In modern ICs, sub-threshold voltage management plays a significant role due to its perspective on energy efficiency and speed performance. Level shifters (LSs) play a critical role in signal exchange among multiple voltage domains by ensuring signal integrity and the reliable operation of ICs. In this article, a Pass-Transistor-Enabled Split Input Voltage Level Shifter (PVLS) is designed for area, delay, and power-efficient applications with a wide voltage conversion range.
View Article and Find Full Text PDFObjectives: The rising cost of anti-seizure medications (ASMs) in the United States (US) is a major concern for patients, healthcare providers, insurance payors, and policymakers. We aim to describe and analyze the spending trends on ASMs in the Medicare Part D (MPD) and Medicaid population in the US.
Methods: A retrospective study was conducted on the databases of MPD and Medicaid Spending by Drug from 2012 to 2022, which was published by the Centers for Medicare and Medicaid Services (CMS).
Bosniak classification version 2019 (v2019) was a major revision to version 2005 (v2005) that defined cystic renal mass subclasses based on wall or septa features. To determine the proportion of malignancy within cystic renal masses stratified by Bosniak classification v2019 class and feature-based subclass. MEDLINE and EMBASE databases were searched on July 24, 2023 for studies published in 2019 or later that reported cystic renal masses that underwent renal-mass CT or MRI, were assessed using Bosniak v2019, and had a reference standard (histopathology indicating benignity or malignancy or ≥5-year imaging follow-up indicating benignity).
View Article and Find Full Text PDFIn robotic arm controllers, the ability to shift signal levels is crucial for interfacing between different voltage domains in a processor. The level shifter (LS) has been used to convert signals operating near threshold voltage to signals operating well above the threshold voltage. Researchers have developed current mirror-based LSs to employ current mirrors, which duplicate the current from one transistor and accurately replicate it in another, ensuring precise current matching.
View Article and Find Full Text PDFBackground/objectives: We assessed the influence of local patients and clinical characteristics on the performance of commercial deep learning (DL) segmentation models for head-and-neck (HN), breast, and prostate cancers.
Methods: Clinical computed tomography (CT) scans and clinically approved contours of 210 patients (53 HN, 49 left breast, 55 right breast, and 53 prostate cancer) were used to train and validate segmentation models integrated within a vendor-supplied DL training toolkit and to assess the performance of both vendor-pretrained and custom-trained models. Four custom models (HN, left breast, right breast, and prostate) were trained and validated with 30 (training)/5 (validation) HN, 34/5 left breast, 39/5 right breast, and 30/5 prostate patients to auto-segment a total of 24 organs at risk (OARs).