相转移催化氧化合成对硝基苯甲酸

考察了以KMnO4为氧化剂,相转移催化氧化合成对硝基苯甲酸的有关影响因素．结果表明十六烷基三丁基溴化磷的催化效果最好,收率达76.6％;以价廉易得的季铵盐A-1为氧化剂,能进行缓和反应,且收率提高,达74.3％．此外,KMnO4的自身分解严重影响它对底物的氧化作用,由此确定反应以在中性介质中发生为宜．     

环境友好型脱硫醇催化剂的研制

在脱硫醇反应中采用微波分散MgO的方法制备固体碱以替代传统工艺中的液体碱。通过甲醇助剂的使用可以明显地提高催化剂的活性和寿命。研究表明，催化剂性能的提高和甲醇改善催化剂表面的浸润状况，与促进磺化酞菁钴在水相中的分散有关。     

可见光诱导的光氧化还原催化的CB键氧化反应

利用可见光催化的氧气参与的烯基硼酸的CB键氧化断裂,成功实现了多取代醛、酮等羰基化合物的制备.该反应适用于芳基烯基硼酸、烷基烯基硼酸、直链的烯基硼酸以及环状的烯基硼酸,为羰基化合物的合成提供了一条新的途径.此外,有机染料也可以成功催化该类CB键氧化反应,均能高收率的得到相应的羰基化合物(收率高达86%).     

光化学催化氧化法处理含硫废水的研究

采用溶胶 -凝胶法制备了TiO2 薄膜催化剂 ,实现了催化与分离的一体化。同时采用了UV H2 O2 TiO2 工艺对高浓度的含硫废水进行了处理研究 ,结果表明 ,在反应温度为 30℃ ,每升废碱液投加H2 O2 (体积分数为 30 % ) 10mL L ,反应时间为 180min等条件下 ,硫化物的去除率可达 95 %以上 ,显示了该工艺良好的应用前景     

生物活性物质--低聚半乳糖

介绍了生物活性物质-低聚半乳糖的生理功能及生产方法,并对其在食品工业的应用与发展前景进行了展望.     

PTC法以苯为原料电化学氧化合成对苯醌的研究

报道以苯为原料，H2SO4，NaSO4为支持电解质，采用PTC法，在铅基二氧化铅阳极上电化学氧化合成对苯醌的方法和合适工艺条件的研究。实验结果表明这是一条很有潜力的工艺路线。     

微电解--催化氧化组合工艺处理染料废水的探讨

用微电解--催化氧化组合工艺对染料废水处理试验结果表明氧化剂二氧化氯在催化剂作用下将染料废水中有机物氧化分解,可使废水中CODcr去除率达70%,色度去除率达95%,染料废水经此法处理后继续生化处理,其出水可达排放指标.     

Stochastic sensors inspired by biology

Sensory systems use a variety of membrane-bound receptors, including responsive ion channels, to discriminate between a multitude of stimuli. Here we describe how engineered membrane pores can be used to make rapid and sensitive biosensors with potential applications that range from the detection of biological warfare agents to pharmaceutical screening. Notably, use of the engineered pores in stochastic sensing, a single-molecule detection technology, reveals the identity of an analyte as well as its concentration.     

受证据理论启发的传感器数据融合算法

受证据理论的启发,提出了一种新的传感器数据融合算法.借鉴证据理论中的基本信任分配思想和证据组合思想,将所有测量值构成的集合视为辨识框架,并对各个测量值的精度进行判断,在此基础上将各个测量值分别转换为相应的证据;再利用基于冲突分配法的证据组合规则对所有证据进行组合,所得合成证据的Mass函数即为各个测量值的权值分配函数;...     

基于危险模式免疫算法模型

本文阐述了危险模式的基本概况及运行机理,提出了一种新型的基于危险模式的免疫算法模型,并探讨了基于危险理论免疫算法的应用领域。     

Immunodominance and clonal selection inspired multiobjective clustering

The biological immune system is a highly parallel and distributed adaptive system. The information processing abilities of the immune system provide important insights into the field of computation. Based on immunodominance in the biological immune system and the clonal selection mechanism, a novel data mining method, Immune Dominance Clonal Multiobjective Clustering algorithm (IDCMC), is presented. The algorithm divides an individual population into three sub-populations according to three different measurements, and adopts different evolution and selection strategies for each sub-population. The update of each sub-population, however, is not carried out in isolation. The periodic combination operation of the analysis of the three sub-populations represents considerable advantages in its global search ability. The clustering task is a multiobjective optimization problem, which is more robust with respect to the variety of cluster structures of different datasets than a single-objective clustering algorithm. In addition, the new algorithm can determine the number of clusters automatically, which should identify the most promising clustering solutions in the candidate set. The experimental results, using artificial datasets with different manifold structure and handwritten digit datasets, show that the IDCMC outperforms the PESAII-based clustering method, the genetic algorithm-based clustering technique and the original K-Means algorithm in solving most of the problems tested.     

基于危险模式免疫算法模型

本文阐述了危险模式的基本概况及运行机理,提出了一种新型的基于危险模式的免疫算法模型,并探讨了基于危险理论免疫算法的应用领域.     

固体超强酸催化剂研究综述

固体超强酸的性能独特,在工业上有很好的应用前景,本身又是一种无污染的催化剂。SO42-/MxOy型固体催化剂具有超强酸,MxOy包括TiO2,ZrO2,Fe2O3,本文详细介绍了固体超强酸,尤其是SO42-/MxOy型的表面结构特征和制备条件对其性能的影响,以及在化学反应中的催化作用。     

群体组织启示下的粒子群算法改进

从社会学的视角分析粒子群的组织结构,试图通过增强群体组织管理来防止算法的早熟收敛.借鉴社会管理学中发挥个体能动性和规范成员行为并重的管理理念,构造基于团队式管理的粒子群算法.使用标准测试函数对该算法进行仿真实验,并用正交试验法进行了参数优化.实验结果表明,相对现有的一些改进方案,文中提出的改进算法简单易实现,在一些问题求解上表现出较好的寻优和收敛性能.     

一种由人类步行启发的半被动双足步行机器人

针对半被动双足步行机器人所采用的控制方法大多较为复杂,并且缺乏控制参数对机器人性能影响的定量分析,从人类步行的生物力学研究得到启发,提出一种半被动双足步行机器人的控制方法,并定量分析该控制方法各参数对机器人性能的影响.在机器人摆动腿与地发生碰撞后开始于髋关节处施加方波力矩,作为动力输入.采用步态敏感范数衡量机器人的稳定性,采用无量纲步行能耗衡量机器人的步行效率.以稳定性、效率和步行速度作为机器人性能评价指标,通过仿真得到力矩、力矩作用时间和斜坡角度3个参数对机器人性能的影响.由于力矩在1个步行周期中没有做负功,该机器人具有与人类步行相似的高能量效率.该机器人能够实现沿上坡、下坡和平地步行.     

Immune secondary response and clonal selection inspired optimizers

The immune system's ability to adapt its B-cells to new types of antigen is powered by processes known as clonal selection and affinity maturation. When the body is exposed to the same antigen, immune system usually calls for a more rapid and larger response to the antigen, where B cells have the function of negative adjustment. Based on the clonal selection theory and the dynamic process of immune response, two novel artificial immune system algorithms, secondary response clonal programming algorithm (SRCPA) and secondary response clonal multi-objective algorithm (SRCMOA), are presented for solving single and multi-objective optimization problems, respectively. Clonal selection operator (CSO) and secondary response operator (SRO) are the main operators of SRCPA and SRCMOA. Inspired by the clonal selection theory, CSO reproduces individuals and selects their improved maturated progenies after the affinity maturation process. SRO copies certain antibodies to a secondary pool, whose members do not participate in CSO, but these antibodies could be activated by some external stimulations. The update of the secondary pool pays more attention to maintain the population diversity. On one hand, decimal-string representation makes SRCPA more suitable for solving high-dimensional function optimization problems. Special mutation and recombination methods are adopted in SRCPA to simulate the somatic mutation and receptor editing process. Compared with some existing evolutionary algorithms, such as OGA/Q, IEA, IMCPA, BGA and AEA, SRCPA is shown to be able to solve complex optimization problems, such as high-dimensional function optimizations, with better performance. On the other hand, SRCMOA combines the Pareto-strength based fitness assignment strategy, CSO and SRO to solve multi-objective optimization problems. The performance comparison between SRCMOA, NSGA-II, SPEA, and PAES based on eight well-known test problems shows that SRCMOA has better performance in converging to approximate Pareto-optimal fronts with wide distributions.