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
Independent inventors in Italy: The role of sex and age in career development
Get PDFIndependent inventors are considered important contributors to technological progress. However, empirical studies on individuals who retain the intellectual property rights in patented inventions are relatively scarce. This work analyzes a sample of more than 20k inventors who debuted between 1994 and 2017 and who filed at least one patent at the Italian Patent Office (UIBM) as independent: i.e., they were also the applicant/assignee. UIBM reports independent inventors’ national tax IDs, which allow identification of sex, date of birth, and place of birth. Inventors’ patent portfolios are reconstructed using their names and through filtering criteria to reduce the presence of false positives and negatives. The analyses confirm the presence of female underrepresentation (slowly declining), and sex-related differences in mean age and age distribution at the patenting debut. The dynamic process from independent to organizational inventor is then examined. Results of the survival analyses suggest that, ceteris paribus, female inventors are less likely to become organizational inventors than their male counterparts, while the role of age is negligible. However, within the subsample of inventors becoming organizational, female innovators transition more quickly, suggesting a potential selection effect that excludes many female patentees from pursuing their careers
Activation analysis of a compact Tokamak using Deuterium–Helium3 fuel
Get PDFRecent advancements in high-temperature superconducting (HTS) magnets have enabled tokamaks to reduce dimensions and operate with higher plasma parameters. This opens to the possibility of using advanced fuel mixtures such as Deuterium-3Helium (DHe3). Compared to traditional fuels, DHe3 offers the potential to reduce neutron-induced activation and minimize the presence of tritium in the fuel cycle. In a fusion reactor that uses a 50% Deuterium-50% 3Helium mixture, neutrons are produced solely in the Deuterium-Deuterium (DD) and Deuterium-Tritium (DT) side reactions, while Tritium can be produced during DD reactions and is therefore absent at the startup of the machine. This study proposes a comprehensive neutronic and activation analysis of a compact, high-field tokamak employing DHe3 fuel. Using OpenMC, an open-source Monte Carlo code, the feasibility and performance of this fuel mixture within the confines of a compact fusion reactor will be investigated. The activation analysis will be limited to the First Wall, Blanket and inboard Toroidal Field coils, with some simplifications in the tokamak layout. By analyzing neutron interactions and activation processes, as well as design modifications, we seek to assess the potential benefits and challenges associated with implementing DHe3 as fuel
Circuit modeling and analysis of matching configurations for the DTT ICRF system
Get PDFIn Ion Cyclotron Range of Frequencies (ICRF) systems, transmission lines, based on rigid coaxial cables, deliver the power from the radiofrequency generators to the antennas, matching the impedances to protect the generator, and maximizing the power transfer to the plasma. This work performs such an analysis, employing a circuital approach to evaluate different matching configurations, checking the maximum voltage all over the cables and avoiding heavy electromagnetic simulation for a quick and easy comparison. Simulations were run for the ICRF system of the Divertor Tokamak Test facility (DTT) using the circuit simulation tool of Ansys Electronic Desktop. After choosing which circuit fits the best in this application, the whole antenna matching circuit was modelled and verified through the active S-parameters. The analysis has proceeded by drawing the ELM resilient schemes for two antennas with-3dB hybrid couplers, showing the power ref lected into the dummy load varying the power ratio and the phasing between the straps by a source tapering of amplitude and phase at the generator
Preservation of wood structure using stabilized alkaline bleaching agents: a novel and mild approach to enhance whitening for lignin-rich wood template
Get PDFPreservation and enhancement of wood properties have become increasingly important due to the growing demand for sustainable materials in the construction and manufacturing industries. This study focuses on bleaching processes as an alternative to conventional delignification, aiming to retain a significant amount of lignin while achieving the desired whiteness of the wood template. In particular, stabilized alkaline hydrogen peroxide is investigated as a bleaching agent under mild conditions. The treatment preserves more than 90% of the native mass in both balsa and birch, ensuring that most of the wood’s structural components are maintained. This results in a high wood volume fraction, corresponding to reduced porosity and a structure closely resembling that of unbleached wood. Fourier Transform Infrared spectroscopy (FTIR) semi-quantitative analysis further confirms that the relative lignin content is retained, ranging between 70 and 80% depending on the species. Unlike conventional approaches that often degrade the wood structure and require long treatment times at high temperatures, the proposed process is performed at room temperature under mild conditions and completed in less than 2–4 h, thus reducing energy demand and avoiding harmful byproducts. Overall, this work provides a bleaching strategy that combines efficiency, structural integrity, and sustainability, offering bleached wood templates suitable for further characterization and advanced applications
Robust Adaptive Model Predictive Control for Tracking in Interconnected Systems via Distributed Optimization
Get PDFThis study presents a novel Distributed Robust Adaptive Model Predictive Control (DRAMPC) for tracking in multi-agent systems. The framework is designed to work with dynamically coupled subsystems and limited communication, which is restricted to local neighborhoods. The proposed approach explicitly accounts for parametric uncertainties and additive disturbances by employing a tube-based formulation to bound the system response for any possible uncertainty realizations. To ensure recursive feasibility and asymptotic stability, contractivity properties for the terminal cross-section are derived alongside a structured stabilizing gain for the closed-loop dynamics. The conservativeness of the tube-based formulation is relaxed by exploiting a distributed set membership via recursive identification of the parameter uncertainty set. The control problem is formulated by leveraging the Artificial Reference method for piecewise reference signals to ensure feasibility even when the desired reference is not directly reachable. The consensus ADMM algorithm is employed to solve the distributed optimization problem efficiently while maintaining scalability as the number of agents increases. Furthermore, the artificial reference formulation is extended to trajectory tracking, allowing the controller to track time-varying references while preserving feasibility. The effectiveness of the proposed method is demonstrated through illustrative examples, highlighting its capability to achieve accurate and robust tracking in multi-agent uncertain systems
Robust Synthetic Air Data Estimation via Kalman-Aided Deep Learning Approach for Analytical Redundancy
No full textReliable air data is essential for safe and stable flight operations. However, physical air data sensors are susceptible to failure due to environmental disturbances, especially in ultralight manned aircraft where hardware redundancy is often impractical due to strict size, weight, and power constraints. Although model-based synthetic air data systems (SADS) have been proposed to reduce sensor reliance, they require precise aerodynamic coefficients and are sensitive to modeling errors. To address these limitations, this study proposes a lightweight, data-driven SADS framework based on a hybrid deep learning model that combines temporal and trend-based features. An unscented Kalman filter (UKF) is applied as a post-processing step to enhance robustness against noise and anomalous inputs. The system is trained and validated on real-world flight data and demonstrates improved accuracy and stability over conventional deep learning baselines. These results suggest that the proposed method offers a robust and complementary alternative to model-based SADS, particularly in resource-constrained flight environments
Extending Kubernetes for Pods Integrity Verification
Get PDFCloud computing is driving a substantial shift of tenant applications and services to external infrastructures administered by third-party providers. As a result, tenants experience an almost complete loss of control over deployment and execution monitoring, which in turn limits their ability to enforce security mechanisms and policies. In this context, the risk of executing unintended and potentially harmful operations increases, along with the likelihood that such actions may go unnoticed. Integrity verification is a key countermeasure, ensuring code integrity and detecting compromises. Cloud computing extensively leverages resource virtualisation, and in this context, Kubernetes has emerged as the de facto standard for cloud application management, orchestrating workloads into groups of containers known as Pods. However, virtualisation represents an additional obstacle to security assurance, and providing integrity verification in such environments remains an open challenge due to the absence of standardised procedures. Despite the transition to cloud environments, tenants must still verify application integrity and indirectly assess the underlying infrastructure security. Trusted computing techniques offer a practical approach to this challenge, mainly through remote attestation, which allows systems to generate verifiable proofs about their integrity state, validated then by a trusted external entity. This paper presents a remote attestation architecture integrated into the Kubernetes framework, allowing tenants to obtain non-repudiable evidence of the security posture of their applications and the integrity of the hosting platforms, thereby restoring visibility and control in cloud environments. The proposed solution was evaluated through functional and performance tests, demonstrating both effectiveness and minimal overhead
Explainable Machine Learning and Deep Learning Models for Predicting TAS2R-Bitter Molecule Interactions
Get PDFThis work aims to develop explainable models to predict the interactions between bitter molecules and TAS2Rs via traditional machine-learning and deep-learning methods starting from experimentally validated data. Bitterness is one of the five basic taste modalities that can be perceived by humans and other mammals. It is mediated by a family of G protein-coupled receptors (GPCRs), namely taste receptor type 2 (TAS2R) or bitter taste receptors. Furthermore, TAS2Rs participate in numerous functions beyond the gustatory system and have implications for various diseases due to their expression in various extra-oral tissues. For this reason, predicting the specific ligand-TAS2Rs interactions can be useful not only in the field of taste perception but also in the broader context of drug design. Considering that in-vitro screening of potential TAS2R ligands is expensive and time-consuming, machine learning (ML) and deep learning (DL) emerged as powerful tools to assist in the selection of ligands and targets for experimental studies and enhance our understanding of bitter receptor roles. In this context, ML and DL models developed in this work are both characterized by high performance and easy applicability. Furthermore, they can be synergistically integrated to enhance model explainability and facilitate the interpretation of results. Hence, the presented models promote a comprehensive understanding of the molecular characteristics of bitter compounds and the design of novel bitterants tailored to target specific TAS2Rs of interest
OMNIS: Semantic RAN Slicing via Dynamic Split Neural Networks
No full textEdge computing enables resource-constrained de- vices to execute machine learning applications via task offloading. To this aim, radio access network (RAN) slicing is instrumental to provide the necessary network resources. However, current RAN slicing approaches rely on static computing models, thereby constraining their ability to leverage the dynamic semantic data representation capabilities enabled by recent neural architec- tures. In this paper, we propose OMNIS, a semantic RAN slicing framework for edge computing built on dynamic split neural models. OMNIS embeds a dynamic form of neural compression paired with adaptive data encoding for task offloading, enabling flexible communication payload and computing options. We explicitly study the interplay between neural compression and information quantization in computer vision tasks and design a novel “Box” quantization scheme that improves resiliency to bit errors as a function of compression rate. Considering partial observability and differing objectives of mobile devices and the edge server, we formulate neural gate control and resource slicing optimization problems and solve them via multi- agent contextual multi-armed bandits and convex optimization algorithms. Experimental results show that OMNIS improves inference accuracy by up to 22.85% and reduces quality of service violations by up to 10x. The evaluation code is available at https://github.com/qlt315/OMNIS
The effect of macro and microstructure on the mechanical and corrosion behaviour of an innovative LPBF-processed AlSi9Cu3 alloy
Get PDFThe rapid solidification associated with laser powder bed fusion (LPBF) promotes oversaturation of the α-Al matrix with alloying elements and redistribution of second phases, resulting in a fine-grained melt pool microstructure that strongly affects corrosion behaviour. This work investigates the corrosion response of LPBF- processed AlSi9Cu3 alloy under different heat treatments, focusing on the correlation between melt pool features, microstructure, and corrosion mechanisms. In the as-built condition, the alloy exhibited a well-defined melt pool structure with Si- and Cu-rich precipitates concentrated along the melt pool boundaries. These precipitates acted as efficient cathodic sites, driving micro-galvanic interactions and leading to an inter-melt pool corrosion morphology. Low-temperature heat treatments preserved the melt pool structure while promoting the precipitation of additional Cu- and Si-rich phases. This increased the galvanic interactions and exacerbated selective corrosion, producing a more severe inter-melt pool attack. Solution treatments dissolved and redistributed the precipitates, producing a more homogeneous microstructure. The resulting reduction of galvanic coupling significantly improved corrosion resistance and promoted a more uniform corrosion morphology. Conversely, artificial aging promoted the precipitation and coarsening of Cu-rich phases, which increased intergranular corrosion susceptibility. These findings emphasize the pivotal role of melt pool structure control and tailored heat treatments in designing LPBF aluminium alloys with improved corrosion resistance