Storm-Water Management Using Street Sweeping

Although street sweeping is commonly regarded as a cost-effective storm-water best management practice, there is little quantitative evidence that street sweeping directly improves runoff water quality. In this paper, several previous street sweeping studies were reevaluated using statistical power analysis. Two-group, independent-sample one-sided t-test power analyses were performed using log-transformed event mean concentrations (EMCs) of total suspended solids, suspended sediment concentration or chemical oxygen demand. The effect size between the two groups was estimated using the sweepers’ pickup efficiency, which showed that the failure to detect the difference between mean EMCs of the two sample groups (i.e., unswept and swept groups) is likely due to limited sample numbers. Too few samples, which also resulted in a high coefficient of variation, were analyzed to detect the likely difference between swept and unswept observations. In addition, the temporal gap between street sweeping and subsequent storm events was not controlled to improve statistical power.     

First-Order Reliability Method for Probabilistic Liquefaction Triggering Analysis Using CPT

The potential for liquefaction triggering of a soil under a given seismic loading is measured herein by probability of liquefaction. The first order reliability method (FORM) is used to calculate reliability index, from which the probability of liquefaction is obtained. This approach requires the knowledge of parameter and model uncertainties; the latter is the focus of this paper. An empirical model for determining liquefaction resistance based on cone penetration test (CPT) is established through “neural network learning” of case histories. This resistance model along with a reference seismic loading model forms a performance function or limit state for liquefaction triggering analysis. Within the framework of the FORM, the uncertainty of this limit state model is characterized through an extensive series of sensitivity studies using Bayesian mapping functions that have been calibrated with a set of quality case histories. In addition, a deterministic model for assessing liquefaction potential in terms of factor of safety is presented, and the probability-safety factor mapping functions for estimating the probability of liquefaction for a given factor of safety in the absence of the knowledge of parameter uncertainty are also established. Examples are presented to illustrate the proposed methods.     

Statistical Modeling of the Serviceability of Sewage Pumps

Sewage pumping stations represent an element of the sewer system, which is directly responsible for affecting serviceability; i.e., failing pumps may result in combined sewer overflows or flooding. However, failures of sewage pumps are not yet incorporated in sewer assessments due to lack of data. This paper presents the analysis of pump failure data provided by two sewer management authorities in The Netherlands. Pump failures have been studied accounting for the nature of the failures, the operation and maintenance procedures of the management authority, the aging of the pumps, and the changes in the environment of pumps. The analysis shows that sewage pumps fail relatively often due to the composition of sewage and the discontinuous operation of the pumps. The interarrival time and the duration of failures are highly variable and independent of the specific function of the pump. Resulting pump failure characteristics are applied in a Monte Carlo simulation to calculate the impact of failures on combined sewer overflow volumes. The results indicate that the serviceability of sewer systems is significantly affected by failing pumps. Therefore, including pump availability in sewer system assessments should be considered.     

使用存储过程实现复杂的数据统计查询

存储过程是一种重要的数据库对象，恰当地使用存储过程可以简化应用程序的开发，提高系统的运行性能。本文通过两个具体的例子，介绍了在SQL Server数据库中如何使用存储过程实现复杂的数据统计查询。     

射线溅井测量物理量之间关系以及KALMAN滤波处理

本文从射线统计误差理论出发,以自然伽马测井为例,分析了在射线测井中由于仪器运动测量所产生的纵向分辨率,它与测量有关的诸测量物理这间的关系,以及测量数据的滤波处理原理。     

Classifying Construction Contractors Using Unsupervised-Learning Neural Networks

Contractor prequalification involves the screening of contractors by a project owner to determine their competence to complete the project on time, within budget, and to expected quality standards. The process of prequalification involves a large number of contractors, each being represented by many attributes. A neural network model was applied to aid in the prequalification process by classifying contractors into groups based on similarity in performance using the financial ratios of liquidity, activity, profitability, and leverage. Contractors are represented in this model by patterns in four-dimensional space. Patterns of similar performance tend to form clusters intercepting regions of low pattern density in between. A neuron with weights is used as a classifier to set a decision boundary between clusters. The method basically iterates the neuron weights to move the decision boundary to a place of low pattern density. Then, the statistical hypothesis testing of the mean difference of two independent samples was used to validate the classification of the parent class to the two child classes considering the four ratios separately. The method was used hierarchically to classify a group of 245 contractors into classes of small numbers. Finally, the inferred procedure of classification proves that the neural network model classified the four-dimension pattern representing contractors efficiently.     

A FORTRAN IV program for comparing ranking algorithms in quantitative biostratigraphy

A program for evaluating the performance of competing ranking algorithms in stratigraphic paleontology is presented. The program (1) generates a hypothetical, and thus known, succession of taxa in time and (2) simulates their succession in strata at several local sample sites. If desired, (1) and (2) may be repeated for several (=50 or 100 for example) iterations and the local site data for each sent to two user routines for inferred rankings (inferred succession of events in time). First data for first and last occurrences (fads and lads) taken together, then data for for lads-only, then data for fads-only is sent. For each submission of data to a user routine, Kendall rank correlation coefficients and Spearman coefficients are computed comparing the inferred rankings generated by the user routine with the known succession of events in time. The performance of two competing ranking algorithms may be compared by (1) obtaining for each submitted dataset the differences between corresponding Kendall (and/or Spearman) coefficients computed for the two algorithms, and (2) testing the observed differences for statistical significance. A simple two-sided t-test may be used to test whether the observed mean difference between two corresponding coefficients differs significantly from zero; if ct-tests are performed, the level of significance of each should be set to alpha/c to obtain a maximum experimentwise error rate of less than alpha. The program is used to compare three ranking algorithms provided by Agterberg and Nel (1982a, b) as well as to determine whether the algorithms work as well for datasets combining lads and fads vs datasets for lads-only or fads-only. Agterberg and Nel's Presorting algorithm performed better than their Ranking or Scaling algorithm. All three performed slightly but significantly better on data for lads-only or fads-only as opposed to combined data.     

Precise calculation of the cumulative distribution function and its inverse function for Fisher's F and Student's t tests

A computer program to calculate precise solutions (exact to the limit of computer accuracy) for the cumulative distribution function of the statistical t and F distributions is detailed. A routine that makes iterative calls of this algorithm to calculate precisely the inverse function of these distributions by successive approximation is also provided. The program provided yields precise levels of probability confidence for both Fisher's F and Student's t tests, and the inverse calculation of critical values given p. It is written in a subset of BASIC which should run on any small computer, requiring only 1538 bytes on the Commodore 64.     

Validity of the Space-Time Enlargement Law for vacuum breakdown

This paper investigates, theoretically and experimentally, the applicability of the Space-Time Enlargement Law to vacuum-insulated systems. A discussion on how characteristics of possible vacuum breakdown mechanisms determine the distribution function of the breakdown voltage random variable is presented. By superimposing effects of electrode surface enlargement and inter-electrode gap enlargement, expressions for the mean value and standard deviation of the breakdown voltage random variable are obtained. In the case of time extensions, the assumption of complete independence of consecutive discharge processes is discussed. Experimental testing of the Enlargement Law was performed on Rogowski type two-electrode systems, with different electrode surface areas, inter-electrode gaps and vacuum pressures. Measurements were conducted using industrial ac voltage, dc voltage with 50 V/s rate of rise, standard atmospheric pulse voltage (1,2/50 μs), and commutational pulse voltage (250/2500 μs). The final conclusion, based on the comparison of theoretical considerations and the experimental results, is that the Space-Time Enlargement Law can be applied in the design phase during the development of vacuum devices, with certain limitations, regardless of the type of the applied voltage.     

Learning LOGO: effects on learning BASIC and statistics

Abstract Two experiments were conducted to examine the effects of a period of training in the use of the turtle graphics aspects of LOGO on both attitudes towards computers and the learning of BASIC and statistics. A group of 15 subjects spent 10 hours learning LOGO . Their performance on subsequent learning of BASIC was significantly better than that of a control group, but no differences were found in attitudes towards computers or in the learning of statistics.