Search

Background: Due to the high nursing pressure of patients with Cerebral hemorrhage and the general shortage of clinical nurses, nursing support workers often participate in clinical nursing work, but the influence of nursing support workers' participation on the negative emotion, quality of life and life satisfaction of patients with intracerebral hemorrhage is unknown. Methods: A total of 107 ICH patients admitted to our hospital from January 2022 to March 2022 were enrolled, including 60 patients receiving conventional care (CG control group) and 47 patients receiving nursing support worker participation (RG research group). All patients were recorded with self-perceived Burden Scale (SPBS), Hamilton Depression Scale (HAMD), Quality of Life Scale (SF-36), Somatic Self rating Scale (SSS), Patient self-care ability assessment scale（Barthel）and Satisfaction with life scale (SWLS) scores. Results: Patients with high negative emotion were more willing to participate in clinical nursing work (P). Nursing support workers involved in cerebral hemorrhage patients can alleviate negative emotions, improve life quality, improve nursing satisfaction (P). Conclusion: The participation of nursing support workers can alleviate the negative emotions of ICH patients, enhance their self-management ability, and improve their life quality.

With the increase of non-linear loads in power grid and the development of power electronics, the growing serious harmonic pollution has put forward higher requirements for the accuracy and real-time performance of the measurement method. For this problem, this paper proposes a wide-frequency harmonic signal measurement method based on window interpolation FFT and adaptive neural network. After the window function is selected, the frequency of harmonic is estimated by double-spectrum-line interpolation algorithm based on three-term third derivative Nuttall window, and then the smoothed frequencies are input into RLS-Adaline neural network to estimate the amplitude and phase of each harmonic. The simulation results show that the proposed method has higher measurement accuracy and stability under few data conditions and low signal-to-noise ratios than conventional window interpolation algorithms. High accuracy measurement of harmonics with short time window in the range of 0∼2.5kHz is effectively realized.

In fermentation process, fuzzy neural networks (FNN) is a novel machine learning method of soft sensor modeling, while the typical algorithm of FNN is inefficient because they can not optimize fuzzy rules and has long training time. Biological parameters can be measured online in real time which is helpful for the control of process optimization. So this paper introduces the use of the particle swarm optimization (PSO) for training FNN. Unlike the conventional back-propagation technique, the adaptation of the weights of the FNN approximator is done on-line using PSO. The PSO is based on the least squares error minimization with random initial condition and without any off-line pre-training. Experiment results show that, in contrast to the traditional fuzzy neural networks, the method has good prediction and is suitable to practical applications.