Heart failure (HF) is the end stage of cardiovascular disease and is characterized by the loss of myocytes caused by cell death. Puerarin has been found to improve HF clinically, and animal study ...findings have confirmed its anti-cell-death properties. However, the underlying mechanisms remain unclear, especially with respect to the impact on ferroptosis, a newly defined mechanism of iron-dependent non-apoptotic cell death in HF. Here, ferroptosis-like cell death was observed in erastin- or isoprenaline (ISO)-treated H9c2 myocytes in vitro and in rats with aortic banding inducing HF, characterized by reduced cell viability with increased lipid peroxidation and labile iron pool. Interestingly, the increased iron overload and lipid peroxidation observed in either rats with HF or H9c2 cells incubated with ISO were significantly blocked by puerarin administration. These results provide compelling evidence that puerarin plays a role in inhibiting myocyte loss during HF, partly through ferroptosis mitigation, suggesting a new mechanism of puerarin as a potential therapy for HF.
•Ferroptosis is involved in the loss of myocytes during heart failure.•Puerarin exerted protective effects against heart failure through inhibition of ferroptosis.•Regulation of Nox4 signaling might be involved in puerarin inhibiting ferroptosis.
A new photoelectrochemical (PEC) "signal-on" sensing platform based on photoactive material Bi
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-ZnO and CdS quantum dots (QDs) sensitizer was fabricated for ultrasensitive determination of thrombin ...by constructing supersandwich nanowires. The CdS/ZnO/Bi
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sensitization structure with excellent energy level arrangement remarkably improved photoelectric conversion efficiency because of the efficient separation of the electron-hole. Moreover, the DNA supersandwich nanowire is ingeniously synthesized in one step by simple dislocation hybridization, which could carry a large amount of sensitized material CdS QDs. More importantly, with Exonuclease III (Exo III)-assisted multiple amplification, the proposed "signal-on" platform demonstrated a detection range of 10 fM to 1 μM with the detection limit of 1.41 fM for thrombin. Impressively, the PEC platform can successfully detect human serum samples with good accuracy. Above all, the CdS/ZnO/Bi
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sensitization photoelectric biosensing platform by using DNA nanowire in combination with Exo III-multiple amplification opens new sensitized amplification paths for supersensitive biosensing and bioanalysis.
•A method integrating the artificial neural network with genetic algorithm is proposed.•A three-dimensional multiphysics model is employed for data generation and validation.•The fuel cell maximum ...power and corresponding operation conditions are identified.•The maximum powers @ various temperatures & their operation conditions are identified.•Deep-learning results reflect major physical/electrochemical processes in fuel cells.
The maximum achievable power of a polymer electrolyte membrane (PEM) fuel cell under specific operating temperature is important to its application. In this paper, we propose a method that integrates an artificial neural network (ANN) with the genetic algorithm (GA) to predict the performance of a PEM fuel cell and identify its maximum powers and corresponding conditions for operational control purpose. A validated three-dimensional (3D) multiphysics model is employed to generate total 1500 data points for training, testing, and verifying the ANN, which consists of two hidden layers with eight and four neurons on each hidden layer, respectively. After the ANN is properly trained, it is incorporated into the GA for deep learning to identify the maximum power and corresponding operating conditions, which shows that the fuel cell configuration could achieve a maximum power of about 0.78 W/cm2 at 368.8 K. Additionally, the combined ANN-GA method is employed to identify the maximum powers and their operating conditions under eight typical operation temperatures in the range of 323–373 K. The deep-learning results reflect the major physical and electrochemical processes that govern fuel cell performance and are validated against the 3D multiphysics model. The results demonstrate that the combined ANN-GA method is suitable to predicting fuel cell performance and identifying operation parameters for the maximum powers under various temperatures, which is important to practical system design and rapid control in fuel cell applications.
Myocardial infarction is one of the leading causes of mortality globally. Currently, the pleiotropic inflammatory cytokine interleukin-6 (IL-6) is considered to be intimately related to the severity ...of myocardial injury during myocardial infarction. Interventions targeting IL-6 are a promising therapeutic option for myocardial infarction, but the underlying molecular mechanisms are not well understood. Here, we report the novel role of IL-6 in regulating adverse cardiac remodeling mediated by fibroblasts in a mouse model of myocardial infarction. It was found that the elevated expression of IL-6 in myocardium and cardiac fibroblasts was observed after myocardial infarction. Further, fibroblast-specific knockdown of
significantly attenuated cardiac fibrosis and adverse cardiac remodeling and preserved cardiac function induced by myocardial infarction. Mechanistically, the role of Il6 contributing to cardiac fibrosis depends on signal transduction and activation of transcription (STAT)3 signaling activation. Additionally, Stat3 binds to the
promoter region and contributes to the increased expression of
, which exacerbates cardiac fibrosis. In conclusion, these results suggest a novel role for IL-6 derived from fibroblasts in mediating Stat3 activation and substantially augmented
expression in promoting cardiac fibrosis, highlighting its potential as a therapeutic target for cardiac fibrosis.
Milling is a main processing mode of the modern manufacturing industry, which seriously affects the quality and precision of the machined workpiece. However, it is difficult to monitor the tool wear ...condition in the continuous cutting process, especially under a variable speed condition. The existing tool wear condition monitoring methods only carry out analysis with a constant engine speed. Different from the general monitoring methods, this paper put forward a milling cutter wear condition monitoring method based on order analysis (OA) and stacked sparse autoencoder (SSAE). The methodology in the research include signals feature extraction and tool wear state monitoring and were designed to analyze the three-phase spindle current signals instead of the traditional force signals and vibration signals. The variable speed signals were transformed into angle domain stationary signals by order analysis, and the SSAE neural network was used to monitor the tool wear state. The proposed method was verified on the laboratory signals and the results showed a better performance than the other methods and a better applicability in actual industrial manufacturing.
Severe air pollution poses a significant threat to public safety and human health. Predicting future air quality conditions is crucial for implementing pollution control measures and guiding ...residents' activity choices. However, traditional single-module machine learning models suffer from long training times and low prediction accuracy. To improve the accuracy of air quality forecasting, this paper proposes a ISSA-LSTM model-based approach for predicting the air quality index (AQI). The model consists of three main components: random forest (RF) and mRMR, improved sparrow search algorithm (ISSA), and long short-term memory network (LSTM). Firstly, RF-mRMR is used to select the influential variables affecting AQI, thereby enhancing the model's performance. Next, ISSA algorithm is employed to optimize the hyperparameters of LSTM, further improving the model's performance. Finally, LSTM model is utilized to predict AQI concentrations. Through comparative experiments, it is demonstrated that the ISSA-LSTM model outperforms other models in terms of RMSE and R
, exhibiting higher prediction accuracy. The model's predictive performance is validated across different time steps, demonstrating minimal prediction errors. Therefore, the ISSA-LSTM model is a viable and effective approach for accurately predicting AQI.
•A decoupling method for blade tip vibration is proposed.•A phase shift method for blade tip vibration is proposed.•An improved method is proposed to reconstruct full-field dynamic response.•The ...full-field dynamic response reconstruction method is verified by simulation and experimental data.
Online monitoring and health assessment of rotating bladed disks have always been a hot topic in the engineering field. However, the existing monitoring methods cannot fully perceive the vibration state of the bladed disk. Therefore, a dynamic response field reconstruction method based on blade tip timing (BTT) data is proposed to evaluate the global vibration state of the rotating bladed disk online in this paper. Firstly, a blade tip vibration decoupling method is proposed to realize the decoupling of the blade tip vibration in Cartesian coordinate system. Secondly, the blade tip vibration phase shift method is proposed, and then the reconstruction of dynamic displacement field and dynamic strain field is realized by combining the improved modal reduction/expansion method. Finally, the proposed dynamic response field reconstruction method is verified by using BTT simulation data and experimental data, and the sources of reconstruction error are analyzed. The numerical results show that the reconstruction errors of the dynamic displacement field and the dynamic strain field are both less than 10% except for the area near the nodal diameter. The experimental results also show that the dynamic strain reconstruction error is less than 15% due to the influence of test noise. The response field reconstruction method proposed in this paper can provide sufficient data support for the online monitoring and fatigue life prediction of the rotating bladed disks. In particular, it can provide strong support for the application of digital twin technology in the field of structural health monitoring of the rotating bladed disks.
Non-Rayleigh distributed radar clutter is widely reported in studies of radar scattering from sea and land surfaces. Existing models of scattered field amplitude distributions have been developed ...primarily through empirical fits to the statistics of radar backscatter measurements. In contrast, this paper investigates a physics-based approach to determine the amplitude distributions of fields scattered from rough surfaces using Monte Carlo simulations and analytical methods, for both backscattering and bistatic configurations. The rough surface is represented using a "two-scale" model. An individual surface facet contains "small-scale" roughness, for which scattered fields are evaluated using the second-order small slope approximation. Individual surface facets are tilted by the slopes of the "large-scale" roughness in a given observation. The results show that non-Rayleigh amplitude distributions are obtained when tilting is performed, and that the departure from the Rayleigh distribution becomes more significant as the variance of the tilting slope increases. Further analysis shows that this departure results from variations in the mean scattering amplitude from a facet (the texture) as tilting occurs. The distribution of the texture is studied and compared with existing models. Finally, the distribution of the scattered field amplitude is modeled through the compound Gaussian model, first using the distribution of the texture, and then in terms of the probability density function of tilting slopes (which avoids the requirement of the knowledge of the texture distribution). The results from the above two methods are in good agreement and both agree well with the Monte Carlo simulation.
Variational mode decomposition (VMD) is widely used in the condition monitoring and fault diagnosis of rotary machinery for its unique advantages. An adaptive parameter optimized VMD (APOVMD) is ...proposed in order to adaptively determine the suitable decomposed parameters and further enhance its performance. The traditional singular value decomposition (SVD) method cannot effectively select the reconstructed order, which often leads to unsatisfactory results for signal reconstruction. Thus, a singular kurtosis difference spectrum method is proposed to accurately determine the effective reconstructed order for signal noise reduction. In addition, because the fault signal of the planetary gearbox at the early fault stage is weak and susceptible to ambient noise and other signal interference, the fault feature information is difficult to extract. To address this issue, a novel method for early fault feature extraction of planetary gearbox based on APOVMD and singular kurtosis difference spectrum is proposed in this paper. First, the APOVMD is applied to decompose the planetary gearbox vibration signal into a series of band-limited intrinsic mode functions adaptively and non-recursively. Second, the sensitive component is selected from the IMFS according to the cosine similarity index. Third, the Hankel matrix is constructed for the sensitive component in the phase space and decomposed by SVD. Here, the effective reconstructed order is automatically selected by the singular kurtosis difference spectrum method for noise reduction. Finally, the Hilbert envelope spectrum analysis is carried out on the reconstructed signal, and the fault characteristic frequency information of planetary gearbox can be accurately extracted from the envelope spectrum to realize the fault identification and location. The results of simulation studies and actual experimental data analysis demonstrate that the proposed method has superior ability to extract the early weak fault characteristics of the planetary gearbox compared with the VMD-SVD and EEMD-SVD methods, and the validity and feasibility of the presented method are proved.
Rolling bearings are the vital components of large electromechanical equipment, thus it is of great significance to develop intelligent fault diagnoses for them to improve equipment operation ...reliability. In this paper, a fault diagnosis method based on refined composite multiscale reverse dispersion entropy (RCMRDE) and random forest is developed. Firstly, rolling bearing vibration signals are adaptively decomposed by variational mode decomposition (VMD), and then the RCMRDE values of 25 scales are calculated for original signal and each decomposed component as the initial feature set. Secondly, based on the joint mutual information maximization (JMIM) algorithm, the top 15 sensitive features are selected as a new feature set and feed into random forest model to identify bearing health status. Finally, to verify the effectiveness and superiority of the presented method, actual data acquisition and analysis are performed on the bearing fault diagnosis experimental platform. These results indicate that the presented method can precisely diagnose bearing fault types and damage degree, and the average identification accuracy rate is 97.33%. Compared with the refine composite multiscale dispersion entropy (RCMDE) and multiscale dispersion entropy (MDE), the fault diagnosis accuracy is improved by 2.67% and 8.67%, respectively. Furthermore, compared with the RCMRDE method without VMD decomposition, the fault diagnosis accuracy is improved by 3.67%. Research results prove that a better feature extraction technique is proposed, which can effectively overcome the deficiency of existing entropy and significantly enhance the ability of fault identification.
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