Nonlinear Abel type integral equation in modelling creep crack propagation

Copyright @ 2011 Birkhäuser BostonA nonlinear Abel-type equation is obtained in this paper to model creep crack time-dependent propagation in the infinite viscoelastic plane. A finite time when the integral equation solution becomes unbounded is obtained analytically as well as the equation parameters when solution blows up for all times. A modification to the Nyström method is introduced to numerically solve the equation and some computational results are presented

Appoximation-assisted [sic] estimation of eigenvectors under quadratic loss

Improved estimation of eigen vector of covariance matrix is considered under uncertain prior information (UPI) regarding the parameter vector. Like statistical models underlying the statistical inferences to be made, the prior information will be susceptible to uncertainty and the practitioners may be reluctant to impose the additional information regarding parameters in the estimation process. A very large gain in precision may be achieved by judiciously exploiting the information about the parameters which in practice will be available in any realistic problem. Several estimators based on preliminary test and the Stein-type shrinkage rules are constructed. The expressions for the bias and risk of the proposed estimators are derived and compared with the usual estimators. We demonstrate that how the classical large sample theory of the conventional estimator can be extended to shrinkage and preliminary test estimators for the eigenvector of a covariance matrix. It is established that shrinkage estimators are asymptotically superior to the usual sample estimators. For illustration purposes, the method is applied to three datasets

Structural dynamic eutrophication models

This article discusses problems of modelling the seasonal succession of algal species in lakes and reservoirs, and the adaptive selection of certain groups of algae in response to changes in the inputs and relative concentrations of nutrients and other environmental variables. A new generation of quantitative models is being developed which attempts to translate some important biological properties of species (survival, variation, inheritance, reproductive rates and population growth) into predictions about the survival of the fittest, where ”fitness” is measured or estimated in thermodynamic terms. The concept of ”exergy” and its calculation is explored to examine maximal exergy as a measure of fitness in ecosystems, and its use for calculating changes in species composition by means of structural dynamic models. These models accomodate short-term changes in parameters that affect the adaptive responses (species selection) of algae

DEPASCALISATION OF SMARANDACHE PASCAL DERIVED SEQUENCES AND BACKWARD EXTENDED FIBONACCI SEQUENCE

We call the process of extracting the base sequence from the Pascal derived sequence as Depascalisation. The interesting observation is that this again involves the Pascal's triangle though with a difference

Reducing regression test size by exclusion.

Operational software is constantly evolving. Regression testing is used to identify the unintended consequences of evolutionary changes. As most changes affect only a small proportion of the system, the challenge is to ensure that the regression test set is both safe (all relevant tests are used) and unclusive (only relevant tests are used). Previous approaches to reducing test sets struggle to find safe and inclusive tests by looking only at the changed code. We use decomposition program slicing to safely reduce the size of regression test sets by identifying those parts of a system that could not have been affected by a change; this information will then direct the selection of regression tests by eliminating tests that are not relevant to the change. The technique properly accounts for additions and deletions of code. We extend and use Rothermel and Harrold’s framework for measuring the safety of regression test sets and introduce new safety and precision measures that do not require a priori knowledge of the exact number of modification-revealing tests. We then analytically evaluate and compare our techniques for producing reduced regression test sets

Episodic memory and episodic future thinking in adults with autism.

The ability to remember past experiences (episodic memory) is thought to be related to the ability to imagine possible future experiences (episodic future thinking). Although previous research has established that individuals with autism spectrum disorder (ASD) have diminished episodic memory, episodic future thinking has not previously been investigated within this population. In the present study, high-functioning adults with ASD were compared to closely matched typical adults on a task requiring participants to report a series of events that happened to them in the past and a series of events that might happen to them in the future. For each event described, participants completed two modified Memory Characteristics Questionnaire items to assess self-reported phenomenal qualities associated with remembering and imagining, including self-perspective and degree of autonoetic awareness. Participants also completed letter, category, and ideational fluency tasks. Results indicated that participants with ASD recalled/imagined significantly fewer specific events than did comparison participants and that participants with ASD demonstrated impaired episodic memory and episodic future thinking. In line with this finding, participants with ASD were less likely than comparison participants to report taking a field (first-person) perspective and were more likely to report taking an observer (third-person) perspective during retrieval of past events (but not during simulation of future events), highlighting that they were less likely to mentally reexperience past events from their own point of view. There were no group differences in self-reported levels of autonoetic awareness or fluency task performance

Reducing regression test size by exclusion.

Operational software is constantly evolving. Regression testing is used to identify the unintended consequences of evolutionary changes. As most changes affect only a small proportion of the system, the challenge is to ensure that the regression test set is both safe (all relevant tests are used) and unclusive (only relevant tests are used). Previous approaches to reducing test sets struggle to find safe and inclusive tests by looking only at the changed code. We use decomposition program slicing to safely reduce the size of regression test sets by identifying those parts of a system that could not have been affected by a change; this information will then direct the selection of regression tests by eliminating tests that are not relevant to the change. The technique properly accounts for additions and deletions of code. We extend and use Rothermel and Harrold’s framework for measuring the safety of regression test sets and introduce new safety and precision measures that do not require a priori knowledge of the exact number of modification-revealing tests. We then analytically evaluate and compare our techniques for producing reduced regression test sets

Language models and probability of relevance

this document; the equation then represents the probability that the document that the user had in mind was in fact this one. Hiemstra [1] gives the same equation a slightly di#erent justification. The basic assumption is the same (the user is assumed to have a specific document in mind and to generate the query on the basis of this document), but instead of smoothing, the user is assumed to assign a binary importance value to each term position in the query. An important term-position is filled with a term from the document; a non-important one is filled with a general language term. If we define # i = P(term position i is important), then we get P (D, T 1 , T 2 , . . . , T n ) = P (D) n # i=1 ((1 - # i )P (T i ) +&lt

Relevance feedback for best match term weighting algorithms in information retrieval

Personalisation in full text retrieval or full text filtering implies reweighting of the query terms based on some explicit or implicit feedback from the user. Relevance feedback inputs the user's judgements on previously retrieved documents to construct a personalised query or user profile. This paper studies relevance feedback within two probabilistic models of information retrieval: the first based on statistical language models and the second based on the binary independence probabilistic model. The paper shows the resemblance of the approaches to relevance feedback of these models, introduces new approaches to relevance feedback for both models, and evaluates the new relevance feedback algorithms on the TREC collection. The paper shows that there are no significant differences between simple and sophisticated approaches to relevance feedback