State Estimation for Discrete-Time Fuzzy Cellular Neural Networks with Mixed Time Delays

This paper is concerned with the exponential state estimation problem for a class of discrete-time fuzzy cellular neural networks with mixed time delays. The main purpose is to estimate the neuron states through available output measurements such that the dynamics of the estimation error is globally exponentially stable. By constructing a novel Lyapunov-Krasovskii functional which contains a triple summation term, some sufficient conditions are derived to guarantee the existence of the state estimator. The linear matrix inequality approach is employed for the first time to deal with the fuzzy cellular neural networks in the discrete-time case. Compared with the present conditions in the form of M-matrix, the results obtained in this paper are less conservative and can be checked readily by the MATLAB toolbox. Finally, some numerical examples are given to demonstrate the effectiveness of the proposed results

Transformation of worst weed into N-, S-, and P-tridoped carbon nanorings as metal-free electrocatalysts for the oxygen reduction reaction

Substituting sustainable/cost-effective catalysts for scarce and costly metal ones is currently among the major targets of sustainable chemistry. Herein we report the synthesis of N-, S-, and P-tridoped, worst-weed-derived carbon nanorings (WWCNRs) that can serve as metal-free and selective electrocatalyst for the oxygen reduction reaction (ORR). The WWCNRs are synthesized via activation-free polymerization of worst weed, Eclipta prostrate, and then removal of the metallic residues by HCl. The WWCNRs exhibit good catalytic activity towards the 4 electron-transfer ORR with low onset potential and high kinetic limiting current density, along with high selectivity (introducing CO, the sample loses onl

Laboratory Study on Improving Recovery of Ultra-Heavy Oil Using High-Temperature-Resistant Foam

After multiple rounds of steam huff-and-puff processes, an ultra-heavy oil reservoir is prone to excessive steam injection pressure, large heat loss, small sweep range of steam, and steam channeling, thus severely affecting the effective utilization of the oil reservoir. To solve these problems, one-dimensional and three-dimensional (3D) physical simulation tools were used to study the plugging performance of high-temperature composite foams by adding tanning extract and alkali lignin under the influence of some factors such as the reservoir temperature, salinity of formation water, and injection methods. The ultra-heavy oil used in the experiment comes from Shengli Oilfield. Under the condition of surface degassing, the viscosity of ultra-heavy oil could reach 145169 mPa.s at 60 °C. The experimental results show that the foam can produce a synergistic effect with both gel systems, indicating that the gel increases the stability of the foam. The foam can transfer more gel into the high-permeability formation, which can efficiently control the foam. The 3D physical simulation experiments indicated that both the systems enhance the recovery of heavy oil reservoir and reduce its moisture content significantly using steam injection. The method involving tannin extract foam and steam injection increased the recovery by 20% compared to the foam involving only steam injection. The method involving alkali lignin foam and steam injection increased the recovery by 11%

Enhanced Light Output of Dipole Source in GaN-Based Nanorod Light-Emitting Diodes by Silver Localized Surface Plasmon

The light output of dipole source in three types of light-emitting diodes (LEDs), including the conventional planar LED, the nanorod LED, and the localized surface plasmon (LSP) assisted LED by inserting silver nanoparticles in the gaps between nanorods, was studied by use of two-dimensional finite difference time domain method. The height of nanorod and the size of silver nanoparticles were variables for discussion. Simulation results show that a large height of nanorod induces strong wavelength selectivity, which can be significantly enhanced by LSP. On condition that the height of nanorod is 400 nm, the diameter of silver nanoparticle is 100 nm, and the wavelength is 402.7 nm, the light-output efficiency for LSP assisted LED is enhanced by 190% or 541% as compared to the nanorod counterpart or the planar counterpart, respectively. The space distribution of Poynting vector was present to demonstrate the significant enhancement of light output at the resonant wavelength of LSP

RESEARCH ON FATIGUE PERFORMANCE TEST AND LIFE PREDICTION FOR STATOR RUBBER OF PCP

The rubber stator of PCP used for oil recovery is studied in this paper,Through the fatigue tension test of stator rubber material,establish of stator rubber S-N curve; The analysis of the finite element mechanical properties of PCP,determine the most likely fatigue failure position of screw pump is near the high pressure chamber spiral sealing belt; Prediction of fatigue crack formation life of stator rubber of PCP by Fe-safe,determine the the minimum fatigue crack formation life and safety factor of each model of the stator rubber of PCP,and it decreases with the increase of chamber pressure,And the relation between the fatigue crack formation life and the chamber pressure of the rubber stator is obtained

The Histone Modifications of Neuronal Plasticity

Nucleosomes composed of histone octamer and DNA are the basic structural unit in the eukaryote chromosome. Under the stimulation of various factors, histones will undergo posttranslational modifications such as methylation, phosphorylation, acetylation, and ubiquitination, which change the three-dimensional structure of chromosomes and affect gene expression. Therefore, the combination of different states of histone modifications modulates gene expression is called histone code. The formation of learning and memory is one of the most important mechanisms for animals to adapt to environmental changes. A large number of studies have shown that histone codes are involved in the formation and consolidation of learning and memory. Here, we review the most recent literature of histone modification in regulating neurogenesis, dendritic spine dynamic, synapse formation, and synaptic plasticity

Pulse Wave Analysis Method of Cardiovascular Parameters Extraction for Health Monitoring

Objective: A pulse waveform is regarded as an information carrier of the cardiovascular system, which contains multiple interactive cardiovascular parameters reflecting physio-pathological states of bodies. Hence, multiple parameter analysis is increasingly meaningful to date but still cannot be easily achieved one by one due to the complex mapping between waveforms. This paper describes a new analysis method based on waveform recognition aimed for extracting multiple cardiovascular parameters to monitor public health. The objective of this new method is to deduce multiple cardiovascular parameters for a target pulse waveform based on waveform recognition to a most similar reference waveform in a given database or pattern library. Methods: The first part of the methodology includes building the sub-pattern libraries and training classifier. This provides a trained classifier and the sub-pattern library with reference pulse waveforms and known parameters. The second part is waveform analysis. The target waveform will be classified and output a state category being used to select the corresponding sub-pattern library with the same state. This will reduce subsequent recognition scope and computation costs. The mainstay of this new analysis method is improved dynamic time warping (DTW). This improved DTW and K-Nearest Neighbors (KNN) were applied to recognize the most similar waveform in the pattern library. Hence, cardiovascular parameters can be assigned accordingly from the most similar waveform in the pattern library. Results: Four hundred and thirty eight (438) randomly selected pulse waveforms were tested to verify the effectiveness of this method. The results show that the classification accuracy is 96.35%. Using statistical analysis to compare the target sample waveforms and the recognized reference ones from within the pattern library, most correlation coefficients are beyond 0.99. Each set of cardiovascular parameters was assessed using the Bland-Altman plot. The extracted cardiovascular parameters are in strong agreement with the original verifying the effectiveness of this new approach. Conclusion: This new method using waveform recognition shows promising results that can directly extract multiple cardiovascular parameters from waveforms with high accuracy. This new approach is efficient and effective and is very promising for future continuous monitoring of cardiovascular health

A Novel Radial Artery P-S Curve Model Based on Radial Vibration of Vascular Wall

In pulse wave analysis, the changing curve of pulse wave strength with continuous increasing pressure, that is, the P-S (pressure-strength) curve, contains abundant human physiological information, but there is no accurate model to describe the formation mechanism of the curve. Therefore, this paper proposes a modeling method of the radial artery P-S curve based on the radial vibration of the vascular wall. The modeling method includes three parts. Firstly, based on hemodynamics, we proposed the blood motion equation in the pulsation process of healthy people. Secondly, the motion equation of the vascular wall based on the fluid–structure interaction between blood motion and vascular wall was established. Finally, according to the elastic theory of the vascular wall, the relationship between pulse strength and extravascular pressure of blood vessels was found. To verify the accuracy and applicability of the model, this paper simulated the changes in the vascular wall stress and the intravascular pressure with the extravascular pressure during the process of vascular deformation. In addition, 69 healthy volunteers were selected to participate in this study. Based on the gradient compression, the pulse strength envelope under the continuous pressure sequence of the radial artery, namely the pulse P-S curve, was extracted. We also analyzed the relationship between the individual P-S curve difference and BMI. The results show that the actual human body data collection and analysis results are consistent with the theoretical model established in this paper, which indicates that the model can provide a novel idea for the evaluation of the state of the human body

Optimal Heart Sound Segmentation Algorithm Based on K-Mean Clustering and Wavelet Transform

The accurate localization of S1 and S2 is essential for heart sound segmentation and classification. However, current direct heart sound segmentation algorithms have poor noise immunity and low accuracy. Therefore, this paper proposes a new optimal heart sound segmentation algorithm based on K-means clustering and Haar wavelet transform. The algorithm includes three parts. Firstly, this method uses the Viola integral method and Shannon’s energy-based algorithm to extract the function of the envelope of the heart sound energy. Secondly, the time–frequency domain features of the acquired envelope are extracted from different dimensions and the optimal peak is searched adaptively based on a dynamic segmentation threshold. Finally, K-means clustering and Haar wavelet transform are implemented to localize S1 and S2 of heart sounds in the time domain. After validation, the recognition rate of S1 reached 98.02% and that of S2 reached 96.76%. The model outperforms other effective methods that have been implemented. The algorithm has high robustness and noise immunity. Therefore, it can provide a new method for feature extraction and analysis of heart sound signals collected in clinical settings