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Transforming CCTV cameras into NO sensors at city scale for adaptive policymaking.
Sci Rep
January 2025
The Alan Turing Institute, London, UK.
Air pollution in cities, especially NO, is linked to numerous health problems, ranging from mortality to mental health challenges and attention deficits in children. While cities globally have initiated policies to curtail emissions, real-time monitoring remains challenging due to limited environmental sensors and their inconsistent distribution. This gap hinders the creation of adaptive urban policies that respond to the sequence of events and daily activities affecting pollution in cities.
View Article and Find Full Text PDFA hybrid deep learning air pollution prediction approach based on neighborhood selection and spatio-temporal attention.
Sci Rep
January 2025
School of Architecture and Urban Planning, Beijing University of Civil Engineering and Architecture, Beijing, 100055, China.
Air pollution is a critical global environmental issue, further exacerbated by rapid industrialization and urbanization. Accurate prediction of air pollutant concentrations is essential for effective pollution prevention and control measures. The complex nature of pollutant data is influenced by fluctuating meteorological conditions, diverse pollution sources, and propagation processes, underscores the crucial importance of the spatial and temporal feature extraction for accurately predicting air pollutant concentrations.
View Article and Find Full Text PDFA synergetic approach for multidimensional drought assessment in the Indian agro-climatic zone using coherency, propagation and AHP techniques.
Environ Sci Pollut Res Int
January 2025
Department of Agricultural Engineering, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, India.
Drought is one of the most detrimental natural calamities to the economy. Despite its significant consequences, the evolution from meteorological to agricultural and hydrological droughts still needs to be explored. A thorough investigation was carried out in India's eastern hills and plateau region to determine the extent of drought's impact through indices.
View Article and Find Full Text PDFRadiogenomic explainable AI with neural ordinary differential equation for identifying post-SRS brain metastasis radionecrosis.
Med Phys
January 2025
Deparment of Radiation Oncology, Duke University, Durham, North Carolina, USA.
Background: Stereotactic radiosurgery (SRS) is widely used for managing brain metastases (BMs), but an adverse effect, radionecrosis, complicates post-SRS management. Differentiating radionecrosis from tumor recurrence non-invasively remains a major clinical challenge, as conventional imaging techniques often necessitate surgical biopsy for accurate diagnosis. Machine learning and deep learning models have shown potential in distinguishing radionecrosis from tumor recurrence.
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