Dynamic model of field modulated magnetic gear system

Considering the special working principle and the structure of field modulation magnetic gear (FMMG), the magnetic coupling stiffnesses (MCS) are calculated and analyzed by finite element method. A magnetic coupling dynamic model is presented and the dynamic differential equations are founded. The natural frequencies and mode shapes of FMMG system are investigated. The effects of main design parameters on the natural frequencies are discussed. The results show that MCS are much smaller than the meshing stiffnesses of mechanical gears and are affected greatly by system parameters. Six modes of FMMG system have distinctive features, and six natural frequencies change differently as main parameters increase

Study on nonlinear detection and identification for rubber isolation bearing

The isolation bearing nonlinear mechanical model is described based on the simplified Bouc-wen hysteresis model. In order to provide reasonable mechanical properties state assessment for isolation bearing of base isolated structure under seismic excitation, wavelet coefficient energy ratio index are proposed to detect the time-domain evolution characteristics during the nonlinear phase of isolation bearing, then the nonlinear characteristics and parameters of the isolation bearing are identified using wavelet multi-resolution analysis theory. Numerical examples show that the wavelet coefficient energy ratio (WCER) can be a good way to detect the nonlinear states of the isolation bearing under different seismic excitation levels, and the nonlinear characteristics and the model parameters of the isolation bearing can be accurately identified by using the wavelet multi-resolution identification methods

Combination resonances of parametric vibration system of the field modulated magnetic gear

Considering the time-varying magnetic coupling stiffness caused by the component eccentricity, the parametric vibration model of the field modulated magnetic gear (FMMG) system is founded and the corresponding dynamic differential equations are deduced. The expressions of the combination resonances are worked out when the excited frequency is close to the combination frequency between the meshing frequency and the natural frequencies, and the resonance responses are discussed. The results show that the resonance amplitudes are much bigger when the excited frequency is close to the combination frequency between the meshing frequency and the natural frequency of the inner rotor torsional mode than when the frequency is close to other combination frequencies. Meanwhile, because the magnetic coupling stiffnesses are much smaller than the supporting stiffness, the resonance displacement of only one degree of freedom is always much bigger than the displacements of other degrees of freedom. The combination resonances make the stability regions of the FMMG system decrease and worsen the dynamic characteristics. All these can lay the foundation for the parameter optimization of the FMMG system

Dynamics of a model for the degradation mechanism of aggregated α-synuclein in Parkinson's disease

Accumulation of the misfolded synaptic protein α-synuclein (αSyn*) is a hallmark of neurodegenerative disease in Parkinson's disease (PD). Recent studies suggest that the autophagy lysosome pathway (ALP) including both the Beclin1-associated and mTOR-signaling pathways is involved in the αSyn* clearance mechanism. In this study, a mathematical model is proposed for the degradation of αSyn* by ALP with the crosstalk element of mTOR. Using codimension-1 bifurcation analysis, the tri-stability of αSyn* is surveyed under three different stress signals and, in addition, consideration is given to the regulatory mechanisms for the Beclin1- and mTOR-dependent rates on αSyn* degradation using the codimension-1 and−2 bifurcation diagrams. It was found that, especially under internal and external oxidative stresses (S1), the bistable switch of the aggregation of αSyn* can be transformed from an irreversible to a reversible condition through the ALP degradation pathways. Furthermore, the robustness of the tri-stable state for the stress S1 to the parameters related to mTOR-mediated ALP was probed. It was confirmed that mTOR-mediated ALP is important for maintaining the essential dynamic features of the tri-stable state. This study may provide a promising avenue for conducting further experiments and simulations of the degradation mechanism of dynamic modeling in PD

Evaluation of different b-values in DWI and 1H MRS for pancreatic cancer and pancreatitis: a rabbit model

Pancreatic cancer is a common malignant tumor with high incidence of metastasis. Currently, there is no absolute standard for the choice of b-value for diffusion-weighted imaging (DWI) for pancreatic cancer. The b-value is rarely reported in animal model study, especially in pancreatic cancer/mass pancreatitis rabbit models. The authors\u27 aim was to determine the different b-values to differentiate the diagnosis of pancreatic cancer and mass pancreatitis in rabbit models using DWI. When comparing the effect of different b-values in diagnostic process, the pathological results could be regarded as the gold standard. In this research, 30 healthy New Zealand rabbits were selected and divided into three groups by random number table method: group 1 (pancreatic cancer), group 2 (mass pancreatitis) and the control group (healthy). After DWI (three different b-values 333, 667, 1000 s/mm2, respectively) and MRI examination, the model rabbits were then killed. Afterward, the tumor mass was removed for biopsy, and occupation anatomy and tumor histopathology were examined. Fat-suppressing sequences of T2WI, DWI, ADC, difference of ADC (DADC), and MRS were used. The present study determined that the effective differential diagnosis of pancreatic cancer and pancreatitis was determined at low b-values (333 s/mm2) when performed DWI inspection in rabbit models

A novel clustering algorithm based on mathematical morphology for wind power generation prediction

Wind power has the characteristic of daily similarity. Furthermore, days with wind power variation trends reflect similar meteorological phenomena. Therefore, wind power prediction accuracy can be improved and computational complexity during model simulation reduced by choosing the historical days whose numerical weather prediction information is similar to that of the predicted day as training samples. This paper proposes a new prediction model based on a novel dilation and erosion (DE) clustering algorithm for wind power generation. In the proposed model, the days with similar numerical weather prediction (NWP) information to the predicted day are selected via the proposed DE clustering algorithm, which is based on the basic operations in mathematical morphology. And the proposed DE clustering algorithm can cluster automatically without supervision. Case study conducted using data from Yilan wind farm in northeast China indicate that the performance of the new generalized regression neural network (GRNN) prediction model based on the proposed DE clustering algorithm (DE clustering-GRNN) is better than that of the DPK-medoids clustering-GRNN, the K-means clustering-GRNN, and the AM-GRNN in terms of day-ahead wind power prediction. Further, the proposed DE clustering-GRNN model is adaptive

Effect of nematic order on the low-energy spin fluctuations in detwinned BaFe1.935_{1.935}Ni0.065_{0.065}As2_2

The origin of nematic order remains one of the major debates in iron-based superconductors. In theories based on spin nematicity, one major prediction is that the spin-spin correlation length at (0,$\pi$) should decrease with decreasing temperature below the structural transition temperature $T_s$. Here we report inelastic neutron scattering studies on the low-energy spin fluctuations in BaFe$_{1.935}$Ni$_{0.065}$As$_2$ under uniaxial pressure. Both intensity and spin-spin correlation start to show anisotropic behavior at high temperature, while the reduction of the spin-spin correlation length at (0,$\pi$) happens just below $T_s$, suggesting strong effect of nematic order on low-energy spin fluctuations. Our results favor the idea that treats the spin degree of freedom as the driving force of the electronic nematic order.Comment: 5 pages, 4 figure

Compound optimal control of harmonic drive considering hysteresis characteristic

Hysteresis behavior widely exists in the transmission process of harmonic drives. Eliminating the hysteresis effect is highly desired in the high-precision mechanical transmission, which results in challenges in the control design. This paper aims to improve the tracking accuracy of the motor-harmonic drive serial system. Firstly, a modified Bouc-Wen model based on uniform smooth approximating function is applied to describe the hysteresis behavior of the harmonic drive. By using coordinate transformation and accurate state feedback linearization, we then obtain the mathematical model of the serial system of the motor-harmonic drive. Finally, the reference trajectory is tracked by a compound optimal controller that is based on a linear quadratic regulator. Simulation results show that compared with the disturbance observer-based control (DOBC) using a linear observer, the new compound optimal controller in this paper presents a smoother control signal with the elimination of large amount of high-frequency oscillations. Furthermore, the relative error in the steady state tracking tends to approach to zero and no cyclic fluctuations appears. With the employing of optimal control, the output of the harmonic drive can trace more complex trajectory

Frailty index based on laboratory tests improves prediction of short-and long-term mortality in patients with critical acute myocardial infarction

BackgroundPrevious studies have shown that the frailty index based on laboratory tests (FI-Lab) can identify older adults at increased risk of adverse health outcomes. This study aimed to determine whether the FI-Lab is associated with mortality risk and can provide incremental improvements in risk stratification of patients with critical acute myocardial infarction (AMI).Materials and methodsWe conducted a secondary analysis of data from the Medical Information Mart for Intensive Care (MIMIC)-IV database. A 33-item FI-Lab was constructed. Outcomes of interest were in-hospital and 1-year mortality. Logistic regression models were used to investigate the association between the FI-Lab and outcomes. For the assessment of the incremental predictive value, the FI-Lab was added to several risk stratification scoring systems for critically ill patients, and the following indices were calculated: Δ C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI).ResultsOut of 2,159 patients, 477 died in hospital (22.1%), and 898 died during the 1-year follow-up period. After adjustment for confounders, the FI-Lab was associated with increased in-hospital mortality [odds ratio (OR) = 1.06, 95% confidence interval (CI): 1.05–1.07] and 1-year mortality (OR = 1.05, 95% CI: 1.04–1.06) when assessed as a continuous variable (per 0.01-score increase). When assessed as a categorical variable, the FI-Lab was associated with in-hospital mortality (2nd Quartile: OR = 1.89, 95% CI: 1.18–3.03; 3rd Quartile: OR = 3.46, 95% CI: 2.20–5.46; and 4th Quartile: OR = 5.79, 95% CI: 3.61–9.28 compared to 1st Quartile) as well as 1-year mortality (2nd Quartile: OR = 1.66, 95% CI: 1.23–2.24; 3rd Quartile: OR = 2.40, 95% CI: 1.76–3.26; and 4th Quartile: OR = 3.76, 95% CI: 2.66–5.30 compared to 1st Quartile) after adjustment for confounders. The addition of the FI-Lab to all disease severity scores improved discrimination and significantly reclassified in-hospital and 1-year mortality risk.ConclusionThe FI-Lab was a strong predictor of short- and long-term mortality in patients with critical AMI. The FI-Lab improved the ability to predict mortality in patients with critical AMI and therefore might be useful in the clinical decision-making process