A model of assessment of collateral damage on power grids based on complex network theory

As power grids are gradually adjusted to fit into a smart grid paradigm, a common problem is to identify locations where it is most beneficial to introduce distributed generation. In order to assist in such a decision, we work on a graph model of a regional power grid, and propose a method to assess collateral damage to the network resulting from a localized failure. We perform complex network analysis on multiple instances of the network, looking for correlations between estimated damages and betweenness centrality indices, attempting to determine which model is best suited to predict features of our network

Criteria for Modification of Complex Infrastructure Networks

Complex network theory enables the analysis and comparison of graphs with a very large number of nodes, or with non-trivial topological properties. Graph models exist for many kinds of networks, ranging from computer networks to representation of protein-protein interactions, and analysis techniques are often shared between fields of application. Infrastructure networks are an active field of application of complex network analysis, which is frequently aimed at finding ways to improve on the structure of a network, while respecting budget constraints. In this activity, complex network analysis is often cross-referenced with simulations or operational research. Power grids stand out among the most prominent examples of infrastructure network analyzed with techniques derived from complex network theory, due to their importance as a service, their properties of quick response to events, and the desired transition to a smart grid paradigm. With the growing interest for the protection of endangered species and habitats, the modeling and analysis of green infrastructure has also received increasing attention from scholars. These classes of infrastructure provide case studies for the exemplification of a common process for the analysis of various kinds of infrastructure networks, which involves the identification of vulnerabilities, the exploration of a search space for possible modifications, and the definition of a comparable measure of health of the network.Complex network theory enables the analysis and comparison of graphs with a very large number of nodes, or with non-trivial topological properties. Graph models exist for many kinds of networks, ranging from computer networks to representation of protein-protein interactions, and analysis techniques are often shared between fields of application. Infrastructure networks are an active field of application of complex network analysis, which is frequently aimed at finding ways to improve on the structure of a network, while respecting budget constraints. In this activity, complex network analysis is often cross-referenced with simulations or operational research. Power grids stand out among the most prominent examples of infrastructure network analyzed with techniques derived from complex network theory, due to their importance as a service, their properties of quick response to events, and the desired transition to a smart grid paradigm. With the growing interest for the protection of endangered species and habitats, the modeling and analysis of green infrastructure has also received increasing attention from scholars. These classes of infrastructure provide case studies for the exemplification of a common process for the analysis of various kinds of infrastructure networks, which involves the identification of vulnerabilities, the exploration of a search space for possible modifications, and the definition of a comparable measure of health of the network

Graph models of network behavior in environmental planning

Policies to protect the environment in Europe and in the rest of the world have been adjusted to take into account the network behavior of conglomerates of nature protection areas. Network behavior can emerge from the natural configuration of habitat patches, or be induced by the establishment of habitat corridors. Careful planning is required to protect and improve the network behavior in existing sites; this has prompted researchers to build graph models of ecological networks, and apply complex network analysis to improve the understanding of their features. However, the most common approach is to keep the focus on a single species, meant to be representative of most species within the area under analysis, or especially important with respect to conservation issues. In this paper, data pertaining to land use types found within sites making up the "Natura 2000" ecological network is used to provide a high-level view of the network, and propose a framework for study, in which similarity measures are used as a criterion to suggest guidelines for land management

Modelli funzionali delle reti ecologiche: dal particolare al generale

La protezione degli habitat e delle specie a rischio avviene oggi in Europa attraverso l'istituzione del progetto Natura 2000. La caratteristica principale di Natura 2000 è il comportamento reticolare delle aree protette, il quale deve emergere da una gestione del territorio che avviene in ambito locale, le cui finalità non si limitino alla protezione di uno specifico habitat, ma si integrino con obiettivi di larga scala e di lungo periodo. La rete Natura 2000 è frequentemente oggetto di studio con tecniche derivate dalla teoria delle reti complesse, tuttavia nella maggior parte dei casi i modelli matematici utilizzati per rappresentare la rete tengono conto solamente di una specie scelta come obiettivo, o un insieme limitato di specie. La ricerca di un modello che catturi il comportamento reticolare della rete in senso più generale può passare attraverso l'integrazione tra i dati raccolti per aree protette diverse, oltre a quella con dati provenienti da altre fonti; integrazione necessaria poiché i dati sulla rete Natura 2000 provengono da attività di rilevamento operate sul territorio e si intendono riferite a singole aree protette, sebbene talora assai vaste. Questo lavoro, considerando come caso di studio i siti della Regione Sardegna, mira alla generazione di modelli funzionali della rete ecologica, tramite l'integrazione dei dati raccolti dai rilevatori nell'ambito del progetto Natura 2000 con i dati sull'uso del suolo, operata tramite software GIS, e conseguentemente allo studio delle proprietà della rete risultante con la teoria delle reti complesse. Questa nuova tipologia di modello risulta utile per il confronto con modelli relativi a singole specie, allo scopo di valutare la portata delle modificazioni conseguenti a interventi proposti sul territorio.The protection of endangered habitats and species is coordinated in Europe under the project denominated Natura 2000. The main aspect of this project is the notion that nature protection areas are considered part of an ecological network, and they have to be established and maintained while taking into account a number of large-scale goals, especially concerning the protection of biodiversity. The Natura 2000 network has frequently been an object of study using complex network analysis, but in most cases, the graph models under analysis are built considering only a single species or a very limited set of species. In an endeavour toward a graph model with a higher degree of generality, the data sources that are part of the project should be integrated with external sources, as within the Natura 2000 project, no information is collected on territories outside nature protection areas. This paper aims at building general graph models by cross-referencing land use data with the employment of GIS software, and providing guidelines to the analysis of these models using complex network analysis techniques. The proposed models can be useful to make comparisons with single-species models and perform the assessment of proposed network modifications

Fatality rate and predictors of mortality in an Italian cohort of hospitalized COVID-19 patients

Clinical features and natural history of coronavirus disease 2019 (COVID-19) differ widely among different countries and during different phases of the pandemia. Here, we aimed to evaluate the case fatality rate (CFR) and to identify predictors of mortality in a cohort of COVID-19 patients admitted to three hospitals of Northern Italy between March 1 and April 28, 2020. All these patients had a confirmed diagnosis of SARS-CoV-2 infection by molecular methods. During the study period 504/1697 patients died; thus, overall CFR was 29.7%. We looked for predictors of mortality in a subgroup of 486 patients (239 males, 59%; median age 71 years) for whom sufficient clinical data were available at data cut-off. Among the demographic and clinical variables considered, age, a diagnosis of cancer, obesity and current smoking independently predicted mortality. When laboratory data were added to the model in a further subgroup of patients, age, the diagnosis of cancer, and the baseline PaO2/FiO2 ratio were identified as independent predictors of mortality. In conclusion, the CFR of hospitalized patients in Northern Italy during the ascending phase of the COVID-19 pandemic approached 30%. The identification of mortality predictors might contribute to better stratification of individual patient risk

Abdominal aortic aneurysm is associated with a variant in low-density lipoprotein receptor-related protein 1

Abdominal aortic aneurysm (AAA) is a common cause of morbidity and mortality and has a significant heritability. We carried out a genome-wide association discovery study of 1866 patients with AAA and 5435 controls and replication of promising signals (lead SNP with a p value < 1 × 10-5) in 2871 additional cases and 32,687 controls and performed further follow-up in 1491 AAA and 11,060 controls. In the discovery study, nine loci demonstrated association with AAA (p < 1 × 10-5). In the replication sample, the lead SNP at one of these loci, rs1466535, located within intron 1 of low-density-lipoprotein receptor-related protein 1 (LRP1) demonstrated significant association (p = 0.0042). We confirmed the association of rs1466535 and AAA in our follow-up study (p = 0.035). In a combined analysis (6228 AAA and 49182 controls), rs1466535 had a consistent effect size and direction in all sample sets (combined p = 4.52 × 10-10, odds ratio 1.15 [1.10-1.21]). No associations were seen for either rs1466535 or the 12q13.3 locus in independent association studies of coronary artery disease, blood pressure, diabetes, or hyperlipidaemia, suggesting that this locus is specific to AAA. Gene-expression studies demonstrated a trend toward increased LRP1 expression for the rs1466535 CC genotype in arterial tissues; there was a significant (p = 0.029) 1.19-fold (1.04-1.36) increase in LRP1 expression in CC homozygotes compared to TT homozygotes in aortic adventitia. Functional studies demonstrated that rs1466535 might alter a SREBP-1 binding site and influence enhancer activity at the locus. In conclusion, this study has identified a biologically plausible genetic variant associated specifically with AAA, and we suggest that this variant has a possible functional role in LRP1 expression

Connectivity analysis of ecological landscape networks by cut node ranking

Ecological landscape networks represent the current paradigm for the protection of biodiversity. In the analysis of land features that precedes the establishment of land management plans, graph-theoretic approaches become increasingly popular due to their aptness for the representation of connectivity. Ecological corridors, seen as connecting elements for geographically distant areas dedicated to the preservation of endangered species, can be analyzed for the identification of critical land patches, by ranking cut nodes according to a score that encompasses various criteria for prioritized intervention

Modeling user interactions for conversion rate prediction in M-Commerce

Recent developments such as the introduction of new mobile banking and mobile payment services represent both an opportunity and a challenge for banks. While there is great potential to increase revenue by providing new services to customers, this goes together with the need to improve the understanding of customer data through deeper analysis, and to react quickly to changes in customer demands. It becomes increasingly important to maintain and update mobile apps with rapid release cycles. However, evaluating the results of changes in data analysis tools and their applications, such as recommender systems, sometimes requires live experiments on deployed systems. In this paper, a model based on stochastic process algebra is described for the interaction between a user and a recommending engine through a mobile app, and quantitative analysis is performed to show how changing features and parameters at the engine side may have an effect on user experience. This activity can be replicated on models representing an existing system, as a way to assess possible impacts before experimenting with live changes