Gap nodes induced by coexistence with antiferromagnetism in iron-based superconductors

We investigate the pairing in iron pnictides in the coexistence phase, which displays both superconducting and antiferromagnetic orders. By solving the pairing problem on the Fermi surface reconstructed by long-range magnetic order, we find that the pairing interaction necessarily becomes angle-dependent, even if it was isotropic in the paramagnetic phase, which results in an angular variation of the superconducting gap along the Fermi surfaces. We find that the gap has no nodes for a small antiferromagnetic order parameter M, but may develop accidental nodes for intermediate values of M, when one pair of the reconstructed Fermi surface pockets disappear. For even larger M, when the other pair of reconstructed Fermi pockets is gapped by long-range magnetic order, superconductivity still exists, but the quasiparticle spectrum becomes nodeless again. We also show that the application of an external magnetic field facilitates the formation of nodes. We argue that this mechanism for a nodeless-nodal-nodeless transition explains recent thermal conductivity measurements of hole-doped Ba_{1-x}K_xFe_2As_2. [J-Ph. Read et.al. arXiv:1105.2232].Comment: 13 pages, 10 figures, submitted to PR

Very Singular Similarity Solutions and Hermitian Spectral Theory for Semilinear Odd-Order PDEs

Very singular self-similar solutions of semilinear odd-order PDEs are studied on the basis of a Hermitian-type spectral theory for linear rescaled odd-order operators.Comment: 49 pages, 12 Figure

Enhancement of TcT_{c} by disorder in underdoped iron pnictides

We analyze how disorder affects the transition temperature $T_{c}$ of the $s^{+-}$superconducting state in the iron pnictides. The conventional wisdom is that $T_{c}$ should rapidly decrease with increasing inter-band non-magnetic impurity scattering, but we show that this behavior holds only in the overdoped region of the phase diagram. In the underdoped regime, where superconductivity emerges from a pre-existing magnetic state, disorder gives rise to two competing effects: breaking of the Cooper pairs, which tends to reduce $T_{c}$, and suppression of the itinerant magnetic order, which tends to bring $T_{c}$ up. We show that for a wide range of parameters the second effect wins, leading to an increase of $T_{c}$ with disorder in the coexistence state. Our results explain several recent experimental findings and provide another evidence for $s^{+-}$-pairing in the iron pnictides.Comment: 5 pages, 3 figures; revised version accepted in PRB-R

Preditores de fibrilação atrial de novo em unidade de cuidados intensivos não cardíaca

OBJECTIVE: To assess the predictors of de novo atrial fibrillation in patients in a non-cardiac intensive care unit. METHODS: A total of 418 hospitalized patients were analyzed between January and September 2016 in a non-cardiac intensive care unit. Clinical characteristics, interventions, and biochemical markers were recorded during hospitalization. In-hospital mortality and length of hospital stay in the intensive care unit were also evaluated. RESULTS: A total of 310 patients were included. The mean age of the patients was 61.0 ± 18.3 years, 49.4% were male, and 23.5% presented de novo atrial fibrillation. The multivariate model identified previous stroke (OR = 10.09; p = 0.016) and elevated levels of pro-B type natriuretic peptide (proBNP, OR = 1.28 for each 1,000pg/mL increment; p = 0.004) as independent predictors of de novo atrial fibrillation. Analysis of the proBNP receiver operating characteristic curve for prediction of de novo atrial fibrillation revealed an area under the curve of 0.816 (p 5,666pg/mL. There were no differences in mortality (p = 0.370), but the lengths of hospital stay (p = 0.002) and stay in the intensive care unit (p = 0.031) were higher in patients with de novo atrial fibrillation. CONCLUSIONS: A history of previous stroke and elevated proBNP during hospitalization were independent predictors of de novo atrial fibrillation in the polyvalent intensive care unit. The proBNP is a useful and easy- and quick-access tool in the stratification of atrial fibrillation risk.Objetivo: Avaliar quais os preditores de fibrilação atrial de novo em doentes de uma unidade de cuidados intensivos não cardíaca. Métodos: Foram analisados 418 doentes internados entre janeiro e setembro de 2016 em uma unidade de cuidados intensivos não cardíaca. Registaram-se as características clínicas, as intervenções efetuadas e os marcadores bioquímicos durante a internação. Avaliaram-se ainda a mortalidade hospitalar e o tempo de internação hospitalar e na unidade de cuidados intensivos. Resultados: Foram incluídos 310 doentes, com média de idades de 61,0 ± 18,3 anos, 49,4% do sexo masculino, 23,5% com fibrilação atrial de novo. O modelo multivariável identificou acidente vascular cerebral prévio (OR de 10,09; p = 0,016) e valores aumentados de proBNP (OR de 1,28 por cada aumento em 1.000pg/mL; p = 0,004) como preditores independentes de fibrilação atrial de novo. A análise por curva Característica de Operação do Receptor do proBNP para predição de fibrilação atrial de novo revelou área sob a curva de 0,816 (p 5.666pg/mL. Não se verificaram diferenças na mortalidade (p = 0,370), porém a duração da internação hospitalar (p = 0,002) e na unidade de cuidados intensivos (p = 0,031) foi superior nos doentes com fibrilação atrial de novo. Conclusões: História de acidente vascular cerebral prévio e proBNP elevado em internação constituíram preditores independentes de fibrilação atrial de novo na unidade de cuidados intensivos polivalente. O proBNP pode constituir ferramenta útil, de fácil e rápido acesso na estratificação do risco de fibrilação atrial.info:eu-repo/semantics/publishedVersio

Modeling the input history of programs for improved instruction-memory performance

When a program is loaded into memory for execution, the relative position of its basic blocks is crucial, since loading basic blocks that are unlikely to be executed first places them high in the instruction-memory hierarchy only to be dislodged as the execution goes on. In this paper we study the use of Bayesian networks as models of the input history of a program. The main point is the creation of a probabilistic model that persists as the program is run on different inputs and at each new input refines its own parameters in order to reflect the program's input history more accurately. As the model is thus tuned, it causes basic blocks to be reordered so that, upon arrival of the next input for execution, loading the basic blocks into memory automatically takes into account the input history of the program. We report on extensive experiments, whose results demonstrate the efficacy of the overall approach in progressively lowering the execution times of a program on identical inputs placed randomly in a sequence of varied inputs. We provide results on selected SPEC CINT2000 programs and also evaluate our approach as compared to the gcc level-3 optimization and to Pettis-Hansen reordering