Archivio della ricerca della Scuola Superiore Sant'Anna
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Impact of glomerular filtration rate estimation formulas on MECKI score performance and prognostic accuracy in heart failure: The MECKI-RENAL study
No full text“Frames of Injustice: Comics As Constitutional Critique; Marcos De Injusticia: Los cómics Como crítica Constitucional”. Constitutional Studies 11 (2): 511-38
No full text“It wasn’t me”: Why context matters in comparative constitutional studies on populisms—A reply
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Design and Performance Assessment of a Cable-Driven, 3-DoFs Exoskeleton for Orthopedic and Poststroke Rehabilitation of the Wrist
No full textThis research presents the mechanical design and performance evaluation of a novel 3-Degrees-of-Freedom (DoFs) wrist exoskeleton (W-EXOS) for orthopedic and poststroke patients' upper limbs rehabilitation. The device covers the 93.3% of the human Range of Motion (RoM), simulating the pronosupination, radioulnar deviation, and flexion–extension motion. W-EXOS is actuated through electric motors via an efficient cable transmission, having high torque/weight and torque/volume ratios. Its kinematics is a serial chain of three rotational joints with nonperpendicular axes competing at the wrist rotation center. So, the device joints are coupled but the structure is compact and with good mass distribution. Theoretical modeling allowed the study of the human wrist and the device axes matching, evaluating the RoM and torques at each joint. With the W-EXOS integrated into a rehabilitation station, the performance assessment was done using: 1) a position control test, for the device RoM validation and 2) a virtual reality serious game test, to prove the device assistance strategy during wrist motion tasks performed by healthy subjects in a typical rehabilitation session. Further, the W-EXOS handle has been replaced by a hand exoskeleton and the whole system has been mounted on a 4-DoFs shoulder–elbow exoskeleton, proving the W-EXOS integrability in multiple, highly wearable, compact, and usable, bimanual, upper limb robotic setups
L’implementazione delle politiche di efficientamento energetico in sanità: la strategia green della Azienda Usl Toscana sud est
No full textL’obiettivo del presente lavoro è illustrare le scelte effettuate da una azienda sanitaria di grandi dimensioni, quale l’Usl Toscana sud est, nell’ambito delle politiche di sostenibilità ambientale e transizione ecologica in sanità. Tra gli insegnamenti emersi dalla pandemia da covid-19, spicca sicuramente l’approccio One health che evidenzia l’importanza di relazionare i bisogni di cura delle persone con la necessità di ridurre l’utilizzo dei combustibili fossili e la propria impronta ambientale. Per tale ragione, in questo contesto, si sono analizzate le azioni attuate dall'Azienda, partendo dalle scelte di finanziamento adottate, per giungere alle effettive strategie messe in opera, oltre che alle modalità di monitoraggio e misurazione delle medesime strategie, attraverso la costituzione di uno specifico indicatore di valutazione
EAFvision: Real-Time Automated Safety Surveillance in Electric Arc Furnaces Using Deep Learning Models
No full textEnsuring safety and operational efficiency in Electric Arc Furnace (EAF) steel manufacturing is critical due to the extreme hazards such as intense heat, toxic emissions, and heavy machinery present in these environments. We propose EAFvision, a real-time automated pipeline for safety surveillance EAFs, leveraging advanced deep learning architectures. EAFvision enables real-time detection of critical safety-related situations, including personnel, electrode clamps, and smoke emissions, to enhance situational awareness and operational safety in industrial environments. We collected and carefully annotated a comprehensive image dataset from an active EAF facility to benchmark a variety of models, including YOLO versions 8 through 11, RT-DETR, and established two-stage detectors like Faster R-CNN and Mask R-CNN. Our results demonstrate that lightweight, single-stage detectors deliver superior accuracy and faster inference times compared to more complex models, enabling efficient real-time testing on edge devices for immediate hazard detection and automated response. This approach highlights the transformative potential of AIpowered real-time monitoring systems to enhance workplace safety and optimize steel production processes
Contrast-Enhanced Robotic Capsule Tracking in Ultrasound Using a Dynamic Acoustic Retroreflector
No full textIngestible robotic capsules are a minimally-invasive option for diagnosing and treating conditions of the gastrointestinal tract. Ultrasound has the potential to provide the accurate and real-time image guidance that is necessary for site-specific procedures; however, existing ultrasound-based methods have limited clinical utility due to factors such as low capsule contrast, high background noise, unsuitable capsule motion requirements, low frame rates, and reliance on raw ultrasound radiofrequency data, which is rarely accessible in clinical systems. This work presents a high-contrast ultrasound tracking target that can be equipped on a robotic capsule, and that emits a periodic flashing signal in clinically-available B-mode images. The tracking target is a shape-changing acoustic retroreflector whose retroreflection can be constructed and destroyed by changing the device’s configuration under magnetic actuation. Subsequent spatial localization of the periodic intensity signal in B-mode is accomplished with an efficient Fourier-based network. Tracking of a stationary mock capsule was evaluated in an ex-vivo porcine stomach-agar phantom using a stationary and moving ultrasound probe. The mean tracking error was 2.2 mm with a stationary probe and 3.0 mm with a moving probe. Tracking update rates reached up to 120 Hz. Our method is compatible with B-mode, stationary capsules, and a moving probe, demonstrating potential for clinical use in the localization of robotic capsules
INTEGRATING BIO-CO2 WITH RENEWABLE HYDROGEN FOR THE SYNTHESIS OF MARITIME METHANOL
No full textRecognizing the growing emissions from the maritime sector and recent EU regulatory developments, this study explores the utilization of bio-CO2 with renewable hydrogen to produce e-MeOH as a renewable maritime fuel. Process models are developed in Aspen PlusTM, including a conventional single-reactor setup and a novel four-reactor configuration with intermediate cooling and separation. First, the performance of the conventional, once-through process is assessed through a sensitivity analysis, which demonstrates that maximum reactant conversion remains below 40% under typical operating conditions. Recycling improves conversion but causes inert buildup, leading to higher compression needs, larger equipment and slower response. The four-reactor system, by contrast, achieves higher conversion without recycling, reducing feedstock demand by 65% under once-through conditions. Finally, to assess the industrial relevance of the proposed technologies, three methanol production scales (150–600 tpd) are evaluated. Depending on those three production scales, 0.1-0.4 Mtpa of bio-CO2 are required, indicating that only large bio-CO2 emitters can meet this demand directly whereas smaller facilities would need to aggregate CO2 at regional hubs. Green hydrogen requirements range from 13–150 ktpa (0.1–1.2 GW), indicating an additional limiting factor based on current EU capacities. However, future expansion of electrolyser technologies, CO2 capture processes and planned EU initiatives could support e-MeOH adoption in the maritime sector
