Analytical model of pass-by-pass strain in rod (or bar) rolling and its applications to prediction of austenite grain size

Rod rolling is a process where the deformation state of the workpiece between the work rolls is quite different from the strip rolling process. However, in most microstructure evolution models, the simple area strains (natural logarithm of the area reduction ratio) multiplied by a constant have been used to compute pass-by-pass evolution of austenite grain size (AGS) in rod (or bar) rolling, without any verification. The strains at a given pass play a crucial role in determining the recrystallization behavior (static or dynamic). In this study, an analytical model that calculates the pass-by-pass strain and strain rate in rod rolling has been developed and verified by conducting four-pass (oval–round) bar and plate rolling experiments. Numerical simulations have then been carried out for the four-pass rolling sequence using the area strain model and the new analytical model, focusing on the effect of the method for calculating the strain on the recrystallization behavior and evolution of AGS. The AGS predicted was compared with those obtained from hot torsion tests. It is shown that the analytical model developed in this study is more appropriate in the analysis of bar (or rod) rolling. It was found that the recrystallization behavior and evolution of AGS during this process were influenced significantly by the calculation method for the deformation parameters (strain and strain rate). The pass-by-pass strain obtained from the simple area strain model is inadequate to be used as an input to the equations for recrystallization and AGS evolution under these rolling conditions.

Psychotic-like experiences in a community sample of adolescents: implications for the continuum model of psychosis and prediction of schizophrenia

Studies conducted in community samples suggest that psychotic-like experiences are common in the general population, leading to suggestions that they are either variations of normal personality or are different expressions of underlying vulnerability to psychotic disorder. Different types of psychotic symptoms may exist, some being normal variants and some having implications for mental health and functioning. The aim of the present study was to determine if different subtypes of psychotic-like experiences could be identified in a community sample of adolescents and to investigate if particular subtypes were more likely to be associated with psychosocial difficulties, that is, distress, depression and poor functioning, than other subtypes. Eight hundred and seventy-five Year 10 students from 34 schools participated in a cross-sectional survey that measured psychotic-like experiences using the Community Assessment of Psychic Experiences; depression using the Centre for Epidemiologic Studies Depression Scale; and psychosocial functioning using the Revised Multidimensional Assessment of Functioning Scale. Factor analysis was conducted to identify any subtypes of psychotic experiences. Four subtypes of psychotic-like experiences were identified: Bizarre Experiences, Perceptual Abnormalities, Persecutory Ideas, and Magical Thinking. Intermittent, infrequent psychotic experiences were common, but frequent experiences were not. Bizarre Experiences, Perceptual Abnormalities and Persecutory Ideas were strongly associated with distress, depression and poor functioning. Magical Thinking was only weakly associated with these variables. Overall these findings may suggest that infrequent psychotic-like experiences are unlikely to be a specific risk factor for onset of a psychotic disorder in community samples. Given that the different subtypes had varying associations with current difficulties it is suggested that not all subtypes confer the same risk for onset of psychotic disorder and poor outcome. Bizarre Experiences, Perceptual Abnormalities and Persecutory Ideas may represent expressions of underlying vulnerability to psychotic disorder, but Magical Thinking may be a normal personality variant.

Effect of mean stress on multiaxial ratcheting life: a simplified life prediction model based on average equivalent stress amplitude

This article proposes a model to predict uniaxial and multiaxial ratcheting life by addressing the three primary parameters that influence failure life: fatigue damage, ratcheting damage and the multiaxial loading path. These three factors are addressed in the present model by (a) the stress amplitude for fatigue damage, (b) mean stress-dependent Goodman equation for ratcheting damage and (c) an inherent weight factor based on average equivalent stress to account for the multiaxial loading. The proposed model requires only two material constants which can be easily determined from uniaxial symmetric stress-controlled fatigue tests. Experimental ratcheting life data collected from the literature for 1025 and 42CrMo steel under multiaxial proportional and nonproportional constant amplitude loading ratcheting with triangular sinusoidal and trapezoidal waveform (i.e. linear, rhombic, circular, elliptical and square stress paths) have shown good agreement with the proposed model.

A partnership model of corporate ethics

The stock market crash of 1987 had a profound effect on corporate Australia and the Australian community in general. The fall-out revealed that some of our most respected business figures had not been as ethical, or even as lawful, as we would have hoped. This impropriety produced in Australia an awakening to business ethics. Whilst many companies endeavoured to introduce ethical practices into their corporations, they perceived ethics as a way of minimising damage to the corporation and in some cases as a means of competitive advantage. What was lost was the reason that one should embark on business ethics; and that is to make the society and corporate Australia a more ethical place in which to exist.This paper proposes a model based on 2 factors: commitment and partnerships, as a means of enabling corporate Australia to refocus attention on the main purpose of being inherently ethical in all that we do. This ethical model requires a commitment to partnerships with all stakeholders both internal and external in an attempt to enhance the level of ethical business practices that are contemplated and pursued within corporate Australia. Whilst the research agenda and the information collected is Australian-based, it is hoped that the ideas contained within this paper will have a wider appeal to corporations in similar cultural settings.

Cardiovascular risk prediction model development and assessment

The aim of this article is to review the development and assessment of cardiovascular risk prediction models and to discuss the predictive value of a risk factor as well as to introduce new assessment methods to evaluate a risk prediction model. Many cardiovascular risk prediction models have been developed during the past three decades. However, there has not been consistent agreement regarding how to appropriately assess a risk prediction model, especially when new markers are added to an existing model. The area under the receiver operating characteristic (ROC) curve has traditionally been used to assess the discriminatory ability of a risk prediction model. However, recent studies suggest that this method has its limitations and cannot be the sole approach to evaluate the usefulness of a new marker. New assessment methods are being developed to appropriately assess a risk prediction model and they will be gradually used in clinical and epidemiological studies.

An artificial neural network-based hairiness prediction model for worsted wool yarns

This study evaluated the performance of multilayer perceptron (MLP) and multivariate linear regression (MLR) models for predicting the hairiness of worsted-spun wool yarns from various top, yarn and processing parameters. The results indicated that the MLP model predicted yarn hairiness more accurately than the MLR model, and should have wide mill specific applications. On the basis of sensitivity analysis, the factors that affected yarn hairiness significantly included yarn twist, ring size, average fiber length (hauteur), fiber diameter and yarn count, with twist having the greatest impact on yarn hairiness.

A performance model of speculative prefetching in distributed information systems

Previous studies in speculative prefetching focus on building and evaluating access models for the purpose of access prediction. This paper investigates a complementary area which has been largely ignored, that of performance modelling. We use improvement in access time as the performance metric, for which we derive a formula in terms of resource parameters (time available and time required for prefetching) and speculative parameters (probabilities for next access). The performance maximization problem is expressed as a stretch knapsack problem. We develop an algorithm to maximize the improvement in access time by solving the stretch knapsack problem, using theoretically proven apparatus to reduce the search space. Integration between speculative prefetching and caching is also investigated, albeit under the assumption of equal item sizes.

Rebound or resignation: developing a predictive model of return to subjective wellbeing set-point

Though subjective wellbeing (SWB) is generally stable and consistent over time, it can fall below its set-point in response to adverse life events. However, deviations from set-point levels are usually only temporary, as homeostatic processes operate to return SWB to its normal state. Given that income and close interpersonal relationships have been proposed as powerful external resources that are coincident with higher SWB, access to these resources may be an important predictor of whether or not a person is likely to recover their SWB following a departure from their set-point. Under the guiding framework of SWB Homeostasis Theory, this study considers whether access to a higher income and a committed partner can predict whether people who score lower than normal for SWB at baseline will return to normal set-point levels of SWB (rebound) or remain below the normal range (resigned) at follow-up. Participants were 733 people (53.3 % female) from the Australian Unity Longitudinal Wellbeing Study who ranged in age from 20 to 92 years (M = 59.65 years; SD = 13.15). Logistic regression analyses revealed that participants’ demographic characteristics were poor predictors of whether they rebounded or resigned. Consistent with homeostasis theory, the extent of departure from the proposed normal SWB set-point at baseline was significantly associated with rebound or resignation at time 2. These findings have implications for the way that SWB measures can be used in professional practice to identify people who are particularly vulnerable to depression and to guide the provision of appropriate and effective therapeutic interventions.

A hybrid model of fuzzy ARTMAP and genetic algorithm for data classification and rule extraction

A two-stage hybrid model for data classification and rule extraction is proposed. The first stage uses a Fuzzy ARTMAP (FAM) classifier with Q-learning (known as QFAM) for incremental learning of data samples, while the second stage uses a Genetic Algorithm (GA) for rule extraction from QFAM. Given a new data sample, the resulting hybrid model, known as QFAM-GA, is able to provide prediction pertaining to the target class of the data sample as well as to give a fuzzy if-then rule to explain the prediction. To reduce the network complexity, a pruning scheme using Q-values is applied to reduce the number of prototypes generated by QFAM. A 'don't care' technique is employed to minimize the number of input features using the GA. A number of benchmark problems are used to evaluate the effectiveness of QFAM-GA in terms of test accuracy, noise tolerance, model complexity (number of rules and total rule length). The results are comparable, if not better, than many other models reported in the literature. The main significance of this research is a usable and useful intelligent model (i.e., QFAM-GA) for data classification in noisy conditions with the capability of yielding a set of explanatory rules with minimum antecedents. In addition, QFAM-GA is able to maximize accuracy and minimize model complexity simultaneously. The empirical outcome positively demonstrate the potential impact of QFAM-GA in the practical environment, i.e., providing an accurate prediction with a concise justification pertaining to the prediction to the domain users, therefore allowing domain users to adopt QFAM-GA as a useful decision support tool in assisting their decision-making processes.

DeepSoft: a vision for a deep model of software

Although software analytics has experienced rapid growth as a research area, it has not yet reached its full potential for wide industrial adoption. Most of the existing work in software analytics still relies heavily on costly manual feature engineering processes, and they mainly address the traditional classification problems, as opposed to predicting future events. We present a vision for \emph{DeepSoft}, an \emph{end-to-end} generic framework for modeling software and its development process to predict future risks and recommend interventions. DeepSoft, partly inspired by human memory, is built upon the powerful deep learning-based Long Short Term Memory architecture that is capable of learning long-term temporal dependencies that occur in software evolution. Such deep learned patterns of software can be used to address a range of challenging problems such as code and task recommendation and prediction. DeepSoft provides a new approach for research into modeling of source code, risk prediction and mitigation, developer modeling, and automatically generating code patches from bug reports.