Politecnio die Bari - Catalogo di prodotti della Ricerca
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Preface
No full textThe impact of the technological, social, and cultural transformations in the fields of urban planning, architecture, design, and engineering occurred in the last decades have radically changed the contemporary interpretations of sustainability and innovation, as well as the con- cept of development itself has become fluid, hybrid and sometimes ephemeral: the role of digital technologies, human interaction and environmental protection are now interested by new sensibilities and approaches within a framework of a common system of ethical values which draw a new cultural paradigm and, on the other hand, renovate relationships between local and global dimension of things.
The book presents these approaches offering tools, critical argumentations, and concrete examples to understand these phenomena in different specific ambits better: from architec- tural design, urban planning, and infrastructure to electrical, mechanical engineering and fabrication, from computation and telecommunication engineering to aviation and aerospace engineering, from design, art and media production to manufacturing engineering, the differ- ent contributions redefine with new senses interesting interpretative perspectives the contem- porary sustainable development goals and practices, as well as the visions on the smart cities and landscapes, the methodological aspects of using innovative materials and techniques, the impacts of renewable energy, re-use of waste and recycling approaches, always including a deep analysis on the human factor in the construction of new contexts for livable commu- nities. Furthermore, in their different approaches, the contributions constitute fundamental parts of an interdisciplinary unity linked together by a fil rouge of necessity in relationship with the issues of sustainability, innovation, and cultural heritage, at different scales
Distributed reasoning for the autonomous coordination of smart object networks
Get PDFL'evoluzione dell'Internet of Things (IoT) verso l'Internet of Everything (IoE) riflette il progresso dei dispositivi di rete e delle tecnologie di calcolo, comprendendo non solo oggetti, ma anche ambienti, persone, processi e dati. Questo sviluppo consente una raccolta e un'analisi dei dati su larga scala, con il potenziale di trasformare le interazioni tra molteplici attività umane e il mondo fisico. Sebbene questa transizione migliori l'efficienza operativa e il processo decisionale basato sui dati, la sua piena realizzazione richiede il superamento di problematiche relative alla larghezza di banda di rete, al consumo energetico, alla sicurezza dei dati e alla privacy. Soprattutto, nell'IoE, l'interoperabilità e la gestione intelligente delle informazioni diventano fondamentali per supportare processi autonomi flessibili e architetture orientate ai servizi sofisticate, adatte a interazioni estese tra macchine e tra esseri umani e macchine.
Una strategia chiave per affrontare queste sfide è l’edge computing, che avvicina le attività computazionali alle sorgenti di dati. Questa trasformazione è essenziale per gestire i grandi volumi di dati e la rapidità con cui questi sono generati nell'IoE, mitigando al contempo i problemi di latenza e larghezza di banda associati ai sistemi di elaborazione centralizzata. Un primo esempio di framework intelligente che sfrutta l’edge computing è il Semantic Web of Things (SWoT). In questo contesto, descrizioni basate sull’utilizzo di ontologie di dispositivi, oggetti ed eventi vengono gestite localmente da agenti intelligenti pervasivi attraverso ragionamenti automatizzati, consentendo operazioni autonome orientate a obiettivi specifici.
L'avanzamento del SWoT verso un Semantic Web of Everything (SWoE) richiede un'integrazione più profonda delle tecnologie semantiche nelle interazioni di calcolo pervasivo. Questa visione implica una pervasività di strumenti di rappresentazione della conoscenza e capacità di ragionamento automatizzato, anche su dispositivi con limitate capacità di elaborazione, memoria ed energia. Meccanismi di inferenza locale sui dispositivi sono essenziali nello SWoE, considerando l'elevata volatilità e la limitata accessibilità a dispositivi più performanti.
L'implementazione di architetture SWoE presenta difficoltà significative dal punto di vista scientifico e tecnologico. Gli attuali motori di ragionamento per il Semantic Web e i Knowledge Base Management Systems (KBMS) sono principalmente progettati per ambienti di calcolo ad elevate prestazioni, come server e cluster di workstation, rendendoli inadatti a dispositivi su scala nanometrica. I motori di ragionamento che potrebbero funzionare su dispositivi più piccoli spesso mancano di procedure di inferenza essenziali, limitandone l'utilizzo. Per questo motivo, la creazione di piattaforme SWoE richiede una rivalutazione delle metodologie di valutazione e benchmarking per includere i vincoli unici di questo nuovo paradigma.
Questa dissertazione presenta diversi contributi innovativi nel campo del ragionamento distribuito in scenari SWoE, concentrandosi sull'applicazione della rappresentazione della conoscenza e del ragionamento automatizzato al coordinamento di reti di agenti intelligenti incorporati in dispositivi dalle risorse limitate. A tal fine, questo lavoro analizza architetture e strategie di ottimizzazione per vari componenti fondamentali, come: Cowl, una libreria per la rappresentazione della conoscenza leggera e versatile progettata per dispositivi dalle risorse limitate, che supera le restrizioni dei KBMS attuali nei contesti embedded e IoT; Tiny-ME, un innovativo motore di ragionamento e matchmaking multi-piattaforma progettato per lo SWoE, che offre capacità di ragionamento efficienti adatte a dispositivi cloud, desktop, mobili ed edge; evOWLuator, un framework multipiattaforma per il benchmarking di motori di ragionamento del Semantic Web, con enfasi sulla stima del consumo energetico e sul supporto inferenziale su dispositivi remoti; un framework di Cloud-Edge Intelligence (CEI) per sistemi multi-agente e applicazioni basate su sensori, che sfrutta il calcolo serverless per la gestione dei dati e i task di machine learning.
Grande enfasi è posta sulla valutazione delle tecnologie sviluppate attraverso campagne sperimentali estese, che forniscono approfondimenti su prestazioni, efficienza e applicabilità in contesti SWoE. Inoltre, vengono dimostrate applicazioni pratiche attraverso casi di studio in diversi contesti. Il primo scenario presenta un framework per l'adattamento della Quality of Experience (QoE) nello streaming multimediale Web, utilizzando la versione WebAssembly di Tiny-ME come motore di ragionamento. Il secondo evidenzia un sistema di ricerca di eventi locali incentrato sulla privacy, mostrando un caso d'uso di ragionamento client-side per il recupero e la personalizzazione dei dati in applicazioni Web. Il terzo esempio esplora come Tiny-ME è in grado di gestire risorse annotate semanticamente in reti peer-to-peer, migliorando negoziazioni e l’explanation dei risultati di ricerca. Infine, un esempio di smart city mostra come Cowl può essere integrato in sensori su scala nanometrica per lo scambio di dati arricchiti semanticamente, migliorando la mobilità urbana. Gli esperimenti e le applicazioni menzionati evidenziano la flessibilità e la vasta applicabilità dei metodi e delle tecnologie presentati, sottolineando il potenziale esteso dello SWoE.The evolution of the Internet of Things (IoT) into the Internet of Everything (IoE) reflects the evolution of connected devices and computing technologies, encompassing not only things, but also environments, people, processes, and data. It enables large-scale data collection and analytics, with the potential to transform the interactions between many kinds of human activities and the physical world. Although this transition improves operational efficiency and data-driven decision-making, its full realization requires overcoming issues concerning network bandwidth, energy consumption, data security, and privacy. Most importantly, in the IoE interoperability and smart information management become essential for supporting flexible autonomous processing and sophisticated, flexible service-oriented architectures for extensive machine-to-machine and human-machine interactions.
A key strategy for addressing these challenges is edge computing, which brings computational tasks closer to data sources. This transformation is essential for managing the large volumes and rapid pace of data generated in the IoE, while also mitigating latency and bandwidth issues associated with centralized processing systems. An early example of a smart framework that leverages edge computing is the Semantic Web of Things (SWoT). Here, ontology-based descriptions of devices, objects, and events are dealt with locally by pervasive intelligent agents through automated reasoning, enabling autonomous operations towards specific objectives.
The advancement of SWoT towards a Semantic Web of Everything (SWoE) requires deeper embedding of semantic technologies in pervasive computing interactions. This vision requires pervasive knowledge representation and automated reasoning abilities, even on devices with stringent processing, memory, and energy limitations. Local inference mechanisms on devices are essential in the SWoE, considering the high volatility and restricted accessibility of more powerful devices.
The deployment of SWoE architectures discloses considerable difficulties from a scientific and technological standpoint. Current Semantic Web reasoners and Knowledge Base Management Systems (KBMS) are primarily tailored for high-performance computing environments such as servers and workstation clusters, making them unsuitable for nano-scale devices. Reasoning engines that might work on smaller devices frequently lack essential inference support, thus limiting their practicality. For this reason, creating SWoE platforms requires a reassessment of evaluation and benchmarking methodologies to consider the unique constraints of this new paradigm.
This dissertation presents several innovative contributions to the field of distributed reasoning in SWoE scenarios, focusing on applying knowledge representation and automated inferences to the coordination of networks of smart agents embodied into resource-constrained devices. To this aim, this work covers system architectures and optimization strategies for various essential components frameworks, such as: Cowl, a lightweight and versatile knowledge representation library designed for devices with limited resources, overcoming the restrictions of current KBMS in embedded and IoT contexts; Tiny-ME, an innovative multi-platform reasoner and matchmaking engine tailored for the SWoE, providing efficient reasoning capabilities appropriate for cloud, desktop, mobile, and edge devices; evOWLuator, a cross-platform evaluation framework that is mindful of energy consumption for Semantic Web reasoners, emphasizing power usage estimation and supporting inferences on remote devices; a Cloud-Edge Intelligence (CEI) framework for multi-agent systems and sensor-based application, exploiting serverless computing for data management and machine learning tasks.
Great emphasis is placed on the assessment of the developed technologies through extensive experimental campaigns, which provide insights into performance, efficiency, and applicability in SWoE settings. In addition, practical applications are demonstrated through case studies in various contexts. The first scenario demonstrates a framework for adapting Quality of Experience (QoE) in Web multimedia streaming, using the WebAssembly port of Tiny-ME as reasoning engine. The second highlights a privacy-focused local event finder, showing a client-side Web reasoning use case in data retrieval and personalization for Web applications. The third case study explores how Tiny-ME manages semantically annotated resources in peer-to-peer networks, improving negotiation and discovery explanations. Finally, a smart city example shows how Cowl can be integrated in nano-scale sensors to exchange semantically enriched data, enhancing urban mobility. Together, the mentioned experiments and applications underscore the flexibility and wide-ranging usability of the presented methods and technologies, highlighting the extensive potential of the SWoE
A Semi-automatic Pipeline for the Decay Mapping and the State of Conservation Assessment of Architectural Heritage Through Point Clouds
No full textThe integration of automation and artificial intelligence has revolutionized the documentation, conservation, and restoration of architectural cultural heritage. This research presents a pipeline leveraging Machine Learning (ML) algorithms for the automatic decay mapping of digitalized architectures, combined with informative system to support experts in the assessment of a synthetic index of the conservation state. Moreover, the setup of a web-based system as a platform for the expert acknowledgment and management of decays and properties helps them in a fast and coherent final assessment. The proposed pipeline combines such technologies to the architectural recovery theories and ensures a standardized procedure compliant with international and national regulations and standards such as ICOMOS-ISCS Glossary and UNI 11182:2006, UNI 8290–1:1981 and UNI CEN/TS 17385:2019. Method and tools are applied to the Labriola Palace in Tursi, Italy, demonstrating its efficacy in assessing the conservation state of architectural heritage, as well as the higher collaborative levels to reach also among technicians with different levels of computer science skill
Characterization of silicon photomultipliers in spaceborne high-energy astrophysics
Get PDFSilicon photomultipliers (SiPMs) have emerged as key photodetectors in gamma-ray and
cosmic-ray observatories due to their high photon detection efficiency, excellent timing resolution, and robustness. Their applications extend beyond astrophysics, finding use in medical imaging, LiDAR, and security systems. However, deploying SiPMs in space presents challenges, particularly in mitigating the effects of radiation exposure, which can degrade their performance through increased dark count rates (DCR), reduced photon detection efficiency (PDE), and shifts in breakdown voltage. This thesis focuses on the characterization of SiPMs for high-energy astrophysics, with an emphasis on their performance before and after irradiation. I evaluated under standard condition Near Ultraviolet High Density (NUV-HD) "lowCT" SiPM models produced by Fondazione Bruno Kessler (FBK) with sizes of 1 × 1 mm2 (with15 μm cell pitch) and 3 × 3 mm2 (with 40 μm cell pitch). Moreover, I studied NUV-HD "MT" SiPM models produced by FBK and Broadcom with sizes of 1 × 1 mm2 (with 15 μm, 25 μm and 40 μm cell pitch), and of 6 × 6 mm2 (with 40 μm cell pitch). I also evaluated the performance of the NUV-HD-lowCT technology after being subjected to the effects of Ionizing and Non- Ionizing Energy Loss (NIEL and IEL) damage. In particular I tested SPADs and SiPMs of 1x1 mm2 with cell pitch 15 and 40 μm after proton irradiation with fluence up to 1 × 10 11 p/cm2, and SiPMs of 3x3 mm2 with cell pitch of 15, 25 and 40 μm after X-ray irradiation between 69 and 100 kGy. My results are in agreement with the evaluation of the effects of NIEL and IEL damage on SiPMs reported in literature for space application and provide insights for the design of future solutions aimed at mitigating SiPMs and SPADs performance degradation
Leveraging artificial intelligence for enhanced and human-centered healthcare solutions
Get PDFArtificial Intelligence (AI) is increasingly recognized as a transformative force in healthcare, offering unprecedented opportunities to enhance disease diagnosis, management, and prevention. This PhD thesis is rooted in two fundamental research areas: the application of AI to health and epidemiological data for the purposes of disease prevention and monitoring, and the utilization of AI techniques for the analysis of bioelectrical signals to support clinical decision-making.
The first research area delves into the sophisticated analysis of extensive health and epidemiological datasets using cutting-edge machine learning (ML) methodologies. The objective is to uncover significant patterns that can inform and improve the prevention and management of chronic diseases. By identifying these patterns, the research enables the creation of personalized intervention strategies tailored to individual patient profiles, while also optimizing disease management on a broader, population-wide scale. This approach not only contributes to the advancement of public health but also sets the stage for more proactive healthcare practices.
The second research focus of this thesis explores the development and application of advanced ML and deep learning (DL) models for the interpretation of bioelectrical signals, such as electroencephalograms (EEG), electrocardiograms (ECG), and electromyograms (EMG). It is important to point out that non-invasive technologies such as brain-computer interfaces (BCIs) were used for the analysis of EEG signals.
The AI-driven models developed in this PhD thesis aim to enhance the accuracy and reliability of medical diagnostics, facilitating more precise and personalized clinical decisions. The integration of these models into clinical workflows has the potential to revolutionize patient care by providing healthcare professionals with powerful tools for diagnosis and treatment planning.
The practical outcomes of this research are profound, offering novel tools and frameworks that bridge the gap between AI innovation and clinical application. By incorporating explainable Artificial Intelligence (XAI) principles, the models developed in this thesis are designed to be transparent and interpretable, ensuring that healthcare professionals can trust and effectively use these advanced technologies in their daily practice.
In summary, this PhD thesis makes significant contributions to the intersection of AI and medicine, addressing key challenges in the interpretation of health and epidemiological data as well as the analysis of bioelectrical signals. The findings presented here lay a robust foundation for future advancements in personalized medicine and public health, ultimately aiming to improve patient outcomes and the overall efficacy of healthcare systems.
All contributions made in this thesis are detailed in the respective chapters, providing a comprehensive overview of the research conducted and its impact on the field of AI in healthcare
Enhanced antibacterial efficacy of new benzothiazole phthalimide hybrid compounds/methyl-β-cyclodextrin inclusion complexes compared to the free forms: Insights into the possible mode of action
No full textWith the aim to gain further insights into the possible mechanism(s) involved in the cyclodextrin (CD)-mediated enhancement of antimicrobial activity, herein we report the preparation and fully characterization of a series of phenoxy-benzothiazole-phthalimide hybrid compounds and the corresponding complexes with methyl-β-CD (Me-β-CD). Next, these complexes were tested against selected Gram positive and Gram-negative bacterial strains and the observed antibacterial activity compared to those of the free forms. Results from 1H NMR and molecular modeling studies showed that in solution the chloro substituted compounds may give inclusion complex of 1:2 Drug-CD stoichiometry besides the 1:1 complexes, while the para-methyl derivative and the unsubstituted compound may provide complexes of 1:1 stoichiometry only. The antimicrobial tests against selected Gram positive and Gram-negative bacterial strains showed that the most active agents were the chloro substituted compounds/Me-β-CD complexes. Hence, it seems that improving of the efficacy of antimicrobial agents by CD complexation may be accomplished by formation of higher order complexes. Further, the hypothesized mode of action of CDs in reducing the minimal inhibition concentration (MIC) values of antimicrobials should be related to their ability to make permeable the bacterial cell wall. This is now substantiated by the observed increased uptake of Propidium iodide in S. aureus ATCC 29213 strain incubated with the para-chloro substituted benzothiazole-phthalimide compound/Me-β-CD complex compared to the corresponding free form. Overall, such strategy to take advantage of inclusion complexes of higher order may constitute a promising methodology to overcome the microbial resistance issue
An injury severity-based methodology for assessing priority areas for shared micromobility accident risk mitigation
Get PDFRecently, the adoption of micromobility as an alternative mode of transportation on a large scale has been
growing rapidly. However, its operational and safety aspects have not been extensively investigated in the
literature. Following this purpose, we developed a novel methodology that aims at evaluating priority areas for
shared micromobility system users’ accident risk mitigation based on predicted injury severity using a machine
learning-based approach. The methodology proposed in this paper consists of two models: a predictive model,
which is based on an artificial neural network with a pattern recognition algorithm, to estimate the expected
safety indicator of an urban zone, and a clustering method to define the priority areas for intervention through
the application of a geofence speed regulation system. A real case study was carried out in the city of Bari, Italy,
to test the effectiveness of the proposed methodology. The results showed how it is possible to define areas for
intervention with different priorities based on the expected severity index. The proposed methodology can be
seen as a decision support system to assist transport operators and urban planners in regulating shared micromobility
vehicles in urban areas by defining priority areas for intervention through geofencing and, therefore, it
can be useful for improving micromobility adoption, road safety, and urban mobility policies
Continuous Flow Decarboxylative Monofluoroalkylation Enabled by Photoredox Catalysis
No full textHerein, we report a scalable and mild strategy for the monofluoroalkylation of a wide array of Giese acceptors via visible-light-mediated photoredox catalysis in continuous flow. The use of flow technology significantly enhances productivity and scalability, whereas mildness of conditions and functional group tolerance are ensured by leveraging 4CzIPN, a transition-metal-free organic photocatalyst. Structurally diverse secondary and tertiary monofluoroalkyl radicals can thus be accessed from readily available α-monofluorocarboxylic acids. Given the mild reaction conditions, this protocol is also amenable to the late-stage functionalization of biologically relevant molecules such as menthol, amantadine, bepotastine, and estrone derivatives, rendering it suitable for application to drug discovery programs, for which the introduction of fluorinated fragments is highly sought after. This method was also extended to enable a reductive multicomponent radical-polar crossover transformation to rapidly increase the complexity of the assembled fluorinated architectures in a single synthetic operation
Advanced numerical modeling and validation of dynamic thermal behavior in layered composites for active thermography test
Get PDFNon-Destructive Evaluation (NDE) techniques, such as thermography, have become essential for detecting and characterizing defects in composite materials, especially in aerospace and other high-performance applications. While traditional numerical approaches, including those implemented in software like ANSYS and COMSOL, have provided valuable insights into thermal behavior, they face limitations in computational efficiency and accuracy when applied to anisotropic, layered composites.
This research introduces advanced numerical modeling methodologies based on the Carrera Unified Formulation (CUF) and the Sublaminate Generalized Unified Formulation (SGUF). For the first time, CUF and SGUF are implemented for transient thermal analysis using active thermography, specifically in Thermography. These formulations enable efficient and accurate simulations of dynamic thermal behavior, capturing interlaminar interactions and thermal gradients that are critical for defect detection. The study begins with a comprehensive review of traditional thermographic techniques and their numerical counterparts. Numerical approaches commonly implemented in finite element tools like Finite difference models (FDM), ANSYS and
COMSOL are critically analyzed, revealing significant gaps in computational efficiency and their inability to fully capture interlaminar thermal interactions and anisotropic material behaviors. To address these challenges, CUF and SGUF-based models are developed, offering a more streamlined and accurate framework for transient thermal analysis. These models are further applied in various parametric studies, including lay-up sequence analysis, material anisotropy, and thermal gradient effects, to comprehensively evaluate their performance. Validation of the developed models is conducted through comparisons with experimental data and traditional numerical benchmarks, demonstrating their accuracy and robustness.
Key findings demonstrate that CUF and SGUF enhance computational efficiency and
accuracy in predicting thermal responses, such as temperature distribution and heat flux,
making them effective for thermal analysis of composite materials. Their efficiency enables the application to real defect scenarios, such as simulating air-filled gaps in T-joint structures for debonding analysis. Moreover, the versatility of these advanced formulations enables their application to complex geometries and real-world scenarios, providing critical insights into defect detection and thermal behavior in layered composites