Higher Institute on Territorial Systems for Innovation
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Urban and Territorial Resilience. Urbanism Facing Crisis
No full textCities and landscapes are undergoing an ever increasing number of often intertwined crises, which urbanism and territorial governance are called to fully understand and to timely and e!ectively address, in order to protect – in advance and/or in the midst of an emergency – the health and the sound operation of the spatial systems in which human and non-human societies can only live and flourish, i.e. the ecosystems upon which everything depends: the ecological, the social, and the economic spheres, and with them the cultural, the
political, etcetera. Spatial multirisk has been the core of the scientific activities taking place within the Spoke number 5 “Urban and metropolitan settlements” of the extended partnership project “RETURN – multi-Risk sciEnce for resilienT commUnities undeR a chaNging climate”. This special issue brings together studies and
perspectives from those scientific activities, connected projects, and topic-relevant parallel studies,
expanding much beyond the original idea to collect the proceedings of the special session “Urban and territorial resilience: from measuring to building planning solutions”, organised by most of this issue’s Guest Editors (Amenta, Cazzola, Cristiano, Giudice, Trabucco, & Vingelli) within the 63rd Congress of the European Regional Science Association (Terceira, Portugal, 26–30 August 2024)
Analysis of the Evolution of Accidental Transients in the Cooling of a MgB2-LH2 Hybrid Power Cable
No full textThe safe and reliable operation of superconducting cables is critically dependent on efficient cooling strategies, and this point becomes even more relevant when hybrid cables cooled by liquid hydrogen (LH2) considered. This study analyzes the evolution of accidental transients in the cooling of a submarine 30 km hybrid cable in MgB2, capable to transfer 10kA at 30 kV and a mass flow rate up to 1 kg/s of LH2 at 20 K. Three key failure scenarios are examined: a loss-of-flow accident (LOFA), a loss-of coolant accident (LOCA) and a loss-of-vacuum accident (LOVA). For each of the accidents, the phenomenology leading to the cable cooling deterioration is first discussed and the qualitative or quantitative evolution of the thermal-hydraulic transient in an unprotected situation is presented. The diagnostics most suited to detect the accident are then identified. This work, the first of its kind, suggests instrumentation to detect the occurrence of the different scenarios to protect the investment associated with the future hybrid power cables. The analysis shows that the loss of flow is easy to detect and the operators can reduce the cable power without any loss of LH2. The LOCA is more severe, as it requires fast detection that could be achieved using optic fibers, whereas overpressures and opening of relief valves are difficult to avoid especially for large ruptures. The LOVA is the most severe among the accidents investigated here, as it is difficult to detect without optic fibers
SPIFF: Selective Preservation of Image Fidelity for Bandwidth-constrained Heterogeneous Networks
No full textTransmitting rich visual data in resource- constrained environments like Non-Terrestrial Networks (NTNs) poses a significant challenge. While current Semantic Communication (SC) approaches reduce bandwidth consumption, they often lack flexibility and/or compromise the perceptual fidelity of critical details. This paper first analyzes the fundamental trade-offs that exist between perceptual fidelity, semantic fidelity, and bandwidth utilization. It then introduces SPIFF, an SC-Generative AI framework that, by supporting selective fidelity, enables fine-grained control over the above trade-offs while meeting delay requirements. SPIFF features a lightweight, semantic-aware encoder performing semantic segmentation and applying a novel patch preservation strategy that retains perceptually significant regions while adapting lower-relevance areas compression to bandwidth availability. SPIFF also offloads high-complexity reconstruction tasks to a Generative AI-enabled decoder at the receiver, thus addressing asymmetric computation requirements. To support adaptation under dynamic conditions, while meeting system and application constraints, we equip SPIFF with a learning-based decision engine that is able to cope with the system non-linearities and effectively tune SPIFF’s configuration online. We evaluate SPIFF by implementing a full encoder-decoder pipeline. Results show that SPIFF fulfills perceptual reconstruction quality in scenarios where SC fails, and improves over state-of-the-art solutions both bandwidth savings (by up to 21%) and perceptual fidelity (by up to 13%)
A Formal Model of Security Controls’ Capabilities and Its Applications to Policy Refinement and Incident Management
Full text linkEnforcing security requirements in networked information systems relies on technical security controls to mitigate the risks posed by increasingly sophisticated threats. Configuring these controls is challenging; even nowadays, administrators must perform it without adequate tool support. Hence, this process is plagued by errors that result in insecure postures, security incidents, and a lack of promptness in addressing threats. This paper presents the Security Capability Model (SCM), a formal model that abstracts the features that security controls offer for enforcing security policies, which includes an Information Model that depicts the basic concepts related to rules (i.e., conditions, actions, events) and policies (i.e., conditions’ evaluation, resolution strategies, default actions), and a Data Model that covers the capabilities needed to describe different types of filtering and channel protection controls. Following state-of-the-art design patterns, the model enables the generation of abstract versions of the security controls’ languages and a model-driven approach for translating abstract policies into device-specific configuration settings. By validating its effectiveness in real-world scenarios, we demonstrate that SCM enables the automation of various and complex security tasks, including accurate and granular security control comparison, policy refinement, and incident response. Lastly, we present opportunities for extensions and integration with other frameworks and models
Estimation of Urban Fractal Dimension Using a Convolutional Neural Network
No full textA deep learning approach to estimate the urban fractal dimension Df using high-resolution WorldView-2 (WV-2) imagery is proposed. The networks are trained on fractal Brownian Motion (FBM) surfaces generated through computational models to simulate natural textures with varying degrees of roughness. Each surface is characterized by the Hurst exponent (H) related to the fractal dimension as Df=2-H. For the classification task, the images are divided into nine distinct classes, each corresponding to a defined range of H values. The regression task uses the same dataset for training and predicts the value of the fractal dimension. The CNNs learn to detect spatial patterns that reflect differences in the fractal geometry of the surfaces. Trained on a large dataset of synthetic images, the models can accurately estimate the urban fractal dimension Df from unseen satellite data. Our CNN-based predictions are compared against well-established methods for estimating the fractal dimension using real-world satellite data
The Multidimensionality of Just Green Transitions
No full textThis chapter discusses the multidimensional framework of Just Green Transitions that is integral to achieving climate neutrality, while ensuring fairness and inclusivity. This framework encompasses social, economic, spatial, environmental, technical, and governance dimensions, emphasising the need for a holistic approach to transitioning to sustainable, low-carbon economies. The social aspect prioritises justice and equity, addressing vulnerabilities and empowering marginalised groups through distributional, recognitional, and procedural justice. Economically, JGTs provide opportunities for regional economic restructuring and diversification, including the development of circular economies and green innovation ecosystems. This is despite the substantial costs associated with the technical aspects of JGTs, particularly helping high-polluting industries and businesses to shift to carbon-neutral internal technological practices and processes. Spatially, tailored policies mitigate territorial disparities and integrate socio-cultural identities into transition strategies. The environmental dimension focuses on achieving long-term sustainability through carbon neutrality, biodiversity preservation, and resilience-building. Effective governance structures have a key role in delivering this multidimensional JGT framework, with public institutions playing a pivotal role in fostering multi-level and multi-actor collaboration, enhancing citizen engagement, and promoting policy coherence
Undercomplete Autoencoder per Analisi e Clustering di Spettri Raman
Full text linkL’analisi spettroscopica Raman di matrici carboniose complesse, come il biochar, è spesso limitata da un basso rapporto segnale/rumore e da intense interferenze di fluorescenza. In questo lavoro, presentiamo un approccio basato su un Undercomplete Autoencoder (AE) per l’estrazione non supervisionata di "pseudo-spettri". A differenza delle tecniche di filtraggio tradizionali, il modello utilizza un collo di bottiglia (bottleneck) a 32 dimensioni per operare una filtrazione intelligente, eliminando il rumore stocastico e ricostruendo la morfologia archetipica del segnale. Dimostriamo come lo pseudo-spettro \hat{x}, generato dalla decodifica dello spazio latente z, agisca come centroide visibile di cluster di materiali simili. L'errore di ricostruzione (MSE) viene inoltre proposto come sensore di anomalia per l'identificazione di contaminanti o variazioni strutturali non presenti nel dataset di addestramento
Role of surface oxidation in enhancing heat transfer across graphene–water interface via Thermal Boundary Resistance modulation
Full text linkFunctionalization or surface oxidation is a fundamental requirement for carbon-based nanoparticles to prevent self-aggregation and thus be homogeneously dispersed in a fluid. However, the presence of functional or oxidation groups dramatically affects the thermal boundary resistance (TBR) and thus the overall thermal properties of the resulting colloidal suspension. In this work, we systematically investigate through molecular dynamics simulations the effect of oxidation degree on the TBR at the graphene-water interface. We find a linear correlation between the oxidation degree and the thermal boundary conductance (reciprocal of TBR) at low-to-moderate degrees, which can be interpreted through a parallel thermal resistance model, considering the contributions of pristine graphene and hydroxyl (-OH) groups, confirming our previous experimental findings. Results are interpreted in the light of wettability, roughness and phonon density of states, which highlight the higher affinity between water and graphene as the oxidation degree rises. More generally, beyond the specific case study discussed in this work, this systematic approach can be applied to other solid-liquid interfaces to further explore the general correlation between TBR and surface oxidation degree
Efficient Tensor Compression and Reconstruction in Split DNNs for Edge-Based Object Detection
Full text linkComputer Vision (CV) tasks are among the most pivotal, yet challenging, operations for Uncrewed Aerial Vehicles (UAVs), especially in mission-critical applications. They require processing complex image data through Deep Neural Networks (DNNs), which demand computational resources far beyond UAVs’ capacity. To address this limitation, Split DNNs offer a promising solution by partitioning the model into: (i) a lightweight Head, deployed on the UAV for rapid, albeit less precise, initial image representations, and (ii) a more complex Tail, executed at the network edge for refined, higher-accuracy results. However, this solution necessitates transmitting large tensor data from the UAV to the edge server, leading to significant bandwidth consumption. We tackle this challenge by introducing a goal-oriented framework named Compressed Tensor-based DNN Split (CoTeD). Our framework integrates an application- and system-aware optimization model that orches- trates computing and transmission resources in real time. At the UAV, CoTeD dynamically selects relevant tensor information and optimally trades-off between DNN detection quality and bandwidth consumption, guided by application requirements and system operational conditions. At the edge server, CoTeD reconstructs the tensor, enabling efficient inference by the Tail model. This approach effectively balances bandwidth usage with quality of the CV task output. Experimental results, obtained through our hardware-software testbed and using datasets with different sizes and characteristics, show that CoTeD can reduce data transmission over the radio link by up to 90% without noticeable loss in object detection quality and inference latency by up to 70% compared to local DNN deployment onboard the UAV. Also, CoTeD yields an inference request success rate of at least 90%, with an increase of 20%-80% compared to direct DNN splitting, static JPEG compression, and DNN model quantization
From Data to Design. AI, Blockchain, and New Frontiers in Digital Archives
Full text linkThe digital transformation is reshaping the role of corporate archives, expanding the possibilities for preserving, managing, and enhancing companies’ historical heritage. This study stems from the following research question: how are emerging technologies such as Artificial Intelligence (AI) and blockchain altering the practices, meanings, and potential of corporate digital archives for design and communication?Through an interdisciplinary analysis and three significant case studies (Fondazione Fiera Milano, Museimpresa/Google Arts & Culture, and the Riva Historical Archive), the paper examines how AI and blockchain introduce new tools for automatic indexing, multimodal search, metadata generation, document certification, and authenticity safeguarding. The findings show that digitalization not only improves the effi-ciency of archival processes but also expands the ways mate-rials can be accessed and interpreted, transforming archives into true laboratories of innovation for design, brand storytell-ing, and heritage communication strategies. The contribution of this research lies in proposing a theoretical-applied frame-work that demonstrates how the integration of digital archives, AI, and blockchain opens new perspectives for the study of design culture, for the enhancement of Made in Italy excel-lence, and for the development of more transparent, accessi-ble, and future-oriented archival ecosystems