REVIEWS OF GEOGRAPHIC SOFTWARE

Reviews of geographic software in this article: DEMO-GRAPHICS: WORLD POPULATIONS AND PROJECTIONS. ESP GAUSS. CEMODEL S. Damus LIMDEP. William H. Greene MICROSTAT 4.1 OTIS PCIPS. (Personal Computer Image Processing System) . H.J. Meyers and R. Bernstein. REGRESSION ANALYSIS OF TIME SERIES (RATS) SPSS/PC+ URBAN DATA MANAGEMENT SOFTWARE (UDMS)     

Implications of low-pass filtering on power spectra and autocorrelation functions of turbulent velocity signals

Filtering either through the electronics of an instrument or through digital procedure is performed routinely on geophysical data. When velocity fluctuations are measured in turbulent flows using electromagnetic current meters (ECMs), a builtin lowpass Butterworth filter of order n usually attenuates fluctuations at high frequencies. However, the effects of this filter may not be acknowledged in turbulence studies, thus impeding comparisons between data collected with different ECMs. This paper explores the implications of the filters on the characteristics of velocity signals, mainly on variance, power spectra, and correlation analyses. Variance losses resulting from filtering can be important but will vary with the order n of the Butterworth filter, decreasing as n increases. Knowing the filter response, it is possible to reconstruct the original signal spectrum to evaluate the effect of filtering on variance and to allow comparisons between data collected with different instruments. The autocorrelation function also is affected by filtering which increases the value of the coefficients in the first lags, resulting in an overestimation of the integral length scale of coherent structures. These important effects add to those related to size and shape differences in ECM sensors and must be taken into account in comparative studies.     

High resolution paleoenvironmental and chronological investigations of Norse landnám at Tasiusaq, Eastern Settlement, Greenland

High-resolution paleoenvironmental data from a peat profile with a small pollen source area are used to reconstruct the impacts of landnám on vegetation and soils at a Norse farm complex (∅2 at Tasiusaq) comprising two farms in the Eastern Settlement of Greenland. Analyses include the AMS ¹⁴C dating of plant macrofossil samples and the use of Bayesian radiocarbon calibration to construct improved age-depth models for Norse cultural horizons. The onset of a regional landnám may be indicated by the clearance of Betula pubescens woodland immediately prior to local settlement. The latter is dated to AD 950-1020 (2σ) and is characterised by possible burning of Betula glandulosa scrub to provide grassland pasture for domestic stock. Clearance and grazing resulted in accelerated levels of soil erosion at a westerly farm. This was followed by an easterly migration of settlement and agriculture. Site constraints prevent an assessment of the demise of the easterly farm, but pressures of overgrazing and land degradation may have been the major factors responsible for the abandonment of the earlier farm.     

Quantitative textural analysis of packings of elongate crystals

The spatial distribution of grains in a solidifying igneous rock controls the physical properties of the crystal mush, and in turn is controlled by the rate of crystal growth and accumulation. A predominant non-spherical habit for igneous minerals brings into question the use of spherical particles in reference packings used for quantification of spatial distribution. Furthermore, variations of crystal clustering/ordering with length scale require spatial statistics which take into account the distribution of particles beyond nearest neighbours. Using random close packings of spherocylinders, we demonstrate the importance of aspect ratio for the aggregation index (usually known as R) and show that packings of spherical particles have more structure than packings of rods. The spatial distribution functions demonstrate that the plagioclase grains in the colonnade from the Holyoke basalt are clustered on a length scale of 0.5 mm. Understanding the controls on grain spatial distribution in igneous rocks will depend on the application of these techniques to well-understood environments.     

Weibull statistical analysis of granite bending strength

Summary  This paper describes and discusses the adequacy of Weibull statistical analysis to analyse the bending strength of granite. The experimental results show that strength variability is related with a specific origin of failure. This conclusion is based on analysing the influence of the surface condition (extrinsic defects) on the bending strength results treated by the Weibull statistics. The conclusions drawn from this study have been validated by analysing the results of the critical flaw dimension estimated by applying the linear elastic fracture mechanics (LEFM) formulae. Results obtained from fractographic examination also have been used to describe the location of the origin of the fracture and understand the distribution of defects; i.e., there is a unimodal distribution of defects (intrinsic defects), despite the fact that some outlier values are normally observed in the fractured surfaces. Correspndence: P. M. Amaral, Department of Materials Engineering, Instituto Superior Técnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001 Lisboa, Portugal     

土壤渗滤处理技术在海岛生活污水处理中的应用

土壤渗滤处理系统设备简单、投资少、操作管理方便、能耗低,净化效果良好,在处理海岛生活污水领域具有广阔的发展前景.根据鼓浪屿目前污水水质、水量的特点,采用土壤渗滤高度水处理技术对污水进行处理,污染物经过土壤的物理、化学和生物作用,出水达到《城市污水再生利用城市杂用水水质》(GB/T18920-2002),具有不产生污泥,运行简单,氮磷去除效率高,无二次污染等特点.     

Prediction of local scour around circular piles under waves using a novel artificial intelligence approach

Abstract

The scour phenomena around vertical piles in oceans and under waves may influence the structure stability. Therefore, accurately predicting the scour depth is an important task in the design of piles. Empirical approaches often do not provide the required accuracy compared with data mining methods for modeling such complex processes. The main objective of this study is to develop three data-driven methods, locally weighted linear regression (LWLR), support vector machine (SVR), and multivariate linear regression (MLR) to predict the scour depth around vertical piles due to waves in a sand bed. It is the first effort to develop the LWLR to predict scour depth around vertical piles. The models simulate the scour depth mainly based on Shields parameter, pile Reynolds number, grain Reynolds number, Keulegan–Carpenter number, and sediment number. 111 laboratory datasets, derived from several experimental studies, were used for the modeling. The results indicated that the LWLR provided highly accurate predictions of the scour depths around piles (R?=?0.939 and RMSE = 0.075). Overall, this study demonstrated that the LWLR can be used as a valuable tool to predict the wave-induced scour around piles.