Search

BackgroundSpinal cord injury (SCI) severely impairs the physical and mental health of patients, decreasing their self-efficacy in coping with daily life and quality of life (QOL). In China, a large gap remains between the complex long-term health needs of SCI patients and the current community care system. With the prevalence of mobile terminals, the usage of mobile health apps has the potential to fill this gap by extending qualified medical resources to the families of SCI patients. Our team developed the app Together for the transitional care of home-dwelling SCI patients in China. ObjectiveThis study aimed to evaluate the effects of app-based transitional care on the self-efficacy and QOL of SCI patients. MethodsThrough a three-round Delphi process, an Android app was designed. Both medical staff and patients could access the app. Medical staff used it for providing remote transitional care to SCI patients. Patients used it to view transitional care time and send messages to medical staff. Thereafter, a multicenter and assessor-blinded randomized controlled trial was conducted. Participants (n=98) who had SCI and lived at home following discharge were recruited and randomly assigned to a study group (n=49) and control group (n=49) using a randomized number list in four research centers. Patients in both groups received systematic discharge education before discharge. The study group received five follow-ups conducted by trained nurses through the app, which had four core functions, namely remote assessment, health education, interdisciplinary referral, and patient interaction, at weeks 2, 4, 6, 8, and 12 following discharge. The control group received a routine telephone follow-up conducted by nurses at week 12 following discharge. The outcome measures were the Moorong Self-Efficacy Scale (MSES) and 36-item Short-Form Health Survey (SF-36) scores. Data were collected before discharge (T0) and at weeks 12 (T1) and 24 following discharge (T2). Differences between the groups were tested by repeated measures analysis of variance and simple effect analysis. ResultsAfter the follow-up, the total MSES scores in the study group improved over time (T0=67.80, T1=71.90, and T2=76.29) and were higher than those in the control group (T2=64.49) at 24 weeks following discharge (simple effect analysis: F1=8.506, P=.004). Regarding the total SF-36 score, although it was higher in patients from the study group (T2=65.36) than those from the control group (T2=58.77) at 24 weeks following discharge, only time effects were significant (F2,95=6.671, P=.002) and neither the group effects nor the interaction effects influenced the change in QOL (group effects: F1,96=0.082, P=.78; interaction effects: F2,95=3.059, P=.052). ConclusionsThis study confirmed that app-based transitional care improves the self-efficacy of SCI patients. Nevertheless, QOL improvement is not yet evident. Future investigations with larger sample sizes and longer observation periods are warranted to further verify the effects. Trial RegistrationChinese Clinical Trial Registry ChiCTR-IPR-17012317; http://www.chictr.org.cn/showproj.aspx?proj=19828

In the face of the challenges in the field of marine engineering applications in the new era, the goal of automation, high efficiency and accuracy can be achieved by using deep learning-based neural networks in hydroacoustic engineering. However, in the face of objective problems such as the lack of underwater target samples, the complex underwater sound environment, and the poor sample signal-to-noise ratio, the deep learning also becomes less sensitive due to its own limitations. In this paper, by constructing a variety of target feature extraction methods and a deep neural network model, we obtain the target recognition rate network prediction value after matched different target feature extraction with neural network model. Through comparing experimental results, a new idea of solving small sample target identification through deep neural network deep design is proposed.

Underwater wireless acoustic positioning technology uses the geometric relationship between a target and a receiving array to determine the target's position by measuring distances between the target and the array elements, that the receiving array is usually symmetry in space. It is an indirect measurement approach, so ranging errors can significantly impact positioning accuracy due to error transmission effects. To improve precision, a similarity-matched localization algorithm based on the L-p norm (LPM) is introduced. This algorithm constructs a distance vector model with environmental parameters and performs similarity analysis by computing the L-p norm of the distance vector and a reference copy vector for three-dimensional localization in the observation area. Unlike conventional methods, this technology directly matches distance vectors without coupling error transmission, thereby enhancing positioning accuracy even though it remains contingent upon ranging precision. To validate the algorithm's efficacy, Monte Carlo simulations are employed to analyze the distribution patterns of positioning errors in both horizontal and three-dimensional spaces. The results show an improvement from a mean positioning error of 0.0475 m to 0.0250 m and a decrease in error standard deviation from 0.0240 m to 0.0092 m. The results indicate that LPM offers improved accuracy and robustness by circumventing traditional error transmission issues. [ABSTRACT FROM AUTHOR]