Recurrent Neural Network Inspired Finite-Time Control Design

Dear Editor,This letter is concerned with the role of recurrent neural networks(RNNs)on the controller design for a class of nonlinear systems.Inspired by the architectures of RNNs,the system states are stacked according to the dynamic along with time while the controller is represented as the neural network output.To build the bridge between RNNs and finite-time controller,a novel activation function is imposed on RNNs to drive the convergence of states at finite-time and propel the overall control process smoother.Rigorous stability proof is briefly provided for the convergence of the proposed finite-time controller.At last,a numerical simulation example is presented to illustrate the efficiency of the proposed strategy.Neural networks can be classified as static(feedforward)and dynamic(recurrent)nets[1].The former nets do not perform well in dealing with training data and using any information of the local data structure[2].In contrast to the feedforward neural networks,RNNs are constituted by high dimensional hidden states with dynamics.

Exploring the Application of Health Education During Pregnancy in Clinical Nursing of Obstetrics and Gynecology

Objective:To study the methods and effects of health education during pregnancy in the clinical nursing work of obstetrics and gynecology.Methods:Between January 2022 and January 2024 in the hospital 140 cases of pregnant women,can be divided into two groups,an observation group and a control group,respectively,between groups of 70 people.The observation group was given health pregnancy education,while the control group was given routine nursing.Results:After intervention compared two groups of natural childbirth,postpartum breast feeding rate and postpartum bleeding,observation group is better than the control group.The results of the two groups have statistical significance(P<0.05).Conclusion:The application of health education during pregnancy in the nursing of obstetrics and gynecology has a good effect,can be widely welcomed and has clinical value.

Unprecedent green macroalgae bloom:mechanism and implication to disaster prediction and prevention

Green macroalgae bloom(GMB),with the dominant species of Ulva prolifera,has regularly occurred since 2007 along the China coast.Although disaster prevention and control achieved favorable results in 2020,the satellite-observed GMB annual maximum coverage(AMC)rebounded sharply in 2021 to an unprecedented level.The reasons for this rebound and the significant interannual variability over past 15 years are still open questions.Here,by using long-term time-series(2007-2022)optical and Synthetic Aperture Radar satellite observations(1000+scenes),meteorological data and water quality statistics,the mechanism analysis was performed by exploring effects from natural factors and human activities.Two key determinants for AMC are successfully identified from numerous potential factors which are the macroalgae distribution in a key area(the Subei Shoal)during a critical period(from April to May 20)and the nutrient availability.Furthermore,by using these two parameters,a novel model for AMC prediction(R^(2)=0.87,p<0.01)is proposed and independently validated,which can reasonably explain the significant interannual variability(2014-2021)and agree well with the latest observation in 2022(percentage difference 12%).Finally,suggestions are proposed for future disaster prevention and alleviation.This work may aid future bloom prediction and management measure optimization.