Politecnico di Milano

Microtia, along with trauma, represents one of the main causes of external ear malformation. Different clinical techniques were developed for the reconstruction of the auricle, but they all have some drawbacks. This work is focused on the development of an innovative 3D porous scaffold, printed by Fused Deposition Modelling (FDM) and based on laser-scanned images of the healthy contralateral ear of the patient. The scaffold was printed using polycaprolactone (PCL) and thermoplastic polyether urethane (TPU) to mimic the components of the cartilage and adipose tissue, respectively. After the optimization of the printing parameters, the 3D surface obtained as an output of the laser scan was elaborated, sliced and used as input for printing the layered structure. Micro CT investigation confirmed the structure homogeneity and good pore interconnection. Mechanical compression and torsion tests were performed to verify that the mechanical properties of the 3D PCL/TPU structure were adequate. The 3D structure exhibited elastic moduluses comparable to those of the cartilaginous and adipose parts of the auricle. The torsion tests showed the adequacy of the structure without detachment between TPU and PCL printed layers. In vitro cell viability tests confirmed the absence of cytotoxicity in both materials. The PCL/TPU layered scaffold reproduced the anatomy of the patient's healthy contralateral ear, representing a good compromise between flexibility and strength. This, along with the other assessed properties, makes this scaffold a valid alternative for the reconstruction of the external ear.

Walking aids for individuals with musculoskeletal frailty or motor disabilities must ensure adequate physical support and assistance to their users. To this end, sensor-enabled human state monitoring and estimation are crucial. This paper proposes an innovative approach to assessing users' stability while walking with WANDER, a novel gait assistive device, by exploiting the correlation between the eXtrapolated Center of Mass ( XCoM ) and the Base of Support ( BoS ) edges. First, the soundness of this metric in monitoring gait stability is proven. Experiments on 25 healthy individuals show that the median value of Pearson's correlation coefficient (p-value $<$ 0.05) remained high during the forward walk for all subjects. Next, a correlation-based variable admittance (CVA) controller is implemented, whose parameters are tuned to physically support users when a gait perturbation is detected (i.e. low values of Pearson's correlation coefficient). To validate this approach, 13 healthy subjects were asked to compare our controller with a force threshold-based (FVA) one. The CVA controller's performance in discriminating stable and perturbed gait conditions showed a high sensitivity value, comparable to FVA, and improved performance in terms of specificity. The number of false and missed detections of gait perturbation was considerably reduced, independently of walking speed, exhibiting a higher level of safety and smoothness compared to the FVA controller. Overall, the outcome of this study gives promising evidence of the proposed metric capability in identifying user stability and triggering WANDER's assistance.

Accurate tire modeling is crucial for optimizing autonomous racing vehicles, as State-of-the-Art (SotA) modelbased techniques rely on precise knowledge of the vehicle's parameters, yet system identification in dynamic racing conditions is challenging due to varying track and tire conditions. Traditional methods require extensive operational ranges, often impractical in racing scenarios. Machine Learning (ML)-based methods, while improving performance, struggle with generalization and depend on accurate initialization. This paper introduces a novel ontrack system identification algorithm, incorporating a NN for error correction, which is then employed for traditional system identification with virtually generated data. Crucially, the process is iteratively reapplied, with tire parameters updated at each cycle, leading to notable improvements in accuracy in tests on a scaled vehicle. Experiments show that it is possible to learn a tire model without prior knowledge with only 30 seconds of driving data, and 3 seconds of training time. This method demonstrates greater one-step prediction accuracy than the baseline Nonlinear Least Squares (NLS) method under noisy conditions, achieving a 3.3x lower Root Mean Square Error (RMSE), and yields tire models with comparable accuracy to traditional steady-state system identification. Furthermore, unlike steady-state methods requiring large spaces and specific experimental setups, the proposed approach identifies tire parameters directly on a race track in dynamic racing environments.

Digital subcarrier multiplexing (DSCM)-based coherent point-to-multipoint transceivers (P2MP-TRXs) are promising for addressing the shift in traffic patterns from point-to-point (P2P) to hub-and-spoke (H&S), and their application in wavelength-switched optical networks (WSONs) can potentially offer enhanced flexibility and efficiency in handling the mixed traffic therein. In this paper, we study how to secure the survivability of P2MP-TRX-based WSONs against packet layer failures with cross-layer restoration (CLR). By analyzing the unique features of P2MP-TRXs, we first design three CLR strategies to restore the traffic affected by packet layer failure(s) and then formulate an integer linear programming (ILP) model to leverage them for cost-effective CLR, i.e., minimizing the cost introduced during the CLR process. Next, we propose a time-efficient heuristic, namely, hHAG-DP, which leverages hybrid dynamic programming (DP) and a hierarchical auxiliary graph (HAG) to find cost-effective CLR schemes quickly. Extensive simulations confirm the effectiveness of our proposals.

Molecular Field-Coupled Nanocomputing (MolFCN) represents a revolutionary approach to computational technology, exploiting single molecules for encoding and processing logical information. MolFCN permits zero-current logical operations to achieve ultra-low power and hyper-miniaturized computing units. This perspective article explores the current state and future potential of MolFCN, highlighting recent technological advancements, potential applications, and the significant challenges that lie ahead. Despite the challenges, the pathway to practical implementation holds significant promise, with obstacles such as scalability, stability, integration, and practical considerations offering opportunities for innovation and advancement. MolFCN can shape the future of nanocomputing and contribute to current major challenges in nanoelectronics by opening key research directions.

In the past years, climate change has affected honey production more and more and the reduction has become a significant risk for beekeepers. In this paper, we discuss the pricing of a parametric insurance policy drafted to cover the potential losses in terms of honey production due to unfavorable weather conditions: the payment of the insurance benefit is triggered by the breaching of predefined thresholds of a weather index, measured over specific relevant periods. The effectiveness of the coverage is verified by the means of random forests, where the honey production is forecast under different real-world weather scenarios and the beekeepers’ loss is compared with the insurance benefit reimbursed (or not) by the policy. The random forest technique is put along with more common ones, such as ordinary least squares regression and mixed linear models. A practical example is given for the Italian market, where the pricing is derived and assessed for three different zones: North, Centre, and South.

Percutaneous coronary interventions in highly calcified atherosclerotic lesions are challenging due to the high mechanical stiffness that significantly restricts stent expansion. Intravascular lithotripsy (IVL) is a novel vessel preparation technique with the potential to improve interventional outcomes by inducing microscopic and macroscopic cracks to enhance stent expansion. However, the exact mechanism of action for IVL is poorly understood, and it remains unclear whether the improvement in-stent expansion is caused by either the macro-cracks allowing the vessel to open or the micro-cracks altering the bulk material properties. In silico models offer a robust means to examine (a) diverse lesion morphologies, (b) a range of lesion modifications to address these deficiencies, and (c) the correlation between calcium morphology alteration and improved stenting outcomes. These models also help identify which lesions would benefit the most from IVL. In this study, we develop an in silico model of stent expansion to study the effect of macro-crack morphology on interventional outcomes in clinically inspired geometries. Larger IVL-induced defects promote more post-stent lumen gain. IVL seems to induce better stenting outcomes for large calcified lesions. IVL defects that split calcified plaque in two parts are the most beneficial for stenting angioplasty, regardless of the calcified plaque size. Location of the IVL defect does not seem to matter with respect to lumen gain. These findings underscore the potential of IVL to enhance lesion compliance and improve clinical outcomes in PCI. The macroscopic defects induced by IVL seem to have a substantial impact on post-stent outcomes.

The implementation of large-scale universal quantum computation represents a challenging and ambitious task on the road to quantum processing of information. In recent years, an intermediate approach has been pursued to demonstrate quantum computational advantage via non-universal computational models. A relevant example for photonic platforms has been provided by the Boson Sampling paradigm and its variants, which are known to be computationally hard while requiring at the same time only the manipulation of the generated photonic resources via linear optics and detection. Beside quantum computational advantage demonstrations, a promising direction towards possibly useful applications can be found in the field of quantum machine learning, considering the currently almost unexplored intermediate scenario between non-adaptive linear optics and universal photonic quantum computation. Here, we report the experimental implementation of quantum machine learning protocols by adding adaptivity via post-selection to a Boson Sampling platform based on universal programmable photonic circuits fabricated via femtosecond laser writing. Our experimental results demonstrate that Adaptive Boson Sampling is a viable route towards dimension-enhanced quantum machine learning with linear optical devices.

The reduction of pollutants and CO 2 emissions is a compelling topic even for small size, high-performance engines for motorbikes. The turbulent jet ignition (TJI) combustion strategy is a noteworthy solution for such powertrains, especially employing a pre-chamber in its passive configuration, thanks to the minor engine modifications needed and the low additional cost. The passive pre-chamber ignition allows to achieve a significant reduction of the combustion duration, hence opening to a possible increase of engine efficiency and performance. In this work, a 3D computational fluid dynamics (CFD) investigation is carried out over an experimental configuration of a naturally aspirated spark ignition (SI) motorbike engine running at several different operating points employing both the conventional and the TJI combustion strategies. The purpose of the research is to achieve a deeper understanding of the experimental data by means of the CFD analyses, clarifying the advantages and drawbacks of the retrofit of a motorbike engine with a passive pre-chamber. The investigations on the pre-chamber scavenging show that the exhaust gas residuals (EGR) concentration inside it mainly depends on the EGR in cylinder, rather than the pre-chamber geometry. The impact of the flow field development during the pre-chamber filling process on the subsequent combustion for the various tested operating conditions is presented. The flame front propagation in both combustion chambers is differently affected, showing a greater asymmetry at higher engine speeds. The turbulence introduction in the main chamber provided by the jets ignition is demonstrated and quantified by means of the turbulent combustion regimes diagram. The wall heat transfer investigation highlights the greater heat losses experienced by the passive pre-chamber engine layout. The higher heat loss contribution for each of the combustion chambers surfaces is demonstrated and quantitatively estimated, emphasizing the differences among the various operating conditions. The impact of the increased heat losses on the engine indicated mean effective pressure (IMEP) is assessed.

Predictors of coronavirus disease 2019 (COVID-19)-related rehospitalization remain underexplored. This study aims to identify the main risk factors associated with rehospitalizations due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reinfections among residents of Lombardy, northern Italy. A retrospective observational study was conducted using two linked administrative databases covering demographic data, comorbidities, hospital records, and COVID-19 data of Lombardy residents. The study population included patients hospitalized for COVID-19 between February 2020 and August 2021. Rehospitalization was defined as a second COVID-19-related hospitalization occurring at least 90 days after the first admission. The Fine–Gray subdistribution hazard model was used to identify risk factors, accounting for death as a competing risk. Out of 98,369 patients hospitalized for COVID-19 between February 1, 2020 and August 31, 2021, 72,593 were alive 90 days after admission and 610 of these (0.8%) were rehospitalized. A higher rehospitalization risk was observed in older male patients with multiple comorbidities. Renal failure, liver disease, and use of diuretics were significantly associated with rehospitalization risk, while female biological sex and the use of lipid-lowering drugs were associated with a lower risk. This is the first study conducted on regional administrative databases to investigate COVID-19 rehospitalizations. Through the availability of a huge cohort, it provides a groundwork for optimizing care for individuals at higher risk for COVID-19-related rehospitalizations. It underlines the need for patient-management approaches that extend beyond the initial recovery. This stresses the importance of ongoing monitoring and personalized interventions for those at heightened risk not only of SARS-CoV-2 reinfection but also related rehospitalizations.

Bioresorbable fibers are an exciting prospect as probes and implants to provide optical access to the human body. In this work, we demonstrate interstitial spectroscopy with bioresorbable fibers at null distance, using time-domain diffuse optics that disentangles absorption from scattering properties and probes the tissues up to a depth of a few cm around the fiber tips. We exploit a fast-gated single-photon avalanche diode with >55 dB of dynamic range to overcome the burst of ‘early’ photons hiding the information of absorption from deep tissues. We tested the absorption linearity—retrieving the water spectrum in the 700–950 nm range with >85% accuracy over two decades of absorption change—and verified the hypothesis of a scattering-independent absorption retrieval. Further, we were able to detect spectral changes at a distance of 1 cm from an inclusion embedded in a biological tissue. Time-domain diffuse optical spectroscopy with bioresorbable fibers could detect spectral changes without being affected by blood extravasation at the fiber tips and could help for long-term monitoring in tissue healing, thermal treatment, photodynamic therapy and ultimately, towards minimally invasive medical procedures.

Diatoms have been described as “nanometer‐born lithographers” because of their ability to create sophisticated 3D amorphous silica exoskeletons. The hierarchical architecture of these structures provides diatoms with mechanical protection and the ability to filter, float, and manipulate light. Therefore, they emerge as an extraordinary model of multifunctional materials from which to draw inspiration. In this paper, numerical simulations, analytical models, and experimental tests are used to unveil the structural and fluid dynamic efficiency of the Coscinodiscus species diatom. Then a novel 3D printable multifunctional biomimetic material is proposed for applications such as porous filters, heat exchangers, drug delivery systems, lightweight structures, and robotics. The results demonstrate Nature's role as a materials designer for efficient and tunable systems and highlight the potential of diatoms for engineering materials innovation. Additionally, this paper lays the foundation to extend the structure‐property characterization of diatoms.

In this paper, we consider the problem of a strongly nonlinear oscillator with movable algebraic singularities. The paper demonstrates the presence of such singularities and proves the existence and uniqueness theorem of the solution to the equation in the neighborhood of a moving singular point. An analytical approximate solution is constructed and the estimates of its error are obtained. Theoretical results were validated through numerical experiments. Furthermore, the problem of finding the point and interval criteria for the existence of the movable singular point was addressed. This problem allows for the development of an algorithm to find a moving singular point.