A Statistical Framework for Automatic Leakage Detection in Smart Water and Gas Grids

In the last few years, due to the technological improvement of advanced metering infrastructures, water and natural gas grids can be regarded as smart-grids, similarly to power ones. However, considering the number of studies related to the application of computational intelligence to distribution grids, the gap between power grids and water/gas grids is notably wide. For this purpose, in this paper, a framework for leakage identification is presented. The framework is composed of three sections aimed at the extraction and the selection of features and at the detection of leakages. A variation of the Sequential Feature Selection (SFS) algorithm is used to select the best performing features within a set, including, also, innovative temporal ones. The leakage identification is based on novelty detection and exploits the characterization of a normality model. Three statistical approaches, The Gaussian Mixture Model (GMM), Hidden Markov Model (HMM) and One-Class Support Vector Machine (OC-SVM), are adopted, under a comparative perspective. Both residential and office building environments are investigated by means of two datasets. One is the Almanac of Minutely Power dataset (AMPds), and it provides water and gas data consumption at 1, 10 and 30 min of time resolution; the other is the Department of International Development (DFID) dataset, and it provides water and gas data consumption at 30 min of time resolution. The achieved performance, computed by means of the Area Under the Curve (AUC), reaches 90 % in the office building case study, thus confirming the suitability of the proposed approach for applications in smart water and gas grids

Targeting dementias through cancer kinases inhibition

The failures in Alzheimer's disease (AD) therapy strongly suggest the importance of reconsidering the research strategies analyzing other mechanisms that may take place in AD as well as, in general, in other neurodegenerative dementias. Taking into account that in AD a variety of defects result in neurotransmitter activity and signaling efficiency imbalance, neuronal cell degeneration and defects in damage/repair systems, aberrant and abortive cell cycle, glial dysfunction, and neuroinflammation, a target may be represented by the intracellular signaling machinery provided by the kinome. In particular, based on the observations of a relationship between cancer and AD, we focused on cancer kinases for targeting neurodegeneration, highlighting the importance of targeting the intracellular pathways at the intersection between cell metabolism control/duplication, the inhibition of which may stop a progression in neurodegeneration

Light scattering features induced by residual layers in dielectric dewetted nanoparticles

All-dielectric, sub-micrometric particles obtained through solid state dewetting of thin SiGe-films have been shown to support Mie resonances together with a high-quality monocrystalline composition and atomically smooth facets. Recently, a precise study on the impact given by the effective complex morphology of a SiGe dewetted nanoparticle to the Mie scattering properties has been provided and carried on through a novel experimental technique called Dark-field Scanning Optical Microscopy. In this work, by means of the same experimental technique and numerical simulations of light scattering, we show how the presence of a pedestal enriched with silicon placed under the SiGe-nanoparticle results in a sharp peak at high energy in the total scattering cross-section. Exploiting a tilted illumination to redirect scattered light, we are able to discriminate the spatial localization of the pedestal-induced resonance. Our results contribute to extending the practical implementations of dewetted Mie resonators in the field of light scattering directionality, sensing applications and show further engineering options beyond the simple isolated-island case

Thick Does the Trick: Genesis of Ferroelectricity in 2D GeTe-Rich (GeTe)m (Sb2 Te3 )n Lamellae

The possibility to engineer (GeTe)(m)(Sb2Te3)n phase-change materials to co-host ferroelectricity is extremely attractive. The combination of these functionalities holds great technological impact, potentially enabling the design of novel multifunctional devices. Here an experimental and theoretical study of epitaxial (GeTe)(m)(Sb2Te3)n with GeTe-rich composition is presented. These layered films feature a tunable distribution of (GeTe)m(Sb2Te3)(1) blocks of different sizes. Breakthrough evidence of ferroelectric displacement in thick (GeTe)m(Sb2Te3)(1) lamellae is provided. The density functional theory calculations suggest the formation of a tilted (GeTe)m slab sandwiched in GeTe-rich blocks. That is, the net ferroelectric polarization is confined almost in-plane, representing an unprecedented case between 2D and bulk ferroelectric materials. The ferroelectric behavior is confirmed by piezoresponse force microscopy and electroresistive measurements. The resilience of the quasi van der Waals character of the films, regardless of their composition, is also demonstrated. Hence, the material developed hereby gathers in a unique 2D platform the phase-change and ferroelectric switching properties, paving the way for the conception of innovative device architectures

All in the Game. The Wire: un campo di ricerca sociologica

Analyzing with an ethnographic approach The Wire, one of the most important TV series on American ghettos, to understand and question the sociological perspective that emerges from the series, positioning it into the broader scientific debate. This is, in a nutshell, the work presented in the book It's all in the Game, the outcome of a laboratorial research activity carried out in 2020 by students and teachers of the Sociology of Communities and Urban Neighborhoods class, at the University of Bologna. The text is structured into four chapters, resulting from the four topics used to analysis the TV series: forms of social capital, the relationship between structural forces- culture of poverty and individual agency, neighborhood effects mechanism and the relationship between statistics and political action. Four subjects that are the core of many neighborhood- studies related researches and on which the TV series makes a clear stand. We analyzed those topics through a critical perspective, not considering them as a truth about ghettos, but as a very precise way of thinking about life in the American suburbs

Advanced Computational Intelligence for Smart Water and Gas Grids

Negli ultimi anni, il miglioramento delle infrastrutture di contabilizzazione ha permesso di assimilare le reti di distribuzione di acqua e gas alle smart grids ideate per la distribuzione di energia elettrica. Tuttavia, considerando il ruolo delle soluzioni di Computational Intelligence, gli studi relativi ad applicazioni per reti di distribuzione e la disponibilità di dataset adeguati, il divario tra il livello di sviluppo delle reti elettriche e quello delle reti di distribuzione di acqua/gas è particolarmente ampio. All'inizio di questo lavoro lo stato dell'arte delle tecniche applicate alle reti di acqua e gas viene presentato e discusso, con attenzione alle applicazioni di previsione e rilevamento delle perdite. Viene inoltre presentato il risultato della ricerca dei dataset utilizzati, mettendoli a disposizione per la comunità scientifica. Successivamente, gli esperimenti di previsione dei consumi di acqua e gas e di rilevamento delle perdite ad essi connesse, sono presentati con attenzione all'utilizzo di dati eterogenei per ottenere un risultato affidabile. Le previsioni sono eseguite garantendo sia criteri di valutazione omogenei sia l'applicazione di informazioni eterogenee. Le valutazioni sono effettuate per differenti scenari, dal caso residenziale a quello nazionale. Nel rilevamento delle perdite, gli esperimenti sono eseguiti mediante un algoritmo basato sul paradigma della novelty detection. Insieme alla valutazione di differenti approcci di Computational Intelligence per la modellazione della condizione di normalità, viene selezionato anche un set di features adeguate, comprensivo di informazioni temporali e di pressione. La mancanza di dataset adeguati ha limitato gli scenari valutati a quello residenziale e a quello di edificio adibito ad uso ufficio. Infine, in linea con le metodologie già applicate, si sono presentate innovazioni per due temi specifici della Smart Grid elettrica. Tali innovazioni riguardano un manager energetico per micro-grid che tenga conto della previsione del prezzo della risorsa e il monitoraggio non intrusivo del carico basato su approccio statistico e su rete neurale.In the last few years, due to the technological improvement of advanced metering infrastructures, water and natural gas grids can be regarded as Smart Grids, similarly to power ones. However, considering the role played by Computational Intelligence solutions, the number of studies related to applications for distribution grids, and the availability of suitable datasets, the gap between power grids and water/gas ones is notably wide. At first, in this work the state-of-the-art techniques for water and natural gas grids is presented and discussed, focusing on load forecasting and leakage detection applications. The result of an extensive search of used datasets is also presented, and thus made available to the research community. Later, experiments concerning the prediction and the leakage detection based on the analysis of water and natural gas consumption records are presented focusing on how to exploit data heterogeneity to get a reliable outcome. With regard to forecasting experiments, the tests are performed according to two key aspects: homogeneous evaluation criteria and application of heterogeneous data. The evaluations are performed for different grid scenarios, from the residential to the national one. Concerning leakage detection experiments, an algorithm based on the novelty detection paradigm is presented. Different Computational Intelligence approaches to model the normality condition are investigated, as well as the composition of an optimal set of features, including innovative temporal information and pressure ones. Due to the lack of suitable datasets, the target scenarios are limited to the residential and the office building one. Finally, in accordance with the methodologies applied above, advancements for two particular power grid applications are also proposed. Specifically, a proper micro-grid energy manager with the support of a pricing profile forecaster, and improvements for non-intrusive load monitoring based on statistical and deep neural network solutions, are presented and evaluated

A Statistical Framework for Automatic Leakage Detection in Smart Water and Gas Grids

In the last few years, due to the technological improvement of advanced metering infrastructures, water and natural gas grids can be regarded as smart-grids, similarly to power ones. However, considering the number of studies related to the application of computational intelligence to distribution grids, the gap between power grids and water/gas grids is notably wide. For this purpose, in this paper, a framework for leakage identification is presented. The framework is composed of three sections aimed at the extraction and the selection of features and at the detection of leakages. A variation of the Sequential Feature Selection (SFS) algorithm is used to select the best performing features within a set, including, also, innovative temporal ones. The leakage identification is based on novelty detection and exploits the characterization of a normality model. Three statistical approaches, The Gaussian Mixture Model (GMM), Hidden Markov Model (HMM) and One-Class Support Vector Machine (OC-SVM), are adopted, under a comparative perspective. Both residential and office building environments are investigated by means of two datasets. One is the Almanac of Minutely Power dataset (AMPds), and it provides water and gas data consumption at 1, 10 and 30 min of time resolution; the other is the Department of International Development (DFID) dataset, and it provides water and gas data consumption at 30 min of time resolution. The achieved performance, computed by means of the Area Under the Curve (AUC), reaches 90 % in the office building case study, thus confirming the suitability of the proposed approach for applications in smart water and gas grids

Advanced Computational Intelligence for Smart Water and Gas Grids

Negli ultimi anni, il miglioramento delle infrastrutture di contabilizzazione ha permesso di assimilare le reti di distribuzione di acqua e gas alle smart grids ideate per la distribuzione di energia elettrica. Tuttavia, considerando il ruolo delle soluzioni di Computational Intelligence, gli studi relativi ad applicazioni per reti di distribuzione e la disponibilità di dataset adeguati, il divario tra il livello di sviluppo delle reti elettriche e quello delle reti di distribuzione di acqua/gas è particolarmente ampio. All'inizio di questo lavoro lo stato dell'arte delle tecniche applicate alle reti di acqua e gas viene presentato e discusso, con attenzione alle applicazioni di previsione e rilevamento delle perdite. Viene inoltre presentato il risultato della ricerca dei dataset utilizzati, mettendoli a disposizione per la comunità scientifica. Successivamente, gli esperimenti di previsione dei consumi di acqua e gas e di rilevamento delle perdite ad essi connesse, sono presentati con attenzione all'utilizzo di dati eterogenei per ottenere un risultato affidabile. Le previsioni sono eseguite garantendo sia criteri di valutazione omogenei sia l'applicazione di informazioni eterogenee. Le valutazioni sono effettuate per differenti scenari, dal caso residenziale a quello nazionale. Nel rilevamento delle perdite, gli esperimenti sono eseguiti mediante un algoritmo basato sul paradigma della novelty detection. Insieme alla valutazione di differenti approcci di Computational Intelligence per la modellazione della condizione di normalità, viene selezionato anche un set di features adeguate, comprensivo di informazioni temporali e di pressione. La mancanza di dataset adeguati ha limitato gli scenari valutati a quello residenziale e a quello di edificio adibito ad uso ufficio. Infine, in linea con le metodologie già applicate, si sono presentate innovazioni per due temi specifici della Smart Grid elettrica. Tali innovazioni riguardano un manager energetico per micro-grid che tenga conto della previsione del prezzo della risorsa e il monitoraggio non intrusivo del carico basato su approccio statistico e su rete neurale.In the last few years, due to the technological improvement of advanced metering infrastructures, water and natural gas grids can be regarded as Smart Grids, similarly to power ones. However, considering the role played by Computational Intelligence solutions, the number of studies related to applications for distribution grids, and the availability of suitable datasets, the gap between power grids and water/gas ones is notably wide. At first, in this work the state-of-the-art techniques for water and natural gas grids is presented and discussed, focusing on load forecasting and leakage detection applications. The result of an extensive search of used datasets is also presented, and thus made available to the research community. Later, experiments concerning the prediction and the leakage detection based on the analysis of water and natural gas consumption records are presented focusing on how to exploit data heterogeneity to get a reliable outcome. With regard to forecasting experiments, the tests are performed according to two key aspects: homogeneous evaluation criteria and application of heterogeneous data. The evaluations are performed for different grid scenarios, from the residential to the national one. Concerning leakage detection experiments, an algorithm based on the novelty detection paradigm is presented. Different Computational Intelligence approaches to model the normality condition are investigated, as well as the composition of an optimal set of features, including innovative temporal information and pressure ones. Due to the lack of suitable datasets, the target scenarios are limited to the residential and the office building one. Finally, in accordance with the methodologies applied above, advancements for two particular power grid applications are also proposed. Specifically, a proper micro-grid energy manager with the support of a pricing profile forecaster, and improvements for non-intrusive load monitoring based on statistical and deep neural network solutions, are presented and evaluated

Modelling of finger-surface contact dynamics

The tactile perception of a surface texture originates from the scanning of a finger on the surface. This kind of sliding contact activates the mechanoreceptors located into the skin, allowing the brain to identify the object and to perceive information about the scanned surface. In this paper, a numerical model describing finger-surface scanning is introduced in order to investigate the relationship between contact induced vibrations and scanning conditions. The model takes into account finger and surface shapes, material properties, normal contact force, and scanning velocity. Model validation is provided by comparison with experimental tests. Afterwards, the model is applied to clarify the role played by contact/scanning parameters on the induced vibration. The proposed model is useful to develop a comprehensive parametrical analysis of the vibration induced in finger-surface scanning, and to investigate the influence of material and contact properties on tactile perception

Dynamic modelling of finger-surface contacts

The tactile perception, originated from the scanning of the fingertip on object surfaces, is related to the contact stresses and to the vibrations induced by the sliding contact that activate the mechanoreceptors located in the skin, allowing the brain to identify objects and to perceive information about their surfaces [1-3]. The relationship between the friction induced vibration and the tactile sensation is rarely investigated, while a clear understanding of the mechanisms of the tactile sense is basilar for manifold applications, like the development of artificial tactile sensors for intelligent prostheses or robotic assistants, and for the ergonomics. In this context, it is necessary to perform appropriate experiments to find out the frequency characteristics of the vibrations induced by the surface scanning. The aim is to analyze the induced vibrations highlighting their dependence on contact and scanning conditions. The study of a finger that moves on a surface involves different difficulties that are related to the material characteristics and to the measurements themselves. In fact, the goal is the measurement and the analysis of the vibrations induced by the scanning, which are very low in magnitude; thus, it is complicated to isolate them from the vibration noise coming out from the experimental set-up and to detect them without significant alteration. For these reasons, an experimental set-up named TRIBOTOUCH was developed to recover the contact forces and the induced vibrations [4]. While the experimental tests, describing how the spectra of the vibrations measured on finger nail can be related to surface characteristics, to scanning speed and to contact force, are presented in the previous works [5-6], in this paper a numerical model reproducing the finger-surface scanning is introduced. The model takes into account finger and surface geometries, their material properties, normal contact force and scanning speed