An approximate method for the solution of an influence function foil rolling model

Fleck and Johnson (Int. J. Mech. Sci. 29 (1987) 507) and Fleck et al. (Proc. Inst. Mech. Eng. 206 (1992) 119) have developed foil rolling models which allow for large deformations in the roll profile, including the possibility that the rolls flatten completely. However, these models require computationally expensive iterative solution techniques. A new approach to the approximate solution of the Fleck et al. (1992) Influence Function Model has been developed using both analytic and approximation techniques. The numerical difficulties arising from solving an integral equation in the flattened region have been reduced by applying an Inverse Hilbert Transform to get an analytic expression for the pressure. The method described in this paper is applicable to cases where there is or there is not a flat region.

Modelling the foil rolling process

A review of the main rolling models is conducted to assess their suitability for modelling the foil rolling process. Two such models are Fleck and Johnson's Hertzian model and Fleck, Johnson, Mear and Zhang's Influence Function model. Both of these models are approximated through the use of perturbation methods. Decrease in the computation time resulted when compared with the numerical solution. The Hertzian model was approximated using the ratio of the yield stress of the strip to the plane-strain Young's Modulus of the rolls as the small perturbation parameter. The Influence Function model approximation takes advantage of the solution of the well-known Aerofoil Integral Equation to gain an insight into how the choice of interior boundary points affects the stability of numerical solution of the model's equations. These approximations require less computation than their full models and, in the case of the Hertzian approximation, only introduces a small error in the predictions of roll force roll torque. Hence the Hertzian approximate method is suitable for on-line control. The predictions from the Influence Function approximation underestimates the predictions from the numerical results. Better approximation of the pressure in the plastic reduction regions is the main source of this error.

Metacognition in substance misuse

Metacognitive theory provides a novel conceptual framework to understand the development and maintenance of psychopathology. It emphasizes the importance of stored knowledge guiding the individual’s plan for coping with heightened cognitive-affective arousal. According to the metacognitive model individuals experience strong affective responses and engage in a process of metacognitive appraisal and initiation of coping responses in the pursuit of cognitive-affective self-regulation. This chapter outlines the details of this theoretical approach as applied to substance misuse and the metacognitive treatment components aimed at interrupting the selection of maladaptive coping responses.

Parameter estimation of structural commodity price models

Commodity price modeling is normally approached in terms of structural time-series models, in which the different components (states) have a financial interpretation. The parameters of these models can be estimated using maximum likelihood. This approach results in a non-linear parameter estimation problem and thus a key issue is how to obtain reliable initial estimates. In this paper, we focus on the initial parameter estimation problem for the Schwartz-Smith two-factor model commonly used in asset valuation. We propose the use of a two-step method. The first step considers a univariate model based only on the spot price and uses a transfer function model to obtain initial estimates of the fundamental parameters. The second step uses the estimates obtained in the first step to initialize a re-parameterized state-space-innovations based estimator, which includes information related to future prices. The second step refines the estimates obtained in the first step and also gives estimates of the remaining parameters in the model. This paper is part tutorial in nature and gives an introduction to aspects of commodity price modeling and the associated parameter estimation problem.

A new ambiguity acceptance test threshold determination method with controllable failure rate

The ambiguity acceptance test is an important quality control procedure in high precision GNSS data processing. Although the ambiguity acceptance test methods have been extensively investigated, its threshold determine method is still not well understood. Currently, the threshold is determined with the empirical approach or the fixed failure rate (FF-) approach. The empirical approach is simple but lacking in theoretical basis, while the FF-approach is theoretical rigorous but computationally demanding. Hence, the key of the threshold determination problem is how to efficiently determine the threshold in a reasonable way. In this study, a new threshold determination method named threshold function method is proposed to reduce the complexity of the FF-approach. The threshold function method simplifies the FF-approach by a modeling procedure and an approximation procedure. The modeling procedure uses a rational function model to describe the relationship between the FF-difference test threshold and the integer least-squares (ILS) success rate. The approximation procedure replaces the ILS success rate with the easy-to-calculate integer bootstrapping (IB) success rate. Corresponding modeling error and approximation error are analysed with simulation data to avoid nuisance biases and unrealistic stochastic model impact. The results indicate the proposed method can greatly simplify the FF-approach without introducing significant modeling error. The threshold function method makes the fixed failure rate threshold determination method feasible for real-time applications.

Re-conceptualising IS function's support performance : a preliminary model

While IS function has gained widespread attention for over two decades, there is little consensus among information systems (IS) researchers and practitioners on how best to evaluate IS function's support performance. This paper reports on preliminary findings of a larger research effort proceeds from a central interest in the importance of evaluating IS function's support in organisations. This study is the first that attempts to re-conceptualise and conceive evaluate IS function's support as a multi- dimensional formative construct. We argue that a holistic measure for evaluating evaluate IS function's support should consist of dimensions that together assess the variety of the support functions and the quality of the support services provided to end-users. Thus, the proposed model consists of two halves, "Variety" and "Quality" within which resides seven dimensions. The Variety half includes five dimensions: Training; Documentation; Data- related Support, Software-related Support; and Hardware-related Support. The Quality half includes two dimensions: IS Support Staff and Support Services Performance. The proposed model is derived using a directed content analysis of 83 studies; from top IS outlets, employing the characteristics of the analytic theory and consistent with formative construct development procedures.

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

A hybrid quality function deployment and cybernetic analytic network process model for project manager selection

Identifying appropriate decision criteria and making optimal decisions in a structured way is a complex process. This paper presents an approach for doing this in the form of a hybrid Quality Function Deployment (QFD) and Cybernetic Analytic Network Process (CANP) model for project manager selection. This involves the use of QFD to translate the owner's project management expectations into selection criteria and the CANP to weight the expectations and selection criteria. The supermatrix approach then prioritises the candidates with respect to the overall decision-making goal. A case study is used to demonstrate the use of the model in selecting a renovation project manager. This involves the development of 18 selection criteria in response to the owner's three main expectations of time, cost and quality.

Developments of the total entropy utility function for the dual purpose of model discrimination and parameter estimation in Bayesian design

The total entropy utility function is considered for the dual purpose of Bayesian design for model discrimination and parameter estimation. A sequential design setting is proposed where it is shown how to efficiently estimate the total entropy utility for a wide variety of data types. Utility estimation relies on forming particle approximations to a number of intractable integrals which is afforded by the use of the sequential Monte Carlo algorithm for Bayesian inference. A number of motivating examples are considered for demonstrating the performance of total entropy in comparison to utilities for model discrimination and parameter estimation. The results suggest that the total entropy utility selects designs which are efficient under both experimental goals with little compromise in achieving either goal. As such, the total entropy utility is advocated as a general utility for Bayesian design in the presence of model uncertainty.

Smoothing a Discrete Hazard Function for the Number of Patients Colonized with Methicillin-Resistance Staphylococcus Aureus in an Intensive Care Unit

A new method for estimating the time to colonization of Methicillin-resistant Staphylococcus Aureus (MRSA) patients is developed in this paper. The time to colonization of MRSA is modelled using a Bayesian smoothing approach for the hazard function. There are two prior models discussed in this paper: the first difference prior and the second difference prior. The second difference prior model gives smoother estimates of the hazard functions and, when applied to data from an intensive care unit (ICU), clearly shows increasing hazard up to day 13, then a decreasing hazard. The results clearly demonstrate that the hazard is not constant and provide a useful quantification of the effect of length of stay on the risk of MRSA colonization which provides useful insight.

Combining ganglion cell topology and data of patients with glaucoma to determine a structure-function map

PURPOSE: To introduce techniques for deriving a map that relates visual field locations to optic nerve head (ONH) sectors and to use the techniques to derive a map relating Medmont perimetric data to data from the Heidelberg Retinal Tomograph. METHODS: Spearman correlation coefficients were calculated relating each visual field location (Medmont M700) to rim area and volume measures for 10 degrees ONH sectors (HRT III software) for 57 participants: 34 with glaucoma, 18 with suspected glaucoma, and 5 with ocular hypertension. Correlations were constrained to be anatomically plausible with a computational model of the axon growth of retinal ganglion cells (Algorithm GROW). GROW generated a map relating field locations to sectors of the ONH. The sector with the maximum statistically significant (P < 0.05) correlation coefficient within 40 degrees of the angle predicted by GROW for each location was computed. Before correlation, both functional and structural data were normalized by either normative data or the fellow eye in each participant. RESULTS: The model of axon growth produced a 24-2 map that is qualitatively similar to existing maps derived from empiric data. When GROW was used in conjunction with normative data, 31% of field locations exhibited a statistically significant relationship. This significance increased to 67% (z-test, z = 4.84; P < 0.001) when both field and rim area data were normalized with the fellow eye. CONCLUSIONS: A computational model of axon growth and normalizing data by the fellow eye can assist in constructing an anatomically plausible map connecting visual field data and sectoral ONH data.

Molecular architecture of the human sinus node: insights into the function of the cardiac pacemaker.

BACKGROUND: Although we know much about the molecular makeup of the sinus node (SN) in small mammals, little is known about it in humans. The aims of the present study were to investigate the expression of ion channels in the human SN and to use the data to predict electrical activity. METHODS AND RESULTS: Quantitative polymerase chain reaction, in situ hybridization, and immunofluorescence were used to analyze 6 human tissue samples. Messenger RNA (mRNA) for 120 ion channels (and some related proteins) was measured in the SN, a novel paranodal area, and the right atrium (RA). The results showed, for example, that in the SN compared with the RA, there was a lower expression of Na(v)1.5, K(v)4.3, K(v)1.5, ERG, K(ir)2.1, K(ir)6.2, RyR2, SERCA2a, Cx40, and Cx43 mRNAs but a higher expression of Ca(v)1.3, Ca(v)3.1, HCN1, and HCN4 mRNAs. The expression pattern of many ion channels in the paranodal area was intermediate between that of the SN and RA; however, compared with the SN and RA, the paranodal area showed greater expression of K(v)4.2, K(ir)6.1, TASK1, SK2, and MiRP2. Expression of ion channel proteins was in agreement with expression of the corresponding mRNAs. The levels of mRNA in the SN, as a percentage of those in the RA, were used to estimate conductances of key ionic currents as a percentage of those in a mathematical model of human atrial action potential. The resulting SN model successfully produced pacemaking. CONCLUSIONS: Ion channels show a complex and heterogeneous pattern of expression in the SN, paranodal area, and RA in humans, and the expression pattern is appropriate to explain pacemaking.

Fundamental solution and discrete random walk model for time-space fractional diffusion equation

In this paper, we consider a time-space fractional diffusion equation of distributed order (TSFDEDO). The TSFDEDO is obtained from the standard advection-dispersion equation by replacing the first-order time derivative by the Caputo fractional derivative of order α∈(0,1], the first-order and second-order space derivatives by the Riesz fractional derivatives of orders β 1∈(0,1) and β 2∈(1,2], respectively. We derive the fundamental solution for the TSFDEDO with an initial condition (TSFDEDO-IC). The fundamental solution can be interpreted as a spatial probability density function evolving in time. We also investigate a discrete random walk model based on an explicit finite difference approximation for the TSFDEDO-IC.

The development and evaluation of an innovative nursing practice model to improve undergraduate nursing students' competence in paediatric physical assessment

Introduction The purpose of this study was to develop, implement and evaluate the impact of an educational intervention, comprising an innovative model of clinical decisionmaking and educational delivery strategy for facilitating nursing students‘ learning and development of competence in paediatric physical assessment practices. Background of the study Nursing students have an undergraduate education that aims to produce graduates of a generalist nature who demonstrate entry level competence for providing nursing care in a variety of health settings. Consistent with population morbidity and health care roles, paediatric nursing concepts typically form a comparatively small part of undergraduate curricula and students‘ exposure to paediatric physical assessment concepts and principles are brief. However, the nursing shortage has changed traditional nursing employment patterns and new graduates form the majority of the recruitment pool for paediatric nursing speciality staff. Paediatric nursing is a popular career choice for graduates and anecdotal evidence suggests that nursing students who select a clinical placement in their final year intend to seek employment in paediatrics upon graduation. Although concepts of paediatric nursing are included within undergraduate curriculum, students‘ ability to develop the required habits of mind to practice in what is still regarded as a speciality area of practice is somewhat limited. One of the areas of practice where this particularly impacts is in paediatric nursing physical assessment. Physical assessment is a fundamental component of nursing practice and competence in this area of practice is central to nursing students‘ development of clinical capability for practice as a registered nurse. Timely recognition of physiologic deterioration of patients is a key outcome of nurses‘ competent use of physical assessment strategies, regardless of the practice context. In paediatric nursing contexts children‘s physical assessment practices must specifically accommodate the child‘s different physiological composition, function and pattern of clinical deterioration (Hockenberry & Barrera, 2007). Thus, to effectively manage physical assessment of patients within the paediatric practice setting nursing students need to integrate paediatric nursing theory into their practice. This requires significant information processing and it is in this process where students are frequently challenged. The provision of rules or models can guide practice and assist novice-level nurses to develop their capabilities (Benner, 1984; Benner, Hooper-Kyriakidis & Stannard, 1999). Nursing practice models are cognitive tools that represent simplified patterns of expert analysis employing concepts that suit the limited reasoning of the inexperienced, and can represent the =rules‘ referred to by Benner (1984). Without a practice model of physical assessment students are likely to be uncertain about how to proceed with data collection, the interpretation of paediatric clinical findings and the appraisal of findings. These circumstances can result in ad hoc and unreliable nursing physical assessment that forms a poor basis for nursing decisions. The educational intervention developed as part of this study sought to resolve this problem and support nursing students‘ development of competence in paediatric physical assessment. Methods This study utilised the Context Input Process Product (CIPP) Model by Stufflebeam (2004) as the theoretical framework that underpinned the research design and evaluation methodology. Each of the four elements in the CIPP model were utilised to guide discrete stages of this study. The Context element informed design of the clinical decision-making process, the Paediatric Nursing Physical Assessment model. The Input element was utilised in appraising relevant literature, identifying an appropriate instructional methodology to facilitate learning and educational intervention delivery to undergraduate nursing students, and development of program content (the CD-ROM kit). Study One employed the Process element and used expert panel approaches to review and refine instructional methods, identifying potential barriers to obtaining an effective evaluation outcome. The Product element guided design and implementation of Study Two, which was conducted in two phases. Phase One employed a quasiexperimental between-subjects methodology to evaluate the impact of the educational intervention on nursing students‘ clinical performance and selfappraisal of practices in paediatric physical assessment. Phase Two employed a thematic analysis and explored the experiences and perspectives of a sample subgroup of nursing students who used the PNPA CD-ROM kit as preparation for paediatric clinical placement. Results Results from the Process review in Study One indicated that the prototype CDROM kit containing the PNPA model met the predetermined benchmarks for face validity and the impact evaluation instrumentation had adequate content validity in comparison with predetermined benchmarks. In the first phase of Study Two the educational intervention did not result in statistically significant differences in measures of student performance or self-appraisal of practice. However, in Phase Two qualitative commentary from students, and from the expert panel who reviewed the prototype CD-ROM kit (Study One, Phase One), strongly endorsed the quality of the intervention and its potential for supporting learning. This raises questions regarding transfer of learning and it is likely that, within this study, several factors have influenced students‘ transfer of learning from the educational intervention to the clinical practice environment, where outcomes were measured. Conclusion In summary, the educational intervention employed in this study provides insights into the potential e-learning approaches offer for delivering authentic learning experiences to undergraduate nursing students. Findings in this study raise important questions regarding possible pedagogical influences on learning outcomes, issues within the transfer of theory to practice and factors that may have influenced findings within the context of this study. This study makes a unique contribution to nursing education, specifically with respect to progressing an understanding of the challenges faced in employing instructive methods to impact upon nursing students‘ development of competence. The important contribution transfer of learning processes make to students‘ transition into the professional practice context and to their development of competence within the context of speciality practice is also highlighted. This study contributes to a greater awareness of the complexity of translating theoretical learning at undergraduate level into clinical practice, particularly within speciality contexts.