Network-based ranking in social systems: three challenges

Ranking algorithms are pervasive in our increasingly digitized societies, with important real-world applications including recommender systems, search engines, and influencer marketing practices. From a network science perspective, network-based ranking algorithms solve fundamental problems related to the identification of vital nodes for the stability and dynamics of a complex system. Despite the ubiquitous and successful applications of these algorithms, we argue that our understanding of their performance and their applications to real-world problems face three fundamental challenges: (i) Rankings might be biased by various factors; (2) their effectiveness might be limited to specific problems; and (3) agents' decisions driven by rankings might result in potentially vicious feedback mechanisms and unhealthy systemic consequences. Methods rooted in network science and agent-based modeling can help us to understand and overcome these challenges.Comment: Perspective article. 9 pages, 3 figure

Gaucher Disease and Myelofibrosis: A Combined Disease or a Misdiagnosis?

Background: Gaucher disease (GD) and primary myelofibrosis (PMF) share similar clinical and laboratory features, such as cytopenia, hepatosplenomegaly, and marrow fibrosis, often resulting in a misdiagnosis. Case Report: We report here the case of a young woman with hepatosplenomegaly, leukopenia, and thrombocytopenia. Based on bone marrow (BM) findings and on liver biopsy showing extramedullary hematopoiesis, an initial diagnosis of PMF was formulated. The patient refused stem cell transplantation from an HLA-identical sibling. Low-dose melphalan was given, without any improvement. Two years later, a BM evaluation showed Gaucher cells. Low glucocerebrosidase and high chitotriosidase levels were indicative for GD. Molecular analysis revealed N370S/complex I mutations. Enzyme replacement therapy with imiglucerase was commenced, resulting in clinical and hematological improvements. Due to an unexpected and persistent organomegaly, PMF combined with GD were suspected. JAK2V617F, JAK2 exon 12, MPL, calreticulin, and exon 9 mutations were negative, and BM examination showed no marrow fibrosis. PMF was excluded. Twenty years after starting treatment, the peripheral cell count and liver size were normal, whereas splenomegaly persisted. Conclusion: In order to avoid a misdiagnosis, a diagnostic algorithm for patients with hepatosplenomegaly combined with cytopenia is suggested

Principal Component Analysis of RR Lyrae light curves

In this paper, we analyze the structure of RRab star light curves using Principal Component Analysis. We find this is a very efficient way to describe many aspects of RRab light curve structure: in many cases, a Principal Component fit with 9 parameters can describe a RRab light curve including bumps whereas a 17 parameter Fourier fit is needed. As a consequence we show statistically why the amplitude is also a good summary of the structure of a RR Lyrae light curve. We also use our analysis to derive an empirical relation relating absolute magnitude to light curve structure. In comparing this formula to those derived from exactly the same dataset but using Fourier parameters, we find that the Principal Component Analysis approach has disticnt advantages. These advantages are, firstly, that the errors on the coefficients in such formulae are smaller, and secondly, that the correlation between Principal Components is significantly smaller than the correlation between Fourier amplitudes. These two factors lead to reduced formal errors, in some cases estimated to be a factor of 2, on the eventual fitted value of the absolute magnitude. This technique will prove very useful in the analysis of data from existing large scale survey projects concerning variable stars.Comment: 8 pages, 10 figures, revised version, accepted for publication to MNRA

Review of Reactor Neutrino Oscillation Experiments

In this document we will review the current status of reactor neutrino oscillation experiments and present their physics potentials for measuring the $\theta_{13}$ neutrino mixing angle. The neutrino mixing angle $\theta_{13}$ is currently a high-priority topic in the field of neutrino physics. There are currently three different reactor neutrino experiments, \textsc{Double Chooz}, \textsc{Daya Bay} and \textsc{Reno} and a few accelerator neutrino experiments searching for neutrino oscillations induced by this angle. A description of the reactor experiments searching for a non-zero value of $\theta_{13}$ is given, along with a discussion of the sensitivities that these experiments can reach in the near future.Comment: 15 pages, 4 figure

Hybrid reduced-order modeling and particle-Kalman filtering for the health monitoring of flexible structures

MEMS-based, surface-mounted structural health monitoring systems were recently proposed to locate possible damage events in lightweight composite structures. To track the structural dynamics induced by the external actions and identify in real-time the inception of drifts from the virgin, or undamaged state, recursive Bayesian filters are here adopted. As the main drawback of any on-line identification method might be linked to the excessive computational costs, two solutions are jointly enforced: an order-reduction of the numerical model used to track the structural behavior, through the proper orthogonal decomposition in its snapshot-based version; an improved particle filtering strategy, which features an extended Kalman updating of each evolving particle before the resampling stage. While the former method alone can reduce the number of effective degrees-of-freedom of the structure to a few only (depending on the excitation), the latter allows to track the evolution of damage and also locate it thanks to an intricate formulation. To assess the proposed procedure, the case of a thin plate subject to bending is investigated; it is shown that, when the procedure is fed by measurements gathered by a network of inertial MEMS sensors appropriately deployed over the plate, damage is efficiently and accurately estimated and located

Circulating SIRT1 inversely correlates with epicardial fat thickness in patients with obesity

Background and aim: Obesity is increasing worldwide and is related to undesirable cardiovascular outcomes. Epicardial fat (EF), the heart visceral fat depot, increases with obesity and correlates with cardiovascular risk. SIRT1, an enzyme regulating metabolic circuits linked with obesity, has a cardioprotective effect and is a predictor of cardiovascular events. We aimed to assess the relationship of EF thickness (EFT) with circulating SIRT1 in patients with obesity. Methods and results: Sixty-two patients affected by obesity and 23 lean controls were studied. Plasma SIRT1 concentration was determined by enzyme-linked immunosorbent assay (ELISA). EFT was measured by echocardiography. Body mass index (BMI), waist circumference, heart rate (HR), blood pressure, and laboratory findings (fasting glucose, insulin, HbA1c, cholesterol, and triglycerides) were assessed. SIRT1 was significantly lower (P = 0.002) and EFT was higher (P < 0.0001) in patients with obesity compared with lean controls. SIRT1 showed a negative correlation with EFT and HR in the obesity group (rho = -0.350, P = 0.005; rho = -0.303, P = 0.008, respectively). After adjustment for obesity-correlated variables, multiple linear regression analysis showed that EFT remained the best correlate of SIRT1 (beta = -0.352, P = 0.016). Conclusions: Circulating SIRT1 correlates with the visceral fat content of the heart. Serum SIRT1 levels might provide additional information for risk assessment of coronary artery disease in patients with obesity. (C) 2016 The Italian Society of Diabetology, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition, and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved

An optimal sensor placement method for SHM based on Bayesian experimental design and polynomial chaos expansion

We present an optimal sensor placement methodology for structural health monitoring (SHM) purposes, relying on a Bayesian experimental design approach. The unknown structural properties, e.g. the residual strength and stiffness, are inferred from data collected through a network of sensors, whose architecture, i.e., type and position may largely affect the accuracy of the monitoring system. In tackling this issue, an optimal network configuration is herein sought by maximizing the expected information gain between prior and posterior probability distributions of the parameters to be estimated. Since the objective function linked to the network topology cannot be analytically computed, a numerical approximation is provided by means of a Monte Carlo analysis, wherein each realization is obtained via finite element modeling. Since the computational burden linked to this procedure often grows infeasible, a Polynomial Chaos Expansion (PCE) approach is adopted for accelerating the computation of the forward problem. The analysis expands over joint samples covering both structural state and design variables, i.e., sensor locations. Via increase of the number of deployed sensors in the network, the optimization procedure soon turns computationally costly due to the curse of dimensionality. To this end, a stochastic optimization method is adopted for accelerating the convergence of the optimization process and thereby the damage detection capability of the SHM system. The proposed method is applied to thin flexible structures, and the resulting optimal sensor configuration is shown. The effects of the number of training samples, the polynomial degree of the approximation expansion and the optimization settings are also discussed