A robust hierarchical clustering for georeferenced data

The detection of spatially contiguous clusters is a relevant task in geostatistics since near located observations might have similar features than distant ones. Spatially compact groups can also improve clustering results interpretation according to the different detected subregions. In this paper, we propose a robust metric approach to neutralize the effect of possible outliers, i.e. an exponential transformation of a dissimilarity measure between each pair of locations based on non-parametric kernel estimator of the direct and cross variograms (Fouedjio, 2016) and on a different bandwidth identification, suitable for agglomerative hierarchical clustering techniques applied to data indexed by geographical coordinates. Simulation results are very promising showing very good performances of our proposed metric with respect to the baseline ones. Finally, the new clustering approach is applied to two real-word data sets, both giving locations and top soil heavy metal concentrations

Supporting group maintenance through prognostics-enhanced dynamic dependability prediction

Condition-based maintenance strategies adapt maintenance planning through the integration of online condition monitoring of assets. The accuracy and cost-effectiveness of these strategies can be improved by integrating prognostics predictions and grouping maintenance actions respectively. In complex industrial systems, however, effective condition-based maintenance is intricate. Such systems are comprised of repairable assets which can fail in different ways, with various effects, and typically governed by dynamics which include time-dependent and conditional events. In this context, system reliability prediction is complex and effective maintenance planning is virtually impossible prior to system deployment and hard even in the case of condition-based maintenance. Addressing these issues, this paper presents an online system maintenance method that takes into account the system dynamics. The method employs an online predictive diagnosis algorithm to distinguish between critical and non-critical assets. A prognostics-updated method for predicting the system health is then employed to yield well-informed, more accurate, condition-based suggestions for the maintenance of critical assets and for the group-based reactive repair of non-critical assets. The cost-effectiveness of the approach is discussed in a case study from the power industry

Fuzzy clustering of spatial interval-valued data

In this paper, two fuzzy clustering methods for spatial interval-valued data are proposed, i.e. the fuzzy C-Medoids clustering of spatial interval-valued data with and without entropy regularization. Both methods are based on the Partitioning Around Medoids (PAM) algorithm, inheriting the great advantage of obtaining non-fictitious representative units for each cluster. In both methods, the units are endowed with a relation of contiguity, represented by a symmetric binary matrix. This can be intended both as contiguity in a physical space and as a more abstract notion of contiguity. The performances of the methods are proved by simulation, testing the methods with different contiguity matrices associated to natural clusters of units. In order to show the effectiveness of the methods in empirical studies, three applications are presented: the clustering of municipalities based on interval-valued pollutants levels, the clustering of European fact-checkers based on interval-valued data on the average number of impressions received by their tweets and the clustering of the residential zones of the city of Rome based on the interval of price values

RESCUE MANAGEMENT AND ASSESSMENT OF STRUCTURAL DAMAGE BY UAV IN POST-SEISMIC EMERGENCY

Abstract. The increasing frequency of emergencies urges the need for a detailed and thorough knowledge of the landscape. The first hours after a disaster are not only chaotic and problematic, but also decisive to successfully save lives and reduce damage to the building stock. One of the most important factors in any emergency response is to get an adequate awareness of the real situation, what is only possible after a thorough analysis of all the available information obtained through the Italian protocol Topography Applied to Rescue. To this purpose geomatic tools are perfectly suited to create, manage and dynamically enrich an organized archive of data to have a quick and functional access to information useful for several types of analysis, helping to develop solutions to manage the emergency and improving the success of rescue operations. Moreover, during an emergency like an earthquake, the conventional inspection to assess the damage status of buildings requires special tools and a lot of time. Therefore, given the large number of buildings requiring safety measures and rehabilitation, efficient use of limited resources such as time and equipment, as well as the safety of the involved personnel are important aspects. The applications shown in the paper are intended to underline how the above-mentioned objective, in particular the rehabilitation interventions of the built heritage, can be achieved through the use of data acquired from UAV platform integrated with geographic data stored in GIS platforms

A cost-benefit approach for the evaluation of prognostics-updated maintenance strategies in complex dynamic systems

The implementation of maintenance strategies which integrate online condition data has the potential to increase availability and reduce maintenance costs. Prognostics techniques enable the implementation of these strategies through up-to-date remaining useful life estimations. However, a cost-benefit assessment is necessary to verify the scale of potential benefits of condition-based maintenance strategies and prognostics for a given application. The majority of prognostics applications focus on the evaluation of a specific failure mode of an asset. However, industrial systems are comprised of different assets with multiple failure modes, which in turn, work in cooperation to perform a system level function. Besides, these systems include time-dependent events and conditional triggering events which cause further effects on the system. In this context not only are the system-level prognostics predictions challenging, but also the cost-benefit analysis of condition-based maintenance policies. In this work we combine asset prognostics predictions with temporal logic so as to obtain an up-to-date system level health estimation. We use asset level and system level prognostics estimations to evaluate the cost-effectiveness of alternative maintenance policies. The application of the proposed approach enables the adoption of conscious trade-off decisions between alternative maintenance strategies for complex systems. The benefits of the proposed approach are discussed with a case study from the power industry

Supporting group maintenance through prognostics-enhanced dynamic dependability prediction

Condition-based maintenance strategies adapt maintenance planning through the integration of online condition monitoring of assets. The accuracy and cost-effectiveness of these strategies can be improved by integrating prognostics predictions and grouping maintenance actions respectively. In complex industrial systems, however, effective condition-based maintenance is intricate. Such systems are comprised of repairable assets which can fail in different ways, with various effects, and typically governed by dynamics which include time-dependent and conditional events. In this context, system reliability prediction is complex and effective maintenance planning is virtually impossible prior to system deployment and hard even in the case of condition-based maintenance. Addressing these issues, this paper presents an online system maintenance method that takes into account the system dynamics. The method employs an online predictive diagnosis algorithm to distinguish between critical and non-critical assets. A prognostics-updated method for predicting the system health is then employed to yield well-informed, more accurate, condition-based suggestions for the maintenance of critical assets and for the group-based reactive repair of non-critical assets. The cost-effectiveness of the approach is discussed in a case study from the power industry

Fuzzy clustering with entropy regularization for interval-valued data with an application to scientific journal citations

In recent years, the research of statistical methods to analyze complex structures of data has increased. In particular, a lot of attention has been focused on the interval-valued data. In a classical cluster analysis framework, an interesting line of research has focused on the clustering of interval-valued data based on fuzzy approaches. Following the partitioning around medoids fuzzy approach research line, a new fuzzy clustering model for interval-valued data is suggested. In particular, we propose a new model based on the use of the entropy as a regularization function in the fuzzy clustering criterion. The model uses a robust weighted dissimilarity measure to smooth noisy data and weigh the center and radius components of the interval-valued data, respectively. To show the good performances of the proposed clustering model, we provide a simulation study and an application to the clustering of scientific journals in research evaluation

Protein fraction heterogeneity in donkey's milk analysed by proteomic methods

Donkey's milk is often well tolerate by patients affected by cow's milk protein allergy, probably thanks to its protein composition. This empiric evidence, confirmed by some clinical trials, needs to be better investigated. A preliminary survey on the protein fraction of donkey's milk was carried out: fifty-six individual milk samples have been collected and analysed by IEF and SDS-PAGE. Five different IEF patterns have been identified, showing a marked heterogeneity both in casein and whey protein fractions. A single IEF pattern showed an apparent reduced amount of casein fraction highlighted by SDS. Three of the five IEF patterns have been further investigated by Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS)

Synthesis, Characterization and Electrocatalytic Activity of Bi- and Tri-metallic Pt-Based Anode Catalysts for Direct Ethanol Fuel Cells

Three Pt-based anode catalysts supported on Vulcan XC-72R (VC) were prepared by using a modified polyol process. These materials were characterized and tested by X-Ray Diffraction (XRD), X-Ray Fluorescence (XRF) and Transmission Electron Microscopy (TEM). XRD and TEM analysis indicated that especially the ternary anode catalysts consisted of uniform nanosized particles with sharp distribution. The Pt lattice parameter was smaller, in the ternary PtSnIr catalyst whereas it increased with the addition of Sn and Rh, in the corresponding binary and ternary catalysts. Cyclic voltammetry (CV) measurements showed that Sn, Ir and Rh may act as promoter of Pt enhancing ethanol electro-oxidation activity. It was found that the direct ethanol fuel cell (DEFC) performances were significantly improved with these modified anode catalysts. This effect on the DEFC performance is attributed to the so-called bi-tri-functional mechanism and to the electronic interaction between Pt and additives. The performance increased significantly with the temperature. However, it was also possible to observe some decay with time for all catalysts due to the formation of surface poisons, probably consisting in CO-like species. At 60 °C, the PtSnIr catalyst showed the best performance, as a result of a proper morphology and promoting effectFil: D'Urso, C.. Centro Nazionale della Ricerca. ITAE; ItaliaFil: Bonesi, Alejandro Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina. Universidad Nacional de La Plata; ArgentinaFil: Triaca, Walter Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina. Universidad Nacional de La Plata; ArgentinaFil: Castro Luna Berenguer, Ana Maria del Carmen. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina. Universidad Nacional de La Plata; ArgentinaFil: Baglio, V.. Centro Nazionale della Ricerca. ITAE; Italia; ItaliaFil: Aricò, A. S.. Centro Nazionale della Ricerca. ITAE; Italia; Itali