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Power driver devices have functions such as current amplification and power conversion, making them key components of electronic systems. Their degradation is affected by multiple types of stress, making it difficult to establish an accurate degradation model. To monitor the degradation state of electronic devices and predict their remaining useful life at different moments, this paper obtains the degradation data of the samples by carrying out accelerated degradation experiments of power driver devices under the influence of multiple stresses, and proposes a new multi-stress-coupled accelerated degradation model based on the Wiener process. This model associates the accelerated stress with the drift coefficient of the Wiener stochastic degradation model. Finally, the MLE-SA optimization algorithm is used to obtain the unknown parameter values of the model. The method proposed in this paper incorporates accelerated stress factors into the stochastic degradation model, effectively improving the prediction accuracy and interpretability of the stochastic degradation model. To verify the accuracy of the model, the paper conducted comparative experiments on the accelerated degradation data of power driver devices and publicly available data. The results show that the multi-stress coupled accelerated degradation model based on the Wiener process proposed in this paper can well fit the accelerated degradation data of power driver devices, and the goodness-of-fit for the public dataset above 0.9, indicating that the model proposed in the paper has high accuracy.
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http://dx.doi.org/10.1038/s41598-025-03786-y | DOI Listing |
Mol Ecol
September 2025
State Key Laboratory of Soil and Water Conservation and Desertification Control, College of Soil and Water Conservation Science and Engineering, Northwest A&F University, Shaanxi, People's Republic of China.
Increasing evidence indicates that the loss of soil microbial α-diversity triggered by environmental stress negatively impacts microbial functions; however, the effects of microbial α-diversity on community functions under environmental stress are poorly understood. Here, we investigated the changes in bacterial and fungal α- diversity along gradients of five natural stressors (temperature, precipitation, plant diversity, soil organic C and pH) across 45 grasslands in China and evaluated their connection with microbial functional traits. By quantifying the five environmental stresses into an integrated stress index, we found that the bacterial and fungal α-diversity declined under high environmental stress across three soil layers (0-20 cm, 20-40 cm and 40-60 cm).
View Article and Find Full Text PDFACS Catal
August 2025
Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States.
Chlorinated hydrocarbons are widely used as solvents and synthetic intermediates, but their chemical persistence can cause hazardous environmental accumulation. Haloalkane dehalogenase from (DhlA) is a bacterial enzyme that naturally converts toxic chloroalkanes into less harmful alcohols. Using a multiscale approach based on the empirical valence bond method, we investigate the catalytic mechanism of 1,2-dichloroethane dehalogenation within DhlA and its mutants.
View Article and Find Full Text PDFInt J Gen Med
September 2025
Department of Gynecology, Zhongshan Hospital, Fudan University, Shanghai, 200035, People's Republic of China.
Objective: This study aims to investigate the association between the dynamics of routine metabolic markers and endometriosis severity.
Methods: A retrospective analysis was conducted on patients diagnosed with endometriosis at Zhongshan Hospital, Xiamen, affiliated with Fudan University. The collected data encompassed demographic details and biochemical indicators related to lipid, hepatobiliary, renal metabolism, and electrolyte balance.
Mol Ther Methods Clin Dev
June 2025
Eisai Co., Ltd., Tsukuba Research Laboratories, 5-1-3, Tokodai, Tsukuba, Ibaraki 300-2635, Japan.
Liver-humanized chimeric mice (PXB-mice) are widely utilized for predicting human pharmacokinetics (PK) and as human disease models. However, residual metabolic activity of mouse hepatocytes in chimeric mice can interfere with accurate human PK estimation. Lipid nanoparticle (LNP)-formulated small interfering RNA (siRNA) treatment makes it possible to eliminate the shortcomings of chimeras and create new models.
View Article and Find Full Text PDFRadiol Adv
September 2024
Department of Radiology, Northwestern University and Northwestern Medicine, Chicago, IL, 60611, United States.
Background: In clinical practice, digital subtraction angiography (DSA) often suffers from misregistration artifact resulting from voluntary, respiratory, and cardiac motion during acquisition. Most prior efforts to register the background DSA mask to subsequent postcontrast images rely on key point registration using iterative optimization, which has limited real-time application.
Purpose: Leveraging state-of-the-art, unsupervised deep learning, we aim to develop a fast, deformable registration model to substantially reduce DSA misregistration in craniocervical angiography without compromising spatial resolution or introducing new artifacts.