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AI-Driven Morphological Classification of the Italian School Building Stock: Towards a Deep Energy Renovation Roadmap
No full textThe Italian school building stock is largely outdated, with structural and technological inadequacies leading to low comfort and high energy consumption. Addressing this challenge requires large-scale renovation supported by an integrated, data-driven approach. This study conducted a nationwide analysis of over 40,000 school buildings. After incomplete or inconsistent records were filtered out, a refined subset was selected. Building forms were reconstructed by cross-referencing GIS data with multiple open data sources. Using supervised machine learning, the research identifies and classifies recurring morphological patterns to define a set of 3D school building archetypes. These archetypes are enriched with spatial configurations and physical characteristics aligned with national educational standards. The result is a macrotypological classification based on form, conceived as part of an operational tool to support policymakers, designers, and public administrations in selecting effective retrofit strategies. This contributes to the creation of large-scale national renovation strategies, as well as Renovation Roadmaps and Digital Building Logbooks in line with the Energy Performance of Buildings Directive (EPBD IV), specifically tailored to the Italian context. The novelty of this work lies in its unprecedented scale and the use of AI to enable fast, replicable assessments of retrofit potential, thereby supporting informed decisions in energy-efficient renovation planning
Yield and Quality of New Strawberry Advanced Breeding Selections and Commercial Cultivars, Grown Under Warm-Temperate Climatic Conditions
No full textClimate change challenges existing strawberry cultivars, requiring adaptation and the introduction of new varieties better suited to new climate conditions. This research evaluated the response over time of new advanced breeding selections (AN15,07,53, AN16,53,54 and AN12,44,60) derived from intraspecific crosses, proposed for the Mediterranean environment and organic greenhouse cultivation, by comparing plant yield and fruit quality at each harvest stage against five commercial strawberry cultivars (Dina, Arwen, Melissa, Marimbella, and Elide). Results showed that Dina, AN15,07,53, and AN16,53,54 had higher levels of soluble sugars, organic acids, and anthocyanins than the other cultivars evaluated. In addition, AN16,53,54 showed anticipated peak production and plant yield similar to that of commercial cultivars. Elide showed on average the highest total yield (632 g plant−1), while Dina, AN15,07,53 and AN12,44,60 showed lower yields. The lowest and highest percentages of discarded fruits were recorded in Arwen (10%) and AN 12,44,60 (27.7%), respectively. Two genotypes, AN16,53,54 and AN15,07,53 are susceptible to further evaluation; AN16,53,54 showed appropriate features for organic systems management. An important feature related to the environmental conditions of the Mediterranean area is the precocity of production, combined with good quality properties. The genotype AN15,07,53 derived from two parents with high and low chilling requirements, would need to be evaluated for its performance under very different climatic conditions
Bridging Supervised and Unsupervised Learning for Classification of Breast Tissue
No full textUnderstanding the underlying structure of medical data is essential for developing robust and reliable classification models. Supervised learning, which relies on predefined classes, may fail to capture the intrinsic patterns within the data, potentially leading to suboptimal outcomes. This study investigates the application of unsupervised clustering to analyze and validate the structure of a public medical dataset, the Breast Tissue Dataset, with varying class configurations (6 vs. 4 classes). Clustering methods, such as KMeans and Affinity Propagation models, were applied alongside classification models, including Random Forest and XGBoost. Key performance metrics, such as accuracy and confusion matrices, were employed to evaluate classification performance, while clustering results were assessed using the Adjusted Rand Index (ARI) and the Hopkins Test, which evaluates the clustering tendency of datasets. Additionally, the robustness of clustering to measurement uncertainty was examined by introducing synthetic noise (5 % and 10 % perturbations) into the input data, simulating real-world variability. The study further explores how clustering can reveal insights into class labels and assess the separability of different groups. Results demonstrate the utility of combining unsupervised clustering with supervised methods to enhance data exploration, assess the reliability of predefined labels, and improve classification in medical applications, even in the presence of measurement uncertainty
Evaluating the role of lung ultrasound in the diagnosis of rheumatoid arthritis-interstitial lung disease
No full textIntroduction: The involvement of the pulmonary parenchyma in rheumatoid arthritis (RA), characterized by the presence of interstitial lung disease (RA-ILD), represents one of the most common and potentially severe extra-articular manifestations of the disease. High-resolution computed tomography (HRCT) of the chest is considered the gold standard diagnostic technique; however, its reliance on ionizing radiation and the limited availability of imaging equipment make it challenging to perform repeatedly. Over the past decade, lung ultrasound (LUS) has emerged as a noninvasive and easily repeatable technique for detecting the presence of RA-ILD. Areas covered: This narrative review summarizes the currently available evidence on the use of LUS in RA-ILD. It begins by defining the elementary lesions indicative of pulmonary involvement (B-lines and pleural irregularities) and provides an overview of LUS application in other connective tissue disease-associated interstitial lung diseases (CTD-ILDs). Expert opinion: Current evidence suggests a promising role for LUS in the screening of RA-ILD, primarily based on the quantification of B-lines. Initially, a threshold of 10 B-lines was proposed, which has recently been lowered to 5, demonstrating good sensitivity and specificity in detecting RA-ILD. Future directions should focus on the role of pleural irregularities and the further standardization of the technique
Supporting “Build Back Better” in historical towns: a novel methodology to include users’ exposure and vulnerability in strategic function relocation assessment
No full textApplying ‘Build Back Better’ (BBB) principles to Historical Urban Built Environments (HUBEs) means balancing sustainable structural and non-structural strategies with revitalization and preservation tasks, by addressing multiple risk factors. Among them, user exposure (“how many people?”) and vulnerability (“of which typology?”) can describe how the HUBE and its composing parts can be attractive depending on their functions, also impacting potential damage and losses. Relocating strategic functions can directly impact these factors, being strictly linked with urban policies. Existing approaches try to quantify user factors over space and time, but operational implications for decision-makers seem to be still limited. This work aims to develop a methodological framework to evaluate the multi-scale impact of function relocation in HUBEs assessing users' vulnerability and exposure at the: (1) macroscale, to evaluate if relocation can benefit the whole HUBE safety; (2) mesoscale (open space-related), to identify critical “hot-spots” in the HUBE. The framework is showcased on a significant earthquake-prone Italian HUBE. In particular, validated methodologies exploiting geospatial tools are used to generate typical use scenarios (i.e. daytime, night-time, holidays), aggregating micro-scale inputs on indoor and outdoor functions at meso/macroscales. User factors metrics are derived to compare current and relocation scenarios on selected buildings. Results demonstrate the framework capabilities in quantifying relocation impacts at the considered scales, thus providing valuable support to urban planning practices. Its implementation in decision-support systems would enable dynamic monitoring of urban development policies, prioritizing risk-reduction over space, and focusing interventions on physical vulnerability where user factors impact increases
Clinical Characteristics of Anti-Synthetase Syndrome: Analysis From the Classification Criteria for Anti-Synthetase Syndrome Project
No full textObjective: Anti-synthetase syndrome (ASSD) is a rare systemic autoimmune rheumatic disease (SARD) with significant heterogeneity and no shared classification criteria. We aimed to identify clinical and serological features associated with ASSD that may be suitable for inclusion in the data-driven classification criteria for ASSD. Methods: We used a large, international, multicenter “Classification Criteria for Anti-synthetase Syndrome” (CLASS) project database, which includes both patients with ASSD and controls with mimicking conditions, namely, SARDs and/or interstitial lung disease (ILD). The local diagnoses of ASSD and controls were confirmed by project team members. We employed univariable logistic regression and multivariable Ridge regression to evaluate clinical and serological features associated with an ASSD diagnosis in a randomly selected subset of the cohort. Results: Our analysis included 948 patients with ASSD and 1,077 controls. Joint, muscle, lung, skin, and cardiac involvement were more prevalent in patients with ASSD than in controls. Specific variables associated with ASSD included arthritis, diffuse myalgia, muscle weakness, muscle enzyme elevation, ILD, mechanic's hands, secondary pulmonary hypertension due to ILD, Raynaud phenomenon, and unexplained fever. In terms of serological variables, Jo-1 and non–Jo-1 anti-synthetase autoantibodies, antinuclear antibodies with cytoplasmic pattern, and anti-Ro52 autoantibodies were associated with ASSD. In contrast, isolated arthralgia, dysphagia, electromyography/magnetic resonance imaging/muscle biopsy findings suggestive of myopathy, inflammatory rashes, myocarditis, and pulmonary arterial hypertension did not differentiate between patients with ASSD and controls or were inversely associated with ASSD. Conclusion: We identified key clinical and serological variables associated with ASSD, which will help clinicians and offer insights into the development of data-driven classification criteria for ASSD. (Figure presented.)
Towards blue diving: analysis and solutions to prevent scuba diving impact in the Mediterranean sea
Get PDFRecreational scuba diving is a growing tourism sector in the Mediterranean Sea that can be integrated with citizen science initiatives, contributing to the collection of extensive data and increasing public awareness of marine coastal ecosystems. However, over time, scuba diving can pose a threat to the integrity of marine communities, mainly due to mechanical damage that may compromise both the functional role and the aesthetic value of marine organisms. This work aims to provide recommendations for adaptive co-management of scuba diving, with a focus on preventing damage and fostering more responsible behaviours in the Mediterranean marine environment. Three tools were used to lay a solid foundation for the designed strategy: 1. an in-depth review of the literature to understand the factors that contribute to the irresponsible attitude of divers and the consequent environmental damage; the successful practices that have been adopted/proposed and the potential factors that contribute to failures in achieving environmentally responsible behaviour, 2. a spatial index approach to detect the areas where the probability of diving impact is highest, and 3. a behavioural change framework to promote greater environmental awareness and stewardship and to induce a mindset shift in scuba divers. The proposed management flow is built around four key elements: 1. the establishment of a network of dive centres and other stakeholders directly or indirectly connected to scuba diving; 2. the development of a data hub to ensure the collection, storage, and exchange of data, knowledge, and outcomes among all parties; 3. the implementation of a robust communication plan to facilitate multi-directional feedback within the stakeholder network; and 4. the application of a spatial approach to map the distribution and intensity of pressures across the territory. Among priority habitats, marine caves require particular attention from policymakers due to their ecological vulnerability and their potential role as refugia for key habitat-forming species, at least at their entrance. Successful management cases at small scale can then be shared with other systems to foster mutual learning, crowdsource solutions to common challenges, and progressively expand the approach to broader systems through a stepped, cascade model flow of data and experiences
Designing Renewable Energy Communities for Incentive Optimisation: Hydropower vs. Photovoltaic Systems
Get PDFThe increasing adoption of distributed renewable energy sources introduces new challenges for local energy management. Renewable Energy Communities (RECs) offer a promising framework by promoting collective energy self-consumption and citi- zen participation. This study presents a methodology for optimally sizing a REC based on consumers grouped into three typical energy consumption profiles. The objective is to maximise the economic revenue from feed-in tariffs associated with shared renewable energy, using hybrid systems with a total installed capacity of 100 kW in central Italy. Five renewable energy generation scenarios are analysed: i) HYDROpower- (HYDRO) only, ii) Photovoltaic- (PV) only, and iii) three PV-HYDRO hybrid combinations. Each scenario is assessed under two ownership models: i) third party-owned generation and ii) REC-owned generation. Results show that the HYDRO-only configuration delivers the highest economic benefits, namely 71, 450 in total revenue, which €33, 538 comes from in feed-in tariffs, and over 127 tonnes of CO2 avoided annually. Consumer sav- ings peak at €184 per household per year under third party ownership, which, while less financially beneficial to the REC itself, can support broader community initiatives. The analysis confirms that integrating HYDRO into RECs enhances energy efficiency, economic performance, environmental impact, and social value
Triphasic Inter-Dimensional WS2/Magnetic Lithium Iron Oxide Nanocomposite for Electromagnetic Interference Shielding
No full textThe widespread use of wireless devices and telecommunication networks has given rise to electromagnetic interference (EMI) pollution that can cause data corruption, critical device failure, and detrimental effects on wildlife and human health. Developing EMI shielding materials can block these harmful electromagnetic waves. This study explores inter-dimensional composite systems composed of dielectric and magnetic phases (WS2/biphasic lithium iron oxide) for EMI shielding applications. WS2 is a 2D material with unique dielectric properties and flake-like morphology that enhances surface effects. In contrast, biphasic magnetic lithium iron oxide nanocomposites have grain-like morphology with greater magnetic losses. The formation of interfaces between these two phases with different morphologies and dimensionalities leads to enhanced interfacial polarization loss. This work demonstrates that by carefully controlling the weight percentage of the two phases, and thereby the interfaces, the EMI shielding properties can be significantly enhanced. An optimum phase composition is determined that exhibits maximum shielding efficiency (SET ≈55.6 dB at 12.4 GHz) with high absorption shielding (SEA ≈48.8 dB at 12.4 GHz), and an absorption coefficient more than 100% higher than either end member. The studied nanocomposites, with their tunable absorption and reflection capabilities, are suitable for a wide range of EMI shielding applications
Analisi strutturale e strategie di mitigazione del rischio sismico per il patrimonio architettonico storico: il caso della chiesa di Santa Maria di Piazza (Ostra Vetere, Italia)
No full textThis study presents the preliminary results of an ongoing seismic vulnerability assessment of the Church of Santa Maria di Piazza in Ostra Vetere (Marche Region, Italy), a historic building currently closed due to damage sustained during the 2016 Central Italy earthquake sequence. The research integrates architectural documentation, geometric survey, material analysis, and structural modeling to evaluate both local and global seismic behavior. A combined laser scanner and photogrammetric survey enabled the creation of a high-fidelity 3D geometric model, which informed a solid finite element model used for dynamic and nonlinear static (pushover) analyses. Additionally, kinematic limit analysis was employed to identify potential local collapse mechanisms and assess associated risk indices. Results highlight critical structural vulnerabilities, particularly in older masonry portions and the concrete dome support, confirming the need for targeted retrofit strategies. The findings provide a solid basis for the development of tailored intervention solutions in line with conservation principles, aiming to restore structural safety and public accessibility. Future developments will include in-situ diagnostic campaigns to refine material parameters and improve numerical model accurac