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.     

Systematic marine conservation planning in data-poor regions: Socioeconomic data is essential

Systematic planning for conservation is highly regarded but relies on spatially explicit data that are lacking in many areas of conservation concern. The decision support tool Marxan is applied to a reef system in the central Philippines where 30 marine protected areas (MPAs) have been established in communities without much use of biophysical data. The intent was to explore how Marxan might assist with the legally required expansion to protect 15% of marine waters, and how existing MPAs might affect that process. Results show that biophysical information alone did not provide much guidance in identifying patterns of conservation importance in areas where the data are poor. Socioeconomic data were needed to distinguish among possible areas for protection; but here, as elsewhere in marine environments, the availability of such data was very limited. In the final analysis, local knowledge and integrated understanding of socioeconomic realities may offer the best spatially explicit information. The 30 existing MPAs, which encompassed a small proportion of the reef system, did not limit future options in developing a suite of MPAs on a broader scale. Rather, they appeared to generate the support for MPAs that is obligatory for any larger zoning effort. In summary, establishing MPAs based on community-driven criteria has biological and social value, but efforts should be made to collect ecological and socioeconomic data to guide the continued creation of MPAs.     

The new wave overtopping database for coastal structures

Significant effort has been made to generate a homogeneous database on wave overtopping consisting of more than 10,000 irregular wave overtopping tests from more than 160 independent projects or test series, each described by means of 31 parameters. Many coastal structures, including dikes, rubble mound breakwaters, berm breakwaters, caisson structures and combinations have been considered and have been schematised for inclusion in the database. All these overtopping tests are represented by over 300,000 numbers in the database.     

Mixed layer depth climatology of the North Pacific based on Argo observations

A monthly mean climatology of the mixed layer depth (MLD) in the North Pacific has been produced by using Argo observations. The optimum method and parameter for evaluating the MLD from the Argo data are statistically determined. The MLD and its properties from each density profile were calculated with the method and parameter. The monthly mean climatology of the MLD is computed on a 2° × 2° grid with more than 30 profiles for each grid. Two bands of deep mixed layer with more than 200 m depth are found to the north and south of the Kuroshio Extension in the winter climatology, which cannot be reproduced in some previous climatologies. Early shoaling of the winter mixed layer between 20–30°N, which has been pointed out by previous studies, is also well recognized. A notable feature suggested by our climatology is that the deepest mixed layer tends to occur about one month before the mixed layer density peaks in the middle latitudes, especially in the western region, while they tend to coincide with each other in higher latitudes.     

船体可靠性评估

为了研究不同结构船体的寿命和使用时可靠度的关系,提出了一种基于试验的可靠度估计方法.针对不同结构船体寿命服从Weibull分布,在无失效数据情形下、置信度至少为γ和寿命为t时,根据船体寿命试验数据,给出了可靠度的单侧置信下限.该方法是在Weibull分布形状参数下限已知的情况下,能将所有的无失效寿命数据进行累加,大大提高了可靠性分析的精度.最后,对钢结构、钛合金结构、铝合金结构的船体,在寿命为t月、置信度至少为γ时,计算了3种结构船体的可靠度单侧置信下限.结果表明该方法易于计算,便于工程应用.     

春晓气田泥岩垮塌测井数据校正及应用

由于存在部分泥岩井眼垮塌,东海春晓气田测井曲线砂泥岩阻抗部分叠置。针对这一现象,采用未垮塌段泥岩伽马与密度拟合关系校正垮塌段泥岩密度,声波时差经本区伽德那公式拟合后求取。通过泥岩垮塌测井数据校正前后对比、直方交会、岩石物理、正演及反演效果分析,认为校正后的测井数据比较真实地反映垮塌段扩径泥岩地层特征,储层与围岩波阻抗差异明显,为储层反演、砂体空间展布分析提供了有力依据。     

Extending self-organizing maps for supervised classification of remotely sensed data

An extended self-organizing map for supervised classification is proposed in this paper. Unlike other traditional SOMs, the model has an input layer, a Kohonen layer, and an output layer. The number of neurons in the input layer depends on the dimensionality of input patterns. The number of neurons in the output layer equals the number of the desired classes. The number of neurons in the Kohonen layer may be a few to several thousands, which depends on the complexity of classification problems and the classification precision. Each training sample is expressed by a pair of vectors ： an input vector and a class codebook vector. When a training sample is input into the model, Kohonen＇s competitive learning rule is applied to selecting the winning neuron from the Kohouen layer and the weight coefficients connecting all the neurons in the input layer with both the winning neuron and its neighbors in the Kohonen layer are modified to be closer to the input vector, and those connecting all the neurons around the winning neuron within a certain diameter in the Kohonen layer with all the neurons in the output layer are adjusted to be closer to the class codebook vector. If the number of training sam- ples is sufficiently large and the learning epochs iterate enough times, the model will be able to serve as a supervised classifier. The model has been tentatively applied to the supervised classification of multispectral remotely sensed data. The author compared the performances of the extended SOM and BPN in remotely sensed data classification. The investigation manifests that the extended SOM is feasible for supervised classification.     

基于概念分析的空间数据挖掘研究进展

空间数据挖掘的过程与空间概念形成和分析的过程密切相关.本文以空间数据中抽取出各层次、各粒度的概念为主线,以概念形成和分析的形式化理论--概念格和云模型为理论基础,总结了基于概念分析的空间数据挖掘方法及应用,包括概念格的空间关联规则挖掘方法及应用、概念格的概念聚类挖掘方法及应用、云模型的空间关联规则挖掘方法及应用、云模型的空间聚类/分类挖掘方法及应用等.概念格提供了一种形式化的概念描述和概念结构分析的理论和方法,云模型提供了一种综合处理模糊性、随机性以及二者之间关联性的不确定性理论和方法,利用概念格、云模型等形式化的理论和方法分析和描述空间数据挖掘过程中空间概念的形成、空间概念的结构关系、空间概念之间以及空间概念层次之间的不确定性是概念分析的空间数据挖掘研究的进一步的研究方向.     

基于Gabor变换和支持向量机的手写体字符识别算法

针对在手写字符识别中由于书写习惯和风格的不同造成字符模式不稳定的问题,将支持向量机SVM方法用于手写字符的识别.算法首先采用Gabor变换提取手写字符图像的特征参数,然后采用提取的特征训练SVM分类器.再应用SVM分类器分类和判别手写字符.实验表明这种方法具有良好的车牌识别效果,较强的鲁棒性,较大的应用价值.     

集合卡尔曼滤波在流域水文模型流量预报中的应用

针对流域降雨入渗过程,引入集合卡尔曼滤波(EnKF)理论,视整个边坡流域为一个随机动态系统,将边坡流域流量观测值作为系统的输出,用集合卡尔曼滤波模型来描述系统的状态;结合流域流量计算方法,实现水文模型参数的随机动态估计,在有效获得待估参数的同时还给出估计值的不确定性.通过数值算例表明,集合卡尔曼滤波可以有效地对含噪声的量测数据进行处理,能够跟踪水文模型的动态变化.相对于常用最优化算法,集合卡尔曼滤波同时给出反演结果和先验知识的后验分布,显示出更好的实时性和可靠性.