Intelligent methods for optimization design of lightweight fiber-reinforced composite structures: A review and the-state-of-the-art

As the application of lightweight fiber-reinforced composite structures reaches an unprecedented scale in industry, design technology for composite structures becomes crucial for enhancing performance, improving productivity, and reducing cost. In recent years, the rapid development of intelligent technology, such as big data, deep learning, and machine learning, has promoted the development of design technology. However, the current situation and intellectualization of the design technology is not well summarized. This paper reviews the advance in design technologies for fiber-reinforced composite structures, including prediction and optimization methods for composite properties. Then, their intellectualization development is overviewed. Finally, the development trend of intelligent design technologies and intelligent composite structures are discussed. This work can provide a reference for researchers in the related field

Modeling multivariate ocean data using asymmetric copulas

Multivariate descriptions of ocean parameters are quite important for the design and risk assessment of offshore engineering applications. A reliable and realistic statistical multivariate model is essential to produce a representative estimate of the sea state for understanding the ocean conditions. Therefore, an advanced modeling of ocean parameters helps towards improving ocean and coastal engineering practices. In this paper, we introduce the concepts of asymmetric copulas for the modeling of multivariate ocean data. In contrast to extensive previous research on the modeling of symmetric ocean data, this study is focused on capturing asymmetric dependencies among the environmental parameters, which are critical for a realistic description of ocean conditions. This involves particular attention to both nonlinear and asymmetrically dependent variates, which are quite common for the ocean variables. Several asymmetric copula functions, capable of modeling both linear and nonlinear asymmetric dependence structures, are examined in detail. Information on tail dependencies and measures of asymmetric dependencies are exploited. To demonstrate the advantages of asymmetric copulas, the asymmetric copula concept is compared with the traditional copula approaches from the literature using actual environmental data. Each of the introduced copula models is fitted to a set of ocean data collected from a buoy at the US coast. The performance of these asymmetric copulas is discussed and compared based on data fitting and tail dependency characterizations. The accuracy of asymmetric copulas in predicting the extreme value contours is discussed

The numerical classification and grading standards of daylily (Hemerocallis) flower color.

This study collected 183 Hemerocallis varieties to conduct numerical classification of flower color and provide valuable baseline data and foundational theory for normalization and precision of Hemerocallis. The color CIELab phenotypes were collected via colorimeter (CR-10 Plus), which separately measured three sepal and petal parts (throat, eye and limb). The colors of experimental samples were artificially named by the Royal Horticultural Society Colour Chart (RHSCC). All the data were analyzed using R software. The results showed that the throat was predominantly green-yellow, light yellow and yellow; green-yellow accounted for the largest proportion of sepals (67.76%) and petals (69.40%). The eye was more abundant, and there were significant differences between sepals and petals. The limb was clustered into five color groups (orange, yellow, pink, red and purple); the yellow group had the most varieties for both sepals and petals, containing 57.38% and 55.74%, respectively. Both sepals and petals had significant differences (p<0.0001) in color (△E), redness (a*) and color angle (h) for the throat, eye and limb. However, the difference in CIELab phenotypes between the eye and limb were not significant. According to "Dual Classification", the color classification standard was proposed as a 3-level standard. The color of sepal and petal consistency served as the first standard, and the color of limb was the second standard. The color pattern types of pure, gradual change, watermark and eye spot, served as the third standard. It has been proposed that all the 183 experimental varieties were divided into two categories, five groups and finally four types. This study provides a classification basis and reference for numeric and standardized color phenotype description for Hemerocallis

Insulin-like growth factor 1-induced enolase 2 deacetylation by HDAC3 promotes metastasis of pancreatic cancer

Enolase 2 (ENO2) is a key glycolytic enzyme in the metabolic process of glycolysis, but its potential function in pancreatic ductal adenocarcinoma (PDAC) is unclear. In this study, we observed a significant overexpression of ENO2 in PDAC tissues, and its expression was correlated with metastasis and poor prognosis in PDAC patients. K394 was identified as a major acetylation site in ENO2 that regulates its enzymatic activity, cell metabolism and PDAC progression. Knockdown of ENO2 suppressed tumor growth and liver metastasis in PDAC. Re-expression of wild-type (WT) ENO2, but not the K394 acetylation mimetic mutant, could reverse the decreased tumor malignancy. We further characterized histone deacetylase 3 (HDAC3) and P300/CBP-associated factor (PCAF) as the potential deacetylase and acetyltransferase for ENO2, respectively. HDAC3-mediated deacetylation was shown to lead to ENO2 activation and enhancement of glycolysis. Importantly, insulin-like growth factor-1 (IGF-1) was found to decrease K394 acetylation and stimulate ENO2 activity in a dose- and time-dependent manner. The PI3K/AKT/mTOR pathway facilitated the phosphorylation of HDAC3 on S424, which promoted K394 deacetylation and activation of ENO2. Linsitinib, an oral small-molecule inhibitor of IGF-1R, could inhibit IGF-1-induced ENO2 deacetylation by HDAC3 and the PI3K/AKT/mTOR pathway. Furthermore, linsitinib showed a different effect on the growth and metastasis of PDAC depending on the overexpression of WT versus K394-mutant ENO2. Our results reveal a novel mechanism by which acetylation negatively regulates ENO2 activity in the metastasis of PDAC by modulating glycolysis. Blockade of IGF-1-induced ENO2 deacetylation represents a promising strategy to prevent the development of PDAC