Creep crack growth in cast steam turbine casing steels

The creep rupture properties of cast ½Cr½Mo¼V and 1Cr1Mo¼V alloy steel used in the manufacture of power station steam generating plant. have been investigated. The effects of constraint and geometry on the creep rupture properties are also considered. The validity of various criteria controlling macroscopic creep crack growth in cast CrMoV alloys has been examined. It is found that neither the stress intensity factor nor reference stress correlate satisfactorily the creep crack growth rates at the test temperature of 550°C. Certain minimum displacements must be achieved for crack initiation and propagation. It is found that this displacement as measured by crack opening displacement or crack aspect ratio, is the same in both compact tension and centre-cracked panel geometries, is invariant with crack length and decreases with increasing constraint. The effect of constraint on creep crack growth rate in the two geometries is less conclusive. A new model describing creep crack growth in cast CrMoV alloy steels has been developed. The model is based on the results from a numerical finite element creep analysis of the relaxation and redistribution of stress ahead of an incubating creep crack . It is found that macroscopic creep crack growth in a material undergoing either plane stress or plane strain deformation can be described by a fracture stress which is based on the Von Mises equivalent stress. It has been shown that this model is capable of rationalising all of the experimental crack velocity data from the cast CrMoV alloys. The resultant degree of data correlation is far superior to that obtained when using the stress intensity factor or reference stress. A cumulative damage creep fracture model based upon the results from the numerical analysis has been developed. It is found that the model is capable of predicting the behaviour of propagating creep cracks in cast CrMoV alloys from smooth bar creep rupture data.

Influence of ethical beliefs, national culture and institutions on preferences for HRM in Oman

Purpose – The paper seeks to investigate the association between ethical beliefs, aspects of national culture and national institutions, and preferences for specific human resource management practices in the Sultanate of Oman. Design/methodology/approach – A total of 712 individuals working in six organisations (both private and public sectors) responded to a self-administered questionnaire in the Sultanate of Oman. To test the raised research questions of the proposed framework, the methodology of structural equation models was used. Findings – The results highlight significant differences in the belief systems on the basis of different demographic characteristics. The findings also confirm impact of ethical beliefs, and aspects of national culture and national institutions on preferences for human resource management (HRM) practices. Research limitations/implications – Although the goodness-of-fit indexes confirmed the validity of the proposed operational model, some indices were attained at rather flexible levels. Practical implications – Studies on managerial beliefs and values can offer important insights into the extent that work is viewed as an integral life activity. Such information can help differentiate among managerial styles in various cultures, and in predicting managerial behaviour such as ethical decision-making. Based on such understanding, the findings can be used to educate government officials and outside consultants interested in Oman. Originality/value – The study contributes to the accumulation of knowledge about under-researched developing countries such as Oman, as limited data are available on HRM, value orientations and ethical beliefs' issues in this region.

Travelling-wave model of semiconductor optical amplifier based non-linear loop mirror

A travelling-wave model of a semiconductor optical amplifier based non-linear loop mirror is developed to investigate the importance of travelling-wave effects and gain/phase dynamics in predicting device behaviour. A constant effective carrier recovery lifetime approximation is found to be reasonably accurate (±10%) within a wide range of control pulse energies. Based on this approximation, a heuristic model is developed for maximum computational efficiency. The models are applied to a particular configuration involving feedback.

Validação numérica de leis coesivas triangulares para previsão de resistência de juntas adesivas em tração e corte puros

As ligações adesivas surgiram pela necessidade de se encontrar formas de unir componentes, por vezes de materiais distintos, através de técnicas mais vantajosas. Atualmente, qualquer estrutura deve ser resistente, robusta e leve, o que amplificou o interesse industrial e investigação nas ligações adesivas, principalmente na melhoria das propriedades de resistência e fratura de materiais. Desta forma, nos últimos anos, a utilização de juntas adesivas em aplicações industriais tem aumentando gradualmente, substituindo alguns métodos de ligação tradicionais, por apresentarem vantagens, tais como redução de concentração de tensões, reduzido peso e facilidade de processamento/fabrico. Em qualquer área da indústria, a aplicação em larga escala de uma determinada técnica de ligação supõe que estão disponíveis ferramentas confiáveis para o projeto e previsão da rotura. Neste âmbito, os Modelos de Dano Coesivo (MDC) são uma ferramenta fundamental, apesar de ser necessário estimar as leis coesivas do adesivo à tração e corte para entrada nos modelos numéricos. Nesta dissertação o trabalho experimental consistiu no tratamento de dados com vista à obtenção de GIc e GIIc, com a devida comparação de diferentes métodos de redução, bem como potencialidades e limitações dos mesmos. É realizada uma comparação dos três adesivos: Araldite® AV138, Araldite® 2015 e SikaForce® 7752. Neste trabalho estudou-se também numericamente a adequação de leis coesivas triangulares na previsão no comportamento de juntas adesivas, nomeadamente nas curvas forçadeslocamento (P-) de ensaios Double-Cantilever Beam para caracterização à tração e ensaios End-Notched Flexure para caraterização ao corte. Os ensaios foram simulados numericamente pelo software ABAQUS®, recorrendo ao Método de Elementos Finitos (MEF) e um MDC triangular, com o intuito de estimar a lei coesiva de cada um dos adesivos. Para os adesivos Araldite®AV138 e Araldite®2015, à tração e ao corte, a lei triangular previu o comportamento do adesivo com alguma razoabilidade. Para a previsão da resistência do adesivo SikaForce® 7752, a lei triangular não se ajustou convenientemente nem à tração nem ao corte. Considera-se que, para este adesivo, uma lei trapezoidal é a que melhor se adequa, devido à ductilidade do mesmo.

A next application prediction service using the BaranC framework

Predicting user behaviour enables user assistant services provide personalized services to the users. This requires a comprehensive user model that can be created by monitoring user interactions and activities. BaranC is a framework that performs user interface (UI) monitoring (and collects all associated context data), builds a user model, and supports services that make use of the user model. A prediction service, Next-App, is built to demonstrate the use of the framework and to evaluate the usefulness of such a prediction service. Next-App analyses a user's data, learns patterns, makes a model for a user, and finally predicts, based on the user model and current context, what application(s) the user is likely to want to use. The prediction is pro-active and dynamic, reflecting the current context, and is also dynamic in that it responds to changes in the user model, as might occur over time as a user's habits change. Initial evaluation of Next-App indicates a high-level of satisfaction with the service.

Strategies for identifying and predicting islet autoantigen T-cell epitopes in insulin-dependent diabetes mellitus

T cells recognize peptide epitopes bound to major histocompatibility complex molecules. Human T-cell epitopes have diagnostic and therapeutic applications in autoimmune diseases. However, their accurate definition within an autoantigen by T-cell bioassay, usually proliferation, involves many costly peptides and a large amount of blood, We have therefore developed a strategy to predict T-cell epitopes and applied it to tyrosine phosphatase IA-2, an autoantigen in IDDM, and HLA-DR4(*0401). First, the binding of synthetic overlapping peptides encompassing IA-2 was measured directly to purified DR4. Secondly, a large amount of HLA-DR4 binding data were analysed by alignment using a genetic algorithm and were used to train an artificial neural network to predict the affinity of binding. This bioinformatic prediction method was then validated experimentally and used to predict DR4 binding peptides in IA-2. The binding set encompassed 85% of experimentally determined T-cell epitopes. Both the experimental and bioinformatic methods had high negative predictive values, 92% and 95%, indicating that this strategy of combining experimental results with computer modelling should lead to a significant reduction in the amount of blood and the number of peptides required to define T-cell epitopes in humans.

Factors predicting the need for insulin therapy in patients with gestational diabetes mellitus

Aim: To determine the possible factors predicting the insulin requirement in pregnancies complicated by gestational diabetes mellitus (GDM). Method: A total of 294 patients with GDM diagnosed by the 100-g/3-h oral glucose tolerance test (OGTT) were studied. The following factors were analyzed: maternal age, nulliparity, family history of diabetes, prepregnancy BMI, prior GDM, prior fetal macrosomia, multiple pregnancy, polyhydramnios, gestational age at diagnosis of GDM, smoking, hypertension, number of abnormal 100-g/3-h OGTT values, and glycated hemoglobin (HbA1c). The association between each factor and the need for insulin therapy was then analyzed individually. The performance of these factors to predict the probability of insulin therapy was estimated using a logistic regression model. Results: Univariate analysis showed a positive correlation between insulin therapy and prepregnancy BMI, family history of diabetes, hypertension, prior GDM, prior fetal macrosomia, number of abnormal 100-g/3-h OGTT values, and HbA1c (P < 0.05). Prepregnancy BMI, family history of diabetes, number of abnormal 100-g/3-h OGTT values and HbA1c were statistically significant variables in the logistic regression model. Conclusions: The probability of insulin therapy can be estimated in pregnant women with GDM based on prepregnancy BMI, family history of diabetes, number of abnormal 100-g/3-h OGTT values, and HbA1c concentration. (C) 2010 Elsevier Ireland Ltd. All rights reserved.

Factors predicting mortality in type 2 diabetes. With special reference to physical exercise, blood pressure, proteinuria, inflammation and P wave duration

Background: Type 2 diabetes patients have a 2-4 fold risk of cardiovascular disease (CVD) compared to the general population. In type 2 diabetes, several CVD risk factors have been identified, including obesity, hypertension, hyperglycemia, proteinuria, sedentary lifestyle and dyslipidemia. Although much of the excess CVD risk can be attributed to these risk factors, a significant proportion is still unknown. Aims: To assess in middle-aged type 2 diabetic subjects the joint relations of several conventional and non-conventional CVD risk factors with respect to cardiovascular and total mortality. Subjects and methods: This thesis is part of a large prospective, population based East-West type 2 diabetes study that was launched in 1982-1984. It includes 1,059 middle-aged (45-64 years old) participants. At baseline, a thorough clinical examination and laboratory measurements were performed and an ECG was recorded. The latest follow-up study was performed 18 years later in January 2001 (when the subjects were 63-81 years old). The study endpoints were total mortality and mortality due to CVD, coronary heart disease (CHD) and stroke. Results: Physically more active patients had significantly reduced total, CVD and CHD mortality independent of high-sensitivity C-reactive protein (hs-CRP) levels unless proteinuria was present. Among physically active patients with a hs-CRP level >3 mg/L, the prognosis of CVD mortality was similar to patients with hs-CRP levels ≤3 mg/L. The worst prognosis was among physically inactive patients with hs-CRP levels >3 mg/L. Physically active patients with proteinuria had significantly increased total and CVD mortality by multivariate analyses. After adjustment for confounding factors, patients with proteinuria and a systolic BP <130 mmHg had a significant increase in total and CVD mortality compared to those with a systolic BP between 130 and 160 mmHg. The prognosis was similar in patients with a systolic BP <130 mmHg and ≥160 mmHg. Among patients without proteinuria, a systolic BP <130 mmHg was associated with a non-significant reduction in mortality. A P wave duration ≥114 ms was associated with a 2.5-fold increase in stroke mortality among patients with prevalent CHD or claudication. This finding persisted in multivariable analyses. Among patients with no comorbidities, there was no relationship between P wave duration and stroke mortality. Conclusions: Physical activity reduces total and CVD mortality in patients with type 2 diabetes without proteinuria or with elevated levels of hs-CRP, suggesting that the anti-inflammatory effect of physical activity can counteract increased CVD morbidity and mortality associated with a high CRP level. In patients with proteinuria the protective effect was not, however, present. Among patients with proteinuria, systolic BP <130 mmHg may increase mortality due to CVD. These results demonstrate the importance of early intervention to prevent CVD and to control all-cause mortality among patients with type 2 diabetes. The presence of proteinuria should be taken into account when defining the target systolic BP level for prevention of CVD deaths. A prolongation of the duration of the P wave was associated with increased stroke mortality among high-risk patients with type 2 diabetes. P wave duration is easy to measure and merits further examination to evaluate its importance for estimation of the risk of stroke among patients with type 2 diabetes.

Predicting trainees' intentions to transfer training : an application of the theory of planned behaviour

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

Modelling monsoons: understanding and predicting current and future behaviour

The global monsoon system is so varied and complex that understanding and predicting its diverse behaviour remains a challenge that will occupy modellers for many years to come. Despite the difficult task ahead, an improved monsoon modelling capability has been realized through the inclusion of more detailed physics of the climate system and higher resolution in our numerical models. Perhaps the most crucial improvement to date has been the development of coupled ocean-atmosphere models. From subseasonal to interdecadal time scales, only through the inclusion of air-sea interaction can the proper phasing and teleconnections of convection be attained with respect to sea surface temperature variations. Even then, the response to slow variations in remote forcings (e.g., El Niño—Southern Oscillation) does not result in a robust solution, as there are a host of competing modes of variability that must be represented, including those that appear to be chaotic. Understanding the links between monsoons and land surface processes is not as mature as that explored regarding air-sea interactions. A land surface forcing signal appears to dominate the onset of wet season rainfall over the North American monsoon region, though the relative role of ocean versus land forcing remains a topic of investigation in all the monsoon systems. Also, improved forecasts have been made during periods in which additional sounding observations are available for data assimilation. Thus, there is untapped predictability that can only be attained through the development of a more comprehensive observing system for all monsoon regions. Additionally, improved parameterizations - for example, of convection, cloud, radiation, and boundary layer schemes as well as land surface processes - are essential to realize the full potential of monsoon predictability. A more comprehensive assessment is needed of the impact of black carbon aerosols, which may modulate that of other anthropogenic greenhouse gases. Dynamical considerations require ever increased horizontal resolution (probably to 0.5 degree or higher) in order to resolve many monsoon features including, but not limited to, the Mei-Yu/Baiu sudden onset and withdrawal, low-level jet orientation and variability, and orographic forced rainfall. Under anthropogenic climate change many competing factors complicate making robust projections of monsoon changes. Absent aerosol effects, increased land-sea temperature contrast suggests strengthened monsoon circulation due to climate change. However, increased aerosol emissions will reflect more solar radiation back to space, which may temper or even reduce the strength of monsoon circulations compared to the present day. Precipitation may behave independently from the circulation under warming conditions in which an increased atmospheric moisture loading, based purely on thermodynamic considerations, could result in increased monsoon rainfall under climate change. The challenge to improve model parameterizations and include more complex processes and feedbacks pushes computing resources to their limit, thus requiring continuous upgrades of computational infrastructure to ensure progress in understanding and predicting current and future behaviour of monsoons.

Trust-By-Contract: Modelling, Analysing and Predicting Behaviour in Software Architectures

Architecture description languages (ADLs) are used to specify high-level, compositional views of a software application. ADL research focuses on software composed of prefabricated parts, so-called software components. ADLs usually come equipped with rigorous state-transition style semantics, facilitating verification and analysis of specifications. Consequently, ADLs are well suited to configuring distributed and event-based systems. However, additional expressive power is required for the description of enterprise software architectures – in particular, those built upon newer middleware, such as implementations of Java’s EJB specification, or Microsoft’s COM+/.NET. The enterprise requires distributed software solutions that are scalable, business-oriented and mission-critical. We can make progress toward attaining these qualities at various stages of the software development process. In particular, progress at the architectural level can be leveraged through use of an ADL that incorporates trust and dependability analysis. Also, current industry approaches to enterprise development do not address several important architectural design issues. The TrustME ADL is designed to meet these requirements, through combining approaches to software architecture specification with rigorous design-by-contract ideas. In this paper, we focus on several aspects of TrustME that facilitate specification and analysis of middleware-based architectures for trusted enterprise computing systems.

Predicting and breaking sedentary behaviour at work

This thesis aimed at designing and developing a system that can a) infer individuals’ need for a break from sedentary behaviour in the workplace, and b) persuade them to take a break through the use of different techniques from persuasive psychology. We postulated three variables, namely, individuals’ posture, stress levels and involvement in their computer mediated activity. We developed and field-studied a system that could infer these using a web camera and a key presses and mouse clicks log. We found that the system could predict posture from viewing depth and stress from the movement detected. We then created a general formula that predicts individuals’ need for a break using only the posture and stress predictors. Once the first objective was set, we built and field-studied a system that used three ways to communicate a recommendation for a break to a user: implicit, just-in time and ambient feedback. The implicit feedback was operationalized through changes in the users’ computer wallpaper that provided subtle visual cues. The just-in time feedback employed prompting at the bottom right side of the user’s screen. In addition, we implemented an intuitive behind-screen interaction technique where people can snooze a notification using simple gestures. The ambient feedback mechanism employed an origami sculpture sitting on the user’s desk. This prototype was continuously reflecting the user’s posture and performed rhythmic movements when to recommend breaks. A field study demonstrated the overall success of the system, with 69% of the break recommendations received by users were accepted. The study further revealed the strengths and weaknesses of the three persuasive mechanisms.

Waist circumference in predicting gestational diabetes mellitus

Background: To evaluate waist circumference (WC) measured at 20-24 weeks of gestation as a predictor of gestational diabetes mellitus (GDM).Methods: This cross-sectional study included 240 women at 20-24 weeks of gestation. At enrollment, WC was measured, and both prepregnancy and gestational body mass index (BMI) were estimated. According to the results of 75-g oral glucose tolerance test (OGTT) performed at 24-28 weeks, subjects were allocated into two groups, non-GDM and GDM. WC sensitivity and specificity, and odds ratios (OR) and 95% confidence intervals for BMI and WC were estimated, and a receiver operating characteristics curve was generated.Results: Of the 240 pregnant women enrolled, 31 (13%) had GDM. Prepregnancy BMI (OR = 4.21), gestational BMI (OR = 3.17) and WC at 20-24 weeks (OR = 4.02) correlated with GDM risk. At 20-24 weeks, a WC of 85.5-88.5 cm was the optimal cutoff point for predicting GDM (Sens/Spec balance between 87.1/41.1% and 77.4/56.9%).Conclusion: At 20-24 weeks of gestation, WC values in the range of 86-88 cm showed to be a good performance in predicting GDM.

Quality of life and coping behaviour in Type 1 diabetes mellitus: relationship with metabolic control

Uncovering factors possibly leading to insufficient metabolic control in Type 1 diabetes, both on the part of the patient or the treating physician, is of considerable relevance. The present long-term study investigated the relevance of patient-related vs education-related factors for the success in achieving acceptable glycaemic control. Adolescents or young adults with Type 1 diabetes mellitus (n= 26, mean age= 22+/-2 yr, diabetes duration= 11+/-5 yr) were followed during 36+/-5 months. All patients were treated by the same diabetologist. At the beginning of the study coping behaviour, quality of life and evaluation of emotional status were assessed. Changes in HbA1c were used as a parameter of glycaemic control. At follow-up there was a significant decrease in HbA1c of 0.4% (p<0.01). However, this was not in statistically significant correlation with age, gender, aspects of quality of life or coping behaviour. Therefore, glycaemic control and/or improvement of glycaemic control in adolescents or young adults with Type 1 diabetes mellitus seems to be primarily related to other factors, eg continuous education provided in a stable setting.

Who stays, who benefits? Predicting dropout and change in cognitive behaviour therapy for psychosis

This study investigates predictors of outcome in a secondary analysis of dropout and completer data from a randomized controlled effectiveness trial comparing CBTp to a wait-list group (Lincoln et al., 2012). Eighty patients with DSM-IV psychotic disorders seeking outpatient treatment were included. Predictors were assessed at baseline. Symptom outcome was assessed at post-treatment and at one-year follow-up. The predictor x group interactions indicate that a longer duration of disorder predicted less improvement in negative symptoms in the CBTp but not in the wait-list group whereas jumping-to-conclusions was associated with poorer outcome only in the wait-list group. There were no CBTp specific predictors of improvement in positive symptoms. However, in the combined sample (immediate CBTp+the delayed CBTp group) baseline variables predicted significant amounts of positive and negative symptom variance at post-therapy and one-year follow-up after controlling for pre-treatment symptoms. Lack of insight and low social functioning were the main predictors of drop-out, contributing to a prediction accuracy of 87%. The findings indicate that higher baseline symptom severity, poorer functioning, neurocognitive deficits, reasoning biases and comorbidity pose no barrier to improvement during CBTp. However, in line with previous predictor-research, the findings imply that patients need to receive treatment earlier.