Search results

Building information modeling (BIM) is a striking development in the architecture, engineering and construction (AEC) industry, which provides in-depth information on different stages of the building lifecycle. Real estate valuation, as a fully interconnected field with the AEC industry, can benefit from 3D technical achievements in BIM technologies. Some studies have attempted to use BIM for real estate valuation procedures. However, there is still a limited understanding of appropriate mechanisms to utilize BIM for valuation purposes and the consequent impact that BIM can have on decreasing the existing uncertainties in the valuation methods. Therefore, the paper aims to analyze the literature on BIM for real estate valuation practices.

This paper presents a systematic review to analyze existing utilizations of BIM for real estate valuation practices, discovers the challenges, limitations and gaps of the current applications and presents potential domains for future investigations. Research was conducted on the Web of Science, Scopus and Google Scholar databases to find relevant references that could contribute to the study. A total of 52 publications including journal papers, conference papers and proceedings, book chapters and PhD and master's theses were identified and thoroughly reviewed. There was no limitation on the starting date of research, but the end date was May 2022.

Four domains of application have been identified: (1) developing machine learning-based valuation models using the variables that could directly be captured through BIM and industry foundation classes (IFC) data instances of building objects and their attributes; (2) evaluating the capacity of 3D factors extractable from BIM and 3D GIS in increasing the accuracy of existing valuation models; (3) employing BIM for accurate estimation of components of cost approach-based valuation practices; and (4) extraction of useful visual features for real estate valuation from BIM representations instead of 2D images through deep learning and computer vision.

This paper contributes to research efforts on utilization of 3D modeling in real estate valuation practices. In this regard, this paper presents a broad overview of the current applications of BIM for valuation procedures and provides potential ways forward for future investigations.

This paper aims to prepare a new donor–π–acceptor (D–π–A) and acceptor–π– D–π–A (A–π–D–π–A) phenothiazine (PTZ) in conjugation with vinyl isophorone (PTZ-1 and PTZ-2) were designed and their molecular shape, electrical structures and characteristics have been explored using the density functional theory (DFT). The results satisfactorily explain that the higher conjugative effect resulted in a smaller high occupied molecular orbital–lowest unoccupied molecular orbital gap (Eg). Both compounds show intramolecular charge transfer (ICT) transitions in the ultraviolet (UV)–visible range, with a bathochromic shift and higher absorption oscillator strength, as determined by DFT calculations.

The produced PTZ-1 and PTZ-2 sensors were characterized using various spectroscopic methods, including Fourier-transform infrared spectroscopy and nuclear magnetic resonance spectroscopy (¹H/¹³CNMR). UV–visible absorbance spectra of the generated D–π–A PTZ-1 and A–π–D–π–A PTZ-2 dyes were explored in different solvents of changeable polarities to illustrate positive solvatochromism correlated to intramolecular charge transfer.

The emission spectra of PTZ-1 and PTZ-2 showed strong solvent-dependent band intensity and wavelength. Stokes shifts were monitored to increase with the increase of the solvent polarity up to 4122 cm⁻¹ for the most polar solvent. Linear energy-solvation relationship was applied to inspect solvent-dependent Stokes shifting. Quantum yield (ф) of PTZ-1 and PTZ-2 was also explored. The maximum UV–visible absorbance wavelengths were detected at 417 and 419 nm, whereas the fluorescence intensity was monitored at 586 and 588 nm.

The PTZ-1 and PTZ-2 dyes leading to colorimetric and emission spectral changes together with a color shift from yellow to red.

The marketisation of higher education fosters the notion of students as consumers, highlighting the shifting dynamics of student–teacher relationships. This paper aims to contribute to ongoing discussions about students as consumers and their involvement in pedagogical practices. We explore students’ experiences in short-term study abroad (SA) programmes that involve collaborative learning, examining how a consumerism-oriented approach affects students’ perceptions of their pedagogical identities and student–teacher pedagogical relationships.

A qualitative exploratory study was conducted to capture students’ rich and subjective perceptions and experiences. The data were gathered through semi-structured interviews with 15 Chinese undergraduate students who participated in a short-term SA programme at a UK university. Following data translation and transcription, a thematic analysis approach facilitated our exploration.

Chinese students engage in SA programmes as a strategic investment in personal growth and transformation, with their consumer-oriented identity fostering a mutually beneficial relationship with educators and group members. This consumer mindset appears to enhance active student engagement and, to some extent, create reciprocal student–teacher interactions through power sharing and collaborative involvement.

This study presents empirical data exploring the impact of consumer identity on the dynamics of student–teacher relationships in the SA context. It provides recommendations for implementing pedagogical approaches designed to mediate the influence of consumerism on student engagement, particularly in shaping collaborative student–teacher relationships. This study offers insights for future research on the effects of consumerism in higher education within cross-cultural contexts.

This study aimed to propose a performance-oriented approach of automatically generative design and optimization of hospital building layouts in consideration of public health emergency, which intended to conduct reasonable layout design of hospital building to meet different performance requirements for both high efficiency during normal periods and low risk in the pandemic.

The research design follows a sequential mixed methodology. First, key points and parameters of hospital building layout design (HBLD) are analyzed. Then, to meet the requirements of high efficiency and low risk, adjacent preference score and infection risk coefficient are constructed as constraints. On this basis, automatic generative design is conducted to generate building layout schemes. Finally, multi-objective deviation analysis is carried out to obtain the optimal scheme of hospital building layouts.

Automatic generative design of building layouts that integrates adjacent preferences and infection risks enables hospitals to achieve rapid transitions between normal (high efficiency) and pandemic (low risk) periods, which can effectively respond to public health emergencies. The proposed approach has been verified in an actual project, which can help systematically explore the solution for better decision-making.

The form of building layouts is limited to rectangles, and future work can explore conducting irregular layouts into optimization for the framework of generative design.

The contribution of this paper is the developed approach that can quickly and effectively generate more hospital layout alternatives satisfying high operational efficiency and low infection risk by formulating space design rules, which is of great significance in response to public health emergency.

With increasing complexity of construction projects and new construction processes and methods are adopted, more safety hazards are emerging at construction sites, requiring the application of the modern risk management methods. As an emerging technology, digital twin has already made valuable contributions to safety risk management in many fields. Therefore, exploring the application of digital twin technology in construction safety risk management is of great significance. The purpose of this study is to explore the current research status and application potential of digital twin technology in construction safety risk management.

This study followed a four-stage literature processing approach as outlined in the systematic literature review procedure guidelines. It then combined the quantitative analysis tools and qualitative analysis methods to organize and summarize the current research status of digital twin technology in the field of construction safety risk management, analyze the application of digital twin technology in construction safety risk management and identify future research trends.

The research findings indicate that the application of digital twin technology in the field of construction safety risk management is still in its early stages. Based on the results of the literature analysis, this paper summarizes five aspects of digital twin technology's application in construction safety risk management: real-time monitoring and early warning, safety risk prediction and assessment, accident simulation and emergency response, safety risk management decision support and safety training and education. It also proposes future research trends based on the current research challenges.

This study provides valuable references for the extended application of digital twin technology and offers a new perspective and approach for modern construction safety risk management. It contributes to the enhancement of the theoretical framework for construction safety risk management and the improvement of on-site construction safety.

An increasing number of immersive technologies have been adopted in museum tourism in response to shifting consumer habits in the digital era. In contrast, the authenticity experience of museum tourists relies on genuine relics, the environment and activities, which are ancient or traditional. This raises the question of whether tourists can perceive authenticity in immersive technology-based museum tourism. To address this question, this study aims to explore the impact of virtual reality (VR) attributes on tourists’ presence, tourism authenticity and subsequent behavioral intentions in virtual museums.

Data were collected via scenario-based surveys of participants who had taken virtual museum tours based on VR. A total of 174 effective questionnaires were collected for exploratory factor analysis via SPSS 25. Afterward, 597 questionnaires were obtained for confirmatory factor analysis and path analysis via Mplus 7.4.

A conceptual model of how VR attributes influence presence, authenticity and visit intention was developed. There is a chain intermediary between presence and visit intentions, from original authenticity to interactive authenticity and then to emotional authenticity. Technology readiness and museum familiarity moderate some relationships between VR attributes and presence.

The findings can guide museums in improving the use of VR. For example, managers can improve the quality of virtual systems and adopt various interactive forms to enhance tourists’ participation experiences.

These research findings contribute to the research area of immersive technology adoption, enhance the understanding of tourism authenticity in the new context of technology application and extend the presence-emotion-intention theory.

Images taken from construction site interiors often suffer from low illumination and poor natural colors, which restrict their application for high-level site management purposes. The state-of-the-art low-light image enhancement method provides promising image enhancement results. However, they generally require a longer execution time to complete the enhancement. This study aims to develop a refined image enhancement approach to improve execution efficiency and performance accuracy.

To develop the refined illumination enhancement algorithm named enhanced illumination quality (EIQ), a quadratic expression was first added to the initial illumination map. Subsequently, an adjusted weight matrix was added to improve the smoothness of the illumination map. A coordinated descent optimization algorithm was then applied to minimize the processing time. Gamma correction was also applied to further enhance the illumination map. Finally, a frame comparing and averaging method was used to identify interior site progress.

The proposed refined approach took around 4.36–4.52 s to achieve the expected results while outperforming the current low-light image enhancement method. EIQ demonstrated a lower lightness-order error and provided higher object resolution in enhanced images. EIQ also has a higher structural similarity index and peak-signal-to-noise ratio, which indicated better image reconstruction performance.

The proposed approach provides an alternative to shorten the execution time, improve equalization of the illumination map and provide a better image reconstruction. The approach could be applied to low-light video enhancement tasks and other dark or poor jobsite images for object detection processes.

This paper takes Chinese A-share listed companies from 2007 to 2021 as research samples to investigate the influence of supplier concentration on debt maturity structure and its mechanism. It further analyzes whether the relationship between the two is different in the case of different monetary policies, collateral assets, and total debt. The research conclusion is of practical significance for enterprises to construct a balanced debt maturity structure and prevent financial risks.

This paper adopts the empirical research method. The data came from the CSMAR database, which eliminated ST and *ST and companies with missing data, resulting in a sample of 20,328. Stata16 was used for statistical analysis.

There is an inverted U-shaped relationship between supplier concentration and debt maturity structure, and market position and trade credit play an intermediary role. In the case of tight monetary policy, fewer collateral assets, and higher total debt, the inverse U-shaped relationship is more significant.

This paper examines the relationship between supplier concentration and debt maturity structure from a non-linear perspective for the first time, providing theoretical support for enterprises to form a reasonable debt structure, and deepening the theoretical cognition of the relationship between supplier concentration and corporate debt maturity structure.

This study looks to understand the opposing forces that would influence continuance intention. This is significant as users will take into account the positive and negative use experiences in determining their continuance intention. Therefore, this study looks to highlight the opposing forces of users’ continuance intention by proposing the Expectation-Confirmation-Resistance Model (ECRM).

Through an online survey, 411 responses were obtained from mobile payment users. Subsequently, a hybrid approach comprised of the Partial Least Squares-Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) was utilized to analyze the data.

The results revealed that all hypotheses proposed in the ECRM are supported. More precisely, the facilitating and inhibiting variables were found to significantly affect continuance intention. In addition, the ECRM was revealed to possess superior explanatory power over the original model in predicting continuance intention.

This study successfully developed and validated the ECRM which captures both facilitators and inhibitors of continuance intention. Besides, the relevance and significance of users’ innovative resistance to continuance intention have been highlighted. Following this, effective business and research strategies can be developed by taking into account the opposing forces that affect users’ continuance intention.

The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.

We propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.

Expanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.

This study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.

This study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.