1,720,981 research outputs found
Predicting the sources of an outbreak with a spectral technique
No full textThe epidemic spreading of a disease can be described by a contact net-work whose nodes are persons or centers of contagion, and links are het-erogeneous relations among them. We provide a procedure to identify multiple sources of an outbreak or their closer neighbors. Our method-ology is based on a simple spectral technique that requires only the definition of a undirected contact graph. The algorithm is tested on a variety of graphs collected from real influenza outbreaks, both in urban and rural areas. The proposed spectral technique is able to identify the source nodes in cases when the graph sufficiently approximates to a tree
Discriminating 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.
Semi-centralized reconstruction of robot swarm topologies: The largest laplacian eigenvalue and high frequency noise are used to calculate the adjacency matrix of an underwater swarm from time-series
No full textAn important task in underwater autonomous vehicle swarm management is the knowledge of the graph topology, to be obtained with the minimum possible communication exchanges and amid heavy interferences and background noises. Despite the importance of the task, this problem is still partially unsolved. Recently, the Fast Fourier Transform and the addition of white noise to consensus signals have been proposed independently to determine respectively the laplacian spectrum and the adjacency matrix of the graph of interacting agents from consensus time series, but both methodologies suffer technical difficulties. In this paper, we combine them in order to simplify calculations, save energy and avoid topological reconstruction errors using only the largest eigenvalue of the spectrum and instead of white noise, a high frequency, low amplitude noise. Numerical simulations of several swarms (random, small-world, pipeline, grid) show an exact reconstruction of the configuration topologies
Modal identification from motion magnification of ancient monuments supported by blind source separation algorithms
No full textMotion Magnification (MM) is an emerging video processing methodology that acts like a microscope for motion in digital videos. Hardly visible motions are magnified leaving unchanged the general topology of the image. Therefore, the micro-displacements produced by vibrations can be amplified greatly and made available to the standard frequency domain analysis. The MM was recently successfully explored as a viable method to perform modal identification, at least in laboratory. In outdoor environment ambient vibration acquisitions are unavoidably affected by significant noise disturbing the modes identification. However, the first three or four modes, which are usually the most relevant to the dynamic behaviour of most structures, can be identified with little supervision, possibly reducing the calculation requirements as much as possible. All these tasks may be accomplished using MM together with the Blind Source Separation (BSS) algorithm. BSS allows the separation of mixed signals without previously known information about the mixture. MM provides the data while the BSS improves the identification of the modes by separating their contribution within the mixed noisy signals. A case-study is proposed to explore the application of the methodology to large ancient masonry structures, which represent very challenging objects for their structural complexities and heterogeneities. In particular, the studied structure was represented by an ancient bridge, the Ponte delle Torri, Spoleto. Due to the outdoor environmental difficulties, to the state of damage of the bridge and to the high level of noise in the video footages, this case-study has to be considered a very demanding one, nevertheless the modes were identified with good approximation in comparison to the results by Operational Modal Analysis (OMA) techniques, applied to ambient vibration data from seismographs equipped with accurate triaxial velocimeters
Motion Magnification Analysis for structural monitoring of ancient constructions
No full textA new methodology for digital image processing, namely the Motion Magnification (MM), allows to magnify small displacements of large structures. MM acts like a microscope for motion in video sequences, but affecting only some groups of pixels. The processed videos unveil motions hardly visible with the naked eye and allow a more effective frequency domain analysis. We applied the MM method to several historic structures, including a 1:10-scale mockup of Hagia Irene in Constantinople tested on shaking table, the so-called Temple of Minerva Medica in Rome and the Ponte delle Torri of Spoleto. MM algorithms parameters were calibrated by comparison with reference consolidated modal identification methods applied to conventional velocimeters data. Encouraging results were obtained in terms of vibration monitoring and modal analysis for dynamic identification of the studied structures, offering a low-cost, viable support to the standard vibration sensing equipment, such as contact velocimeters, laser vibrometers and others. © 201
Networks to stop the epidemic spreading
No full textToday, only two methods are viable to immunize people against an epidemic spreading: vaccine and quarantine, but a prolonged quarantine extended to the whole population implies unsustainable costs, while vaccinations take a lot of time. Nevertheless, it would be possible to stop the propagation of viruses and alleviate the economic activities lockdown greatly, vaccinating or quarantining only a small percentage of the population using well-known methodologies to select people to immunize. From a practical point of view, it is necessary to provide the social or relational national network, which will constitute the spectral graph analysis, our primary methodological tool. This requires to generate a graph of many nodes (people) and links (relations, of any kind) mapping the whole population. The connections are extracted from the national register, media, web resources, cellular phones and any other source, possibly after an anonymizing step. The procedure is inherently dynamic since relations and people geo-localization change continuously; therefore, a real-time update must be implemented. Fortunately, internet data collection mechanisms can provide vast information to support the update step. Once the National Relation Network is available, individuals that could propagate more dangerously the infection (which is subtly different from propagating to more people the infection) will be identified quickly and immunized with high priority. A careful selection of these individuals may stop or slow down the spreading, safeguarding at the same time, the economic system. Likewise, the National Relational Network can directly indicate the subjects hit financially by the epidemic without additional computational costs. Moreover, the Graph theory usage will allow applying its numerous, impressive achievements to the epidemic containment. We warn that no real experiment has been conducted on a large scale, so no evidence is available; however theoretical demonstrations and computer simulations are encouraging. Finally, we do not intend to present a formal treatment of the issue or foster academic discussions; instead, we propose a practical approach to the epidemic spreading problem
Estimating the epidemic growth dynamics within the first week
No full textInformation about the early growth of infectious outbreaks is indispensable to estimate the epidemic spreading. A large number of mathematical tools have been developed to this end, facing as much large number of different dynamic evolutions, ranging from sub-linear to super-exponential growth. Of course, the crucial point is that we do not have enough data during the initial outbreak phase to make reliable inferences. Here we propose a straightforward methodology to estimate the epidemic growth dynamic from the cumulative infected data of just a week, provided a surveillance system is available over the whole territory. The methodology, based on the Newcomb-Benford Law, is applied to the Italian covid 19 case-study. Results show that it is possible to discriminate the epidemic dynamics using the first seven data points collected in fifty Italian cities. Moreover, the most probable approximating function of the growth within a six-week epidemic scenario is identified
The Network Topology of Connecting Things: Defence of IoT Graph in the Smart City
No full textThe Internet of Things (IoT) is a novel paradigm based on the connectivity among different entities or “things”. IoT environment in the form of interconnected smart “things” represents a great potential in terms of effective and efficient solutions related to urban context (e.g., system architecture, design and development, human involvement, data management and applications). On the other hand, with the introduction of the IoT environment, devices and network security have become a fundamental and challenging issue. Moreover, growing number of users connected via IoT system necessitates focusing on the vulnerability of complex networks and defence challenges at the topological level. This paper addresses these challenges from the perspective of graph theory. In this work, the authors introduce a novel AV11 algorithm to identify the most critical and influential IoT nodes in a Social IoT (SIoT) network in a smart city context using ENEA Portici CRESCO infrastructure
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
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