Construction Technologies Institute

The tumour microenvironment of glioblastoma (GBM) as defined by mechanical heterogeneity, hypoxia, and hyaluronic acid (HA)-rich extracellular matrix (ECM), is a highly dynamic milieu which influences tumour progression and therapeutic resistance. Yet, how these cues converge to regulate mechanosensitive pathways in 3D remains poorly understood. Here, we engineered agar-based porous hydrogels functionalized with HA to independently tune stiffness and ECM composition, creating biomimetic 3D niches for GBM cells. The presence of HA coating showed to increase hydrogel stiffness, promote YAP/TAZ nuclear localisation, and elevate total LATS1/2 expression, consistent with Hippo pathway feedback regulation. Over time, however, hypoxic niches emerged that destabilised this feedback, enabling sustained YAP nuclear activity. HA also modulated OCT4 and Sox2 localisation and attenuated HIF-1α nuclear accumulation, indicating that HA also modulates the spatial distribution and nuclear accumulation of HIF-1α. Also, a cooperative regulation through the HA-CD44-CXCR4 axis, showed integrated biochemical and mechanical signals to reinforce YAP/HIF crosstalk. Together, these results reveal a dynamic interplay between ECM stiffness, HA signalling, and hypoxia in shaping YAP/HIF crosstalk and stem-like phenotypes in GBM and establish our hydrogel platform as a powerful tool to dissect and therapeutically exploit these interactions.

This research investigates the structural-typological characteristics of reinforced concrete (RC) school buildings constructed between 1950 and 1980 in Italy aimed at supporting large-scale seismic vulnerability assessment. Drawing from detailed surveys of 30 schools and archival research, the work identifies four representative expert-judgment based structural archetypes, capturing recurrent features mainly based on typical in-plan characteristics of the buildings and accounting for other parameters such as number of storeys, plan geometry, frame system regularity, and infill layout. The analysis reveals common vulnerabilities, including soft-storey mechanisms, torsional irregularities, short columns, and inadequate reinforcement detailing, all of which critically affect seismic performance. Despite some favourable traits, such as rigid diaphragms and low-rise mass, the structural response is often dominated by irregularities and brittle failure modes. The classification aligns with national seismic risk mitigation frameworks (e.g., School-MARS) and provides the basis for the development of typology-based fragility models. These results may support targeted retrofit prioritization and contribute to a more refined archetype-driven approach to school vulnerability assessment, promoting scalable strategies for seismic resilience.

The photophysical behavior in solution, PMMA films and crystals of PhCHO mono-, TTPhCHO, and tri-substituted, TT(PhCHO)3, cyclic triimidazole (TT) derivatives is here reported and analyzed through computational and X-ray diffraction...

This study investigates the thermal performance of nanoPCM emulsions (nanoPCMEs) as working fluids in parallel plate thermal energy storage system, comparing two configurations: solid aluminum walls and aluminum profile walls filled with PCM + metal foam. Three distinct nanoPCM emulsions were evaluated against pure water as a base fluid. The experimental results demonstrated superior convection heat transfer performance for all nanoPCMEs compared to pure water. In the solid aluminum wall configuration, maximum thermal energy storage reached approximately 900 kJ, with 2.0 wt% nanoPCME achieving this level twice as fast as pure water. The PCM + metal foam configuration exhibited enhanced performance, storing up to 2250 kJ of thermal energy due to PCM's high latent heat, reaching maximum storage 1.6 times faster than pure water. Notable improvements in heat transfer were observed, with nanoPCME achieving the highest average Nusselt numbers across all configurations. Maximum enhancements in Nusselt number were 34.5 % and 41.9 % for solid wall and PCM + metal foam configurations, respectively, using 2.0 wt% nanoPCME. These findings demonstrate the significant potential of nanoPCMEs for improving thermal energy storage systems and thermal control applications. • Forced convection heat transfer experiments were performed with parallel plate TES. • Heat transfer enhancement by adding the PCM nanoparticles and ferro nanoparticles to the base fluid. • Ferro nanoparticles and concentrations have vital effects on heat transfer of emulsions. • Adding 2.0 wt% nanoPCM enhances the heat transfer up to 41.9 % with PCM + metal foam case.

The retrofitting of existing reinforced concrete structures is of paramount importance due to aging structures, changes in design codes, and the need to enhance resilience against seismic events. This paper addresses this critical issue by presenting the results of an experimental campaign focused on evaluating the shear performance of old-to-new concrete interface, considering the application of a thin (5 cm) concrete overlay. Both monotonic and cyclic shear tests were conducted to assess the behavior under static and seismic loading conditions. Mechanical anchors were installed into the existing concrete as connectors. The study investigates the influence of different concrete types, i.e. normal-weight and lightweight concrete, used for the overlay. The experimentally obtained shear strength values were compared with predictions derived from established design codes and guidelines: Eurocode 2 (old and new versions) and EOTA TR066. The aim of the comparison was to understand if the available codes are able to predict the experimental behavior when using an overlay with limited thickness and a lightweight concrete. TR066 results conservative but able to predict the failure mode, while the new Eurocode 2 gives the best prediction of resistance, although it does not cover very short embedment depth and seismic action. Lastly, the evaluation of the reduction coefficient α seis is made to account the behavior of the connectors under seismic action.

This study addresses the global challenge of superficial soil contamination by heavy metals, focusing on differentiating natural geogenic sources from anthropogenic contributions in complex industrial–urban environments. We develop an integrated geostatistical and multivariate framework combining soil metal concentration analysis with AERMOD atmospheric dispersion modeling using a comparative multi-model machine learning approach (including Extreme Gradient Boosting, Random Forest, and Ridge Regression). Applied to the industrialized area of Taranto, Southern Italy, this approach incorporates spatial autocorrelation and multiple environmental predictors to identify contamination patterns and sources. The results reveal variable predictive accuracy across metals, with RF generally outperforming the other algorithms. The model achieved its highest performance for copper (R2 = 0.58, RMSE = 25.82), Tin (R2 = 0.53, RMSE = 5.95), and chromium, while showing instability for others. These disparities highlight the differential influence of remote sensing data on contamination mapping. The framework advances the quantitative assessment of soil pollution by linking atmospheric deposition and spatial processes with causal interpretability.