 adjustments based on force/torque feedback. The success rate for peg-in-hole assembly using the proposed framework was 100%. These results proved the effectiveness of the proposed framework.

This paper proposes a framework for peg-in-hole assembly that combines visual servo and adjustment based on force/torque feedback. The assembly tasks are accomplished using DNN, image processing and BLS. To the best of the authors’ knowledge, no similar methods were found in other people’s work. Therefore, the authors believe that this work is original.

Residing in a densely populated urban area possesses its allure; nonetheless, it can significantly impact physical and mental well-being owing to the persistent stress and information overload inherent in urban settings. This study aims to introduce a neuro-urbanism framework that can guide urban planners and designers in quantitatively evaluating individuals' responses to virtual simulated environments.

Our study consisted of two phases after randomly selecting six locations representing three types of urban areas in Bratislava, Slovakia: urban spaces, urban streets, and public parks. First, we conducted a Mentimeter live polling (dialogic survey fusion), followed by an experiment involving volunteer participants from the Slovak University of Technology. This experiment employed an electroencephalogram (EEG) with virtual reality headsets to virtually explore participants' responses to the selected locations.

The EEG signal analysis revealed significant differences in relaxation levels across the selected locations in this study. Urban streets with commercial activities promote mental well-being more effectively than public parks, challenging the preconception that restorative environments are exclusively confined to public parks.

The results demonstrate a replicable neuro-urbanism framework comprising three distinct stages: problem-based technology rooted in neuroscience, experimental setup and deliverables, and identification of restorative environments.

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.

China's New Rural Pension Program (NRPP) has been implemented for a decade, but the factors that facilitate rural residents' participation have received little attention. This study aims to investigate whether financial literacy has an influence on rural residents' behavior of participation in the NRPP. In particular, this study further verifies if high financial literacy is important and whether financial education can enhance the impact of financial literacy on current, long-term and dynamic pension decisions of rural households.

This study investigates the impact of financial literacy on rural residents' participation in China's NRPP using the China Household Financial Survey (CHFS) Data of 2015 and 2017. This study constructs an analytical framework for current, long-term and dynamic impacts and comprehensively analyzes the value of financial literacy in the decision making of the NRPP. This study uses the instrumental variable method to solve the possible endogeneity problem. In addition, the authors also demonstrate the positive role of high financial literacy in household pension decisions. Further analysis reveals gender and regional heterogeneity in the impact of financial literacy on pension decisions. The moderating effect model explores whether financial education has a significant moderating effect on financial literacy and pension decision making of the NRPP.

Financial literacy can improve the participation behavior of households in rural areas (dynamic effect) and promote their current and long-term participation in the NRPP, choosing a higher pension contribution level in the NRPP. However, financial literacy has no significant effect on the change in the contribution amount of the NRPP. Further research finds that high financial literacy has comparative advantages in household pension decision making in rural areas. There are gender and regional differences in the impact of financial literacy on pension decisions. In addition, effective financial literacy education enhances the current, long-term and dynamic impacts of residents' financial literacy on NRPP participation and pension contributions.

This study comprehensively considers the impact of financial literacy on pension decision making behavior from three aspects: current, long-term and dynamic, making up for the dearth in the existing literature that only focuses on the impact of financial literacy on current financial behaviors and bridging the gap between the theoretical framework and experimental results. Our study proposes new policy implications: (1) Governments and financial institutions should pay attention to financial literacy and education levels in rural areas and carry out financial education and training programs to increase social welfare levels by increasing rural residents' participation and pension contribution. (2) The community can strengthen the policy advocacy of the NRPP and make people develop a stronger sense of trust toward it. The government can also subsidize individual accounts through financial support.

This study comprehensively considers the impact of financial literacy on pension decision-making behavior from three aspects: current, long-term and dynamic, making up for the dearth in the existing literature that only focuses on the impact of financial literacy on current financial behaviors and bridging the gap between the theoretical framework and experimental results. Our study proposes new policy implications: (1) Governments and financial institutions should pay attention to financial literacy and education levels in rural areas and carry out financial education and training programs to increase social welfare levels by increasing rural residents' participation and pension contribution. (2) The community can strengthen the policy advocacy of the NRPP and make people develop a stronger sense of trust toward it. The government can also subsidize individual accounts through financial support.

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.

This paper presents a set of instrumental case studies for the context-based learning of BIM in the milieu of knowledge-based practice in the AEC industry. The study aimed to examine students' actions and perspectives in a simulated learning environment for real-world BIM processes. The core intent was to provide an in-depth understanding of strategic and functional BIM implementation by synthesizing a suggestive pedagogical framework based on context-based learning approaches.

Derived from context-based approaches and experiential learning methods such as role-play, problem-based and active learning, the study involved a set of doctoral-level case studies. In a qualitative research study, these cases were devised and organized around industry-focused simulations on various levels of BIM implementation strategies.

Results from the case studies and the student responses suggest that the comprehensive evaluation of real-world BIM implementation simulations facilitates a solid understanding of the value of BIM. The participation of industry professionals catalyzes the development of strategic and functional BIM competencies.

The study proposes a well-structured and replicable BIM learning framework based on context-based learning approaches. The novel framework is adaptive and flexible for BIM education. It can provide students with the necessary skills, strategic vision and professional competencies for innovative practices in the 21st-century AEC Industry. The simulative learning settings, including the evaluation rubrics and connected instructional methods, can be implemented and further developed for similar education efforts.