Politecnio die Bari - Catalogo di prodotti della Ricerca
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Blockchain and NoSQL: Enhancing Throughput via a Deep Learning-Based Hybrid Architecture
Get PDFAny data structure used to store information can be considered a database. Blockchain technology, at its core, is no more than a ledger to store information about transactions. In recent years, such technology has gained immense popularity in a wide range of applications. However, scalability and throughput are major challenges of the Blockchain technology since applications and APIs have much lower throughput compared to non-Blockchain ones. Several studies showed that the integration of the NoSQL paradigm along with a Blockchain pipeline enhances the overall throughput and scalability of the system, as well as the possibility of handling both on-chain and off-chain data. The goal of this paper is therefore to study how the advantages of both technologies can be capitalized on to enhance the system capabilities and to finally put forward a novel hybrid architecture that mixes NoSQL and Blockchain characteristics using Deep Learning techniques
A spectral method for dispersive solutions of the nonlocal Sine–Gordon equation
No full textMoved by the need for rigorous and reliable numerical tools for the analysis of peridynamic materials, the authors propose a model able to capture the dispersive features of nonlocal soliton-like solutions obtained by a peridynamic formulation of the Sine–Gordon equation. The analysis of the Cauchy problem associated to the peridynamic Sine–Gordon equation with local Neumann boundary condition is performed in this work through a spectral method on Chebyshev polynomials nodes joined with the Störmer–Verlet scheme for the time evolution. The choice for using the spectral method resides in the resulting reachable numerical accuracy, while, indeed, Chebyshev polynomials allow straightforward implementation of local boundary conditions. Several numerical experiments are proposed for thoroughly describe the ability of such scheme. Specifically, dispersive effects of the specific peridynamic kernel are demonstrated, while the internal energy behavior of the specified peridynamic operator is studied
ERUDITE: a Deep Neural Network for Optimal Tuning of Adaptive Video Streaming Controllers
No full textHigh–Q Integrated Resonators for Biosensing and Chemical Sensing: From non-Hermitian Resonators to Quasi–BIC Metasurfaces
No full textTrends on Human Factors in Industrial Human-Robot Collaboration: From Design to Implementation
No full textHuman-Robot Collaboration (HRC) systems will shape industrial scenarios of the near future by integrating collaborative robots (cobots) to enhance performance and well-being of human workers. Despite the increasing research on this topic, investigating how human factors influence the success of HRC systems remains an issue to be further explored. This study reviews the role of human factors in various implementation stages of industrial HRC systems, including design, simulation, laboratory case studies, and industrial case studies. Findings reveal that most studies focus on controlled environments, highlighting gaps in real-world industrial applications where physiological and psychological factors remain overlooked. By addressing these gaps, this study provides insights into integrating human factors, fostering more inclusive industrial HRC systems that prioritize worker safety, ergonomics, ethics, and well-being. (c) Copyright 2025 The Authors
Preface of the 53rd Conference of the Italian Scientific Society of Mechanical Engineering Design (AIAS 2024) †
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Experimental Investigation of Wood’s Hygroscopic Behavior for Adaptive Shading Systems in Building Façades
No full textCharacterization of Human Balance through a Reinforcement Learning-based Muscle Controller
No full textObjective characterization of human balance remains a challenge and clinical observation-based balance tests during physical rehabilitation are often affected by subjectivity. On the other hand, computational approaches mostly rely on center of pressure (COP) tracking and inverted pendulum models, which do not capture the multi-joint and muscle contributions to whole-body balance. This study proposes a novel musculoskeletal modeling and control methodology to investigate human balancing capabilities in the center of mass (COM) state space. A musculoskeletal model is integrated with a balance controller trained through reinforcement learning (RL) to explore the limits of dynamic balance during postural sway. The RL framework consists of two interlinked neural networks (balance recovery and muscle coordination) and is trained using Proximal Policy Optimization (PPO) under multiple training strategies. By exploring recovery from random initial COM states with a trained controller, a balance region (BR) is obtained that encloses successful state-space trajectories. Comparing BRs obtained from different trained controllers with the analytical postural stability limits of a linear inverted pendulum model, we observe a similar trend in COM balanced states, but reduced recoverable areas. Furthermore, the effects of muscle weakness and neural excitation delay on the BRs are investigated, revealing reduced balancing capability in the COM state space. The novel approach of determining regions of stability through learning muscular balance controllers provides a promising avenue for personalized balance assessments and objective quantification of balance capability in humans with different health conditions
VHE γ-ray observations of bright BL Lacs with the Large-Sized Telescope prototype (LST-1) of the CTAO
No full textCherenkov Telescope Array Observatory (CTAO) is the next-generation ground-based -ray observatory operating in the energy range from up to, with two sites in La Palma (Spain) and Paranal (Chile). It will consist of telescopes of three sizes, covering different parts of the large energy range. We report on the performance of Large-Sized Telescope prototype (LST-1) in the detection and characterization of extragalactic -ray sources, with a focus on the reconstructed -ray spectra and variability of classical bright BL Lacertae objects, which were observed during the early commissioning phase of the instrument. LST-1 data from known bright -ray blazars - Markarian 421, Markarian 501, 1ES 1959+650, 1ES 0647+250, and PG 1553 + 113 - were collected between 2020 July 10, and 2022 May 23, covering a zenith angle range of 4 to 57. The reconstructed light curves were analysed using a Bayesian block algorithm to distinguish the different activity phases of each blazar. Simultaneous Fermi-LAT data were utilized to reconstruct the broad-band -ray spectra for the sources during each activity phase. High-level reconstructed data in a format compatible with gammapy are provided together with measured light curves and spectral energy distributions (SEDs) for several bright blazars and an interpretation of the observed variability in long and short time-scales. Simulations of historical flares are generated to evaluate the sensitivity of LST-1. This work represents the first milestone in monitoring bright BL Lacertae objects with a CTAO telescope