River Bifurcation Analysis by Physical and Numerical Modeling

In the framework of a river regulation design of the Po River Delta (Northern Italy), a study on a large physical model of the bifurcation Po di Goro-Po di Venezia was conducted with the main objective of determining the discharge subdivision rate at the river node, in order to assess the inflow conditions in the Po di Goro River for flood risk analysis. In this context, a two-dimensional depth averaged numerical model was tested against measured values, with reference to the prototype. In this paper a comprehensive analysis and discussion of the results is reported in order to highlight the applicability of numerical models in comparison with physical ones in river engineering applications.     

W‐band physical layer design issues in the context of the DAVID–DCE experiment

This paper aims at focusing on the aspects concerning the physical layer design for an innovative satellite communication experiment. Such an experiment, denoted by the acronym DAVID–DCE (Data and Video Interactive Distribution—Data Collection Experiment) is based on the exploitation of the W‐band (75–110 GHz) for high‐bit‐rate satellite transmission. The potential advantages of using of the W‐band are mainly related to the great bandwidth availability, and to the absence of interference. Moreover, an expected result of the experiment is a substantive improvement in the communication system's performances in the presence of meteorological phenomena (e.g. rain) as compared with the more conventional Ka‐band satellite transmission. On the other hand, problems to be faced concern the non‐ideal behaviours of hardware devices employed for high‐frequency digital transmission. In particular, carrier recovery and timing recovery are the most crucial signal‐processing tasks to be carefully considered in the design of the physical level of the system, because they considerably suffer from hardware impairments. The purpose of this work is to illustrate the proposed solutions in terms of the most critical modulation, demodulation and synchronization design issues, together with the effects of non‐ideal behaviours of hardware components on BER performances. Copyright © 2004 John Wiley & Sons, Ltd.     

Isomorphous replacement of cystine with selenocystine in endothelin: oxidative refolding, biological and conformational properties of [Sec3,Sec11,Nle7]-endothelin-1

Air re-oxidation of fully reduced human endothelin-1 under optimized conditions yields the natural isomer with parallel disulfide bridges and the non-natural isomer with crossed disulfide bridges at a ratio of 3:1. In view of the recently determined highly reducing redox potential of selenocysteine (-381 mV) in peptides, the half-cystine residues Cys3 and Cys11 of the natural isomer of endothelin-1 were replaced by selenocysteine. Taking advantage of the high stability of the diselenide group toward reducing agents for disulfides a regioselective disulfide bridging of the second cysteine pair allowed for straightforward preparation of the [Sec3,Sec11, Nle7]-endothelin-1. NMR structural analysis showed conformational preferences of this endothelin analog that were identical to those of the natural hormone. Similarly, the bioactivity data confirmed that replacement of cysteine residues with selenocysteine was without detectable effect on receptor recognition and signal transduction. Both findings strongly support that the exchange of sulfur against selenium produces a fully isomorphous molecule as recently observed for similar exchanges at the level of methionine residues in proteins. Moreover, oxidative refolding of the fully reduced [Sec3,Sec11,Nle7]-endothelin-1 fulfilled the expectation that the redox potential of the selenocysteines would dictate quantitative formation of the natural isomer. These results suggest that the selenocysteine approach, besides offering an interesting chemical tool for induction of correct oxidative folding of multiple cysteine-containing peptides, should even allow for the preparation of non-natural isomers and thus for studying conformational preferences of folding intermediates in peptides and proteins.     

Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography

Neural Computing and Applications - In this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble...     

In Silico Logical Modelling to Uncover Cooperative Interactions in Cancer

The multistep development of cancer involves the cooperation between multiple molecular lesions, as well as complex interactions between cancer cells and the surrounding tumour microenvironment. The search for these synergistic interactions using experimental models made tremendous contributions to our understanding of oncogenesis. Yet, these approaches remain labour-intensive and challenging. To tackle such a hurdle, an integrative, multidisciplinary effort is required. In this article, we highlight the use of logical computational models, combined with experimental validations, as an effective approach to identify cooperative mechanisms and therapeutic strategies in the context of cancer biology. In silico models overcome limitations of reductionist approaches by capturing tumour complexity and by generating powerful testable hypotheses. We review representative examples of logical models reported in the literature and their validation. We then provide further analyses of our logical model of Epithelium to Mesenchymal Transition (EMT), searching for additional cooperative interactions involving inputs from the tumour microenvironment and gain of function mutations in NOTCH.     

Large-scale linked data integration using probabilistic reasoning and crowdsourcing

We tackle the problems of semiautomatically matching linked data sets and of linking large collections of Web pages to linked data. Our system, ZenCrowd, (1) uses a three-stage blocking technique in order to obtain the best possible instance matches while minimizing both computational complexity and latency, and (2) identifies entities from natural language text using state-of-the-art techniques and automatically connects them to the linked open data cloud. First, we use structured inverted indices to quickly find potential candidate results from entities that have been indexed in our system. Our system then analyzes the candidate matches and refines them whenever deemed necessary using computationally more expensive queries on a graph database. Finally, we resort to human computation by dynamically generating crowdsourcing tasks in case the algorithmic components fail to come up with convincing results. We integrate all results from the inverted indices, from the graph database and from the crowd using a probabilistic framework in order to make sensible decisions about candidate matches and to identify unreliable human workers. In the following, we give an overview of the architecture of our system and describe in detail our novel three-stage blocking technique and our probabilistic decision framework. We also report on a series of experimental results on a standard data set, showing that our system can achieve a 95 % average accuracy on instance matching (as compared to the initial 88 % average accuracy of the purely automatic baseline) while drastically limiting the amount of work performed by the crowd. The experimental evaluation of our system on the entity linking task shows an average relative improvement of 14 % over our best automatic approach.     

Mobile Underwater Sensor Networks for Protection and Security: Field Experience at the UAN11 Experiment

The EU‐funded project UAN (Underwater Acoustic Network) was aimed at conceiving, developing, and testing at sea an innovative and operational concept for integrating underwater and above‐water sensors in a unique communication system to protect offshore and coastline critical infrastructures. This work gives details on the underwater part of the project. It introduces a set of original security features and gives details on the integration of autonomous underwater vehicles (AUVs) as mobile nodes of the network and as surveillance assets, acoustically controlled by the command and control center to respond against intrusions. Field results are given of the final UAN project sea trial, UAN11, held in May 2011 in Norway. During the experimental activities, a UAN composed of four fixed nodes, two AUVs, and one mobile node mounted on the supporting research vessel was operated continuously and integrated into a global protection system. In this article, the communication performance of the network is reported in terms of round‐trip time, packet loss, and average delivery ratio. The major results of the experiment can be thus summarized: the implemented network structure was successful in continuously operating over five days with nodes seamlessly entering and exiting the network; the performance of the network varied greatly with fluctuations in the acoustic channel; the addition of security features induced a minor degradation in network performance with respect to channel variation; the AUVs were successfully controlled from a remote station through acoustic signals routed by the network. © 2013 Wiley Periodicals, Inc.     

On vector averaging over the unit hypersphere

Sample averaging is a commonly used way to smooth out irregularities of data and to get rid of random fluctuations in measurements analysis. In adaptive signal processing, where an adaptive system learns its own parameters in order to perform a predefined task, the learnt parameters-pattern may depend on the initial learning state and on the fluctuations of the statistical features of the input signals to the system. In adaptive system learning, averaging may be employed as a method to merge several learnt parameters-patterns in order to get a better representative pattern. Even in the case of scalar parameters, the concept of averaging is not uniquely defined as scalar parameters spaces may exhibit a rich structure to be dealt with. The case of multiple parameter patterns where single parameters are mutually constrained to each other may exhibit an even richer structure. In the present paper, we deal with the case of parameters-patterns belonging to the unit hypersphere and develop an averaging technique based on the differential geometrical structure of such a curved space. Numerical experiments illustrate the behavior of the developed averaging algorithm.     

Robust network monitoring in the presence of non-cooperative traffic queries

We present the design of a predictive load shedding scheme for a network monitoring platform that supports multiple and competing traffic queries. The proposed scheme can anticipate overload situations and minimize their impact on the accuracy of the traffic queries. The main novelty of our approach is that it considers queries as black boxes, with arbitrary (and highly variable) input traffic and processing cost. Our system only requires a high-level specification of the accuracy requirements of each query to guide the load shedding procedure and assures a fair allocation of computing resources to queries in a non-cooperative environment. We present an implementation of our load shedding scheme in an existing network monitoring system and evaluate it with a diverse set of traffic queries. Our results show that, with the load shedding mechanism in place, the monitoring system can preserve the accuracy of the queries within predefined error bounds even during extreme overload conditions.