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The study proposed a human–robot interaction (HRI) framework to enable operators to communicate remotely with robots in a simple and intuitive way. The study focused on the situation when operators with no programming skills have to accomplish teleoperated tasks dealing with randomly localized different-sized objects in an unstructured environment. The purpose of this study is to reduce stress on operators, increase accuracy and reduce the time of task accomplishment. The special application of the proposed system is in the radioactive isotope production factories. The following approach combined the reactivity of the operator’s direct control with the powerful tools of vision-based object classification and localization.

Perceptive real-time gesture control predicated on a Kinect sensor is formulated by information fusion between human intuitiveness and an augmented reality-based vision algorithm. Objects are localized using a developed feature-based vision algorithm, where the homography is estimated and Perspective-n-Point problem is solved. The 3D object position and orientation are stored in the robot end-effector memory for the last mission adjusting and waiting for a gesture control signal to autonomously pick/place an object. Object classification process is done using a one-shot Siamese neural network (NN) to train a proposed deep NN; other well-known models are also used in a comparison. The system was contextualized in one of the nuclear industry applications: radioactive isotope production and its validation were performed through a user study where 10 participants of different backgrounds are involved.

The system was contextualized in one of the nuclear industry applications: radioactive isotope production and its validation were performed through a user study where 10 participants of different backgrounds are involved. The results revealed the effectiveness of the proposed teleoperation system and demonstrate its potential for use by robotics non-experienced users to effectively accomplish remote robot tasks.

The proposed system reduces risk and increases level of safety when applied in hazardous environment such as the nuclear one.

The contribution and uniqueness of the presented study are represented in the development of a well-integrated HRI system that can tackle the four aforementioned circumstances in an effective and user-friendly way. High operator–robot reactivity is kept by using the direct control method, while a lot of cognitive stress is removed using elective/flapped autonomous mode to manipulate randomly localized different configuration objects. This necessitates building an effective deep learning algorithm (in comparison to well-known methods) to recognize objects in different conditions: illumination levels, shadows and different postures.

Weak repeatability is observed in handcrafted keypoints, leading to tracking failures in visual simultaneous localization and mapping (SLAM) systems under challenging scenarios such as illumination change, rapid rotation and large angle of view variation. In contrast, learning-based keypoints exhibit higher repetition but entail considerable computational costs. This paper proposes an innovative algorithm for keypoint extraction, aiming to strike an equilibrium between precision and efficiency. This paper aims to attain accurate, robust and versatile visual localization in scenes of formidable complexity.

SiLK-SLAM initially refines the cutting-edge learning-based extractor, SiLK, and introduces an innovative postprocessing algorithm for keypoint homogenization and operational efficiency. Furthermore, SiLK-SLAM devises a reliable relocalization strategy called PCPnP, leveraging progressive and consistent sampling, thereby bolstering its robustness.

Empirical evaluations conducted on TUM, KITTI and EuRoC data sets substantiate SiLK-SLAM’s superior localization accuracy compared to ORB-SLAM3 and other methods. Compared to ORB-SLAM3, SiLK-SLAM demonstrates an enhancement in localization accuracy even by 70.99%, 87.20% and 85.27% across the three data sets. The relocalization experiments demonstrate SiLK-SLAM’s capability in producing precise and repeatable keypoints, showcasing its robustness in challenging environments.

The SiLK-SLAM achieves exceedingly elevated localization accuracy and resilience in formidable scenarios, holding paramount importance in enhancing the autonomy of robots navigating intricate environments. Code is available at https://github.com/Pepper-FlavoredChewingGum/SiLK-SLAM.

Foreign construction subsidiaries play an important role in the global construction market. How to establish and maintain long-term sustainable performance has attracted increased attention, but only a few studies have considered this issue. The purpose of this study is to explore the relationship between autonomy and the sustainable performance of subsidiaries and to provide support for their management control modes.

From an institutional logics perspective, empirical research using a questionnaire survey was conducted following the methodological framework of this study. Relevant data were collected from 106 experienced managers of foreign construction subsidiaries, and the hypotheses were tested through a regression model.

The results show that foreign construction subsidiaries have a high degree of operational autonomy, which tends to strengthen their embeddedness in the host country and improve their sustainable performance. However, the role of strategic autonomy is not found to be significant. The moderation results show that the positive impact between operational autonomy and external network embeddedness is strengthened by institutional distance. Institutional distance has no significant moderating impact on the relationship between strategic autonomy and external network embeddedness, respectively.

Geographical limitations may exist as the survey is focused on the Chinese construction foreign subsidiaries. However, based on an institutional logics perspective, this study discusses the management control mode of foreign subsidiaries, which enriches the antecedents of sustainable performance and can provide an in-depth explanation of the effects of the organizational strategies of multinational construction enterprises.

This study provides beneficial information for the sustainable performance of foreign construction subsidiaries. It will provide detailed guidance to managers located in different institutional environments on optimally promoting the sustainable development of subsidiaries.

This study identifies autonomy as an important antecedent, making it one of the first studies investigating autonomy on the sustainable performance of foreign construction subsidiaries. The findings of this study can contribute to the construction subsidiaries' sustainable performance literature and provide novel, comprehensive knowledge for academia and practice.

The study aims to identify the evoked “sense of place” that the campus community attributes to a watershed area in a Malaysian higher institution, aiming to enhance their participation in watershed conservation. Central to this objective is the incorporation of the concept of a watershed as a place, serving as the conceptual framework for analysis.

This case study explores an urban lake at Universiti Malaya, Malaysia’s oldest higher institution. It uses diverse qualitative data, including document analysis, semi-structured interviews, vox-pop interviews and a co-production workshop, to generate place-based narratives reflecting the meanings and values that staff and students associate with the watershed. Thematic analysis is then applied for further examination.

The data patterns reveal shared sense of place responses on: campus as a historic place, student, staff and campus identity, in-place learning experiences and interweaving of community well-being and watershed health. Recommendations advocate translating these narratives into campus sustainability communication through empirical findings and continuous co-production of knowledge and strategies with the campus community.

The research findings play a critical role in influencing sustainable campus planning and community inclusion by integrating place-based frameworks into sustainable development and watershed management. The study recommends the process of identifying place-based narratives with implications for the development of sustainability communication in a campus environment.

This paper contributes both conceptually and empirically to the sustainable management of a campus watershed area through place-based thinking. It outlines a process for enhancing campus sustainability communication strategies.

This study aims to build on social capital theory (SCT) and its dimensions by examining the role of social capital in the public procurement process and by identifying related contingencies that may influence procurement performance.

A systematic literature review and a thematic analysis regarding social capital in procurement are conducted. The antecedent–behaviour–consequence (ABC) model is used for illuminating linkages between social capital, contingencies and procurement performance.

The dimensions of social capital are investigated in the procurement process; however, the extent of social capital role can vary between the phases of the process. It is concluded that the contingencies of social dynamics are linked with social capital and may influence the outcomes and performance of the procurement process.

Social capital can ease interactions between public buyers and private suppliers by contributing to effective tendering, improving social interaction in negotiations and balancing rigidity in contract management, supporting the interests of both parties. The provided framework helps decision makers to comprehend the social dynamics in public procurement.

Improving social dynamics and solutions in public procurement.

This study extends social capital research in the field of public procurement and creates a framework connecting social capital and prevailing contingency factors to procurement process performance.

The Thai video game domain has witnessed substantial growth in recent years. However, many games enjoyed by Thai players are in foreign languages, with only a handful of titles translated/localized into the Thai locale. Some Thai video game enthusiasts have taken on the role of unofficial translators/localizers, contributing to a localization domain that accommodates both official and unofficial translation/localization efforts. This general review paper aims to outline the author's experiences in collecting data within the domain of video game translation/localization in Thailand.

Using a descriptive approach, this general review paper employs the netnography method. It sheds light on the complexities of video game translation/localization in Thailand and incorporates semi-structured interviews with a snowball sampling technique for the selection of participants and in-game data collection methods.

The netnography method has proved instrumental in navigating the intricacies of this evolving landscape. Adopting the netnography method for data collection in this research contributes to establishing more robust connections with the research sites. “Inside” professionals and individuals play a significant role in data gathering by recommending additional sources of information for the research.

While netnography is conventionally applied in the market and consumer research, this paper demonstrates its efficacy in unraveling the dynamics of video game translation/localization in Thailand.

This paper aims to explore the key risk factors affecting the Personnel Localization Management of international construction projects under the major public emergencies represented by the novel coronavirus pneumonia pandemic (hereinafter COVID-19) and how the public emergency affected the Personnel Localization Management from three levels: staff turnover rate, the number of different personnel, the salary and performance of workers. The paper also helps to enhance the construction enterprises' response capacity of major public emergencies and provides a comprehensive framework of optimization strategies for the Personnel Localization Management of international construction projects (hereinafter projects).

The main research method of this paper is the case study, and ten representative international construction projects are selected for case study in China construction enterprises (hereinafter CCE). And this study used the failure mode and effects analysis (FMEA) and comparative analysis to find out all potential risk factors under the COVID-19 and analyze how the epidemic affects the Personnel Localization Management of projects which based on the primary data from 10 projects obtained through in-depth interviews and the secondary data from China First Metallurgical Group and Central South Construction Group's Overseas Enterprise.

The findings show that the outbreak of the major public emergencies not only greatly increased eight risk factors but also directly led to an increase in staff turnover rate. Meanwhile, the numbers of Chinese and local managers and workers are all affected, and an increase in the number and the salary performance of local workers can be reduced, to a certain extent, to the cost-to-output ratio of the projects. The findings would help construction enterprises better cope with Personnel Localization Management and enhance the response capacity of major public emergencies.

This study will broaden researchers' horizons regarding “Personnel Localization Management under major public emergencies” and “risk factors of Personnel Localization Management in an international context.” Furthermore, construction enterprises looking for a better mechanism of Personnel Localization Management can benefit from research findings and lessons learned from the authors' case study during or before an outbreak of major public emergency. Lastly, the framework of optimization strategies for Personnel Localization Management can be used both for research purposes and practice issues in international construction projects.

The findings from the authors' case study offer the direction for international construction enterprises in China and other countries to formulate effective measures, strengthen overseas business and establish a crisis management mechanism for Personnel Localization Management under major public emergencies, and the findings provide emergency plans for projects to improve the public crisis handling capacity and respond to major public emergencies such as the COVID-19.

This study analyzes the impact of the COVID-19 on the Personnel Localization Management of international construction projects from the perspective of personnel. This study provides a theoretical reference for the international construction industry to actively respond to major public emergencies. Besides, the research is conducive to improving the emergency response mechanism in the construction industry, and further promoting the high-quality and globalized development of international construction.

This study provides other researchers with a comprehensive understanding of the risk factors affecting the Personnel Localization Management of projects under the COVID-19 and insight for further research on localization management, risk management, and project management.

The prerequisite for most traditional visual simultaneous localization and mapping (V-SLAM) algorithms is that most objects in the environment should be static or in low-speed locomotion. These algorithms rely on geometric information of the environment and restrict the application scenarios with dynamic objects. Semantic segmentation can be used to extract deep features from images to identify dynamic objects in the real world. Therefore, V-SLAM fused with semantic information can reduce the influence from dynamic objects and achieve higher accuracy. This paper aims to present a new semantic stereo V-SLAM method toward outdoor dynamic environments for more accurate pose estimation.

First, the Deeplabv3+ semantic segmentation model is adopted to recognize semantic information about dynamic objects in the outdoor scenes. Second, an approach that combines prior knowledge to determine the dynamic hierarchy of moveable objects is proposed, which depends on the pixel movement between frames. Finally, a semantic stereo V-SLAM based on ORB-SLAM2 to calculate accurate trajectory in dynamic environments is presented, which selects corresponding feature points on static regions and eliminates useless feature points on dynamic regions.

The proposed method is successfully verified on the public data set KITTI and ZED2 self-collected data set in the real world. The proposed V-SLAM system can extract the semantic information and track feature points steadily in dynamic environments. Absolute pose error and relative pose error are used to evaluate the feasibility of the proposed method. Experimental results show significant improvements in root mean square error and standard deviation error on both the KITTI data set and an unmanned aerial vehicle. That indicates this method can be effectively applied to outdoor environments.

The main contribution of this study is that a new semantic stereo V-SLAM method is proposed with greater robustness and stability, which reduces the impact of moving objects in dynamic scenes.

Existing Simultaneous Localization and Mapping (SLAM) algorithms have been relatively well developed. However, when in complex dynamic environments, the movement of the dynamic points on the dynamic objects in the image in the mapping can have an impact on the observation of the system, and thus there will be biases and errors in the position estimation and the creation of map points. The aim of this paper is to achieve more accurate accuracy in SLAM algorithms compared to traditional methods through semantic approaches.

In this paper, the semantic segmentation of dynamic objects is realized based on U-Net semantic segmentation network, followed by motion consistency detection through motion detection method to determine whether the segmented objects are moving in the current scene or not, and combined with the motion compensation method to eliminate dynamic points and compensate for the current local image, so as to make the system robust.

Experiments comparing the effect of detecting dynamic points and removing outliers are conducted on a dynamic data set of Technische Universität München, and the results show that the absolute trajectory accuracy of this paper's method is significantly improved compared with ORB-SLAM3 and DS-SLAM.

In this paper, in the semantic segmentation network part, the segmentation mask is combined with the method of dynamic point detection, elimination and compensation, which reduces the influence of dynamic objects, thus effectively improving the accuracy of localization in dynamic environments.

This study aims to achieve superior localization and mapping performance in point cloud degradation scenarios through the effective removal of dynamic obstacles. With the continuous development of various technologies for autonomous vehicles, the LIDAR-based Simultaneous localization and mapping (SLAM) system is becoming increasingly important. However, in SLAM systems, effectively addressing the challenges of point cloud degradation scenarios is essential for accurate localization and mapping, with dynamic obstacle removal being a key component.

This paper proposes a method that combines adaptive feature extraction and loop closure detection algorithms to address this challenge. In the SLAM system, the ground point cloud and non-ground point cloud are separated to reduce the impact of noise. And based on the cylindrical projection image of the point cloud, the intensity features are adaptively extracted, the degradation direction is determined by the degradation factor and the intensity features are matched with the map to correct the degraded pose. Moreover, through the difference in raster distribution of the point clouds before and after two frames in the loop process, the dynamic point clouds are identified and removed, and the map is updated.

Experimental results show that the method has good performance. The absolute displacement accuracy of the laser odometer is improved by 27.1%, the relative displacement accuracy is improved by 33.5% and the relative angle accuracy is improved by 23.8% after using the adaptive intensity feature extraction method. The position error is reduced by 30% after removing the dynamic target.

Compared with LiDAR odometry and mapping algorithm, the method has greater robustness and accuracy in mapping and localization.