SAMPLING BIAS IN EVALUATING THE PROBABILITY OF SEISMICALLY INDUCED SOIL LIQUEFACTION WITH SPT & CPT CASE HISTORIES

Several deterministic and probabilistic methods are used to evaluate the probability of seismically induced liquefaction of a soil. The probabilistic models usually possess some uncertainty in that model and uncertainties in the parameters used to develop that model. These model uncertainties vary from one statistical model to another. Most of the model uncertainties are epistemic, and can be addressed through appropriate knowledge of the statistical model. One such epistemic model uncertainty in evaluating liquefaction potential using a probabilistic model such as logistic regression is sampling bias. Sampling bias is the difference between the class distribution in the sample used for developing the statistical model and the true population distribution of liquefaction and non-liquefaction instances. Recent studies have shown that sampling bias can significantly affect the predicted probability using a statistical model. To address this epistemic uncertainty, a new approach was developed for evaluating the probability of seismically-induced soil liquefaction, in which a logistic regression model in combination with Hosmer-Lemeshow statistic was used. This approach was used to estimate the population (true) distribution of liquefaction to non-liquefaction instances of standard penetration test (SPT) and cone penetration test (CPT) based most updated case histories. Apart from this, other model uncertainties such as distribution of explanatory variables and significance of explanatory variables were also addressed using KS test and Wald statistic respectively. Moreover, based on estimated population distribution, logistic regression equations were proposed to calculate the probability of liquefaction for both SPT and CPT based case history. Additionally, the proposed probability curves were compared with existing probability curves based on SPT and CPT case histories.

Relevant Aspects on Teaching Probability and Statistics in Basic General Education

Probability and Statistics were included in the Basic General Education curricula by the Ministry of Public Education (Costa Rica), since 1995. To analyze the teaching reality in these fields, a research was conducted in two educational regions of the country: Heredia and Pérez Zeledón. The survey included university training and updating processes of teachers teaching Statistics and Probability in the schools. The research demonstrated the limited university training in these fields, the dissatisfaction of teachers about it, and the poor support of training institutions to their professional exercise.

Detailed analysis of a conservative difference approximation for the time fractional diffusion equation

Diffusion equations that use time fractional derivatives are attractive because they describe a wealth of problems involving non-Markovian Random walks. The time fractional diffusion equation (TFDE) is obtained from the standard diffusion equation by replacing the first-order time derivative with a fractional derivative of order α ∈ (0, 1). Developing numerical methods for solving fractional partial differential equations is a new research field and the theoretical analysis of the numerical methods associated with them is not fully developed. In this paper an explicit conservative difference approximation (ECDA) for TFDE is proposed. We give a detailed analysis for this ECDA and generate discrete models of random walk suitable for simulating random variables whose spatial probability density evolves in time according to this fractional diffusion equation. The stability and convergence of the ECDA for TFDE in a bounded domain are discussed. Finally, some numerical examples are presented to show the application of the present technique.

Integrating science through Bayesian Belief Networks : case study of Lyngbya in Moreton Bay

Bayesian Belief Networks (BBNs) are emerging as valuable tools for investigating complex ecological problems. In a BBN, the important variables in a problem are identified and causal relationships are represented graphically. Underpinning this is the probabilistic framework in which variables can take on a finite range of mutually exclusive states. Associated with each variable is a conditional probability table (CPT), showing the probability of a variable attaining each of its possible states conditioned on all possible combinations of it parents. Whilst the variables (nodes) are connected, the CPT attached to each node can be quantified independently. This allows each variable to be populated with the best data available, including expert opinion, simulation results or observed data. It also allows the information to be easily updated as better data become available ----- ----- This paper reports on the process of developing a BBN to better understand the initial rapid growth phase (initiation) of a marine cyanobacterium, Lyngbya majuscula, in Moreton Bay, Queensland. Anecdotal evidence suggests that Lyngbya blooms in this region have increased in severity and extent over the past decade. Lyngbya has been associated with acute dermatitis and a range of other health problems in humans. Blooms have been linked to ecosystem degradation and have also damaged commercial and recreational fisheries. However, the causes of blooms are as yet poorly understood.

Property cash flow studies : focusing on model consistency and data accuracy

The high degree of variability and inconsistency in cash flow study usage by property professionals demands improvement in knowledge and processes. Until recently limited research was being undertaken on the use of cash flow studies in property valuations but the growing acceptance of this approach for major investment valuations has resulted in renewed interest in this topic. Studies on valuation variations identify data accuracy, model consistency and bias as major concerns. In cash flow studies there are practical problems with the input data and the consistency of the models. This study will refer to the recent literature and identify the major factors in model inconsistency and data selection. A detailed case study will be used to examine the effects of changes in structure and inputs. The key variable inputs will be identified and proposals developed to improve the selection process for these key variables. The variables will be selected with the aid of sensitivity studies and alternative ways of quantifying the key variables explained. The paper recommends, with reservations, the use of probability profiles of the variables and the incorporation of this data in simulation exercises. The use of Monte Carlo simulation is demonstrated and the factors influencing the structure of the probability distributions of the key variables are outline. This study relates to ongoing research into functional performance of commercial property within an Australian Cooperative Research Centre.

Some Remarks on the Approximation of Transitional Density in Stochastic Differential Equations

Aijt-Sahalia (2002) introduced a method to estimate transitional probability densities of di®usion processes by means of Hermite expansions with coe±cients determined by means of Taylor series. This note describes a numerical procedure to ¯nd these coe±cients based on the calculation of moments. One advantage of this procedure is that it can be used e®ectively when the mathematical operations required to ¯nd closed-form expressions for these coe±cients are otherwise infeasible.

Radiobiological model comparison of 3D conformal radiotherapy and IMRT plans for the treatment of prostate cancer

The main aim of radiotherapy is to deliver a dose of radiation that is high enough to destroy the tumour cells while at the same time minimising the damage to normal healthy tissues. Clinically, this has been achieved by assigning a prescription dose to the tumour volume and a set of dose constraints on critical structures. Once an optimal treatment plan has been achieved the dosimetry is assessed using the physical parameters of dose and volume. There has been an interest in using radiobiological parameters to evaluate and predict the outcome of a treatment plan in terms of both a tumour control probability (TCP) and a normal tissue complication probability (NTCP). In this study, simple radiobiological models that are available in a commercial treatment planning system were used to compare three dimensional conformal radiotherapy treatments (3D-CRT) and intensity modulated radiotherapy (IMRT) treatments of the prostate. Initially both 3D-CRT and IMRT were planned for 2 Gy/fraction to a total dose of 60 Gy to the prostate. The sensitivity of the TCP and the NTCP to both conventional dose escalation and hypo-fractionation was investigated. The biological responses were calculated using the Källman S-model. The complication free tumour control probability (P+) is generated from the combined NTCP and TCP response values. It has been suggested that the alpha/beta ratio for prostate carcinoma cells may be lower than for most other tumour cell types. The effect of this on the modelled biological response for the different fractionation schedules was also investigated.

Creative design and the generative evolutionary paradigm

Design as seen from the designer's perspective is a series of amazing imaginative jumps or creative leaps. But design as seen by the design historian is a smooth progression or evolution of ideas that they seem self-evident and inevitable after the event. But the next step is anything but obvious for the artist/creator/inventor/designer stuck at that point just before the creative leap. They know where they have come from and have a general sense of where they are going, but often do not have a precise target or goal. This is why it is misleading to talk of design as a problem-solving activity - it is better defined as a problem-finding activity. This has been very frustrating for those trying to assist the design process with computer-based, problem-solving techniques. By the time the problem has been defined, it has been solved. Indeed the solution is often the very definition of the problem. Design must be creative-or it is mere imitation. But since this crucial creative leap seem inevitable after the event, the question must arise, can we find some way of searching the space ahead? Of course there are serious problems of knowing what we are looking for and the vastness of the search space. It may be better to discard altogether the term "searching" in the context of the design process: Conceptual analogies such as search, search spaces and fitness landscapes aim to elucidate the design process. However, the vastness of the multidimensional spaces involved make these analogies misguided and they thereby actually result in further confounding the issue. The term search becomes a misnomer since it has connotations that imply that it is possible to find what you are looking for. In such vast spaces the term search must be discarded. Thus, any attempt at searching for the highest peak in the fitness landscape as an optimal solution is also meaningless. Futhermore, even the very existence of a fitness landscape is fallacious. Although alternatives in the same region of the vast space can be compared to one another, distant alternatives will stem from radically different roots and will therefore not be comparable in any straightforward manner (Janssen 2000). Nevertheless we still have this tantalizing possibility that if a creative idea seems inevitable after the event, then somehow might the process be rserved? This may be as improbable as attempting to reverse time. A more helpful analogy is from nature, where it is generally assumed that the process of evolution is not long-term goal directed or teleological. Dennett points out a common minsunderstanding of Darwinism: the idea that evolution by natural selection is a procedure for producing human beings. Evolution can have produced humankind by an algorithmic process, without its being true that evolution is an algorithm for producing us. If we were to wind the tape of life back and run this algorithm again, the likelihood of "us" being created again is infinitesimally small (Gould 1989; Dennett 1995). But nevertheless Mother Nature has proved a remarkably successful, resourceful, and imaginative inventor generating a constant flow of incredible new design ideas to fire our imagination. Hence the current interest in the potential of the evolutionary paradigm in design. These evolutionary methods are frequently based on techniques such as the application of evolutionary algorithms that are usually thought of as search algorithms. It is necessary to abandon such connections with searching and see the evolutionary algorithm as a direct analogy with the evolutionary processes of nature. The process of natural selection can generate a wealth of alternative experiements, and the better ones survive. There is no one solution, there is no optimal solution, but there is continuous experiment. Nature is profligate with her prototyping and ruthless in her elimination of less successful experiments. Most importantly, nature has all the time in the world. As designers we cannot afford prototyping and ruthless experiment, nor can we operate on the time scale of the natural design process. Instead we can use the computer to compress space and time and to perform virtual prototyping and evaluation before committing ourselves to actual prototypes. This is the hypothesis underlying the evolutionary paradigm in design (1992, 1995).

Music purchase behaviour : the effect of emotional loyalty on intention to purchase.

This paper explores the influence of emotional loyalty on music purchase behaviour. Specifically, it examines whether emotional attachment to an artist's music influences loyalty to that artist, and how emotional loyalty influences a consumer's decision to purchase music. Data collection involved fifteen semi-structured interviews with young (18-30) subjects recruited through non-probability convenience sampling. Findings show that consumers who are emotionally loyal to an artistes) are more inclined to purchase the music rather than download free of charge.

Error, Bias, and Long-Branch Attraction in Data for Two Chloroplast Photosystem Genes in Seed Plants

Sequences of two chloroplast photosystem genes, psaA and psbB, together comprising about 3,500 bp, were obtained for all five major groups of extant seed plants and several outgroups among other vascular plants. Strongly supported, but significantly conflicting, phylogenetic signals were obtained in parsimony analyses from partitions of the data into first and second codon positions versus third positions. In the former, both genes agreed on a monophyletic gymnosperms, with Gnetales closely related to certain conifers. In the latter, Gnetales are inferred to be the sister group of all other seed plants, with gymnosperms paraphyletic. None of the data supported the modern ‘‘anthophyte hypothesis,’’ which places Gnetales as the sister group of flowering plants. A series of simulation studies were undertaken to examine the error rate for parsimony inference. Three kinds of errors were examined: random error, systematic bias (both properties of finite data sets), and statistical inconsistency owing to long-branch attraction (an asymptotic property). Parsimony reconstructions were extremely biased for third-position data for psbB. Regardless of the true underlying tree, a tree in which Gnetales are sister to all other seed plants was likely to be reconstructed for these data. None of the combinations of genes or partitions permits the anthophyte tree to be reconstructed with high probability. Simulations of progressively larger data sets indicate the existence of long-branch attraction (statistical inconsistency) for third-position psbB data if either the anthophyte tree or the gymnosperm tree is correct. This is also true for the anthophyte tree using either psaA third positions or psbB first and second positions. A factor contributing to bias and inconsistency is extremely short branches at the base of the seed plant radiation, coupled with extremely high rates in Gnetales and nonseed plant outgroups. M. J. Sanderson,* M. F. Wojciechowski,*† J.-M. Hu,* T. Sher Khan,* and S. G. Brady

A multi-hypothesis topological SLAM approach for loop closing on edge-ordered graphs

We present a method for topological SLAM that specifically targets loop closing for edge-ordered graphs. Instead of using a heuristic approach to accept or reject loop closing, we propose a probabilistically grounded multi-hypothesis technique that relies on the incremental construction of a map/state hypothesis tree. Loop closing is introduced automatically within the tree expansion, and likely hypotheses are chosen based on their posterior probability after a sequence of sensor measurements. Careful pruning of the hypothesis tree keeps the growing number of hypotheses under control and a recursive formulation reduces storage and computational costs. Experiments are used to validate the approach.

Investment decision-making under risk (reliability) and uncertainty for infrastructure asset management

Risks and uncertainties are inevitable in engineering projects and infrastructure investments. Decisions about investment in infrastructure such as for maintenance, rehabilitation and construction works can pose risks, and may generate significant impacts on social, cultural, environmental and other related issues. This report presents the results of a literature review of current practice in identifying, quantifying and managing risks and predicting impacts as part of the planning and assessment process for infrastructure investment proposals. In assessing proposals for investment in infrastructure, it is necessary to consider social, cultural and environmental risks and impacts to the overall community, as well as financial risks to the investor. The report defines and explains the concept of risk and uncertainty, and describes the three main methodology approaches to the analysis of risk and uncertainty in investment planning for infrastructure, viz examining a range of scenarios or options, sensitivity analysis, and a statistical probability approach, listed here in order of increasing merit and complexity. Forecasts of costs, benefits and community impacts of infrastructure are recognised as central aspects of developing and assessing investment proposals. Increasingly complex modelling techniques are being used for investment evaluation. The literature review identified forecasting errors as the major cause of risk. The report contains a summary of the broad nature of decision-making tools used by governments and other organisations in Australia, New Zealand, Europe and North America, and shows their overall approach to risk assessment in assessing public infrastructure proposals. While there are established techniques to quantify financial and economic risks, quantification is far less developed for political, social and environmental risks and impacts. The report contains a summary of the broad nature of decision-making tools used by governments and other organisations in Australia, New Zealand, Europe and North America, and shows their overall approach to risk assessment in assessing public infrastructure proposals. While there are established techniques to quantify financial and economic risks, quantification is far less developed for political, social and environmental risks and impacts. For risks that cannot be readily quantified, assessment techniques commonly include classification or rating systems for likelihood and consequence. The report outlines the system used by the Australian Defence Organisation and in the Australian Standard on risk management. After each risk is identified and quantified or rated, consideration can be given to reducing the risk, and managing any remaining risk as part of the scope of the project. The literature review identified use of risk mapping techniques by a North American chemical company and by the Australian Defence Organisation. This literature review has enabled a risk assessment strategy to be developed, and will underpin an examination of the feasibility of developing a risk assessment capability using a probability approach.