This ethnography within ten English and Welsh hospitals explores the significance of boundary work and the impacts of this work on the quality of care experienced by heart attack patients who have suspected non-ST segment elevation myocardial infarction (NSTEMI) /non-ST elevation acute coronary syndrome. Beginning with the initial identification and prioritisation of patients, boundary work informed negotiations over responsibility for patients, their transfer and admission to different wards, and their access to specific domains in order to receive diagnostic tests and treatment. In order to navigate boundaries successfully and for their clinical needs to be more easily recognised by staff, a patient needed to become a stable boundary object. Ongoing uncertainty in fixing their clinical classification, was a key reason why many NSTEMI patients faltered as boundary objects. Viewing NSTEMI patients as boundary objects helps to articulate the critical and ongoing process of classification and categorisation in the creation and maintenance of boundary objects. We show the essential, but hidden, role of boundary actors in making and re-making patients into boundary objects. Physical location was critical and the parallel processes of exclusion and restriction of boundary object status can lead to marginalisation of some patients and inequalities of care (A virtual abstract of this paper can be viewed at: https://www.youtube.com/channel/UC_979cmCmR9rLrKuD7z0ycA).
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http://dx.doi.org/10.1111/1467-9566.12778 | DOI Listing |
Sensors (Basel)
January 2025
School of Geosciences, Yangtze University, Wuhan 430100, China.
Roadside tree segmentation and parameter extraction play an essential role in completing the virtual simulation of road scenes. Point cloud data of roadside trees collected by LiDAR provide important data support for achieving assisted autonomous driving. Due to the interference from trees and other ground objects in street scenes caused by mobile laser scanning, there may be a small number of missing points in the roadside tree point cloud, which makes it familiar for under-segmentation and over-segmentation phenomena to occur in the roadside tree segmentation process.
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January 2025
Faculty of Production Engineering and Materials Technology, Częstochowa University of Technology, al. Armii Krajowej 19, 42-201 Częstochowa, Poland.
The paper presents the results of industrial research and numerical simulations of the chemical homogenization of liquid steel. The research object was a ladle furnace with a working capacity of the ladle of 100 t at the steel plant of Huta Częstochowa, currently Liberty Częstochowa Sp. z o.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China.
With the advancement of service robot technology, the demand for higher boundary precision in indoor semantic segmentation has increased. Traditional methods of extracting Euclidean features using point cloud and voxel data often neglect geodesic information, reducing boundary accuracy for adjacent objects and consuming significant computational resources. This study proposes a novel network, the Euclidean-geodesic network (EGNet), which uses point cloud-voxel-mesh data to characterize detail, contour, and geodesic features, respectively.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, China.
Point cloud registration is pivotal across various applications, yet traditional methods rely on unordered point clouds, leading to significant challenges in terms of computational complexity and feature richness. These methods often use k-nearest neighbors (KNN) or neighborhood ball queries to access local neighborhood information, which is not only computationally intensive but also confines the analysis within the object's boundary, making it difficult to determine if points are precisely on the boundary using local features alone. This indicates a lack of sufficient local feature richness.
View Article and Find Full Text PDFEntropy (Basel)
December 2024
Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China.
Image segmentation is a crucial task in artificial intelligence fields such as computer vision and medical imaging. While convolutional neural networks (CNNs) have achieved notable success by learning representative features from large datasets, they often lack geometric priors and global object information, limiting their accuracy in complex scenarios. Variational methods like active contours provide geometric priors and theoretical interpretability but require manual initialization and are sensitive to hyper-parameters.
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