1,720,964 research outputs found
Populismo come adattamento. Note critiche sull’analisi laclausiana della Lega Nord
No full textPopulismo come adattamento. Note critiche sull’analisi laclausiana della Lega Nor
Quantum simulations of macrorealism violation via the quantum nondemolition measurement protocol
No full textThe Leggett-Garg inequalities have been proposed to identify the quantum behavior of a system; specifically, the violation of macrorealism. They are usually implemented by performing two sequential measurements on quantum systems, calculating the correlators of such measurements and then combining them arriving at Leggett-Garg inequalities. However, this approach only provides sufficient conditions for the violation of macrorealism. Recently, an alternative approach was proposed that uses nondemolition measurements and gives both a necessary and sufficient condition for the violation of macrorealism. By storing the information in a quantum detector, it is possible to construct a quasiprobability distribution whose negative regions unequivocally identify the quantum behavior of the system. Here, we perform a detailed comparison between these two approaches. The use of the IBM quantum simulators allows us to evaluate the performance in real-case situations and to include both the statistical and environmental noise. We find that the nondemolition approach is not only able to always identify the quantum features, but it requires fewer resources than the standard Leggett-Garg inequalities. In addition, while the efficiency of the latter is strongly affected by the presence of the noise, the nondemolition approach results incredibly robust and its efficiency remains unchanged by the noise. These results make the nondemolition approach a viable alternative to the Leggett-Garg inequalities to identify the violation of macrorealism
Il laboratorio politico latinoamericano. Crisi del neoliberalismo, movimenti sociali e nuove esperienze di governance
No full textIl volume discute il contributo teorico-politico di Ernesto Laclau sullo sfondo dei processi politici che hanno prodotto profonde trasformazioni in molti Paesi latinoamericani nell'ultimi decennio, aprendo tra l'altro inedite prospettive di integrazione regionale. In particolare, affronta criticamente categorie come "populismo" e "ritorno dello Stato" dal punto di vista di quello che sembra configurarsi come un nuovo modello di governance "post-neoliberale"
An integrated study of hard and soft cluster analyses for detecting leachate in a MSW landfill site using geoelectrical data
Full text link: An accurate assessment of leachate levels necessitates the integration of various parameters. Traditional geophysical prospecting methods often lack measurable accuracy because they focus on individual parameters rather than effectively integrating data. This may lead to inconsistent estimates of leachate depth and make the evaluation of prediction reliability challenging. In this study, we exploit hard and soft cluster analyses to improve the effectiveness of geoelectrical methods in identifying the extent of leachate accumulation zones. A machine learning-based approach employing hard clustering on resistivity and induced polarization data was recently proposed to obtain integrated model sections that highlight leachate accumulation zones in municipal solid waste landfills. In those models, areas with different colours represent areas characterized by specific ranges of values of the considered physical quantities and have strictly defined boundaries. This is an intrinsic limitation of hard clustering that carries out cluster assignments without providing an assessment of the reliability of the reconstruction. In contrast, soft clustering approaches provide estimation of the cluster membership that allows a refinement of cluster boundaries, improving the identification of groups in the data. We apply hard and soft cluster analyses to geoelectrical data for detecting leachate accumulation zones in a landfill located on a steep slope in Central Italy. There, leachate may not only contaminate groundwater but also trigger instability phenomena. Among the different clustering algorithms, we selected K-means due to its simplicity of implementation, its ability to identify clusters that are both compact and distinct, and its faster performance compared to Fuzzy C-means. The clusters associated with the leachate accumulation zones represent approximately the 11% of total investigated subsoil and are characterized by values of resistivity, chargeability and normalized chargeability in the ranges of about 1.5-5 Ωm, 10-70 mV/V and 4.5-37 mS/m, respectively. Then, we applied the Fuzzy C-means algorithm to obtain the degree of membership of points belonging to such areas and better outline their boundaries. By considering a fuzzy membership greater than 0.5, we achieve an accuracy exceeding 90% in identifying leachate in wells. Furthermore, identifying zones with lower membership we delineate the boundaries of less saturated regions as well as those that are more saturated, providing a reconstruction of potential preferential leachate flows within the waste mass. These findings have important practical implications as they contribute to cost reductions for future drilling and monitoring processes
A machine learning-based approach for mapping leachate contamination using geoelectrical methods
Full text link: Leachate is the main source of pollution in landfills and its negative impacts continue for several years even after landfill closure. In recent years, geophysical methods are recognized as effective tools for providing an imaging of the leachate plume. However, they produce subsurface cross-sections in terms of individual physical quantities, leaving room for ambiguities on interpretation of geophysical models and uncertainties in the definition of contaminated zones. In this work, we propose a machine learning-based approach for mapping leachate contamination through an effective integration of geoelectrical tomographic data. We apply the proposed approach for the characterization of two urban landfills. For both cases, we perform a multivariate analysis on datasets consisting of electrical resistivity, chargeability and normalized chargeability (chargeability-to-resistivity ratio) data extracted from previously inverted model sections. By executing a K-Means cluster analysis, we find that the best partition of the two datasets contains ten and eleven classes, respectively. From such classes and also introducing a distance-based colour code, we get updated cross-sections and provide an easy and less ambiguous identification of the leachate accumulation zones. The latter turn out to be characterized by coordinate values of cluster centroids27 mV/V and 11 mS/m. Our findings, also supported by borehole data for one of the investigation sites, show that the combined use of geophysical imaging and unsupervised machine learning is promising and can yield new perspectives for the characterization of leachate distribution and pollution assessment in landfills
Electrical and Electromagnetic Prospecting for the Characterization of Municipal Waste Landfills: A Review
No full textIn this chapter, we review the main results of electrical and electromagnetic prospecting applied to the characterization and monitoring of municipal waste landfills in the last decade. Among all the geophysical surveys, these methods are the most used for subsurface investigations of landfills since they provide a cost-effective approach that allows for detailed and non-invasive imaging of the subsurface in terms of the electrical properties, down to depths which generally vary from a few tens of centimeters to several tens of meters. Nevertheless, the indirect geophysical mapping needs the direct even if punctual information from boreholes and wells for an accurate reconstruction of the contaminated zones. Electrical and electromagnetic methods are used for multiple purposes that include mapping landfill boundaries, measuring waste volume and composition, as well as identifying and tracking leachate plumes. In particular, electrical methods are widely used for leachate detection (both inside and outside the landfill) and for the geometrical reconstruction of the landfill using electrical conductivity and chargeability as the main proxies. Low-frequency electromagnetic methods are mostly used for a hydrogeological characterization and extensive screening of the high-conductive areas associated to the leachate accumulation. These methods have lower resolution compared to the electrical techniques but often allow greater depth of investigation. High-frequency electromagnetic surveys are instead mainly focused on the shallow part of the landfill for detection of defects on the covering liner and characterization of the covering layer. We discuss recent results related to the topic providing updated references in relation to the specific applications and emphasizing the importance of site-specific validation through direct information. At last, a special focus is given to novel trends, emerging techniques and data integration by machine learning-based approaches for mapping and monitoring of municipal solid waste landfills
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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