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Recent years have seen significant research on oscillations in aircraft structures. A self-excited oscillation known as flutter is known to wear down aircraft equipment. So, in this work, the graphene nanoplatelets reinforced composite (GPLRC) micropanel's nonlinear aerodynamics are modeled in this paper, and the nonlinear aerodynamics properties are validated using an artificial neural network technique. The axial direction and the material characteristics of the current system are graded. The relationship between the input layers of composite weight fraction, mode number, and slenderness ratio and the output layer of nonlinear vibrations derived from mathematical simulation has been estimated using an artificial neural network model. As a training algorithm, Levenberg–Marquardt back-propagation is employed. This study is new in that it attempts for the first time to estimate the nonlinear vibration properties of micropanels built of axially graded GPLRC material using the aforementioned input layer. Without the need to solve any differential equations or go through time-consuming experimental procedures, the suggested artificial neural network model can forecast linear or nonlinear natural frequencies. The findings demonstrate that axially graded GPLRC micropanel linear/nonlinear vibration problems can be effectively addressed using artificial intelligence techniques. Future vibration analyses of the microstructures for engineering reasons can use the provided mode. [ABSTRACT FROM AUTHOR]

The present research bridges the gap between a numerical solution with efficient machine-learning-based algorithms to provide a strong platform for predicting thermomechanical shock behavior of the sandwich cylindrical panels with polymer-made core and circumferentially-graded graphene-plates reinforced (CG-GPLR) nanocomposite face-sheets. The comprehensive declaration of the elasticity theory is considered to establish the governing equations of the system in three orthogonal directions. The well-known Lord-Shulman heat transfer theorem is implemented to present the time-variant nature of the system's heat. The differential quadrature method (DQM) is used as a numerical approach to solve the spatial dependency of the governing differential equations. While the Laplace transform solves the time dependency of the governing differential equations. In order to decline the computational cost of predicting the time-variant analysis of the thermomechanical shock response of the system, an efficient machine-learning-based algorithm is employed in a way that it only requires the thermomechanical shock information of the selective set of points known as the training set. After completion of the training procedure, this machine-learning predictor can easily predict the thermoelastic response of all points included either as the train set or a new point. Validation of the applied solution is performed through a comparative process between the present results with those determined in the published literature. The results of this study are in excellent compatibility with those obtained in a solo numerical solution. [ABSTRACT FROM AUTHOR]

(1)Background-Carbon labeling, as an effective means for countries to achieve low-carbon goals, has become a passport for international trade. With the spread of green low-carbon concept and the development of forest economy, forest foods have become an important part of people' s diet consumption, and play an increasingly important role in sustainable food consumption and forestry economy. In the globalization of green development, consumers' ecological behaviors and forward-looking ecological education are of great significance to promote the sustainable development of China's ecological culture and ecological technology. Understanding the impact of environmental awareness on consumers' behaviors and its realization paths is helpful to cultivate consumers' ecological environmental awareness and promote the construction of ecological civilization. (2)Methods-Based on theoretical analysis, this paper proposed research hypotheses, and selected consumers ' purchase intention of low-carbon forest foods as the explained variable, consumers ' environmental awareness as the core explanatory variable, cognition of carbon label products as the mediating variable, trust in low-carbon products as the moderating variable, and individual characteristics as the control variables. The research data was collected from 559 valid questionnaires obtained from December 2023 to February 2024. This paper first studied the collinearity and correlation among the variables, then analyzed the impact mechanism of environmental awareness on consumers' purchase intention of forest food using the Ologit model, and tested the mediating effect of cognition of carbon label products and the moderating effect of trust in low-carbon products using the Bootstrap sampling method. Finally, the regression model was replaced by the Ologit model for robustness analysis, which provided data support for the conclusions. (3) Results-Environmental awareness has a positive impact on consumers' purchase intention of low- carbon forest foods. The more consumers pay attention to ecological issues, the more willing they are to buy low-carbon forest foods and pay extra for environmental protection. Consumers ' cognition of carbon label products plays an intermediary role between environmental awareness and consumers ' purchase intention of low-carbon forest foods. Trust in low-carbon products positively regulates the environmental awareness of consumers' purchase intention of low-carbon forest foods. In addition, gender, age, income and residential area also have a significant impact on consumers ' purchase intention of low-carbon forest foods. After considering the endogenous factors, there is still a significant positive correlation between environmental awareness and consumers' purchase intention of low-carbon forest foods. Therefore, there is reason to think that the results are robust. (4) Conclusions and Discussions-Based on the above research conclusions, the following policy suggestions are put forward. First, vigorously advocate the concept of low-carbon consumption, gradually cultivate consumers' awareness of ecological environment and enhance the public' s sense of social responsibility in green consumption. Second, widely popularize the relevant knowledge of carbon labeling, so that more consumers can understand the meaning, necessity and benefits of carbon labeling, and closely link carbon emission reduction with people' s daily life, so as to improve the public' s attention, understanding and awareness of carbon label foods. Third, implement safety responsibilities of low-carbon products and enhance the level of trust in low-carbon products. The government should standardize the market order of carbon label products, strengthen market supervision, and improve the transparency of carbon labeling, so as to enhance consumers' trust and recognition of carbon label products, and help residents realize the transformation of green life. [ABSTRACT FROM AUTHOR]

Background: Despite extensive work to identify diagnostic plasma markers for Parkinson's disease (PD), there are still no accepted and validated surrogate biomarkers. Mitophagy‐associated proteins (MAPs), including PTEN‐induced putative kinase 1 (PINK1), Parkin, phosphoglycerate mutase 5 (PGAM5), BCL2 interacting protein 3 (BNIP3), and phosphorylated‐TBK1 (p‐TBK1), are, to our best knowledge, not well studied as a panel of biomarkers of neurodegeneration in PD. Methods: The study population comprised 116 age‐matched controls (HC), 179 PD patients, alongside and 90 PD syndromes (PDs) divided between two cohorts: (i) the modeling cohort (cohort 1), including 150 PD, 97 HC, and 80 PDs; and (ii) the validated cohort (cohort 2), including 29 PD, 19 HC, and 10 PDs. Results: MAPs are elevated in the plasma of PD patients. PINK1, Parkin, and PGAM5 displayed the top three measurable increase trends in amplitude compared to BNIP3 and p‐TBK1. Moreover, the area under the curve (AUC) values of PINK1, PGAM5, and Parkin were ranked the top three MAP candidates in diagnosis accuracy for PD from HC, but the MAPs make it hard to differentiate PD from PDs. In addition, there are higher plasma PINK1‐Parkin levels and prominent diagnostic accuracy in A‐synuclein (+) subjects than in A‐synuclein (−) subjects. Conclusions: These results uncover that plasma MAPs (PINK1, Parkin, and PGAM5) may be potentially useful diagnostic biomarkers for PD diagnosis. Studies on larger cohorts would be required to test whether elevated plasma MAP levels are related to PD risk or prognosis. [ABSTRACT FROM AUTHOR]