Instability and new phases of higher-dimensional rotating black holes

It has been conjectured that higher-dimensional rotating black holes become unstable at a sufficiently large value of the rotation, and that new black holes with pinched horizons appear at the threshold of the instability. We search numerically, and find, the stationary axisymmetric perturbations of Myers-Perry black holes with a single spin that mark the onset of the instability and the appearance of the new black hole phases. We also find new ultraspinning Gregory-Laflamme instabilities of rotating black strings and branes.Comment: 5 pages, 5 figures. The instability of the black hole is argued to appear at the second zero mode. The first zero mode is not associated to a new branch of black hole solution

Study of meta-analysis strategies for network inference using information-theoretic approaches

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Reverse engineering of gene regulatory networks (GRNs) from gene expression data is a classical challenge in systems biology. Thanks to high-throughput technologies, a massive amount of gene-expression data has been accumulated in the public repositories. Modelling GRNs from multiple experiments (also called integrative analysis) has; therefore, naturally become a standard procedure in modern computational biology. Indeed, such analysis is usually more robust than the traditional approaches focused on individual datasets, which typically suffer from some experimental bias and a small number of samples. To date, there are mainly two strategies for the problem of interest: the first one (”data merging”) merges all datasets together and then infers a GRN whereas the other (”networks ensemble”) infers GRNs from every dataset separately and then aggregates them using some ensemble rules (such as ranksum or weightsum). Unfortunately, a thorough comparison of these two approaches is lacking. In this paper, we evaluate the performances of various metaanalysis approaches mentioned above with a systematic set of experiments based on in silico benchmarks. Furthermore, we present a new meta-analysis approach for inferring GRNs from multiple studies. Our proposed approach, adapted to methods based on pairwise measures such as correlation or mutual information, consists of two steps: aggregating matrices of the pairwise measures from every dataset followed by extracting the network from the meta-matrix.Peer ReviewedPostprint (author's final draft

Optimizing periodicity and polymodality in noise-induced genetic oscillators

Many cellular functions are based on the rhythmic organization of biological processes into self-repeating cascades of events. Some of these periodic processes, such as the cell cycles of several species, exhibit conspicuous irregularities in the form of period skippings, which lead to polymodal distributions of cycle lengths. A recently proposed mechanism that accounts for this quantized behavior is the stabilization of a Hopf-unstable state by molecular noise. Here we investigate the effect of varying noise in a model system, namely an excitable activator-repressor genetic circuit, that displays this noise-induced stabilization effect. Our results show that an optimal noise level enhances the regularity (coherence) of the cycles, in a form of coherence resonance. Similar noise levels also optimize the multimodal nature of the cycle lengths. Together, these results illustrate how molecular noise within a minimal gene regulatory motif confers robust generation of polymodal patterns of periodicity.Comment: 9 pages, 6 figure

La ionosfera: comunicare... naturalmente!

La ionosfera è la parte della media-alta atmosfera compresa tra i 60 e i 1000 km di quota. Essa è caratterizzata da una concentrazione di elettroni tale da modificare la propagazione delle onde radio che la attraversano

Post-operative atrial fibrillation is influenced by beta-blocker therapy but not by pre-operative atrial cellular electrophysiology

We investigated whether post-cardiac surgery (CS) new-onset atrial fibrillation (AF) is predicted by pre-CS atrial cellular electrophysiology, and whether the antiarrhythmic effect of beta-blocker therapy may involve pre-CS pharmacological remodeling. Atrial myocytes were obtained from consenting patients in sinus rhythm, just prior to CS. Action potentials and ion currents were recorded using whole-cell patch-clamp technique. Post-CS AF occurred in 53 of 212 patients (25%). Those with post-CS AF were older than those without (67 ± 2 vs 62 ± 1 years, P = 0.005). In cells from patients with post-CS AF, the action potential duration at 50% and 90% repolarization, maximum upstroke velocity, and effective refractory period (ERP) were 13 ± 4 ms, 217 ± 16 ms, 185 ± 10 V/s, and 216 ± 14 ms, respectively (n = 30 cells, 11 patients). Peak L-type Ca2+ current, transient outward and inward rectifier K+ currents, and the sustained outward current were −5.0 ± 0.5, 12.9 ± 2.4, −4.1 ± 0.4, and 9.7 ± 1.0 pA/pF, respectively (13-62 cells, 7-19 patients). None of these values were significantly different in cells from patients without post-CS AF (P > 0.05 for each, 60-279 cells, 29-86 patients), confirmed by multiple and logistic regression. In patients treated >7 days with a beta-blocker pre-CS, the incidence of post-CS AF was lower than in non-beta-blocked patients (13% vs 27%, P = 0.038). Pre-CS beta-blockade was associated with a prolonged pre-CS atrial cellular ERP (P = 0.001), by a similar degree (∼20%) in those with and without post-CS AF. Conclusion: Pre-CS human atrial cellular electrophysiology does not predict post-CS AF. Chronic beta-blocker therapy is associated with a reduced incidence of post-CS AF, unrelated to a pre-CS ERP-prolonging effect of this treatment

Design and Accuracy Analysis of Multilevel State Estimation Based on Smart Metering Infrastructure

© 1963-2012 IEEE. While the initial aim of smart meters is to provide energy readings for billing purposes, the availability of these measurements could open new opportunities for the management of future distribution grids. This paper presents a multilevel state estimator that exploits the smart meter measurements for monitoring both low and medium voltage grids. The goal of this paper is to present an architecture that is able to efficiently integrate smart meter measurements and to show the accuracy performance achievable if the use of real-Time smart meter measurements for state estimation purposes was enabled. The design of the state estimator applies the uncertainty propagation theory for the integration of the data at different hierarchical levels. The coordination of the estimation levels is realized through a cloud-based infrastructure, which also provides the interface to auxiliary functions and the access to the estimation results for other distribution grid management applications. A mathematical analysis is performed to characterize the estimation algorithm in terms of accuracy and to show the performance achievable at different levels of the distribution grid when using the smart meter data. Simulations are presented, which validate the analytical results and demonstrate the operation of the multilevel estimator in coordination with the cloud-based platform

Low voltage system state estimation based on smart metering infrastructure

© 2016 IEEE. The accurate monitoring of distribution grids is essential to enable the intelligent management and control of future Smart Grids. Several challenges prevent an easy development of the state estimation tools needed to assess the operating conditions of distribution networks. The lack of a suitable measurement infrastructure is one of the most challenging aspects to face. However, in last years, several utilities started a massive deployment of smart meters in their networks. The proper use of these measurements is key to enhance the performance of distribution system state estimators. This paper presents a two-level approach conceived to efficiently include smart meter measurements in low voltage grid state estimation. The proposed solution relies on a cloud-based smart metering architecture, which allows scalability and interoperability among different off-the-shelf meters. Moreover, a suitable design of the estimation algorithm, using the uncertainty propagation theory, is proposed in order to maximize the accuracy of the estimation results. Tests performed on a sample low voltage network show the performance and the main features of the proposed state estimation solution

A Distributed IoT Infrastructure to Test and Deploy Real-Time Demand Response in Smart Grids

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. In this paper, we present a novel distributed framework for real-time management and co-simulation of demand response (DR) in smart grids. Our solution provides a (near-) real-time co-simulation platform to validate new DR-policies exploiting Internet-of-Things approach performing software-in-the-loop. Hence, the behavior of real-world power systems can be emulated in a very realistic way and different DR-policies can be easily deployed and/or replaced in a plug-and-play fashion, without affecting the rest of the framework. In addition, our solution integrates real Internet-connected smart devices deployed at customer premises and along the smart grid to retrieve energy information and send actuation commands. Thus, the framework is also ready to manage DR in a real-world smart grid. This is demonstrated on a realistic smart grid with a test case DR-policy

A cloud-based smart metering infrastructure for distribution grid services and automation

© 2017 The Authors The evolution of the power systems towards the smart grid paradigm is strictly dependent on the modernization of distribution grids. To achieve this target, new infrastructures, technologies and applications are increasingly required. This paper presents a smart metering infrastructure that unlocks a large set of possible services aimed at the automation and management of distribution grids. The proposed architecture is based on a cloud solution, which allows the communication with the smart meters from one side and provides the needed interfaces to the distribution grid services on the other one. While a large number of applications can be designed on top of the cloud, in this paper the focus will be on a real-time distributed state estimation algorithm that enables the automatic reconfiguration of the grid. The paper will present the key role of the cloud solution for obtaining scalability, interoperability and flexibility, and for enabling the integration of different services for the automation of the distribution system. The distributed state estimation algorithm and the automatic network reconfiguration will be presented as an example of coordinated operation of different distribution grid services through the cloud