The improved algorithm surface irradiance derived from a range of satellite-based sensors (SIDES) is presented in this article. It calculates various types of surface UV intensities, such as biologically weighted or unweighted UV spectra, integrated doses or irradiance at specific wavelengths, using data from satellite instruments. These surface UV data are mainly useful for environmental impact or process studies where high accuracy or a high temporal resolution is required. In contrast to several previous studies, SIDES has been validated with spectral measurements. By this method an averaging of positive or negative deviations over the complete wavelength range is avoided. This is especially important for UV wavelengths around 300 nm where biological effectiveness is highest. The results of SIDES deviate less than 7% from ground-based observations for wavelengths between 295 and 400 nm. In contrast, the corresponding deviations of the joint research center algorithm escalate for shorter wavelengths, reaching 35% at 295 nm. This large deviation is due to an inaccurate interpolation procedure that has been detected by spectral analysis. Thus, spectral validation is demonstrated to be an appropriate tool to detect weaknesses in such an algorithm and provides information essential for improvement.
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http://dx.doi.org/10.1562/0031-8655(2003)077<0052:aiafsd>2.0.co;2 | DOI Listing |
J Med Syst
January 2025
Department of Computing, University of North Florida, 1 UNF Dr., Jacksonville, 32246, FL, USA.
The "no-show" problem in healthcare refers to the prevalent phenomenon where patients schedule appointments with healthcare providers but fail to attend them without prior cancellation or rescheduling. In addressing this issue, our study delves into a multivariate analysis over a five-year period involving 21,969 patients. Our study introduces a predictive model framework that offers a holistic approach to managing the no-show problem in healthcare, incorporating elements into the objective function that address not only the accurate prediction of no-shows but also the management of service capacity, overbooking, and idle resource allocation resulting from mispredictions.
View Article and Find Full Text PDFJ Med Syst
January 2025
Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
Optimizing operating room (OR) utilization is critical for enhancing hospital management and operational efficiency. Accurate surgical case duration predictions are essential for achieving this optimization. Our study aimed to refine the accuracy of these predictions beyond traditional estimation methods by developing Random Forest models tailored to specific surgical departments.
View Article and Find Full Text PDFSupport Care Cancer
January 2025
Oral Diagnosis Department, Faculdade de Odontolodia de Piracicaba, Universidade de Campinas (UNICAMP), Piracicaba, São Paulo, Brazil.
Purpose: Oral mucositis (OM) reflects a complex interplay of several risk factors. Machine learning (ML) is a promising frontier in science, capable of processing dense information. This study aims to assess the performance of ML in predicting OM risk in patients undergoing head and neck radiotherapy.
View Article and Find Full Text PDFMed Biol Eng Comput
January 2025
Non-Invasive Imaging and Diagnostic Laboratory, Department of Applied Mechanics and Biomedical Engineering, Indian Institute of Technology Madras, Chennai, India.
Detection of early mild cognitive impairment (EMCI) is clinically challenging as it involves subtle alterations in multiple brain sub-anatomic regions. Among different brain regions, the corpus callosum and lateral ventricles are primarily affected due to EMCI. In this study, an improved deep canonical correlation analysis (CCA) based framework is proposed to fuse magnetic resonance (MR) image features from lateral ventricular and corpus callosal structures for the detection of EMCI condition.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Departamento de Genética del Desarrollo y Fisiología Molecular, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos 62210, México.
This study addresses the challenging task of identifying viruses within metagenomic data, which encompasses a broad array of biological samples, including animal reservoirs, environmental sources, and the human body. Traditional methods for virus identification often face limitations due to the diversity and rapid evolution of viral genomes. In response, recent efforts have focused on leveraging artificial intelligence (AI) techniques to enhance accuracy and efficiency in virus detection.
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