Factors shaping prayer frequency among 9- to 11-year-olds

This paper begins by reviewing the evidence from international research concerning the personal and social correlates of prayer frequency during childhood and adolescence. Overall these data continue to support the view that young people who pray not only report higher levels of personal wellbeing but also report higher levels of pro-social attitudes. These findings raise a research question of particular relevance within church schools regarding the factors that predict higher levels of prayer activity among students. The Student Voice Project offers data that can illuminate this research question. Among the 3,101 9- to 11-year old students who participated in the project 11% prayed daily, 9% at least once a week, 32% sometimes, 11% once or twice a year, and 37% never. The present paper tests the power of four sets of predictor variables to account for individual differences in prayer frequency among these students: personal factors (age and sex), psychological factors (using the three dimensional model of personality proposed by Eysenck), church attendance (self, mother, and father), and family discussion about prayer (mother, father, and grandparents). Multiple regression analyses identified the discussion of prayer with the mother as the single most important predictor. These findings locate the development of the practice of prayer within the home, even more than within the church

SMARTS: Exploiting Temporal Locality and Parallelism through Vertical Execution

In the solution of large-scale numerical prob- lems, parallel computing is becoming simultaneously more important and more difficult. The complex organization of today's multiprocessors with several memory hierarchies has forced the scientific programmer to make a choice between simple but unscalable code and scalable but extremely com- plex code that does not port to other architectures. This paper describes how the SMARTS runtime system and the POOMA C++ class library for high-performance scientific computing work together to exploit data parallelism in scientific applications while hiding the details of manag- ing parallelism and data locality from the user. We present innovative algorithms, based on the macro -dataflow model, for detecting data parallelism and efficiently executing data- parallel statements on shared-memory multiprocessors. We also desclibe how these algorithms can be implemented on clusters of SMPS

Fast Fourier transform spectrometer readout for large arrays of microwave kinetic inductance detectors

Microwave kinetic inductance detectors have great potential for large, very sensitive detector arrays for use in, for example, submillimeter imaging. Being intrinsically readout in the frequency domain, they are particularly suited for frequency domain multiplexing allowing ?1000s of devices to be readout with one pair of coaxial cables. However, this moves the complexity of the detector from the cryogenics to the warm electronics. We present here the concept and experimental demonstration of the use of fast Fourier transform spectrometer readout, showing no deterioration of the noise performance compared to the low noise analog mixing while allowing high multiplexing ratios