Higher Institute on Territorial Systems for Innovation
PORTO@iris (Publications Open Repository TOrino - Politecnico di Torino)Not a member yet
146173 research outputs found
Sort by
Efficacy of Xanthan‐Based Chlorhexidine Gel in Peri‐Implant Mucositis Treatment: A Split‐Mouth Randomized Clinical Trial
Get PDFObjectives: To investigate the potential benefits of Xanthan-based chlorhexidine gel application in addition to professional mechanical plaque removal (PMPR) in the treatment of peri-implant mucositis (PM). Material and methods: Subjects diagnosed with PM were consecutively included in this randomized split-mouth study. All participants received a single session of PMPR using titanium curettes, followed by the application of an air-polishing glycine powder device. Implants allocated to the Test group were additionally treated with local delivery of Xanthan-based chlorhexidine gel. Clinical evaluation was performed at T0 (i.e., baseline), at 30 (T1), 90 (T2) and 180 days (T3) after treatment, while treatment success was evaluated at T2 and T3. Change in bleeding on probing (BoP) was considered as primary outcome measure. A logistic multivariate regression model was developed to explore the predictive role of implant and patient-level variables on primary outcome measure. Results: Fifty-nine patients (mean age: 65.4 ± 8.7 years; 54.2% male; 88.1% non-smokers) and 182 implants completed the study. At T1, only the Test group displayed a significant reduction in BoP (p 0.05). T2 Treatment success as well as the frequency distribution of complete (BoP = 0) and partial (BoP ≤ 1, ≤ 2, ≤ 3) disease resolution did not significantly differ between groups (p > 0.05). Multiple regression model revealed that smoking (p = 0.008), and implant position (i.e., premolar p = 0.009) did significantly affect the primary outcome measure. Conclusion: The adjunctive use of XanCHX gel did not result in any statistically significant clinical benefit compared to PMPR alone in the treatment of PM up to 6 months, despite the reported clinical positive effects within the first month after treatment
No-Reference Quality Assessment of Dermoscopic Images Using Minimal Expert Supervision
Get PDFBackground: Assessing image quality is critical in medical imaging to ensure diagnostic reliability. Traditional no-reference image quality assessment (IQA) metrics designed for natural images often fail to address the complexities of medical images. This study proposes DermaIQA, a novel no-reference metric for dermoscopic images that aligns quality scores with clinical perception. Methods: We developed a degradation pipeline simulating realistic artifacts without requiring extensive manual labeling. From 812 expert-classified images, we generated a comprehensive dataset (>125,000 images) using controlled blur and compression techniques. An iterative ranking procedure converted these degradations into a continuous quality scale, which was used to train a vision transformer model. Results: The proposed IQA metric outperformed both heuristic and deep learning techniques, achieving 92% accuracy in distinguishing high-quality vs. low-quality images. The approach demonstrated robust generalization when tested on external datasets with different acquisition characteristics, confirming its relevance across varied imaging conditions. Conclusions: DermaIQA represents the first dermatology-specific quality metric that minimizes expert annotation requirements while maintaining clinical relevance. This tool enhances workflows through real-time acquisition feedback and acts as a gatekeeper for AI diagnostic systems, ensuring only high-quality images are processed. The trained model and inference scripts are publicly available
Bridging Physics and Data in Metal Powder Bed Fusion with Scientific Machine Learning
Get PDFMetal Powder Bed Fusion (PBF) epitomizes the complexity of modern manufacturing, where strongly coupled thermal, mechanical, and fluid-dynamic interactions evolve over multiple spatial and temporal scales. Capturing these multiscale phenomena with predictive fidelity remains a central barrier to reliable, repeatable, and certifiable metal additive manufacturing. Conventional numerical solvers such as finite element or finite volume methods provide physical rigor but are computationally prohibitive for design exploration, uncertainty quantification, or real-time control. This paper positions Scientific Machine Learning (SciML) as a unifying paradigm that bridges the interpretability of physics-based modeling with the adaptability of data-driven inference. By embedding governing physical laws directly into learning procedure, or introducing biases into the architectures, SciML enables models that are both data-efficient and physically consistent, capable of accelerating high-fidelity simulations and supporting in-situ process optimization. The review is structured around three persistent challenges: mathematical complexity stemming from coupled multiphysics interactions, training instability inherent to physics-informed optimization, and practical integration barriers that limit industrial deployment. We analyze how emerging formulations such as Physics-Informed Neural Networks and Neural Operators address these challenges and advance toward robust, generalizable process surrogates. Beyond synthesizing existing methods, the paper outlines a roadmap toward physics-grounded digital twins, signaling a transformative step toward intelligent, self-correcting metal additive manufacturing systems
Electrochemical methanation of carbon dioxide: Advances, challenges, and perspectives for biogas upgrading
No full textThe electrochemical conversion of carbon dioxide to methane is emerging as promising strategy for both mitigating CO2 emissions and enabling renewable energy storage in a chemically stable, energy-dense form. Among the possible products of CO2 electroreduction, methane stands out due to its high volumetric energy density, compatibility with existing natural gas infrastructure, and potential for direct integration into current energy systems. This review provides a comprehensive overview of the recent advancements in the electrochemical CO2to-CH4 conversion, with particular focus on catalyst development, cell configurations, and system-level performance under industrially relevant conditions. Key challenges such as low selectivity, limited current densities and long-term operational stability are critically discussed. In addition, the review also explores the technoeconomic aspects of electrochemical methanation, highlighting pathways toward scalable and sustainable deployment. Special emphasis is then placed on the potential application of this technology for biogas upgrading, which offers a unique advantage by utilizing an already CO2-rich feedstock without the need for additional separation steps. Finally, we outline future directions for research aimed at making electrochemical biogas upgrading a viable component of circular carbon and renewable energy strategies
The making of Alpine Architecture. Media, awards and exhibitions in Vorarlberg and Grisons
No full textDevelopment and qualification of a novel process for the treatment and conditioning of spent ion exchange resins into geopolymers
No full textL'abstract è presente nell'allegato / the abstract is in the attachmen
The Material Technosystem of Cattle Farming
No full textWe interpret the architecture of the dairy barn as an interplay of zootechnical knowledge, hygiene practices, and metabolic and thermodynamic processes, as well as a stage where animal and human labor is subsumed by capital. By analyzing historical and contemporary zootechnical handbooks, manufacturer’s records, catalogs, brochures, advertisements, and other industrial sources, we aim to narrate a multifaceted history of the diverse trends and shifts in the architecture of dairy cattle farming that emerged con- currently across different fields and locations, under the influence of techno-scientific advances, material science, climate, regulations on animal welfare and food safety, and digitalization
A Methodology for Evaluating Economic–Environmental–Social Sustainability
Get PDFThis paper builds on research from a project aimed at promoting the circular economy through processes based on low-impact materials derived from natural fibers. A methodology is developed to assess the economic, environmental, and social sustainability of alternative production scenarios, thereby supporting the ranking of options. Assuming the principles of Life Cycle Thinking and circular economy and the operational aspects of Life Cycle Costing (LCC), Life Cycle Assessment (LCA), Social Life Cycle Assessment (S-LCA) approaches normed by international standards, an integrated approach is proposed based on the construction of a joint Global Cost indicator. Attention is paid to harmonizing impacts assessed in their own units of measurement to arrive at a monetary indicator for summarizing and simplifying the prioritization of alternatives. As a result, the integrated Global Cost calculation methodology is presented to internalize social and environmental impacts, as well as economic ones, and to evaluate the sustainability of materials derived from primary and waste natural fibers
Time-dependent deformations in deep tunnels: Insights into uncertainty and variability of rheological behavior
No full textThe uncertainty in determining rock mass properties significantly impacts tunnel stability. Additionally, squeezing conditions worsen tunnel stability, causing the tunnel to gradually converge over time. This paper addresses this issue and investigates the long-term behavior of deep tunnels using both visco-elastic and viscoelasto-plastic models. This study also includes risk-based analyses to offer a quantitative tool for engineering decision-making. Initially, an analytical method is introduced to calculate tunnel convergence in a visco-elastic rock mass. The uncertainty of key parameters that significantly affect tunnel behavior is also considered. Using MATLAB, the probability distributions of tunnel wall deformations over time are determined. The results indicate that, except in one case, the long-term tunnel convergence follows a right-skewed Gamma distribution, especially with a low GSI in both visco-elastic and visco-elasto-plastic models. This suggests that deterministic methods may not be reliable for ensuring the safety of long-term tunnel designs