Search results

This study aims to provide a new perspective and path to reduce the unsafe behavior of new generation of construction workers (NGCWs) in China. The purpose of this study is to explore the influencing mechanism of work-family balance on the unsafe behavior of NGCWs and test the mediating effect of job satisfaction and the moderating effect of group safety climate.

A theoretical model on the influencing mechanism of work-family balance on unsafe behavior of NGCWs was constructed through theoretical analysis. Research data were collected from 502 NGCWs via a questionnaire survey, and research hypotheses were testified with regression analysis.

The results show that work-family balance not only directly reduces NGCWs’ unsafe behavior but also indirectly reduces it through job satisfaction, which plays a partial mediating role. In addition to positively moderating the relationship between work-family balance and NGCWs’ unsafe behavior, group safety climate can also moderate the relationship between work-family balance and job satisfaction in a positive way.

This study provides practical implications for construction companies to reduce the unsafe behaviors of NGCWs from the perspective of work-family balance. Specifically, construction companies should adopt more flexible work rules, such as flexible organization and rotation systems, to increase their work autonomy. Meanwhile, construction companies need to improve the work environment and basic conditions for NGCWs, establish a reasonable salary system and provide attractive promotion opportunities to increase their job satisfaction. In addition, construction companies should provide active safety lectures and training, and supervisors should improve safety communication and interaction levels. Co-workers should remind workers about their safety attitudes and behaviors promptly. A good group safety climate will be created through the efforts of construction companies, supervisors and co-workers.

This study clarifies the influencing mechanism of work-family balance on the NGCWs’ unsafe behavior and further tests the partial mediating role of job satisfaction and the positively moderating effect of group safety climate on the influence relationship of work-family balance on job satisfaction and NGCWs’ unsafe behavior, which defines the boundary conditions of the relationship between work-family balance and NGCWs’ unsafe behavior, and promotes the effective integration of social exchange theory and theoretical system of influencing mechanism of construction workers’ unsafe behavior.

Although an important facet of modernist architecture in which function plays a prominent role, building flexibility is not entirely a new concept. Its relevance transcends generations, allowing space and structure to evolve through time. This paper investigates the relationship among main building structures, infill elements, and space by studying examples in ancient Chinese architecture. It reveals the role of building owners, users, and craftsmen from a survey of historical documentation. In studying these examples, it is concluded that craftsmen in ancient China were involved not only during the construction phase but throughout the period of use as well. Thus, in select cases, the relationship between craftsmen and owners or users had been preserved for generations. Finally, this paper suggests potential strategies for the building industry and technology in the move towards sustainable development.

Periodic inspection and maintenance are essential for effective pavement preservation. Cracks not only affect the appearance of the road and reduce the levelness, but also shorten the life of road. However, traditional road crack detection methods based on manual investigations and image processing are costly, inefficiency and unreliable. The research aims to replace the traditional road crack detection method and further improve the detection effect.

In this paper, a crack detection method based on matrix network fusing corner-based detection and segmentation network is proposed to effectively identify cracks. The method combines ResNet 152 with matrix network as the backbone network to achieve feature reuse of the crack. The crack region is identified by corners, and segmentation network is constructed to extract the crack. Finally, parameters such as the length and width of the cracks were calculated from the geometric characteristics of the cracks and the relative errors with the actual values were 4.23 and 6.98% respectively.

To improve the accuracy of crack detection, the model was optimized with the Adam algorithm and mixed with two publicly available datasets for model training and testing and compared with various methods. The results show that the detection performance of our method is better than many excellent algorithms, and the anti-interference ability is strong.

This paper proposed a new type of road crack detection method. The detection effect is better than a variety of detection algorithms and has strong anti-interference ability, which can completely replace traditional crack detection methods and meet engineering needs.

The purpose of this paper is to study the effect of managerial education levels on the wealth effect at the time of investment announcements, by testing two competitive hypotheses: the agency theory-based overinvestment hypothesis vs the Q-theory-based organizational legitimacy hypothesis.

The authors construct the sample by hand-collecting the announcement dates of capital investments from major newspapers published in Taiwan from 2006 to 2014. The authors then use the event study methodology to estimate cumulative abnormal returns at the time of investments announcements to measure the wealth effect. Finally, the authors examine the wealth effect for capital-investing firms with higher managerial education vs those with lower managerial education. The authors also conduct a cross-sectional regression to test the relation between the wealth effect of capital investment and managerial education.

The empirical results indicate that the wealth effect at the time of investment announcements is less favorable for firms with better-educated managers; this negative relation is mitigated for firms with higher institutional ownership and is aggravated for family-controlled firms; and the overall findings are supported by the agency theory-based overinvestment hypothesis, suggesting that higher managerial education lead to greater managerial optimism/overconfidence, which in turn increases the likelihood of overinvestment and implies a less favorable wealth effect associated with capital investment.

This study contributes to the literature by proposing a new, unexplored stock market’s reaction channel through which managerial education signals adverse information about potential overinvestment behavior, even though many studies suggests that managerial education serves as an indication of knowledge/capability and improves firm performance.

The purpose of this paper is to develop a statistical-based model to forecast future domestic water demand in the context of climate change, population growth and technological development in Yellow River.

The model is developed through the analysis of the effects of climate variables and population on domestic water use in eight sub-basins of the Yellow River. The model is then used to forecast water demand under different environment change scenarios.

The model projected an increase in domestic water demand in the Yellow River basin in the range of 67.85 × 10⁸ to 62.20 × 10⁸ m³ in year 2020 and between 73.32 × 10⁸ and 89.27 × 10⁸ m³ in year 2030. The general circulation model Beijing Normal University-Earth System Model (BNU-ESM) predicted the highest increase in water demand in both 2020 and 2030, while Centre National de Recherches Meteorologiques Climate Model v.5 (CNRM-CM5) and Model for Interdisciplinary Research on Climate- Earth System (MIROC-ESM) projected the lowest increase in demand in 2020 and 2030, respectively. The fastest growth in water demand is found in the region where water demand is already very high, which may cause serious water shortage and conflicts among water users.

The simple regression-based domestic water demand model proposed in the study can be used for rapid evaluation of possible changes in domestic water demand due to environmental changes to aid in adaptation and mitigation planning.

The development of social networks enhances the interaction between people, which brings new challenges to the research of group decision-making (GDM). This study aims at the problem that the synergy and redundancy due to interaction among decision-makers are ignored in the previous GDM, a trust-enhanced consensus reaching model based on interaction among decision-makers with incomplete preferences is proposed.

Firstly, confidence level is introduced to improve the hesitation phenomenon that should be considered when calculating trust degree; Secondly, a new trust propagation operator is developed to deal with indirect trust relationships; Thirdly, trust degree is transformed into interaction index to quantify the synergy and redundancy in decision-making. Fuzzy capacities of decision-makers are used to replace traditional weights, and the final scores of alternatives are obtained through Choquet integral.

The proposed model using fuzzy capacity can reflect the synergy or redundancy among decision-makers and improve the accuracy of final ranking result and reduce the loss of information.

This study proposes a trust-enhanced consensus reaching model, which develops a new trust propagation operator to ensure the continuous attenuation of trust in propagation process. And the proposed model uses fuzzy capacity to improve the enhancement or attenuation on the scores of alternatives.

The Chinese civilization is an important part of the history of mankind. The purpose of this paper is to show that there are project management lessons to be learned from Chinese history, including that relating to the management of the building process in ancient China.

Through a review of the literature, this paper discusses the key management and economic practices in the building process of ancient China and highlights these practices from an important document, the Yingzao Fashi or (“Treatise on Architectural Methods”), that was compared with the modern‐day project management framework.

This paper explains the official systems instituted for public projects; the management of labour, design and planning of construction works; quantity surveying practices; the use, control and recycling of building materials; and inspection of building elements in ancient China.

The study suggests that lessons in the principles of construction project management in ancient China bear many similarities with the nine areas of modern‐day project management body of knowledge relating to integration, scope, time, cost, quality, human resource, communications, risk, and procurement management. An area for future research would be to compare the Yingzao Fashi with modern‐day codes of practice for building works to determine which of its “ancient” provisions relating to quality management are still relevant today.

It was found that much emphasis was placed by the ancient Chinese on the quality aspects of prominent building projects. This is one facet from which modern‐day project managers and clients can draw lessons.

To solve the problems of annotation noise, ambiguity recognition and nested entity recognition in the field of Chinese furniture, this paper aims to design a new recognition model ALE-BiLSTM-CRF.

This paper addresses the relative independence of text characters in the Chinese furniture domain named entity recognition (NER) task. It also considers the limited information provided by these text characters in this task. Therefore, a model named ALE-BiLSTM-CRF for Chinese furniture domain NER is proposed. First, the ERNIE pre-trained model is used to transform text into a dynamic vector that integrates contextual information. And adversarial learning is combined to generate adversarial samples to enhance the robustness of the model. Next, the BiLSTM module captures the temporal information of the context, and the multi-head attention mechanism integrates long-distance semantic features into the character vectors. Finally, a CRF layer is used to learn the constraints between labels, enabling the model to generate more reasonable and semantically consistent label sequences. This paper conducts comparative experiments with mainstream models on the Weibo data set, achieving an F1 score of 75.52%, demonstrating its generality and robustness. Additionally, comparative and ablation experiments are conducted on a self-constructed furniture data set in the Chinese furniture field, achieving an F1 score of 89.62%, verifying the model’s superiority and effectiveness.

This paper conducts comparative experiments with mainstream models on the Weibo data set, achieving an F1 score of 75.52%, demonstrating its generality and robustness. Additionally, comparative and ablation experiments are conducted on a self-constructed furniture data set in the Chinese furniture field, achieving an F1 score of 89.62%, verifying the model’s superiority and effectiveness.

This paper demonstrates its universality and generalization by conducting comparative experiments with mainstream models on the Weibo data set. It also conducts comparative experiments with representative pre-trained models on the furniture data set and conducts ablation experiments on the model itself, further demonstrating the superiority and effectiveness of the model.

In the furniture domain, NER aims to use various methods, including rule templates, machine learning and deep learning techniques, to extract structured information related to furniture from unstructured text. These pieces of information may include the name, material, brand, style and function of the furniture. By extracting and identifying these named entities, this paper can provide more accurate data support for furniture design, manufacturing and marketing, thereby promoting further development and innovation in the furniture industry.

In the furniture field, NER faces some special challenges, which are different from entity recognition in general fields. Furniture terminology is often highly specialized and complex in structure. At the same time, there may be a large number of nested entities in the text of the furniture field. For example, the furniture name “sofa bed” contains two entities “sofa” and “bed.” Current sequence labeling methods often find it difficult to recognize such nested entity structures simultaneously. Additionally, because furniture terminology and descriptions may change with trends and design styles, the model also needs to have a certain degree of adaptability and update capabilities. These reasons make it more difficult to extract information in the furniture field, and NER in the furniture field faces huge challenges.

This paper conducts comparative experiments with mainstream models on the Weibo data set, achieving an F1 score of 75.52%, demonstrating its generality and robustness. Additionally, comparative and ablation experiments are conducted on a self-constructed furniture data set in the Chinese furniture field, achieving an F1 score of 89.62%, verifying the model’s superiority and effectiveness.