IRIS Università degli Studi dell'Aquila
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ADVANCING WATER CIRCULARITY: A SIMULATION OF HYDRODYNAMIC CAVITATION AND MEMBRANE PROCESSES FOR SUSTAINABLE INDUSTRIAL WASTEWATER REUSE
No full textWater scarcity is a growing global challenge, exacerbated by population growth, industrialization, and climate change. As freshwater resources become more limited, water reuse has emerged as a promising solution to alleviate shortages. Reusing treated wastewater for agricultural use in arid and semi-arid regions is considered a sustainable approach to conserving freshwater and supporting food production. However, for this to be viable, treated wastewater must undergo rigorous treatment processes to ensure both food safety and environmental sustainability. A water circular economy is central to this approach, emphasizing the recycling and reuse of water in a closed-loop system to minimize waste and maximize resource efficiency. In this model, wastewater is treated, purified, and reused across multiple sectors, reducing overall water consumption. For agriculture, reusing treated wastewater transforms what would be waste into a valuable resource, reducing dependence on freshwater sources. This study examines the integration of advanced treatment technologies into wastewater treatment plants to facilitate the safe and effective reuse of agricultural water. The research focuses on a simulation of a wastewater treatment plant designed for agricultural reuse, starting from a real industrial effluent. The treatment process incorporates innovative methods such as hydrodynamic cavitation, advanced oxidation processes, and membrane filtration to remove organic contaminants and inactivate pathogens. These technologies ensure that the treated wastewater meets the stringent water quality standards required for agricultural irrigation of all types of crops. This work demonstrates how integrating cutting-edge treatment technologies within a circular economic framework can significantly contribute to water sustainability, especially in water-scarce regions
Size-dependent effect of nano-confinement of water in an ionic liquid matrix at low temperature
Full text linkOne of the leading hypotheses explaining water's anomalies is a metastable liquid-liquid phase transition (LLPT) at high pressure and low temperatures, which remains experimentally elusive due to homogeneous nucleation. Infrared spectroscopy experiments have shown that adding hydrazinium trifluoroacetate to water induces a sharp, reversible LLPT at ambient pressure, potentially originating from the same underlying mechanism as in pure water. In a previous work, we demonstrated that this transition can be attributed to the behavior of pure water only when nanosegregation of the aqueous component is brought into play. Here, by means of molecular dynamics simulations and the structural order parameter ζ, we explicitly analyze the effect of the ionic compound on the structure of liquid water at low temperature, both in a mixed solution and nanoconfined in spherical clusters of varying size. Our findings indicate that the ions surrounding the water induce structural perturbations that disrupt the water hydrogen-bond network up to a depth of approximately 0.70-0.75 nm from the surface toward the center of the sphere. This suggests that, in order to preserve a low-density liquid state within this ionic matrix, and more in general highly ionic matrices, water must be confined within pockets with radii greater than approximately 0.70-0.75 nm
Total Knee Arthroplasty and the Evolution of Coronal Alignment: From Mechanical to Personalized Strategies
Full text linkTotal knee arthroplasty (TKA) remains a cornerstone of orthopedic surgery, with optimal coronal alignment playing a pivotal role in determining both clinical outcomes and implant longevity. Traditionally, mechanical alignment has been regarded as the gold standard. However, the emergence of alternative philosophies—such as kinematic alignment and hybrid techniques—has shifted the focus toward individualized approaches. Recent advancements in robotic and computer-assisted systems have significantly enhanced the precision of implant positioning, allowing surgeons to better replicate native knee biomechanics and improve patient satisfaction. This narrative review examines current alignment philosophies in TKA, including mechanical, kinematic, and hybrid methods. It analyzes each technique’s principles, functionalities, benefits, and limitations while highlighting ongoing debates regarding their clinical application. Special attention is given to the role of technology in enabling more accurate, patient-specific surgical execution. Despite promising developments, challenges remain in standardizing these techniques and validating their long-term efficacy. To ensure a comprehensive evaluation relevant literature was reviewed, focusing on studies that explore alignment strategies, biomechanical outcomes, and the integration of technology in TKA. This review aims to synthesize current evidence, identify gaps in knowledge, and outline directions for future research needed to optimize alignment strategies in modern knee arthroplasty
An efficient numerical tool for 1-dof nonlinear systems towards large-scale seismic structural health monitoring of masonry buildings
Full text linkA nonlinear constitutive model, combined with an ad-hoc developed numerical strategy, is proposed to study the nonlinear dynamics of masonry buildings approached through single degree-of-freedom systems (1-dof). This proposal is explicitly aimed at providing enhancements in designing tools for seismic structural health monitoring in large-scale urban contexts. The model incorporates plasticity and damage induced by friction and wear, and it is capable of capturing the nonlinear response of structures subject to general external time-dependent loads. A numerical algorithm to solve the ensuing piecewise nonlinear equations is devised and explicitly implemented in a low-level language, thus being optimized for specific hardware systems for structural health monitoring. The proposed formulation is initially tested to validate the model’s constitutive parameters by identifying them in pseudo-static regimes, according to the experimental behavior of a real-scale single-storey masonry building. Then, the numerical stability of the proposed code is examined in comparison with traditional numerical solvers. Finally, the effects of real seismic actions are investigated, with particular emphasis on the accumulated damage when subsequent quakes are considered
Development of a human RPE in vitro model with AMD-like features reveals blue light-induced modulation of the endocannabinoid system
Full text linkRecognition of breast cancer from heterogeneous ultrasound images: A multi-level deep learning approach
Full text linkBreast ultrasound is a medical imaging technique that employs sound waves to produce breast images, and it has been primarily used to diagnose breast cancer and other related issues. With various machine learning algorithms being applied, many applications have shown promising results and demonstrated outstanding efficiency in giving doctors early accurate diagnoses. By investigating existing state-of-the-art approaches to breast lesion detection, given ConvNeXt-Small architecture as an example, we observe that although they bring a satisfactory performance in classification, their ability to detect small lesions is limited. Therefore, there is still room for improving the performance of DL-based approaches. In this paper, we present a practical Deep Learning-based solution for breast lesion detection, using DetectoRS with Gaussian Receptive Field-based Label Assignment (RFLA) and SegFormer-B4 for recognizing and segmenting small breast lesions, including malignant tumors. Our proposed solution involves three distinct models: ConvNeXt-Small for classification, Swin-Base combined with DetectoRS and RFLA for object detection, and SegFormer-B4 for segmentation. Each model is tailored specifically to address its respective task in breast cancer detection and analysis. The proposed approach has been evaluated on diverse ultrasound datasets. Our deep learning model achieves an Average Precision of 0.270 for small objects (AP_S), and records the highest mean Intersection over Union at 81.55%. The results show that the proposed model outperforms various well-established baselines. We suppose that our method can be integrated into computer-aided diagnosis systems to assist physicians in their clinical activities
An investigation of thermoelastic behavior in periodic and functionally graded multilayered composite structures using tolerance modeling and finite element methods
No full textIn response to the increasing demand for accurate yet computationally efficient methods for analyzing modern composites under thermal loading, this study introduces a thermoelastic model for multicomponent, multilayer step-wise functionally graded materials (FGMs), based on the tolerance modeling approach. For the first time, the tolerance modeling method has been extended to the thermoelasticity equations of FGM structures comprising more than two components, which marks a clear departure from earlier studies mostly limited to two-component materials or, if for multi-component, then only for periodic structures. This extension enables a significantly more realistic representation of modern layered composites. Numerical verification of the model was performed using Mathematica and COMSOL Multiphysics, enabling assessment of both accuracy and predictive capability. Deviations from COMSOL-based reference results did not exceed 7 % for both periodic and step-wise FGM structures, confirming the high reliability of the approach. Unlike existing methods, which are often either oversimplified or extremely computationally demanding, the developed model provides a realistic description of structural behavior while requiring considerably less computational effort than classical finite element methods (FEM). The analysis has direct practical relevance to the design of components exposed to high thermal gradients, such as thermal shields in aerospace applications, layered partitions in energy-efficient buildings, and electronic device components. Accurate prediction of displacement and thermal stress distributions facilitates optimization of geometry and material layout, reducing stress concentrations and minimizing damage risk. A comparative analysis of analytical and numerical results under identical boundary conditions and geometries confirms that the tolerance model successfully captures the behavior of FGM structures while keeping computational costs low. The conclusions further indicate that ordered, symmetric layering reduces deformation, whereas asymmetry and abrupt material transitions lead to localized stress concentrations. The novel methodology not only broadens existing modeling capabilities but is also flexible and can be adapted to a wide spectrum of modern layered structures, enhancing its potential for practical engineering applications
Advanced constitutive modeling of flexoelectric materials incorporating higher-order gradient effects: Towards the design and optimization of nanoscale devices
No full textThis work presents a comprehensive theoretical framework for flexoelectric materials by incorporating higher-order strain gradient and polarization gradient effects into the constitutive modeling. Using an extended strain gradient elasticity (SGE) approach, coupled with a generalized Toupin-like variational formulation, we derive governing equations, balance laws, and boundary conditions based on an enriched internal energy density function. Analytical solutions, expressed in terms of modified Bessel functions, provide key insights into the role of higher-order gradients in influencing displacement, polarization, and electric fields. The study highlights the critical impact of size effects on flexoelectric response, revealing that reducing material thickness enhances sensitivity and energy conversion efficiency. Furthermore, numerical simulations validate the theoretical model and demonstrate its applicability in the design of nanoscale flexoelectric sensors and energy harvesters. These findings establish a robust theoretical foundation for optimizing nanoscale electromechanical devices, with potential applications in biomedical sensors, structural health monitoring, and energy-efficient electronics
Efficiency and resilience of temporary housing complexes in L’Aquila 16 years after the earthquake
Full text linkThe housing modules designed after the 2009 L’Aquila earthquake aimed to support the populations affected by the seismic events, with a primary objective of combining safety with energy efficiency aspects. For this reason, the 185 buildings built as part of the “Progetto C.A.S.E.” (Complessi Antisismici Sostenibili ed Ecocompatibili), although different from each other, are all oriented towards the pursuit of the same strategic objectives: technological innovation, architectural and construction quality, safety, energy efficiency, and environmental sustainability. The houses, built as part of this project, were initially intended as temporary and provisional structures, but without an expiration date. The aim of the contribution is to verify whether, 16 years after the implementation of the intervention, the houses have maintained their initial characteristics. In this context, the thermal properties of the buildings have been investigated through the infrared thermography (IRT) technique, comparing two different residential typologies. The study revealed that prolonged use of the buildings without adequate maintenance has significantly compromised their thermal performance, resulting in a deterioration of up to 37 % in transmittance
