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The ecosophy and the cosmology of the unity and coexistence between human and nature, as well as the aesthetic view of natural beauty in ancient Chinese philosophy, all reflect the deep love for nature and all creatures. One of these representative themes related to ecological view is the reflection on life and death, which represents as the eternal issue of art expression. This ecological aesthetic consciousness that was embodied in countless works including poems, music, dance and paintings has been inherited by contemporary artists in China. Some artists represent the desire for life and the fear of death in the artworks, arousing empathized experience and feelings of the audience during the appreciation. Others focus on the transcendence of life and death, discussing the representation of rebirth. The artworks of Shen Shaomin, Xu Bing, Song Dong and Jin Lipeng are introduced as representatives in this paper. In the practice of artistic creation, any reflection and expression on life and death ultimately serves the aesthetic education of eco-literacy. In order to make greater contribution to an ecological aesthetic education, eco-artists are supposed to take the love for both life and earth as the inner spirit. To form a new paradigm of eco-friendly living is the main goal of ecological aesthetic education.

To solve the problems of the high temperature and poor temperature uniformity of lithium batteries, a liquid cooling topology optimization using a neural network as a proxy model is proposed. The reduction of average cell temperature and cold plate pressure drop are taken as the optimization objectives. The effects of basin volume fraction, Reynolds number and boundary conditions on the topological results are investigated. A proxy model is established using neural networks and transfer learning. The differences between the predicted and true values of the source model and the target model do not exceed 10% and 15%, respectively. The improved optimization algorithm is combined with the proxy model. When the number of inlets and outlets is both three, the fitness value reaches 5.33. Compared to before optimization, the target value increased by 37.7%. The maximum battery temperature was reduced by 2.3% and the maximum temperature difference was reduced by 35.3%. [ABSTRACT FROM AUTHOR]

Accurately analyzing the reliability of driveshaft systems is crucial in engineering vehicles and mechanical equipment. A complex system reliability modeling and analysis method based on a dynamic Bayesian network (DBN) is proposed to repair accurately and reduce the cost in time. Considering the logical structure of the drive shaft system, the reliability block diagram (RBD) of the manufacturing system is constructed in a hierarchical and graded manner, and a method of obtaining the Bayesian network (BN) directly from the RBD is adopted based on the conversion relationship between the RBD, fault tree and BN. A variable‐structure DBN model of the system is constructed based on a static BN extended in time series and incorporating dynamic reliability parameters of the components. Reliability analyses based on DBN reasoning, including reliability assessment, significance metrics, and sensitivity analyses, were performed to identify critical subsystems and critical components. This research contributes to enhancing product reliability, equipment utilization, and improving economic efficiency. [ABSTRACT FROM AUTHOR]

Aims/Background Pregnancy can affect various bodily functions, including metabolism, cardiovascular function, and eyesight. Pathological ocular changes observed during pregnancy are linked to the development of pregnancy-specific conditions, such as preeclampsia/eclampsia and gestational diabetes. This study aims to analyze clinical data disease history and maternal characteristics collected during pregnancy, to determine ocular parameters and develop a risk prediction model for adverse ocular outcomes. Methods We retrospectively analyzed the medical records of 760 pregnant women (1520 eyes) from September 2020 to September 2022 at The Third Affiliated Hospital of Guangzhou Medical University. We identified maternal variables that could influence adverse ocular outcomes, including maternal age, pregnancy-induced hypertension (PIH), gestational diabetes mellitus (GDM), eclampsia, pre-eclampsia, uterine disease, fetal abnormalities, in vitro fertilization with embryo transfer, hypoproteinemia, and major comorbidities during pregnancy. Univariate and multivariate logistic regression analyses were conducted to evaluate the effects of these independent predictors on adverse ocular outcomes. Additionally, receiver operating characteristic (ROC) curve analysis was performed to determine the cut-off probability with for optimal sensitivity and specificity. Results Eclampsia, pre-eclampsia, GDM, a history of chronic hypertension, and hypoproteinemia were identified as independent predictors of adverse ocular outcomes during pregnancy (p < 0.05). Maternal age, PIH, intrauterine growth retardation (IUGR), obesity, and pregnancy with immunoglobulin A nephropathy were predictors of moderate and severe retinal arteriole sclerosis during pregnancy (p < 0.05). Additionally, hemolysis, elevated liver enzymes, and low platelets (HELLP) syndrome were predictors of retinal hemorrhage and exudate during pregnancy (p < 0.05). The area under the ROC curve for adverse ocular outcomes were 0.75 and 0.74, respectively. Conclusion Our predictive model effectively forecasts adverse ocular outcomes during pregnancy, incorporating risk factors such as maternal age, eclampsia and pre-eclampsia, GDM, obesity, a history of chronic hypertension, hypoproteinemia, IUGR, pregnancy with immunoglobulin A nephropathy, and HELLP syndrome. [ABSTRACT FROM AUTHOR]