Use of context-awareness in mobile peer-to-peer networks

Mobile ad-hoc network are an emerging research field due to the potential range of applications that they support and for the problems they present due to their dynamic nature. Peer-to-peer is an example of a class of applications that have recently been deployed on top of ad-hoc networks. In this paper we propose an approach based on context-awareness to allow peer-to-peer applications to exploit information on the underlying network context to achieve better performance and better group organization. Information such as availability of resources, battery power, services in reach and relative distances can be used to improve the routing structures of the peer-to-peer network, thus reducing the routing overhead

Incentives in peer-to-peer and grid networking

Today, most peer-to-peer networks are based on the assumptionthat the participating nodes are cooperative. Thisworks if the nodes are indifferent or ignorant about the resourcesthey offer, but limits the usability of peer-to-peernetworks to very few scenarios. It specifically excludes theirusage in any non-cooperative peer-to-peer environment, beit Grid networks or mobile ad-hoc networks. By introducingsoft incentives to offer resources to other nodes, we seean overall performance gain in traditional file-sharing networks.We also see soft incentives promoting the convergenceof peer-to-peer and Grid networks, as they increasethe predictability of the participating nodes, and thereforethe reliability of the services provided by the system as awhole. Reliability is what is required by Grid networks, butmissing in peer-to-peer networks

Mining open datasets for transparency in taxi transport in metropolitan environments.

Uber has recently been introducing novel practices in urban taxi transport. Journey prices can change dynamically in almost real time and also vary geographically from one area to another in a city, a strategy known as surge pricing. In this paper, we explore the power of the new generation of open datasets towards understanding the impact of the new disruption technologies that emerge in the area of public transport. With our primary goal being a more transparent economic landscape for urban commuters, we provide a direct price comparison between Uber and the Yellow Cab company in New York. We discover that Uber, despite its lower standard pricing rates, effectively charges higher fares on average, especially during short in length, but frequent in occurrence, taxi journeys. Building on this insight, we develop a smartphone application, OpenStreetCab, that offers a personalized consultation to mobile users on which taxi provider is cheaper for their journey. Almost five months after its launch, the app has attracted more than three thousand users in a single city. Their journey queries have provided additional insights on the potential savings similar technologies can have for urban commuters, with a highlight being that on average, a user in New York saves 6 U.S. Dollars per taxi journey if they pick the cheapest taxi provider. We run extensive experiments to show how Uber's surge pricing is the driving factor of higher journey prices and therefore higher potential savings for our application's users. Finally, motivated by the observation that Uber's surge pricing is occurring more frequently that intuitively expected, we formulate a prediction task where the aim becomes to predict a geographic area's tendency to surge. Using exogenous to Uber data, in particular Yellow Cab and Foursquare data, we show how it is possible to estimate customer demand within an area, and by extension surge pricing, with high accuracy.This is the final version of the article. It was first available from Springer via http://dx.doi.org/10.1140/epjds/s13688-015-0060-

Model-Based Decomposition of Dual-Pol SAR Data: Application to Sentinel-1

In this study, we advance a new family of model-based decompositions adapted for dual-pol synthetic aperture radar data. These are formulated using the Stokes vector formalism, coupled to mappings from full quad-pol decomposition theory. A generalized model-based decomposition is developed, which allows separation of an arbitrary Stokes vector into partially polarized and polarized wave components. We employ the widely used random dipole cloud as a volume model but, in general, non-dipole options can be used. The cross-polarized phase δ, and the α angle, which is a function of the ratio between wave components, measure the transformation of polarization state on reflection. We apply the decomposition to dual-pol data provided by Sentinel-1 covering different scenarios, such as agricultural, forest, urban and glacial land-ice. We show that the polarized term of received polarization state is not usually the same as the transmitted one, and can therefore be used for key applications, e.g., classification and geo-physical parameter estimation. We show that, for vegetated terrain, depolarization is not the only influencing factor to Sentinel-1 backscattered intensities and, in the case of vertical crops (e.g., rice), this allows the crop orientation effects to be decoupled from volume scattering in the canopy. We demonstrate that coherent dual-pol systems show strong phase signatures over glaciers, where the polarized contribution significantly affects the backscattered state, resulting in elliptical polarization on receive. This is a key result for Sentinel-1, for which dual-pol phase analysis coupled to dense time series offer great opportunities for land-ice monitoring.This work was funded by the Spanish Ministry of Science and Innovation, the State Agency of Research (AEI) and the European Funds for Regional Development (EFRD) under Projects TEC2017-85244-C2-1-P and PID2020-117303GB-C22, and by the University of Alicante under grant VIGROB-114

Some Considerations on the Behaviour of Bolted Stainless-Steel Beam-to-Column Connections: A Simplified Analytical Approach

Stainless-steel has proven to be a first-class material with unique mechanical properties for a variety of applications in the building and construction industry. High ductility, strain hardening, durability and aesthetic appeal are only a few of them. From a specific point of view, its nonlinear stress–strain behaviour appears capable of providing a significant increase in the rotational capacity of stainless-steel connections. This, in turn, may provide significant benefits for the overall response of a structure in terms of capacity and ductility. However, the bulk of the research on stainless-steel that has been published so far has mostly ignored the analysis of the deformation capabilities of the stainless-steel connections and has mostly focused on the structural response of individual members, such as beams or columns. For such a reason, the present study aims to contribute to the general understanding of the behaviour of stainless-steel connections from a conceptual, numerical and design standpoint. After a brief review of the available literature, the influence of the use of stainless-steel for column–beam connections is discussed from a theoretical standpoint. As a novel contribution, a different approach to compute the pseudo-plastic moment resistance that takes into account the post-elastic secant stiffness of the stainless-steel is proposed. Successively, a refined finite element model is employed to study the failure of stainless-steel column–beam connections. Finally, a critical assessment of the employment of carbon-steel-based design guidelines for stainless-steel connections provided by the Eurocode 3 design (EN 1993-1-8) is performed. The findings prove the need for the development of novel design approaches and more precise capacity models capable of capturing the actual stainless-steel joint response and their impact on the overall ductility and capacity of the whole structure

Developing and Deploying a Taxi Price Comparison Mobile App in the Wild: Insights and Challenges.

As modern transportation systems become more complex, there is need for mobile applications that allow travelers to navigate efficiently in cities. In taxi transport the recent proliferation of Uber has introduced new norms including a flexible pricing scheme where journey costs can change rapidly depending on passenger demand and driver supply. To make informed choices on the most appropriate provider for their journeys, travelers need access to knowledge about provider pricing in real time. To this end, we developed OpenStreetcab a mobile application that offers advice on taxi transport comparing provider prices. We describe its development and deployment in two cities, London and New York, and analyse thousands of user journey queries to compare the price patterns of Uber against major local taxi providers. We have observed large heterogeneity across the taxi transport markets in the two cities. This motivated us to perform a price validation and measurement experiment on the ground comparing Uber and Black Cabs in London. The experimental results reveal interesting insights: not only they confirm feedback on pricing and service quality received by professional drivers users, but also they reveal the tradeoffs between prices and journey times between taxi providers. With respect to journey times in particular, we show how experienced taxi drivers, in the majority of the cases, are able to navigate faster to a destination compared to drivers who rely on modern navigation systems. We provide evidence that this advantage becomes stronger in the centre of a city where urban density is high

Quantifying Privacy Loss of Human Mobility Graph Topology

Abstract Human mobility is often represented as a mobility network, or graph, with nodes representing places of significance which an individual visits, such as their home, work, places of social amenity, etc., and edge weights corresponding to probability estimates of movements between these places. Previous research has shown that individuals can be identified by a small number of geolocated nodes in their mobility network, rendering mobility trace anonymization a hard task. In this paper we build on prior work and demonstrate that even when all location and timestamp information is removed from nodes, the graph topology of an individual mobility network itself is often uniquely identifying. Further, we observe that a mobility network is often unique, even when only a small number of the most popular nodes and edges are considered. We evaluate our approach using a large dataset of cell-tower location traces from 1 500 smartphone handsets with a mean duration of 430 days. We process the data to derive the top−N places visited by the device in the trace, and find that 93% of traces have a unique top−10 mobility network, and all traces are unique when considering top−15 mobility networks. Since mobility patterns, and therefore mobility networks for an individual, vary over time, we use graph kernel distance functions, to determine whether two mobility networks, taken at different points in time, represent the same individual. We then show that our distance metrics, while imperfect predictors, perform significantly better than a random strategy and therefore our approach represents a significant loss in privacy.</jats:p

Late diagnosis of celiac disease in an asymptomatic infant with growth failure

The clinical spectrum for celiac disease (CD) is broad and includes cases with either typical (intestinal) or atypical (extraintestinal) features, often making the diagnosis of CD very difficult. We describe the case of a girl presenting with stunted growth and malnourishment. She was evaluated at 14 months for decreased growth rate without any signs of gastrointestinal, renal or endocrine disorders. She was evaluated for CD, but resulted negative for anti-tTG antibodies. At the age of 4.1 years, she exhibited basal dental enamel hypoplasia, iron deficiency anaemia despite repeated iron supplementation, with persistent reduced height (-2.79 SDS), BMI (-0.76 SDS), growth velocity (-1.79 SDS) and delayed bone age (1.5 year). The CD screening was repeated and very high anti-tTG-IgA (128 IU/ml, normal values 40 IELs/100 epithelial cells) confirming the diagnosis of CD. A gluten-free diet was started and after only four months, her growth velocity increased from 4.83 cm/year (-1.79 SDS) to 6.53 cm/year (-0.15 SDS). In conclusion, we report the development of a positive serology for CD in an asymptomatic child with growth retardation, who previously was investigated for CD and resulted negative. Therefore, when faced with retarded growth in young patients, after excluding other malabsorption conditions and even when CD serological markers are negative, the paediatric endocrinologist should request HLA genotyping, before the intestinal biopsy, in order to check for the presence of risk alleles