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In recent years, augmented reality (AR) has shown its potential to assist various construction activities. Its use commonly requires additional refinement to be integrated into the building information modeling (BIM) process. Nevertheless, few studies have investigated AR implementation in BIM-enabled projects because of numerous challenges related to its implementation. This study aims to investigate the implementation of AR in construction and identify the critical mechanisms for implementing BIM-AR successfully.

A mixed methodology was adopted for this study. First, this work presents a bibliometric analysis covering articles obtained from Scopus database published between 2000 and 2022. A sample size of 65 research papers pertinent to AR in construction was analyzed using VOSviewer software. Second, a participatory case study was conducted for a BIM-enabled project in China to gain insight into how BIM-AR implementation in construction is achieved.

The findings from the bibliometric analysis show an increasing interest in AR research within construction. The results indicate that AR research focuses on four clusters: real-time communication, project management, construction activities and education. Findings from the case study provide an empirical experience of AR application scenarios in a BIM-enabled project. Concomitantly, 15 critical success factors that influence BIM-AR implementation were finally identified and demonstrated.

This study provides a rich insight into the understanding and awareness of implementing AR. First, the findings are beneficial to construction practitioners and researchers because they provide a concentrated perspective of AR for emerging activities in the construction industry. Second, the results obtained from the case study could provide a useful guide for effectively implementing AR in a BIM-enabled construction project. Overall, this study may stimulate further research on AR-related studies in construction, such as BIM integration, factor analysis and construction education.

The purpose of this work is to apply the Fourier–Ritz method to study the vibration behavior of the moderately thick functionally graded (FG) parabolic and circular panels and shells of revolution with general boundary conditions.

The modified Fourier series is chosen as the basis function of the admissible functions of the structure to eliminate all the relevant discontinuities of the displacements and their derivatives at the edges, and the vibration behavior is solved by means of the Ritz method. The complete shells of revolution can be achieved by using the coupling spring technique to imitate the kinematic compatibility and physical compatibility conditions of FG parabolic and circular panels at the common meridian of θ = 0 and 2π. The convergence and accuracy of the present method are verified by other contributors.

Some new results of FG panels and shells with elastic restraints, as well as different geometric and material parameters, are presented and the effects of the elastic restraint parameters, power-law exponent, circumference angle and power-law distributions on the free vibration characteristic of the panels are also presented, which can be served as benchmark data for the designers and engineers to avoid the unpleasant, inefficient and structurally damaging resonant.

The paper could provide the reference for the research about the moderately thick FG parabolic and circular panels and shells of revolution with general boundary conditions. In addition, the change of the boundary conditions can be easily achieved by just varying the stiffness of the boundary restraining springs along all the edges of panels without making any changes in the solution procedure.

The purpose of this paper is to present a measure method of the uncertainty for rough fuzzy set based on general binary relation.

Rough set and fuzzy set are two different but complementary theories for expressing uncertainty information, and based on the combination of these two uncertainty theories of expressing and handling uncertainty information, the rough fuzzy set model and uncertainty measure based on general relation are discussed.

This paper reveals the intrinsic of the uncertainty for rough fuzzy set based on general relation and presents a new measure method by introducing the Shannon entropy to generalized approximation space.

The paper contributes to the discussion on the research of rough set and fuzzy set. The conclusions are useful in information processing with uncertainty.

The purpose of this paper is to explore the means for enhancing the image and business legitimacy of a socially discredited industry – pawnbroking in pre-1949 China – are explored. Previous studies suggest companies operating within such industries cannot solely rely on hard marketing strategies “to maximize sales and profits as they do with soaps and shoes” (Davidson, 2003, p. 7). Instead, they must find soft strategies for improving company and industry image and legitimacy.

This research relies on qualitative analysis of historical data and documents.

Soft strategies deployed by Chinese pawnbrokers – such as interpretations, moral value advocacies and institutionalized arrangements – contributed substantially to improving pawnbroking’s image and business legitimacy.

Interconnections among ethical values, image, business legitimacy and select marketing strategies are clarified. The efficacy of historically analyzing previously implemented business strategies and their embedding contexts is discussed.

Strategies Chinese pawnbrokers used to mitigate their previously negative image and boost their business legitimacy suggest strategies current socially disapproved companies can use to improve their image and business legitimacy.

A historical analysis of pre-1949 Chinese pawnbroking can suggest soft marketing strategies for overcoming consumers’ negative company and industry impressions.

The purpose of this paper is to develop a coiling robot in the production of coated elevator compensation chains to replace the manual coiling operations and improve the quality of compensation chains.

This paper introduces both mechanical and servo control system designs of the coiling robot. The structure of two friction wheels stabilizes the conveying speed of compensation chain, so the chain speed matches with the car speed. A centering mechanism pushes the chain to its original position. Seven servo motors are integrated into the system, and they are controlled by a servo control system based on programmable logic controller, positioning controller, analog output block and touch screen.

The results of the project show that the coiling robot can both greatly reduce the number of workers and the intensity of the work and improve the quality of the chain. The chain lid by the robot is not only neat, but also uniform in its inner stress.

When the output speed of the compensation chain from the rear friction wheel does not match the coiling speed, the coiling operation has to be halted. Then, the operator adjusts the chain speed and restarts the coiling operation.

The coiling robot is proven working. It has been adopted by a leading company manufacturing compensation chains.

This is the first coiling robot which is practically used in a production line of compensation chains. Its design, mechanism and control systems are of great reference values to people.

Organizations in many domains generate a considerable amount of heterogeneous data every day. Such data can be processed to enhance these organizations’ decisions in real time. However, storing and processing large and varied datasets (known as big data) is challenging to do in real time. In machine learning, streaming feature selection has always been considered a superior technique for selecting the relevant subset features from highly dimensional data and thus reducing learning complexity. In the relevant literature, streaming feature selection refers to the features that arrive consecutively over time; despite a lack of exact figure on the number of features, numbers of instances are well-established. Many scholars in the field have proposed streaming-feature-selection algorithms in attempts to find the proper solution to this problem. This paper presents an exhaustive and methodological introduction of these techniques. This study provides a review of the traditional feature-selection algorithms and then scrutinizes the current algorithms that use streaming feature selection to determine their strengths and weaknesses. The survey also sheds light on the ongoing challenges in big-data research.

In order to further improve the accuracy of nonlinear simulation models, the purpose of this paper is to make some improvements to the general mode.

This paper adopts the calculation method of variable specific heat, taking the variation of gas‐oil ratios and discharge pressure into account, and establishes a nonlinear dynamic simulative model of biaxial gas turbine considering about the inertia of volume based on MATLAB/SIMULINK.

The simulation result, compared with test data, shows that this simulative model is more accurate to reflect the system's dynamic characteristics.

The hot inertial has not been taken into account. Meanwhile, the quantity of data is less, so this paper adopts interpolation method.

It is used to simulate calculation of gas turbines.

This paper adopts the calculation method of variable specific heat, considering the effect of the gas‐oil ratio variation and the discharge pressure variation (the total pressure of discharge duct) on the system.

The trade cost is a significant factor which restricts the trade potential between two nations. This paper aims to make a measurement of agricultural bilateral trade costs of China.

Based on Novy model, this paper makes a measurement of agricultural bilateral trade costs before and after China joining the WTO (1995‐2007).

This paper finds that China's agricultural trade costs with its five major trade partners have not got a pronounced downward trend during 1995‐2007. In ascending order, these are: Malaysia, the USA, Japan, Brazil and Argentina in 2007. Otherwise, there is an obvious corresponding relationship between the trade potential and costs of agricultural products, which is that high costs lead to inadequate trade. With a simple regression, distance and free trade agreement are found to be main factors influencing agricultural trade costs.

Based on the revised gravity model, this paper especially calculates the agricultural bilateral trade costs before and after China joining the WTO, which expands the understanding of trade costs in an industrial perspective. It can prove the agricultural market opening extent, and also help us to learn more about how China participates in the division of the world farm produce market.

The purpose of this paper is to construct a novel grey dominance variable precision rough model.

To deal with the problems that the attribute values of the decision-making object are often not exact numbers but interval grey numbers, and the decision-making attributes satisfy a certain preference relationship in the decision-making information because of the complexity and uncertainty of the real world, the authors take advantage of the theoretical thinking of the grey systems, dominance rough set theory and variable precision rough set theory, and construct a novel dominance variable precision rough set model. On the basis of the thinking logic of grey systems, the authors first define the concepts of balance degree, dominance degree and inferior degree, and then the grey dominance relationship based on the comparison of interval grey numbers. Then the authors use the grey dominance relationship to substitute for the indiscernibility relationship of the variable precision rough set so that the grey dominance variable precision rough model is naturally utilized to reduce the system's attributes in order to derive the needed decision rules. At the end, the authors use a decision-making example of the radar target selection to demonstrate the feasibility and effectiveness of the novel model.

The results show that the proposed model possesses certain fault tolerance ability and can well-realize decision rule extraction and knowledge discovery out of a given incomplete information system.

The method exposed in the paper can be used to deal with the decision-making problems with the grey information, preference information and noise data.

The paper succeeds in realizing both the grey decision-making information with preference information and noise data and the extraction of decision-making rules.

This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations.

This study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques.

A comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions.

Based on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.