Fine particulate matter (PM) is an airborne pollutant associated with negative acute and chronic human health outcomes. Although the majority of PM research has focused on outdoor exposures, people spend the majority of their time indoors, where PM of outdoor origin can penetrate. In this work, we measured indoor PM continuously for one year in 37 urban commercial offices with mechanical or mixed-mode ventilation in China, India, the United Kingdom, and the United States. We found that indoor PM concentrations were generally higher when and where outdoor PM was elevated. In India and China, mean workday indoor PM levels exceeded the World Health Organization's 24-hour exposure guideline of 25 g/m about 17% and 27% of the time, respectively. Our statistical models found evidence that the operation of mechanical ventilation systems could mitigate the intrusion of outdoor PM: during standard work hours, a 10 g/m increase in outdoor PM was associated with 19.9% increase in the expected concentration of indoor PM (<0.0001), compared to a larger 23.4% increase during non-work hours (<0.0001). Finally, our models found that using filters with ratings of MERV 13-14 or MERV 15+ was associated with a 30.9% (95% CI: -55.0%, +6.2%) or 39.4% (95% CI: -62.0%, -3.4%) reduction of indoor PM, respectively, compared to filters with lower MERV 7-12 ratings. Our results demonstrate the potential efficacy of mechanical ventilation with efficient filtration as a public health strategy to protect workers from PM exposure, particularly where outdoor levels of PM are elevated.
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http://dx.doi.org/10.1016/j.buildenv.2021.107975 | DOI Listing |
Sensors (Basel)
December 2024
Department of Intelligent Systems & Robotics, Chungbuk National University, Cheongju 28644, Republic of Korea.
Handheld LiDAR scanners, which typically consist of a LiDAR sensor, Inertial Measurement Unit, and processor, enable data capture while moving, offering flexibility for various applications, including indoor and outdoor 3D mapping in fields such as architecture and civil engineering. Unlike fixed LiDAR systems, handheld devices allow data collection from different angles, but this mobility introduces challenges in data quality, particularly when initial calibration between sensors is not precise. Accurate LiDAR-IMU calibration, essential for mapping accuracy in Simultaneous Localization and Mapping applications, involves precise alignment of the sensors' extrinsic parameters.
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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.
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December 2024
Institute of Computer Science, Zurich University of Applied Sciences, 8400 Winterthur, Switzerland.
Simultaneous localization and mapping (SLAM) techniques can be used to navigate the visually impaired, but the development of robust SLAM solutions for crowded spaces is limited by the lack of realistic datasets. To address this, we introduce InCrowd-VI, a novel visual-inertial dataset specifically designed for human navigation in indoor pedestrian-rich environments. Recorded using Meta Aria Project glasses, it captures realistic scenarios without environmental control.
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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.
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December 2024
School of Health and Society, University of Salford, Salford M6 6PU, UK.
This study investigated the relationship between stepping-defined daily activity levels, time spent in different postures, and the patterns and intensities of stepping behaviour. Using a thigh-mounted triaxial accelerometer, physical activity data from 3547 participants with seven days of valid data were analysed. We classified days based on step count and quantified posture and stepping behaviour, distinguishing between indoor, community, and recreation stepping.
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