Results 211 to 220 of about 770,641 (335)

Machine Learning‐Augmented Loop‐Mediated Isothermal Amplification‐Enabled Point‐of‐Care for Mpox‐Specific Detection

open access: yesAdvanced Intelligent Systems, EarlyView.
A low‐cost, portable point‐of‐care platform for rapid Mpox detection using loop‐mediated isothermal amplification is reported. The device integrates fluorescence readout and mobile monitoring. A machine‐learning model analyzes temperature data and correlates thermal changes with DNA concentration, enabling sensitive and reliable molecular diagnosis in ...
Nazente Atceken   +4 more
wiley   +1 more source

Optimal selection and placement of BMPs and LID practices with a rainfall-runoff model

open access: yesEnvironmental Modelling & Software, 2016
Yaoze Liu   +5 more
semanticscholar   +1 more source

Ultralow‐Power Real‐Time On‐Chip Thermal Prediction via Finite Element Method–Machine Learning Codesign and Field‐Programmable Gate Array Deployment

open access: yesAdvanced Intelligent Systems, EarlyView.
A lightweight machine learning (ML)‐based thermal prediction framework is demonstrated and implemented on a field‐programmable gate array (FPGA). Using measured temperature data from a real chiplet, the approach enables real‐time, die‐level heat‐map inference with low power consumption, validating practical on‐chip thermal monitoring for advanced ...
Jun Ho Lee   +4 more
wiley   +1 more source

Clinical Insights From a Case of Sifrim‐Hitz‐Weiss Syndrome With a CHD4 Variant: Expanding the Phenotypic Spectrum and Its Response to Growth Hormone Therapy

open access: yesAmerican Journal of Medical Genetics Part A, EarlyView.
ABSTRACT To enhance clinicians' understanding of Sifrim‐Hitz‐Weiss syndrome (SIHIWES), this study investigated the clinical phenotypes, genetic characteristics, and response to growth hormone therapy in a patient. A case of a patient with global developmental delay and distinctive facial features is presented.
Jianmei Zhang   +6 more
wiley   +1 more source

Home - About - Disclaimer - Privacy