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Parametric modeling of cradle-to-gate carbon emissions from gas-atomized AISI 316L powders under closed-loop feedstock strategies
No full textThis study introduces a parametric framework for the cradle-to-gate assessment of carbon emissions associated with the production of gas-atomized AISI 316L stainless steel powders intended for use in additive manufacturing and other powder metallurgy processes. The model provides a detailed representation of upstream material flows and includes all major unit operations involved in powder production, such as feedstock preparation, gas atomization, sieving and blending, and packing. By varying the composition of the feedstock charged into the atomizer crucible, the framework enables the estimation of carbon emissions across a wide range of scenarios reflecting alternative sourcing strategies. The case study on AISI 316L highlights the environmental benefits of integrating closed-loop material flows, including the recirculation of off-specification powders and the direct use of compatible metallic scrap. Furthermore, broadening the acceptable powder size range significantly improves atomization yield, thereby reducing the specific carbon intensity of usable powder output. Such an approach lays the foundation for the development of robust decision-support tools for process planning in gas atomization, with direct implications for industrial-scale powder production
The rehabilitation of atrophic jaws using short implants with different surface treatment. A multicentred cross-over randomized trial
No full textL'abstract è presente nell'allegato / the abstract is in the attachmen
Signal-Based Dynamic Identification of Composite Steel–Concrete Bridges Using Short-Duration Records
Get PDFtructural Health Monitoring (SHM) of existing bridges increasingly relies on dynamic measurements to assess structural performance and detect potential damage. However, the practical implementation of long-term vibration-based monitoring is still constrained by the volume of data required and the complexity of continuous acquisition systems. In the context of ensuring the safety and performance of existing bridge infrastructure, vibration-based monitoring offers a powerful tool for detecting changes in structural behavior. This study presents an extended investigation of dynamic monitoring applied to composite steel–concrete viaducts, focusing particularly on the signal-analysis framework and methodological enhancements. Short-duration accelerometric records are processed through an automated signal-selection pipeline and advanced modal-parameter extraction algorithms to yield identification of modal features. Emphasis is placed on the statistical evaluation of modal-parameter stability, effects of operational and environmental variability, and the potential for long-term trend detection. The results highlight the limits of short-length recordings when OMA techniques are applied. Nevertheless, appropriate signal processing and data handling can provide acceptable insights into the dynamic characteristics of large bridge systems. The methodological findings provide a foundation for improved monitoring workflows, showing the amount of information that can be retrieved using a cost-effective hardware deployment and supporting further development toward structural digital twins
Multi-fidelity probabilistic failure onset analysis of composite structures under uncertainties
Get PDFAdvancements in composite manufacturing have enabled innovative design strategies to enhance stress distribution, stiffness, and overall performance of composite structures. However, uncertainties related to material variability, load fluctuations, and manufacturing defects continue to pose serious challenges for structural reliability and early failure prediction. This study presents a novel multi-fidelity probabilistic framework for analyzing composite laminates under uncertainties. The methodology integrates low- and high-fidelity structural theories generated via the Carrera Unified Formulation (CUF). An adaptive Gaussian Process Regression (GPR) model is employed to construct a probabilistic surrogate that selectively uses high-fidelity theories only where needed, based on uncertainty-driven learning. Compared to conventional Monte Carlo Simulations (MCS) based entirely on high-fidelity models, the proposed framework achieves comparable accuracy, with R2 > 0.99 and nRMSE < 1%, while reducing the computational cost by a factor of two to ten. Convergence is obtained with only 20–300 high-fidelity simulations, against 400–800 required by the reference benchmark. The approach is applied to a composite plate, a free-edge laminate, and an open-hole configuration, where the adaptive multi-fidelity model accurately captures through-thickness stresses and failure indices
Le retour à la ville. L’Architecture d’Aujourd’hui di Bernard Huet, tra Francia e Italia. 1974-1976
No full textBiochar-Coated Drywall Panels for Electromagnetic Shielding Applications in the K-Band
No full textWith the rise of telecommunication systems in recent decades, the implications for human health have prompted a search for ways to reduce the impact of electromagnetic waves in buildings when necessary. A viable and promising solution to realize electromagnetic shielding could be the use of drywall panels coated with a biochar paste, as proposed in this study. Biochar (bio-charcoal), a low-cost and carbon-based material, can be obtained by the thermochemical conversion of different biomass sources. A commercial wood-based biochar thermally treated at 750 °C is considered in this work. Transmission coefficients of several gypsum board elements with a biochar coating are measured in the frequency K-band (18–27 GHz). In addition, the SE of a double panel configuration, obtained by joining two coated boards to form a multilayer structure, is evaluated. The results show that the biochar coating significantly enhances the SE compared to uncoated drywall. At the highest biochar loading investigated (0.20 g/cm2), the shielding effectiveness consistently
exceeds 27 dB for single panels and 46 dB for double panels across the entire frequency band. These findings indicate that biochar-coated drywall systems offer a practical and sustainable solution for integrating electromagnetic shielding into building envelopes, paving the way for innovative applications in indoor exposure control
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
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
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