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

Environmental productivity index GIS-based model to estimate prickly pear biomass potential availability for biogas production

Nowadays, climate change is the environmental issue facing the world. To reach the 2030 European Union goals, recently, biogas production through anaerobic digestion has developed significantly, by using alternative biomass sources due to the competition between food and no-food products. In this regard, Opuntia ficus-indica (OFI) has been suggested as a suitable new biomass for producing biomethane within the context of circular economy. In this study, a predictive methodology was applied by combining the Nobel model of environmental productivity index (EPI) and geographic information system (GIS), with the aim of estimating OFI biomass amount, as well as biogas and electricity potential production. The GIS analyses allowed the identification of the most suitable territorial areas for producing biogas from OFI, and an estimation of electricity production. The achieved results are highly valuable information for strategic planning of biogas sector development and could be relevant to the intervention priorities established by the EU

Gambling Disorders Among Young Women Regular Gamblers: The Unique and Common Contribution of Executive Thinking Style and Mindfulness

The aim of this study is to investigate the role played by mindfulness in the relationship between cognitive styles and gambling disorders in a sample of female young adults. Participants in this study (125 women; Mage = 18.64 years; SD = 1.7) were recruited in betting or bingo halls. They completed the South Oaks Gambling Screen, the Child and Adolescent Mindfulness Measure, and Sternberg's questionnaire on thinking styles. The results from the mediation analyses revealed that the executive thinking style increases gambling and that the deficit in mindfulness ability mediates this relationship. Theoretical and clinical implications are discussed

Robust DTW-based entropy fuzzy clustering of time series

Time series are complex data objects whose partitioning into homogeneous groups is still a challenging task, especially in the presence of outliers or noisy data. To address the problem of robustness against outliers in clustering techniques, this paper proposes a robust fuzzy C-medoids method based on entropy regularization. In-depth, we use an appropriate exponential transformation of the dissimilarity based on Dynamic Time Warping, which can be computed also for time series of different length. In addition, the fuzzy framework provides the necessary flexibility to cope with the complexity of the features space. It allows a time series to be assigned to more than one group, considering potential switching behaviours. Moreover, the use of a medoids-based approach enables the identification of observed representative objects within the dataset, thus enhancing interpretability for practical applications. Through an extensive simulation study, we successfully demonstrate the effectiveness of our proposal, comparing and emphasizing its strengths. Finally, our proposed methodology is applied to the daily mean concentrations of three air pollutants in 2022 in the Province of Rome. This application highlights its potential, namely the capability to intercept outliers and switching time series while preserving group structures

Assessing influence factors on daily ammonia and greenhouse gas concentrations from an open-sided cubicle barn in hot mediterranean climate

Measurement of gas concentrations constitutes basic knowledge for the computation of emissions from livestock buildings. Although it is well known that hot climate conditions increase gas emissions, in the literature the relation between gas concentrations from open barns and animalrelated parameters has not been investigated yet. This study aimed at filling this gap by evaluating daily gas concentrations within an open-sided barn in hot Mediterranean climate. The influence of microclimatic parameters (MC) and cow behavior and barn management (CBBM) were evaluated for ammonia (NH3 ), methane (CH4 ), and carbon dioxide (CO2 ) concentrations. Results showed that both MC and CBBM affected concentrations of NH3 (p < 0.02), CH4 (p < 0.001), and CO2 (p < 0.001). Higher values of NH3 concentration were detected during the cleaning of the floor by a tractor with scraper, whereas the lowest NH3 concentrations were recorded during animal lying behavior. Measured values of CO2 and CH4 were highly correlated (C = 0.87–0.89) due to the same sources of production (i.e., digestion and respiration). The different management of the cooling systems during the two observation periods reduced significantly CH4 concentrations in the barn when the cooling system in the feeding area was switched off. Based on methodological choices due to the specific barn typology, parameters related to animals can provide information on the variation of gas concentrations in the barn environment in hot climate conditions

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

Cross Sectional and Longitudinal Fuzzy Clustering of the NUTS and Positioning of the Italian Regions with Respect to the Regional Competitiveness Index (RCI) Indicators with Contiguity Constraints

In socio-economical clustering often the empirical information is represented by time-varying data generated by indicators observed over time on a set of subnational (regional) units. Usually among these units may exist contiguity relations, spatial but not only.In this paper we propose a fuzzy clustering model of multivariate time-varying data, the longitudinal fuzzy C-Medoids clustering with contiguity constraints. The temporal aspect is dealt with by using appropriate measures of dissimilarity between time trajectories. The contiguity among units is dealt with adding a contiguity matrix as a penalization term in the clustering model.The cross sectional fuzzy C-Medoids clustering with contiguity constraints is obtained considering one instant of time. The model is applied to the classification of the European NUTS on the basis of the observed dynamics of the Basic, Efficiency and Innovation subindexes of the Regional Competitiveness Index (RCI) 2013 and 2016. The positioning of the Italian regions is analyzed through the values of the medoids of the clusters and shows the peculiarities of the regions with respect to the subindexes either in single times or in the dynamic. Two contiguity constraints, one based on the European Western, Southern, Central and Northern geographic areas and one on the level of GDP—taken into account in the computation of the RCI—are also introduced in the models

Entropy-based fuzzy clustering of interval-valued time series

This paper proposes a fuzzy C-medoids-based clustering method with entropy regularization to solve the issue of grouping complex data as interval-valued time series. The dual nature of the data, that are both time-varying and interval-valued, needs to be considered and embedded into clustering techniques. In this work, a new dissimilarity measure, based on Dynamic Time Warping, is proposed. The performance of the new clustering procedure is evaluated through a simulation study and an application to financial time series

The Central Laser Facility at the Pierre Auger Observatory

The Central Laser Facility is located near the middle of the Pierre Auger Observatory in Argentina. It features a UV laser and optics that direct a beam of calibrated pulsed light into the sky. Light scattered from this beam produces tracks in the Auger optical detectors which normally record nitrogen fluorescence tracks from cosmic ray air showers. The Central Laser Facility provides a "test beam" to investigate properties of the atmosphere and the fluorescence detectors. The laser can send light via optical fiber simultaneously to the nearest surface detector tank for hybrid timing analyses. We describe the facility and show some examples of its many uses.Comment: 4 pages, 5 figures, submitted to 29th ICRC Pune Indi