1,721,077 research outputs found
Knowledge-Based Coordination in cyber-physical systems via distributed ledger technologies
Full text linkRecentemente, si può osservare un trend tecnologico sempre più preminente di fusione dei domini fisico e digitale, arrivando a coinvolgere non solo gli oggetti, ma anche luoghi, processi e persino esseri viventi. In questo scenario, gli oggetti di uso quotidiano acquisiscono la capacità di raccogliere, memorizzare, elaborare e scambiare informazioni. Questo nuovo contesto tecnologico è supportato dalla ricerca sull’Internet of Things (IoT) e sul più ampio dominio dell’Internet of Everything (IoE). In tale ambito, il Cyber-Physical System (CPSs) è emerso come paradigma di sistemi complessi che integrano sensoristica, computazione, comunicazione e attuazione per controllare processi fisici, i cui requisiti chiave includono responsività, affidabilità e sicurezza. Per far fronte alla natura decentralizzata, eterogenea e volatile dei CPS su larga scala basati su IoT, questa dissertazione propone un Multi-Agent System (MAS), in cui ogni dispositivo diventa un’entità intelligente dotata di capacità decisionali e comportamento adattabile. Tuttavia, la mobilità continua dei nodi e la connettività intermittente ostacolano interazioni basate sulla fiducia tra questi agenti. Le Distributed Ledger Technologies (DLT) – in particolare quelle basate su strutture di Directed Acyclic Graph (DAG) – offrono soluzioni promettenti a queste sfide di coordinamento nei contesti CPS, garantendo uno scambio di dati robusto alle manomissioni e senza intermediari centralizzati. In questa prospettiva, il presente lavoro propone un nuovo framework per la comunicazione fra diverse piattaforme DLT che si articola in due livelli. L’infrastruttura progettata permette di far interagire molteplici piattaforme blockchain attraverso un substrato basato su DLT DAG. Questo approccio possibile la definizione di un’architettura a microservizi che migliora la scalabilità, separando i componenti del sistema e distribuendo l’elaborazione necessaria per rispondere alle richieste attraverso il DLT DAG. Ciò rende possibile una gestione dei servizi trasparente tenendo traccia di eventi che coinvolgono diversi ledger. Inoltre, il lavoro analizza metodi per integrare le tecnologie di Knowledge Representation and Reasoning (KRR) con l’obiettivo di supportare task più sofisticati di resource discovery, composition e negotiation tramite Smart Contract (SC) che sfruttano motori di ragionamento automatico. A questo scopo, è stato implementato il prototipo SeeSaw, dimostrando come SC abilitati alla semantica possano facilitare una coordinazione robusta e spiegabile su base logica in ambienti CPS complessi. La necessità di integrare capacità di dialogo più articolate tra i componenti di un CPS ha motivato lo sviluppo di un framework basato sulla teoria della Computational Argumentation, in cui un formalismo basato su grafi permette di analizzare le interrelazioni tra gli argomenti, rilevando informazioni in accordo e in conflitto. Infine, lo sviluppo di prototipi in diversi casi di studio ha permesso di dimostrare l’applicabilità e i vantaggi delle tecnologie KRR e delle componenti dell’infrastruttura DLT a due livelli progettata per favorire scalabilità, affidabilità e intelligenza in CPS distribuiti in diversi domini. Il lavoro svolto propone innovazioni metodologiche e tecnologiche con cui agenti intelligenti possono collaborare in modo fluido, dialogare, adattarsi ed evolvere per soddisfare le esigenze sempre più complesse dei CPS nell’era del Digital Twin.The physical and digital domains are becoming increasingly intertwined, encompassing not just objects, but also locations, processes and living entities. In this scenario, everyday objects acquire the capability of collecting, storing, processing and exchanging information. This technological trend has been supported by the research on the Internet of Things (IoT) and the broader domain of Internet of Everything (IoE). Within such context, Cyber-Physical Systems (CPSs) have emerged as complex systems that tightly integrate sensing, computation, communication and actuation capabilities to control physical processes. Key CPS requirements include responsiveness, reliability and safety. To cope with the decentralized, heterogeneous and volatile nature of large-scale IoT-based CPSs, this dissertation adopts a Multi-Agent System (MAS) perspective, where each device becomes a smart entity endowed with decision-making abilities and adaptable behaviour. However, continuous node mobility and intermittent connectivity hinder trust-based interactions among these agents. Distributed Ledger Technologies (DLTs) - particularly those built upon Directed Acyclic Graph (DAG) structures - provide promising solutions to these coordination challenges in CPS scenarios by ensuring tamper-resistant data exchange without centralized intermediaries. This dissertation proposes a novel dual-layer interledger framework that unifies multiple blockchain platforms through a shared DAG-based backbone. This enables the design of a microservice architecture that enhances scalability by decoupling system components and distributing the computation required for servicing requests, with a DAG-based substrate to enable transparent data management and tracking of events involving multiple different ledgers. Furthermore, it investigates methods to integrate Knowledge Representation and Reasoning (KRR) for supporting more sophisticated tasks of resource discovery, composition and negotiation by means of Smart Contracts (SCs) leveraging automated reasoning. For this purpose, the Seesaw prototype has been implemented demonstrating how semantic-enabled SCs can facilitate robust, logically explainable coordination in complex CPS environments. Moreover, the need to allow more articulate dialogue capabilities among components of pervasive CPSs has motivated the work on a framework based on Computational Argumentation theory, in which a graph-based formalism allows the analysis of interrelation among arguments, detecting agreements and conflicts. As a further step, prototypes in several case studies show how KRR technologies have been integrated into the dual-layer infrastructure to foster scalability, trustworthiness, and intelligence in interconnected cyber-physical domains. In this context, intelligent agents can seamlessly collaborate, engage in dialogue, adapt and evolve to meet the increasingly complex demands of CPS in the Digital Twin era
Estimating long memory in the mark-dollar exchange rate with high frequency data
No full texthigh frequency data, long memory, exchange rate
Breaks and persistency: macroeconomic causes of stock market volatility
No full textThe paper studies the relation between stock market volatility and the volatility of relevant macroeconomic variables. The paper innovates over pre-existing literature in the use of an econometric model which may allow for structural breaks in the volatility series
Structural breaks in the volatility of the Fama-French factors
No full textWe study the volatility of the Fama-French risk factors for the US stock market on the basis of a realized volatility econometric model that allows for structural break
Comovements in international stock markets
No full textIl paper analizza i comovimenti tra i mercati azionari. I momenti di varianza e covarianza vengono stimati mediante la tecnica dei momenti realizzati a partire dai dati giornalieri
Statistical benefits of value-at-risk with long memory
No full textThe paper uses a volatility model that allows for stochastic volatility of volatility. The problem is crucial to understand and model unconditional non-normality of returns. Implications for financial risks are important due to the assumptions of normality that are generally made in practtioners models. Such implications are discussed in the paper
Deterministic and stochastic methods for estimation of intra-day seasonal components with high frequency data
No full textWe introduce a model for the analysis of intra-day volatility based on unobserved components. The stochastic seasonal component is essential to model time-varing intra-day effects. The model is estimated with high frequency data for Deutsche mark-US dollar for 1993 and 1996. The model performs well in terms of coherence with the theoretical aggregation properties of GARCH models, it is effective in terms of both forecasting ability and describing reactions to macroeconomic news. © Banca Monte dei Paschi di Siena SpA, 2001. Published by Blackwell Publishers
Does the stock market affect income distribution? Some empirical evidence for the US
No full textThe paper looks at bthe long relation between stock market valuation and labor income distribution in the US
Climate change awareness: Empirical evidence for the European Union
Full text linkIn this paper, we assess public attitudes on climate change in Europe over the last decade. Using aggregate figures from the Special Eurobarometer surveys on Climate Change, we find that environmental concern is directly related to per capita income, social trust, secondary education, the physical distress associated with hot weather, media coverage, the share of young people in the total population, and monetary losses caused by extreme weather episodes. It is also inversely related to greenhouse gas emissions, relative power position of right-wing parties in government and tertiary education. Moreover, we find a significant, opposite impact for two dummies for years 2017 and 2019, which we respectively associate with the effects of Donald Trump's denial campaigns and the U.S. Paris Agreement withdrawal announcement, and Greta Thunberg's environmental activism
Core inflation in the Euro area
No full textUsing a common trends model, a forward-looking 'core' inflation measure is estimated for the Euro area based on long-run relations among major macroeconomic variables, bearing the interpretation of long-run inflation forecast. The proposed measure may be particularly suitable for the 'two-pillar' monetary policy strategy of the ECB which focuses on medium-term inflation prospects
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