XLOOPS -- A Program Package calculating One- and Two-Loop Feynman Diagrams

The aim of XLOOPS is to calculate one-particle irreducible Feynman diagrams with one or two closed loops for arbitrary processes in the Standard model of particles and related theories. Up to now this aim is realized for all one-loop diagrams with at most three external lines and for two-loop diagrams with two external lines.Comment: 84 pages, Postscript, program package and this manual also available at http://wwwthep.physik.uni-mainz.de/~xloops/, minor changes and bug fixes are included no

Proximity to clinical care and time to resolution following an abnormal cancer screening in an urban setting

Thesis (M.S.)--Boston UniversityBarriers to care have been identified as a major factor in cancer health disparities. Previous research at Boston Medical Center (BMC) found that women referred from community health centers (CHCs) following abnormal breast cancer screening took longer to achieve diagnostic resolution than women referred from a BMC-based practice, consistent with research showing longer delays and worse outcomes for disadvantaged urban populations. It is not known whether this difference relates to the additional distance to BMC. To evaluate the effect of proximity from subjects' residence to the site of clinical care on time to diagnostic resolution in this urban setting we conducted a secondary analysis using data collected as part of the Boston Patient Navigation Research Program (PNRP). The database included all women who had a breast or cervical cancer screening abnormality at six Federally-qualified CHCs from January 2007 to June 2009. Using geocoded home address data captured at the time of registration, we calculated straight-line distances to the location of the diagnostic evaluation, which was the CHC for subjects with a cervical abnormality or BMC for subjects with a breast abnormality, and plotted the time to diagnostic resolution versus distance to site of care. We used proportional hazards regression models to examine the effect of distance to site of care on time to resolution, adjusting for CHC, subject age, race/ethnicity, language, and insurance. Results. We geocoded addresses for 1512 of 1544 subjects (98%). Among the diverse group of subjects with a breast screening abnormality (36% Black, 33% Hispanic; 44% non-English speaking), there was no significant difference in adjusted hazard ratios based on distance to care in 1,000 meter units (adjusted Hazard Ratio 1.00, 95% CI 0.99 -1.01). Similarly, among those with a cervical screening abnormality (22% Black, 21% Hispanic; 15% non-English), there was no significant difference in adjusted hazard ratios based on distance to care in 1,000 meter units (adjusted Hazard Ratio 1.01, 95% CI 1.00- 1.02). Conclusions. Increased distance between residence and clinic alone is not a barrier to diagnostic resolution for this vulnerable urban population receiving care at a CHC who had an abnormal cancer screening exam

Study of Tools Interoperability

Interoperability of tools usually refers to a combination of methods and techniques that address the problem of making a collection of tools to work together. In this study we survey different notions that are used in this context: interoperability, interaction and integration. We point out relation between these notions, and how it maps to the interoperability problem. We narrow the problem area to the tools development in academia. Tools developed in such environment have a small basis for development, documentation and maintenance. We scrutinise some of the problems and potential solutions related with tools interoperability in such environment. Moreover, we look at two tools developed in the Formal Methods and Tools group1, and analyse the use of different integration techniques

Workshop on Verification and Theorem Proving for Continuous Systems (NetCA Workshop 2005)

Oxford, UK, 26 August 200

Design a photovoltaic system based on maximum power point tracking under partial shading

Photovoltaic systems have been given special attention given their long-term potential advantages. Solar panels can produce maximum power at specific operating points called maximum power points (MPP). Solar panels must work at this particular stage in order to ensure that solar panels produce maximum power and maximize efficiency. The performance of the solar photovoltaic unit is strongly affected by the level of radiation, heat and partial shading condition. The partial shedding condition is one of vectors that can affect the PV cell performance. To overcome on this problem, this project proposes photovoltaic system based on maximum power point tracking of partial shading condition. The MPPT algorithm has many methods like P&O and PSO. P&O it had limitation that is not capable to cover the multi-peaks curves. Beside that the PSO method is more effective in partial shading condition. The voltage and current of MSX60 PV module are subjected to various insolation conditions. The Particle Swarm Optimization (PSO) algorithm based MPPT has been implemented to track maximum power partial shading condition. So, in normal condition the power reach 245 W which is higher than the power under partial shading condition that reach 100 W. The PV module is designed using MATLAB/SIMULINK. The accurateness of this simulator is verified with PV module, the result is practiced during normal condition and under partial shading condition meanwhile, multiple curves of I-V and P-V will produce during normal condition and partial shading condition

Automatic Differentiation of Algorithms for Machine Learning

Automatic differentiation---the mechanical transformation of numeric computer programs to calculate derivatives efficiently and accurately---dates to the origin of the computer age. Reverse mode automatic differentiation both antedates and generalizes the method of backwards propagation of errors used in machine learning. Despite this, practitioners in a variety of fields, including machine learning, have been little influenced by automatic differentiation, and make scant use of available tools. Here we review the technique of automatic differentiation, describe its two main modes, and explain how it can benefit machine learning practitioners. To reach the widest possible audience our treatment assumes only elementary differential calculus, and does not assume any knowledge of linear algebra.Comment: 7 pages, 1 figur