Publications by authors named "Z Di"

Objective: This study aims to investigate the predictive effectiveness of bedside lung ultrasound score (LUS) in conjunction with rapid shallow breathing index (RSBI) and oxygenation index (P/F ratio) for weaning pediatric patients from mechanical ventilation.

Methods: This was a retrospective study. Eighty-two critically ill pediatric patients, who were admitted to the Pediatric Intensive Care Unit (PICU) and underwent mechanical ventilation from January 2023 to April 2024, were enrolled in this study.

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Freestanding nanomembranes fabricated by lift-off technology have been widely utilized in microelectromechanical systems, soft electronics, and microrobotics. However, a conventional chemical etching strategy to eliminate nanomembrane adhesion often restricts material choice and compromises quality. Herein, we propose a nanomembrane-on-graphene strategy that leverages the weak van der Waals adhesion on graphene to achieve scalable and controllable release and 3D construction of nanomembranes.

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A wavelength demodulation method for ultra-short fiber Bragg grating (US-FBG) sensors based on an arrayed waveguide grating (AWG) and a convex optimization algorithm is proposed and demonstrated. Instead of measuring the output power ratio of the two adjacent AWG channels as previously done, in this work the wavelength demodulation is realized by reconstructing the US-FBG spectrum. The principle of spectral reconstruction involves using an AWG to sample the spectral information of US-FBG and constructing underdetermined matrix equations with the obtained prior information on transmission responses and the detected output power from multiple AWG channels.

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Background: Pulsatilla chinensis (PC) is a traditional Chinese medicine (TCM) known for its beneficial activities. It has been historically used to treat dysentery, vaginal trichomoniasis, bacterial infections, and malignant tumors. The therapeutic potential of PC in the management of hypercholesterolemia remains largely unexplored.

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Article Synopsis
  • The study presents a novel artificial neural network (ANN) model for image classification, inspired by the neural circuit for aversive olfactory learning in the simple organism C. elegans.
  • Despite the impressive performance of existing ANNs, they often face issues like high parameter demands and training costs, which this new model aims to address.
  • Results show that this bio-inspired ANN performs better in accuracy, consistency, and speed compared to traditional models, highlighting the potential for biological insights to enhance future ANN designs.
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