Politecnio die Bari - Catalogo di prodotti della Ricerca
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Percolation thresholds and connectivity in quantum networks
No full textWe study entanglement percolation in qubit-based planar quantum network models of arbitrary topology, where neighboring nodes are initially connected by pure states with quenched disorder in their entanglement. To address this, we develop a physics-informed heuristic algorithm designed to find a sequence of entanglement swapping and distillation operations to connect any pair of distant nodes. The algorithm combines locally optimal percolation strategies between nodes at a maximum distance of one swapping operation. If this fails to produce a maximally entangled state, it looks for alternative paths surrounding intermediate states within the process. We analytically find and numerically verify thresholds in quantum percolation, which depend on the initial network configuration and entanglement, and are associated with specific percolation strategies. We classify these strategies based on the connectivity, a quantity that relates the entanglement in the final state and the level of integrity of the network at the end of the process. We find distinct regimes of quantum percolation, which are clearly separated by the percolation thresholds of the employed strategies and vastly vary according to the network topology
7 incontri per Sante Simone (1823-1894). Ciclo di conferenze per il Bicentenario della nascita dell’architetto conversanese
No full textLe celebrazioni del Bicentenario della nascita dell’architetto Sante Simone hanno previsto un ciclo di confe-renze che hanno indagato la figura di Simone, architetto, restauratore e intellettuale del XIX secolo, protago-nista della cultura architettonica meridionale postunitaria. Il volume 7 Incontri per Sante Simone (1823-1894) ne raccoglie e riorganizza i contributi multidisciplinari che spaziano dai temi della storia e della teoria dell’ar-chitettura al restauro, dall’urbanistica alla rappresentazione, evidenziando il ruolo di Simone nella costruzione dell’identità artistica e patrimoniale pugliese. Emerge un ritratto complesso di tecnico e studioso, capace di coniugare rigore scientifico e sensibilità storica, tradizione e modernità. Il volume si configura come sintesi tra ricerca, divulgazione e valorizzazione del patrimonio, offrendo una lettura organica della cultura architettoni-ca ottocentesca del Mezzogiorn
Heart Disease Diagnosis Using Machine Learning
No full textThe growing impact of heart disease on global health requires to improve diagnostic techniques for more timely and accurate diagnosis. Machine Learning (ML) has demonstrated significant potential in supporting the identification and classification of heart diseases, thanks to its ability to analyze large volumes of data and learn complex patterns. The aim of this work is to explore the application of ML algorithms for heart disease diagnosis, using two datasets ‘Heart Disease Cleveland’ and ‘Heart Failure Prediction Dataset’ available on the web. Each dataset is enriched with 1000 synthetic instances, generated by a designed Generative Adversarial Network model. Different ML-based classification models including Random Forest, Logistic Regression, Stochastic Gradient Descent and XGBoost are compared based on standard performance metrics. In addition, a stacking model as an ensemble method based on the combination of the above four models has been developed and tested. The obtained results show the effectiveness of ML models in the diagnosis of cardiac diseases, with the stacking model standing out for its superior performance according to the majority of metrics
Assessment of a consistent multi-internal-temperature kinetic model for hypersonic neutral air flows using a finite volume solver
No full textA multi-internal-temperature approach for hypersonic air kinetics has been consistently derived from the state-specific vibrational kinetics. Vibrational levels have been grouped in a limited number of subsets (one to five), each one characterized by its own concentration and temperature, approximating the entire distribution as a piecewise Boltzmann. The capability of the reduced-order model in terms of accuracy and computational savings has been tested comparing the results with those obtained using the state-to-state approach. Firstly, a 0D heat bath evolution in thermochemical non-equilibrium is considered. Then, the proposed model has been implemented in a finite volume solver for the solution of the Euler equations, employing a Flux Vector Splitting scheme with MUSCL reconstruction, and used to solve an axisymmetric hypersonic flow past a sphere
Cell adhesion on substrates with variable curvature: Effects on genetic transcription processes
No full textSeveral studies suggest that changes in nuclear morphology due to forces and deformations as result of cell adhesion on biological substrates can induce molecular streaming through nuclear pore openings and alter chromatin structure. The condensed state of chromatin hinders transcription and replication, while its decompaction, induced by adhesion, plays a key role in differentiation. However, assessing nuclear stress/strain in vivo remains challenging, and the impact of substrate curvature on nuclear mechanics and chromatin structures is still unclear.
In this study, we developed an axisymmetric finite element model of a mesenchymal stem cell adhering to substrates with different curvatures to analyze nuclear stress distribution and identify locations where adhesion-induced gene expression may occur. Results reveal a nuclear stress field with principal stresses in radial and circumferential directions, leading to chromatin decondensation and nuclear pore opening. The predicted forces acting on chromatin fibers, estimated and compared with experimental data, remain slightly below 5 pN—the threshold at which internucleosomal attraction is disrupted, triggering chromatin condensation-decondensation transition—. During early spreading, nuclear forces achieved through adhesion on convex substrates approach this threshold more closely than in concave or flat cases.
These findings provide insights for tissue engineering and regenerative medicine, where early control of stem cell fate through substrate design is crucial. Understanding how mesenchymal stem cells respond to substrate curvature could lead to improved biomaterial surface topographies for guiding cell behavior. Tailoring curvature and mechanical properties may enhance early lineage commitment, optimizing regenerative strategies for tissue repair and organ regeneration
Aportaciones formales a la arquitectura defensiva en Pulia durante el periodo de la Corona de Aragón
No full textApulia, with its counties and duchies, was part of the Kingdom of Naples during the medieval period. After French domination, it was integrated into the Crown of Aragon, which meant an italianisation of the kingdom, governed by a faction of the House of Aragon-Italian, started by Alfonso V of Aragon and continued by his illegitimate son Ferrante. During this brief period, two important factors occurred: the introduction of gunpowder and the military technology of firearms, and the change in social organisation
at the turn of the Middle Ages and the Renaissance. During this period, with abundant conflicts in the area, the evolution of the castles can be observed in a few years, incorporating new construction solutions that would modify the shape of the castle, such as: barbicans, bastions, escarpments and counter-escarpments, fortified towers and ravelins. To obtain these results, a virtual model of some of the most representative castles was recreated using photogrammetric and laser scanning techniques
A scientific approach to the formulation of digital innovation strategies in high-risk and high-uncertainty environments
Get PDFThe thesis provides a scientific approach to the formulation of digital innovation strategies in high-risk and high-uncertainty environment
Lattice Models: Non-Conventional simulation methods for mechanobiology
No full textComputational methods represent a powerful tool to explore biophysical phenomena occurring at small scales and hence difficult to observe through experimental setups. In detail, they can provide a support to mechanobiology, with the aim of understanding the behavior of living cells interacting with the surrounding environment. To this end, lattice models can provide a simulation framework that is highly reliable and easy to implement, even for simulations involving large deformations and topological changes during time evolution. In this review article, elastic network models for studying biological molecules are described, several lattice spring models for investigating cell behaviors are discussed, and the adoption of lattice beam models for biomimetic structures design is presented. The lattice modelling approaches could be regarded as a valuable option to conduct in-silico experiments and consolidate the emergent mechanobiology research field
VREM-FL: mobility-aware computation-scheduling co-design for vehicular federated learning
Get PDFAssisted and autonomous driving are rapidly gaining momentum and will soon become a reality. Artificial intelligence and machine learning are regarded as key enablers thanks to the massive amount of data that smart vehicles will collect from onboard sensors. Federated learning is one of the most promising techniques for training global machine learning models while preserving data privacy of vehicles and optimizing communications resource usage. In this article, we propose vehicular radio environment map federated learning (VREM-FL), a computation-scheduling co-design for vehicular federated learning that combines mobility of vehicles with 5G radio environment maps. VREM-FL jointly optimizes learning performance of the global model and wisely allocates communication and computation resources. This is achieved by orchestrating local computations at the vehicles in conjunction with transmission of their local models in an adaptive and predictive fashion, by exploiting radio channel maps. The proposed algorithm can be tuned to trade training time for radio resource usage. Experimental results demonstrate that VREM-FL outperforms literature benchmarks for both a linear regression model (learning time reduced by 28%) and a deep neural network for semantic image segmentation (doubling the number of model updates within the same time window
Frequency-dependent damping in the linear wave equation
No full textWe propose a model for frequency-dependent damping in the linear wave equation. After proving well-posedness of the problem, we study qualitative properties of the energy. In the one-dimensional case, we provide an explicit analysis for special choices of the damping operator. Finally, we show, in special cases, that solutions split into a dissipative and a conservative part