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Composite Electrospun Fibers Containing Optimized B- and Cu-Doped Bioactive Glass Sol-Gel Particles for Potential Soft Tissue Engineering Applications
No full textElectrospinning (ES) is largely used to produce polymeric nano- and microfibers for various applications, including the biomedical fields. ES enables the production of biomimetic fibrous composite scaffolds that resemble the morphology of the extracellular matrix (ECM) of human tissues. However, literature lacks research on composite electrospun fiber tissue engineering (TE). The synergistic effect of biologically active ions with multitarget activity, in comparison to single-target approaches, is also underexplored. In this work, composite fibers based on poly(epsilon-caprolactone) (PCL) incorporating spherical sol-gel B- and Cu-doped bioactive glass (BG) particles, with potential use in soft TE, are fabricated and characterized with scanning electron microscopy, energy-dispersive X-ray spectroscopy, Fourier-transform infrared spectroscopy, contact angle measurements, acellular bioactivity, and mechanical and preliminary cell tests. The composite fibers are obtained by using benign solvents for the ES. The results showed good retention of the BG particles inside the PCL matrix, leading to bioactive behavior. Preliminary in vitro cellular tests with bone marrow stromal cells confirmed the biocompatibility of these fibrous composites. The dual ion release from the bioactive fillers in the PCL matrix is expected to enhance angiogenesis, making the composites relevant for both soft and bone TE
Topographical and mechanical properties of sputtered composite coatings with silver nanoparticles for functional surfaces
No full textThe development of durable, effective functional coatings is a pressing challenge in biomedical and surface engineering applications. This work investigates the morphological, topographical, and mechanical properties of nanocomposite coatings functionalized by silver nanoparticles (Ag NPs) embedded in SiO2 and ZrO2 matrices, deposited by co-sputtering. The effects of matrix material and deposition duration are investigated in terms of their impact on composition, morphology, topography and mechanical response, which are tested, respectively, by EDS, FESEM, conductive AFM, topographical microscopy and nanoindentation. Results show a complex combined effect of nano structure and average structural and topographical properties and composition on the mechanical response. Results highlight that for longer depositions, average properties are controlled by micro-scale topography and average structure and composition. Conversely, for shorter depositions, local structural differences strongly influence the average mechanical response and result in a greater dispersion of the mechanical properties. These findings demonstrate the tunability of coating properties through process control. Future research will apply these insights to optimize the functionality of fibers and textiles
High Albedo Interlocking Concrete Block Pavement for Urban Heat Island Mitigation
No full textThe combined effects of global warming and urbanisation have intensified the urban heat island (UHI) phenomenon and thermal stress, especially in the summer season. This study develops an integrated multi-scale framework to quantify the sustainability in terms of the thermal performance of high albedo interlocking concrete block pavement (ICBP) in the city of Turin, Italy. The framework combines: (1) experimental campaigns to establish baseline albedo values, using an albedometer (in accordance with the standard ASTM E1918-21 and E1980-24); (2) in situ measurements to assess the performance of ICBP in three parking areas; (3) satellite analysis using Landsat 8-9 and Sentinel-2 images to derive the land surface temperature (LST) and quantify changes in the surface urban heat island intensity (SUHII). In situ measurements showed an average albedo of 0.20 for ICBP, lower values for aged surfaces and about 0.08 for asphalt. Satellite analysis confirmed the effectiveness of the substitution of asphalt surface pavements with ICBP, revealing an increase of over 30% in both the average albedo and the solar reflectance index (SRI). These results are also combined with the 15% decrease in SUHII. Combining on-site measurements and satellite analysis provides a comprehensive framework for quantifying surface urban heat island effects and thermal performances of more sustainable road pavements. These findings support high albedo ICBP as an effective strategy for UHI mitigation
Geometric deep learning-based coronary wall shear stress estimation from real-world patients
No full textBackground
Coronary wall shear stress (WSS) derived from computational fluid dynamics (CFD) provides mechanistic insight and prognostic information, but its clinical translation is hindered by modeling complexity and computation time. We evaluated a geometric deep learning framework based on gauge-equivariant mesh graph convolutional network (GEM-GCN) to estimate coronary WSS directly in geometries reconstructed from coronary angiography in real-world patients.
Methods
A total of 1078 coronary arteries from 748 patients were reconstructed from invasive angiography. Time-averaged WSS computed by transient CFD served as reference labels for GEM-GCN training and testing. Two experiments were conducted: (i) random splitting of the full dataset with 10-fold cross-validation, and (ii) a clinical split, to assess whether GEM-GCN–derived WSS preserved the ability to predict myocardial infarction (MI) compared with CFD-derived WSS.
Results
GEM-GCN produced patient-specific WSS maps in < 5s per vessel. GEM-GCN slightly underestimated lesion- and vessel-averaged WSS in the random split, with absolute and percentage errors equal to 0.48 [0.26–0.78] Pa and 23.6 [14.8–42.6]%, respectively. High spatial agreement was found for high-WSS regions (Dice distance 0.88 [0.81–0.92]). Similar performance was observed in the clinical split (absolute error 0.65 [0.41–1.12] Pa; Dice distance 0.84 [0.71–0.90]). After normalization by vessel-averaged WSS, the correlation between GEM-GCN-derived and CFD lesion-averaged WSS improved from R = 0.67 to R = 0.89 (p < 0.0001). Lesion-averaged WSS and the lesion-to-vessel WSS ratio achieved comparable MI prediction performance for CFD and GEM-GCN.
Conclusions
Geometric deep learning enables fast, CFD-free coronary WSS estimation from routine angiography, supporting its potential for large-scale, real-world risk stratification
A Second-Order Perspective on Pruning at Initialization and Knowledge Transfer
No full textThe widespread availability of pre-trained vision models has enabled numerous deep learning applications through their transferable representations. However, their computational and storage costs often limit practical deployment. Pruning-at-Initialization has emerged as a promising approach to compress models before training, enabling efficient task-specific adaptation. While conventional wisdom suggests that effective pruning requires task-specific data, this creates a challenge when downstream tasks are unknown in advance. In this paper, we investigate how data influences the pruning of pre-trained vision models. Surprisingly, pruning on one task retains the model’s zero-shot performance also on unseen tasks. Furthermore, fine-tuning these pruned models not only improves performance on original seen tasks but can recover held-out tasks’ performance. We attribute this phenomenon to the favorable loss landscapes induced by extensive pre-training on large-scale datasets
Time-Dependent Numerical Modelling of the Effectiveness of a Drainage Intervention on a Large Landslide
No full textThe creep behaviour of large slow landslides may pose threats to interacting infrastructures, thus reducing their service life. A case study is here described where a large landslide is threatening the safety of a motorway. To reduce the displacement rate of the landslide, an extensive drainage intervention has been designed, comprising wells and drains. This paper illustrates the evaluation of the intervention’s effectiveness through Finite Difference numerical modelling. To this extent, a time-dependent constitutive model has to be selected for the simula-tion of the landslide movement. First, a realistic hydro-geological and geotechnical model for the site is defined. Then, the drainage system’s effectiveness is eval-uated by running transient flow analyses. Lastly, the time-dependent mechanical behaviour of the landslide is obtained before and after the intervention. To build representative and reliable scenarios of future evolution, it is fundamental to vali-date the numerical model against observations and the available monitoring data. In this case, satellite (InSAR) detected surface displacements and inclinometer mea-surements were used to compare and calibrate the viscous parameters. Numerical results are discussed by underlying the role of the geotechnical parameters and appropriate modelling to get reliable predictions
On the impact of migratory flows on the residential species of a region
No full textMigrations can trigger biological invasions. Indeed, when a migrant population finds favorable conditions in a region, it can settle there permanently. This can pose a threat to local biodiversity. Moreover, even when a biological invasion does not occur, a migratory transit affecting a territory can harm residential species. In this paper, we focus on a migrant population that either only touches the border of the region occupied by residents or crosses it altogether. We investigate the possible negative consequences due to a migratory flow for a native species that interacts with the migrants in some particular scenarios. Specifically, we consider the following situations: migratory interference with negative or positive effects on the native species, migrant predation on the residential population, competition and symbiosis of the two populations. By analytically and numerically exploring the mathematical models for the migrant-resident interactions of interest, we find that in the interference scenarios migrant biological invasions cannot occur, while in the others they do. The only migrant disturbance action, however, could still have serious consequences for the native population, which may even be driven to extinction. We can also have the resident extinction in the models of migrant predation on residents and competition. In the positive interference and symbiosis scenarios, instead, the system can only evolve toward coexistence. The residents, in these last two situations, benefit from the migratory transit. These general results are valid for both marginal and non-marginal migrant-resident contacts, but we also observe some differences in these two cases
Dynamic identification of prestressed reinforced concrete railway bridges through Automated Operational Modal Analysis: an example on two case studies
No full textStructural Health Monitoring (SHM) of strategic transportation infrastructures is becoming increasingly important due to ageing and degradation, particularly in the case of railway bridges and viaducts that support high-speed train operations. This study presents the experimental dynamic identification of two prestressed reinforced concrete (PRC) railway bridges, representative of two common short-to-medium span typologies. The accelerometric data were acquired under operational conditions, hence, recording both ambient vibrations and train passage-induced high-amplitude vibrations. After discarding these latter disturbances, Ambient Vibration Tests (AVT) were performed through a recently-introduced Automated Operational Modal Analysis (AOMA) algorithm to identify the modal parameters (natural frequencies, damping ratios, and mode shapes), which serve as damage-sensitive features. The results of this dynamic identification are then benchmarked against those obtained with state-of-the-art commercial software (ARTeMIS), confirming the accuracy and reliability of the proposed approach
Effect of Torch Power and Thickness on APS Al2O3 Coatings on 100Cr6 Bearing Steel: Microstructure, Adhesion and Flexural Response
No full textThis research examines how atmospheric plasma spraying torch power and coating thickness jointly affect the adhesion strength, microstructure, porosity, and flexural behavior of Al2O3 coatings on 100Cr6 steel substrates. Optical microscopy, SEM and EDS mapping, 3D surface-roughness analysis, Vickers hardness testing (HV2) on polished crosssections, and three-point bending of extracted beams were employed to develop a processing– structure–property map. This multi-technique approach enables the cross-validation of processing–structure–property relationships and supports a robust identification of the optimal power–thickness condition by jointly considering porosity (densification), adhesion strength, flexural response and failure mode. All conditions resulted in an average surface roughness Ra of approximately 1.0 μm. Increasing torch power to 45 kW generally reduced cross-sectional porosity, except at 500 μm, where globular pores appeared. Hardness (HV2) increased with power and peaked at the intermediate thickness (500 μm); adhesion up to 63 MPa was recorded for the 300 μm/45 kW coating. Flexural strength was highest at 500 μm and was consistently greater at 45 kW than at 39 kW. Fractography showed a shift in failure mode from interface-driven delamination at 39 kW to more cohesive, tortuous intra-coating cracks at 45 kW, aligned with improved splat bonding and crack-path deflection. An intermediate thickness of 500 μm deposited at 45 kW is thus identified as an optimal condition to balance densification and crack-path tortuosity, leading to enhanced hardness and flexural performance