Nonlinear Dynamic Parameters Research Articles

Modelling and parameter identification of penicillin fermentation using physics‐informed neural networks

AbstractWith the rapid development of machine learning technology and computer science, artificial neural networks have become an effective and popular method in the existing modelling research of penicillin fermentation process. Although these networks can capture the complexity of the fermentation process, they may lead to overfitting and require large amounts of data. In addition, the inference of the model on the data may not satisfy the physical laws. In this paper, a penicillin fermentation modelling method based on physics‐informed neural networks is proposed. The fermentation mechanism equations are combined with the neural networks to develop the model as constraints. First, a general penicillin fermentation mechanism model is built according to known prior knowledge, and then its unknown nonlinear dynamic parameters are identified by physics‐informed neural networks. Finally, the successfully trained model exhibits a high prediction accuracy, which not only satisfies the physical laws in the loss function, but also verifies the effectiveness of the proposed mechanism model.

Reinforcement learning based fractional fuzzy controller for photovoltaic systems

This article presents an innovative artificial nonlinear control technique for photovoltaic (PV) systems. A boost converter extracts maximum power from the PV system under different weather conditions. A fuzzy logic maximum power point (MPP) finding algorithm is used to determine the reference voltage of the PV system under various atmospheric conditions. It is assumed that the nonlinear dynamic model, parameters, and uncertainties of the converter are unknown. To estimate a precise model that contains nonlinearities, model uncertainties, and disturbances of the converter, proximal policy optimization (PPO) with a special reward function is applied. PPO is optimal in policy learning and sampling, fast to implement, and is a simple reinforcement learning (RL) algorithm. Then, estimations of dynamics and uncertainties are used to enhance the robustness of the suggested fractional order sliding mode controller. Furthermore, the controller coefficients are optimally adjusted by the PPO algorithm. The Lyapunov theorem is used for proving the control system's closed-loop stability. Also, the proposed methods can be generalized and used for other control systems. The simulation is conducted by MATLAB to evaluate the suggested controller's performance effectiveness. Robustness against the presence of parameter uncertainty is investigated. Moreover, the suggested technique is also compared with sliding mode controller.

High‐Dissolution Mechanism of Ti–48Al–2Cr–2Nb Alloy in Electrochemical Machining from a Nonlinear Dynamic Perspective

High‐rate dissolution process of Ti–48Al–2Cr–2Nb alloy in electrochemical machining exhibits strong nonlinear dynamic characteristics, but the quantitative relationship between these nonlinear characteristics and surface topography, as well as the electro‐dissolution mechanism, is unknown. Therefore, the fractal and recursive features of dissolution behavior and quantitative characterization of the dissolved surface topography were investigated. Additionally, an electro‐dissolution mechanism model in electrochemical machining process was presented. Applying a higher potential of 11 V can enhance the stability of the electrochemical system, resulting in a smoother and uniform surface. The saturation correlation dimension, surface roughness, and integral area of lacunarity initially decrease before increasing with higher electrolytic pressure, while laminarity and determinism show an initial increase followed by a decrease. These five characteristic parameters reach their optimal values at an electrolyte pressure of 0.4 MPa, indicating that achieving a stable and deterministic electrochemical system is crucial for obtaining a uniform smooth surface. These nonlinear dynamic characteristic parameters can be considered as in‐situ monitoring parameters for surface quality in ECM. The anodic dissolution process comprises three stages: initial passive film dissolution and breakdown, rapid dissolving with surface coarsening, and stable dissolving with polishing.

Bayesian model selection for COVID-19 pandemic state estimation using extended Kalman filters: Case study for Saudi Arabia.

Quantifying the uncertainty in data-driven mechanistic models is fundamental in public health applications. COVID-19 is a complex disease that had a significant impact on global health and economies. Several mathematical models were used to understand the complexity of the transmission dynamics under different hypotheses to support the decision-making for disease management. This paper highlights various scenarios of a 6D epidemiological model known as SEIQRD (Susceptible-Exposed-Infected-Quarantined-Recovered-Deceased) to evaluate its effectiveness in prediction and state estimation during the spread of COVID-19 pandemic. Then we investigate the suitability of the classical 4D epidemiological model known as SIRD (Susceptible-Infected-Recovered-Deceased) in the long-term behaviour in order to make a comparison between these models. The primary aim of this paper is to establish a foundational basis for the validity and epidemiological model comparisons in long-term behaviour which may help identify the degree of model complexity that is required based on two approaches viz. the Bayesian inference employing the nested sampling algorithm and recursive state estimation utilizing the Extended Kalman Filter (EKF). Our approach acknowledges the potential imperfections and uncertainties inherent in compartmental epidemiological models. By integrating our proposed methodology, these models can consistently generate predictions closely aligned with the observed data on active cases and deaths. This framework, implemented within the EKF algorithm, offers a robust tool for addressing future, unknown pandemics. Moreover, we present a systematic methodology for time-varying parameter estimation along with uncertainty quantification using Saudi Arabia COVID-19 data and obtain the credible confidence intervals of the epidemiological nonlinear dynamical system model parameters.

Model-free predictive current control of Syn-RM based on time delay estimation approach

Abstract This paper investigates an optimal model-free control design for a synchronous reluctance motor (Syn-RM) with unknown nonlinear dynamic functions, parameter variations, and disturbances. The idea is to combine a predictive control with a time-delay estimation technique (TDE) in order to successfully deal with the system’s uncertainties and make the Syn-RM control scheme easy to implement in real-time. This model-free control strategy comprises two cascade control loops namely outer and inner loops. The outer loop is designed for the mechanical part of Syn-RM to ensure the convergence of the speed dynamics by using a proportional-integral controller while the inner loop is developed to control the uncertain dynamics of currents via an optimal robust controller. In the proposed current loop, the predictive control is enhanced by the inclusion of ultra-local model theory where dynamic functions and disturbances are estimated by instantaneous input-output measurements of the Syn-RM using the TDE approach. Moreover, a particle swarm optimization (PSO) algorithm is also proposed to find the optimal design parameters to improve the dynamic performances of the closed-loop control system. Numerical validation tests of the proposed TDE-based model-free predictive current control (TDE-MFPCC) method are performed in the simulation environment of the Syn-RM system, and the results show the robustness and the effectiveness of the proposed TDE-MFPCC compared to the conventional model-based PCC.

Nonlinear Dynamic Model-Based Position Control Parameter Optimization Method of Planar Switched Reluctance Motors

Currently, there are few systematic position control parameter optimization methods for planar switched reluctance motors (PSRMs); how to effectively optimize the control parameters of PSRMs is one of the critical issues that needs to be urgently solved. Therefore, a nonlinear dynamic model-based position control parameter optimization method of PSRMs is proposed in this paper. First, to improve the accuracy of the motor dynamics model, a Hammerstein–Wiener model based on the BP neural network input–output nonlinear module is established by combining the linear model and nonlinear model structures so that the nonlinear and linear characteristics of the system are characterized simultaneously. Then, a position control parameter optimization system of PSRMs is developed using the established Hammerstein–Wiener model. In addition, with a self-designed simulated annealing adaptive particle swarm optimization algorithm (SAAPSO), the position control parameter optimization system is performed offline iteratively to obtain the optimal position control parameters. Simulations and experiments are carried out and the corresponding results show that the optimal position control parameters obtained by the proposed method can be directly applied in the actual control system of PSRMs and the control performance is improved effectively using the obtained optimal control parameters.

Swarm Intelligence Based Nonlinear Friction and Dynamic Parameters Identification for a 6-DOF Robotic Manipulator

Swarm Intelligence Based Nonlinear Friction and Dynamic Parameters Identification for a 6-DOF Robotic Manipulator

Changes in oscillatory and nonlinear dynamic processes of microcirculation in patients with obliterating atherosclerosis of lower limb arteries after revascularization

Aim: To study the nature of changes in oscillatory and nonlinear dynamic processes in the skin microcirculatory system by laser Doppler flowmetry in patients with obliterating atherosclerosis of the lower limbs arteries (OALLA) after limb revascularization.Material and Methods. 27 male patients with OALLA before and after endovascular revascularization of the affected limb (median age 63.0 [60.0; 69.0] years) were studied. Microcirculation (MC) of the foot skin with assessment of nonlinear dynamic processes and spectral wavelet analysis of blood flow fluctuations was studied by laser Doppler flowmetry. The normalized amplitude indices of blood flow fluctuations were determined in frequency ranges reflecting: endothelial, neurogenic, myogenic, respiratory, pulse factors of hemocirculation. Bypass parameters and nutritive blood flow were assessed. An occlusion test was performed to determine capillary blood flow reserve. The study of nonlinear dynamic processes included assessment of fractal dimension, entropy determination and phase portrait analysis.Results. Limb revascularization in patients with OALLA resulted in improvement of the clinical picture accompanied by statistically significant increase in nutritive blood flow (+9.7%) and reserved dilatation potential of microvessels (+43.2%), decrease in arteriolo-venular blood shunting (–5.0%) and venous plethora (–14.3%). The analysis of nonlinear dynamic processes of the MC showed that after angioplasty, along with the remaining deficit of oscillatory processes energy, there was a decrease in the fractal dimension index (–14.3%), indicating the limitation of lability of the functional system of the microvascular bed. At the same time, an increase in the chaotization of the regulatory mechanisms of the peripheral blood flow was established.Conclusions. The results showed positive functional changes of the MC system associated with the improved clinical picture in patients with OALLA after limb revascularization. At the same time, changes in the nonlinear dynamics parameters indicate a compensatory increase in the chaotization of the system together with the remaining limitation of its functional lability and the energy deficit of oscillatory processes.

Non-linear dynamic processes and their correlation with indicators of microcirculation in patients with obliterating atherosclerosis of the lower extremities arteries according to laser doppler flowmetry

Objective: To study features of nonlinear dynamic and oscillatory processes in the skin microcirculatory flow in patients with obliterating atherosclerosis in lower extremities arteries (OALEA).Material and methods. 56 male patients with obliterating atherosclerosis in the arteries of lower extremities and 15 practically healthy individuals were taken into the study. Microcirculation in the skin on feet with the assessment of nonlinear dynamic processes and spectral wavelet analysis of blood fl ow fl uctuations was studied by laser Doppler flowmetry, device “LACK-M” (firm Lazma, Russia). Normalized amplitude parameters of blood flow fluctuations in frequency ranges of hemocirculation tonus-forming factors were analyzed: endothelial, neurogenic, myogenic. Myogenic microvascular tone and capillary blood flow indices were calculated. To study nonlinear dynamic processes, the following parameters were assessed: fractal dimension, entropy and phase portrait analysis.Results. Spastic-atonic microcirculation disorders were revealed in patients with OALEA. They were characterized with metarteriole constriction and with restriction of capillary blood flow, with an increased myogenic tone and dilated arterioles. Statistically significant shifts in parameters of nonlinear dynamics in this case- in particular, decrease in entropy (–10.6 %) and in dimensions of phase portrait (–9.3%) under energy deficit in oscillatory microcirculation processes (–20.8 %) – can be interpreted as a decrease in complexity of laser Doppler flowmetric signal and better ordering in regulatory mechanisms of the peripheral blood flow.Conclusion. In patients with obliterating atherosclerosis in the arteries of lower extremities, the obtained results indicate significant functional microcirculation disorders of the spastic-atonic nature leading to the restriction of nutritive blood flow and accompanied by the lack of energy in oscillatory processes. These results have also shown the decrease in the system chaos severity which indicates limitation of connections of microcirculation control factors, simplification of the mechanisms of functioning of the microvascular bed and, as a consequence, decrease in the compensatory adaptive potential.

Identification and modelling of dynamic parameters for round link chains subject to axial loads

A round link chain subject to axial dynamic loads composes a nonlinear viscoelastic system. Unlike the classical pounding problems, the round link chain will not only suffer linear elastic deformation, but also nonlinear plastic or impacting deformation. Based on theoretical formulation and experiments, a new approach is presented in this paper to model and identify the nonlinear dynamic parameters, namely the stiffness and damping for the round link chain. With linear deformation, nonlinear deformation and energy dissipation considered, a modified nonlinear viscoelastic model is developed to describe the vibrational behavior of the chain with numbers of round links. The linear elastic model and impacting model are combined to derive the equivalent nonlinear stiffness, while experiments and the least square fitting method are employed to identify the nonlinear damping according to the modified nonlinear viscoelastic model. The influences of the key parameters such as the length of the chain, elastic module and loading frequency on the dynamic stiffness and damping are investigated. Another test is performed to validate the identification model and good agreements are observed.

Nonlinear Dynamic Modeling of Joints between Solar Panels on Spacecraft

Multiple linear and nonlinear dynamic parameters of the joints at the root of solar panels and between solar panels on spacecraft, both of which have complex nonlinear dynamic properties, were identified by using the force state mapping method for modeling complex nonlinear joints in deployable mechanisms of spacecraft, and the differential equations representing the nonlinear dynamic model of the joints were derived. On the basis of the actual force characteristics of the joints, test systems were developed to investigate the vibration response of the complex nonlinear joints in spacecraft so as to acquire test data necessary for the identification. The relation between the moment and bending angle of the panel root and inter-panel joints on spacecraft was obtained through vibration tests under various frequencies and excitation forces. The validity and effectiveness of the dynamic model have been verified by vibration tests of the joints under pulsed excitation. The parameters identified in this paper reflect the nonlinear stiffness, friction and damping characteristics of the joints. The dynamic model established based on these parameters can describe multiple linear and nonlinear dynamic properties of the joints and can be further applied in modeling and control of the entire spacecraft system.

Dynamic modeling of practice effects across the healthy aging-Alzheimer's disease continuum.

Standardized tests of learning and memory are sensitive to changes associated with both aging and superimposed neurodegenerative diseases. Unfortunately, repeated behavioral test administration can be confounded by practice effects (PE), which may obscure declines in level of abilities and contribute to misdiagnoses. Growing evidence, however, suggests PE over successive longitudinal measurements may differentially predict cognitive status and risk for progressive decline associated with aging, mild cognitive impairment (MCI), and dementia. Thus, when viewed as a reflection of neurocognitive plasticity, PE may reveal residual abilities that can add to our understanding of age- and disease-related changes in learning and memory. The present study sought to evaluate differences in PE and verbal recall in a clinically characterized aging cohort assessed on multiple occasions over 3 years. Participants included 256 older adults recently diagnosed as cognitively unimpaired (CU; n = 126), or with MCI of amnestic (n = 65) or non-amnestic MCI (n = 2085), and multi-domain amnestic dementia of the Alzheimer’s type (DAT; n = 45). We applied a continuous time structural equation modeling (ctsem) approach to verbal recall performance on the Hopkins Verbal Learning Test in order to distinguish PE from individual occasion performance, coupled random changes, age trends, and differing measurement quality. Diagnoses of MCI and dementia were associated with lower recall performance on all trials, reduced PE gain per occasion, and differences in non-linear dynamic parameters. Practice self-feedback is a dynamic measure of the decay or acceleration in PE process changes over longitudinal occasions. As with PE and mean recall, estimated practice self-feedback followed a gradient from positive in CU participants to null in participants with diagnosed MCI and negative for those with dementia diagnoses. Evaluation of sensitivity models showed this pattern of variation in PE was largely unmodified by differences in age, sex, or educational attainment. These results show dynamic modeling of PE from longitudinal performance on standardized learning and memory tests can capture multiple aspects of behavioral changes in MCI and dementia. The present study provides a new perspective for modeling longitudinal change in verbal learning in clinical and cognitive aging research.

Using Non-linear Dynamics of EEG Signals to Classify Primary Hand Movement Intent Under Opposite Hand Movement.

Decoding human hand movement from electroencephalograms (EEG) signals is essential for developing an active human augmentation system. Although existing studies have contributed much to decoding single-hand movement direction from EEG signals, decoding primary hand movement direction under the opposite hand movement condition remains open. In this paper, we investigated the neural signatures of the primary hand movement direction from EEG signals under the opposite hand movement and developed a novel decoding method based on non-linear dynamics parameters of movement-related cortical potentials (MRCPs). Experimental results showed significant differences in MRCPs between hand movement directions under an opposite hand movement. Furthermore, the proposed method performed well with an average binary decoding accuracy of 89.48 ± 5.92% under the condition of the opposite hand movement. This study may lay a foundation for the future development of EEG-based human augmentation systems for upper limbs impaired patients and healthy people and open a new avenue to decode other hand movement parameters (e.g., velocity and position) from EEG signals.

Phenomenological Model of Nonlinear Dynamics and Deterministic Chaotic Gas Migration in Bentonite: Experimental Evidence and Diagnostic Parameters

Understanding gas migration in compacted clay materials, e.g., bentonite and claystone, is important for the design and performance assessment of an engineered barrier system of a radioactive waste repository system, as well as many practical applications. Existing field and laboratory data on gas migration processes in low-permeability clay materials demonstrate the complexity of flow and transport processes, including various types of instabilities, caused by nonlinear dynamics of coupled processes of liquid–gas exchange, dilation, fracturing, fracture healing, etc., which cannot be described by classical models of fluid dynamics in porous media. We here show that the complexity of gas migration processes can be explained using a phenomenological concept of nonlinear dynamics and deterministic chaos theory. To do so, we analyzed gas pressure and gas influx (i.e., input) and outflux (i.e., output), recorded during the gas injection experiment in the compact Mx80-D bentonite sample, and calculated a set of the diagnostic parameters of nonlinear dynamics and chaos, such a global embedding dimension, a correlation dimension, an information dimension, and a spectrum of Lyapunov exponents, as well as plotted 2D and 3D pseudo-phase-space strange attractors, based on the univariate influx and outflux time series data. These results indicate the presence of phenomena of low-dimensional deterministic chaotic behavior of gas migration in bentonite. In particular, during the onset of gas influx in the bentonite core, before the breakthrough, the development of gas flow pathways is characterized by the process of chaotic gas diffusion. After the breakthrough, with inlet-to-outlet movement of gas, the prevailing process is chaotic advection. During the final phase of the experiment, with no influx to the sample, the relaxation pattern of gas outflux is resumed back to a process of chaotic diffusion. The types of data analysis and a proposed phenomenological model can be used to establish the basic principles of experimental data-gathering, modeling predictions, and a research design.

Specificity study of visualization analysis of electroencephalogram diagnosis of depression based on CiteSpace

This paper analyzed literatures on the specificity study of electroencephalogram (EEG) in the diagnosis of depression since 2010 to 2020, summarized the recent research directions in this field and prospected the future research hotspots at home and abroad. Based on databases of China National Knowledge Infrastructure (CNKI) and the core collection of Web of Science (WOS), CiteSpace software was used to analyze the relevant literatures in this research field. The number of relevant literatures, countries, authors, research institutions, key words, cited literatures and periodicals related to this research were analyzed, respectively, to explore research hotspots and development trends in this field. A total of 2 155 articles were included in the WOS database. The most published institution was the University of Toronto, the most published country was the United States, China occupied the third place, and the hot keywords were anxiety, disorder, brain and so on. A total of 529 literatures were included and analyzed in CNKI database. The institution with the most publications was the Mental Health Center of West China Hospital of Sichuan University, and the hot keywords were EEG signal, event-related potential, convolutional neural network, schizophrenia, etc . This study finds that EEG study of depression is developing rapidly at home and abroad. Research directions in the world mainly focus on exploring the characteristics of spontaneous EEG rhythm and nonlinear dynamic parameters during sleep in depressed patients. In addition, synchronous transcranial magnetic stimulation (TMS) and EEG technologies also attract much attention abroad, and the future research hotspot will be on the mechanism of EEG on patients with major depression. Domestic research directions mainly focus on the classification of resting EEG and the control study of resting EEG power spectrum entropy in patients with schizophrenia and depression, and future research hotspot is the basic and clinical EEG study of depressed patients complicated with anxiety.

Generalized refined composite multiscale fuzzy entropy and multi-cluster feature selection based intelligent fault diagnosis of rolling bearing

Extracting the failure related information from vibration signals is a very important aspect of vibration-based fault detection for rolling bearing Multiscale entropy and its improvement, multiscale fuzzy entropy (MFE), are significant complexity measure tools of time series. They have been successfully applied to extract vibration failure features for rolling bearing condition monitoring . However, MFE over different scales will fluctuate with increase of scale factor. A new nonlinear dynamic parameter termed generalized refined composite multiscale fuzzy entropy (GRCMFE) is firstly developed to enhance the performance of MSE and MFE in data complexity measurement. Then three algorithms are developed and compared with MSE and MFE, as well as two algorithms of generalized MFE to verify the availability and superiority by analyzing two kinds of noise signals. In addition, based on three algorithms of GRCMFE, a novel fault diagnosis approach for rolling bearing is proposed with linking multi-cluster feature selection for supervised learning and the gravitational search algorithm optimized support vector machine for failure pattern recognition. Last, the proposed fault diagnostic approach was utilized to analyze two kinds of bearing test data sets. Analysis results indicate that our proposed fault diagnosis approach could effectively extract nonlinear dynamic complexity information and gets the highest identifying rate and the best performance among the comparative approaches.

Measured and investigated nonlinear dynamics parameters on bolted flange joints of combined rotor

The complex micro-slip phenomenon of the contact interface will lead to the nonlinear stiffness of the connection structure, as well as the structural damping and energy dissipation. As the most important connection structure of the combination rotor, the mechanical properties of bolted flange joint interface are needed in the dynamic analysis of the combined rotor. Therefore, it is urgent to model and test the friction contact interface in the nonlinear dynamic analysis of rotor. In this paper, two sets of mechanical characteristics test system were built to test the dynamic parameters of tangential and bending directions of the bolted flange joint interface. Then, the mechanical behavior and the change regularities of dynamics parameters were studied under different external excitation, bolt distribution and tightening torque. The results show that once the bolt preload is above the rated torque, stiffness softening behavior is not significant; and then the tangential stiffness of the joint interface tends to be stable, with the variation range of 8.08∼8.96e8 N/m; the equivalent bending stiffness coefficient is about 3.38∼3.83e6 N·m/rad. With the decrease of bolt preload, the external excitation and the number of bolts have a significant effect on the stiffness reduction of the joint. Finally, the change interval of the dynamics parameters of the interface obtained by the experiment provide basis for the uncertainty dynamic analysis and optimization of the rotor.

Iterative learning sliding mode control for output-constrained upper-limb exoskeleton with non-repetitive tasks

This paper investigates an iterative learning control scheme for a 5 degrees of freedom upper-limb exoskeleton with time-varying constraints. No priori knowledge of nonlinear dynamic parameters is needed in the modeling process by applying the iterative recursive method, thereby the time-consumption modelling work can be averted. Unlike most iterative learning algorithms that require identical initial condition between different iterations, the estimation errors of learning part in our work can be arbitrary small along the iterations under random initial status. To further improve the safety of human-robot system, a tan-type barrier Lyapunov function is utilized in convergence analysis to guarantee the constraint requirements of tracking errors between human body and robot. Furthermore, in order to handle the nonparametric uncertainties and external disturbances, sliding mode control strategy is constructed where the chattering phenomenon in control torques is suppressed by adaptive method. The stability over the time domain and convergence over the iterative domain are proved by composite energy function. Finally, comparative study and co-simulation results of Matlab/Simulink and ADAMS are presented to verify the advantages and effectiveness of proposed algorithm.

128 Nonlinear Dynamic Analysis of Heart Rate Variability Can Detect Sepsis in Severe Burns

Abstract Introduction Sepsis after burn injury affects 50–84% of adult patients and ~55% of pediatric patients. The diagnosis of sepsis in patients with severe burns is complicated by the overlap of clinical signs of post-burn hypermetabolic response with those of sepsis. Earlier detection of sepsis could lead to improved outcomes and decreased morbidity and mortality. Nonlinear dynamic (ND) analysis of heart rate variability (HRV) can identify non-random relationships between different HRV parameters by using a complex oscillogram. ND can be obtained even when heart rate recordings are weak or poor in quality, which is common in the setting of burns. We hypothesized that ND could detect differences in HRV after severe burns. Methods All patients enrolled during the study period at our institution with >20% total body surface area (TBSA) burn were eligible for this study. High-definition heart rate monitoring was collected three times for each patient in five-minute intervals. The first collection occurred after patient ICU admission, and the third collection occurred 16–24 hours. Matlab was used to analyze heart rate using the following common ND methods: Correlation dimension (CD), detrended fluctuation (DF), largest Lyapunov exponent (LLE), approximate entropy (AE), fractal dimension (FD), Hurst exponent (HE), and recurrence quantification analysis (RQA). Additionally, several linear time-domain parameters were analyzed, including heart rate (R), RR interval (RR), SD1, and SD2. Results A total of six non-septic (NS) and four septic (S) patients were enrolled in the study. There were no significant differences between the two groups in age, sex, percent TBSA, or percent TBSA of third-degree burns. Two nonlinear parameters were significantly different between NS and S patients: AE and the density of recurrence points in RQA. AE was significantly lower in NS than S patients at all time points [(0.931±0.011 vs. 0.960±0.024, p=0.001); (0.923±0.019 vs. 0.959±0.025, p=0.008); (0.933±0.011 vs. 0.964±0.025, p=0.013)]. The density of recurrence points in RQA were significantly higher than in NS than S patients at all time points. [(90.4±40.2 vs. 41.4± 27.8, p=0.029), (102.2±39.4 vs. 40.1±22.9, p=0.008), (92.5±34.4 vs. 45.5±33.9, p=0.043)]. Conclusions The two ND parameters AE and RQA, can be used to distinguish between patients with and without sepsis in the setting of severe burns.

A Study on Constitutive Model of the Cohesive Soil Considering Soil-Structure Interactions

Transformer is key equipment in the power transmission and transformation system. When the seismic calculation is carried out, the foundation soil is generally considered to be absolutely rigid, and the bottom of the structure is considered as the fixed end, the soil-structure interaction is ignored. This paper is aimed to study the soil-structure interaction’s impact on the seismic performance of the transformer and other power facilities by carrying out cyclic shear test on cohesive soil, simulating the dynamic response of clay soil under the seismic load, and get the nonlinear dynamic characteristics parameters of the soil in the loading process of seismic cycles. On the basis of the study, the fitting equation of the dynamic shear modulus and damping ratio of the soil is put forward through analyzing the cohesive soil dynamic hysteresis constitutive model.