1,721,032 research outputs found
Obserbot: A Totally Automated Watcher to Monitor Essential Services
No full text“Obserbot” is the name of a long term project resulting from the crasis of the terms “observe” and “bot” (abridged form for robot). In fact it represents a framework to monitor internet and extract some knowledge without any human supervision. The service is available 24 h a day and 365 days a year. Obserbot targets are presently two: official news media and twitter. A rough mass of textual data are collected form those two sources. A in-line semantic analysis of the collected stream allows to extract information of a specific Domain of Interest (DoI). The hardware and software architecture allowing collection is rather versatile and can be employed for several different purposes, however the semantic analytics is strictly DoI dependent. In its present form, Obserbot can handle information related to all essential services. Essential services are those activities performed by network of utilities that allow good provision: water supply, energy supply (e.g. gas and electricity), fuel supply, fresh food supply etc. and responding to other fundamental human needs such as transports, mobility and social connectivity
Resources allocation in disaster response using Ordinal Optimization based approach
No full textRecent events, such as Hurricane Katrina, have revealed the need for coordinated and effective disaster responses. An optimal distribution of available resources is essential for disaster response effectiveness. Emergency responders are faced with the challenges of increased size and complexity of critical infrastructures that provide vital resources for disaster response operations. In this paper, we propose a simulation-based tool to assist emergency responders in finding the optimal distribution of available resources during a disaster event. The proposed tool utilizes the Disaster Response Network Enabled Platform (DR-NEP) which is an infrastructure interdependencies simulation platform for disaster response support. DR-NEP is a simulation network platform that integrates different simulators for different infrastructures to form a universal simulation platform. We employ a new concept in Discrete Event Systems optimization called Ordinal Optimization to address the problem of resources allocation during a disaster event. The objective of the optimization problem is maximizing the operational capacity of a critical infrastructure, a hospital in this case. Due to the huge combinatorial feasible search space, an Ordinal Optimization based approach is used to solve the problem using two main concepts: goal softening and order comparison. This approach aims at finding a Good Enough solution set (G) with an acceptable probability and efficient computational effort. This paper describes early results of our work that shows the use of our approach in optimizing resources allocation in a simulated disaster event. © 2014 IEEE
Un criterio quantitativo per la graduazione degli scenari associati al terrorismo radiologico e nucleare
No full textDiscriminating chaotic time series with visibility graph eigenvalues
No full text"Time series can be transformed into graphs called horizontal visibility graphs (HVGs) in order to gain useful insights. Here, the maximum eigenvalue of the adjacency matrix associated to the HVG derived from several time series is calculated. The maximum eigenvalue methodology is able to discriminate between chaos and randomness and is suitable for short time series, hence for experimental results. An application to the United States gross domestic product data is given.
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Toward ECListener: An Unsurpervised Intelligent System to Monitor Energy Communities
No full textAbstract This work describes the architecture of the “Obserbot Cluster”. Obserbot is the name of a long term project resulting from the crasis of the terms “observe” and “bot” (abridged form for robot). The Obserbot Cluster is formed by a number of different components running as micro-services on an dependable and scalable platform. Obserbot main objective is to monitor the web social media to extract some knowledge without any human supervision. The service is available 24 h a day and 365 days a year. Obserbot targets are presently two: official news media and twitter. A rough mass of textual data are collected form those two sources. In-line semantics and sentiment analysis of the collected stream allows to extract information on a specific Domain of Interest (DoI). In particular, Natural Language Processing and machine learning techniques are extensively employed for recognizing named entities, performing events classification and building up taxonomies. The hardware and software architecture enabling such a collection is rather versatile and it can be exploited to accomplish several different purposes. However, the semantic analytics is strictly DoI dependent. In its present form, Obserbot is able to handle information related to essential services and monitor publications on emerging Energy Communities. Essential services are sustained by networks of utilities providing basic goods: water supply, energy supply (e.g. gas and electricity), fuel supply, fresh food supply etc. and responding to other fundamental human needs such as transports, mobility and social connectivity
Computational models of myocardial endomysial collagen arrangement
No full textCollagen extracellular matrix is one of the factors related to high passive
stiffness of cardiac muscle. However, the architecture and the mechanical aspects
of the cardiac collagen matrix are not completely known. In particular,
endomysial collagen contribution to the passive mechanics of cardiac muscle as
well as its micro anatomical arrangement is still a matter of debate. In order to
investigate mechanical and structural properties of endomysial collagen, we
consider two alternative computational models of some specific aspects of the
cardiac muscle. These two models represent two different views of endomysial
collagen distribution: (1) the traditional view and (2) a new view suggested by
the data obtained from scanning electron microscopy (SEM) in NaOH macerated
samples (a method for isolating collagen from the other tissue). We model the
myocardial tissue as a net of spring elements representing the cardiomyocytes
together with the endomysial collagen distribution. Each element is a viscous
elastic spring, characterized by an elastic and a viscous constant. We connect
these springs to imitate the interconnections between collagen fibers. Then we
apply to the net of springs some external forces of suitable magnitude and
direction, obtaining an extension of the net itself. In our setting, the ratio
forces magnitude /net extension is intended to model the stress /strain ratio of
a microscopical portion of the myocardial tissue. To solve the problem of the
correct identification of the values of the different parameters involved, we use
an artificial neural network approach. In particular, we use this technique to
learn, given a distribution of external forces, the elastic constants of the
springs needed to obtain a desired extension as an equilibrium position. Our
experimental findings show that, in the model of collagen distribution structured
according to the new view, a given stress /strain ratio (of the net of springs,
in the sense specified above) is obtained with much smaller (w.r.t. the other
model, corresponding to the traditional view) elasticity constants of the
springs. This seems to indicate that by an appropriate structure, a given
stiffness of the myocardial tissue can be obtained with endomysial collagen
fibers of much smaller siz
Towards a Systemic Asset Characterization of Electrical Systems for Multi-Risk Resilience
No full textTraditional reliability analysis tools for electrical systems must be advanced to represent the potential impacts from climate change and other natural hazards and must embed system-of-systems approaches to account for critical infrastructures' (CIs) interdependencies. The Italian project RETURN, funded by the PNRR, within the activity-line 'Multi-Risk Resilience Assessment of Critical Infrastructures' is adopting an interdisciplinary and holistic approach for developing impact modelling methods with an all-hazards perspective. The paper provides an overview of the methodology under development towards the systemic modelling and analysis of CIs, including the electrical transmission and distribution systems, aiming to support decision making processes targeting multi-risk resilience
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