A Petri Nets Model for Blockchain Analysis

A Blockchain is a global shared infrastructure where cryptocurrency transactions among addresses are recorded, validated and made publicly available in a peer- to-peer network. To date the best known and important cryptocurrency is the bitcoin. In this paper we focus on this cryptocurrency and in particular on the modeling of the Bitcoin Blockchain by using the Petri Nets formalism. The proposed model allows us to quickly collect information about identities owning Bitcoin addresses and to recover measures and statistics on the Bitcoin network. By exploiting algebraic formalism, we reconstructed an Entities network associated to Blockchain transactions gathering together Bitcoin addresses into the single entity holding permits to manage Bitcoins held by those addresses. The model allows also to identify a set of behaviours typical of Bitcoin owners, like that of using an address only once, and to reconstruct chains for this behaviour together with the rate of firing. Our model is highly flexible and can easily be adapted to include different features of the Bitcoin crypto-currency system

Automatically evaluating the quality of textual descriptions in cultural heritage records

AbstractMetadata are fundamental for the indexing, browsing and retrieval of cultural heritage resources in repositories, digital libraries and catalogues. In order to be effectively exploited, metadata information has to meet some quality standards, typically defined in the collection usage guidelines. As manually checking the quality of metadata in a repository may not be affordable, especially in large collections, in this paper we specifically address the problem of automatically assessing the quality of metadata, focusing in particular on textual descriptions of cultural heritage items. We describe a novel approach based on machine learning that tackles this problem by framing it as a binary text classification task aimed at evaluating the accuracy of textual descriptions. We report our assessment of different classifiers using a new dataset that we developed, containing more than 100K descriptions. The dataset was extracted from different collections and domains from the Italian digital library "Cultura Italia" and was annotated with accuracy information in terms of compliance with the cataloguing guidelines. The results empirically confirm that our proposed approach can effectively support curators (F1 $$\sim$$ ∼ 0.85) in assessing the quality of the textual descriptions of the records in their collections and provide some insights into how training data, specifically their size and domain, can affect classification performance

Automatically evaluating the quality of textual descriptions in cultural heritage records

Metadata are fundamental for the indexing, browsing and retrieval of cultural heritage resources in repositories, digital libraries and catalogues. In order to be effectively exploited, metadata information has to meet some quality standards, typically defined in the collection usage guidelines. As manually checking the quality of metadata in a repository may not be affordable, especially in large collections, in this paper we specifically address the problem of automatically assessing the quality of metadata, focusing in particular on textual descriptions of cultural heritage items. We describe a novel approach based on machine learning that tackles this problem by framing it as a binary text classification task aimed at evaluating the accuracy of textual descriptions. We report our assessment of different classifiers using a new dataset that we developed, containing more than 100K descriptions. The dataset was extracted from different collections and domains from the Italian digital library \u201cCultura Italia\u201d and was annotated with accuracy information in terms of compliance with the cataloguing guidelines. The results empirically confirm that our proposed approach can effectively support curators (F1 3c 0.85) in assessing the quality of the textual descriptions of the records in their collections and provide some insights into how training data, specifically their size and domain, can affect classification performance

Analisi della relazione esistente tra il training neuromotorio riabilitativo e i fattori psicologici di soggetti sportivi operati di r-lca: uno studio pilota

BACKGROUND: Ad oggi circa un terzo degli atleti che si lesionano il LCA non ritorna a praticare il suo sport al livello pre-infortunio. Le evidenze scientifiche riportano tra le cause principali che determinano questo dati insoddisfacente i fattori psicologici. Al momento ancora non è chiaro se e come la fisioterapia riesca ad agire su tali fattori. Il Training Neuromotorio (TNM) si pone l’obiettivo di migliorare e correggere quei pattern motori responsabili dell’aumento del rischio primario e di re-infortunio del LCA. OBIETTIVO: il principale obiettivo di questo studio pilota è stato quello di valutare se il TNM potesse migliorare, oltre che la funzionalità dei soggetti, anche i fattori psicologici come kinesiofobia, ottimismo/pessimismo, resilenza, readiness, paura, fiducia e percezione di supporto sociale in sportivi con R-LCA. MATERIALI E METODI: nei mesi da ottobre 2019 a febbraio 2020 sono stati reclutati 6 pazienti inseriti in un programma riabilitativo di TNM e gli sono stati somministrati dei questionari di valutazione psicologica. È stata effettuata poi un’analisi statistica sulle medie dei valori ottenuti ed elaborati grafici di andamento per ogni singolo fattore psicologico rilevato. RISULTATI: l’analisi dei dati non ha indicato una variazione statisticamente significativa (p-value >>0,05). Gli outcome fisici del TNM sono significativamente migliorati (p-value = 0,05). Gli andamenti della kinesiofobia, resilienza, pessimismo, paura, fiducia e percezione di supporto sociale hanno mostrato un lieve miglioramento. La readiness non ha riportato variazioni tra l’inizio e la fine del trattamento mentre l’ottimismo invece un leggero calo. CONCLUSIONI: dai risultati emersi, questo studio non è stato in grado di dimostrare l’esistenza di una correlazione statistica significativa tra il Training Neuromotorio e i fattori psicologici indagati ma i dati rilevati ci hanno permesso di formulare nuove ipotesi che necessitano di essere rivalutate con ulteriori studi

High-Speed Pipeline Analog-to-Digital Converter: Transistor-Level Design and Calibration Issues

La tesi riguarda la progettazione dei blocchi essenziali di un convertitore pipeline ad alta velocità (250MHz) a capacità commutate. Il lavoro inoltre include uno studio approfondito su due possibili tecniche di calibrazione del guadagno, delle non-linearità e del mismatch capacitivo

CMOS Interface Circuits for High-Voltage Automotive Signals

Abstract: The acquisition of high-voltage signals from sensors and actuators in an internal-combustion engine is often required for diagnostic purposes or in the case of conversion to alternative fuels, such as hydrogen, natural gas, or biogas. The integration of electronic interfaces and acquisition circuits in a single device provides benefits in terms of component-count reduction and performance. Nonetheless, the high voltage level of the involved signals makes on-chip design challenging. Addi- tionally, the circuits should be compatible with the CMOS technology, with limited use of high-voltage options and a minimum number of off-chip components. This paper describes the design and the implementation in 350 nm CMOS technology of electronic interfaces and acquisition circuits for typical high-voltage signals of automotive context. In particular, a novel co-design of dedicated voltage clamps with electro-static discharge (ESD) protections is described. The proposed circuits require only a single off-chip resistor, and they are suitable for the acquisition of signals with peak voltages up to 400 V. The measured performance of the silicon prototypes, in the [−40 °C, +125 °C] temperature range, make the proposed electronic interfaces suitable for the automotive domain

Blockchain in the Energy Sector for SDG Achievement

Blockchain technology finds application in multiple sectors, including renewable energy. Numerous blockchain-based applications aim to provide support in the production, management, distribution, and consumption of green energy. The benefits offered are not only technological but also social, environmental, and economic. The purpose of this study is to examine how the application of blockchain in the energy industry may affect the achievement of the Sustainable Development Goals (SDGs). This study is composed of two parts. The first part concerns the identification and analysis of the most relevant categories of blockchain applications in the energy sector and their ability to contribute to the achievement of the SDGs. A knowledge base, comprising scientific articles, gray literature, and real-world applications, has been created and analyzed. With a keyword-based approach, each application was associated with one or more SDGs. In the second part, the Sustainability Awareness Framework (SuSAF) was used to examine the findings of the first part of the study and discuss them in terms of five dimensions of sustainability. Finally, potential risks associated with the use of blockchain in the energy sector are also covered. Results reveal that tracking energy production and consumption and renewable energy communities are the applications that have the most beneficial effects, and that the benefits linked to blockchain adoption go beyond the energy sector to include the environment, the economy, industry, infrastructure, smart cities, and society

High efficiency room temperature laser emission in heavily doped Yb:YLF

We report the tunable, CW and quasi CW laser operation at room temperature of an highly doped (30% at.) Yb:YLF crystal longitudinally pumped by a fiber coupled laser diode array. The CW output power is 1.15 W vs. an absorbed pump power of 6 W, with a slope efficiency of 31%. In quasi-CW operation (20% duty factor @10 Hz) an output power of 4 W with an absorbed power of 9.5 W, and a slope efficiency of 62.8% were obtained. The tuning range spans from 1022 to 1075 nm. To our knowledge, these are among the best experimental results obtained at room temperature with Yb doped YLF

Unusual effectiveness of systemic steroids in Whipple disease.

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A Blockchain-based traceability system in Agri-Food SME: case study of a traditional bakery

In this paper we present a blockchain based system for the supply chain management of a particular Italian bread. Goal of the system is to guarantee a transparent and auditable traceability of the Carasau bread where each actor of the supply chain can verify the quality of the products and the conformity to the normative about the hygienic-sanitary conditions along the chain. To realize this system we relied on the Blockchain and the Internet of Thing technologies in order to provide a trustless environment, in which trust is placed in cryptography, in mathematical operations and on the network, and not in public or private companies. Thanks to the use of digital technologies the system aims to reduce the data entry errors and the risk of tampering. Our system is designed so that along the supply chain, the nodes equipped with several sensors directly communicate their data to Raspberry Pi units that elaborate and transmit them to Interplanetary File System and to the Ethereum Blockchain. Furthermore, we designed ad hoc Radio Frequency Identification and Near Field communication tags to shortly supply the proposed system with information about the products and batches. The dedicated RFID tags robustness during on-bread operation was numerically tested. The system will easily allow end consumers to have a transparent view on the whole journey from raw material to purchased final product and a supervisory authority to perform online inspections on the products’ quality and on the good working practices