125,631 research outputs found
Spatial Complex Network Analysis and Accessibility Indicators: the Case of Municipal Commuting in Sardinia, Italy
No full textIn this paper a contribution is presented with respect to accessibility indicators modelling for commuters moving through the municipalities of Sardinia, in Italy. In this case, spatial complex network analysis is integrated into the construction of accessibility measures: one of the most relevant outcomes of the first tool –the detection of shortest road paths and distances- is adopted as an input for the second in modelling accessibility indicators. Instead of Euclidean distances often adopted in the literature, shortest road distances are chosen, as commuting implies movements that are usually repeated daily and very likely subjected, even unconsciously, to space and time minimization strategies.
In particular, two commuter accessibility indicators are constructed according to approaches based on a travel cost and a spatial interaction model with impedance function calibrated in exponential and in power form. The accessibility indicators are confronted each other and with relevant socio-economic and infrastructure characteristics of Sardinia.
In addition, they are described, with respect to their spatial distribution and their different implications, when adopted in decision-making and planning. The travel cost based accessibility indicator has a municipal spatial distribution strongly influenced by the main road infrastructure of the Island. By contrast, spatial interaction model based accessibility indicators are more reliable, with respect to their capacity to confirm a leading socio-economic role of the municipalities comprehended in the metropolitan area of the capital town Cagliari
Unsupervised human process discovery in smart homes
No full textThe advances in the Internet of Things (IoT) have enabled the automation of various tasks like switching on the heating at home from work, seeing who is at your front door from the couch, supporting nurses in elderly homes, or the efficient delivery of packages. By enabling the connection between the physical and digital worlds, the IoT has shown how environments can be augmented with technology to enhance their capabilities, making them more intelligent, responsive, and adaptive. This widespread adoption of embedded systems turned pervasive (or ubiquitous) computing into reality: while sensors gather real-time data about the environment, actuators are used to automate the execution of many tasks that help the users of such environments. These environments, referred to as smart environments or smart spaces, represent an emerging class of IoT-based applications and are centered on their human users. Among smart spaces, smart homes and offices are representative examples. The goal is to enhance the quality of life, improve productivity, and provide personalized services by understanding and responding to the needs and preferences of the users, realizing the paradigm known as Ambient Intelligence (AmI). The literature presents various definitions of AmI systems and a set of distinct features that characterize them: sensitivity, responsiveness, adaptivity, ubiquity, and transparency. Sensitivity pertains to the AmI system's ability to perceive and comprehend the surrounding environment and its interaction context. Responsiveness and adaptivity, closely tied to sensitivity, indicate the system's capacity to promptly react, either proactively or reactively, to changes in the context in accordance with user preferences. Collectively, sensitivity, responsiveness, and adaptivity contribute to the overarching concept of context awareness. Lastly, the terms ubiquity and transparency directly relate to the idea of pervasive computing. Smart environments process and analyze the data collected from sensors to extract meaningful information. In this context, AmI is realized by utilizing techniques such as machine learning, artificial intelligence, and human-computer interaction (HCI). The rich data automatically collected via IoT sensors in smart spaces is used to get insights about the human behavior of the user (e.g., sleep tracking) or to perform automated actions for the user (e.g., automatically opening the blinds). For instance, current applications of human behavior monitoring in smart spaces include smart thermostats (e.g., Google Nest Learning Thermostat) and ambient assisted living (e.g., elderly fall detection systems). Modeling human activities and habits is not a simple task, due to the flexible and unstructured nature of human behavior. Recently, although it is still difficult to represent them following a precise flow of tasks, approaches have been proposed that model human habits as workflows. In particular, the research community and manufacturers have shown a great interest in applying process mining (PM) to smart spaces. Process mining is a fairly recent research discipline that combines data mining techniques with techniques used in Business Process Management (BPM), such as process modeling and process analysis. Process mining aims to extract, monitor, and improve processes based on real-world data. In particular, process discovery is a process mining technique used to discover and generate the process model describing the underlying behavior shown in the event log. The mined process model can be visualized in different forms, such as Petri nets, process flowcharts, or BPMN diagrams. Visualization helps to understand the structure and dynamics of processes within the smart space. However, even though process models could be extracted from smart space data, multiple important challenges arose. This thesis presents an overview of how some of the aforementioned research challenges are handled and to what degree they are addressed by the author
Spatial Correlations in Attribute Communities
No full textCommunity detection is an important tool for exploring and classifying the properties of large complex networks and should be of great help for spatial networks. Indeed, in addition to their location, nodes in spatial networks can have attributes such as the language for individuals, or any other socio-economical feature that we would like to identify in communities. We discuss in this paper a crucial aspect which was not considered in previous studies which is the possible existence of correlations between space and attributes. Introducing a simple toy model in which both space and node attributes are considered, we discuss the effect of space-attribute correlations on the results of various community detection methods proposed for spatial networks in this paper and in previous studies. When space is irrelevant, our model is equivalent to the stochastic block model which has been shown to display a detectability-non detectability transition. In the regime where space dominates the link formation process, most methods can fail to recover the communities, an effect which is particularly marked when space-attributes correlations are strong. In this latter case, community detection methods which remove the spatial component of the network can miss a large part of the community structure and can lead to incorrect results
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe 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
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Grouping complex systems: a weighted network comparative analysis
No full textIn this study, the authors compare two inter-municipal commuting networks (MCN) pertaining to the Italian islands of Sardinia and Sicily, by approaching their characterization through a weighted network analysis. They develop on
the results obtained for the MCN of Sardinia (De Montis et al. 2007) and attempt to use network analysis as a mean of detection of similarities or dissimilarities between the systems at hand
Emergent topological and dynamical properties of a real inter-municipal commuting network - perspectives for policy-making and planning
No full textA variety of phenomena can be explained by means of a description of the features of their underlying network structure. In addition, a large number of scientists (see the reviews, eg. Barabasi, 2002; Watts, 2003) demonstrated the emergence of large-scale properties common to many different systems. These various results and studies led to what can be termed as the “new science of complex networks” and to emergence of the new “age of connectivity”. In the realms of urban and environmental planning, spatial analysis and regional science, many scientists have shown in the past years an increasing interest for the research developments on complex networks. Their studies range from theoretical statements on the need to apply complex network analysis to spatial phenomena (Salingaros, 2001) to empirical studies on quantitative research about urban space syntax (Jiang and Claramunt, 2004). Concerning transportation systems analysis, interesting results have been recently obtained on subway networks (Latora and Marchiori, 2002; Gastner and Newman, 2004) and airports (Barrat et al, 2004). In this paper, we study the inter-municipal commuting network of Sardinia (Italy). In this complex weighted network, the nodes correspond to urban centres while the weight of the links between two municipalities represents the flow of individuals between them. Following the analysis developed by Barrat et al. (2004), we investigate the topological and dynamical properties of this complex weighted network. The topology of this network can be accurately described by a regular small-world network while the traffic structure is very rich and reveals highly complex traffic patterns. Finally, in the perspective of policy-making and planning, we compare the emerging network behaviors with the geographical, social and demographical aspects of the transportation system.
Development of a real-time PCR for detection of Mycoplasma agalactiae in bulk tank milk samples and epidemiology of infection in Sardinia
No full textDEVELOPMENT OF A REAL-TIME PCR FOR DETECTION OF MYCOPLASMA AGALACTIAE IN BULK TANK
MILK SAMPLES AND EPIDEMIOLOGY OF INFECTION IN SARDINIA
Carta T. [1], Mannu F.[2], Fadda M.[1], Ibba I.[3], Muggianu D.[3], Turrini F.[2], Pittau M.[1], Chessa B.[1]
[1]Dipartimento di Medicina Veterinaria, Università degli Studi di Sassari ~ Sassari ~ Italy, [2]Nurex s.r.l. ~ Sassari ~ Italy, [3]Associazione
Regionale Allevatori Sardegna (ARAS) ~ Oristano ~ Italy
In this work the Mycoplasma agalactiae p48 gene was used as a diagnostic marker for contagious agalactia (CA) of sheep and goats by Real-Time
PCR. The p48 gene encodes an invariable, constantly expressed, immunodominant surface lipoprotein belongs to the basic membrane protein
family. The Real-Time PCR test based on p48 resulted specific and sensible. The test performance were evaluated on bulk tank milk samples
collected from 1064 ovine and 66 goat farms in sardinian region. 4.8% of sheep farms and 4.5 % of goat farms tested positive. Our results showed
that the test based on the p48 gene can be used on bulk tank milk for detection and epidemiological surveillance of Mycoplasma agalactiae
infections
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