Refractive index of soft contact lens materials measured in packaging solution and standard phosphate buffered saline and the effect on back vertex power calculation

PurposeTo measure the refractive index (RI) of commonly available soft contact lens (CL) materials, their packaging solutions and compare to the manufacturers’ nominal RI. The relationship between RI versus water content, and the effect of inaccurate RI when converting lens power measured in solution to in-air back vertex power were examined.MethodsThe RI of 18 single vision soft CL materials were measured using CLR 12–70 digital refractometer. Three lenses of each material were measured, in their packaging solution and then after soaking in standard phosphate buffered saline (PBS). The RIs of packaging solution were also measured. Accuracy requirements for correct wet to dry power conversion based on thick lens formula were projected.ResultsThe standard deviation between three samples was less than 0.005. The measured RI ranged from 1.3744 ± 0.001–1.4265 ± 0.0004 for PBS soaked and from 1.3739 ± 0.0003–1.4264 ± 0.0024 for packaging solution soaked materials. Comparing nominal with mean measured PBS and packaging solution RIs, 5 and 3 lens materials, respectively, fell outside ISO tolerance. The packaging solution RI of DailiesAquaComfortPlus had the largest difference of 0.0040, compared to RI of standard PBS. For converting lens power measured in PBS to in-air power, the difference between measured and nominal RI of 0.0104 would result in wrongly calculated in-air power 0.99 D for a -6.00 D lens.ConclusionThe CLR 12–70 is reliable and accurate refractometer for the measurement of soft CL materials. Accurate RI measurements are of relevance with increased use of wavefront sensors to measure lens power while they are immersed in solution. Even small errors in solution or material RI can lead to significant errors in converted in-air power. To obtain valid in-air lens power results, measurement conditions must match the material and solution RIs used for the conversion.     

A fuzzy irregular cellular automata-based method for the vertex colouring problem

ABSTRACT

Vertex colouring is among the most important problems in graph theory which has been widely applied across different real-world problems. In vertex colouring problem (VCP), the goal is to assign a distinct colour to each vertex of the graph in such a way that no two adjacent vertices have the same colour. This paper presents a fuzzy irregular cellular automaton (FICA) for finding a near-optimal solution for the VCP. FICA is an extension fuzzy cellular automaton (FCA) in which the cells of the automaton can be arranged in an irregular structure. The aim of the proposed method is to reap the benefits of both FCA and irregular cellular automata while minimising their drawbacks. To evaluate the proposed method, various computer simulations have been conducted on a variety of graphs. The results suggest that the proposed method is able to achieve better results in terms of the minimum number of required colours and the execution time of the algorithm, compared to other peer algorithms.     

混合实验法在W-Ni-Cu合金组分设计中的应用

采用极端顶点设计法设计W-Ni-Cu合金组分,将组分自变量与相对应的性能因变量(相对密度、显微硬度、抗弯强度)进行回归分析和规划求解,同时采用冷压烧结法制备不同组分的W-Ni-Cu合金,测定合金的密度、显微硬度和力学性能,研究组分对合金性能的影响。结果表明:回归方程复相关系数R~2=1,方程精确度高;合金性能随组分变化而呈规律变化;当Ni含量与Cu含量(均为质量分数)分别为3%和5%时,合金的综合力学性能最佳:相对密度为94.295%、显微硬度286.55、抗弯强度931.51 MPa。W-Ni-Cu合金的相对密度计算值与实验结果的误差为-0.45%~0.06%,显微硬度计算值与实验结果的误差为-8.48%~4.46%,抗弯强度计算值与实验结果的误差为-5.19%~4.15%。误差很小,说明混合实验和极端顶点设计法能优化W-Ni-Cu合金组分,并可靠预测合金性能。     

水基液体磁性磨具复合分散剂配方实验研究

为提高水性液体磁性磨具的稳定性,制备以六偏磷酸钠与聚丙烯酸(PAA)为表面活性剂、纳米二氧化硅为辅助剂的复合分散剂。通过Minitab软件的DOE实验设计模块,以水基液体磁性磨具的沉淀率和零场黏度为评价指标,采用极端形顶点设计复合分散剂的配方并实验研究复合分散剂各组分对沉淀率和零场黏度的影响。结果表明:六偏磷酸钠与PAA之间存在协同作用,纳米二氧化硅对水基液体磁性磨具的零场黏度起到重要作用,三者能够很好地提高水基液体磁性磨具的稳定性。综合考虑沉降率和零场黏度,确定复合分散剂的最佳配比,并通过实验验证了预测值的准确性。     

基于顶点索引的三维建模与可视化方法研究

分析了影响三维建模与可视化效率的主要因素,设计了一种新的基于顶点索引的三维模型数据组织方式,相对于传统的3DS模型文件,该方法避免了大量冗余数据的处理,并能充分发挥图形显卡高效的数据传输特性,经验证:显著提高了模型绘制的效率,较好地解决了复杂模型高效三维可视化的问题。     

A result on certain coset subalgebras of the βγ-system

In this paper, for the highest weight module V4 of sl（2,C） with the highest weight 4, we describe subalgebras Sβ（V4）＋ and Sγ（V4）＋ of the βγ-system coset S（V4）＋ by giving their generators. These eoset subalgebras are interesting, new examples of strongly f     

A kind of conditional vertex connectivity of Cayley graphs generated by 2-trees

Let G be a graph. Then T ⊆ V(G) is called an R^k-vertex-cut if G − T is disconnected and each vertex in V(G) − T has at least k neighbors in G − T. The size of a smallest R^k-vertex-cut is the R^k-vertex-connectivity of G and is denoted by κ^k(G). In this paper, we determine the numbers κ¹ and κ² for Cayley graphs generated by 2-trees, including the popular alternating group graphs.     

Depth-based complexity traces of graphs

In this paper we aim to characterize graphs in terms of a structural measure of complexity. Our idea is to decompose a graph into layered substructures of increasing size, and then to measure the information content of these substructures. To locate dominant substructures within a graph, we commence by identifying a centroid vertex which has the minimum shortest path length variance to the remaining vertices. For each graph a family of centroid expansion subgraphs is derived from the centroid vertex in order to capture dominant structural characteristics of the graph. Since the centroid vertex is identified through a global analysis of the shortest path length distribution, the expansion subgraphs provide a fine representation of a graph structure. We then show how to characterize graphs using depth-based complexity traces. Here we explore two different strategies. The first strategy is to measure how the entropies on the centroid expansion subgraphs vary with the increasing size of the subgraphs. The second strategy is to measure how the entropy differences vary with the increasing size of the subgraphs. We perform graph classification in the principal component space of the complexity trace vectors. Experiments on graph datasets abstracted from some bioinformatics and computer vision databases demonstrate the effectiveness and efficiency of the proposed graph complexity traces. Our methods are competitive to state of the art methods.