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The management of building information modeling (BIM)-enabled construction projects is challenging and unstructured in nature, particularly in terms of contract administration. Even though previous studies have revealed various legal issues related to BIM, little is known regarding the contractual practices of BIM. Hence, the purpose of this paper is to explore the contractual practices between the BIM consultant and employer in detail.

An explanatory case study was carried out on four large BIM-enabled construction projects in China.

The contractual practices differed from one project to another in terms of ownership and intellectual property rights (IPRs) of the BIM model, roles of the BIM consultant, liability of the BIM consultant in the event of errors and delays of the BIM model, and BIM-related costs and payments. Some of the interesting findings are as follows: the employer shall retain the ownership and IPRs of the BIM model, the BIM consultant shall provide a warranty to ensure usability of the BIM model after project handover, the BIM consultant shall pay for damages or losses if the BIM model fails to deliver and the costs of BIM implementation shall be borne by both contracting parties.

This study provides a fresh, realistic insight on the development of plausible contractual practices between the BIM consultant and employer and the findings can be used to improve BIM contract protocols in future projects.

The purpose of this paper is to propose an algorithm based on genetic algorithm (GA) to solve the block erection scheduling problem in shipbuilding.

The block erection scheduling problem is defined as the identical parallel machine‐scheduling problem with precedence constraints and machine eligibility (PCME) restrictions. A GA is proposed to find near optimal solution, and a few lower bounds and the percentage of the reduced makespan are defined to evaluate the performance of the proposed algorithm. Finally, the GA for block erection scheduling problem in a shipyard is illustrated by using the real data from a local shipyard.

The proposed GA produces lesser values of makespan against the random heuristic algorithm for the collected real instances.

The proposed GA can solve other similar parallel machine‐scheduling problems with PCME to minimize makespan.

Based on the proposed GA, the developed scheduling system for block erection in a shipyard can reduce the makespan of block erection, and contribute to the productivity improvement.

The allocation of block erection to the crane is modeled as a parallel machine‐scheduling problem with PCME, and the GA is developed to solve this problem to minimize makespan.

The main research problem is the relationship between the use motivation (UM) and use behavior (UB) of middle-aged and old short videos users and the difference of the influence of the use motivation of middle-aged and old users, a special group, on the usage behavior is analyzed.

By means of a questionnaire survey and in-depth interview, the middle-aged and old-aged users' short video use motivation and behavior and their influence relationship are discussed.

It is found that information appeal is the main motivation of middle-aged and old-aged users to use short videos and users with different social attributes have differences in motivation and there is a significant relationship between motivation and behavior.

The research objective is middle-aged and elderly short video users. And how to promote the transformation of middle-aged and old-aged users' use mentality and record and create their unique bright spots in the big stage of short videos with a more active and independent attitude, is of unique significance not only to the users themselves but also to the media content of this era and the users' existence in the technological change.

Casing damage problems are increasingly prominent in oil fields, most of which were caused by casing external squeezing loads. The traditional calculation method of casing external squeezing loads is not very accurate now, especially in complex formation. The purpose of this paper is to propose a new calculation method to solve the problem of actual casing loads under above conditions.

Based on Lame’s model of elastic mechanics, a new calculation method of casing external squeezing loads is deduced. Comprehensive influence laws of the loads which caused by in-situ stress, internal pressure, formation parameters, cement annulus parameters and casing parameters are analyzed.

The paper provides a new calculation method of casing external squeezing loads, by which the dispersion effect of internal liquid pressure caused by casing wall material is eliminated. The main influence factors of casing external squeezing loads are in-situ stress and formation elastic modulus.

The model and boundary conditions used in the paper is based on elastic mechanics. The accuracy of the calculation results depends on the quality and accuracy of the input formation parameters.

This paper proposes a new method to calculate casing external squeezing loads. And compared with traditional methods, this method is more practical.

This paper addresses the limitations of current graph neural network-based recommendation systems, which often neglect the integration of side information and the modeling of complex high-order interactions among nodes. The research motivation stems from the need to enhance recommendation performance by effectively utilizing all available data. We propose a novel method called MSHCN, which leverages hypergraph neural networks to integrate side information and model complex interactions, thereby improving user and item representations.

The MSHCN method employs a hypergraph structure to incorporate various types of side information, including social relationships among users and item attributes, which are essential for enriching user and item representations. The k-means clustering algorithm is utilized to create item-associated hypergraphs, while sentiment analysis on user reviews refines the modeling of user interests. Additionally, hypergraphs are constructed for user-user and item-item interactions based on interaction similarity. MSHCN also incorporates contrastive learning as an auxiliary task to enhance the representation learning process.

Extensive experiments demonstrate that MSHCN significantly outperforms existing recommendation models, particularly in its ability to capture and utilize side information and high-order interactions. This results in superior user and item representations and improved recommendation performance.

The novelty of MSHCN lies in its use of a hypergraph structure to integrate diverse side information and model intricate high-order interactions. The incorporation of contrastive learning as an auxiliary task sets it apart from other hypergraph-based models, providing a significant enhancement in recommendation accuracy.

Short-form video advertisements have recently gained popularity and are widely used. However, creating attractive short video advertisements remains a challenge for sellers. Based on the visual-audio perspective and signaling theory, this study investigated the impacts of three visual features (number of shots, pixel-level image complexity and vertical versus horizontal formats) and two audio features (speech rate and average spectral centroid) on user engagement behavior.

We conducted a field study on TikTok. To test our various hypotheses, we used regression analysis on 2,511 videos containing product promotion information posted by 60 sellers between January 1, 2020 and November 20, 2021.

For visual variables, the number of shots and pixel-level image complexity were found to have nonlinear (inverted U-shaped) relationships with user engagement behavior. The vertical video form was found to have a positive effect on comments and shares. In the case of audio variables, speech rate was found to have a significant positive effect on shares but not on likes and comments. The average spectral centroid was found to have significant negative influences on likes and comments.

This study provides specific suggestions for sellers who create short-form videos to improve user engagement behavior.

This study contributes to the literature on short-form video advertising by extending the potential drivers of user engagement behavior. Additionally, from a methodological perspective, it contributes to the literature by using computer vision and speech-processing techniques to analyze user behavior in a video-related context, effectively overcoming the limitations of the widely adopted survey method.

This paper aims to analyse the role of government-led innovative knowledge management platforms in innovation knowledge management, social network effects and innovative resource clusters in the context of academician workstations in China. Specifically, this paper empirically studies the impact of academician workstations on corporate innovation capabilities and the mechanisms behind this impact.

This study uses the propensity matching score method and difference-in-differences method to test the relationship between academician workstations and corporate innovation capabilities. Baron and Kenny’s (1986) mediation method is used to test two potential mechanisms.

Academician workstations significantly improve corporate innovation capabilities because of their contribution to knowledge and innovation management. The facilitation effects are stronger in non-state-owned firms, high-tech firms and firms in industries with low levels of competition. Further, academician workstations enhance corporate innovation capabilities through their funding effect.

This paper encourages policymakers to create a better market environment and stable support policies to facilitate sustainable scientific and technological innovation.

To the best of authors’ knowledge, this study is among the first to empirically analyse the impacts of innovative knowledge management platforms on corporate innovation. It enriches the theoretical perspective of innovation platforms and provides an excellent research perspective for effectively analysing the impacts of innovation platforms. This study also contributes to the literature on the determinants of innovation.

This paper aims to address the problem of complex accurate segmentation of target objects due to chaotic stacking of multiple objects of the same kind in the vision-based robotic arm order review scenarios. This paper proposes an improved fully convolutional one-stage object detection (FCOS) rotation detection network for order review.

An improved FCOS rotation detection network for the vision-based robotic arm order review scenario is proposed. This method solves the problem of difficulty in accurately segmenting multiple objects when overlapped and stacked by introducing a sliding window multi-head self-attention (SW-MSA) and angle parameters. Secondly, the AdamW optimization algorithm is introduced to obtain an adaptive learning rate and improve the training efficiency of the network. Thirdly, the Rotation IoU Loss is introduced as the loss function for bounding box regression to enhance the model’s precise positioning of the rotation target position.

In the same conditions, the proposed improved FCOS rotation detection network was trained for 12 epochs, which took 121 s less than the original FCOS detection network, a speed increase of 22.9%. With two images loaded at a time, the memory used was reduced by 254 MB. The detection speed also increased from 2.5 to 3.4 images per second. Finally, the comparative and ablation experiments on the DOTA-v1.0 data set and the self-made data set further verified through experiments that the improved FCOS rotation detection network is practical and effective in terms of detection accuracy and operational efficiency in real-world environments.

An improved FCOS rotation detection network for the robotic arm in the order review scenario is proposed. This method effectively solves the problem of inefficient segmentation of the target object caused by the chaotic stacking of multiple similar objects.

Optimized plating conditions, included proper designs of insulating shield (IS), auxiliary cathode (AC) and different patterns, contribute to the uniformity enhancement of copper deposition.

Plating experiments were implemented in vertical continuous plating (VCP) line for manufacturing in different conditions. Multiphysics coupling simulation was brought to investigate and predict the plating uniformity improvement of copper pattern. In addition, the numerical model was based on VCP to approach the practical application.

With disproportionate current distribution, different plating pattern design formed diverse copper thickness distribution (CTD). IS and AC improved plating uniformity of copper pattern because of current redistribution. Moreover, optimized plating condition for effectively depositing more uniformed plating copper layer in varied pattern designs were derived by simulation and verified by plating experiment.

The comparison between experiment and simulation revealed that multiphysics coupling is an efficient, reliable and of course environment-friendly tool to perform research on the uniformity of pattern plating in manufacturing.