Passive air samplers (PASs) are simple, versatile devices that are increasingly used to determine the concentrations of semivolatile organic compounds (SVOCs) in the atmosphere. Using PAS and interpreting PAS-derived data with confidence requires a detailed understanding of the factors that control the uptake kinetics. A number of experiments were aimed at clarifying the role that the housing has in limiting the uptake of SVOCs in a PAS. Specifically, we quantified the gradient in the amount of polychlorinated biphenyls (PCBs) accumulated in XAD-filled mesh cylinders with increasing distance from the PAS housing's opening. That gradient was non-existent in an artificially ventilated housing (i.e. different segments of a cylinder contained the same amount of PCBs), minor during outdoor deployments (i.e. the bottom third of the cylinder sampled approximately 20% more PCBs than the top third), and strong during indoor deployments (i.e. the bottom third of the cylinder sampled twice the amount sampled by the top third). This is consistent with the thickness of the air boundary layer surrounding the XAD-resin increasing with increasing distance from the housing's opening and decreasing with increasing air turbulence. An experiment with housings absorbing different amounts of sunlight revealed that heat-induced convection has a minor effect on the gradient within the mesh cylinder and on the total amount of accumulated PCB. Similarly, this gradient and the total amount sorbed was also not influenced by the number of XAD-filled mesh cylinders placed within a housing as long as they were deployed outdoors. However, if four mesh cylinders were placed in one housing in a calm indoor setting, the top third of the mesh cylinders was notably starved of PCBs, suggestive of an air concentration gradient within the sampler housing.
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Sensors (Basel)
November 2024
Department of System Information Sciences, Graduate School of Information Sciences, Tohoku University, Aoba-ku, Sendai 980-8579, Japan.
The integration of robotics in the garment industry remains relatively limited, primarily due to the challenges in the highly deformable nature of garments. The objective of this study is thus to explore a vision-based garment recognition and environment reconstruction model to facilitate the application of robots in garment processing. Object SLAM (Simultaneous Localization and Mapping) was employed as the core methodology for real-time mapping and tracking.
View Article and Find Full Text PDFAnn Vasc Surg
January 2025
CHU Rennes, Service de Chirurgie Cardiothoracique et Vasculaire, Rennes, France; INSERM, Rennes, France; Université de Rennes 1, Laboratoire de Traitement du Signal et de l'Image (LTSI), Rennes, France. Electronic address:
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Biomimetics (Basel)
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Jiangsu Kingkind Industrial Furnace Co., Ltd., Yancheng 224100, China.
According to the Magnus principle, a rotating cylinder experiences a lateral force perpendicular to the incoming flow direction. This phenomenon can be harnessed to boost the lift of an airfoil by positioning a rotating cylinder at the leading edge. In this study, we simulate flapping-wing motion using the sliding mesh technique in a heaving coordinate system to investigate the energy harvesting capabilities of Magnus effect flapping wings (MEFWs) featuring a leading-edge rotating cylinder.
View Article and Find Full Text PDFHeliyon
April 2024
Department of Mechanical Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria.
This work aimed at investigating blends of biodiesel in a compression ignition engine, attempting to improve engine performance and reduce CO emission compared with conventional diesel. Analysis of System (ANSYS) was used to predict in-cylinder behavior of the fuel. ANSYS SpaceClaim generated the geometric model on which 5° sector and mesh refinement was on ANSYS Internal Combustion Engine Modeler (ICEM).
View Article and Find Full Text PDFNeural Netw
July 2024
Applied Modelling and Computation Group, Department of Earth Science and Engineering, Imperial College London, SW7 2AZ, UK; Data Assimilation Laboratory, Data Science Institute, Imperial College London, SW7 2AZ, UK.
This paper presents the first classical Convolutional Neural Network (CNN) that can be applied directly to data from unstructured finite element meshes or control volume grids. CNNs have been hugely influential in the areas of image classification and image compression, both of which typically deal with data on structured grids. Unstructured meshes are frequently used to solve partial differential equations and are particularly suitable for problems that require the mesh to conform to complex geometries or for problems that require variable mesh resolution.
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