Synthesis and properties of PDMS modified waterborne polyurethane–acrylic hybrid emulsion by solvent-free method

A new type of polysiloxane modified polyurethane–acrylic hybrid emulsion was synthesized by solvent-free method and the polysiloxane was introduced into the soft segment of polyurethane chains using dihydroxybutyl-terminated polydimethylsiloxane (PDMS). The formed film from the hybrid emulsion could provide obviously higher water-resistance property. The preparation technologies such as the content of carboxy group and acrylic monomer, the rate and the time of emulsification were discussed systematically. The chain structure and the particle size were confirmed by the analysis of Fourier transform infrared spectroscopy and transmission electron microscopy, respectively. The effect of PDMS content on the water resistance and the mechanical property were investigated by absorbed water ratio, water contact angle and dynamic mechanical measurement.     

An efficient parallel strategy for the perfect domination problem on distance-hereditary graphs

A graph is distance-hereditary if the distance stays the same between any of two vertices in every connected induced subgraph containing both. Two well-known classes of graphs, trees and cographs, both belong to distance-hereditary graphs. In this paper, we first show that the perfect domination problem can be solved in sequential linear-time on distance-hereditary graphs. By sketching some regular property of the problem, we also show that it can be easily parallelized on distance-hereditary graphs.     

Particle distribution and interfacial reactions of Al–7%Si–10%B<Subscript>4</Subscript>C die casting composite

Aluminum–boron carbide particle reinforced composite is an advanced material which can be used in applications such as neutron-shielding components, aircraft, and aerospace structures. In the microstructural characterization of an Al–7%Si–10%B₄C die casting, attention is particularly focused on particle distribution and interface reaction products between B₄C particles and the aluminum matrix. The quantitative analysis results show that, in a cross-section of the cast part, more particles concentrate in the center and fewer particles are present in the wall regions. Moreover, some particle segregation bands have been observed. The mechanisms of the particle migration are proposed to describe the phenomenon. However, the average particle fraction in any cross-section of the cast part is almost the same. A barrier layer consisting of several sublayers was detected on the surface of B₄C particles. Using electron diffraction in selected areas, it is found that these sublayers are composed of Al₃BC crystals, TiB₂ crystals, Si crystals, and coarse stick-shaped TiB₂ particles. In addition, it is observed that Si plays an important role in the formation of a dense barrier layer. The barrier layer can limit B₄C decomposition and improve B₄C stability in the aluminum melt.     

An empirical investigation of bias and variance in time series forecasting: modeling considerations and error evaluation

Bias and variance play an important role in understanding the fundamental issue of learning and generalization in neural network modeling. Several studies on bias and variance effects have been published in classification and regression related research of neural networks. However, little research has been done in this area for time-series modeling and forecasting. We consider modeling issues related to understanding error components given the common practices associated with neural-network time-series forecasting. We point out the key difference between classification and time-series problems in consideration of the bias-plus-variance decomposition. A Monte Carlo study on the role of bias and variance in neural networks time-series forecasting is conducted. We find that both input lag structure and hidden nodes are important in contributing to the overall forecasting performance. The results also suggest that overspecification of input nodes in neural network modeling does not impact the model bias, but has significant effect on the model variance. Methods such as neural ensembles that focus on reducing the model variance, therefore, can be valuable and effective in time-series forecasting modeling.     

Composite group vector space method for estimating melting and boiling point of pure organic compound

Composition Group Vector Space (CGVS) method for estimating melting and boiling point T _m , T _b of organic compound has been proposed, and the principle of this method has been elucidated. The models for estimating T _m , T _b have been established and the numerical values of relative parameters have been presented. The average percentage deviations of T _m , T _b estimation are 7.53 and 1.58, respectively, which show that the present method demonstrates significant improvement in applicability to predict the above properties, compared to conventional group methods.     

导数火焰原子吸收法对调味品中锌的测定

将微量脉冲进样技术和导数火焰原子吸收法结合，建立了测定调味品中锌含量的新方法。该方法的检出限为0．013μg／mL，精密度4．46％，回收率96．67～113．33。     

A Simple Quasi-2D Numerical Model of a Thermogage Furnace

A simple quasi-2D model for the temperature distribution in a graphite tube furnace is presented. The model is used to estimate the temperature gradients in the furnace at temperatures above which contact sensors can be used, and to assist in the redesign of the furnace heater element to improve the temperature gradients. The Thermogage graphite tube furnace is commonly used in many NMIs as a blackbody source for radiation thermometer calibration and as a spectral irradiance standard. Although the design is robust, easy to operate and can change temperature rapidly, it is limited by its effective emissivity of typically 99.5–99.8%. At NMIA, the temperature gradient along the tube is assessed using thermocouples up to about 1,500°C, and the blackbody emissivity is calculated from this. However, at higher operating temperatures (up to 2,900°C), it is impractical to measure the gradient, and we propose to numerically model the temperature distributions used to calculate emissivity. In another paper at this conference, the model is used to design an optimized heater tube with improved temperature gradients. In the model presented here, the 2-D temperature distribution is simplified to separate the axial and radial temperature distributions within the heater tube and the surrounding insulation. Literature data for the temperature dependence of the electrical and thermal conductivities of the graphite tube were coupled to models for the thermal conductivity of the felt insulation, particularly including the effects of allowing for a gas mixture in the insulation. Experimental measurements of the temperature profile up to 1,500°C and radial heat fluxes up to 2,200°C were compared to the theoretical predictions of the model and good agreement was obtained.     

两相管流工艺计算软件的研制

利用流体力学理论 ,结合目前国内外最新的两相流理论和实验研究成果 ,在描述气液两相流水力计算、热力计算和流型判断的数学物理模型基础上 ,研制出适用于海洋、沙漠、丘陵地区原油与伴生气、凝析天然气与凝液输送工艺的一套新的计算模拟软件TFTCS。介绍TFTCS软件的结构、功能、适用范围。用该软件对锦州 2 0 - 2海底湿天然气管道进行模拟计算 ,并与管线生产数据进行对比 ,用国外同类软件PIPEPHASE进行验算。实例计算分析表明 ,该软件功能完善 ,操作方便 ,计算结果可靠 ,能满足油气混输管道的设计计算和生产管理的需要