Politecnio die Bari - Catalogo di prodotti della Ricerca
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Environmental Impacts and Housing Deprivation: A Study of the Effects of Industrial Polluting Sites in the Italian Context
No full textThe disadvantaged populations often bear a disproportionate burden of environmental pollution, especially when air quality exceeds regulatory standards. This link is particularly pronounced in areas with housing deprivation. As deprivation levels rise, the number of people exposed to excessive pollution increases. The industrial sector is one of the major contributors to air pollution and greenhouse gas emissions. Given the close relationship between industrial sites and socioeconomic, environmental, and health factors in cities, analyzing the real estate market can provide valuable insights for guiding sustainable development strategies. The present research aims to determine if a correlation exists between residential property prices and polluting sites, by considering factors that could represent housing deprivation. By examining the type of relationship and changes in property prices, the study intends to inform decision-making on housing deprivation policies. Through a genetic algorithm (Multi-Case Strategy of Evolutionary Polynomial Regression) applied to a sample of a limited available sample of polluting sites in Italy, first results have been obtained, that align with empirical observations and local user expectations. The results highlight the importance of implementing effective housing deprivation policies that address the environmental impacts of polluting industrial sites on real estate market dynamics. The innovative contribution of this work lies in identifying critical ‘pollution-poverty’ areas that require urgent remedial action
The Implementation of Multicriteria Analysis for Heritage Assets Enhancement: An Application for the HBU Identification of a Historical Building in an Italian Municipality
No full textThe enhancement of properties with historical and cultural value should be consistent with the need for their preservation, the interests of stakeholders and the urge of obtaining public and/or private funding. These aspects, defined through both qualitative and quantitative criteria, can be simultaneously considered by implementing Multi-Criteria Decision Analysis (MCDA). In the present work, through the application to a case study concerning a building located in the ancient part of a municipality in Southern Italy, the potential of MCDA is tested as a support tool for evaluations related to the functional reconversion of heritage assets aimed at identifying the Highest and Best Use
Stereotomic Fiber Architecture: Applicazione delle nuove tecniche costruttive in Fibra per gli spazi complessi stereotomici
No full textThe aim of this research is to define the new frontiers of fibre architecture starting from the steroethomic field and converting the heaviness of stone into the lightness of fibre filament.
This will be achieved in two ways: a theoretical-critical and a practical-experimental. The thesis, therefore, provides a wide coverage of stereotomic issues related to the development of a new architecture, that in fiber in various fields from design to architectural systems.
The same themes are at the heart of the conceptual and operational reflections which
have led to the definition of design and construction strategies
the development of a number of prototypes presented in the current thesis.
Those have the task of being demonstrators of research and to play the role of reference points for the next steps of research and concretion of theories tested in the field of construction and architecture. The new proposals require the use of the latest and most varied methods and tools for parametric computational design and digital and robotic manufacturing
Rethinking the design of upholstered seating from a circular and acoustics-conscious perspective
No full textThe study presents a sustainable strategy to mitigate the environmental impacts of textile industry waste by converting them into innovative raw materials for the furniture sector, preventing landfill disposal. The approach involves replacing conventional polyurethane foam in armchair padding with nonwoven made from thermo-bonded fibers derived from pre-consumer textile waste of varied compositions (i.e., cotton, wool, synthetic). As well as the environmental outcomes, the research aims to provide a seat with excellent acoustic performance to enhance indoor comfort. Comprehensive acoustic and microstructural characterizations (i.e., normal incidence sound absorption, open porosity, pore size distribution, specific surface area and airflow resistivity) were performed on both the nonwoven textile waste and polyurethane foam before assembling and testing full seat prototypes. The fibrous matrix showed lower air permeable than the foamy one, with a reduced pore volume concentrated in micropores and mesopores. However, the greater specific surface area of the nonwoven textile led to higher normal incidence absorption, with differences up to 0.2 compared to polyurethane. These results affected the acoustic performance of the textile seat which exhibited a greater sound absorption area then the polyurethane seat, with differences up to 0.5 m2 in the frequency range between 315 and 800 Hz. Further analysis combining nonwoven materials with eco-leather and fabric layers, highlighted the importance of using sound-transparent coverings to maximize acoustic performance. Life cycle assessment emphasized the sustainability of using nonwoven textile waste, showing reduced impacts on all categories. Transportation scenarios were also evaluated to minimize the environmental footprint associated with material logistics
Rationalizing Evaluation Legal Models for Affordable Home Ownership and Leasehold Interests in Italy
No full textWith reference to the issue of the evaluation of the purchase prices for leasehold properties, in Italy the transfer of properties built as part of regulated housing initiatives is restricted by a series of constraints, the removal of which entails a set of fees to be borne by the housing lessees. This study, starting from an analysis of similar tools applied in other European territorial contexts, provides a methodological approach for estimating these fees while simultaneously considering the framework established by the current regulations, the trends in the residential market, and the inflation dynamics
Comparative Study of Different Constructions of Morphological Leveling Decompositions for Spatial Multi-scale Image Analysis
No full textMathematical morphology is a powerful tool for analyzing the geometrical properties of land cover classes while mitigating spectral confusion in remote sensing data, particularly in hyperspectral images. Among various approaches for modeling spatial information, hierarchical image processing via morphological multi-scale decomposition is a prominent technique for structural image characterization. A key component of this decomposition is morphological leveling, which provides a robust framework for hierarchical image analysis. This study systematically investigates and compares different morphological leveling decomposition strategies using a hyperspectral image acquired in 2002 over the University of Pavia campus in Italy. Two experiments were conducted to assess the impact of different morphological leveling configurations on classification performance. The first experiment evaluated four morphological leveling constructions, each defined by distinct reference and marker parameters combined with three filtering techniques (Gaussian Convolution, Alternate Sequential Filter, and Averaged Alternate Sequential Filter). The second experiment assessed the effectiveness of residual leveling images derived from the results of the first experiment. Outputs from both experiments were integrated with the spectral information and used as input to a two-dimensional convolutional neural network to improve classification accuracy. The analysis of overall accuracy and agreement with ground truth data shows that incorporating morphological leveling significantly enhances classification performance. One configuration achieved nearly 99% accuracy with a very high level of agreement, highlighting the potential of multi-scale spectral–spatial feature extraction. These results offer valuable insights for future applications in remote sensing and environmental monitoring
Comparing XAI Explanations and Synthetic Data Augmentation Strategies in Neuroimaging AI
No full textBrain age, a biomarker of neurological health, is widely used in neuroimaging for early detection of neurodegenerative diseases. While deep learning models have shown promise in brain age prediction from MRI, data imbalance and model interpretability remain key challenges.
This study investigates the impact of data augmentation (DA) on both predictive accuracy and explanation stability in convolutional neural networks (CNNs) for brain age prediction. We compare three training strategies: (i) a baseline model, (ii) a model augmented with real MRI scans from OASIS-3, and (iii) a model trained with synthetic data generated by a diffusion model. Model performance is evaluated using mean absolute error (MAE), while interpretability is assessed through Explainable AI (XAI) methods, including DeepSHAP, Grad-CAM, and Occlusion.
Our findings indicate that synthetic augmentation improves predictive accuracy, particularly for underrepresented age groups (individuals aged 40–80 years), while real-data augmentation provides more stable feature attributions. However, differences in XAI methods suggest that explanation reliability varies across training strategies.
These results highlight the trade-offs between accuracy and interpretability in AI-driven neuroimaging, emphasizing the need for balanced augmentation strategies to develop clinically trustworthy models
Ppb-level HF sensor in GeF4 gas matrices with a 76 m TDLAS cell
No full textThe detection of trace hydrogen fluoride (HF) impurities in germanium tetrafluoride (GeF4) is to maintaining the purity and safety standards required in semiconductor manufacturing and nuclear industry. Currently, few studies have measured HF concentrations, and none have addressed its detection as an impurity in high-purity GeF4. We present here a highly sensitive gas sensor based on tunable diode laser absorption spectroscopy (TDLAS) for HF monitoring in GeF4 matrices. The system employs a distributed feedback diode laser operating at 1.278 μm, targeting the HF first-overtone absorption line, and an optimized White cell having a 345 mm length cavity and providing an effective optical path length of 76 m. Wavelength modulation spectroscopy was employed and the sensor response was validated using HF samples at varying concentrations, achieving excellent linearity with R2 > 0.999. The minimum detection limit was determined to be 48 ppb (3σ), highlighting superior sensitivity among near-infrared HF detection approaches. These results highlight the system’s strong potential for high-purity gas analysis and real-time process control in semiconductor environments
The Urban One Health Approach for the Planning of Effective Interventions on Territory: A Systematic Analysis of the Scientific Literature
No full textThe aims of this research is to conduct an analysis of the existing scientific literature on the concept of Urban One Health that is an innovative and integrated approach that connects human, animal and environmental health within urban planning and development processes. This multidisciplinary paradigm is a concrete and necessary response to the challenges posed by contemporary urbanisation, environmental sustainability and the well-being of populations. The Urban One Health approach promotes an holistic vision that recognises the interconnection between urban ecosystems and public health. The main purpose of this study is to expand the theoretical and practical knowledge and understanding of the Urban One Health concept, highlighting its main implications for contemporary urban planning. For this scope, the analysis focuses on identifying interdisciplinary tools, strategies and methodologies that can support a more resilient, inclusive and conscious urban transformation in line with the principles of sustainability. Through the examination of the scientific literature, it was possible to identify three criteria that characterise the Urban One Health approach: firstly, the definition of specific research objectives focused on the integration of health and urban planning; secondly, the analysis of the context as a field of interaction between biological, social and environmental factors; and finally, the use of interdisciplinary research methodologies, capable of understanding the complexity of the issue
Quantitative Analysis of Urban Heat Island and ISA Density Effects on Land Surface Temperature: A Remote Sensing Study Using Local Morphological Density Analysis and Hybrid Optimization Model
Full text linkUrban Heat Island (UHI) phenomenon, driven by rapid urbanization and increased Impervious Surface Areas (ISA) density, is a significant challenge to sustainable urban development and climate resilience. A robust understanding of the relationship between ISA density and Land Surface Temperature (LST) is critical for mitigating UHI effects. A key component in UHI analysis is the accurate extraction of UHI zones, which depends on the chosen threshold values. Traditional approaches often rely on static methods to identify UHI thresholds and simple regression models, which may not fully capture the complex, nonlinear interactions between ISA density and LST. Therefore, this study introduces a hybrid optimization model that integrates Genetic Algorithm-Simulated Annealing with Generalized Additive Models to assess the impact of ISA density on LST and thus define UHI thresholds. The model is applied to Algiers (2012-2021) and Taranto (2000-2014) cities using ASTER data. A local morphological density analysis was employed to quantify ISA and green space densities. Findings reveal a distinct positive nonlinear relationship between the ISA density and LST, identifying critical thresholds beyond which temperature escalates sharply. UHI zones exhibited significantly higher mean LST values, reaching 42.11°C in Taranto and 38.38°C in Algiers. Moreover, green space density was found to mitigate LST, reinforcing the pivotal role of vegetation in urban climate regulation. The findings of this adaptive modeling framework highlight the necessity of sustainable urban planning and the potential of data-driven approaches for refining climate adaptation strategies