University of Ljubljana

About: University of Ljubljana is a education organization based out in Ljubljana, Slovenia. It is known for research contribution in the topics: Population & Liquid crystal. The organization has 17210 authors who have published 47013 publications receiving 1082684 citations. The organization is also known as: Univerza v Ljubljani.

Search at the Mainz Microtron for Light Massive Gauge Bosons Relevant for the Muon g-2 Anomaly

Abstract: A massive, but light, Abelian U(1) gauge boson is a well-motivated possible signature of physics beyond the standard model of particle physics. In this Letter, the search for the signal of such a U(1) gauge boson in electron-positron pair production at the spectrometer setup of the A1 Collaboration at the Mainz Microtron is described. Exclusion limits in the mass range of 40 MeV/c^2 to 300 MeV/c^2, with a sensitivity in the squared mixing parameter of as little as e^2=8×10^−7 are presented. A large fraction of the parameter space has been excluded where the discrepancy of the measured anomalous magnetic moment of the muon with theory might be explained by an additional U(1) gauge boson.

Robust network community detection using balanced propagation

Abstract: Label propagation has proven to be an extremely fast method for detecting communities in large complex networks. Furthermore, due to its simplicity, it is also currently one of the most commonly adopted algorithms in the literature. Despite various subsequent advances, an important issue of the algorithm has not yet been properly addressed. Random (node) update orders within the algorithm severely hamper its robustness, and consequently also the stability of the identified community structure. We note that an update order can be seen as increasing propagation preferences from certain nodes, and propose a balanced propagation that counteracts for the introduced randomness by utilizing node balancers. We have evaluated the proposed approach on synthetic networks with planted partition, and on several real-world networks with community structure. The results confirm that balanced propagation is significantly more robust than label propagation, when the performance of community detection is even improved. Thus, balanced propagation retains high scalability and algorithmic simplicity of label propagation, but improves on its stability and performance.

Nonalcoholic Fatty Liver Disease: Focus on Lipoprotein and Lipid Deregulation

Abstract: Obesity with associated comorbidities is currently a worldwide epidemic and among the most challenging health conditions in the 21st century. A major metabolic consequence of obesity is insulin resistance which underlies the pathogenesis of the metabolic syndrome. Nonalcoholic fatty liver disease (NAFLD) is the hepatic manifestation of obesity and metabolic syndrome. It comprises a disease spectrum ranging from simple steatosis (fatty liver), through nonalcoholic steatohepatitis (NASH) to fibrosis, and ultimately liver cirrhosis. Abnormality in lipid and lipoprotein metabolism accompanied by chronic inflammation is the central pathway for the development of metabolic syndrome-related diseases, such as atherosclerosis, cardiovascular disease (CVD), and NAFLD. This paper focuses on pathogenic aspect of lipid and lipoprotein metabolism in NAFLD and the relevant mouse models of this complex multifactorial disease.

Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

Abstract: . Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorological and chemical data; however, because CCMM are fairly recent, data assimilation in CCMM has been limited to date. We review here the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data assimilation in CCMM. We first review the methods available for data assimilation in atmospheric models, including variational methods, ensemble Kalman filters, and hybrid methods. Next, we review past applications that have included chemical data assimilation in chemical transport models (CTM) and in CCMM. Observational data sets available for chemical data assimilation are described, including surface data, surface-based remote sensing, airborne data, and satellite data. Several case studies of chemical data assimilation in CCMM are presented to highlight the benefits obtained by assimilating chemical data in CCMM. A case study of data assimilation to constrain emissions is also presented. There are few examples to date of joint meteorological and chemical data assimilation in CCMM and potential difficulties associated with data assimilation in CCMM are discussed. As the number of variables being assimilated increases, it is essential to characterize correctly the errors; in particular, the specification of error cross-correlations may be problematic. In some cases, offline diagnostics are necessary to ensure that data assimilation can truly improve model performance. However, the main challenge is likely to be the paucity of chemical data available for assimilation in CCMM.