The creep behaviour of pressure diecast zinc-aluminium based alloys

The creep behaviour of three pressure diecast commercial zinc-aluminium based alloys: Mazak 3, corresponding to BS 1004A, and the new alloys ZA.8 and ZA.27 with a series of alloys with compositions ranging from 0% to 30% aluminium was investigated. The total creep elongation of commercial alloys was shown to be well correlated using an empirical equation. Based on this a parametrical relationship was derived which allowed the total creep extension to be related to the applied stress, the temperature and the time of test, so that a quantitative assessment of creep of the alloys could be made under different conditions. Deviation from the normal creep kinetics occurred in alloys ZA.8 and ZA.27 at very low stresses, 150°C, due to structural coarsening combined with partial transformation of ε -phase into T' phase. The extent of primary creep was found to increase with aluminium content, but secondary creep rates decreased in the order Mazak 3, ZA.8 and ZA.27. Thus, based on the above equation, ZA.8 was found to have a substantially better total creep resistance than ZA.27, which in turn was marginally better than Mazak 3 for strains higher than 0.5%, but inferior for smaller strains, due to its higher primary creep extension. The superior creep resistance of ZA.8 was found to be due to the presence of strictly-orientated, thin plate-like precipitates of ε(CuZn4) phase in the zinc matrix of the eutectic and the lamellarly decomposed β phase, in which the precipitation morphology and orientation of ε in the zinc matrix was determined. Over broad ranges of temperature and stresses, the stress exponents and activation energies for creep were found to be consistent with some proposed creep rate mechanisms; i.e. viscous glide for Mazak 3, dislocation climb over second phase particles for ZA.8 and dislocation climb for ZA.27, controlled by diffusion in the zinc-rich phase. The morphology of aluminium and copper-rich precipitates formed from the solid solution of zinc was clearly revealed. The former were found to further increase the creep rate of inherently low creep resistant zinc, but the latter contributed significantly to the creep resistance. Excess copper in the composition, however, was not beneficial in improving the creep resistance. Decomposition of β in copper-containing alloys was found to be through a metastable Zn-Al phase which is strongly stabilised by copper, and the final products of the decomposition had a profound effect on the creep strength of the alloys. The poor creep resistance of alloy ZA.27 was due to the presence of particulate products derived from decomposed β-phase and a large volume of fine, equiaxed products of continuously decomposed α-dendrites.

Do stressful times call for team-based working? A study of psychiatric nurses in UK mental healthcase

Background: Team-based working is now an inherent part of effective health care delivery. Previous research has identified that team working is associated with positive mental health and well-being outcomes for individuals operating in an effective team environment. This is a particularly important topic in the health services context, although little empirical attention has been paid to mental-health services. Psychiatric nurses work on a day-to-day basis with a particularly stressful and demanding client group in an environment which is characterised by high demands, uncertainty, and limited resources. This paper specifically focuses on psychiatric nurses working in National Health Service (NHS) and casts some light on the ways in which effective team-based working can help to alleviate a number of occupational stressors and strains. Method: A questionnaire method (2005 NHS Staff Survey) was employed to collect data from 6655 psychiatric nurses from 64 different NHS Trusts. The hypotheses were concerned with four overall measures from the survey; effective team working, occupational stress, work pressure and social support. Hypothesis 1 stated that effective team working will have a significant negative relationship with occupational stress and work pressure. Further, Hypothesis 2 stated that social support from supervisors and co-workers will moderate this relationship. Findings: Data was treated with a series of regression analyses. For Hypothesis 1, working in a real team did have main effects on work pressure and accounted for 1.6 per cent of the variance. Using the Nagelkerke R square value, working in a real team also had main effects on occupational stress an accounted for approximately 2.8 per cent of the variance. Further, the Exp (B) value of 0.662 suggests that the odds of suffering from occupational stress are cut by 33.8 per cent when a psychiatric nurse works in a real team. Results failed to provide support for Hypothesis 2. The analysis then went on to adopt a unique approach for assessing the extent of real team-based working, distinguishing between real teams, and a number of pseudo team typologies, as well as the absence of teamwork all together. As was hypothesised, results demonstrated that psychiatric nurses working in real teams (ones with clear objectives, where-by team members work closely with one another to achieve team objectives and meet regularly to discuss team effectiveness and how it can be improved) experienced the lowest levels of stress and work pressure of the sample. However, contrary to prediction, results indicated that psychiatric nurses working in any type of pseudo team actually experienced significantly higher levels of stress and work pressure than those who did not report as working in a team at all. Discussion: These findings have serious implications for NHS Mental Health Trusts, which may not be implementing, structuring and managing their nursing teams adequately. Indeed, results suggest that poorly-structured team work may actually facilitate stress and pressure in the workplace. Conversely, well-structured real teams serve to reduce stress and work pressure, which in turn not only enhances the working lives and well-being of psychiatric nurses, but also greatly improves the service that the NHS provides to its users.

Stressful times call for team-based measures:psychiatric nursing in the UK National Health Service

Background: Team-based working is now an inherent part of effective health care delivery. Previous research has identified that team working is associated with positive mental health and well-being outcomes for individuals operating in an effective team environment. This is a particularly important topic in the health services context, although little empirical attention has been paid to mental-health services. Psychiatric nurses work on a day-to-day basis with a particularly stressful and demanding client group in an environment which is characterised by high demands, uncertainty, and limited resources. This paper specifically focuses on psychiatric nurses working in National Health Service (NHS) and casts some light on the ways in which effective team-based working can help to alleviate a number of occupational stressors and strains. Method: A questionnaire method (2005 NHS Staff Survey) was employed to collect data from 6655 psychiatric nurses from 64 different NHS Trusts. The hypotheses were concerned with four overall measures from the survey; effective team working, occupational stress, work pressure and social support. Hypothesis 1 stated that effective team working will have a significant negative relationship with occupational stress and work pressure. Further, Hypothesis 2 stated that social support from supervisors and co-workers will moderate this relationship. Findings: Data was treated with a series of regression analyses. For Hypothesis 1, working in a real team did have main effects on work pressure and accounted for 1.6 per cent of the variance. Using the Nagelkerke R square value, working in a real team also had main effects on occupational stress an accounted for approximately 2.8 per cent of the variance. Further, the Exp (B) value of 0.662 suggests that the odds of suffering from occupational stress are cut by 33.8 per cent when a psychiatric nurse works in a real team. Results failed to provide support for Hypothesis 2. The analysis then went on to adopt a unique approach for assessing the extent of real team-based working, distinguishing between real teams, and a number of pseudo team typologies, as well as the absence of teamwork all together. As was hypothesised, results demonstrated that psychiatric nurses working in real teams (ones with clear objectives, where-by team members work closely with one another to achieve team objectives and meet regularly to discuss team effectiveness and how it can be improved) experienced the lowest levels of stress and work pressure of the sample. However, contrary to prediction, results indicated that psychiatric nurses working in any type of pseudo team actually experienced significantly higher levels of stress and work pressure than those who did not report as working in a team at all. Discussion: These findings have serious implications for NHS Mental Health Trusts, which may not be implementing, structuring and managing their nursing teams adequately. Indeed, results suggest that poorly-structured team work may actually facilitate stress and pressure in the workplace. Conversely, well-structured real teams serve to reduce stress and work pressure, which in turn not only enhances the working lives and well-being of psychiatric nurses, but also greatly improves the service that the NHS provides to its users.

Applying Information Foraging Theory to understand user interaction with content-based image retrieval

The paper proposes an ISE (Information goal, Search strategy, Evaluation threshold) user classification model based on Information Foraging Theory for understanding user interaction with content-based image retrieval (CBIR). The proposed model is verified by a multiple linear regression analysis based on 50 users' interaction features collected from a task-based user study of interactive CBIR systems. To our best knowledge, this is the first principled user classification model in CBIR verified by a formal and systematic qualitative analysis of extensive user interaction data. Copyright 2010 ACM.

The measurement and decomposition of economy-wide productivity growth:Assessing the accuracy and selecting between different approaches

Productivity at the macro level is a complex concept but also arguably the most appropriate measure of economic welfare. Currently, there is limited research available on the various approaches that can be used to measure it and especially on the relative accuracy of said approaches. This thesis has two main objectives: firstly, to detail some of the most common productivity measurement approaches and assess their accuracy under a number of conditions and secondly, to present an up-to-date application of productivity measurement and provide some guidance on selecting between sometimes conflicting productivity estimates. With regards to the first objective, the thesis provides a discussion on the issues specific to macro-level productivity measurement and on the strengths and weaknesses of the three main types of approaches available, namely index-number approaches (represented by Growth Accounting), non-parametric distance functions (DEA-based Malmquist indices) and parametric production functions (COLS- and SFA-based Malmquist indices). The accuracy of these approaches is assessed through simulation analysis, which provided some interesting findings. Probably the most important were that deterministic approaches are quite accurate even when the data is moderately noisy, that no approaches were accurate when noise was more extensive, that functional form misspecification has a severe negative effect in the accuracy of the parametric approaches and finally that increased volatility in inputs and prices from one period to the next adversely affects all approaches examined. The application was based on the EU KLEMS (2008) dataset and revealed that the different approaches do in fact result in different productivity change estimates, at least for some of the countries assessed. To assist researchers in selecting between conflicting estimates, a new, three step selection framework is proposed, based on findings of simulation analyses and established diagnostics/indicators. An application of this framework is also provided, based on the EU KLEMS dataset.

A survey based analysis of IT adoption and 3PLs' performance

Purpose: In today's competitive scenario, effective supply chain management is increasingly dependent on third-party logistics (3PL) companies' capabilities and performance. The dissemination of information technology (IT) has contributed to change the supply chain role of 3PL companies and IT is considered an important element influencing the performance of modern logistics companies. Therefore, the purpose of this paper is to explore the relationship between IT and 3PLs' performance, assuming that logistics capabilities play a mediating role in this relationship. Design/methodology/approach: Empirical evidence based on a questionnaire survey conducted on a sample of logistics service companies operating in the Italian market was used to test a conceptual resource-based view (RBV) framework linking IT adoption, logistics capabilities and firm performance. Factor analysis and ordinary least square (OLS) regression analysis have been used to test hypotheses. The focus of the paper is multidisciplinary in nature; management of information systems, strategy, logistics and supply chain management approaches have been combined in the analysis. Findings: The results indicate strong relationships among data gathering technologies, transactional capabilities and firm performance, in terms of both efficiency and effectiveness. Moreover, a positive correlation between enterprise information technologies and 3PL financial performance has been found. Originality/value: The paper successfully uses the concept of logistics capabilities as mediating factor between IT adoption and firm performance. Objective measures have been proposed for IT adoption and logistics capabilities. Direct and indirect relationships among variables have been successfully tested. © Emerald Group Publishing Limited.

A fuzzy decision tree-based duration model for Standard Yorùbá text-to-speech synthesis

In this paper, we present syllable-based duration modelling in the context of a prosody model for Standard Yorùbá (SY) text-to-speech (TTS) synthesis applications. Our prosody model is conceptualised around a modular holistic framework. This framework is implemented using the Relational Tree (R-Tree) techniques. An important feature of our R-Tree framework is its flexibility in that it facilitates the independent implementation of the different dimensions of prosody, i.e. duration, intonation, and intensity, using different techniques and their subsequent integration. We applied the Fuzzy Decision Tree (FDT) technique to model the duration dimension. In order to evaluate the effectiveness of FDT in duration modelling, we have also developed a Classification And Regression Tree (CART) based duration model using the same speech data. Each of these models was integrated into our R-Tree based prosody model. We performed both quantitative (i.e. Root Mean Square Error (RMSE) and Correlation (Corr)) and qualitative (i.e. intelligibility and naturalness) evaluations on the two duration models. The results show that CART models the training data more accurately than FDT. The FDT model, however, shows a better ability to extrapolate from the training data since it achieved a better accuracy for the test data set. Our qualitative evaluation results show that our FDT model produces synthesised speech that is perceived to be more natural than our CART model. In addition, we also observed that the expressiveness of FDT is much better than that of CART. That is because the representation in FDT is not restricted to a set of piece-wise or discrete constant approximation. We, therefore, conclude that the FDT approach is a practical approach for duration modelling in SY TTS applications. © 2006 Elsevier Ltd. All rights reserved.

Microstructural degradation of aluminide coatings on single crystal nickel based superalloys during high temperature exposure

An investigation, employing edge-on transmission electron microscopy, of the microstructure of aluminide diffusion coatings on a single crystal y' strengthened nickel base super alloy is reported. An examination has been made of the effect of postcoating exposure at 1100°C on the stability of the coating matrix, a B2 type phase, nominally NiAl. Precipitation in the coating is considered with respect to both decomposition of the B2 matrix to other Ni-Al (plus titanium) phases and the formation of chromium bearing precipitates. A comparison is drawn with behaviour at lower temperatures (850-950°C). © 1995 The Institute of Materials.

Predicting class I major histocompatibility complex (MHC) binders using multivariate statistics:comparison of discriminant analysis and multiple linear regression

The accurate in silico identification of T-cell epitopes is a critical step in the development of peptide-based vaccines, reagents, and diagnostics. It has a direct impact on the success of subsequent experimental work. Epitopes arise as a consequence of complex proteolytic processing within the cell. Prior to being recognized by T cells, an epitope is presented on the cell surface as a complex with a major histocompatibility complex (MHC) protein. A prerequisite therefore for T-cell recognition is that an epitope is also a good MHC binder. Thus, T-cell epitope prediction overlaps strongly with the prediction of MHC binding. In the present study, we compare discriminant analysis and multiple linear regression as algorithmic engines for the definition of quantitative matrices for binding affinity prediction. We apply these methods to peptides which bind the well-studied human MHC allele HLA-A*0201. A matrix which results from combining results of the two methods proved powerfully predictive under cross-validation. The new matrix was also tested on an external set of 160 binders to HLA-A*0201; it was able to recognize 135 (84%) of them.

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Background - The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. Results - We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. Conclusion - As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.

Optimising market share and profit margin:SMDP-based tariff pricing under the smart grid paradigm

Smart grid technologies have given rise to a liberalised and decentralised electricity market, enabling energy providers and retailers to have a better understanding of the demand side and its response to pricing signals. This paper puts forward a reinforcement-learning-powered tool aiding an electricity retailer to define the tariff prices it offers, in a bid to optimise its retail strategy. In a competitive market, an energy retailer aims to simultaneously increase the number of contracted customers and its profit margin. We have abstracted the problem of deciding on a tariff price as faced by a retailer, as a semi-Markov decision problem (SMDP). A hierarchical reinforcement learning approach, MaxQ value function decomposition, is applied to solve the SMDP through interactions with the market. To evaluate our trading strategy, we developed a retailer agent (termed AstonTAC) that uses the proposed SMDP framework to act in an open multi-agent simulation environment, the Power Trading Agent Competition (Power TAC). An evaluation and analysis of the 2013 Power TAC finals show that AstonTAC successfully selects sell prices that attract as many customers as necessary to maximise the profit margin. Moreover, during the competition, AstonTAC was the only retailer agent performing well across all retail market settings.

Laser-induced breakdown spectroscopy quantitative analysis method via adaptive analytical line selection and relevance vector machine regression model

Abstract A new LIBS quantitative analysis method based on analytical line adaptive selection and Relevance Vector Machine (RVM) regression model is proposed. First, a scheme of adaptively selecting analytical line is put forward in order to overcome the drawback of high dependency on a priori knowledge. The candidate analytical lines are automatically selected based on the built-in characteristics of spectral lines, such as spectral intensity, wavelength and width at half height. The analytical lines which will be used as input variables of regression model are determined adaptively according to the samples for both training and testing. Second, an LIBS quantitative analysis method based on RVM is presented. The intensities of analytical lines and the elemental concentrations of certified standard samples are used to train the RVM regression model. The predicted elemental concentration analysis results will be given with a form of confidence interval of probabilistic distribution, which is helpful for evaluating the uncertainness contained in the measured spectra. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples have been carried out. The multiple correlation coefficient of the prediction was up to 98.85%, and the average relative error of the prediction was 4.01%. The experiment results showed that the proposed LIBS quantitative analysis method achieved better prediction accuracy and better modeling robustness compared with the methods based on partial least squares regression, artificial neural network and standard support vector machine.

High performance liquid level monitoring system based on polymer fiber Bragg gratings embedded in silicone rubber diaphragm

Liquid-level sensing technologies have attracted great prominence, because such measurements are essential to industrial applications, such as fuel storage, flood warning and in the biochemical industry. Traditional liquid level sensors are based on electromechanical techniques; however they suffer from intrinsic safety concerns in explosive environments. In recent years, given that optical fiber sensors have lots of well-established advantages such as high accuracy, costeffectiveness, compact size, and ease of multiplexing, several optical fiber liquid level sensors have been investigated which are based on different operating principles such as side-polishing the cladding and a portion of core, using a spiral side-emitting optical fiber or using silica fiber gratings. The present work proposes a novel and highly sensitive liquid level sensor making use of polymer optical fiber Bragg gratings (POFBGs). The key elements of the system are a set of POFBGs embedded in silicone rubber diaphragms. This is a new development building on the idea of determining liquid level by measuring the pressure at the bottom of a liquid container, however it has a number of critical advantages. The system features several FBG-based pressure sensors as described above placed at different depths. Any sensor above the surface of the liquid will read the same ambient pressure. Sensors below the surface of the liquid will read pressures that increase linearly with depth. The position of the liquid surface can therefore be approximately identified as lying between the first sensor to read an above-ambient pressure and the next higher sensor. This level of precision would not in general be sufficient for most liquid level monitoring applications; however a much more precise determination of liquid level can be made by linear regression to the pressure readings from the sub-surface sensors. There are numerous advantages to this multi-sensor approach. First, the use of linear regression using multiple sensors is inherently more accurate than using a single pressure reading to estimate depth. Second, common mode temperature induced wavelength shifts in the individual sensors are automatically compensated. Thirdly, temperature induced changes in the sensor pressure sensitivity are also compensated. Fourthly, the approach provides the possibility to detect and compensate for malfunctioning sensors. Finally, the system is immune to changes in the density of the monitored fluid and even to changes in the effective force of gravity, as might be obtained in an aerospace application. The performance of an individual sensor was characterized and displays a sensitivity (54 pm/cm), enhanced by more than a factor of 2 when compared to a sensor head configuration based on a silica FBG published in the literature, resulting from the much lower elastic modulus of POF. Furthermore, the temperature/humidity behavior and measurement resolution were also studied in detail. The proposed configuration also displays a highly linear response, high resolution and good repeatability. The results suggest the new configuration can be a useful tool in many different applications, such as aircraft fuel monitoring, and biochemical and environmental sensing, where accuracy and stability are fundamental. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

A novel algorithm of posture best fit based on key characteristics for large components assembly

Measurement and variation control of geometrical Key Characteristics (KCs), such as flatness and gap of joint faces, coaxiality of cabin sections, is the crucial issue in large components assembly from the aerospace industry. Aiming to control geometrical KCs and to attain the best fit of posture, an optimization algorithm based on KCs for large components assembly is proposed. This approach regards the posture best fit, which is a key activity in Measurement Aided Assembly (MAA), as a two-phase optimal problem. In the first phase, the global measurement coordinate system of digital model and shop floor is unified with minimum error based on singular value decomposition, and the current posture of components being assembly is optimally solved in terms of minimum variation of all reference points. In the second phase, the best posture of the movable component is optimally determined by minimizing multiple KCs' variation with the constraints that every KC respectively conforms to its product specification. The optimal models and the process procedures for these two-phase optimal problems based on Particle Swarm Optimization (PSO) are proposed. In each model, every posture to be calculated is modeled as a 6 dimensional particle (three movement and three rotation parameters). Finally, an example that two cabin sections of satellite mainframe structure are being assembled is selected to verify the effectiveness of the proposed approach, models and algorithms. The experiment result shows the approach is promising and will provide a foundation for further study and application. © 2013 The Authors.

Comprehensive secondary pyrolysis in fluidized-bed fast pyrolysis of biomass, a fluid dynamics based modelling effort

Homogenous secondary pyrolysis is category of reactions following the primary pyrolysis and presumed important for fast pyrolysis. For the comprehensive chemistry and fluid dynamics, a probability density functional (PDF) approach is used; with a kinetic scheme comprising 134 species and 4169 reactions being implemented. With aid of acceleration techniques, most importantly Dimension Reduction, Chemistry Agglomeration and In-situ Tabulation (ISAT), a solution within reasonable time was obtained. More work is required; however, a solution for levoglucosan (C6H10O5) being fed through the inlet with fluidizing gas at 500 °C, has been obtained. 88.6% of the levoglucosan remained non-decomposed, and 19 different decomposition product species were found above 0.01% by weight. A homogenous secondary pyrolysis scheme proposed can thus be implemented in a CFD environment and acceleration techniques can speed-up the calculation for application in engineering settings.