The lung is one of the most important organs exposed to environmental agents. People spend approximately 90% of their time indoors, and risks to health may thus be greater from exposure to poor air quality indoors than outdoors. Multiple indoor pollutants have been linked to chronic respiratory diseases. Environmental tobacco smoke (ETS) is known as an important source of multiple pollutants, especially in indoor environments. Indoor PM (particulate matter with aerodynamic diameter < 2.5 μm) was reported to be the most reliable marker of the presence of tobacco smoke. Recent studies have demonstrated that PM is closely correlated with chronic lung diseases. In this paper, we reviewed the relationship of tobacco smoking and indoor PM and the mechanism that underpin the link of tobacco smoke, indoor PM and chronic lung diseases.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.envres.2019.108910 | DOI Listing |
Plants (Basel)
January 2025
Department of Plant and Soil Sciences, Faculty of Agriculture, Chiang Mai University, Chiang Mai 50200, Thailand.
Nitrogen (N) is an essential determinant of strawberry growth and productivity. However, plants exhibit varying preferences for sources of nitrogen, which ultimately affects its use efficiency. Thus, it is imperative to determine the preferred N source for the optimization of indoor strawberry production.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Engineering Design, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden.
Topography estimation is essential for autonomous off-road navigation. Common methods rely on point cloud data from, e.g.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Free-Space Optical Communication Technology Research Center, Harbin Institute of Technology, Harbin 150001, China.
To achieve real-time deep learning wavefront sensing (DLWFS) of dynamic random wavefront distortions induced by atmospheric turbulence, this study proposes an enhanced wavefront sensing neural network (WFSNet) based on convolutional neural networks (CNN). We introduce a novel multi-objective neural architecture search (MNAS) method designed to attain Pareto optimality in terms of error and floating-point operations (FLOPs) for the WFSNet. Utilizing EfficientNet-B0 prototypes, we propose a WFSNet with enhanced neural architecture which significantly reduces computational costs by 80% while improving wavefront sensing accuracy by 22%.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Smart Diagnostic and Online Monitoring, Leipzig University of Applied Sciences, Wächterstraße 13, 04107 Leipzig, Germany.
This paper presents a comparative study of different AI models for indoor positioning systems, emphasizing improvements in localization accuracy and processing time. This study examines Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNNs), and the Kalman filter using a real Received Signal Strength Indicator (RSSI) and 9-axis ICM-20948 sensor. An in-depth analysis is provided in this paper for data cleaning and feature selection to reduce errors for all the models.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Civil Engineering and Engineering Management, National Quemoy University, Kinmen 89250, Taiwan.
Ground-based LiDAR technology has been widely applied in various fields for acquiring 3D point cloud data, including spatial coordinates, digital color information, and laser reflectance intensities (I-values). These datasets preserve the digital information of scanned objects, supporting value-added applications. However, raw point cloud data visually represent spatial features but lack attribute information, posing challenges for automated object classification and effective management.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!