Higher Institute on Territorial Systems for Innovation
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Methods of Analytical Mechanics in Continuum Mechanics: A unifying approach to growth, remodeling, and evolution of defects in simple and grade-one materials
No full textL'abstract è presente nell'allegato / the abstract is in the attachmen
Design and development of Cu-based alloys for AM in the aerospace context
No full textL'abstract è presente nell'allegato / the abstract is in the attachmen
Bridging the agri-food and concrete industries. A systemic design approach for reducing waste and CO2 emissions
No full textThis essay explores the use of agri-food waste within circular design models to develop more sustainable building materials. Addressing the construction sector’s high energy demands and CO2 emissions, the text examines innovative approaches such as utilizing spent grain, a byproduct of beer production, and the chaff (husk of grains) from rice production as a replacement for fine aggregates in concrete. The study highlights the role of design in optimizing material and energy flows between systems to minimize environmental impact, aligning with principles of Industrial Symbiosis and Industrial Ecology. By examining two distinct socio-economic and industrial contexts, Mexico and Italy, the research emphasizes the significance of geographic and cultural factors. The beer industry is identified as a promising source of raw materials due to its substantial waste output, particularly spent brewing grains. Concrete modified with spent grain is evaluated for its physical-mechanical properties, considering both technical feasibility and broader economic and environmental implications. Likewise, byproducts like chaff and straw from rice cultivation, once considered mere waste suitable only for combustion, are now being recognized as valuable raw materials for a variety of applications, including concrete production. The analysis underscores the potential synergies between agricultural and industrial supply chains, contributing to the reduction of resource waste and CO2 emissions
La fabbrica prefabbricata. Marco Zanuso, Eduardo Vittoria, Stabilimento Olivetti, Crema, 1969
No full textBuilt in just seven months, between February and August 1969, the Olivetti plant in Crema represents one of the three occasions for the application of the prefabricated reinforced concrete construction system designed by Marco Zanuso and Eduardo Vittoria in 1967 for the construction of the Italian factories of the company. The system, defined in collaboration with the engineer Antonio Migliasso, is in continuity with the previous projects for industrial buildings drawn up by Zanuso during the fifties, aimed at the integration of structures and plant equipment, but differs from the latter for the unprecedented comparison of the same design principles with prefabrication.
The result is characterized first by the standardization of the three prefabricated structural components (a column, a main beam and a secondary beam) and by the definition of their design starting not only from the needs of the structure in place, but also from its construction through a prefabrication process capable of optimizing materials, energy, time, and labor.
A real industrial design project applied to the spaces of that production which Marco Zanuso interpreted in structure and shape
CLEAR: Scheduling of Multi-model Mobile Workloads on Chiplet Edge Platforms
No full textTo support multiple AI-based applications, mobile systems need to collaboratively execute DNN architectures on heterogeneous AI accelerators. At the same time, the increasing DNN complexity and high degree of diversity in workloads on multichip module (MCM) accelerators are pushing AI processing off mobile nodes onto the edge. This has made computationally intensive, edge-based solutions the dominant approach for the deployment of modern neural networks. However, the rigid structure of fully-executed DNNs fails to align with the mod- ular nature of MCM architectures, limiting their potential for efficient execution. In this paper, we introduce CLEAR, a novel optimization framework based on geometric programming that leverages both transformer-based and more canonical DNNs with early exits. CLEAR enables fast, coordinated decision- making across DNN design, workload distribution, and resource allocation, with the overarching goal of minimizing inference energy consumption. To our knowledge, this is the first work to integrate dynamic DNN optimization with decisions at both the communication infrastructure and hardware accelerator levels. We evaluate CLEAR using real-world wireless measurements and dynamic DNNs applied to computer vision inference tasks. Our results demonstrate that CLEAR achieves near-optimal performance and reduces energy consumption and resource usage by over 80% and 70%, respectively, compared to its benchmark
Mechanistic Insights Into How Rewiring and Bifurcation Angle Affect DK‐Crush Stent Deployment
No full textBackground: Double kissing crush (DKC) is a preferred two-stent technique for complex coronary bifurcation lesions. Proximal cell rewiring is routinely recommended to reduce technical failure, and DKC is considered effective across various bifurcation angles. However, it remains unclear whether this standard approach is optimal for all patients. Aims: This study investigates the interaction between bifurcation angle and rewiring configuration to identify anatomy-specific strategies. Methods: Computational modeling of the DKC procedure was used to simulate 12 DKC procedures across three left main bifurcation angles (45°, 70°, and 100°) and four rewiring configurations: proximal−proximal (P−P), proximal−distal (P−D), distal−proximal (D−P), and distal−distal (D−D). Evaluation metrics included stent malapposition, side branch ostium clearance, arterial wall stress, low time-averaged endothelial shear stress, and high shear rates. Results: DKC performed in wide bifurcations (100°) resulted in worse outcomes, with malapposition reaching 18%, side branch clearance down to 23%, and up to twice the exposure to adverse high shear rates compared to narrower angles. In contrast, intermediate (70°) and narrow (45°) angles generally resulted in more favorable outcomes, though optimal rewiring varied by angle. Proximal strategies, that is, P−P and P−D, were most effective at 70°, while D−D performed best at 45°. No single strategy was consistently superior across all bifurcation angles. Conclusions: DKC outcomes depend on bifurcation angle and can be optimized by tailoring rewiring strategies, challenging the current clinical understanding. These findings support anatomy-specific procedural planning and intravascular imaging to guide rewiring. This study provides a mechanistic rationale to improve clinical decision-making and tailor bifurcation interventions
Reduced-Dynamic Filtering for GNSS-Only Orbit Determination in Cislunar Space: An Experimental Assessment with LuGRE Data
No full textGlobal Navigation Satellite Systems (GNSSs) are increasingly regarded as an enabling technology for onboard Positioning, Navigation and Timing (PNT) beyond the Space Service Volume (SSV), where navigation autonomy is becoming central to sustained lunar exploration. In cislunar space, however, GNSS-based navigation is challenged by intermittent satellite visibility and poor measurement geometry, motivating the development of Orbit Determination and Time Synchronization (ODTS) architectures that remain reliable under sparse radiometric conditions while being computationally feasible for onboard use. Reduced-dynamic filtering provides a practical compromise by combining a physics-based orbit propagator with stochastic dynamic compensation, offering robustness against dynamic mismodeling compared to purely-dynamic approaches. This work presents the first experimental assessment of GNSS-only reduced-dynamic ODTS in cislunar space using real spaceborne data from the Lunar GNSS Receiver Experiment (LuGRE). Raw pseudorange and Doppler-shift measurements, recorded by the GNSS payload during three representative operations across the Earth-Moon phasing orbits and on the lunar surface, are processed using a first-order Extended Kalman Filter (EKF). To account for the different perturbation environments in each orbit regime, the ODTS filter incorporates a distance-adaptive lunar gravity model, ensuring physical consistency across Earth-Moon regimes. The results obtained across three LuGRE operations, spanning geocentric distances from 15.03 to 51.69 Earth radii (RE) and including one surface operation, demonstrate the viability of pseudorange-based reduced-dynamic filtering despite poor satellite geometry and depleted observability. In the most favorable low altitude scenario, the position error reaches 1.36 km at the 95th percentile. In the more challenging cislunar and lunar-surface operations, the orbit estimation accuracy remains within 3.91 km. Velocity errors are below 1 m/s across all cases. While the filter provides stable and continuos solutions even under degraded conditions, the analysis reveals sensitivity to residual systematic measurement errors, which can induce biased and inconsistent estimates. These preliminary results provide an experimentally grounded benchmark for GNSS-based ODTS in cislunar space and motivate further improvements in measurement modeling and adaptive process noise tuning
Measures of extended fractional Deng entropy and extropy with applications
Get PDFRecently, Zhang and Shang introduced modifications to the concept of fractional entropy and proved some properties based on the inverse Mittag-Leffler function (MLF). The Deng entropy serves as a valuable measure in the Dempster-Shafer evidence theory (DST) to tackle uncertainty. In this study, we extend the fractional Deng entropy measure, introducing two distinct versions: (Formula presented.) and (Formula presented.) We call this new measure the extended fractional Deng entropy, EFDEn. Additionally, we apply a similar approach to the fractional Deng extropy measure, resulting in (Formula presented.) and (Formula presented.) We call this new measure the extended fractional Deng extropy, EFDEx. These two measures are complementary, leading to provide a deeper analysis of known and unknown information. Subsequently, we conduct a comparative analysis of these measures within the DST framework. We also propose the decomposable fractional Deng entropy, an extension of the decomposable entropy for Dempster–Shafer evidence theory, which effectively decomposes fractional Deng entropy. Finally, we delve into a pattern recognition classification problem to highlight the importance of these new measures