The increasing interest in switchable and tunable wideband perfect absorbers for applications such as modulation, energy harvesting, and spectroscopy has significantly driven research efforts. In this study, we present a dual-function terahertz (THz) metamaterial absorber supported by deep neural networks (DNN). This absorber achieves dual-wideband perfect absorption through the use of graphene and vanadium dioxide (VO₂), enabling both switching and tuning functionalities. Simulation results show that, in the insulating phase of VO₂, a high-frequency wideband absorption ranging from 9.31 to 9.77 THz is achieved, with an absorption rate exceeding 90%. In contrast, in the metallic phase of VO₂, a full-band wideband absorption above 90% is observed from 8.44 to 9.75 THz. The corresponding fractional bandwidths are 61.3% and 174.6%, respectively. Additionally, electrical tuning of graphene's Fermi level from 0.01 to 1 eV enables continuous modulation of absorption intensity between 48 and 100%. The absorber also exhibits polarization insensitivity to TE and TM waves due to its symmetric design and broad incidence angle. This design holds significant potential for various THz applications, including switching, electromagnetic shielding, stealth technology, filtering, and sensing.
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http://dx.doi.org/10.1038/s41598-024-75705-6 | DOI Listing |
Clin Oral Implants Res
January 2025
Department of Oral and Maxillofacial Radiology, School of Dentistry, Kashan University of Medical Sciences, Kashan, Iran.
Objective: This study evaluated ResNet-50 and U-Net models for detecting and segmenting vertical misfit in dental implant crowns using periapical radiographic images.
Methods: Periapical radiographs of dental implant crowns were classified by two experts based on the presence of vertical misfit (reference group). The misfit area was manually annotated in images exhibiting vertical misfit.
J Oral Microbiol
January 2025
Integrative Microecology Clinical Center, Shenzhen Clinical Research Center for Digestive Disease, Shenzhen Technology Research Center of Gut Microbiota Transplantation, The Clinical Innovation & Research Center, Shenzhen Key Laboratory of Viral Oncology, Department of Clinical Nutrition, Shenzhen Hospital, Southern Medical University, Shenzhen, China.
Background: This study aims to develop an oral microbiota-based model for gastric cancer (GC) risk stratification and prognosis prediction.
Methods: Oral microbial markers for GC prognosis and risk stratification were identified from 99 GC patients, and their predictive potential was validated on an external dataset of 111 GC patients. The identified bacterial markers were used to construct a Deep Neural Network (DNN) model, a Random Forest (RF) model, and a Support Vector Machine (SVM) model for predicting GC prognosis.
Front Plant Sci
January 2025
Information and Communication Engineering, Yeungnam University, Gyeongsan, Republic of Korea.
Smart farming is a hot research area for experts globally to fulfill the soaring demand for food. Automated approaches, based on convolutional neural networks (CNN), for crop disease identification, weed classification, and monitoring have substantially helped increase crop yields. Plant diseases and pests are posing a significant danger to the health of plants, thus causing a reduction in crop production.
View Article and Find Full Text PDFCureus
December 2024
Internal Medicine, Belgaum Institute of Medical Science, Belgaum, IND.
Several studies explored the application of artificial intelligence (AI) in magnetic resonance imaging (MRI)-based rectal cancer (RC) staging, but a comprehensive evaluation remains lacking. This systematic review aims to review the performance of AI models in MRI-based RC staging. PubMed and Embase were searched from the inception of the database till October 2024 without any language and year restrictions.
View Article and Find Full Text PDFJ Biomed Opt
January 2025
Columbia University, Department of Electrical Engineering, New York, United States.
Significance: Radiofrequency ablation to treat atrial fibrillation (AF) involves isolating the pulmonary vein from the left atria to prevent AF from occurring. However, creating ablation lesions within the pulmonary veins can cause adverse complications.
Aim: We propose automated classification algorithms to classify optical coherence tomography (OCT) volumes of human venoatrial junctions.
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