Facebook faith - social networking in a faith based community

This paper views the increasing social networking as an efficient emerging ministry to the moveable generation. Through social network such as Facebook, ministry from a pastoral perspective can become more authentic and meaningful. Ministry is relational. Social Networking sites provide a strong platform to being part in other people’s life. Social networking and living online builds community beyond geographical boarders. Young adults and youths digital identity often reflects their faith, this is supported by research which suggests a practice of more openness to share and expose private issues online. Spiritual and religious views are freely shared, creating sacred spaces in the midst of life practising a holistic faith identity in a secular community. Providing a strong platform for information flow, Social Network is attractive in a postmodern society where inviting people to join in events are perceived as non threatening, making church community events transparent and available to people who do not attend church, inviting spiritual friendships and relationships. Social Networking strengthens relationship in a non hierarchical manner and invites the minister into lives where there previously would have been barriers, engaging in prayer and bible study as well as pastoral care through social networking, thus relationships deepens via social networking making people real. It has been observed that, although community building happens on the net, church affiliation loyalty remains to the local community. Therefore presence ministry though social networks emerges as a core form of ministry, where relations to youth who move from local church to university campuses are kept alive. The asynchronous nature of communication within social networking eases the minister in her work. The minister is able to engage with many individuals at the same time. Before the minister could visit one person at a time, now she visits 5-6 individuals at any given time. Therefore social networking not only increases the quality of the work, but also empowers the minister to be more efficient.

Self-tuning proportional, integral and derivative controller based on genetic algorithm least squares

A self-tuning proportional, integral and derivative control scheme based on genetic algorithms (GAs) is proposed and applied to the control of a real industrial plant. This paper explores the improvement in the parameter estimator, which is an essential part of an adaptive controller, through the hybridization of recursive least-squares algorithms by making use of GAs and the possibility of the application of GAs to the control of industrial processes. Both the simulation results and the experiments on a real plant show that the proposed scheme can be applied effectively.

Blind equalization of nonminimum phase channels: higher order cumulant based algorithm

Higher order cumulant analysis is applied to the blind equalization of linear time-invariant (LTI) nonminimum-phase channels. The channel model is moving-average based. To identify the moving average parameters of channels, a higher-order cumulant fitting approach is adopted in which a novel relay algorithm is proposed to obtain the global solution. In addition, the technique incorporates model order determination. The transmitted data are considered as independently identically distributed random variables over some discrete finite set (e.g., set {±1, ±3}). A transformation scheme is suggested so that third-order cumulant analysis can be applied to this type of data. Simulation examples verify the feasibility and potential of the algorithm. Performance is compared with that of the noncumulant-based Sato scheme in terms of the steady state MSE and convergence rate.

Modula-2 applied

Programming is a skill which requires knowledge of both the basic constructs of the computer language used and techniques employing these constructs. How these are used in any given application is determined intuitively, and this intuition is based on experience of programs already written. One aim of this book is to describe the techniques and give practical examples of the techniques in action - to provide some experience. Another aim of the book is to show how a program should be developed, in particular how a relatively large program should be tackled in a structured manner. These aims are accomplished essentially by describing the writing of one large program, a diagram generator package, in which a number of useful programming techniques are employed. Also, the book provides a useful program, with an in-built manual describing not only how the program works, but also how it does it, with full source code listings. This means that the user can, if required, modify the package to meet particular requirements. A floppy disk is available from the publishers containing the program, including listings of the source code. All the programs are written in Modula-2, using JPI's Top Speed Modula-2 system running on IBM-PCs and compatibles. This language was chosen as it is an ideal language for implementing large programs and it is the main language taught in the Cybernetics Department at the University of Reading. There are some aspects of the Top Speed implementation which are not standard, so suitable comments are given when these occur. Although implemented in Modula-2, many of the techniques described here are appropriate to other languages, like Pascal of C, for example. The book and programs are based on a second year undergraduate course taught at Reading to Cybernetics students, entitled Algorithms and Data Structures. Useful techniques are described for the reader to use, applications where they are appropriate are recommended, but detailed analyses of the techniques are not given.

On combination of SMOTE and particle swarm optimization based radial basis function classifier for imbalanced problems

The combination of the synthetic minority oversampling technique (SMOTE) and the radial basis function (RBF) classifier is proposed to deal with classification for imbalanced two-class data. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to generate synthetic instances for the positive class to balance the training data set. Based on the over-sampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier structure and the parameters of RBF kernels are determined using a particle swarm optimization algorithm based on the criterion of minimizing the leave-one-out misclassification rate. The experimental results on both simulated and real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.

B-spline neural network based digital baseband predistorter solution using the inverse of De Boor algorithm

In this paper a new nonlinear digital baseband predistorter design is introduced based on direct learning, together with a new Wiener system modeling approach for the high power amplifiers (HPA) based on the B-spline neural network. The contribution is twofold. Firstly, by assuming that the nonlinearity in the HPA is mainly dependent on the input signal amplitude the complex valued nonlinear static function is represented by two real valued B-spline neural networks, one for the amplitude distortion and another for the phase shift. The Gauss-Newton algorithm is applied for the parameter estimation, in which the De Boor recursion is employed to calculate both the B-spline curve and the first order derivatives. Secondly, we derive the predistorter algorithm calculating the inverse of the complex valued nonlinear static function according to B-spline neural network based Wiener models. The inverse of the amplitude and phase shift distortion are then computed and compensated using the identified phase shift model. Numerical examples have been employed to demonstrate the efficacy of the proposed approaches.

A combined SMOTE and PSO based RBF classifier for two-class imbalanced problems

This contribution proposes a powerful technique for two-class imbalanced classification problems by combining the synthetic minority over-sampling technique (SMOTE) and the particle swarm optimisation (PSO) aided radial basis function (RBF) classifier. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to generate synthetic instances for the positive class to balance the training data set. Based on the over-sampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier's structure and the parameters of RBF kernels are determined using a PSO algorithm based on the criterion of minimising the leave-one-out misclassification rate. The experimental results obtained on a simulated imbalanced data set and three real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.

The decomposition of some RDX and HMX based materials in the one dimensional time to explosion apparatus. Part 2. Estimating the violence of the cook-off event

Various methods of assessment have been applied to the One Dimensional Time to Explosion (ODTX) apparatus and experiments with the aim of allowing an estimate of the comparative violence of the explosion event to be made. Non-mechanical methods used were a simple visual inspection, measuring the increase in the void volume of the anvils following an explosion and measuring the velocity of the sound produced by the explosion over 1 metre. Mechanical methods used included monitoring piezo-electric devices inserted in the frame of the machine and measuring the rotational velocity of a rotating bar placed on the top of the anvils after it had been displaced by the shock wave. This last method, which resembles original Hopkinson Bar experiments, seemed the easiest to apply and analyse, giving relative rankings of violence and the possibility of the calculation of a “detonation” pressure.

The decomposition of some RDX and HMX based materials in the one-dimensional time to explosion apparatus. Part 1. Time to explosion and apparent activation energy

A One-Dimensional Time to Explosion (ODTX) apparatus has been used to study the times to explosion of a number of compositions based on RDX and HMX over a range of contact temperatures. The times to explosion at any given temperature tend to increase from RDX to HMX and with the proportion of HMX in the composition. Thermal ignition theory has been applied to time to explosion data to calculate kinetic parameters. The apparent activation energy for all of the compositions lay between 127 kJ mol−1 and 146 kJ mol−1. There were big differences in the pre-exponential factor and this controlled the time to explosion rather than the activation energy for the process.

Neural network based control in food processing

A number of commonly encountered simple neural network types are discussed, with particular attention being paid to their applicability in automation and control when applied to food processing. In the first instance n-tuple networks are considered, these being particularly useful for high speed production checking operations. Subsequently backpropagation networks are discussed, these being useful both in a more familiar feedback control arrangement and also for such things as recipe prediction.

Implementation of microprocessor based self-tuning control on a heating system

This paper describes the implementation, using a microprocessor, of a self-tuning control algorithm on a heating system. The algorithm is based on recursive least squares parameter estimation with a state-space, pole placement design criterion and shows how the controller behaves when applied to an actual system.

Life histories of academics who become heads of department: socialisation, identity and career trajectory

Although the role of the academic head of department (HoD) has always been important to university management and performance, an increasing significance given to bureaucracy, academic performance and productivity, and government accountability has greatly elevated the importance of this position. Previous research and anecdotal evidence suggests that as academics move into HoD roles, usually with little or no training, they experience a problem of struggling to adequately manage key aspects of their role. It is this problem – and its manifestations – that forms the research focus of this study. Based on the research question, “What are the career trajectories of academics who become HoDs in a selected post-1992 university?” the study aimed to achieve greater understanding of why academics become HoDs, what it is like being a HoD, and how the experience influences their future career plans. The study adopts an interpretive approach, in line with social constructivism. Edited topical life history interviews were undertaken with 17 male and female HoDs, from a range of disciplines, in a post-1992 UK university. These data were analysed using coding, categorisation and theme formation techniques and developing profiles of each of the respondents. The findings from this study suggest that academics who become HoDs not only need the capacity to assume a range of personal and professional identities, but need to regularly adopt and switch between them. Whether individuals can successfully balance and manage these multiple identities, or whether they experience major conflicts and difficulties within or between them, greatly affects their experiences of being a HoD and may influence their subsequent career decisions. It is claimed that the focus, approach and analytical framework - based on the interrelationships between the concepts of socialisation, identity and career trajectory - provide a distinct and original contribution to knowledge in this area. Although the results of this study cannot be generalised, the findings may help other individuals and institutions move towards a firmer understanding of the academic who becomes HoD - in relation to theory, practice and future research.

Effective elements of cognitive behaviour therapy for psychosis: results of a novel type of subgroup analysis based on principal stratification

Background. Meta-analyses show that cognitive behaviour therapy for psychosis (CBT-P) improves distressing positive symptoms. However, it is a complex intervention involving a range of techniques. No previous study has assessed the delivery of the different elements of treatment and their effect on outcome. Our aim was to assess the differential effect of type of treatment delivered on the effectiveness of CBT-P, using novel statistical methodology. Method. The Psychological Prevention of Relapse in Psychosis (PRP) trial was a multi-centre randomized controlled trial (RCT) that compared CBT-P with treatment as usual (TAU). Therapy was manualized, and detailed evaluations of therapy delivery and client engagement were made. Follow-up assessments were made at 12 and 24 months. In a planned analysis, we applied principal stratification (involving structural equation modelling with finite mixtures) to estimate intention-to-treat (ITT) effects for subgroups of participants, defined by qualitative and quantitative differences in receipt of therapy, while maintaining the constraints of randomization. Results. Consistent delivery of full therapy, including specific cognitive and behavioural techniques, was associated with clinically and statistically significant increases in months in remission, and decreases in psychotic and affective symptoms. Delivery of partial therapy involving engagement and assessment was not effective. Conclusions. Our analyses suggest that CBT-P is of significant benefit on multiple outcomes to patients able to engage in the full range of therapy procedures. The novel statistical methods illustrated in this report have general application to the evaluation of heterogeneity in the effects of treatment.

A global blended tropopause based on ERA data, Part II: trends and tropical broadening

A new tropopause definition involving a flow-dependent blending of the traditional thermal tropopause with one based on potential vorticity has been developed and applied to the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses (ERA), ERA-40 and ERA-Interim. Global and regional trends in tropopause characteristics for annual and solsticial seasonal means are presented here, with emphasis on significant results for the newer ERA-Interim data for 1989-2007. The global-mean tropopause is rising at a rate of 47 m decade−1 , with pressure falling at 1.0 hPa decade−1 , and temperature falling at 0.18 K decade−1 . The Antarctic tropopause shows decreasing heights,warming,and increasing westerly winds. The Arctic tropopause also shows a warming, but with decreasing westerly winds. In the tropics the trends are small, but at the latitudes of the sub-tropical jets they are almost double the global values. It is found that these changes are mainly concentrated in the eastern hemisphere. Previous and new metrics for the rate of broadening of the tropics, based on both height and wind, give trends in the range 0.9◦ decade−1 to 2.2◦ decade−1 . For ERA-40 the global height and pressure trends for the period 1979-2001 are similar: 39 m decade−1 and -0.8 hPa decade−1. These values are smaller than those found from the thermal tropopause definition with this data set, as was used in most previous studies.