Archivio della ricerca della Scuola Superiore Sant'Anna
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Guiding the Transition Toward H2-DRI-Based Steelworks Through a Related Simulation Toolkit
No full textDirect reduction is a promising process to reduce emissions in steelmaking, especially if the reducing gas contains significant amount of hydrogen. However, the introduction of the related plant into existing integrated steelworks may lead to not completely known effects on production and energy management. In the European MaxH2DR project, a multipurpose simulation toolkit was developed to help industrial managers in the transition from current configurations to H2-DRI based steelworks. Models were developed to consider production and energy management aspects. The contribution describes the toolkit and the models of gas and energy management area and of DRI production processes
Beating of eukaryotic flagella via Hopf bifurcation of a system of stalled molecular motors
Get PDFThe modeling of the beating of cilia and flagella in fluids is a particularly active field of study, given the
biological relevance of these organelles. Various mathematical models have been proposed to represent the
nonlinear dynamics of flagella, whose motion is powered by the work of molecular motors attached to filaments
composing the axoneme. Here, we formulate and solve a nonlinear model of activation based on the sliding
feedback mechanism, capturing the chemical and configurational changes of molecular motors driving axone-
mal motion. This multiscale model bridges microscopic motor dynamics with macroscopic flagellar motion,
providing insight into the emergence of oscillatory beating. We validate the framework through linear stability
analysis and fully nonlinear numerical simulations, showing the onset of spontaneous oscillations. To make
the analysis more comprehensive, we compare our approach with two established sliding feedback models
Il principio costituzionale di sussidiarietà orizzontale e l'Unione europea: il caso della disciplina delle comunità energetiche
Get PDFNOTAZIONI SPARSE SU COSTITUZIONE, ALGORITMI E PROTEZIONE DELLA DEMOCRAZIA: LE CONTRAPPOSTE VIE DI STATI UNITI E BRASILE
Get PDFMyocarditis and pericarditis during COVID-19 pandemic: a study of the Italian Society of Cardiology
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A hardware accelerator to support deep learning processor units in real-time image processing
Get PDFDeep neural networks are becoming crucial in many cyber–physical systems involving complex perceptual tasks. For those embedded systems requiring real-time interactions with dynamic environments, as autonomous robots and drones, it is of paramount importance that such algorithms are efficiently executed onboard on properly designed hardware accelerators to meet the required performance specifications. In particular, some neural network architectures for object detection and tracking, as You Only Look Once (YOLO), include heavy computational stages that need to be executed before and after the model inference. Such stages are typically not incorporated in traditional accelerators and are executed on general-purpose processors, thus introducing a bottleneck in the overall processing pipeline. To overcome such a problem, this paper presents a general-purpose accelerator on a field-programmable gate array (FPGA) able to run pre-processing and post-processing operations typically required by vision tasks. The proposed solution has been tested in combination with a YOLO object detector accelerated on an Advanced Micro Devices (AMD) Xilinx Kria KR260 board mounting an UltraScale+ multiprocessor system-on-chip, achieving a significant improvement in terms of both timing performance and power consumption, and enabling onboard visual processing into drones. The proposed solution is able to boost the traditional object detection process by a factor of 4.4, allowing the execution of the full processing pipeline at 60 frames per second (fps), versus 13.6 fps reachable without the proposed accelerator. As a result, this work enables the use of high-speed cameras for developing more reactive systems that can respond to incoming events with lower latency
Experimental evidence on the determinants of citizens' expectations toward public services
Get PDFWe conducted three randomized experiments to investigate whether and to what extent citizens' expectations toward waiting times for public service delivery are influenced by reference points, either in the form of social or numerical references. Consistent with our theoretical expectations, our results provide convergent evidence of reference dependence. Specifically, informing citizens that waiting times are longer (shorter) relative to a social reference causes an increase (decrease) in expected waiting times. Additionally, due to an anchoring bias, priming citizens with a higher numerical value for waiting times extends their expected waiting times. Furthermore, in line with the expectancy-disconfirmation model, citizens' satisfaction with the service is causally impacted by the extent to which actual performance exceeds their expectations
Augmented Reality in the DESIRE6G Cloud-native and Programmable Infrastructure with Multi-Agent System and Pervasive Monitoring
No full text6G networks promise ultra-low latency and adaptive digital services, including immersive experiences like Augmented Reality (AR). This demo showcases DESIRE6G, an integrated 6G architecture validated on the ARNO testbed in Pisa, Italy. The system combines cloud-native orchestration, programmable data plane infrastructure, secure multi-agent monitoring, and in-band telemetry to support latency-sensitive AR applications. A real-time use case involving camera-equipped drones and AR headsets showcases enhanced situational awareness. The architecture enables scalable service assurance, with latency recovery triggers ranging from microseconds to hundreds of milliseconds across multiple layers
