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The purpose of this paper is to develop a coupled lattice Boltzmann model for the simulation of the freezing process in unsaturated porous media.

In the developed model, the porous structure with complexity and disorder was generated by using a stochastic growth method, and then the Shan-Chen multiphase model and enthalpy-based phase change model were coupled by introducing a freezing interface force to describe the variation of phase interface. The pore size of porous media in freezing process was considered as an influential factor to phase transition temperature, and the variation of the interfacial force formed with phase change on the interface was described.

The larger porosity (0.2 and 0.8) will enlarge the unfrozen area from 42 mm to 70 mm, and the rest space of porous medium was occupied by the solid particles. The larger specific surface area (0.168 and 0.315) has a more fluctuated volume fraction distribution.

The concept of interfacial force was first introduced in the solid–liquid phase transition to describe the freezing process of frozen soil, enabling the formulation of a distribution equation based on enthalpy to depict the changes in the water film. The increased interfacial force serves to diminish ice formation and effectively absorb air during the freezing process. A greater surface area enhances the ability to counteract liquid migration.

The purpose of this paper is to use samples from Chinese-listed companies to investigate the effects of interest rate deregulation and earnings transparency on company’s capital structure in China over the period of 2003–2015. In particular, the authors study the link between state-owned enterprises (SOEs), economic growth targets and marketization in China’s unique institutional context.

Based on the methodology of quantitative analysis, the authors use baseline and cluster analysis for all samples with full set of controls, for robustness tests of alternative proxy of interest rate control by using a cluster analysis at the firm level, regarding endogeneity tests conducted fixed effect model with adding instrument variables (IV), two-period factors regression method via IV and system generalized method of moments for dynamic analysis.

The results show that earnings transparency increases firm leverage and the additional tests suggest that such an effect takes place via a mechanism by reducing the cost of debt finance. However, information transparency could moderate the effects of interest rate deregulation on corporate capital structure. In addition, it finds that SOEs are less sensitive toward the changes of interest rates in China because lending to SOEs is policy-oriented and lacks of market evaluation of business risk. Government control is conducive to enhancing the transparency of the whole industry; however, market-oriented reform is conducive to enhancing the transparency of the company’s own information.

The paper makes contribution to the relationship between earnings disclosure quality and capital structure in the Chinese unique institutional context, such as taking the progressive interest rate reform, SOES, different economic growth target and different marketization level in each province of China. The authors suggest that investors will pay more attention to the company’s own unique information transparency in the provinces with a high degree of marketization. As a potential direction for future research, the authors will investigate how the earnings transparency has impact on capital structure, and how such impact would depend on the transparency of specific business, the cap of foreign shareholding and the convenience of investment.

This research would be the target of banking market reform in order to bring a fair financing environment for all businesses in China. It implies that current experiment of interest rate liberalization in China is not as efficient as it could be in allocating funds across all businesses. State banks, SOEs and local governments are still the biggest players on both the demand and supply sides of the Chinese credit markets.

The social implication of this paper lies in the fact that first, it provides additional evidence on the effect of market-oriented reforms through how the information transparency interacts with the financial decisions making of corporations. Second, it offers policy implication to banking market deregulation in China.

The paper makes contribution to the relationship between earnings disclosure quality and capital structure in the Chinese unique institutional context. This research tests the existing literature, such as Francis et al. (2004) and Zhang and Lu (2007), and suggests that informationally transparent firms have a higher debt ratio and lower effective interest costs on bank loans. In addition, this paper further explores the role played by interest rate deregulation in corporate finance, and in turn market fund allocation. This paper sheds new light on information transparency and explores the relationship between earnings disclosure quality and debt financing behaviors of Chinese publicly listed companies over the period of 2003–2015.

This paper aims to investigate how a prospective buyer’s optimal home-size purchase can be determined by means of a stochastic-dominance (SD) analysis of the historical data of Hong Kong.

By means of SD analysis, the paper uses monthly property yields in Hong Kong over a 15-year period to illustrate how buyers of different risk preference may optimize their home-size purchase.

Regardless of whether the buyer eschews risk, embraces risk or is indifferent to it, in any adjacent pairing of five well-defined housing classes, the smaller class provides the optimal purchase. In addition, risk-averters focusing on total yield would prefer to invest in the smallest and second-smallest classes than in the largest class.

As the smaller class provides the optimal purchase, the smallest class affords the buyer the optimal purchase over all classes in this important housing market – at least where rental yields are of primary concern.

The findings suggest that in the Hong Kong housing market, long-term investors may be better off purchasing smaller homes. For other type of investors, it depends on their risk preference.

There is a very small body of empirical literature on housing investment, especially if the focus is on the optimal home-size purchase.

The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.

The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.

The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.

The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

This paper aims to improve the kinematic modeling accuracy of a spatial three-degrees-of-freedom compliant micro-motion parallel mechanism by proposing a modified modeling method based on the structural matrix method (SMM).

This paper analyzes the problem that the torsional compliance equation of the circular notched hinge is no longer applicable because it is subject to bilateral restrained torsion. The torsional compliance equation is modified by introducing the relative length coefficient. The input coupling effect, which is often neglected, is considered in kinematic modeling. The symbolic expression of the input coupling matrix is obtained. Theory, simulation and experimentation are presented to show the validity of the proposed kinematic model.

The results show that the proposed kinematics model can improve the modeling accuracy by comparing the theoretical, finite element method (FEM) and experimental method.

This work provides a feasible scheme for CMPM kinematics modeling. It can be better applied to the optimization design based on the kinematic model in the future.

This study aims to establish an evaluation method for cross-regional major infrastructure project (CRMIP) supportability. The focus is to identify evaluation indicators from a complexity perspective and develop an evaluation model using qualitative and quantitative methods. Case studies are carried out to verify the reliability of the evaluation model, thereby providing theoretical and practical guidance for CRMIP operations and maintenance (O&M).

Guided by the idea of complexity management, the evaluation indicators of CRMIP supportability are determined through literature analysis, actual O&M experience and expert interviews. A combination of qualitative and quantitative methods, consisting of sequential relationship analysis, entropy weighting, game theory and cloud model, is developed to determine the indicator weights. Finally, the evaluation model is used to evaluate the supportability of the Hong Kong–Zhuhai–Macao Bridge (HZMB), which tests the rationality of the model and reveals its supportability level.

The results demonstrate that CRMIPs' supportability is influenced by 6 guideline-level and 18 indicator-level indicators, and the priority of the influencing factors includes “organization,” “technology,” “system,” “human resources,” “material system,” and “funding.” As for specific indicators, “organizational objectives,” “organizational structure and synergy mechanism,” and “technical systems and procedures” are critical to CRMIPs' O&M supportability. The results also indicate that the supportability level of the HZMB falls between good and excellent.

Under the guidance of complexity management thinking, this study proposes a supportability evaluation framework based on the combined weights of game theory and the cloud model. This study provides a valuable reference and scientific judgment for the health and safety of CRMIPs' O&M.

The purpose of this study is to establish a modified accident causation model for highway construction accidents (ACM-HC) and describe the establishment process of the ACM-HC.

Based on the 2–4 Model, a framework of the ACM-HC was constructed, and the accident causal factors (CF) were extracted from four aspects of human, material, environment and management. In addition, association rule mining (ARM) was introduced to analyze accident investigation reports to obtain the interrelationships between the factors. Based on the framework, factors and ARM results, the ACM-HC was established. Finally, the ACM-HC was verified with a tunnel collapse accident.

Both the external and internal causes of contractor cause accidents. The flaws of safety management of other stakeholders are external causes. In terms of the internal causes, there are four stages: direct causes, indirect causes, radical causes and root causes. More specifically, the direct causes refer to the unsafe acts and the unsafe conditions; ineffective safety supervision and poor individual factors of frontline workers constitute the indirect causes; the radical causes lie in the flaws of construction procedures and technical schemes; the root causes are related to the poor individual factors of decision makers and managers.

The ACM-HC expresses the causes, sequence and mechanism of highway construction accidents in a visual way. In addition, this study describes a process of using a qualitative–quantitative hybrid approach to establish a modified ACM, which provides a different perspective for the establishment of an ACM.

This paper aimed to introduce a systematic body of knowledge via a scientometric review, guiding the sustainable transition from conventional construction to prefabricated construction. The construction industry currently faces a challenge to balance sustainable development and the construction of new buildings. In this context, one of the most recent debates is prefabricated construction. As an emerging construction approach, although existing knowledge makes contributions to the implementation of prefabricated construction, there is a lack of a comprehensive and in-depth overview of the critical knowledge themes and gaps.

This study uses the scientometric analysis to review the state-of-the-art knowledge of prefabricated construction. It retrieved data from the Web of Science core collection database. CiteSpace software was used to conduct the analysis and visualization; three analysis methods identify the knowledge hotspots, knowledge domains and knowledge topics. Finally, according to integrating the hidden connections among results, a body of knowledge for prefabricated construction application can be inferred.

The results show that 120 knowledge hotspots, five critical knowledge domains and five prominent knowledge topics are vital for promoting implementation of prefabricated construction. Based on the afore analysis, a body of knowledge for prefabricated construction that can systematically cover a broad knowledge of prefabricated construction-related research and activities are integrated and proposed in this paper.

Body of knowledge systematically covers a broad knowledge of prefabricated construction applications and is vital to guide researchers and practitioners to conduct related research and activities, thereby promoting the sustainable transition to prefabricated construction implementation.

The purpose of this study is to understand the impact mechanism of grassland transfer on herders' production behaviour in pastoral areas. The impact of grassland transfer on herders' livestock production and grazing intensity is quantified.

Using the survey data collected for 356 herder households from Inner Mongolia and Gansu, China, quantile regression is employed to assess the heterogeneous effects of grassland transfer on livestock production and grazing intensity. To correct the potential self-selection bias of grassland transfer, the propensity score matching technique is used.

Results show that labour, percentage of livestock income and livestock stock are the main factors affecting herders' choice to transfer grassland. The positive effect of grassland transfer on livestock numbers on behalf of those who rented additional grassland is statistically significant but declines with livestock numbers. The sustainability-enhancing effect of grassland transfer on grazing intensity is significant, and the effect becomes larger amongst herder households with higher grazing intensity. The analysis on the impact mechanism shows that grassland transfer significantly promotes the adoption of sustainable grazing modes, such as rotational and seasonal rest grazing, which in turn increases herders' livestock numbers and decreases grazing intensity.

Few studies have empirically analysed the influence of grassland transfer on livestock numbers and grazing intensity. This study fills this gap by employing a quantile regression to assess the heterogeneous effects of grassland transfer on livestock numbers and grazing intensity, while accounting for self-selection bias. In addition, the authors have examined the influencing mechanisms under which grassland transfer impacts on livestock numbers and grazing intensity.

Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.