Multiscale characterization and constitutive parameters identification of polyamide (PA12) processed via selective laser sintering

This study is focused on multiscale characterization and constitutive parameters identification of selectively laser sintered PA12 specimens. The process parameters and change in crystallinity due to 3D printing were identified via differential scanning calorimetry (DSC). Tension, compression, shear, flexural and fracture tests performed on samples fabricated, both in 0° and 90° directions reveal that the tensile toughness of samples printed in 0° orientation, outperform samples printed in 90° orientation by an average of 24%. Cryogenically fractured samples were analyzed via SEM and micro-computed tomography to analyze 2D/3D defects and correlate the microstructure with macroscopic properties. The constitutive parameters for a strain-rate and temperature-dependent Three Network (TN) material model were identified using the measured mechanical properties. Furthermore, mechanical response of micro-architected Kelvin lattice structure was analyzed by Finite Element Method employing the TN constitutive model and the predictions were corroborated with measurements.     

Numerical and empirical modeling of peak deceleration and stress analysis of polyurethane elastomer under impact loading test

In this work, we present the modeling of the peak deceleration (PD) using data of the experimental drop test. Specimens with different thicknesses and areas tested in the drop test device which has adaptable height and weight. In the empirical modeling of the PD, the thickness, area, drop mass and drop height considered as separable functions. An analytical model and Neural Network (NN) was used as the empirical models. Further, the stress on the material was calculated using differential equations and the Finite Element Method (FEM). The Obtained PD from the experimental test, analytical and NN models was converted to the stress on the material using a derived differential equation. Finally, the best model for analyzing the PD and Stress on the material was presented.     

可拉伸超韧水凝胶的制备和应用

综述了可拉伸超韧水凝胶的设计原理及其在组织工程和柔性电子器件领域的应用. 通过将网络结构层次、 化学结构、 增韧机制与宏观力学性能相结合, 重点讨论了单网络水凝胶、 双网络水凝胶、 纳米复合水凝胶及其它水凝胶等可拉伸超韧水凝胶的研究进展, 并总结和展望了新思路和新方向.     

Effect of aspect ratio of vertically aligned copper nanowires in the presence of cellulose nanofibers on the thermal conductivity of epoxy composites

The composites comprising vertically aligned network of copper nanowires (CuNWs) in the presence of cellulose nanofibers were fabricated by using the freeze‐templating method and the effect of aspect ratio (A/R) of CuNWs on the thermal conductivity of epoxy composites was investigated. The thermal conductivity of epoxy composites increased to 0.79 W m^?1 K^?1 at 1.12 vol% of high A/R CuNWs loading, corresponding to the thermal conductivity enhancement of 365% as compared to the pure epoxy. The thermal conductivity of vertically aligned higher A/R CuNWs/epoxy, which is 38.5% and 51.9% higher than those of the lower A/R CuNWs and the randomly aligned CuNWs, respectively. The application of the epoxy composites in heat dissipation was demonstrated by the temperature changes of composites on a hot plate with the increase of heating time. These results indicate that the thermally conductive composites in this study could be applied for thermal dissipating materials in electronic devices.     

lncRNA-miRNA-mRNA interaction network for colorectal cancer; An in silico analysis

BackgroundColorectal cancer (CRC) is one of the most frequent and diagnosed diseases. Accumulating evidences showed that mRNAs and noncoding RNAs play important regulatory roles in tumorigenesis. Identification and determining the relationship between them can help diagnosis and treatment of cancer.MethodsHere we analyzed three microarray datasets; GSE110715, GSE32323 and GSE21510, to identify differentially expressed lncRNAs and mRNAs in CRC. The adjusted p-value ≤0.05 was considered statistically significant. Gene set enrichment analysis was carried out using DAVID tool. The miRCancer database was searched to obtain differentially expressed miRNAs in colorectal cancer, and the miRDB database was used to attain the targets of the obtained miRNAs. To predict the lncRNA-miRNA interactions we used DIANA-LncBase v2 and RegRNA 2.0. Finally the lncRNA-miRNA-mRNA-signaling pathway network was constructed using Cytoscape v3.1.ResultsBy analyzing the three datasets, a total of 21 mRNAs (15 up- and 6 down-regulated) and 24 lncRNAs (18 up- and 6 down-regulated) were identified as common differentially expressed genes between CRC tumor and marginal tissues. Nevertheless, the constructed lncRNA-miRNA-mRNA-signaling pathway network revealed a convergence on 6 lncRNAs (3 up- and 3 downregulated), 7 mRNAs (2 up- and 5 downregulated) and 6 miRNAs (3 up- and 3 downregulated). We found that dysregulation of lncRNAs such as PCBP1-AS1, UCA1 and SNHG16 could sequester several miRNAs such as hsa-miR-582-5p and hsa-miR-198 and promote the proliferation, invasion and drug resistance of colorectal cancer cells.ConclusionsWe introduced a set of lncRNAs, mRNAs and miRNAs differentially expressed in CRC which might be considered for further experimental research as potential biomarkers of CRC development.     

Identifying critical states of hepatocellular carcinoma based on landscape dynamic network biomarkers

Hepatocellular carcinoma (HCC) is the major histological form of primary liver cancer. It has usually reached the disease state once the patient is diagnosed since there are no specific symptoms in the early stages of HCC. This fact increases the difficulty of curing HCC. Recently, quantities of evidence have shown that many mathematical methods (such as dynamic network biomarkers, DNB) can be used to detect critical states or tipping points of complex diseases. However, it is difficult to apply the DNB theory to the clinic since multiple samples are generally unavailable for individual patient. This paper constructs a novel method based on landscape dynamic network biomarkers (L-DNB), which aims to detect early warning signals from cirrhosis state to very advanced HCC state in individual patient. The selected dataset contains multiple samples for each HCC state. A score that indicates the disease characteristics is calculated for each sample by RNA-seq data, and several scores constitute a distribution in the same state. Quantifying the statistical characteristics of these distributions and determining that low-grade dysplastic and high-grade dysplastic are the critical states of HCC. These results can provide scientific advice for early warning indicators and optimal treatment time for HCC.     

Combining convolutional neural networks and in‐line near‐infrared spectroscopy for real‐time monitoring of the chromatographic elution process in commercial production of notoginseng total saponins

The chromatographic elution process is a key step in the production of notoginseng total saponins. Due to quality variability of loading samples and resin capacity decreasing over cycle time, saponins, especially the five main saponins of notoginseng total saponins, need to be monitored in real time during the elution process. In this study, convolutional neural networks, one of the most popular deep learning methods, were used to develop quantitative calibration models based on in‐line near‐infrared spectroscopy for notoginsenoside R₁, ginsenosides Rg₁, Re, Rb₁ and Rd, and their sum concentration, with root mean square error of prediction values of 0.87, 2.76, 0.60, 1.57, 0.28, and 4.99 mg/mL, respectively. Partial least squares calibration models were also developed for model performance comparison. Results show predicted concentration profiles outputted by both the convolutional neural network models and partial least squares models show agreements with the real trends defined by reference measurements, and can be used for elution process monitoring and endpoint determination. To the best of our knowledge, this is the first reported case study of combining convolutional neural networks and in‐line near‐infrared spectroscopy for monitoring of the chromatographic elution process in commercial production of botanical drug products.     

Design and synthesis of interpenetrating polymer networks for second-order nonlinear optics

There has been a tremendous recent interest in the development of second-order nonlinear optical (NLO) polymeric materials for photonic applications. However, a major drawback of second-order NLO polymers that prevents them from being used in device applications is the instability of their electric field induced dipolar alignment. The randomization of the dipole orientation leads to the decay of second-order optical nonlinearities. Numerous efforts have been made to increase the stability of the second-order NLO properties of polymers. The search for new approaches to develop NLO polymers with optimal properties has been an active research area since the past decade. A novel approach, combining the hybrid properties of high glass transition temperatures, extensively extensively crosslinked networks and permanent entanglements, based on interpenetrating polymer networks (IPN) is introduced to develop stable second-order NLO materials. Two types of IPN systems are prepared and their properties are investigated. The designing criteria and the rationale for the selection of polymers are discussed. The IPN samples show excellent temporal stability at elevated temperatures. Long-term stability of the optical nonlinearity at 100°C has been observed in these materials. Temporal stability of the NLO properties of these IPNs is synergistically enhanced. Relaxation behavior of the optical nonlinearity of an IPN system has been studied and compared with that of a typical guest/host system. The improved temporal stability of the second-order NLO properties of this IPN system is a result of the combination of the high rigidity of the polymer backbones, crosslinked matrices and permanent entanglements of the polymer networks. A slight modification of the chemical structure resulted in an improvement of the optical quality of the sample.     

变时滞Hopfield神经网络的全局吸引性和全局指数稳定性

对一类具时滞的Hopfeild型神经网络模型,在非线性神经元激励函数只要求满足Lipschitz连续的条件下,利用推广的Halanay时延微分析不等式、Dini导数以及泛函微分析技术,给出了这类模型的平衡点全局指数稳定性和全局吸引性的充分条件,这些条件易于检验,且改进和推广了前人的结论.此外,此文给出了研究神经网络模型的全局吸引性的微分不等式比较方法.