Higher Institute on Territorial Systems for Innovation
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Combining Hyaluronic Acid and Amino Acids for Improved Healing of Post-Extraction Tooth Socket in Type 2 Diabetes Mellitus Subjects: A Randomized Clinical Trial
No full textBackground/Objectives: Conventional wound care often fails to address the complex pathology of diabetic wounds adequately. Research shows that hyaluronic acid and its derivatives promote tissue regeneration in the later stages of wound healing. We evaluated the efficacy of a novel topical formulation in promoting socket healing following post-extraction in patients with type-2 diabetes mellitus, by combining sodium hyaluronate and six amino acids involved in collagen synthesis. Methods: A single-center, two-arm randomized controlled trial was conducted in adults aged 18 and over with type 2 diabetes requiring extraction of at least one non-impacted tooth. Forty-three participants were randomized to receive either the intervention or no treatment. Primary outcomes included a modified Landry’s healing index and rate of socket closure. Results: Comparative analysis showed significantly improved healing index scores in the intervention group by day 7 and day 14 compared to control, with no improvements in the rate of socket closure. Conclusions: This research provides evidence on the therapeutic efficacy of the gel formulation under study in promoting wound healing of post-extraction sites in diabetic patients undergoing tooth extraction. Further research is needed to compare its efficacy with standard treatments and adjunct therapies
Close-range real-time camera pose estimation and AR-guided alignment for large-scale industrial components
No full textPrecise alignment of large-scale industrial components on machine tools is essential to ensure machining accuracy, product quality, and process efficiency. Errors introduced during the setup phase can propagate throughout the manufacturing process, often resulting in costly rework. Conventional alignment methods rely on laser tracker systems, which, despite their high precision, require specialized equipment, skilled operators, and long setup times, making them expensive and operationally demanding. To overcome these limitations, this work presents a real-time collaborative camera pose estimation framework that simplifies and accelerates the alignment process. The proposed solution integrates predictive simulation, acquisition trajectory planning, and augmented reality (AR) to enable fast, accurate, and intuitive alignment, even for non-expert users. The system built upon the IDEKO VSET solution was further developed within the TACCO project, co-funded by EIT Manufacturing and the European Union. The framework starts from high-fidelity 3D models and employs a Monte Carlo-based optimization strategy to determine the optimal placement of auxiliary components, including calibrated scale bars, coded targets (igloos), and cross reference frame. Configurable image acquisition strategies allow users to balance accuracy and computational complexity. Candidate configurations are evaluated through a least-squares bundle block adjustment simulation, based on collinearity equations and a Structure from Motion approach, enabling uncertainty propagation analysis and the generation of 95% confidence error ellipsoids prior to data acquisition. A key innovation lies in the system’s ability to translate optimized planning solutions into real-time immersive AR guidance. Operators are guided in the placement of auxiliary components and camera positioning through an AR head-mounted display, ensuring complete and accurate data capture. The proposed approach achieves alignment accuracies closer to laser tracker systems while significantly reducing setup time, cost, and dependence on specialized personnel, offering a scalable and cost-effective alternative for industrial component alignment
Data-Driven AI Approach to Address Territorial Strategies. Why Investing in Agri-Food Sector to Enhance the Valsesia Inner Area
No full textIn recent years, data-driven artificial intelligence (AI) approaches have gained prominence in territorial planning and economic development, enabling policymakers to analyse large datasets and formulate evidence-based strategies. This study aims to develop and apply a data-driven AI approach to analyse past funded projects to support decision-making processes and the development of strategic actions in Italy’s inner territories, focusing on the Valsesia SNAI Inner Area and the enhancement of the Agri-food chains and rural development sector. The study employs data mining techniques such as Latent Dirichlet Allocation (LDA) topic modeling and clustering to analyse thematic and financial data from the "OpenCoesione projects" dataset. Findings highlight that while infrastructure investments are substantial, funding for research, innovation, and business competitiveness in the agri-food sector remains underdeveloped. The study underscores the importance of private-public financing mechanisms and strategic investment to enhance regional development. Conducted within the Branding4Resilience (B4R) project by the Politecnico di Torino Research Unit, this research can contribute to optimizing SNAI strategy implementation and broader territorial policies, fostering the competitiveness of agri-food SMEs and supporting sustainable socio-economic growth in Valsesia
Seeing is Believing: Assessing and Enhancing Android Privacy Indicators Through Eye-Tracking Analysis
No full textToday, mobile device privacy is more crucial than ever, pushing Android to introduce Privacy Indicators (PIs) to enhance transparency and protect users. These visual alert systems signal when sensitive resources, like the camera or microphone, are in use. The effectiveness of these visual elements is clearly linked to their ability to capture the users’ gaze. In this paper, we leverage eye-tracking technology to explore PIs’ ability to catch the users’ attention. In a controlled experiment with 29 participants, we uncovered significant gaps in PI effectiveness, particularly during high-engagement tasks, showing that changes in the PI implementation may affect its visibility, still highlighting the need for more attention-grabbing privacy notifications. Building on these findings, a second experiment with 14 participants assessed the Disk PI—the best performer from the initial study—across passive (video watching) and active (app usage) usage contexts. Even concerning these two factors, the results show the limits of the proposed solution, suggesting the need for careful analysis of the UI elements that are most effective in capturing the user’s gaze to create a better solution. Heatmap analysis revealed that users consistently focus on centrally located, dynamic elements and text while ignoring static and peripheral areas. Inspired by these insights, we developed a new Popup PI, strategically positioned at the top center of the screen with dynamic animations and textual information. This Popup PI significantly increased user attention and retention, proving to be a more effective solution for privacy notifications. Our research underscores the urgent need for intuitive and user-friendly privacy indicators in the Android ecosystem. The compelling evidence points to the Popup PI as a superior alternative, greatly enhancing user awareness and privacy protection. These findings are a pivotal step towards evolving privacy mechanisms, fostering a safer and more transparent digital environment for all users, and advancing the methodology of utilizing eye tracking in user experience research
The Impact of Transition and Turbulence Modeling on the SPLEEN High-Speed Low-Pressure Turbine Cascade
Get PDFIn high speed low pressure turbines (LPTs) for geared turbofan engine applications, transonic flow conditions combined with low Reynolds number operation depict a flow scenario where shock waves can interact with laminar or turbulent boundary layers, and the resulting flow topologies pose serious challenges for computational fluid dynamics (CFD) analyses. In this work, two different in house developed Reynolds Averaged Navier Stokes (RANS) solvers are applied to the study of a transonic low pressure turbine cascade over a range of Mach and Reynolds numbers, with a focus on the performance of transition and turbulent closures. The selected test case consists of the SPLEEN (Secondary and Leakage Flow Effects in High Speed Low Pressure Turbines) C1 cascade, a state of the art high speed low pressure turbine blade section that has been investigated in an extensive experimental campaign at the von Karman Institute, in the framework of the SPLEEN European Research Programme. The considered transition sensitive turbulence closures are representative of the most advanced techniques for RANS methods and range from correlation based intermittency transport approaches to phenomenological model based on the laminar kinetic energy (LKE) concept and the k v'2 w framework. It is shown how realistic transition modeling is crucial for predicting blade loading distributions and then addresses design challenges for transonic LPT bladings. A discussion concerning the reproduction of wake loss profiles demonstrates how classical linear eddy viscosity closures can be adequate in the case of attached flow even in transonic flow conditions but fall short in predicting the intense wake mixing brought about by the thick turbulent boundary layers that are formed past laminar separation bubbles
Cambiamenti economici e territoriali in aree alpine: riflessioni urbanistiche dalla Valtellina
No full textQuali sono le ricadute territoriali degli attuali processi di riassetto produttivo ed energetico e di diversificazione turistica nelle aree alpine? Quali forme di ricerca e innovazione possono contribuire a orientare tali processi verso obiettivi di transizione ecologica e digitale? Quali dispositivi di pianificazione e quali progetti di rigenerazione spaziale possono supportare l’integrazione di tali processi in territori montani caratterizzati da forti polarizzazioni determinate da eccellenze produttive e paesisticoambientali e profonde fragilità connesse al cambiamento climatico e a dinamiche antropiche transscalari? A partire da queste domande, il servizio esplora prospettive d’azione per la ridefinizione dei comparti produttivi in Valtellina al fine di delineare futuri scenari territoriali di rigenerazione diffusa
Parametric Evaluation of Morphed Wing Effectiveness
No full textRecently, continuous improvements in aircraft manoeuvrability and fuel consumption reduction have led researchers to investigate additional wing configurations based on morphing concepts. Morphing is also a potential solution for noise level reduction and may therefore represent an additional benefit. The advantages of morph-type schemes over traditional control surfaces during specific manoeuvres become a key parameter in the preliminary design stage. In this work, three types of airfoil morphing applied to a typical basic wing are considered and analysed: leading-edge morphing, trailing-edge morphing, and rib twist. The aerodynamic performance of each configuration is evaluated through a numerical procedure combining a panel method and a vortex lattice method. Drag reduction in morphed versus conventional wings under identical flight conditions is quantified, allowing the identification of the most efficient configuration. The analyses consider both roll manoeuvres and high-lift flight phases by evaluating changes in design parameters—such as chord-wise hinge positions, span-wise morph distribution, and morphing angles—which are compared and discussed. For the rolling manoeuvre, increasing the span-wise morphing region improves drag reduction, but not by more than 5%. When shifting the hinge position from 60% to 80% of the chord, similar drag reduction levels can be achieved, although the required morph angle differs under the same conditions. The effect of different drag components is also assessed, showing that the induced drag component is predominant for low aspect ratio wings, whereas parasite drag becomes significant at higher aspect ratios. Optimal geometrical configurations are presented and discussed for both manoeuvres. For the rolling, hinge positions yielding typical rolling moment coefficients (i.e., −0.05, −0.06, and −0.08) lie between 65% and 75% of the chord, with span-wise morphing ranges 40% < yrib < 60% producing drag reduction up to 40% compared with a conventional wing. For the high-lift conditions, configurations between 65% < xhinge < 80% and 50% < yrib < 90% allow a drag reduction which can go up to 60%. Another beneficial effect is also observed for the yawing moment coefficient Cn with a reduction of more than 20% for larger aileron surfaces
Wind Power Low-output Event Identification and Accurate Prediction Strategy Based on Global Heterogeneous Information Dynamic Fusion with Dynamic Dual Graph Neural Networks
No full textThe frequent occurrence of wind power low-output events (LOE) poses a serious challenge to the dispatching safety and operation stability of power systems. The existing research lacks a systematic identification and prediction scheme for such events. So, this paper proposes a dynamic dual graph networks to achieve accurate capture of LOE. In the identification graph, used a dynamic graph convolutional network (DGCN) that integrates the time-domain encoding convolutional attention mechanism (TDECAM) to integrate the global meteorological and power information of the wind farm cluster (WFC) to accurately identify the LOE of the target wind farm. By constructing the conditional weighted distance (CWD), the multi-dimensional feature dynamic correlation between wind farms is depicted. In the prediction graph, established a dynamic spatiotemporal heterogeneous graph attention network (STHGAT) that integrates the delay effect of wind speed (DEWS) to characterize the spatiotemporal heterogeneous connections between wind farms of different output types, so as to achieve accurate prediction of power evolution process under LOE. The proposed method was applied to a WFC in Inner Mongolia, China for validation. Compared to traditional models, the accuracy, precision, recall, and F1 score for LOE identification were on average 10.34%, 10.29%, 10.34%, and 10.35% higher, respectively, with identification accuracy exceeding 96%. In power prediction for low-output scenarios, compared to traditional models, the IRMSE and IMAE were reduced by an average of only 4.16% and 3.45%, respectively, while R2 increased by an average of 24.56%. At the wind farm level, overall prediction accuracy improved to over 95%
A gentle introduction to interpolation on the Grassmann manifold
No full textThis paper offers a self-contained exposition of the fundamental mathematical and computational tools for interpolation on the Grassmann manifold, including detailed derivations of geodesics and explicit formulations of the exponential and logarithmic maps. The presentation emphasizes intuition and draws continuous parallels with the Euclidean setting. This pedagogical approach facilitates the understanding of linear, piecewise linear, and high-order interpolation algorithms, as well as their extension to more general manifolds. Two numerical examples are finally used to illustrate the potential of these algorithms: one in the context of parametric model order reduction, and another drawn from stationary iterative methods for linear systems
Invited - Machine Learning for Accelerating Multi-band Optical Communication Systems Optimization
Get PDFMulti-band systems have demonstrated to be a viable solution to sustain capacity growth required by optical communication systems, thanks to the availability of wide bandwidth amplification technologies, like the Raman amplifier (RA). However, extreme levels of optimization are needed to extract all the potential, requiring super-fast and accurate evaluation of the impact of nonlinear effects. This is a tricky task when the transmission bandwidth is very large, as all fiber parameters becomes frequency dependent and the number of data channels and RA pumps is large. Also, the inter-channel stimulated Raman scattering (ISRS) become impactful. Optimization approaches based on Gaussian Noise (GN) models turn to be very complex, with a consequent slow down of the whole design process. Resorting to the fast GN-based closed-form-models (CFMs), it requires a full spectral and spatial knowledge of the signal power profile along the fiber span. This is particularly computational heavy when backward RA is considered. We propose an approach based on machine learning (ML) and neural networks (NN) to accelerate the process. The method, tested for a super-(C+L) system (12 THz bandwidth) and backward Raman amplification, guarantees a high level of accuracy and a significant speed increase