A fast and accurate full-wave technique based on the dual-primal finite element tearing and interconnecting method and the second-order transmission condition is presented for large-scale three-dimensional photonic device simulations. The technique decomposes a general three-dimensional electromagnetic problem into smaller subdomain problems so that parallel computing can be performed on distributed-memory computer clusters to reduce the simulation time significantly. With the electric fields computed everywhere, photonic device parameters such as transmission and reflection coefficients are extracted. Several photonic devices, with simulation volumes up to 1.9×10(4) (λ/n(avg))3 and modeled with over one hundred million unknowns, are simulated to demonstrate the application, efficiency, and capability of this technique. The simulations show good agreement with experimental results and in a special case with a simplified two-dimensional simulation.
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http://dx.doi.org/10.1364/OE.22.004437 | DOI Listing |
BMC Chem
January 2025
Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh, 11451, Saudi Arabia.
For paediatric patients suffering from neurofibromatosis, Selumetinib (SEL) is the only approved drug. Here an original ecofriendly and high pace method is introduced using 96- microwell spectrophotometric assay (MW-SPA) to measure SEL content in bulk and commercial pharmaceutical formulation (Koselugo capsules). This assay was relied on in-microwell formation of a coloured charge transfer complex (CTC) upon interaction of SEL with 2,3-dichloro-5,6-dicyano-1,4-benzoquinone (DDQ).
View Article and Find Full Text PDFAnal Chim Acta
March 2025
Department of Obstetrics and Gynecology the Second Affiliated Hospital of Nanchang University, China. Electronic address:
Rapid, sensitive, and specific molecular detection methods are crucial for diagnosing, treating and prognosing cancer patients. With advancements in biotechnology, molecular diagnostic technology has garnered significant attention as a fast and accurate method for cancer diagnosis. CRISPR-Cas12a (Cpf1), an important CRISPR-Cas family member, has revolutionized the field of molecular diagnosis since its introduction.
View Article and Find Full Text PDFBr J Anaesth
February 2025
Transfusion Research Unit, Department of Public Health and Preventative Medicine, Monash University, Melbourne, VIC, Australia; Department of Clinical Haematology, Monash Health, Clayton, VIC, Australia.
Accurate and timely diagnostic information is a vital adjunct to clinical assessment to inform therapeutic decision-making, including decisions to transfuse, or not transfuse, blood components. A prospective cohort study of diagnostic point-of-care (POC) haemoglobin measurements on arterial or central venous samples from adults undergoing major noncardiac surgery compared three widely used devices, HemoCue®, i-STAT™, and the Rad-67™ pulse CO-Oxymeter® finger sensor device, against standard laboratory haemoglobin measurements, but importantly not against a blood gas analyser. The study focused on haemoglobin results below 100 g L to establish the utility of these devices to guide red cell transfusion decisions.
View Article and Find Full Text PDFJ Clin Neurosci
January 2025
Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, NSW, Australia; Computational NeuroSurgery (CNS) Lab, Macquarie University, NSW, Australia.
Purpose: This literature review aims to synthesise current research on the application of artificial intelligence (AI) for the segmentation of brain neuroanatomical structures in magnetic resonance imaging (MRI).
Methods: A literature search was conducted using the databases Embase, Medline, Scopus, and Web of Science, and captured articles were assessed for inclusion in the review. Data extraction was performed for the summary of the AI model used, and key findings of each article, advantages and disadvantages were identified.
J Med Internet Res
January 2025
Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, United Arab Emirates.
Background: Neuroimaging segmentation is increasingly important for diagnosing and planning treatments for neurological diseases. Manual segmentation is time-consuming, apart from being prone to human error and variability. Transformers are a promising deep learning approach for automated medical image segmentation.
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