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A text generation based multidisciplinary problem identification method is proposed, which does not rely on a large amount of data annotation.

Hand-eye calibration is crucial for vision-based robots using open-loop visual control. Based on the product of exponentials model, this study introduces a novel hand-eye calibration algorithm to improve calibration accuracy. As opposed to the traditional method of solving the AX = XB type in the hand-eye calibration using the marker, we transform the solution into an AX = B type without requiring accurate camera parameters. Firstly, the nominal marker pose in the robot base is described by the nominal kinematic parameters of the robot, camera, and end-effector-to-camera. The actual marker pose is obtained by physically contacting the marker with the probe. Subsequently, the error parameter of the hand-eye pose is defined as linear to the error between the nominal and actual marker poses and can be solved by an iterative least-squares algorithm. Finally, the calibrated hand-eye pose is acquired by multiplying its nominal pose and the exponential mapping of the error parameter. The simulations and real experiments illustrate the effectiveness and stability of the proposed algorithm. Noteworthy, the mean position accuracy of the vision-based robot is improved nearly 20 times after calibration. Furthermore, the comparison experiment demonstrates that the proposed algorithm is preferable for improving hand-eye calibration accuracy. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to propose meta-synthetic ideas and knowledge asset management approaches to build a comprehensive strategic framework for Beijing City in China. Design/methodology/approach: Methods include a review of relevant literature in both English and Chinese, case studies of different types of support frameworks in the UK, the USA, Singapore and Hong Kong, formulation of a meta-synthetic support framework for Beijing City, and justification of its application to policy development by various studies. Three stages of meta-synthetic support frameworks are proposed. Findings: The suggested meta-synthetic support frameworks are highly appropriate for the optimisation of, and innovation in, management and services systems of government information resources. The proposed knowledge asset management approaches offer significant practical value in improving the competence and capabilities of service-oriented government, providing a set of solutions to identified, urgent problems, including a joint administration system for creating value, a release and distribution management system for sharing and protecting value, and a licensing and authorisation management system for adding value. Research limitations/implications: This paper focuses on the formulation of a theoretical support framework for the reuse of government information resources and the justification of its effectiveness to guide policy development at strategic level. Case studies of its application at operational level are ongoing and will be discussed in future papers. Practical implications: The suggested meta-synthetic support frameworks support the efficiency, effectiveness and economy of intelligent traffic administration, good governance of value-added services based on government information resources, and intellectual activity around city travel and traffic. The study has wide implications for the improvement of service-oriented government performance, public satisfaction and the image of government. Originality/value: The paper presents the adaptation of meta-synthetic ideas and knowledge asset management approaches to collaboration, optimisation, innovation and compliance management issues in the reuse of government information resources. The advantages of different types of support systems and frameworks are integrated as a coherent whole for a strategic framework of legal, regulatory and standards support to China and Beijing. (Contains 3 figures and 3 tables.)

The check-in behaviors of users are ubiquitous in location-based social networks in urban living. Understanding user preferences is critical to improving the recommendation services of social platforms. In addition, great quality of recommendation is also beneficial to sustainable urban living since the user can easily find the point of interest (POI) to visit, which avoids unnecessary consumption, such as a longer time taken for searching or driving. To capture user preferences from their check-in behaviors, advanced methods transform historical records into graph structure data and further leverage graph deep learning-based techniques to learn user preferences. Despite their effectiveness, existing graph deep learning-based methods are limited to the capture of the deep graph's structural information due to inherent limitations, such as the over-smoothing problem in graph neural networks, further leading to suboptimal performance. To address the above issues, we propose a novel method built on Transformer architecture named spatiotemporal aware transformer (STAT) via a novel graphically aware attention mechanism. In addition, a new temporally aware sampling strategy is developed to reduce the computational cost and enable STAT to deal with large graphs. Extensive experiments on real-world datasets have demonstrated the superiority of the STAT compared to state-of-the-art POI recommendation methods. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to propose meta-synthetic ideas and knowledge asset management approaches to build a comprehensive strategic framework for Beijing City in China. Design/methodology/approach – Methods include a review of relevant literature in both English and Chinese, case studies of different types of support frameworks in the UK, the USA, Singapore and Hong Kong, formulation of a meta-synthetic support framework for Beijing City, and justification of its application to policy development by various studies. Three stages of meta-synthetic support frameworks are proposed. Findings – The suggested meta-synthetic support frameworks are highly appropriate for the optimisation of, and innovation in, management and services systems of government information resources. The proposed knowledge asset management approaches offer significant practical value in improving the competence and capabilities of service-oriented government, providing a set of solutions to identified, urgent problems, including a joint administration system for creating value, a release and distribution management system for sharing and protecting value, and a licensing and authorisation management system for adding value. Research limitations/implications – This paper focuses on the formulation of a theoretical support framework for the reuse of government information resources and the justification of its effectiveness to guide policy development at strategic level. Case studies of its application at operational level are ongoing and will be discussed in future papers. Practical implications – The suggested meta-synthetic support frameworks support the efficiency, effectiveness and economy of intelligent traffic administration, good governance of value-added services based on government information resources, and intellectual activity around city travel and traffic. The study has wide implications for the improvement of service-oriented government performance, public satisfaction and the image of government. Originality/value – The paper presents the adaptation of meta-synthetic ideas and knowledge asset management approaches to collaboration, optimisation, innovation and compliance management issues in the reuse of government information resources. The advantages of different types of support systems and frameworks are integrated as a coherent whole for a strategic framework of legal, regulatory and standards support to China and Beijing. [ABSTRACT FROM AUTHOR]

Abstract: Cellular acetylation signaling is important for viral gene regulation, particularly during the transactivation of retroviruses. The regulatory protein of bovine foamy virus (BFV), BTas, is a transactivator that augments viral gene transcription from both the long terminal repeat (LTR) promoter and the internal promoter (IP). In this study, we report that the histone acetyltransferase (HAT), p300, specifically acetylates BTas both in vivo and in vitro. Further studies demonstrated that BTas acetylation markedly enhances its transactivation activity. Mutagenesis analysis identified three lysines at positions 66, 109 and 110 in BTas that are acetylated by p300. The K110R mutant lost its binding to BFV promoter as well as its ability to activate BFV promoter. The acetylation of K66 and K109 may contribute to increased BTas binding ability. These results suggest that the p300-acetylated lysines of BTas are important for transactivation of BFV promoters and therefore have an important role in BFV replication. [Copyright &y& Elsevier]

Abstract: The BTas protein of bovine foamy virus (BFV) is a 249-amino-acid nuclear regulatory protein which transactivates viral gene expression directed by the long terminal repeat promoter (LTR) and the internal promoter (IP). Here, we demonstrate the BTas protein forms a dimeric complex in mammalian cells by using mammalian two hybrid systems and cross-linking assay. Functional analyses with deletion mutants reveal that the region of 46–62aa is essential for dimer formation. Furthermore, our results show that deleting the dimerization region of BTas did not affect the localization of BTas, but that it did result in the loss of its transactivational activity on the LTR and IP. Furthermore, BTas (Δ46–62aa) retained binding ability to the LTR and IP similar to that of the wild-type BTas. These data suggest the dimerization region is necessary for the transactivational function of BTas and is crucial to the replication of BFV. [Copyright &y& Elsevier]