Magnetic and Dynamic Properties of the Hubbard Model in Infinite Dimensions

An essentially exact solution of the infinite dimensional Hubbard model is made possible by using a self-consistent mapping of the Hubbard model in this limit to an effective single impurity Anderson model. Solving the latter with quantum Monte Carlo procedures enables us to obtain exact results for the one and two-particle properties of the infinite dimensional Hubbard model. In particular we find antiferromagnetism and a pseudogap in the single-particle density of states for sufficiently large values of the intrasite Coulomb interaction at half filling. Both the antiferromagnetic phase and the insulating phase above the N\'eel temperature are found to be quickly suppressed on doping. The latter is replaced by a heavy electron metal with a quasiparticle mass strongly dependent on doping as soon as $n<1$. At half filling the antiferromagnetic phase boundary agrees surprisingly well in shape and order of magnitude with results for the three dimensional Hubbard model.Comment: 32 page

Effort-reward imbalance at work and risk of type 2 diabetes in a national sample of 50,552 workers in Denmark: A prospective study linking survey and register data

Objective: To examine the prospective relation between effort-reward imbalance at work and risk of type 2 diabetes.Methods: We included 50,552 individuals from a national survey of the working population in Denmark, aged 30-64 years and diabetes-free at baseline. Effort-reward imbalance was defined, in accordance with the literature, as a mismatch between high efforts at work (e.g. high work pace, time pressure), and low rewards received in return (e.g. low recognition, job insecurity) and assessed as a continuous and a categorical variable. Incident type 2 diabetes was identified in national health registers. Using Cox regression we calculated hazard ratios (HR) and 95% confidence intervals (95% CI) for estimating the association between effort-reward imbalance at baseline and risk of onset of type 2 diabetes during follow-up, adjusted for sex, age, socioeconomic status, cohabitation, children at home, migration background, survey year and sample method.Results: During 136,239 person-years of follow-up (mean = 2.7 years) we identified 347 type 2 diabetes cases (25.5 cases per 10,000 person-years). For each one standard deviation increase of the effort-reward imbalance score at baseline, the fully adjusted risk of type 2 diabetes during follow-up increased by 9% (HR: 1.09, 95% CI: 0.98-1.21). When we used effort-reward imbalance as a dichotomous variable, exposure to effort-reward imbalance was associated with an increased risk of type 2 diabetes with a HR of 1.27 (95% CI: 1.02-1.58).Conclusion The results of this nationwide study of the Danish workforce suggest that effort-reward imbalance at work may be a risk factor for type 2 diabetes

Clustering Algorithms: Their Application to Gene Expression Data

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and iden-tify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure