Network theory of the nonlinear behaviour of polymer melts. Simple shear flow

Summary The network model developed in a previous paper is applied to the simple shear flow of polymer melts. The constitutive equation obtained consists of two terms. One of them describes the stress due to the network strands which exist at the onset of the deformation, dissociate during the deformation and result in a single integral constitutive equation with a strain dependent damping function. The formulation of the damping function in invariant form seems to be almost impossible.The second normal stress differenceN ₂ of the model is not zero,but has negative values. According to our model this is a consequence of the deformation dependence of the disentanglement process. The theory is compared with experimental data for a LDPE melt. It is found that the model explains the main features of the shear flow behaviour of the LDPE melt investigated preciously.

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Zusammenfassung Das Netzwerk-Modell, das in einer vorangegangenen Arbeit entwickelt wurde, wird für die einfache Scherströmung von Polymerschelzen angewendet. Die abgeleitete rheologische Zustandsgleichung besteht aus zwei Gliedern. Das erste beschreibt die Spannung-Dehnung-Beziehung der Kettensegmente, die zu Beginn der Deformation existieren und während der Deformation aufgelöst werden. Es hat die Form der einfachen Integralbeziehung mit einer Gedächtnisfunktion. Es ist kaum möglich, die dabei erhaltene Gedächtnisfunktion als Funktion der Invarianten der der Tensoren darzustellen. Die zweite Normal-SpannungsdifferenzN ₂ des Modells ist nicht Null und hat einen negativen Wert. Dies ist nach unserem Modell eine Folge der Deformationsabhängigkeit des Entschlaufungsprozesses. Die Theorie wird mit dem experimentellen Daten für eine LDPE-Schmelze verglichen, wobei sich zeigt, daß das Modell die wesentlichen Merkmale des Scherverhaltens der LDPE-Schmelze gut erklärt.

     

Recent developments on creation of artificial metalloenzymes

Organic synthesis using biocatalysts has been developed over many years and is still a prominent area of research. In this context, various hybrid biocatalysts composed of a synthetic metal complex catalyst and a protein scaffold (i.e. “artificial metalloenzymes”) have been constructed. One of the most recent research areas in biocatalysts-mediated synthesis is CC bond/cleavage, the most important type of reaction in organic chemistry. Some of the artificial enzymes were applied to in-cell reactions as well as in vitro systems. The effects of the structural fluctuation in biomacromolecules on their functions have also been realized. This review article includes recent research examples of artificial metalloenzymes used to CC bond formation/cleavage. As a perspective, we also focus on how we apply protein dynamics factor for the creation of new generation artificial metalloenzymes.     

人工神经元网络辅助丙烷氨氧化催化剂设计

报道了利用人工神经元网络辅助丙烷氨氧化制丙烯腈催化剂设计的研究进展结果．通过筛选得到了可以拟合该反应体系的神经元网络模型，从而使丙烷转化率和丙烯腈选择性可以表示为催化剂组成的函数，利用培训后的神经元网络模型所得到的权值文件，结合作者自编的优化程序，该网络可用于预测最优的催化剂组成．实验得到丙烯腈收率和选择性可分别达到４３．０％和５６．２４％．     

Application of Short-wave Near-infrared Reflectance Spectroscopy in Controlling Extract of Fructus Cnidii Using Supercritical Carbon Dioxide

Fructus cnidii （Chinese name shechuangzi） is the fruit produced by Cnidium monnieri （L.） Cusson （Umbelliferae）. It is a perennial herb that is used to treat skin-related diseases and gynecopathyell. Recent pharmacological studies have revealed crude extracts or components isolated from fructus cnidii possess antiallergic, antipruritic, antidermatophytic, antibacterial, antifungal, and antiosteoporotic activities. Osthole and imperatorin are the major compounds present in shechuangzi. They are often used as standards for the evaluation of the quality of shechuangzi products.     

神经网络法用于多元混酸同时测定

利用多层神经网络误差反向传播算法处理酸碱电位滴定数据，求得出多元混合酸各组分的浓度，优化了神经网络的结构和参数，测定了三组分有机酸混合样品，结果良好，平均相对偏差ＲＳＤ≤４％。     

Unraveling the mystery of efficacy in Chinese medicine formula: New approaches and technologies for research on pharmacodynamic substances

Traditional Chinese medicine (TCM) is the key to unlock treasures of Chinese civilization. TCM and its compound play a beneficial role in medical activities to cure diseases, especially in major public health events such as novel coronavirus epidemics across the globe. The chemical composition in Chinese medicine formula is complex and diverse, but their effective substances resemble “mystery boxes”. Revealing their active ingredients and their mechanisms of action has become focal point and difficulty of research for herbalists. Although the existing research methods are numerous and constantly updated iteratively, there is remain a lack of prospective reviews. Hence, this paper provides a comprehensive account of existing new approaches and technologies based on previous studies with an in vitro to in vivo perspective. In addition, the bottlenecks of studies on Chinese medicine formula effective substances are also revealed. Especially, we look ahead to new perspectives, technologies and applications for its future development. This work reviews based on new perspectives to open horizons for the future research. Consequently, herbal compounding pharmaceutical substances study should carry on the essence of TCM while pursuing innovations in the field.     

Modeling of the relationship between electroosmotic flow and separation parameters in capillary zone electrophoresis using artificial neural networks and experimental design

The prediction of migration time of electroosmotic flow (EOF) marker was achieved by applying artificial neural networks (ANN) model based on principal component analysis (PCA) and standard normal distribution simulation to the input variables. The voltage of performance, the temperature in the capillary, the pH and the ionic strength of background electrolytes (BGE) were applied as the input variables to ANN. The range of the performance voltage studied was from 15 to 27 kV, and that of the temperature in the capillary was from 20 to 30 °C. For the pH values studied, the range was from 5.15 to 8.04. The range of the ionic strength investigated in this paper was from 0.040 to 0.097. The prediction abilities of ANN with different pre-processing procedure to the input variables were compared. Under the same performance conditions, the average prediction error of the migration time of the EOF marker was 5.46% with RSD = 1.76% according to 10 parallel runs of the optimized ANN structure by the proposed approach, and that of the 10 parallel predictions of the optimal ANN structure for the different performance conditions was 12.95% with RSD = 2.29% according to the proposed approach. The study showed that the proposed method could give better predicted results than other approaches discussed.     

Entrapment and release of sodium polystyrene sulfonate (SPS) from calcium alginate gel beads

The release of sodium polystyrene sulfonate (SPS) from calcium alginate hydrogel beads has been studied. It has been shown that the structure of the cross-linked calcium alginate network is of primary importance in the retention and/or release of the SPS. This has been evidenced by studying the influence of Ca²⁺ concentration, molar masses (M_n) and the ratio of mannuronic acid/guluronic acid components. A minimum in the SPS release is observed in relation with the organization of the network structure. Conditions inducing the organization of a strong gel (e.g. high Ca²⁺ concentration for example) are not always related to a low release. A good control of release is found when a compromise between a well-structured hydrogel and sterical consideration of SPS is reached.     

Cost operator algorithms for the transportation problem

A primal transportation algorithm is devised via post-optimization on the costs of a modified problem. The procedure involves altering the costs corresponding to the basic cells of the initial (primal feasible) solution so that it is dual feasible as well. The altered costs are then successively restored to their true values with appropriate changes in the optimal solution by the application of cell or area cost operators discussed elsewhere. The cell cost operator algorithm converges to optimum within (2T – 1) steps for primal nondegenerate transportation problems and [(2T + 1) min (m, n)] – 1 steps for primal degenerate transportation problems, whereT is the sum of the (integer) warehouse availabilities (also the sum of the (integer) market requirements) andm andn denote the number of warehouses and markets respectively. For the area cost operator algorithm the corresponding bounds on the number of steps areT and (T + 1) min (m, n) respectively.This report was prepared as part of the activities of the Management Sciences Research Group, Carnegie—Mellon University, under Contract N00014-67-A-0314-0007 NR 047-048 with the U.S. Office of Naval Research.     

Optimization of the Microwave-Assisted Saponification and Extraction of Organic Pollutants from Marine Biota Using Experimental Design and Artificial Neural Networks

Several improvements in sample pretreatment for the determination of organic pollutants (i.e. n-alkanes, polycyclic aromatic hydrocarbons, PAHs) in marine biota (mussels) are presented. The use of liquid nitrogen and homogenization of the samples are shown to be an alternative to the time consuming liophilization step required for the analysis of biota samples. Microwave-assisted hydrolysis and extraction are combined to isolate organic pollutants (19 n-alkanes and 27 PAHs) from biota matrices. Experimental design (ED) and artificial neural networks (ANNs) were used to optimize the experimental conditions. NIST-CRM 2978 was used to test the validity of the developed method which shows a good agreement with certified values.