Conceptual overlap between moral norms and anticipated regret in the prediction of intention: implications for theory of planned behaviour research

Moral norms and anticipated regret are widely used extensions to the theory of planned behaviour, yet there is some evidence to suggest that these constructs may conceptually overlap as predictors of intention. Two health-related behaviours with distinct moral implications (Study 1: organ donation registration, N = 352 and Study 2: condom usage, N = 1815) were therefore examined to ascertain whether moral norms and anticipated regret are indeed conceptually distinct. While evidence consistent with conceptual overlap was identified in Study 1, the evidence for such overlap in Study 2 was more ambiguous. In Study 3, a meta-analysis of existing literature revealed that the relationship between moral norms and anticipated regret was moderated by the extent of the moral implications arising from the behaviour under examination. Taken together, these findings suggest that conceptual overlap between moral norms and anticipated regret is more likely to occur among behaviours with obvious moral implications. Researchers wishing to examine the predictive utility of moral norms and anticipated regret among such behaviours would therefore be advised to aggregate these measures to form a composite variable (personal norms).

Robust extended kalman filter applied to location tracking and trajectory prediction for PCS networks

Provisioning of real-time multimedia sessions over wireless cellular network poses unique challenges due to frequent handoff and rerouting of a connection. For this reason, the wireless networks with cellular architecture require efficient user mobility estimation and prediction. This paper proposes using robust extended Kalman filter (REKF) as a location heading altitude estimator of mobile user for next cell prediction in order to improve the connection reliability and bandwidth efficiency of the underlying system. Through analysis we demonstrate that our algorithm reduces the system complexity (compared to existing approach using pattern matching and Kalman filter) as it requires only two base station measurements or only the measurement from the closest base station. Further, the technique is robust against system uncertainties due to inherent deterministic nature in the mobility model and more effective in comparison with the standard Kalman filter.

Chaos theory and assessment of forest stakeholder attitudes towards Australian forest policy

The Regional Forest Agreement process has dominated Australian forest policy for the past decade. The RFA process set in place a mechanism by which benchmark conservation values were established for forest  ecosystems, whilst addressing the needs of the timber industry. The outcomes of a number of RFA's have been fraught with controversy. Key stakeholder groups have shown disagreement with processes and  outcomes of methods employed by government both in establishing conservation reserves and areas allocated to timber harvesting. This research uses non-linear techniques to examine the dynamical behavior in stakeholder responses and to identify patterns of behavior that may lead to prediction of stakeholder responses. The method developed in this research provides a bridge between social sciences and Chaos theory.¹

A methodology for springback prediction

The springback of simple geometries can be predicted through theoretical analysis, however problems arise when transferring this analysis to the manufacturing environment. To determine why this is the case, a study
of small curvature free bending through theoreticalanalysis, manufacturing data and Finite Element (FE)simulation was completed.The theoretical analysis provided an understanding of the behavior of springback and gave accurate predictions in a controlled environment. The manufacturing and Finite Element data verified the trends predicted by theory, but lacked in accuracy. The paper concludes by proposing a prediction method based solely on the geometry that is well defined in both environments.

Analytic prediction of structural stress-strain relations of microstructured metal

Nanostructured and ultra-fine grained metals have higher strength but extremely limited ductility compared to coarse grained metals. However, their ductility can be greatly improved by introducing a specific range of grain sizes in the microstructures. In the paper, multiscale unit cell approach (UCA) is developed and applied to predict the averaged stress-strain relations of the multiscale microstructure metals. The unit cell models are three-phase structured at different scale lengths of 100 nm, 1 μm and 10 μm with different volume fractions and periodic boundary conditions. The contributions of multi-scale microstructures to the macroscopic structural properties of metals are also studied using a analytic approach—two-step mean-field method (TSMF), where three microstructural parameters are introduced and thus mechanical properties such as strength and ductility are presented as a function of these parameters. For verification of these proposed numerical and theoretical algorithms, the structural properties of the pure nickel with three-grain microstructures are studied and the results from FEA and the proposed theory have good agreement.

An illustration of variable precision rough set theory : The gender classification of the European barn swallow (Hirundo rustica)

This paper introduces a new technique in the investigation of object classification and illustrates the potential use of this technique for the analysis of a range of biological data, using avian morphometric data as an example. The nascent variable precision rough sets (VPRS) model is introduced and compared with the decision tree method ID3 (through a ‘leave n out’ approach), using the same dataset of morphometric measures of European barn swallows (Hirundo rustica) and assessing the accuracy of gender classification based on these measures. The results demonstrate that the VPRS model, allied with the use of a modern method of discretization of data, is comparable with the more traditional non-parametric ID3 decision tree method. We show that, particularly in small samples, the VPRS model can improve classification and to a lesser extent prediction aspects over ID3. Furthermore, through the ‘leave n out’ approach, some indication can be produced of the relative importance of the different morphometric measures used in this problem. In this case we suggest that VPRS has advantages over ID3, as it intelligently uses more of the morphometric data available for the data classification, whilst placing less emphasis on variables with low reliability. In biological terms, the results suggest that the gender of swallows can be determined with reasonable accuracy from morphometric data and highlight the most important variables in this process. We suggest that both analysis techniques are potentially useful for the analysis of a range of different types of biological datasets, and that VPRS in particular has potential for application to a range of biological circumstances.

Chaos theory based detection against network mimicking DDoS attacks

DDoS attack traffic is difficult to differentiate from legitimate network traffic during transit from the attacker, or zombies, to the victim. In this paper, we use the theory of network self-similarity to differentiate DDoS flooding attack traffic from legitimate self-similar traffic in the network. We observed that DDoS traffic causes a strange attractor to develop in the pattern of network traffic. From this observation, we developed a neural network detector trained by our DDoS prediction algorithm. Our preliminary experiments and analysis indicate that our proposed chaotic model can accurately and effectively detect DDoS attack traffic. Our approach has the potential to not only detect attack traffic during transit, but to also filter it.

Intervention efficacy among 'At Risk' adolescents: a test of subjective wellbeing homeostasis theory

 This study tests a number of theoretical predictions based on subjective wellbeing (SWB) Homeostasis Theory. This theory proposes that SWB is actively maintained and defended within a narrow, positive range of values around a 'set-point' for each person. Due to homeostatic control, it is predicted to be very difficult to substantially increase SWB in samples operating normally within their set-point-range. However, under conditions of homeostatic defeat, where SWB is lower than normal, successful interventions should be accompanied by a substantial increase as each person's SWB returns to lie within its normal range of values. This study tests these propositions using a sample of 4,243 participants in an Australian Federal Government Program for 'at-risk' adolescents. SWB was measured using the Personal Wellbeing Index and results are converted to a metric ranging from 0 to 100 points. The sample was divided into three sub-groups as 0-50, 51-69, and 70+ points. The theoretical prediction was confirmed. The largest post-intervention increase in SWB was in the 0-50 group and lowest in the 70+ group. However, a small increase in SWB was observed in the normal group, which was significant due to the large sample size. The implications of these findings for governments, schools and policy makers are discussed.

Reinforcement sensitivity theory and coping behaviours in alcohol use

This research examined how individual differences in coping styles and drinking motives are associated with personality in the prediction of alcohol use.

Incremental criterion prediction of personality facets over factors: obtaining unbiased estimates and confidence intervals

 Many researchers have argued that higher order models of personality such as the Five Factor Model are insufficient, and that facet-level analysis is required to better understand criteria such as well-being, job performance, and personality disorders. However, common methods in the extant literature used to estimate the incremental prediction of facets over factors have several shortcomings. This paper delineates these shortcomings by evaluating alternative methods using statistical theory, simulation, and an empirical example. We recommend using differences between Olkin-Pratt adjusted r-squared for factor versus facet regression models to estimate the incremental prediction of facets and present a method for obtaining confidence intervals for such estimates using double adjusted-. r-squared bootstrapping. We also provide an R package that implements the proposed methods.

An integrated framework for suicide risk prediction

Suicide is a major concern in society. Despite of great attention paid by the community with very substantive medico-legal implications, there has been no satisfying method that can reliably predict the future attempted or completed suicide. We present an integrated machine learning framework to tackle this challenge. Our proposed framework consists of a novel feature extraction scheme, an embedded feature selection process, a set of risk classifiers and finally, a risk calibration procedure. For temporal feature extraction, we cast the patient’s clinical history into a temporal image to which a bank of one-side filters are applied. The responses are then partly transformed into mid-level features and then selected in 1-norm framework under the extreme value theory. A set of probabilistic ordinal risk classifiers are then applied to compute the risk probabilities and further re-rank the features. Finally, the predicted risks are calibrated. Together with our Australian partner, we perform comprehensive study on data collected for the mental health cohort, and the experiments validate that our proposed framework outperforms risk assessment instruments by medical practitioners.

Constructing optimal prediction intervals by using neural networks and bootstrap method

This brief proposes an efficient technique for the construction of optimized prediction intervals (PIs) by using the bootstrap technique. The method employs an innovative PI-based cost function in the training of neural networks (NNs) used for estimation of the target variance in the bootstrap method. An optimization algorithm is developed for minimization of the cost function and adjustment of NN parameters. The performance of the optimized bootstrap method is examined for seven synthetic and real-world case studies. It is shown that application of the proposed method improves the quality of constructed PIs by more than 28% over the existing technique, leading to narrower PIs with a coverage probability greater than the nominal confidence level.