Background: Estimation of long-term risk for cardiovascular events using the SMART (Secondary Manifestations of Arterial Disease) risk score can be potentially valuable in devising risk mitigation strategies.
Objectives: The objective of this study was to apply the SMART risk score to compute the risk for major adverse cardiovascular events (MACE) in the U.S.
Complex-valued neural networks process both amplitude and phase information, in contrast to conventional artificial neural networks, achieving additive capabilities in recognizing phase-sensitive data inherent in wave-related phenomena. The ever-increasing data capacity and network scale place substantial demands on underlying computing hardware. In parallel with the successes and extensive efforts made in electronics, optical neuromorphic hardware is promising to achieve ultra-high computing performances due to its inherent analog architecture and wide bandwidth.
View Article and Find Full Text PDFHigh-dimensional photon states (qudits) are pivotal to enhance the information capacity, noise robustness, and data rates of quantum communications. Time-bin entangled qudits are promising candidates for implementing high-dimensional quantum communications over optical fiber networks with processing rates approaching those of classical telecommunications. However, their use is hindered by phase instability, timing inaccuracy, and low scalability of interferometric schemes needed for time-bin processing.
View Article and Find Full Text PDFJ Midwifery Womens Health
December 2024
Introduction: First-trimester prenatal care is an important component of quality care during pregnancy and is associated with improved perinatal outcomes. Despite its importance, many pregnant people delay prenatal care initiation or receive no prenatal care. This scoping review assessed multilevel factors associated with first-trimester prenatal care initiation in the United States among studies that included a measure of prenatal care timing, using the socioecological model as an organizing framework.
View Article and Find Full Text PDFObjective: Magnetic resonance imaging (MRI) is a crucial tool for identifying brain abnormalities in a wide range of neurological disorders. In focal epilepsy, MRI is used to identify structural cerebral abnormalities. For covert lesions, machine learning and artificial intelligence (AI) algorithms may improve lesion detection if abnormalities are not evident on visual inspection.
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