Covariation between reading and arithmetic skills from Grade 1 to Grade 7

This study examined the extent to which reading and arithmetic skills show covariation at Grade 1 and at Grade 7, to what extent this covariation is time-invariant or time-specific, and to what extent different antecedents will predict these time-invariant and time-specific portions of the covariation. The reading and arithmetic skills of a total of 1335 Finnish children were assessed at the end of Grade 1 and then again at the end of Grade 7. Phonological awareness, letter knowledge, rapid automatized naming (RAN), counting, and parental education levels were measured in kindergarten; working memory at Grade 1 and nonverbal reasoning at Grade 3. The results showed that reading and arithmetic had a substantial amount of covariation at grades 1 and 7, and that most of the covariation between these grades was time-invariant and could be predicted by RAN, counting, letter knowledge, working memory, and nonverbal reasoning. The time-specific portion of the covariation between reading and arithmetic in Grade 1 was predicted by phonological awareness, letter knowledge, and counting; while time-specific covariation in Grade 7 was predicted by parental education level and nonverbal reasoning.     

Multi-layer self-organizing polynomial neural networks and their development with the use of genetic algorithms

In this paper, we introduce a new architecture of genetic algorithms (GA)-based self-organizing polynomial neural networks (SOPNN) and discuss a comprehensive design methodology. Let us recall that the design of the “conventional” PNNs uses an extended group method of data handling (GMDH) and exploits polynomials (such as linear, quadratic, and modified quadratic functions) as well as considers a fixed number of input nodes (as being selected in advance by a network designer) at polynomial neurons (or nodes) located in each layer. The proposed GA-based SOPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional PNNs. The design procedure applied in the construction of each layer of a PNN deals with its structural optimization involving the selection of preferred nodes (or PNs) with specific local characteristics (such as the number of input variables, the order of the polynomial, and a collection of the specific subset of input variables) and addresses specific aspects of parametric optimization. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the network. To evaluate the performance of the GA-based SOPNN, the model is experimented with using chaotic time series data. A comparative analysis reveals that the proposed GA-based SOPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.     

交通运输行业低碳多式联运路径优化研究

交通运输行业是仅次于电力行业的第二大温室气体排放源，近年来各国对其“低碳化”发展的关注不断提高，运输产业如何承担相应的低碳减排责任，积极变革寻求一种绿色低碳的运输方式成为行业发展的热点问题。低碳多式联运作为一种环境友好型的运输模式，通过综合多种运输方式的技术经济优势可以有效降低运输过程中产生的碳排放量，并逐渐被各大运输公司所关注。本文基于成本和时间双重约束下，将二氧化碳排放量纳入到多式联运研究中，通过碳价函数将二氧化碳的排放量换算为二氧化碳的排放成本，形成考虑整个运输过程的总成本，并在设定时间约束下，建立低碳多式联运优化模型，采用遗传算法求解，得出最佳低碳多式联运组合方式和路线，研究对基于企业层面如何建立多重约束下的低碳联运模式进行探索，并提出了相应的政策建议，研究结论对于促进我国交通运输业供给侧低碳结构性改革具有参考意义。     

GSTP1基因Ile105Val多态性与最大运动状态下的心肺表现

人体谷胱苷肽S-转移酶在肺和心血管系统免疫反应方面扮演一定角色,不少研究已经显示谷胱苷肽S-转移酶P1(glutathione S-transferaseP1,GSTP1)基因Ile105Val多态性可能与肺疾病的发生发展相关联,而该多态是否影响正常人体心肺功能少见报道。选取198名健康汉族成年男性,首次对GSTPl基...     

自适应遗传模拟退火的Web日志关联挖掘

提出一种基于自适应遗传模拟退火策略的Web日志关联规则挖掘算法。该算法在遗传模拟退火策略基础上,引入自适应的交叉概率和变异概率,使其具有较强的全局搜索能力,有效地避免了早熟的现象。实验结果证明,该算法能有效地解决Web日志关联规则挖掘问题。     

基于改进遗传算法的GSM基站选址问题研究

本文首先对于罚函数遗传算法构造了合适的适应度计算方式,其次将适当的修补算子加入修补遗传算法中,保证修补的随机性和有效性;然后在两者的交叉、变异操作之后都加入进化突变算子,增强了他们的局部搜索能力;最后针对不同规模的基站选址问题,分别采用加入进化突变前后的罚函数遗传算法和修补遗传算法进行仿真,结果验证加入进化突变的修补遗传算法在求解大规模的基站选址问题时效率最高。     

Tackling the storage problem through genetic algorithms

The capability automated warehouse in of a company to implement an an optimized way might be nowadays a crucial leverage in order to gain competitive advantage to satisfy the demand. The order picking is a warehouse function that needs to deal with the retrieval of articles from their storage locations. Merging several single customer orders into one, a picking order can increase efficiency of warehouse operations. The aim of this paper is to define throughout the use of ad-hoc genetic algorithm （GA） how better a warehouse can be set up. The paper deals with order batching, which has a major effect on efficiency of warehouse operations to avoid wastes of resources in terms of processes and to control possibility of unexpected costs in advance.     

一种引入遗传算法的最大类间距离法在脑部图像分割中的运用

最大类间距离法已经被证明是一种较好的阈值化方法[1],但在用于双阈值或多阈值分割时存在实现效率较低等问题,而遗传算法高效的随机搜索能力正好能弥补其不足.根据脑部图像分为灰质、白质等三类的特点,通过把遗传算法引入最大类间距离法来实行对脑部图像的分割,实验验证取得了较好的分割效果.     

以学习为中心:学习设计的结构与层次——以小学阶段为例

学习设计作为学习范式落地的抓手,成为范式转型之际教育领域关注的重点研究话题。文章首先分析了学习设计的本质是关注认识发生和学习要素,并以此为基础梳理已有文献发现,当前学习设计对学习关注不足;其次,以“五维学习设计”为理论基础,阐释了学习设计的结构关系;最后,以“发生认识论”为学理基础,以小学阶段为例阐释了1~3年级和4~6年级两学段学习设计的层次:“物”经历“具体形象型”学习空间到“形象—抽象型”学习空间的变化,“人”经历主动学习到自主学习的变化,“事”经历序列化学习场景到关联化学习场景的变化,“境”经历原生之境到派生之境的变化,“脉”经历具体知识到知识专题的变化。     

连续型遗传算法和遗传规划在投影变换中的应用初探

回顾了地图投影变换的基本方法,阐述连续型遗传算法和遗传规划的原理和方法,探讨这两优化方法在地图投影变换中的应用.