超高分子量聚乙烯纤维增强中空蜂窝模压复合材料性能研究

为研制车船等壳体所用的轻质、高强复合板材,选用超高分子量聚乙烯(UHMWPE)短纤维纱,制备成单层经纬为120根/(10 cm)的平纹组织,采用多组经纱持续更替交织层的方法制成2L(1+0)型、4L(2+1)型、6L(3+2)型3种多层角联锁结构织物,采用扦插芯棒、模压成型方法制成菱形蜂窝状的热固性环氧树脂基中空板,并与2块真空吸液法制成的面板组成“三合一”复合板,同时测定了复合板材的结构特征及其平拉、平压和弯曲性能。结果表明:3种类型复合板的密度均远小于水的密度,其中6L(3+2)型最小,为0.48 g/cm³;复合板层数越多,环氧树脂越难渗透尤其是在中空板菱形交叉点处,复合板平拉、平压、抗弯曲强度则呈现递增,制成的6L(3+2)型复合板试样平压强度可达到1.03 MPa。     

保健食品标准中重(装)量差异指标的分析及应用

近年来, 保健食品注册和备案技术指引要求保健食品制剂制订重量差异或装量差异指标, 如片剂等剂型应制订重量差异指标, 胶囊剂等剂型应制订装量差异指标, 以替代原先对包装食品通用的净含量与负偏差指标。在执行过程中, 检测机构众多检验结果显示有不少生产企业可能未能完全理解重量差异和装量差指标的具体分析算法和应用细节要求, 导致抽检结果与生产企业出厂检验结果不一致的情况。本文着重对重量差异和装量差异测定方法的质控目的、限度设定、计算与判定方法等进行详细解读, 以帮助相关企业更好地理解重量差异和装量差异的指标特性和分析应用要点, 阐明重量差异和装量差异指标与包装食品通用的净含量与负偏差指标的不同, 使规范制订保健食品标准并正确执行分析检验。     

Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems

ABSTRACT

Feature selection is an important task to improve the classifier’s accuracy and to decrease the problem size. A number of methodologies have been presented for feature selection problems using metaheuristic algorithms. In this paper, an improved self-adaptive inertia weight particle swarm optimisation with local search and combined with C4.5 classifiers for feature selection algorithm is proposed. In this proposed algorithm, the gradient base local search with its capacity of helping to explore the feature space and an improved self-adaptive inertia weight particle swarm optimisation with its ability to converge a best global solution in the search space. Experimental results have verified that the SIW-APSO-LS performed well compared with other state of art feature selection techniques on a suit of 16 standard data sets.     

The control of paracetamol particle size and surface morphology through crystallisation in a spray dryer

The parameters governing the crystallisation of paracetamol using various conventional techniques has been extensively studied, however the factors influencing the drug crystallisation using spray drying is not as well understood. The aim of this work was to investigate the crystallisation of an active pharmaceutical ingredient through evaporative crystallisation using a spray dryer to study the physicochemical properties of the drug and to use semi-empirical equations to gain insight into the morphology and particle size of the dried powder. Paracetamol solutions were spray dried at various inlet temperatures ranging from 60 °C to 120 °C and also from a series of inlet feed solvent compositions ranging from 50/50% v/v ethanol/water to 100% ethanol and solid-state characterisation was done. The size and morphology of the dried materials were altered with a change in spray drying parameters, with an increase in inlet temperature leading to an increase in particle Sauter mean diameter (from 3.0 to 4.4 µm) and a decrease in the particle size with an increase in ethanol concentration in the feed (from 4.6 to 4.4 µm) as a result of changes in particle density and atomised droplet size. The morphology of the dried particles consisted of agglomerates of individual crystallites bound together into larger semi-spherical agglomerates with a higher tendency for particles having crystalline ridges to form at higher ethanol concentrations of the feed.     

基于改进雷达图的配电系统电能质量评估方法

本文针对配电系统电能质量综合评估存在的评估指标权重分配过于主观或客观的问题,提出一种基于改进雷达图法的配电系统电能质量评估方法。该方法利用最小偏差组合权重法计算各评估指标的最优组合权重。并在传统雷达图的基础上对各指标轴间夹角值的确定和特征向量的选取两方面做了进一步的改进。首先采用扇形区域取代原始的三角形区域,并利用最小偏差组合权重法计算各指标轴间夹角值。其次选择新的特征向量构造评估函数对配电系统电能质量进行综合评估。最后通过对某钢铁厂棒材变电站10kV配电系统电能质量进行综合评估,验证了该方法的可行性和合理性。较传统雷达图而言,改进雷达图法解决了传统雷达图法中各指标间信息共用以及评估结果不唯一的问题,使评估结果更加准确合理。     

Length–weight relationships of five catfish species (Siluriformes) from the Ganga River,India

Knowledge of the length–weight relationships (LWRs) of fish is an important tool to understand fish body form, growth pattern, stock management and their conservation. The present study focused on investigating the length–weight relationships for five catfish species, Pachypterus atherinoides (Bloch, 1794), belonging to family Horabagridae; Batasio batasio (Hamilton, 1822) family Bagridae; Bagarius yarrelli (Sykes, 1839), family Gogangra viridescens (Hamilton, 1822); and Sisor rhabdophorus (Hamilton, 1822) belonging to family Sisoridae. Specimens were collected from the middle stretch of the Ganga River in India from November 2016 to May 2018. A total of 174 specimens of five fish species were collected, and their total lengths were measured to the nearest centimetre and the body weight to the nearest gram. The value of the parameter slope (b) of LWRs of the five species ranged from 2.86 (B. yarrelli) to 3.16 (G. viridescens), with a mean value of 2.99. The results of the present study documented the new maximum total length (TL) for P. atherinoides and S. rhabdophorus. The present study also provides the first reference regarding LWRs for S. rhabdophorus.     

考虑决策者时序偏好的时域证据融合方法

针对时域不确定信息的融合难题，为充分体现时域信息融合的动态性特点和时间因素对融合的影响，在证据理论的基础上，提出一种考虑决策者时序偏好的时域证据融合方法。首先将决策者对时序的偏好融入时域证据融合，通过分析时域证据序列的特点，在定义时序记忆因子的基础上，对决策者的时序偏好进行度量；然后通过构建优化模型求解时序权重，再结合证据信任度的概念，对证据源进行修正；最后利用Dempster组合规则对修正后的证据进行融合。数值算例表明，与没有考虑时间因素的融合方法相比，考虑决策者时序偏好的证据融合方法可以有效处理时域信息序列中的冲突信息，得到合理的融合结果；同时，所提方法充分考虑了时域证据序列的信任度和决策者的主观偏好，可以反映决策者主观因素对时域证据融合的影响，较好地体现了时域证据融合的动态性特点。     

化学微发泡成型外观表面技术研究

从汽车内饰轻量化方向考虑,研究了化学微发泡技术,从成核和气泡长大阶段进行研究,通过特殊的柔性后退core-back工艺,在充模过程中控制气泡的变形和破裂以保证成型制品的表面质量,实现化学微发泡成型制品无外观缺陷、质量减轻20%以上的目的。     

Input-to-state learning of recurrent neural networks with delay and disturbance

This article deals with the issue of input-to-state stabilization for recurrent neural networks with delay and external disturbance. The goal is to design a suitable weight-learning law to make the considered network input-to-state stable with a predefined -gain. Based on the solution of linear matrix inequalities, two schemes for the desired learning law are presented via using decay-rate-dependent and decay-rate-independent Lyapunov functionals, respectively. It is shown that, in the absence of external disturbance, the proposed learning law also guarantees the exponential stability of the network. To illustrate the applicability of the present weight-learning law, two numerical examples with simulations are given.