Politecnio die Bari - Catalogo di prodotti della Ricerca
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Measurement of the inclusive cross section in final states with at least one lepton and additional jets with 302 pb−1 of pp collisions at = 5.02 TeV
No full textAdvancing the building materials reclamation: An evaluation method for the disassembly potential of glass facade systems
No full textThe construction sector is responsible for producing and landfilling high volumes of construction and demolition waste. Design for disassembly (DfD) is deemed a promising strategy to establish a circular building process, enabling the easy separation of material and components at the end of life. Despite efforts to promote DfD, only a few applications of this strategy currently exists, and most buildings are not designed to be disassembled at the end of life. These non-DfD compliant building systems represent a possible source of materials and components, according to the concept of building as a materials bank. The ease of deconstruction of these systems should be evaluated to understand their harvesting capacity and circular potentiality. The purpose of this research is to evaluate the DfD level of the widely distributed facade systems in the high-rise building. Specifically, the unitized curtain wall, the stick facades, and the structural glass facades. In this research, a method to evaluate the DfD level of these facade systems has been developed, including various parameters grouped in three sets, such as the technological, economic and environmental ones. The application and validation of this method allowed to identify, among those investigated, the facade technology that is easier to disassemble. Further improvements to the evaluation method would involve fine-tuning the identified parameters to enhance accuracy
Measurements of the Higgs boson production cross section in the four-lepton final state in proton-proton collisions at = 13.6 TeV
No full textGenerating an Iris-based Seed for Key-pairs in a Blockchain Platform
No full textis a critical concern in cybersecurity, and the challenge of combining security with ease of use remains a pressing Authentication is a critical concern in cybersecurity, and the challenge of combining security with ease of use remains a pressing issue. Biometric authentication methods, particularly iris recognition, leverage unique physical characteristics to confirm identity, presenting an effective solution to enhance security over traditional password systems. However, the management of sensitive biometric data requires privacy measures to protect user information from potential misuse. This work proposes a novel Blockchain-based system that utilizes biometric data, specifically iris scans, as a seed for generating cryptographic key-pairs. By employing feature extraction and hashing techniques, the system ensures that sensitive biometric information is neither stored centrally nor accessible in its original form, thereby preserving user anonymity and privacy. The architecture is designed to distribute fragmented biometric data across multiple nodes in the Blockchain, enhancing scalability and security. The system’s functionality is validated through extensive testing scenarios that demonstrate its reliability and robustness in various operational conditions. This research highlights the potential of combining Blockchain technology with biometric authentication to create secure and privacy-preserving identity management solutions, paving the way for applications in sectors such as finance, healthcare, and secure voting systems
Comparing neural network architectures for simulating pollutant loads and first flush events in urban watersheds: Balancing specialization and generalization
No full textThis study investigates the effectiveness of artificial neural networks (ANNs) models in predicting urban water quality, specifically focusing on first flush (FF) event classification and pollutant event mean load (EML) predictions for total suspended solids (TSS), total nitrogen (TN), and total phosphorus (TP). The importance of accurate water quality models in urban areas is underscored by their role in addressing pollution challenges, ensuring public health, and maintaining ecosystem integrity. This research compares single-output, double-output, and multi-output feedforward neural network models to assess their performance in varying levels of specialization and generalization. The models were trained and evaluated using a comprehensive dataset, that consists of 577 data points, including observed events, simulated values, and generated data. Their performance achieved average F1 scores of 0.70 and accuracy of 0.77 for TSS, TN, and TP classification tasks. For EML predictions, Nash–Sutcliffe efficiency (NSE) values averaged 0.85 for TSS, 0.77 for TN, and 0.83 for TP, indicating strong predictive capabilities. Results reveal that while single-output models excel in precision for specific tasks, multi-output models offer improved efficiency and adaptability, completing training in about 6.75% of the total time required for the single-output models. Multi-output models generalize across multiple pollutants, making them more suitable for integrated urban water quality management, though with a slight reduction in precision. These findings highlight the potential of ANN-based machine learning approaches to enhance urban water quality management, supporting regulatory compliance and environmental sustainability
Experimental, statistical and numerical study of the single laser spot condition and influence parameters of a Neodymium-Doped Yttrium Lithium Fluoride laser
No full textThis study investigates the influence of key laser processing parameters—namely laser power and pass number—on AISI 301LN stainless steel, utilizing a Neodymium-Doped Yttrium Lithium Fluoride (Nd) laser. Employing a Design of Experiments (DOE) approach coupled with Finite Element Analysis (FEA), the research examines single-spot laser effects on surface morphology, with specific focus on spot width, depth, and roughness. Through systematic experimentation, ANOVA, and response surface methodology, significant findings reveal that pass number is a dominant factor affecting spot width and depth, while laser power subtly impacts roughness. The study provides regression models for predictive analysis, enabling parameter optimization to achieve precise microstructural changes. Complementary simulations further illustrate thermal distribution, ablation effects, and fluid dynamics in the laser-affected zone. Results support tailored laser parameters selection to improve surface integrity and performance, positioning this approach as valuable for precision applications in advanced materials processing
Physical and Mechanical Properties of Cement Mortars with Recycled Polyethylene Terephthalate: Influence of Grain Size and Composition
No full textPolyethylene terephthalate (PET) with different grain size after grinding (fine and coarse) was recycled and used as aggregate for non-conventional lightweight cement mortars. The physical and mechanical characteristics were compared to conventional sand-based composites. The workability in the fresh state was evaluated. Accordingly, the composites showed decreases in fluidity with increases in PET percentage weight. Higher thermal insulation and lower mechanical strengths were observed with the increase in plastic dosage due to a density decrease and porosity increase in the composites. Finer grain size PET samples were more resistant (~12–24 MPa) than the coarse-grain samples (~3–23 MPa) due to the higher density and specific surface area of the aggregate. Conversely, higher thermal insulation was obtained with coarse PET addition (~0.6–0.2 W/mK vs. ~0.7–0.35 W/mK). A ductile behavior with discrete cracks after failure was observed after plastic addition to the mixture. Low wettability was observed in PET samples which, although more porous than the sand specimens, showed a hydrophobic behavior which contributed to water repellency. The reported physical, mechanical, thermal, wettability and microstructural features suggest the potential of these composites for both inside and outside applications of non-structural objects
Biodiversity-Climate-Society Nexus assessment through Participatory System Dynamics Model. The case study of Amazon forest-based value chain
No full textThe Biodiversity-Climate-Society (BCS) Nexus arises from the awareness of defining the integration of social, ecological and climate dimensions to address complex global challenges. The complexity related to feedback loops and combined interactions at spatial, temporal and organisational scales highlights the need for innovative and transdisciplinary analytical tools that consider local contexts, multi-scale interactions and social and political dimensions. To this aim, this study offers a methodological framework based on the Participatory System Dynamics Model (PSDM) to analyse and map the complex interaction of Nexus. The model-building process also integrates local knowledge, Ecosystem Services (ES), and value chain impacts. Using this method in Rondônia's forest-based value chain, the study shows how forest cover changes and unstainable activities threaten vital ecosystem services, especially for Indigenous communities. Through a participatory approach including local communities and stakeholders, the study generated shared knowledge and promoted dialogue to identify the main challenges toward transformative change
L'aggiornamento liturgico dell'aula ecclesiastica nella Fabbrica religiosa monumentale storica. Alcune riflessioni applicative sul caso di studio della Cattedrale di Bitonto
No full textIl processo di ricerca si occupa dell'analisi dell'allestimento liturgico a seguito tra il Concilio Vaticano II e la Riforma Liturgica e di come questo si inserisca nei progetti di valorizzazione del patrimonio storico italiano
Search for the Higgs boson decays to a ρ0, φ, or K⁎0 meson and a photon in proton-proton collisions at s=13TeV
No full textThree rare decay processes of the Higgs boson to a ρ(770)0, φ(1020), or K⁎(892)0 meson and a photon are searched for using s=13TeV proton-proton collision data collected by the CMS experiment at the LHC. Events are selected assuming the mesons decay into a pair of charged pions, a pair of charged kaons, or a charged kaon and pion, respectively. Depending on the Higgs boson production mode, different triggering and reconstruction techniques are adopted. The analyzed data sets correspond to integrated luminosities up to 138fb−1, depending on the reconstructed final state. After combining various data sets and categories, no significant excess above the background expectations is observed. Upper limits at 95% confidence level on the Higgs boson branching fractions into ρ(770)0γ, φ(1020)γ, and K⁎(892)0γ are determined to be 3.7×10−4, 3.0×10−4, and 3.0×10−4, respectively. In case of the ρ(770)0γ and φ(1020)γ channels, these are the most stringent experimental limits to date