Turin Polytechnic University

ABSTRACT This study presents a novel class of glass‐ceramics (GCs) and GC/zirconia composites designed for dental applications, developed within the CaO‐MgO‐SiO 2 ternary system through the synergistic addition of 3.40 wt.% Al 2 O 3 and 25 wt.% 3Y‐TZP. This combined reinforcement strategy effectively enhances mechanical reliability while preserving favorable tribological behavior. XRD and SEM/EDS analyses revealed well‐defined crystalline assemblages, consisting of diopside, wollastonite, and zirconium yttrium oxide in K‐free systems and diopside, α‐potassium magnesium silicate, and zirconium yttrium oxide in K‐containing materials. The incorporation of 3Y‐TZP significantly improved flexural strength and Weibull modulus, indicating increased structural reliability, while maintaining coefficients of dynamic friction comparable to dental enamel. All developed materials meet the flexural strength requirements of ISO 6872, supporting their suitability for dental fixed restorations. Moreover, their mechanical properties closely resemble those of natural dentine and enamel, ensuring harmonious interaction with surrounding hard tissues without causing injury. Brittleness index values further confirm their suitability for CAD/CAM processing, highlighting the strong potential of these novel GCs and GC/zirconia composites as next‐generation dental restorative materials.

This study introduces and compares online rule-based and optimization-based energy management strategies for a mild hybrid electric vehicle, with their performance evaluated against an offline Dynamic Programming benchmark. A structured rule-based strategy is proposed to enforce engine operation along its optimal efficiency line, while the remaining power demand is balanced by the electric motor. To achieve charge-sustaining battery operation, a soft state of charge regulation mechanism is incorporated. An Equivalent Consumption Minimization Strategy (ECMS) is also developed using a precise formulation of battery equivalent fuel consumption computed from instantaneous engine and electric path efficiencies, instead of constant efficiencies used in the literature. DP, which provides a globally optimal solution over the entire driving cycle, is employed as a benchmark for assessing the rule-based and ECMS strategies. The control strategies are compared under charge-sustaining conditions, considering engine and motor operation characteristics, overall fuel consumption, and battery usage intensity. Furthermore, the influence of load shifting between the internal combustion engine and the electric motor on overall vehicle performance is analyzed. Fuel consumption decreases by 13.5% relative to the engine-only baseline with the proposed ECMS with precise equivalent fuel consumption, and DP yields an additional 1.6% benefit. Compared with the developed rule-based controller, ECMS nearly halves the battery usage intensity, and DP provides 3.1% further reduction relative to ECMS.

Digital Twin technology has become one of the key component of Industry 4.0 and smart manufacturing systems. The concept enables real-time synchronization between physical industrial assets and their virtual representations. This research investigates the architecture, mathematical models, and predictive algorithms used in Digital Twin systems for industrial automation. A comprehensive mathematical framework based on dynamic system modeling, data-driven analytics, and predictive maintenance algorithms is proposed. Experimental analysis demonstrates that the implementation of Digital Twin technology improves monitoring accuracy, reduces downtime, and increases production efficiency. The proposed system integrates sensor networks, industrial IoT, cloud computing, and machine learning algorithms to optimize industrial operations.

BACKGROUND: The isolation of DNA or RNA is a crucial step that must be carried out before many biochemical and diagnostic procedures can be performed. To date, several methods have been developed for the purification of biological entities. The most common magnetic materials/systems used in biological separations involve the use of nanoparticles with an iron oxide core. However, the use of such systems for the separation of nucleic acids from biological fluids does not seem to be free of problems. It required laborious, multi-step and lengthy procedures, skilled chemists and expensive reactants. Such considerations explain the growing demand for alternative techniques. A patented process developed by some of us, could be used for the production of reliable magnetic adsorbents. The process uses commercially available zeolites as raw materials and the final product is a metal-ceramic nanocomposite. The same metal-ceramic nanocomposites, which have magnetic properties, have already been successfully used for a wide range of applications. The aim of this study was to compare RNA extraction from blood samples from pediatric patients with RSV bronchiolitis extracted with the manual commercial Promega ReliaPrep silica column kit and a manual protocol using magnetic nanocomposites derived from zeolite.METHODS: Thirty pediatrics bronchiolitis patients and 10 healthy pediatrics control. Each sample was extracted in duplicate, with both automated extraction and nanocomposites extraction. These extracts were compared in terms of yield and quality, measured by traditional UV spectroscopy, and amplifiability, the latter measured by evaluating the results obtained from type 1 interferon signature with real-time PCR.RESULTS: The zeolite-derived nanocomposite method for RNA extraction yielded a higher RNA quantity compared to the commercial magnetic silica bead kit (Promega), although with lower purity and amplification efficiency. Nevertheless, both extraction methods consistently confirmed the interferon signature data.CONCLUSIONS: Although the zeolite-based method produced higher RNA yields, residual contaminants likely compromised RNA purity and reduced amplification efficiency compared to the commercial kit. Nonetheless, the final IFN score was comparable between methods. Ongoing optimizations aim to improve nanocomposite purity and amplification efficiency, highlighting their potential as low-cost alternatives to commercial magnetic systems for diagnostic and research applications.

Abstract This paper presents a methodological analysis of wind gust spectra, with a focus on estimating the wind gust power spectral density (PSD). Three spectral estimation approaches are systematically investigated: zero-padding, Welch’s method, and a length-weighted PSD formulation. The main objective is to evaluate the accuracy, robustness, and practical suitability of these techniques when applied to wind-gust signals obtained from two distinct sources: onboard sensors collected during a dedicated flight test campaign and measurements from a low-Earth orbit (LEO) satellite platform. Comparative results show that Welch’s method yields the most stable and reliable PSD estimates for flight-test data, particularly in terms of variance reduction and spectral smoothness. The same approach can be extended to satellite-based signals; however, the inherently low sampling frequency of LEO data severely constrains their usable bandwidth. As a consequence, satellite-derived gust spectra are of limited relevance for detailed assessments of aircraft structural response and fatigue, although they may remain valuable for large-scale atmospheric characterization.

Composite materials are known to excel in high-performance applications, particularly in the aerospace industry. Due to this fact, there is a growing concern regarding the maintenance, repair, and overhaul operations of such materials. Within this context, the best approaches for restoring the degraded mechanical properties of composites converge toward the use of fiber-reinforced adhesive patches. The present work proposes a novel methodology for the shape optimization of patches for the recovery of locally damaged composite plates. A shape optimization problem was formulated with the aim of minimizing the error associated with the modal response of the repaired structure to that of the undamaged one. Sequential linear programming was employed alongside an interior point algorithm to attain the optimized dimensions of rectangular single-sided patches, which were used in the restoration of simply supported damaged square panels. The damage was introduced by the mechanical removal of material along the central region. Modal and three-point bending tests were conducted to evaluate the performance of the patch repair. The modal response of the repaired panels indicated an efficiency of 98.2 % restoration of the first natural frequency. The effectiveness in terms of mechanical strength was 94.0 % in restoring the maximum resisted load, and 96.5 % in terms of the ultimate displacement. • A novel modal-based shape optimization methodology is proposed for conventionally shaped repair patches. • The approach incorporates structural inertia, targeting dynamic response rather than only static stiffness. • Patches optimized solely on a dynamic criterion recovered 98.2% the first natural frequency and over 94% static failure metrics. • Failure modes of repaired panels were governed by the parent laminate, not by the patch or bondline. • Modal response optimization is demonstrated as an effective proxy for restoring overall mechanical performance.