Archivio della ricerca della Scuola Superiore Sant'Anna
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The evolution of search engines and their application to Justice: opportunities and challenges: A technical perspective
Get PDFDesign of a Soft Robotic Artificial Cardiac Wall
No full textBackground: In cardiovascular engineering, the recent introduction of soft robotic technologies sheds new light on the future of implantable cardiac devices, enabling the replication of complex bioinspired architectures and motions. To support human heart function, assistive devices and total artificial hearts have been developed. However, the system's functionality, hemocompatibility, and overall implantability are still open challenges. Methods: Here, the design of a soft robotic artificial cardiac wall is presented: the action of a bioinspired myocardium of pneumatic McKibben actuators in a double helix is coupled with an engineered passive and deformable endocardial layer made of silicone. The correlation between the helix angle of the actuators and the ejection fraction of the artificial cardiac wall was preliminarily studied with a simplified analytical model. A FEM model was introduced to represent the complex deformation of the endocardial layer during the actuation of the cardiac wall. Results: Experimental tests report an ejection fraction of 68%, i.e., 77.2 ± 0.4 mL against 90 mmHg, satisfying the minimum physiological requirements and, therefore, proving the concept's functionality. Conclusions: The conceived device paves the way for a new generation of innovative approaches where engineered bioinspiration might be the key to future artificial cardiac pumps that could support or even substitute the human failing heart
Physics informed neural network framework for unsteady discretized reduced order system
No full textThis work addresses the development of a physics-informed neural network (PINN) with a loss term derived from a discretized time-dependent full-order and reduced-order system. In this work, first, the governing equations are discretized using a finite difference scheme (whereas any other discretization technique can be adopted), then projected on a reduced or latent space using the Proper Orthogonal Decomposition (POD)-Galerkin approach, and next, the residual arising from discretized reduced order equation is considered as an additional loss penalty term alongside the data-driven loss term using different variants of deep learning method such as Artificial neural network (ANN), Long Short-Term Memory based neural network (LSTM). The LSTM neural network has been proven to be very effective for time-dependent problems in a purely data-driven environment. The current work demonstrates the LSTM network's potential over ANN networks in PINN as well. The major difficulties in coupling PINN with external forward solvers often arise from the inability to access the discretized forms of the governing equation directly through the PINN solver and also to include those forms in the computational graph of the network. This poses a significant challenge, especially when a gradient-based optimization approach is considered in the neural network. Therefore, we propose an additional step in the PINN algorithm to overcome these difficulties. The proposed methods are applied to a pitch-plunge airfoil motion governed by rigid-body dynamics and a one-dimensional viscous Burgers' equation. The potential of using discretized governing equations instead of a continuous form lies in the flexibility of input to the PINN. The current work also demonstrates the prediction capability of various discretized-physics-informed neural networks outside the domain where the data is available or where the governing equation-based residuals are minimized
Multi-Channel Integrated Microwave Photonic Transmitter for Radio-Over-Fiber Systems
No full textIn this paper, we present the first multi-channel RF transmitter based on integrated photonics for enabling multichannel radio-over-fiber systems on chip. The photonic integrated circuit has been implemented on monolithic Indium Phosphide platform. The characterization of the transmitter confirms its potential for RF systems up to Ka band
EAF Steelmaking: AI applications for estimating energy consumption|Ciclo EAF: applicazioni dell’IA per stimare i consumi energetici
No full textSteel production through the electric cycle suffers from low diversification of energy supply sources and, with a view to efficiency and cost reduction, needs to be optimized on several aspects, among which production planning. Optimizing production costs means, for example, leveling out the electricity consumption along the day, whose prices may fluctuate in the short/medium term (variating on hour/daily basis) or if peak consumption exceeds a certain threshold. Production planning can, therefore, benefit from models predicting the energy consumed as a function of the quality of steel to be produced and the specific production recipe. These models can be effectively used by intelligent scheduling optimization systems. This paper presents a set of models based on neural networks that can predict with good accuracy the power consumption in the electric arc and ladle furnaces as a function of main production information. The models were trained and validated through real production and process data from a plant in Croatia with encouraging results
Rooting for survival: OsCDPK5 and OsCDPK13 are crucial for rice acclimation to low-oxygen conditions
No full textLate gadolinium enhancement dispersion for predicting malignant arrhythmic events in patient with non-ischaemic dilated cardiomyopathy
No full textLe operazioni straordinarie nel concordato preventivo. Dalla Legge Fallimentare al Correttivo ter del Codice della Crisi
No full textContemporary medical therapy for heart failure across the ejection fraction spectrum: The OPTIPHARM-HF registry
No full textVerso la codificazione dell’obbligo di riduzione del rischio di disastri nel diritto internazionale: stato dell’arte, sfide e alcune proposte
No full textTOWARDS CODIFYING THE DUTY TO REDUCE DISASTER RISK IN INTERNATIONAL LAW: CURRENT FRAMEWORK, CHALLENGES, AND PROPOSALS
On 4 December 2024, the United Nations General Assembly decided to elaborate and conclude, by the end of 2027, a legally binding instrument on the protection of persons in the event of disasters. The draft articles adopted by the International Law Commission (ILC) in 2016 on the same topic will serve as the basis for the negotiations. Although the ILC’s work primarily focused on disaster response – particularly on the external assistance to be provided to a State affected by a disaster – it also included a disaster risk reduction (DRR) component, albeit not fully developed or integrated. This contribution argues that the future treaty should enshrine a clear and comprehensive obligation to reduce disaster risk. To this end, it first reviews the current state of DRR in international law and then advances specific proposals on how to amend the ILC text in order to fully integrate DRR into the forthcoming convention