Synergistic Damage Mechanic Model for Stiffness Properties of Early Fatigue Damage in Composite Laminates

AbstractIn the initial period of the life in the composite laminates, the principal types of damage are diffused ones, such as matrix crack, diffused fiber breaking and local delamination. On account of these diffused damages, a synergistic damage mechanic model was proposed for the stiffness properties. The model included the microcosmic responses of the physical damage and macroscopic performance of the material's stiffness. In micro-level, mesoscopic RVE(representative volume element) model was established to obtain crack opening displacement and crack sliding displacement, which were used to define the damage tensor. In macro- level, through homogenizing the material strain and the surface displacement of the damage, the relationship of the stiffness matrix of unidirectional laminate or laminates in damage statue and damage tense was set up. Due to restriction of NDT (non- destructive testing) technology development, only the constitutive relations of matrix cracks were constructed. The influences of the transverse matrix cracks on the stiffness properties of the laminates [0/±45]s was analyzed with the present model and showed that it is capable to predict the reduction of the stiffness properties resulted from the fatigue diffused damage in the laminates

Neural-Symbolic Recursive Machine for Systematic Generalization

Despite the tremendous success, existing machine learning models still fall short of human-like systematic generalization -- learning compositional rules from limited data and applying them to unseen combinations in various domains. We propose Neural-Symbolic Recursive Machine (NSR) to tackle this deficiency. The core representation of NSR is a Grounded Symbol System (GSS) with combinatorial syntax and semantics, which entirely emerges from training data. Akin to the neuroscience studies suggesting separate brain systems for perceptual, syntactic, and semantic processing, NSR implements analogous separate modules of neural perception, syntactic parsing, and semantic reasoning, which are jointly learned by a deduction-abduction algorithm. We prove that NSR is expressive enough to model various sequence-to-sequence tasks. Superior systematic generalization is achieved via the inductive biases of equivariance and recursiveness embedded in NSR. In experiments, NSR achieves state-of-the-art performance in three benchmarks from different domains: SCAN for semantic parsing, PCFG for string manipulation, and HINT for arithmetic reasoning. Specifically, NSR achieves 100% generalization accuracy on SCAN and PCFG and outperforms state-of-the-art models on HINT by about 23%. Our NSR demonstrates stronger generalization than pure neural networks due to its symbolic representation and inductive biases. NSR also demonstrates better transferability than existing neural-symbolic approaches due to less domain-specific knowledge required

Anti-cancer natural products isolated from chinese medicinal herbs

In recent years, a number of natural products isolated from Chinese herbs have been found to inhibit proliferation, induce apoptosis, suppress angiogenesis, retard metastasis and enhance chemotherapy, exhibiting anti-cancer potential both in vitro and in vivo. This article summarizes recent advances in in vitro and in vivo research on the anti-cancer effects and related mechanisms of some promising natural products. These natural products are also reviewed for their therapeutic potentials, including flavonoids (gambogic acid, curcumin, wogonin and silibinin), alkaloids (berberine), terpenes (artemisinin, β-elemene, oridonin, triptolide, and ursolic acid), quinones (shikonin and emodin) and saponins (ginsenoside Rg3), which are isolated from Chinese medicinal herbs. In particular, the discovery of the new use of artemisinin derivatives as excellent anti-cancer drugs is also reviewed

Prioritization of feasible physiological parameters in drought tolerance evaluation in sorghum: a grey relational analysis

Abstract Identification and evaluation of drought tolerant germplasm is the primary step for sorghum (Sorghum bicolor L. Moench) breeding and utilization under drought conditions. The objective of this study was to use a grey relational analysis to investigate the role of feasible physiological parameters in evaluating drought tolerance in sorghum. Four sorghum varieties were cultivated in pots with two water treatments, including normal watering (75-80% of the soil moisture capacity) and water deficit (45-50% of the soil moisture capacity), which occurred at jointing stage, anthesis and filling stage, respectively. Drought tolerance index of yield was used as the key indicator to evaluate sorghum performance under drought. The grey relational degree of the investigated parameters decreased in the order of transpiration rate, stomatal conductance, photosynthetic rate, soluble sugar content, proline content, relative water content, activity of catalase, activity of superoxide dismutase and activity of peroxidase, implying that drought tolerance for guaranteeing sorghum yield formation was the most related to gas exchange parameters. Water content was a very sensitive parameter of plant growth under drought stress and was more important as compared to the activities of antioxidant enzymes. Results of this research suggested that feasible physiological parameters could be used in the evaluation of drought tolerance to improve the efficiency and accuracy of selection