An assessment of a nowcast/forecast system for the straits of Florida/Florida Current regime

The Florida Current （FC） largely fills the Straits of Florida and is variable on a broad spectrum of time and space scales. Some portions of the variability are due to variable forcing by tides, winds, heating/cooling, and throughflow; other portions are due to intrinsic instabilities of the FC. To predict, as well as to better understand this complex regime, a nowcast/forecast system （East Florida Shelf Information System （EFSIS）） has been implemented and assessed （http：//efsis. rsmas. miami. edu）. EFSIS is based on an implementation of the Princeton Ocean Model （POM） with mesoscale-admitting resolution on a curvilinear grid. It is forced by a mesoscale numerical weather prediction system （called Eta） run operationally by the National Centers for Environmental Prediction （NCEP）, eight tidal constituents from a global tidal model, and lateral boundary conditions from an operational global ocean prediction model, i.e., the Navy Coastal Ocean Model （NCOM）. Real-time observations of coastal sea level, coastal sea surface temperature, coastal HF radar-derived surface current maps, and FC volume transport are used to verify and validate EFSIS. EFSIS is part of an evolving strategy for real-time predictive coastal ocean modeling methodology, and for fostering the understanding of the variability of the regime on several time and space scales. Here, some of the verification and validation results are provided, as well as diagnostic analyses of dynamical processes. The central point is that an example is provided of a ＇scientific revolution＇ in progress that combines real-time observations and numerical circulation models to yield a credible sequence of synoptic views of coastal ocean circulation for the first time.     

数据缺乏条件下的渔业资源评估方法研究进展

全球范围内绝大多数渔业处于数据缺乏的状态,而常规的资源评估方法需要准确的生物学信息和完整的渔获数据,通常并不适合此类渔业资源的评估。数据缺乏方法(DLM)正是适用于此类情况的渔业资源评估方法,DLM的研究已经成为渔业资源研究的热点。作者对DLM的近期发展作了简要回顾,重点对主要的DLM方法和数据需求进行了分析比较,指出这些方法的应用需要注意的问题。历史渔获量是大多数DLM方法运用的必要数据(如DCAC、Catch-MSY模型),而关于资源丰度的相对指标和重要生活史参数(如种群内禀增长率、自然死亡系数),则是运用这些方法的必要补充。此外,对运用DLM所需的主要生物学参数的估算方法进行了简介。最后对DLM方法的使用及其在国内渔业中的运用提出了建议。     

房屋面积测量误差研究

结合珠海市房屋面积测量的实际情况简要分析房屋面积测量的各项误差,以及误差的来源、种类,介绍了房屋面积测量误差的各项指标,并对实地量距法和坐标解析法的精度进行了比较。     

Enhancement of scale-free network attack tolerance

Despite the large size of most communication and transportation systems, there are short paths between nodes in these networks which guarantee the efficient information, data and passenger delivery; furthermore these networks have a surprising tolerance under random errors thanks to their inherent scale-free topology. However, their scale-free topology also makes them fragile under intentional attacks, leaving us a challenge on how to improve the network robustness against intentional attacks without losing their strong tolerance under random errors and high message and passenger delivering capacity. Here we propose two methods (SL method and SH method) to enhance scale-free network's tolerance under attack in different conditions.     

某工程蚀变岩孔隙特征及其软弱程度研究

孔隙作为岩体的初始损伤,对岩体的强度及变形特性的影响尤为明显,而孔隙发育是研究区内一类力学性质较软弱岩石的显著特征。对蚀变岩孔隙特征的研究是了解蚀变岩的软弱程度及孔隙度对其软弱程度的影响的有效方法。针对此蚀变岩进行岩矿及铸体薄片分析得出蚀变岩孔隙有原生、浅表生及构造3种成因,其中以原生为主。从饱水蚀变岩的常规单轴压缩试验应力-应变全过程曲线上求取蚀变岩的饱和单轴抗压强度及割线模量,再与相应试样的孔隙度进行相关性分析,结果显示出蚀变岩的饱和单轴抗压强度及割线模量均随孔隙度的增加而减小,且呈幂函数关系。由于蚀变岩的饱和单轴抗压强度(R c)及割线模量(E s)作为软硬岩评判标准具有一致性,且两者与孔隙度(n)存在较好的相关性,以此将3者共同作为蚀变岩软弱程度的划分标准,并得出软硬岩分界处n为12%、R c为30 M Pa、E s为5 GPa。     

西藏冈底斯B型山链南缘松多群的构成及其变质变形特征

松多群是冈底斯B型山链变质基底的组成部分之一 ,并于晚三叠世—晚第三纪雅江缝合带俯冲超碰撞阶段演化为该B型山链活化根带逆冲推覆超岩片 ,系冈瓦纳大陆北缘于震旦—寒武纪小洋盆及陆缘火山沉积的产物。松多群岔萨岗组、马布库组、雷龙库组及罗马岭超镁铁质岩带 ,彼此间均为构造接触 ,实为一套构造岩石组合体 ,由时代相近而来源、性质不同的地质体构成 ,并经历了低绿片岩相—低角闪岩相变质作用和 4期主要构造变形。岔萨岗组主体为变质基性火山岩 ,化学成分相当TMORB ;马布库组、雷龙库组片岩、石英岩相当于被动陆缘泥砂质复理石建造及石英质复理石建造。罗马岭超镁铁质岩经综合分析判别则形成于伸展裂陷小洋盆构造环境。因而得出松多群为一较完整有限小洋盆火山沉积产物的结论。     

一种基于数据外插改进的ART迭代算法

本文针对有限角度的投影数据的CT图像重建问题,提出了一种基于数据外插改进的ART算法.该算法的基本思想足运用已知角度的投影数据来补全未知角度的投影数据,再用ART算法进行图像重建.最后用模拟的投影数据进行了重建图像的数值实验.实验结果表明该算法不但提高了重建图像质量,同时也提高了图像达代的收敛速度.     

Diffraction‐limited imaging and beyond – the concept of super resolution‡

It is well‐known that experimental or numerical backpropagation of waves generated by a point‐source/‐scatterer will refocus on a diffraction‐limited spot with a size not smaller than half the wavelength. More recently, however, super‐resolution techniques have been introduced that apparently can overcome this fundamental physical limit. This paper provides a framework of understanding and analysing both diffraction‐limited imaging as well as super resolution. The resolution analysis presented in the first part of this paper unifies the different ideas of backpropagation and resolution known from the literature and provides an improved platform to understand the cause of diffraction‐limited imaging. It is demonstrated that the monochromatic resolution function consists of both causal and non‐causal parts even for ideal acquisition geometries. This is caused by the inherent properties of backpropagation not including the evanescent field contributions. As a consequence, only a diffraction‐limited focus can be obtained unless there are ideal acquisition surfaces and an infinite source‐frequency band. In the literature various attempts have been made to obtain images resolved beyond the classical diffraction limit, e.g., super resolution. The main direction of research has been to exploit the evanescent field components. However, this approach is not practical in case of seismic imaging in general since the evanescent waves are so weak – because of attenuation, they are masked by the noise. Alternatively, improvement of the image resolution of point like targets beyond the diffraction limit can apparently be obtained employing concepts adapted from conventional statistical multiple signal classification (MUSIC). The basis of this approach is the decomposition of the measurements into two orthogonal domains: signal and noise (nil) spaces. On comparison with Kirchhoff prestack migration this technique is showed to give superior results for monochromatic data. However, in case of random noise the super‐ resolution power breaks down when employing monochromatic data and a limited acquisition aperture. For such cases it also seems that when the source‐receiver lay out is less correlated, the use of a frequency band may restore the super‐resolution capability of the method.     

Predicting daily pan evaporation by soft computing models with limited climatic data

Abstract

Accurate prediction of daily pan evaporation (PE) is important for monitoring, surveying, and management of water resources as well as reservoir management and evaluation of drinking water supply systems. This study develops and applies soft computing models to predict daily PE in a dry climate region of south-western Iran. Three soft computing models, namely the multilayer perceptron-neural networks model (MLP-NNM), Kohonen self-organizing feature maps-neural networks model (KSOFM-NNM), and gene expression programming (GEP), were considered. Daily PE was predicted at two stations using temperature-based, radiation-based, and sunshine duration-based input combinations. The results obtained by the temperature-based 3 (TEM3) model produced the best results for both stations. The Mann-Whitney U test was employed to compute the rank of different input combination for hypothesis testing. Comparison between the soft computing models and multiple linear regression model (MLRM) demonstrated the superiority of MLP-NNM, KSOFM-NNM, and GEP over MLRM. It was concluded that the soft computing models can be successfully employed for predicting daily PE in south western Iran.

Editor D. Koutsoyiannis