Dark matter line search using a joint analysis of dwarf galaxies with the Fermi Gamma-ray Space Telescope

We perform a joint analysis of dwarf galaxy data from the Fermi Gamma-ray Space Telescope in search of dark matter annihilation into a gamma-ray line. We employ a novel statistical method that takes into account the spatial and spectral information of individual photon events from a sample of seven dwarf galaxies. Dwarf galaxies show no evidence of a gamma-ray line between 10 GeV and 1 TeV. The subsequent upper limit on the annihilation cross section to a two-photon final state is 3.9(+7.1)(-3.7) x 10^-26 cm^3/s at 130 GeV, where the errors reflect the systematic uncertainty in the distribution of dark matter within the dwarf galaxies.Comment: 5 pages, 3 figures. Replaced with version accepted for publication as a Rapid Communication in PR

Diagnostic accuracy of dynamic CZT-SPECT in coronary artery disease. A systematic review and meta-analysis

Background: With the appearance of cadmium-zinc-telluride (CZT) cameras, dynamic myocardial perfusion imaging (MPI) has been introduced, but comparable data to other MPI modalities, such as quantitative coronary angiography (CAG) with fractional flow reserve (FFR) and positron emission tomography (PET), are lacking. This study aimed to evaluate the diagnostic accuracy of dynamic CZT single-photon emission tomography (SPECT) in coronary artery disease compared to quantitative CAG, FFR, and PET as reference. Materials and Methods: Different databases were screened for eligible citations performing dynamic CZT-SPECT against CAG, FFR, or PET. PubMed, OvidSP (Medline), Web of Science, the Cochrane Library, and EMBASE were searched on the 5th of July 2020. Studies had to meet the following pre-established inclusion criteria: randomized controlled trials, retrospective trails or observational studies relevant for the diagnosis of coronary artery disease, and performing CZT-SPECT and within half a year the methodological references. Studies which considered coronary stenosis between 50% and 70% as significant based only on CAG were excluded. Data extracted were sensitivity, specificity, likelihood ratios, and diagnostic odds ratios. Quality was assessed with QUADAS-2 and statistical analysis was performed using a bivariate model. Results: Based on our criteria, a total of 9 studies containing 421 patients were included. For the assessment of CZT-SPECT, the diagnostic value pooled analysis with a bivariate model was calculated and yielded a sensitivity of 0.79 (% CI 0.73 to 0.85) and a specificity of 0.85 (95% CI 0.74 to 0.92). Diagnostic odds ratio (DOR) was 17.82 (95% CI 8.80 to 36.08, P < 0.001). Positive likelihood ratio (PLR) and negative likelihood ratio (NLR) were 3.86 (95% CI 2.76 to 5.38, P < 0.001) and 0.21 (95% CI 0.13 to 0.33, P < 0.001), respectively. Conclusion: Based on the results of the current systematic review and meta-analysis, dynamic CZT-SPECT MPI demonstrated a good sensitivity and specificity to diagnose CAD as compared to the gold standards. However, due to the heterogeneity of the methodologies between the CZT-SPECT MPI studies and the relatively small number of included studies, it warrants further well-defined study protocols

On finite-time ruin probabilities for classical risk models

info:eu-repo/semantics/publishe

Evaluating Process Quality Based on Change Request Data – An Empirical Study of the Eclipse Project

Abstract. The information routinely collected in change request management systems contains valuable information for monitoring of the process quality. However this data is currently utilized in a very limited way. This paper presents an empirical study of the process quality in the product portfolio of the Eclipse project. It is based on a systematic approach for the evaluation of process quality characteristics using change request data. Results of the study offer insights into the development process of Eclipse. Moreover the study allows assessing applicability and limitations of the proposed approach for the evaluation of process quality

Insurance Risk Models

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