A multilevel graph approach for IoT-based complex scenario management through situation awareness and semantic approaches

Modern reality and the environments in which we live are characterized by technology components geared toward automatic management through pervasive services. Thanks to the advent of the Internet of Things, such environments can provide information such as pollution levels, public transport conditions, efficiency of energy distribution networks, and identification of suspicious activities by generating complex scenarios. The profitable management of such scenarios can be performed through context modeling and methodologies that can extract and understand environmental information by preventing certain events through artificial intelligence techniques by increasing Situation Awareness. This paper focuses on developing a methodology with predictive capabilities and context adaptability for managing complex scenarios. The use of semantic and graph-based approaches, unlike many approaches used, leads to better integration of knowledge, resulting in improved system performance. In addition, such approaches allow understanding of what is happening in the system at a given time, enabling manipulation and integration of semantic information. Graph-based approaches chosen for this purpose are Ontologies, Context Dimension Trees, and Bayesian Networks, which are able to support the end-user or expert user in handling complex scenarios. The proposed methodology has been validated and applied to real complex scenarios based on the IoT paradigm. The proposed approach validation was conducted using open data from the city of London; a practical scenario case study was conducted in the field of automated management of a Smart Home. In both cases, the system achieved promising results

Revolutionizing cultural heritage preservation: an innovative IoT-based framework for protecting historical buildings

Italy offers a cultural heritage of considerable value that needs to be protected. Indeed, natural deterioration linked to the passage of time affects ancient artifacts and buildings. Sometimes, the deterioration compromises the functionality of cultural assets, pushing them toward decay. In this scenario, effective intervention seems impossible on the various critical points because of the wide variability of factors involved and the wide range of possible treatments. However, the spread of low-cost technologies has led to the possibility of having different devices and sensors able to communicate and interact with each other and humans: the Internet of Things (IoT). In this scenario, the IoT paradigm makes it possible to map reality by defining a coherent virtual representation (Digital Twin), which could help preserve Cultural Heritage. This work introduces an IoT-based system combining monitoring, predictive maintenance, and decision-making regarding the implementable interventions for protecting cultural heritage buildings. For this purpose, deep and machine learning techniques allow for the detection and classification of damages on specific materials. The experimental phase consists of two phases: the first aims to evaluate the accuracy of the proposed architecture, and the second exploits a prototype capable of interacting with expert users. The results of the experimental campaign are promising

A Context Aware Approach for Promoting Tourism Events: The Case of Artist's Lights in Salerno

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MuG: A Multilevel Graph Representation for Big Data Interpretation

Our society is oriented towards data production. The increasingly massive spread of mobile devices and the Internet of Things is transforming our society into a data factory. Data, however, does not immediately lead to knowledge and, in fact we can become overwhelmed with a mass of information that is difficult to understand: often the desire to predict the future from data analysis turns into the nightmare of data overload. There are numerous approaches, automatic and manual, present in the literature that try to interpret data by extracting information. Among the various methodologies proposed, none seems to have resolved the problem in a definitive and universal way, perhaps because every data analysis problem needs to be faced from a different point of view. This paper introduces an approach for the interpretation of data from sensors located within a city. Three graphs (Ontologies, Context Dimension Tree and Bayesian Networks) were chosen for the representation of the scenarios both from the point of view of the sensors involved and of the services and events connected to the data. Through the Ontologies and the Context Dimension Tree it is possible to analyze the scenario from a syntactic and semantic point of view constructing Bayes networks that enable the estimation of the probability that some events happen. A first empirical analysis conducted on some districts of London seems to confirm the effectiveness of the proposed method

Steps towards the hyperfine splitting measurement of the muonic hydrogen ground state: pulsed muon beam and detection system characterization

The high precision measurement of the hyperfine splitting of the muonic-hydrogen atom ground state with pulsed and intense muon beam requires careful technological choices both in the construction of a gas target and of the detectors. In June 2014, the pressurized gas target of the FAMU experiment was exposed to the low energy pulsed muon beam at the RIKEN RAL muon facility. The objectives of the test were the characterization of the target, the hodoscope and the X-ray detectors. The apparatus consisted of a beam hodoscope and X-rays detectors made with high purity Germanium and Lanthanum Bromide crystals. In this paper the experimental setup is described and the results of the detector characterization are presented.Comment: 22 pages, 14 figures, published and open access on JINS

Game analysis on general purpose technology cooperative R&D with fairness concern from the technology chain perspective

General purpose technologies (GPTs) are regarded as a major source of productivity advancement and economic growth. As a kind of platform technology, GPTs have strong knowledge spillovers, which causes a single subject to lack R&D motivation and adopt a wait-and-see strategy. Cooperation R&D is an effective mode choice for GPTs. For this, three models based on upstream-led, downstream-led and balanced power structures were constructed to study the cooperation R&D modes of GPTs and influencing factors from a technology chain perspective. This study aims to reveal the effects of fairness concerns and power structures on three models. This study also focuses on the roles of knowledge spillovers and government support. The results indicate that different power structures will lead to an unequal distribution of profits between firm U and firm D in the technology chain. The balanced power structure should be the preferred model. The profits of firms in the leading position are always higher than those of firms in the following position. In addition, fairness concerns negatively impact the performance of firms, which may improve the bargaining ability of firms in the following position, but this does not bring a sustainable benefit. Government support (e.g., knowledge and technology support and R&D subsidies) and knowledge spillovers are two key factors influencing the decisions and outcomes of the technology chain. When a firm's relative innovation contribution level is greater, its profits in the leading position are the highest, followed by those in the balanced power structure, and they are lowest in the following position. In contrast, profits under balanced power are the highest, and those in the following position are still the lowest. This study enables a theoretical understanding of how and why the R&D process of GPTs can be regarded as a technology chain. It also sheds light on the fact that the balance power structure model should be the preferred choice and that both fairness concerns and government support should be considered for improving the R&D efficiency of GPT cooperation R&D in practice

First measurement of the temperature dependence of muon transfer rate from muonic hydrogen atoms to oxygen

We report the first measurement of the temperature dependence of muon transfer rate from muonic hydrogen atoms to oxygen between 100 and 300 K. Data were obtained from the X-ray spectra of delayed events in a gaseous target, made of a H2/O2 mixture, exposed to a muon beam. This work sets constraints on theoretical models of muon transfer and is of fundamental importance for the measurement of the hyperfine splitting of muonic hydrogen ground state as proposed by the FAMU collaboration

First measurement of the temperature dependence of muon transfer rate from muonic hydrogen atoms to oxygen

We report the first measurement of the temperature dependence of muon transfer rate from muonic hydrogen atoms to oxygen between 100 and 300 K. Data were obtained from the X-ray spectra of delayed events in a gaseous target, made of a H2/O2 mixture, exposed to a muon beam. This work sets constraints on theoretical models of muon transfer and is of fundamental importance for the measurement of the hyperfine splitting of muonic hydrogen ground state as proposed by the FAMU collaboration

First FAMU observation of muon transfer from \u3bcp atoms to higher-Z elements

Abstract: The FAMU experiment aims to accurately measure the hyperfine splitting of the ground state of the muonic hydrogen atom. A measurement of the transfer rate of muons from hydrogen to heavier gases is necessary for this purpose. In June 2014, within a preliminary experiment, a pressurized gas-target was exposed to the pulsed low-energy muon beam at the RIKEN RAL muon facility (Rutherford Appleton Laboratory, U.K.). The main goal of the test was the characterization of both the noise induced by the pulsed beam and the X-ray detectors. The apparatus, to some extent rudimental, has served admirably to this task. Technical results have been published that prove the validity of the choices made and pave the way for the next steps. This paper presents the results of physical relevance of measurements of the muon transfer rate to carbon dioxide, oxygen, and argon from non-thermalized excited \u3bcp atoms. The analysis methodology and the approach to the systematics errors are useful for the subsequent study of the transfer rate as function of the kinetic energy of the \u3bcp currently under way