Pier Luigi Pau

Evaluating complex network indices for vulnerability analysis of a territorial power grid

In order to meet power demands in a sustainable way, power grids are gradually being adjusted to fit into a smart grid paradigm. A common problem in this kind of transition is to identify locations where it is most beneficial to invest in distributed generation. In order to assist in such a decision, we work on a graph model of a regional power grid. We apply optimization strategies on power flows, and verify the current degree of self-sufficiency of the network, with special reference to the effect of natural variations in wind-based production. We propose a method to assess collateral damage to the network resulting from a localized failure, and proceed to perform complex network analysis on multiple instances of the network, looking for correlations between estimated damages and betweenness centrality indices, with the purpose of attempting to determine which model is best suited to predict features of our network.

An Analysis of Features and Tendencies in Mobile Banking Apps

Abstract Mobile devices such as smartphones and tablets are being employed alongside personal computers, and even replacing them in some applications. Banks are increasingly investing on mobility, by enabling the mobile web and mobile app channels for online banking, and by providing new mobile payment services. In this paper, the services for off-branch banking offered by several Italian banks are analyzed, showing that mobile apps have surpassed the mobile web channel in completeness of the offer, due to the fact that additional capabilities of mobile devices make possible advanced features and applications. An outlook on the near future is provided, remarking that mobile marketing and mobile recommender systems can greatly take advantage of being run natively on devices, making it desirable for businesses to invest on designing mobile apps.

Functional models and extending strategies for ecological networks

Complex network analysis is rising as an essential tool to understand properties of ecological landscape networks, and as an aid to land management. The most common methods to build graph models of ecological networks are based on representing functional connectivity with respect to a target species. This has provided good results, but the lack of a model able to capture general properties of the network may be seen as a shortcoming when the activity involves the proposal for modifications in land use. Similarity scores, calculated between nature protection areas, may act as a building block for a graph model intended to carry a higher degree of generality. The present work compares several design choices for similarity-based graphs, in order to determine which is most suitable for use in land management.

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

Abstract 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.

Identification of Critical Habitat Corridor Patches by Cut Node Ranking

Graph-theoretic approaches are increasingly popular in the representation and analysis of ecological networks, acting as a complement to traditional techniques, particularly to perform connectivity analysis. Local network indices are useful to identify portions of an ecological network that are worth inspecting, such as bottlenecks. However, the simple identification of cut nodes may turn out to be insufficient. By providing criteria to rank identified cut nodes, land managers are able to assess priorities for further local analysis.

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 and Extending Ecological Networks Using Land Similarity

Complex network analysis is being applied on topological models of ecological networks, to extrapolate their advanced properties and as part of the activity of land management. Commonly employed methods tend to focus on single target species. This is satisfactory for cognitive analysis, but the limited view provided by these models results in a lack of general information needed for land planning. Similarity scores computed for pairs of nature protection areas are proposed as a building block of a general model to address this shortcoming.

Topological and Conceptual Complex Network Models for Environmental Planning

Abstract The growing importance of environmental planning has encouraged researchers to apply complex network analysis on topological models of environmental networks. Relevant features of current green infrastructure can be derived with common and ad hoc techniques, but results tend to expose only a limited view, whether by geographical areas or by species. In this paper, the possibility to extend complex network analysis to conceptual models with a higher degree of abstraction is explored.

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.

An analysis of native apps for mobile banking

Apps for iPhone and Android smartphones score roughly the same, meaning that banks invest in these two platforms virtually with the same degree of interest. There is a moderate degree of investment toward Windows Phone, whereas support for the BlackBerry platform is low. In this study, the maturity of existing services offered through mobile channels by Italian banks is evaluated, and an outlook for the evolution of mobile banking services is given.