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The success of near-infrared spectroscopy (NIRS) analysis hinges on the precision and robustness of the calibration model. Shallow learning (SL) algorithms like partial least squares discriminant analysis (PLS-DA) often fall short in capturing the interrelationships between adjacent spectral variables, and the analysis results are easily affected by spectral noise, which dramatically limits the breadth and depth of applications of NIRS. Deep learning (DL) methods, with their capacity to discern intricate features from limited samples, have been progressively integrated into NIRS. In this paper, two discriminant analysis problems, including wheat kernels and Yali pears as examples, and several representative calibration models were used to research the robustness and effectiveness of the model. Additionally, this article proposed a near-infrared calibration model, which was based on the Gramian angular difference field method and coordinate attention convolutional neural networks (G-CACNNs). The research results show that, compared with SL, spectral preprocessing has a smaller impact on the analysis accuracy of consensus learning (CL) and DL, and the latter has the highest analysis accuracy in the modeling results using the original spectrum. The accuracy of G-CACNNs in two discrimination tasks was 98.48% and 99.39%. Finally, this research compared the performance of various models under noise to evaluate the robustness and noise resistance of the proposed method. [ABSTRACT FROM AUTHOR]

The change in the internal contact state caused by the failure of the rolling bearing is an important factor that causes the change in its vibration characteristics. According to the contact form of the fault and the rolling element,this paper considered the different sizes of the fault evolution process,and established the dynamic finite element model of the multi-body contact of the cylindrical roller bearing with different degrees of failure. The effectiveness of the model was verified by the experiment on the outer ring fault of the rolling bearing. Based on the explicit dynamics method,the dynamic contact force between the rolling element and each component and the contact strain of the fault area was analyzed,then the influence law of different fault degrees on the internal dynamic characteristics of the bearing and the change law of the contact deformation under the fault condition were revealed. The results show that the contact force of the rolling elements with the components is reduced when the defects are crossed. As the degree of failure increases,the fluctuation of the internal contact force of the bearing becomes more and more intense,but the fluctuation frequency and amplitude of the contact strain decrease. In the loading area,the rolling elements of faulty bearing are in contact with both sides of the cage pocket,which causing a negative contact force. The fault tends to expand along the rolling direction of the rolling element. The research conclusion can provide a theoretical basis for fault diagnosis and residual life prediction of rolling bearings.

The electromechanical planetary gear system has high work efficiency and long service life, but factors such as heat, wear, and electrical signals can affect the transmission performance of electromechanical planetary gears. This article comprehensively considers factors such as temperature, tooth surface wear, lubrication, current and voltage, damping ratio, etc. Based on the principle of thermal deformation, Archard wear model, and equivalent circuit principle, a dynamic model of electromechanical planetary gears is established. The existing literature has not comprehensively analyzed the effects of temperature, wear, and electrical signal changes on the nonlinear characteristics of electromechanical planetary gear systems. The results show that when the motor current is between 7 A and 18 A and the voltage is lower than the rated voltage, the system is in a stable state; as the temperature rises, the system tends to stabilize; the gear wear exceeds 30 μm, and the bifurcation characteristics of the system are more pronounced. [ABSTRACT FROM AUTHOR]

Background: TMEM106B, a lysosomal transmembrane protein, has been reported to be associated with Parkinson's disease (PD). However, the precise physiopathologic mechanism of TMEM106B in PD remains unclear., Objective: This study aimed to explore the influence of TMEM106B on the autophagy-lysosome pathway (ALP) in PD., Methods: 55 patients with PD and 40 healthy controls were enrolled. RT-qPCR and ELISA were employed to assess the levels of TMEM106B. In vitro and in vivo models of PD, Lentivirus-shTMEM106B and AAV-shTMEM106B were used to knockdown the expression of TMEM106B. Behavioral experiments, western blot, immunofluorescence, and immunohistochemistry were used to detect the effect of TMEM106B on the ALP process., Results: We found that the levels of TMEM106B were increased in the PD patients and PD models. TMEM106B knockdown markedly improved the motor deficits and tyrosine hydroxylase (TH) expression of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-lesioned mice. TMEM106B knockdown promoted α-syn clearance by regulating the ALP process in MPP + -induced SH-SY5Y cells and MPTP-treated mice. Further studies revealed that TMEM106B knockdown might activate ALP through activating AMPK-mTOR-TFEB axis. Furthermore, TMEM106B may play a vital role in the ALP by mediating the expression of TDP43., Conclusions: Taken together, our study suggests that TMEM106B knockdown mediates the ALP pathway, leading to a decrease in α-syn, providing a new direction and perspective for the regulation of autophagy in PD., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Breast cancer affects one in eight women in America and is a leading cause of death from cancer worldwide. In the current study, four types of Omics data including copy number variation, gene expression, proteome and phosphoproteome were collected from seventy-seven breast cancer patients. Individual types of Omics data were used to separately construct predictive models to predict ten-year survival, an important clinical hallmark. The predictive models constructed with proteome data achieved decent predictivity (mean AUC = 0.725) and outperforms the models constructed with other types of Omics data. This indicates that high quality, large scale protein data is more effective for survival prediction compared to other types of omics data. Further, we experimented with ten different data fusion techniques (generic and Multi-kernel learning based) to test whether combining multi-Omics data can result in improved predictive performance. None of the data fusion techniques tested in the current study outperforms the predictive models built with the proteome data.