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Associations of positive end-expiratory pressure (PEEP) with extubation failure and clinical outcomes in invasively ventilated patients with acute brain injury: A secondary analysis of the ENIO study
No full textBackground: Invasive mechanical ventilation (IMV) is crucial for managing acute brain injury (ABI) patients, yet the effects of positive end-expiratory pressure (PEEP) on outcomes are not well understood. This study aimed to evaluate the relationship between PEEP levels and risk of extubation failure as well as intensive care unit (ICU) mortality in ABI patients. Methods: This post-hoc analysis of the ENIO study included 1512 ABI patients from the ENIO cohort, excluding those without available data on PEEP at day 1 and who never received an extubation trial. PEEP levels were recorded at days 1, 3, 7, and on the day of extubation. Logistic regression assessed the association between PEEP and extubation failure, while Cox proportional hazards regression analyzed ICU mortality. Results: Among 1154 included patients, extubation failure occurred in 21.2 % and ICU mortality was 3.7 %. Higher median PEEP at days 1, 3, and 7 was independently associated with increased odds ratio (OR) of extubation failure (OR = 1.13; 95 %CI = 1.01-1.26; p = 0.0294). At the time of extubation, higher PEEP was also significantly associated with extubation failure (OR = 1.13; 95 %CI = 1.02-1.25; p = 0.0218) and ICU mortality (Hazard Ratio, HR = 1.38; 95 %CI = 1.12-1.69; p = 0.0026). However, at sensitivity analyses adjusted for acute respiratory distress syndrome (ARDS), PEEP was no longer significantly associated with outcomes, while ARDS itself was an independent predictor of extubation failure. Conclusions: Extubating ABI patients at higher PEEP levels was associated with an increased risk of extubation failure and ICU mortality. However, this association likely reflects underlying respiratory pathology or disease severity. Our findings suggest that PEEP level may serve as a surrogate marker for extubation readiness, rather than a modifiable risk factor, and highlight the need for individualized assessment prior to extubation
Exploring New Vitality Forms in Human-Robot Interaction
No full textHumans communicate their internal psychological and affective states through movement, which varies in the form with which it is performed. These forms, known as vitality forms, play a crucial role in enhancing the quality of human-robot interaction, particularly when they can be recognized by artificial agents such as humanoid robots. The present study aims to develop and validate forty short stories designed to elicit four distinct vitality forms: fed-up, rude, gentle, and enthusiastic. The stories were generated with the support of a large language model to minimize potential bias related to researchers’ subjective interpretations and were validated through an online questionnaire. In the questionnaire, participants read each story and selected up to three emotional labels from a set of fifty-one. The data were analysed using two complementary methods, percentage-based and weighted frequency analyses, which yielded largely consistent results. The most effective stories for each vitality form will be used in a future human-robot interaction study to investigate the motor behaviours associated with each form during interaction with a humanoid robot such as iCub
Hybrid Rigid–Soft Industrial Gripper: Actuation, Design Enhancement, Multi-Modal Sensorization, and Real-Time Coordinated Control for Automotive Assembly
No full textThe growing complexity of industrial automation and the shift toward human–robot collaborative manufacturing demand robotic grippers that combine mechanical precision, adaptive compliance, and intelligent sensing. This doctoral thesis addresses these needs through comprehensive research on actuation mechanisms, kinematic analysis, design enhancement, advanced sensorization, and industrial implementation of a versatile universal gripper system capable of handling components from delicate items to complex rigid parts. In parallel, it contributes to industrial robot control within the EU Horizon SESTOSENSO project.
The work begins with a detailed kinematic analysis of a novel three-finger gripper architecture. Each rigid mechanical finger integrates a Chebyshev–parallelogram linkage mechanism with a thermoplastic polyurethane (TPU) contact interface. The mechanism produces near-linear trajectories with a deviation of ±0.033 mm and a mechanical advantage of 6.06:1. Independent actuation is achieved using JVL stepper motors with embedded programmable logic controllers (PLCs), communicating via Modbus remote terminal unit (RTU). The control architecture supports torque-based and velocity-based stall detection, both operating in real time with configurable thresholds. These strategies enable reliable grasping without dedicated force sensors by leveraging internal motor feedback parameters.
Gripper enhancement is guided by a quantitative deflection coefficient to assess finger wrapping and by quasi-static force–displacement testing. The original four-bar parallelogram was redesigned into a six-bar linkage with compliant pads. This eliminates link interference limitations while preserving the essential kinematics, resulting in adaptive grasping capability. Pull-out tests demonstrated improved force profiles for complex automotive parts and reliable manipulation of objects from 100 g to 7.5 kg.
Vision-based sensorization was achieved through an embedded Raspberry Pi Camera V3.
The integration of vision-based sensing within the additively manufactured soft finger structure establishes the feasibility of achieving multiple sensing modalities with a single compact embedded system while retaining the characteristic properties of the fingers. The proposed system successfully estimates normal interaction forces, measures internal deformation (Z-displacement), classifies the position of the applied force, and detects slip events with the complete sensing pipeline processed on an embedded platform while avoiding complex signal disambiguation challenges and occlusion issues. Complementing this, a fully flexible resistive sensor was fabricated via fused deposition modeling (FDM) printing and embedded in the finger for contact and bending detection. A novel light-angle sensor array was also developed using a custom four-layer rigid-flex printed circuit board (PCB), where prototype sensors successfully demonstrate distributed tactile sensing capabilities.
The universal gripper and sensing systems were validated on a COMAU six-degrees-of-freedom (6-DOF) industrial robot in diverse grasping trials, confirming adaptability, robustness, and sensing reliability. Separately, within the SESTOSENSO project, real-time control strategies were developed for coordinating a KUKA KR150 robot with a UR10 cobot via robot sensor interface (RSI) and robot operating system (ROS) in an automotive roof assembly task. This work addressed control architecture, real-time trajectory correction, and safe human–robot collaboration in confined, visually occluded environments.
This thesis advances the state of the art in hybrid gripper systems by integrating rigid precision, soft adaptability, and intelligent sensing with industrially validated control strategies. The outcomes directly support Industry 4.0/5.0 objectives, enabling flexible, high-performance automation adaptable to diverse manufacturing requirements
Revisiting Abdominal Pain in IBS: From Pathophysiology to Targeted Management with Algerine Citrate/Simeticone
No full textCinque cartoline di forza e speranza per il nuovo anno
No full textContributo pubblicato da Il Secolo XIX , nel primo numero 2026 del suo 140 anno di vita editoriale. Quando le opinioni trovano spazio è sempre un privilegio raro. Quando le voci del mondo universitario trovano spazio sui quotidiani - oggi e per paradosso - è sempre più un'opportunità preziosa
Stereo-EEG in an epilepsy surgery program: Initial experience in a tertiary pediatric hospital in Italy
No full textValorisation of tomato peel waste for lycopene encapsulation: Optimization and comparison of two green techniques
No full textLycopene, a lipid-soluble carotenoid with potent antioxidant properties, is typically found in tomato peels, which are often discarded as by-products in the food industry. This study focused on extracting lycopene using solvent extraction and encapsulating it in polycaprolactone (PCL), a biodegradable polymer, using two different methods: solvent evaporation and supercritical emulsion extraction (SEE). Both methods were used to produce microparticles for nutraceutical applications. An optimization study based on Box-Behnken design and response surface modelling was conducted to assess the effects of emulsification stirring speed, emulsification time, and polymer amount on encapsulation efficiency and particle size. Particle sizes, measured by laser diffraction, ranged between 1.77 ± 0.10 and 2.82 ± 0.17 μm for solvent evaporation, and between 1.12 ± 0.03 and 2.72 ± 0.15 μm for SEE. Encapsulation efficiencies, measured by UV–vis spectroscopy, ranged between 28.45 ± 0.28 % and 89.94 ± 1.70 % for solvent evaporation, and between 66.52 ± 0.64 % and 89.45 ± 1.31 % for SEE. Results show that SEE yields more consistent encapsulation efficiencies compared to solvent evaporation. Additionally, the design of experiments (DoE) approach helped identify optimal conditions that minimize waste and maximize productivity. This work offers a sustainable method for converting agro-industrial waste into valuable nutraceutical products
Trajectory Planning and Optimization for Human-Robot Collaboration
No full textCollaborative robotics is reshaping the landscape of automation by enabling safe, efficient, and intuitive interaction between humans and robots in shared workspaces. Unlike traditional industrial robots that operate in isolation, cobots are designed to work alongside humans, requiring advanced capabilities in perception, decision-making, and motion planning to ensure seamless cooperation.
Trajectory planning in collaborative robotics involves generating feasible paths that respect kinematic constraints, avoid collisions, and adapt to real-time changes in the workspace.
This thesis investigates safety strategies for shared workspaces, emphasizing speed and separation monitoring to prevent collisions while maintaining workflow fluency. Several robot stopping strategies are experimentally compared using a 7-degrees-of-freedom (DOFs) Franka Emika Panda robot. The best-performing approach dynamically scales safety zones to improve collaboration fluency and is successfully validated in an industrial case study, automating the precise placement of small components on car rear lamps, meeting both safety and cycle time requirements.
Minimum-jerk trajectory planning for generating smooth and predictable robot movements are also investigated. A mixed time–jerk optimization framework is implemented and tested on the same robotic platform, ensuring reduced acceleration and jerk while maintaining task efficiency. An extended method for redundant manipulators further optimizes both timing and joint configurations, minimizing end-effector jerk.
Moreover, this thesis proposes a neural network-based trajectory planner that enables the robot to mimic human arm movements. Trained on a custom dataset, the network selects kinematic configurations that yield human-like motion patterns, improving trust, predictability, and cooperation between humans and robots. Simulations and experimental validations demonstrate the effectiveness of this approach in achieving intuitive, human-like robotic behavior