Identification of Nonlinear Time Lag Systems by Improved Genetic Algorithm

In this paper. a new identification method is proposed which can obtain a good accuracy of identification of nonlinear time lag system on the basis of combination of genetic algorithm and sequence method. The nonlinear system may be described as a discrete model of a polynomial type with unknown parameters using Kolmogorov-Gabor's method. The task of system identification is to determine these parameters. Though the system parameters can be obtained through the search of GA. there is a potential risk. in using a simple GA, that a solution is usually stuck at a local minimum. In order to solve this problem. a new GA search method is proposed by adding a sequence search. which is carried out nearby the value of each estimated parameter coming from a simple GA. By this method. the individual whose fitness is larger can be found. As a result. the solution escapes from a local minimum and converges to the optimum one. The effectiveness of the proposed method is demonstrated through simulation of the identification of nonlinear time lag systems. As an application. the proposed identification method is applied to explosion-proof pneumatic robots, which are modeled as nonlinear time lag systems because the controller links with an actuator by a long pneumatic tube to prevent explosion

A Steady-State Impedance Model for a PEMFC Cathode

A model for the simulation of the steady-state impedance response of a polymer electrolyte membrane fuel cell (PEMFC) cathode is presented. The catalyst layer of the electrode is assumed to consist of many flooded spherical agglomerate particles surrounded by a small volume fraction of gas pores. Stefan-Maxwell equations are used to describe the multicomponent gas-phase transport occurring in both the gas diffusion layer and the catalyst layer of the electrode. Liquid-phase diffusion of O2 is assumed to take place in the flooded agglomerate particles. Newman’s porous electrode theory is applied to determine over-potential distributions. © 2004 The Electrochemical Society. All rights reserved

Cubic Spline Regression for the Open-Circuit Potential Curves of a Lithium-Ion Battery

A cubic spline regression model was used to fit the experimental open-circuit potential (OCP) curves of two intercalation electrodes of a lithium-ion battery. All the details of an OCP curve were accurately predicted by the resulting model. The number of regression intervals used to fit an OCP curve was determined in a way such that in each regression interval the OCP exhibits a profile predictable by a third-order polynomial. The locations of the data points used to separate regression intervals were optimized. Compared to a polynomial model with the same number of fitting parameters, the cubic spline regression model is more accurate. The cubic spline regression model presented here can be used conveniently to fit complicated profiles such as the OCP curves of lithium-ion battery electrodes

Parameter Estimation and Model Discrimination for a Lithium-Ion Cell

Two different models were used to obtain transport and kinetic parameters using nonlinear regression from experimental charge/discharge curves of a lithium-ion cell measured at 35°C under four rates, C/5, C/2, 1C, and 2C, where the C rate is 1.656A . The Levenberg-Marquardt method was used to estimate parameters in the models such as the diffusion of lithium ions in the positive electrode. A confidence interval for each parameter was also presented. The parameter values lie within their confidence intervals. The use of statistical weights to correct for the scatter in experimental data as well as to treat one set of data in preference to other is illustrated. An F-test was performed to discriminate between the goodness of fit obtained from the two models

Study of Ionic Conductivity Profiles of the Air Cathode of a PEMFC by AC Impedance Spectroscopy

A characterization of the ionic conduction of the active layer of a polymer electrolyte membrane fuel cell (PEMFC) cathode by ac impedance measurement at open-circuit potential conditions was conducted. Porous electrode theory was used to derive a compact equation, ∂2Φ̑2/∂y2+∂lnf(y)/∂y×∂Φ̑2/∂y−R/f(y)(1+jΩ)Φ̑2=0, to solve for the impedance response of a cathode at open-circuit potential conditions. This equation includes a parameter R, the ratio of an ionic resistance (evaluated at the active layer/membrane interface), to the total charge-transfer resistance of the active layer. The influence of an assumed ionic conductivity distribution profile f(y) on the error in the estimation of total double-layer capacitance of the active layer from the −1/(ZImω) vs. ZRe plot was also investigated in this work. The increase of ionic conductivity in the active layer of an air cathode with an increase in the ionomer loading was revealed from both impedance data and surface area measurements. A nonlinear parameter estimation method was used to extract the ionic resistance from the high-frequency region of the impedance data at open-circuit potential conditions. The assumed ionic conductivity distribution profile in the active layer was found to vary with ionomer loadings

Parameter Estimates for a PEMFC Cathode

Five parameters of a model of a polymer electrolyte membrane fuel cell (PEMFC) cathode (the volume fraction of gas pores in the gas diffusion layer, the volume fraction of gas pores in the catalyst layer, the exchange current density of the oxygen reduction reaction, the effective ionic conductivity of the electrolyte, and the ratio of the effective diffusion coefficient of oxygen in a flooded spherical agglomerate particle to the square of that particle radius) were determined by least-squares fitting of experimental polarization curves. The values of parameters obtained in this work indicate that ionic conduction and gas-phase transport are two processes significantly influencing the performance of PEMFC air cathodes. While ionic conduction influences cathode performance over a wide range of current densities, gas-phase transport influences cathode performance only at high current densitie

Evaluation of Anti-tumor and Chemoresistance-lowering Effects of Pectolinarigenin from Cirsium japonicum Fisch ex DC in Breast Cancer

Purpose: To investigate the antitumor and chemoresistance-lowering effects of pectolinarigenin on breast cancer cells.Methods: Pectolinarigenin was purified by a combination of silica gel and Sephadex LH-20 column chromatography from ethanol extracts of the aerial parts of C. japonicum DC. Breast cancer selfrenewal properties were tested by colony formation and tumor sphere formation assays. Thereafter, real-time polymerase chain reaction (PCR) was used to detect breast cancer stem cell markers. Furthermore, the effect of pectolinarigenin on breast cancer cell was evaluated by chemoresistance using 3-(4,5-dimethyl-2 thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) assay. Finally, tumor formation in nude mice was used to test the effect of pectolinarigenin on tumorigenicity of breast cancer cells in vivo.Results: The results showed that pectolinarigenin, extracted from Cirsium japonicum Fisch. ex DC., inhibited tumor cell self-renewal in MCF-7 breast cancer cells. Pectolinarigenin (25 μM) caused significant inhibition of colony formation (61.23 %, p < 0.001) and tumor sphere formation (59.49 %, p < 0.01) in MCF-7. The inhibitory effects were associated with changes in breast cancer stem cell markers. Treatment of breast cancer cells with pectolinarigenin reduced the chemoresistance of the cells to doxorubicin. At the same time, mRNA expression of chemoresistance genes (ATP binding cassette subfamily G member 2, ABCG2 and ATP binding cassette subfamily B member 1, MDR1) was repressed by pectolinarigenin. The inhibition efficiency of MDR1 and ABCG2 by 10 μM pectolinarigenin treatment was about 59.29 (p < 0.01) and 46.48 % (p < 0.01), respectively. Furthermore, pectolinarigenin reduced tumor mass in nude mice xenograft model.Conclusion: Pectolinarigenin inhibits breast cancer stem cell-like properties and lowers the chemoresistance of the cancer cells to chemotherapy. The results provide an insight into the mechanism of the anti-breast tumor effects and an experimental basis for the use of pectolinarigenin to enhance treatment of patients with breast cancer.Keywords: Pectolinarigenin, Cancer stem cells, Breast cancer, Chemoresistance, Cirsium japonicum Fisch. ex D

Tumor Mutation Burden Predicts Relapse in Papillary Thyroid Carcinoma With Changes in Genes and Immune Microenvironment

BackgroundThe risk factors of papillary thyroid carcinoma (PTC) recurrence are meaningful for patients and clinicians. Tumor mutation burden (TMB) has been a biomarker for the effectiveness of immune checkpoint inhibitor (ICI) and prognosis in cancer. However, the role of TMB and its latent significance with immune cell infiltration in PTC are still unclear. Herein, we aimed to explore the effect of TMB on PTC prognosis.Material and MethodsRNA-seq and DNA-seq datasets of PTC patients were downloaded from The Cancer Genome Atlas (TCGA) database. The Gene Ontology (GO) and gene set enrichment analysis (GSEA 4.0.1) were applied further to explore potential differences in PTC patients’ biological functions. The differentially expressed genes (DEGs) and immune microenvironment between the high and low TMB groups were determined.ResultsTMB had the highest AUC score than other clinical indicators in ROC analysis on recurrence-free survival, and a higher TMB score was related to a worse prognosis. Further, GSEA showed a higher level of oxidative phosphorylation (OXPHOS) in the high TMB group, and four genes correlated with recurrence-free survival rate were identified. The abundance of CD8+ T cells and M1 macrophages in the high TMB group was significantly lower than that in the low TMB group.ConclusionsOur study found that TMB was a better predictor variable at evaluating the risk of PTC recurrence. Moreover, TMB-related genes conferred dramatically correlated prognosis, which was worth exploring in guiding postoperative follow-up and predicting recurrence for PTC patients