University Of Jinan

This study examines the protective and risk factors for suicide among nursing home residents as well as strategies to prevent suicide. In this study, semi-structured interviews are used to interpret the experiences and perceptions of nursing home residents regarding suicide attempts. The research is conducted using the technique of content analysis with Nvivo. Researchers have assessed this study for clarity. The experiences of nursing home residents with suicidal ideation were divided into three categories, nine sub-themes, and relevant theme clusters. The three categories were: (a) protective factors for suicide prevention; (b) risk factors for suicide attempts; and (c) suicide prevention strategies. These factors and strategies were found to be associated with suicide ideation and attempts. Factors and strategies influencing suicidal ideation and attempts among nursing home residents revealed some new issues and problems during COVID-19, allowing healthcare providers to better understand nursing home residents and improve intervention strategies in practice.

Sleep monitoring is an important part of health management because sleep quality is crucial for restoration of human health. However, current commercial products of polysomnography are cumbersome with connecting wires and state-of-the-art flexible sensors are still interferential for being attached to the body. Herein, we develop a flexible-integrated multimodal sensing patch based on hydrogel and its application in unconstraint sleep monitoring. The patch comprises a bottom hydrogel-based dual-mode pressure–temperature sensing layer and a top electrospun nanofiber-based non-contact detection layer as one integrated device. The hydrogel as core substrate exhibits strong toughness and water retention, and the multimodal sensing of temperature, pressure, and non-contact proximity is realized based on different sensing mechanisms with no crosstalk interference. The multimodal sensing function is verified in a simulated real-world scenario by a robotic hand grasping objects to validate its practicability. Multiple multimodal sensing patches integrated on different locations of a pillow are assembled for intelligent sleep monitoring. Versatile human–pillow interaction information as well as their evolution over time are acquired and analyzed by a one-dimensional convolutional neural network. Track of head movement and recognition of bad patterns that may lead to poor sleep are achieved, which provides a promising approach for sleep monitoring.

Due to its complexity, the problem of predefined-time synchronization in multimodal memristive neural networks has rarely been explored in the literature. This paper is the first to systematically study this issue, filling a research gap in the field and further enriching the related theoretical framework. First, a novel predefined-time stability theorem is proposed, which features more lenient judgment conditions compared to existing methods. This significantly enhances the generality of the stability theorem, making it applicable to a wider range of practical engineering projects. Second, based on the proposed predefined-time stability theorem, as well as the theories of differential inclusion, Filippov solutions, and set-valued mapping, a simple and practical feedback controller is developed. This controller establishes the necessary criteria for achieving predefined-time projective synchronization in multimodal memristive neural networks. Finally, two intricate simulation experiments are carefully designed. These experiments validate the effectiveness and feasibility of the theoretical derivations presented in this paper.