Air pollution directional risk (APDR) is an essential factor to be assessed when selecting an appropriate landfill site. Because air pollutants generated from a landfill are diffused and transported by wind in different directions and speeds, areas surrounding the landfill will be subject to different associated risks, depending on their relative position from the landfill. This study assesses potential APDRs imposed from a candidate landfill site on its adjacent areas on the basis of the pollutant distribution simulated by a dispersion model, wind directions and speeds from meteorological monitoring data, and population density. A pollutant distribution map layer was created using a geographic information system and layered onto a population density map to obtain an APDR map layer. The risk map layer was then used in this study to evaluate the suitability of a candidate site for placing a landfill. The efficacy of the proposed procedure was demonstrated for a siting problem in central Taiwan, Republic of China.
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http://dx.doi.org/10.3155/1047-3289.58.12.1539 | DOI Listing |
Transl Vis Sci Technol
January 2025
Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom, Thailand.
Purpose: The purpose of this study was to develop a deep learning approach that restores artifact-laden optical coherence tomography (OCT) scans and predicts functional loss on the 24-2 Humphrey Visual Field (HVF) test.
Methods: This cross-sectional, retrospective study used 1674 visual field (VF)-OCT pairs from 951 eyes for training and 429 pairs from 345 eyes for testing. Peripapillary retinal nerve fiber layer (RNFL) thickness map artifacts were corrected using a generative diffusion model.
Nanoscale
January 2025
Dept. of Chemical and Biomolecular Engineering, Lehigh University, Bethlehem, PA 18015, USA.
Identifying facile strategies for hierarchically structuring crystalline porous materials is critical for realizing diffusion length scales suitable for broad applications. Here, we elucidate synthesis-structure-function relations governing how room temperature catalytic conditions can be exploited to tune covalent organic framework (COF) growth and thereby access unique hierarchical morphologies without the need to introduce secondary templates or structure directing molecules. Specifically, we demonstrate how scandium triflate, an efficient catalyst involved in the synthesis of imine-based COFs, can be exploited as an effective growth modifier capable of selectively titrating terminal amines on 2D COF layers to facilitate anisotropic crystal growth.
View Article and Find Full Text PDFFront Neurosci
January 2025
School of Data Science, Lingnan University, Hong Kong SAR, China.
Accurate monitoring of drowsy driving through electroencephalography (EEG) can effectively reduce traffic accidents. Developing a calibration-free drowsiness detection system with single-channel EEG alone is very challenging due to the non-stationarity of EEG signals, the heterogeneity among different individuals, and the relatively parsimonious compared to multi-channel EEG. Although deep learning-based approaches can effectively decode EEG signals, most deep learning models lack interpretability due to their black-box nature.
View Article and Find Full Text PDFQuant Imaging Med Surg
January 2025
Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
Background: The major of anticancer therapies induce a wide spectrum of cardiotoxic effects. Early identification of anticancer treatment-associated cardiotoxicity is critical to informing decisions on subsequent interventions. Myocardial extracellular volume (ECV) has been proposed as a useful parameter for quantifying the early cardiotoxicity of cancer-related treatment.
View Article and Find Full Text PDFSci Rep
January 2025
Institute of Big Data and Artificial Intelligence, Southwest Forestry University, Kunming, China.
Warehouses are critical logistics nodes, with food freezer warehouses playing a key role in ensuring food quality while facing challenges such as high-density item distribution and extremely low temperatures required for occupational safety. Traditional management methods struggle to meet these demands, underscoring the need for intelligent and digital solutions to improve efficiency and mitigate safety risks. This study proposes the YOLOv8-RSS model, a lightweight and high-precision approach tailored for food freezer warehouse scenarios.
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