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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]

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]

Objectives: The ATN's different modalities (fluids and neuroimaging) for each of the Aβ (A), tau (T), and neurodegeneration (N) elements are used for the biological diagnosis of Alzheimer's disease (AD). We aim to identify which ATN category achieves the highest potential for diagnosis and predictive accuracy of longitudinal cognitive decline. Methods: Based on the availability of plasma ATN biomarkers (plasma‐derived Aβ42/40, p‐tau181, NFL, respectively), CSF ATN biomarkers (CSF‐derived Aβ42/Aβ40, p‐tau181, NFL), and neuroimaging ATN biomarkers (18F‐florbetapir (FBP) amyloid‐PET, 18F‐flortaucipir (FTP) tau‐PET, and fluorodeoxyglucose (FDG)‐PET), a total of 2340 participants were selected from ADNI. Results: Our data analysis indicates that the area under curves (AUCs) of CSF‐A, neuroimaging‐T, and neuroimaging‐N were ranked the top three ATN candidates for accurate diagnosis of AD. Moreover, neuroimaging ATN biomarkers display the best predictive ability for longitudinal cognitive decline among the three categories. To note, neuroimaging‐T correlates well with cognitive performances in a negative correlation manner. Meanwhile, participants in the "N" element positive group, especially the CSF‐N positive group, experience the fastest cognitive decline compared with other groups defined by ATN biomarkers. In addition, the voxel‐wise analysis showed that CSF‐A related to tau accumulation and FDG–PET indexes more strongly in subjects with MCI stage. According to our analysis of the data, the best three ATN candidates for a precise diagnosis of AD are CSF‐A, neuroimaging‐T, and neuroimaging‐N. Conclusions: Collectively, our findings suggest that plasma, CSF, and neuroimaging biomarkers differ considerably within the ATN framework; the most accurate target biomarkers for diagnosing AD were the CSF‐A, neuroimaging‐T, and neuroimaging‐N within each ATN modality. Moreover, neuroimaging‐T and CSF‐N both show excellent ability in the prediction of cognitive decline in two different dimensions. [ABSTRACT FROM AUTHOR]

This review aims to provide a comprehensive overview of the research progress on soy protein (SP) film. SP film has gained significant attention due to its natural, renewable, and biodegradable characteristics. The review begins by introducing the film formation mechanisms and preparation methods of SP film, including solution casting, dry‐film formation, and other techniques, and discusses the associated preparation conditions and processes. Subsequently, the review explores the structural features, plasticization modifications, mechanical properties, antimicrobial modifications, UV shielding properties, and conductivity of SP film. Lastly, the review summarizes the current challenges and development directions in SP film research. This includes improving the mechanical performance and stability of the film, exploring novel functionalization approaches, and enhancing the sustainability of the film. Through this review of SP film, the study aims to enhance the understanding of their potential in materials science and various applications while providing guidance and insights for future research and applications. [ABSTRACT FROM AUTHOR]

Abstract 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]