A tokamak-independent analysis suite has been developed to process data from Motional Stark Effect (mse) diagnostics. The software supports multi-spectral line-polarization mse diagnostics which simultaneously measure emission at the mse σ and π lines as well as at two "background" wavelengths that are displaced from the mse spectrum by a few nanometers. This analysis accurately estimates the amplitude of partially polarized background light at the σ and π wavelengths even in situations where the background light changes rapidly in time and space, a distinct improvement over traditional "time-interpolation" background estimation. The signal amplitude at many frequencies is computed using a numerical-beat algorithm which allows the retardance of the mse photo-elastic modulators (pem's) to be monitored during routine operation. It also allows the use of summed intensities at multiple frequencies in the calculation of polarization direction, which increases the effective signal strength and reduces sensitivity to pem retardance drift. The software allows the polarization angles to be corrected for calibration drift using a system that illuminates the mse diagnostic with polarized light at four known polarization angles within ten seconds of a plasma discharge. The software suite is modular, parallelized, and portable to other facilities.
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http://dx.doi.org/10.1063/1.4958914 | DOI Listing |
J Orthop Surg Res
January 2025
Department of Spine Surgery, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning, 530021, China.
Objective: To assess the stability of odontoid parameters on flexion-extension motion and to validate the accuracy of the physiological cervical lordosis (CL) predictive formula across different cervical positions.
Methods: Standard cervical spine lateral radiographs in neutral, flexion, and extension positions were collected to measure odontoid incidence (OI), odontoid tilt (OT), C2 slope (C2S), CL, T1 slope (T1S), and T1S minus CL (T1S-CL). Friedman's test was used to assess the differences in parameters among the three cervical spine positions.
Sci Rep
January 2025
Department of Electronics and Communication Engineering, Sri Ramakrishna Institute of Technology, Coimbatore, Tamilnadu, India, 641010.
The global spread of COVID-19, particularly through cough symptoms, necessitates efficient diagnostic tools. COVID-19 patients exhibit unique cough sound patterns distinguishable from other respiratory conditions. This study proposes an advanced framework to detect and predict COVID-19 using deep learning from cough audio signals.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Sport & Health, Exercise Science & Neuroscience Unit Universität Paderborn, Warburger Straße 100, 33098, Paderborn, Germany.
Anterior cruciate ligament injuries (ACLi) impact football players substantially leading to performance declines and premature career endings. Emerging evidence suggests that ACLi should be viewed not merely as peripheral injuries but as complex conditions with neurophysiological aspects. The objective of the present study was to compare kicking performance and associated cortical activity between injured and healthy players.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Computer Science Department, Instituto Nacional de Astrofísica Óptica y Electrónica, Luis Enrrique Erro No. 1, Sta. María Tonantzintla, Puebla 72840, Mexico.
Accurate synthetic image generation is crucial for addressing data scarcity challenges in medical image classification tasks, particularly in sensor-derived medical imaging. In this work, we propose a novel method using a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and nearest-neighbor interpolation to generate high-quality synthetic images for diabetic retinopathy classification. Our approach enhances training datasets by generating realistic retinal images that retain critical pathological features.
View Article and Find Full Text PDFDiagnostics (Basel)
December 2024
Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84108, USA.
: This study aimed to explore machine learning approaches for predicting physical exertion using physiological signals collected from wearable devices. : Both traditional machine learning and deep learning methods for classification and regression were assessed. The research involved 27 healthy participants engaged in controlled cycling exercises.
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