Eight-node conforming straight-side quadrilateral element with high-order completeness (QH8-C1)

A new eight-node conforming quadrilateral element with high-order completeness, denoted as QH8-C1, is proposed in this article. First, expressions for the interpolation displacement function satisfying the requirements for high-order completeness in the global coordinate system are constructed. Second, the displacement function expression in global coordinates is transformed into isoparametric coordinates, and the relationships between the two series of coefficients for the two kinds of displacement function expressions are found. Third, the displacement function expression is modified to satisfy the requirements of nodal freedom and interelement boundary continuity. The key to the new element construction is the derivation of the linear relationship expressions among 12 coefficients of element displacement interpolation polynomials in the global and isoparametric coordinate systems. As a result, the relationship between quadratic completeness and interelement continuity is explicitly given, and a proof of the completeness and the continuity was conducted to theoretically guarantee the validity of the derivation results. Furthermore, in order to verify the correctness of the theoretical work, nine numerical examples were performed. The computation results from these examples demonstrate that QH8-C1 exhibited excellent performance, including high simulation accuracy, fast convergence, insensitivity to mesh distortion, and monotonic convergence.     

R2D2: A scalable deep learning toolkit for medical imaging segmentation

Deep learning has gained a significant popularity in recent years thanks to its tremendous success across a wide range of relevant fields of applications, including medical image analysis domain in particular. Although convolutional neural networks (CNNs) based medical applications have been providing powerful solutions and revolutionizing medicine, efficiently training of CNNs models is a tedious and challenging task. It is a computationally intensive process taking long time and rare system resources, which represents a significant hindrance to scientific research progress. In order to address this challenge, we propose in this article, R2D2, a scalable intuitive deep learning toolkit for medical imaging semantic segmentation. To the best of our knowledge, the present work is the first that aims to tackle this issue by offering a novel distributed versions of two well-known and widely used CNN segmentation architectures [ie, fully convolutional network (FCN) and U-Net]. We introduce the design and the core building blocks of R2D2. We further present and analyze its experimental evaluation results on two different concrete medical imaging segmentation use cases. R2D2 achieves up to 17.5× and 10.4× speedup than single-node based training of U-Net and FCN, respectively, with a negligible, though still unexpected segmentation accuracy loss. R2D2 offers not only an empirical evidence and investigates in-depth the latest published works but also it facilitates and significantly reduces the effort required by researchers to quickly prototype and easily discover cutting-edge CNN configurations and architectures.     

A survey of multiscale modeling: Foundations,historical milestones,current status,and future prospects

Research problems in the domains of physical, engineering, biological sciences often span multiple time and length scales, owing to the complexity of information transfer underlying mechanisms. Multiscale modeling (MSM) and high-performance computing (HPC) have emerged as indispensable tools for tackling such complex problems. We review the foundations, historical developments, and current paradigms in MSM. A paradigm shift in MSM implementations is being fueled by the rapid advances and emerging paradigms in HPC at the dawn of exascale computing. Moreover, amidst the explosion of data science, engineering, and medicine, machine learning (ML) integrated with MSM is poised to enhance the capabilities of standard MSM approaches significantly, particularly in the face of increasing problem complexity. The potential to blend MSM, HPC, and ML presents opportunities for unbound innovation and promises to represent the future of MSM and explainable ML that will likely define the fields in the 21st century.     

高效液相色谱-串联质谱测定香辛料中的去甲乌药碱

建立了高效液相色谱-串联质谱(HPLC-MS/MS)快速测定香辛料中去甲乌药碱的分析方法。实验优化了样品前处理条件和色谱质谱条件。在优化条件下,样品经体积比为80%甲醇-水提取,纯水进行一定倍数的稀释,以Waters XBridge C18(150 mm×2.1mm,5μm)色谱柱分离,电喷雾正离子扫描,多反应监测(MRM)模式检测,基质匹配标准溶液外标法定量,实现了香辛料中去甲乌药碱的精确定量分析。去甲乌药碱在0.05~20.0 ng/mL范围内线性关系良好,相关系数为0.9999,方法检出限为0.7μg/kg,定量限为2.33μg/kg,在3个添加水平条件下八角的平均回收率为91.80%~99.97%,相对标准偏差为1.43%~2.35%。该方法简单、灵敏、准确性高、稳定性好,适用于香辛料中去甲乌药碱的测定。     

Two-Step Processing Method for Blending High-Performance Polymers with Notable Thermal and Rheological Differences: PEI and PBT

Blends between high-performance polymers (HPP) are barely studied, especially those produced by melting processing. In this work, it is proposed a novel methodology to prepare blends between polymers with notable processing temperature differences: poly(ether imide) (PEI) and poly(butylene terephthalate) (PBT). Processing parameters are settled after thermal and rheological evaluation of pure materials, those results suggest these blends need to be produced by steps. It is found a synergistic effect such as lowering PEI processing temperature and reducing PBT hydrolysis at high temperatures. Propose methodology allows to produce blends between HPP in the whole composition range with the same processing conditions.     

Quality control method of sterols in fermented Cordyceps sinensis based on combined fingerprint and quantitative analysis of multicomponents by single marker

In this study, we established a new pattern for differentiating and comprehensively evaluating the quality of fermented Cordyceps sinensis based on high-performance liquid chromatography (HPLC) fingerprint analysis combined with similar analysis (SA), principal component analysis (PCA), hierarchical cluster analysis (HCA), and the quantitative analysis of multicomponents by single marker (QAMS). These methods indicated that fermented Cordyceps sinensis samples could be categorized into one class by PCA and HCA. The fingerprints of fermented Cordyceps sinensis were established, and four HPLC peaks were identified as ergosterol, daucosterol, stigmasterol, and β-sitosterol in Jinshuibao capsules and tablets (two products of fermented Cordyceps sinensis). Ergosterol was chosen as the internal reference substance, and the relative correction factors (RCFs) between ergosterol and the other three sterols were calculated using the QAMS method. Moreover, the accuracy of the QAMS method was confirmed by comparing the relative error between the results of the method used with those of an external standard method (ESM). No significant difference between the two methods was observed. The total sterols content in Jinshuibao products were calculated by the QAMS method, and the total sterols content of the two products were similar. This study showed that the method established herein was efficient and successful in the identification fermented Cordyceps sinensis and may further act to facilitate systematic quality control of fermented Cordyceps sinensis products.     

Designing amorphous formulations of polyphenols with naringin by spray-drying for enhanced solubility and permeability

Naringin (NAR), a major flavanone (FVA) glycoside, is a component of food mainly obtained from grapefruit. We used NAR as a food additive to improve the solubility and permeability of hydrophobic polyphenols used as supplements in the food industry. The spray-dried particles (SDPs) of NAR alone show an amorphous state with a glass transition temperature (T_g) at 93.2 °C. SDPs of hydrophobic polyphenols, such as flavone (FVO), quercetin (QCT), naringenin (NRG), and resveratrol (RVT) were prepared by adding varying amounts of NAR. All SDPs of hydrophobic polyphenols with added NAR were in an amorphous state with a single T_g, but SDPs of hydrophobic polyphenols without added NAR showed diffraction peaks derived from each crystal. The SDPs with NAR could keep an amorphous state after storage at a high humidity condition for one month, except for SDPs of RVT/NAR. SDPs with NAR enhanced the solubility of hydrophobic polyphenols, especially NRG solubility, which was enhanced more than 9 times compared to NRG crystal. The enhanced solubility resulted in the increased membrane permeability of NRG. The antioxidant effect of the hydrophobic NRG was also enhanced by the synergetic effect of NAR. The findings demonstrated that NAR could be used as a food additive to enhance the solubility and membrane permeability of hydrophobic polyphenols.     

复合外加剂对低温高性能混凝土抗渗性的影响

主要研究了在低温高性能混凝土中掺入复合外加剂对混凝土抗渗性能的影响,并按选定配比以最佳掺量掺入低温高性能混凝土中进行抗渗性试验,测试结果满足设计和施工要求.     

超细粉体在复合材料中的功能作用及加工技术

矿物超细粉体作为高性能混凝土、塑料、纸张等复合材料的填充组分，具有降低成本、改善制品性能的作用。论述了矿物超细粉体的功能作用及产生原因和超细粉碎、表面改性技术。