Enhancing the significance of gravitational wave bursts through signal classification

The quest to observe gravitational waves challenges our ability to discriminate signals from detector noise. This issue is especially relevant for transient gravitational waves searches with a robust eyes wide open approach, the so called all- sky burst searches. Here we show how signal classification methods inspired by broad astrophysical characteristics can be implemented in all-sky burst searches preserving their generality. In our case study, we apply a multivariate analyses based on artificial neural networks to classify waves emitted in compact binary coalescences. We enhance by orders of magnitude the significance of signals belonging to this broad astrophysical class against the noise background. Alternatively, at a given level of mis-classification of noise events, we can detect about 1/4 more of the total signal population. We also show that a more general strategy of signal classification can actually be performed, by testing the ability of artificial neural networks in discriminating different signal classes. The possible impact on future observations by the LIGO-Virgo network of detectors is discussed by analysing recoloured noise from previous LIGO-Virgo data with coherent WaveBurst, one of the flagship pipelines dedicated to all-sky searches for transient gravitational waves

Presence of New Delhi metallo-β-lactamase gene (NDM-1) in a clinical isolate of Acinetobacter junii in Argentina

Here we report the presence of a clinically significant A. junii blaNDM-1 positive in a 38-year-old woman who was admitted to the emergency department with a fever and leg ulcers with signs of infection. The NDM-1 carbapenemase has been dramatically spread among Gram-negative bacilli, thus imposing a new challenge on the health system to fight bacterial infections.These data expand the number of Acinetobacter species harbouring blaNDM-1. The wide existence of Acinetobacter harbouring and dispersing this carbapenemase emphasizes the importance of non-previously recognized pathogens as reservoirs of dangerous resistance determinants. These resistance determinants can be later easily transferred to other menacing pathogens.Fil: Montaña, Sabrina Daiana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones en Microbiología y Parasitología Médica. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones en Microbiología y Parasitología Médica; ArgentinaFil: Cittadini, Roxana. Sanatorio Mater Dei; ArgentinaFil: Del Castillo M,. Sanatorio Mater Dei; ArgentinaFil: Uong, S.. California State University; Estados UnidosFil: Lazzaro, T.. California State University; Estados UnidosFil: Almuzara, Marisa. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Bioquímica Clínica; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Hospital de Clínicas General San Martín; ArgentinaFil: Barberis, Claudia. Sanatorio Mater Dei; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Bioquímica Clínica; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Hospital de Clínicas General San Martín; ArgentinaFil: Vay, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Bioquímica Clínica; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Hospital de Clínicas General San Martín; ArgentinaFil: Ramírez, M. S.. California State University; Estados Unido

How People Update Beliefs about Climate Change: Good News and Bad News

People are frequently exposed to competing evidence about climate change. We examined how new information alters people’s beliefs. We find that people who doubt that man-made climate change is occurring, and who do not favor an international agreement to reduce greenhouse gas emissions, show a form of asymmetrical updating: They change their beliefs in response to unexpected good news (suggesting that average temperature rise is likely to be less than previously thought) and fail to change their beliefs in response to unexpected bad news (suggesting that average temperature rise is likely to be greater than previously thought). By contrast, people who strongly believe that man-made climate change is occurring, and who favor an international agreement, show the opposite asymmetry: They change their beliefs far more in response to unexpected bad news (suggesting that average temperature rise is likely to be greater than previously thought) than in response to unexpected good news (suggesting that average temperature rise is likely to be smaller than previously thought). The results suggest that exposure to varied scientific evidence about climate change may increase polarization within a population due to asymmetrical updating. We explore the implications of our findings for how people will update their beliefs upon receiving new evidence about climate change, and also for other beliefs relevant to politics and law

Recent advances on data-driven services for smart energy systems optimization and pro-active management

Optimization and proactive management of energy systems are crucial for achieving sustainability, efficiency and resilience in future smart energy networks. Data-driven approaches offer promising solutions for tackling the complex and dynamic challenges of energy systems, such as uncertainty, variability, and heterogeneity. Meanwhile, recent advances in decreasing hardware costs and improving data accessibility have allowed for the collection of high-quality data, leading to the development of more accurate and robust data-driven models of different energy systems. In this study, a comprehensive overview of current and future trends in data-driven optimization for smart energy systems is presented. After introducing the motivation and the background of this research field, the potential applications and benefits of optimization in various domains is discussed, such as electric vehicles charge, district heating networks and energy districts. Subsequently this review focuses on different methods and techniques for data-driven optimization and proactive management, ranging from scientific models to machine learning algorithms. Finally, the novel European project, DigiBUILD, is introduced, where different case studies are tested in several pilots, including electric vehicle charging management for increasing renewable energy source consumption, district heating network operative costs optimization and building energy and comfort management

Water-energy-ecosystem nexus in small run-of-river hydropower : Optimal design and policy

Acknowledgment This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Support from the Helmholtz Centre for Environmental Research - UFZ is gratefully acknowledged.Peer reviewedPublisher PD

Social Conformity in Autism

Humans are extremely susceptible to social influence. Here, we examine whether this susceptibility is altered in autism, a condition characterized by social difficulties. Autistic participants (N=22) and neurotypical controls (N=22) completed a memory test of previously seen words and were then exposed to answers supposedly given by four other individuals. Autistic individuals and controls were as likely to alter their judgements to align with inaccurate responses of group members. These changes reflected both temporary judgement changes (public conformity) and long-lasting memory changes (private conformity). Both groups were more susceptible to answers believed to be from other humans than from computer algorithms. Our results suggest that autistic individuals and controls are equally susceptible to social influence when reporting their memories

Mitigation of 42^{42}Ar/42^{42}K background for the GERDA Phase II experiment

Background coming from the $^{42}$Ar decay chain is considered to be one of the most relevant for the GERDA experiment, which aims to search of the neutrinoless double beta decay of $^{76}$Ge. The sensitivity strongly relies on the absence of background around the Q-value of the decay. Background coming from $^{42}$K, a progeny of $^{42}$Ar, can contribute to that background via electrons from the continuous spectrum with an endpoint of 3.5 MeV. Research and development on the suppression methods targeting this source of background were performed at the low-background test facility LArGe. It was demonstrated that by reducing $^{42}$K ion collection on the surfaces of the broad energy germanium detectors in combination with pulse shape discrimination techniques and an argon scintillation veto, it is possible to suppress the $^{42}$K background by three orders of magnitude. This is sufficient for Phase II of the GERDA experiment