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

A Novel Deep Learning Framework to Identify Latent Neuroendophenotypes from Multimodal Brain Imaging Data

The expertise required to ensure adequate treatment for patients with complex cases is significantly deficient, which leads to the high demand for subtyping or clustering analysis on different clinical situations. The identification and refinement of disease-related subtypes will support both medical treatments and pathological research. Clinically, clustering can narrow down the possible causes and provide effective treatment options. However, the clustering on non-invasive multimodal brain imaging data has not been well addressed. In this thesis, we explore this clustering issue using a deep unsupervised embedded clustering (DEMC) method on multimodal brain imaging data. T1-weighted magnetic resonance imaging (MRI) features and resting-state functional MRI-derived brain networks are learned by a sparse autoencoder and a stacked autoencoder separately and then transformed into the embedding space. Then, the K-Means approach was adopted to set the initial center of the deeply embedded clustering structure (DEC) as the centroids, after which DEC clusters with the KL divergence. In the entire processing, the deep embedding and clustering are optimized simultaneously. This new framework was tested on 994 subjects from Human Connectome Project (HCP) and the results show that this new framework has better clustering performance in comparison with other benchmark algorithms

Distributed Relay Selection for Heterogeneous UAV Communication Networks Using A Many-to-Many Matching Game Without Substitutability

This paper proposes a distributed multiple relay selection scheme to maximize the satisfaction experiences of unmanned aerial vehicles (UAV) communication networks. The multi-radio and multi-channel (MRMC) UAV communication system is considered in this paper. One source UAV can select one or more relay radios, and each relay radio can be shared by multiple source UAVs equally. Without the center controller, source UAVs with heterogeneous requirements compete for channels dominated by relay radios. In order to optimize the global satisfaction performance, we model the UAV communication network as a many-to-many matching market without substitutability. We design a potential matching approach to address the optimization problem, in which the optimizing of local matching process will lead to the improvement of global matching results. Simulation results show that the proposed distributed matching approach yields good matching performance of satisfaction, which is close to the global optimum result. Moreover, the many-to-many potential matching approach outperforms existing schemes sufficiently in terms of global satisfaction within a reasonable convergence time.Comment: 6 pages, 4 figures, conferenc

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

Abiotrophia defectiva causing infective endocarditis with brain infarction and subarachnoid hemorrhage: a case report

IntroductionA rare pathogen of Infective Endocarditis (IE), the Abiotrophia defectiva, has been known to trigger life-threatening complications. The case discussed here is of a teenager with brain infarction and subarachnoid hemorrhage caused by IE due to A. defectiva.Case reportA 15-year-old girl with movement disorders involving the left limbs and intermittent fevers was admitted to the hospital. A head CT scan revealed cerebral infarction in the right basal ganglia and subarachnoid hemorrhage. Moreover, vegetation on the mitral valve were confirmed by echocardiography. The blood cultures were found to be positive for Gram-positive streptococcus and identified by Vitek mass spectrometry as A. defectiva. She was prescribed vancomycin antibacterial therapy and underwent a surgical mitral valve replacement.ConclusionThis case is suggestive of the fact that A. defectiva is a rare but crucial pathogen of IE-associated stroke. Obtaining early blood cultures and using microbial mass spectrometry could help achieve an accurate diagnosis. Moreover, reasonable anti-infective medications and surgical interventions need to be combined to avoid and/or manage severe complications

Associations of miR-499 and miR-34b/c Polymorphisms with Susceptibility to Hepatocellular Carcinoma: An Evidence-Based Evaluation

Background. Hepatocellular carcinoma (HCC) represents the sixth common cancer in the world. Single nucleotide polymorphisms (SNPs) in microRNA genes may be associated with susceptibility to HCC. Recently, several studies have reported possible associations of SNPs miR-499 T>C rs3746444 and miR-34b/c T>C rs4938723 with the risk of HCC. However the results are inconsistent and inconclusive. In this present study, we conducted a meta-analysis to comprehensively evaluate potential associations between the two SNPs and HCC susceptibility. Methods. Through a systematic literature search, 8-case-control studies involving 5464 subjects were identified and included in this meta-analysis. The association between the two common SNPs and HCC risk was estimated by pooled odds ratios (ORs) and 95% confidence intervals (95% CIs). Our results showed no significant association between rs3746444 and susceptibility to HCC, whereas variant genotypes of rs4938723 were associated with increased HCC risk in allele frequency model and heterozygous model (C versus T, OR=1.11, 95% CI: 1.01–1.23, P=0.04; TC versus TT, OR=1.19, 95% CI: 1.03–1.37, P=0.02). Conclusions. The current evidence did not support association between rs3746444 and HCC risk. SNP rs4938723 may be associated with susceptibility to HCC. Further well-designed studies are required to clarify the relationships between the two SNPs and HCC risk

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