The influence of light availability and predatory behavior of the decapod crustacean Nephrops norvegicus on the activity rhythms of continental margin prey decapods

The day–night cycle is one of the strongest geophysical cycles modulating species' behavioral rhythms. However, in deep-water continental margins, where light intensity decreases over depth, interspecific competition may alter behavioral responses to day–night cycles. The burrowing decapod crustacean Nephrops norvegicus is a large-size predator in benthic communities, exerting despotic territorial behavior. In this study, we analysed how the effect of light intensity cycles on decapod behavioral rhythms is reduced as one moves from shelves to slopes. In the Western Mediterranean, the predatory behavior and interspecific competition for substrate use of Nephrops increases moving from the shelf (100–110 m) to the slope (400–430 m). Vector fitting and generalized additive models were used to assess the effect of light intensity and behavioral rhythms of N. norvegicus on the temporal variation of prey decapods co-occurring in trawl tow catches carried out on the shelf and the slope during October 1999 and June 2000. The combination of diel variations in light intensity and N. norvegicus abundance influences the activity rhythms of prey decapods in a depth- and seasonal-dependent manner. Light modulation is stronger on the shelf and weaker on the slope, where Nephrops population size is greater. Although present regression analysis does not necessarily imply a direct cause–effect relationship between rhythms of predators and prey, we suggest that Nephrops alters the temporal patterning in the behavior of its prey on the slope, where light intensity is reduced. This alteration is stronger in endobenthic species than in benthopelagic species; the former rely on bottom substrate for the expression of behavioral rhythms, experiencing stronger interspecific competitions with Nephrops at time of activity.     

非线性效应对浅水水波变形的影响

本文采用波数矢量无旋和波能守恒方程建立了一个考虑非线性作用的浅水水波变形数值模型,模型中采用Ｂａｔｔｊｅｓ关系与波数矢量无旋,波能守恒方程一起来求解波浪在浅水中变形的波浪要素,在波能守恒方程中考虑了底摩擦的影响。利用本文提出的数值模型对一个斜坡浅滩水域波浪折射绕射现象进行了验证,验证计算中用一个非线性经验弥散关系近似浅水水波变形的非线性效应并与用线性弥散关系的计算结果进行了比较,结果说明使用非线性     

水下圆形浅滩附近波浪绕射的计算

采用波数矢量无旋和波能守恒方程对圆形浅滩附近水域波浪绕射进行了数值计算,计算模型中采用Ｂａｔｔｊｅｓ关系与波数矢量无旋,波能量守恒方程一起联合求解圆形浅滩附近水域波浪折射影响下的波浪要素。本文的数值计算模型对圆形浅滩水域波浪折射绕射现象的验证结果表明,计算所得结果与试验结果是吻合的,数学模型是可靠和合理的,具有实用价值。     

流速逆变张量隐式求解方法及其在航道港池流场计算中的应用

运用高分辨率的边界适应网格进行流体动力学数值计算时,如何提高计算稳定性和减少计算量成为数值求解的关键性问题.在非正交的边界适应坐标系中,每个动量方程中同时出现了两个交叉方向的水位偏导数项,给隐式求解带来困难,而显式格式下的时间步长由于受与空间步长有关的Courant-Friedrichs-Lewy条件限制,计算量成倍增加.本文从广义曲线坐标系下浅海动力学方程组出发,导出了流速的逆变张量所满足的动量方程组,使方程中的水位偏导数项变成了沿某一协变基向量方向占优的形式,方便地采用了交替方向隐式差分格式,从而提高了计算稳定性并减小了计算量.本文通过对澳门海域航道和港池中流场的计算,证实了该模式是一种进行高分辩率数值计算的有效方法.     

个性化大地测量报表服务的实现

报表服务是大地测量信息系统的一项基本功能,但往往会因为格式的微小调整而导致系统的整体重构。首先分析了当前报表生成方法的缺点,提出了基于XML相关技术生成报表的新思路;然后介绍了以XML为基本语法的统一数据交换接口,屏蔽了各种报表数据源的差异;重点讨论了如何运用XSL技术将接口文件变换成通用的HTML,并对报表中的图形信息用SVG进行描述,从而将报表转换成通用浏览器可以识别的信息模型;最后对此技术进行了实践。通过在系统外部更改报表样式文件,能够实现报表服务的个性化,增强系统的扩展性。     

利用模板阴影体算法实现矢量数据在三维场景中的绘制

提出了一种基于模板阴影体算法的矢量数据绘制方法,实现了矢量数据在三维场景中的高质量实时叠加显示。由于该方法基于屏幕空间,所以具有像素级的精度,不会出现传统的基于纹理方法所产生的绘制走样现象;而且不受地形几何数据的约束,其执行效率与地形数据的复杂度无关,仅取决于矢量数据本身的复杂度。详细论述了基于模板阴影体算法矢量数据绘制的关键技术,并通过试验验证了该方法的有效性。     

海底双相随机介质声反射的SVM神经网络分类识别研究

考虑海底沉积介质为双相介质,为了更好地模拟实际海底底质的不均匀性,将随机介质理论引入双相介质理论。首先,通过基于随机-双相介质理论的高阶有限差分数值技术模拟计算海底底质分别为泥质砂、泥、泥质砾时的地震反射波信号。然后利用小波变换分别求取不同底质的一次反射波的包络作为其特征向量,最后利用基于粒子群智能算法优化的支持向量机神经网络对这些反射波信号进行分类识别。为了进一步考察所用方法的抗噪能力,对正演得到的海底底质反射波信号分别加入10%、30%、50%的高斯白噪音之后再进行分类,支持向量机仍然取得了较好的分类预测效果。基于上述正演模拟及分类识别方法的论证,提出了一套行之有效的微机软件模拟海底沉积物分类识别的一般化流程,这将有利于开展海底沉积物反射特征的进一步研究。     

运用GD-PBIBD编码的矢量空间数据数字指纹算法

针对矢量空间数据叛逆者追踪难及编码效率低的问题,本文提出了一种运用GD-PBIBD码的指纹算法。首先运用GDPBIBD构造指纹编码,将待嵌入指纹序列运用Logistic映射置乱,通过D-P算法提取矢量空间数据的特征点,对特征点实施DFT变换得到相位系数和幅度系数;然后运用QIM方法将指纹嵌入DFT变换域的幅度系数上;最后应用DFT逆变换得到含指纹的矢量空间数据。试验选取部分中国路网数据和某区域绿地数据,运用该算法嵌入指纹后,对其进行线性和非线性模拟攻击,成功提取指纹序列,利用汉明距离追踪出合谋者。该算法编码构造简单,在码长一定的情况下,较BIBD码可容纳更多的用户,效率高;算法抗单用户指纹攻击及多重攻击的稳健性较好,也能够抵抗多用户最小值和最大最小值攻击,追踪到所有叛逆者,可用于矢量空间数据版权保护,为矢量空间数据叛逆者追踪提供依据。     

Site characterization model using least‐square support vector machine and relevance vector machine based on corrected SPT data (Nc)

Statistical learning algorithms provide a viable framework for geotechnical engineering modeling. This paper describes two statistical learning algorithms applied for site characterization modeling based on standard penetration test (SPT) data. More than 2700 field SPT values (N) have been collected from 766 boreholes spread over an area of 220 sqkm area in Bangalore. To get N corrected value (N_c), N values have been corrected (N_c) for different parameters such as overburden stress, size of borehole, type of sampler, length of connecting rod, etc. In three‐dimensional site characterization model, the function N_c=N_c (X, Y, Z), where X, Y and Z are the coordinates of a point corresponding to N_c value, is to be approximated in which N_c value at any half‐space point in Bangalore can be determined. The first algorithm uses least‐square support vector machine (LSSVM), which is related to a ridge regression type of support vector machine. The second algorithm uses relevance vector machine (RVM), which combines the strengths of kernel‐based methods and Bayesian theory to establish the relationships between a set of input vectors and a desired output. The paper also presents the comparative study between the developed LSSVM and RVM model for site characterization. Copyright © 2009 John Wiley & Sons, Ltd.