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Project scheduling plays an essential role in the implementation of a project due to the limitation of resources in practical projects. However, the existing research tend to focus on finding suitable algorithms to solve various scheduling problems and fail to find the potential scheduling rules in these optimal or near-optimal solutions, that is, the possible intrinsic relationships between attributes related to the scheduling of activity sequences. Data mining (DM) is used to analyze and interpret data to obtain valuable information stored in large-scale data. The goal of this paper is to use DM to discover scheduling concepts and obtain a set of rules that approximate effective solutions to resource-constrained project scheduling problems. These rules do not require any search and simulation, which have extremely low time complexity and support real-time decision-making to improve planning/scheduling.

The resource-constrained project scheduling problem can be described as scheduling a group of interrelated activities to optimize the project completion time and other objectives while satisfying the activity priority relationship and resource constraints. This paper proposes a new approach to solve the resource-constrained project scheduling problem by combining DM technology and the genetic algorithm (GA). More specifically, the GA is used to generate various optimal project scheduling schemes, after that C4.5 decision tree (DT) is adopted to obtain valuable knowledge from these schemes for further predicting and solving new scheduling problems.

In this study, the authors use GA and DM technology to analyze and extract knowledge from a large number of scheduling schemes, and determine the scheduling rule set to minimize the completion time. In order to verify the application effect of the proposed DT classification model, the J30, J60 and J120 datasets in PSPLIB are used to test the validity of the scheduling rules. The results show that DT can readily duplicate the excellent performance of GA for scheduling problems of different scales. In addition, the DT prediction model developed in this study is applied to a high-rise residential project consisting of 117 activities. The results show that compared with the completion time obtained by GA, the DT model can realize rapid adjustment of project scheduling problem to deal with the dynamic environment interference. In a word, the data-based approach is feasible, practical and effective. It not only captures the knowledge contained in the known optimal scheduling schemes, but also helps to provide a flexible scheduling decision-making approach for project implementation.

This paper proposes a novel knowledge-based project scheduling approach. In previous studies, intelligent optimization algorithm is often used to solve the project scheduling problem. However, although these intelligent optimization algorithms can generate a set of effective solutions for problem instances, they are unable to explain the process of decision-making, nor can they identify the characteristics of good scheduling decisions generated by the optimization process. Moreover, their calculation is slow and complex, which is not suitable for planning and scheduling complex projects. In this study, the set of effective solutions of problem instances is taken as the training dataset of DM algorithm, and the extracted scheduling rules can provide the prediction and solution of new scheduling problems. The proposed method focuses on identifying the key parameters of a specific dynamic scheduling environment, which can not only reproduces the scheduling performance of the original algorithm well, but also has the ability to make decisions quickly under the dynamic interference construction scenario. It is helpful for project managers to implement quick decisions in response to construction emergencies, which is of great practical significance for improving the flexibility and efficiency of construction projects.

In today’s digital era, talent recruitment can help address the growing shortage of skilled labor in the construction industry and promote sustainable growth. While existing research has explored the association between talent acquisition and local labor productivity or economic progress, the impact on construction growth deserves further study. This study aims to (1) explore the influence of talent recruitment on the growth of the construction industry and (2) analyze whether different regional characteristics shape the differential impact of talent acquisition on construction growth.

This research employs a quantitative approach, focusing on 35 major cities in China. A panel data regression model is utilized to analyze annual data from 2013 to 2018, considering variables like the construction talent recruitment index, value added in construction, gross regional product per capita and others. The study also examines regional heterogeneity and conducts robustness tests to validate the findings.

The results reveal a positive and significant correlation between talent recruitment and construction industry growth. This correlation is more pronounced in economically advanced and infrastructure-rich regions. The study also finds that factors like capital investment, educational attainment and housing prices significantly contribute to industry growth. Talent recruitment not only transforms local labor market dynamics but also drives demand for construction services, promoting industry growth through economies of scale.

This research constructs a new measurement for talent recruitment and provides new insights into the pivotal role of talent recruitment in the sustainable growth of the construction industry. It underscores the need for construction firms to tailor talent acquisition policies to their specific circumstances and regional developmental conditions. The findings offer practical guidance for driving regional growth within the sector, emphasizing the importance of talent recruitment as a key yet previously underappreciated factor in industry development.

This paper aims to present the mechanical design and kinematics of a novel rigid-flexible coupling hybrid robot to develop a promising aeroengine blades in situ repair technology.

According to requirements analysis, a novel rigid-flexible coupling hybrid robot is proposed by combining a three degrees of freedom (DOF) parallel mechanism with a flexible continuum section. Then the kinematics models of both parallel mechanism and flexible continuum section are derived respectively. Finally, based on equivalent joint method, a two-step numerical iterative inverse kinematics algorithm is proposed for the whole robot: (1) the flexible continuum section is equivalently transformed to a 2-DOF spherical joint, thus the approximate analytical inverse kinematic solution can be obtained; (2) the accurate solution is derived by an iterative derivation of both parallel mechanism and flexible continuum section.

To verify structure scheme and the proposed kinematics modeling method, numerical simulations and prototype experiments are implemented. The results show that the proposed kinematics algorithm has sufficient accuracy and computational efficiency in the whole available workspace, that is end-effector position error and orientation error are less than 0.2 mm and 0.01° respectively, and computation time is less than 0.22s.

A novel rigid-flexible coupling hybrid robot for aeroengine blades in situ repair is designed. A two-step numerical iterative inverse kinematics algorithm is proposed for this unique hybrid robots, which has good accuracy and computational efficiency.

The missing travel time data for roads is a common problem encountered by traffic management departments. Tensor decomposition, as one of the most widely used method for completing missing traffic data, plays a significant role in the intelligent transportation system (ITS). However, existing methods of tensor decomposition focus on the global data structure, resulting in relatively low accuracy in fibrosis missing scenarios. Therefore, this paper aims to propose a novel tensor decomposition model which further considers the local spatiotemporal similarity for fibrosis missing to improve travel time completion accuracy.

The proposed model can aggregate road sections with similar physical attributes by spatial clustering, and then it calculates the temporal association of road sections by the dynamic longest common subsequence. A similarity relationship matrix in the temporal dimension is constructed and incorporated into the tensor completion model, which can enhance the local spatiotemporal relationship of the missing parts of the fibrosis type.

The experiment shows that this method is superior and robust. Compared with other baseline models, this method has the smallest error and maintains good completion results despite high missing rates.

This model has higher accuracy for the fibrosis missing and performs good convergence effects in the case of the high missing rate.

The purpose of this paper is to demonstrate that the sustainable development thought is one good reason why Chinese civilization is continuously developing, and it can be used as a reference for the development of Chinese agriculture today.

The paper employs a historical analysis approach to examine the sustainable thoughts concerning Chinese traditional agriculture, including view of sancai, farming season, fertility, the nature of matters, recycling, and economization.

The results reveal that the nature of Chinese traditional agriculture is akin to ecological agriculture, which is precious heritage for China and the whole world.

The originality of this paper is that it confirms the fundamental reason of the continuous development of Chinese civilization which, based on organization of sustainable development thought, lies in traditional agriculture.

The purpose of this paper is to reveal the co-editing mechanism aiming at content creation, and an entry of online encyclopedia is taken as a case, for the purpose of promoting and enhancing the development of wiki-based digital humanities projects (WDHPs), specifically, the projects that focus on gathering contextual information in the culture heritage domain.

An exploratory study was conducted by three steps. A representative entry’s editorial records were reorganized to obtain a data set of discussion statements (n=608), based on which linked-structures were built, and PageRank algorithm was used to analyze the co-editing process. Skewness statistic was applied to measure the consensus of co-editing, and consensus evolution over time was explored. Linear or curve fitting was performed to analyze the correlation between consensus evolution and its influential factors.

In WDHPs, co-editing activity of content creation can be considered as a large-scale group discussion, consensus can evaluate the efficiency of co-editing, which evolves with time and is influenced by the number of statements, breadth and depth of argumentation structure. Taking “Mogao Grottoes” as an example, group discussions around 15 key issues dominate the content creating process, consensus is on a rise with time, finally reaches a relatively high level, and consensus evolution is more influenced by breadth than by depth of argumentation structure, which indicates that co-editing efficiency of “Mogao Grottoes” is fine and more argumentation in a depth manner should be guided.

For researchers of WDHPs, it is beneficial to apply online encyclopedia platform combining with consensus analysis to develop WDHPs. For designers of WDHPs, the elements related to argumentation structure can be absorbed into the design to promote co-editing in an effective manner. For DH researchers, the analytic procedure can be beneficial of revealing the interest of contributors in a specific DH field.

This research is novel in comprehensively understanding co-editing mechanism of content creation in WDHPs, resulting in a three-step analytic procedure of presenting co-editing process, evaluating and improving co-editing efficiency.