In this paper, an extremely sensitive microwave sensor is designed based on a complementary symmetric S shaped resonator (CSSSR) to evaluate dielectric characteristics of low-permittivity material. CSSSR is an artificial structure with strong and enhanced electromagnetic fields, which provides high sensitivity and a new degree of freedom in sensing. Electromagnetic simulation elucidates the effect of real relative permittivity, real relative permeability, dielectric and magnetic loss tangents of the material under test (MUT) on the resonance frequency and notch depth of the sensor. Experiments are performed at room temperature using low-permittivity materials to verify the concept. The proposed design provides differential sensitivity between 102% to 95% as the relative permittivity of MUT varies from 2.1 to 3. The percentage error between simulated and measured results is less than 0.5%. The transcendental equation has been established by measuring the change in the resonance frequency of the fabricated sensor due to interaction with the MUT.
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http://dx.doi.org/10.3390/s20071916 | DOI Listing |
Cureus
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Pharmacology, Ministry of National Guard, AlAhsa, SAU.
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View Article and Find Full Text PDFDigit Health
January 2025
Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan.
Objective: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition influenced by various genetic and environmental factors. Currently, there is no definitive clinical test, such as a blood analysis or brain scan, for early diagnosis. The objective of this study is to develop a computational model that predicts ASD driver genes in the early stages using genomic data, aiming to enhance early diagnosis and intervention.
View Article and Find Full Text PDFFront Oncol
January 2025
School of Nursing, Chengdu Medical College, Chengdu, China.
Objective: Presentation delay of cancer patients prevents the patient from timely diagnosis and treatment leading to poor prognosis. Predicting the risk of presentation delay is crucial to improve the treatment outcomes. This study aimed to develop and validate prediction models of presentation delay risk in gastric cancer patients by using various machine learning models.
View Article and Find Full Text PDFJ Res Med Sci
December 2024
Department of Biostatistics, Student Research Committee, University of Medical Sciences, Kermanshah, Iran.
Background: The initial assessment of trauma is a time-consuming and challenging task. The purpose of this research is to examine the diagnostic effectiveness and usefulness of machine learning models paired with radiomics features to identify blunt traumatic liver injury in abdominal computed tomography (CT) images.
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Indian J Psychol Med
January 2025
Dept. of Community Medicine and School of Public Health, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
Background: Depression among the elderly is a growing public health concern, especially in India. This study aimed to investigate the predictive validity of physiological, psychological, and functional health factors in classifying the level of depressive symptoms among the elderly using the extreme gradient boosting (XGBoost) technique. Additionally, we compared the performance of models trained on original and resampled data.
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