Modeling the mechanical behavior of coated masonry elements using surface stress theory

The Gurtin–Murdoch Surface Stress Model (SSM) is employed to model thin coatings of Steel Fiber Reinforced Mortar (SFRM) applied to masonry structures. This approach introduces a non-classical mechanical boundary condition, which expresses in-plane surface traction on the masonry facades in terms of surface stress and inertia. Finite Element (FE) analyses are performed within the elastic regime on coated masonry wall samples, both under static and dynamic loading conditions, to validate the accuracy of the theoretical model. Finally, a more realistic masonry structural system is analyzed to demonstrate the effectiveness of the proposed reinforcement and highlight the computational efficiency of the proposed surface model

On the role of search budgets in model-based software refactoring optimization

Software model optimization is a process that automatically generates design alternatives aimed at improving quantifiable non-functional properties of software systems, such as performance and reliability. Multi-objective evolutionary algorithms effectively help designers identify trade-offs among the desired non-functional properties. To reduce the use of computational resources, this work examines the impact of implementing a search budget to limit the search for design alternatives. In particular, we analyze how time budgets affect the quality of Pareto fronts by utilizing quality indicators and exploring the structural features of the generated design alternatives. This study identifies distinct behavioral differences among evolutionary algorithms when a search budget is implemented. It further reveals that design alternatives generated under a budget are structurally different from those produced without one. Additionally, we offer recommendations for designers on selecting algorithms in relation to time constraints, thereby facilitating the effective application of automated refactoring to improve non-functional properties

Experimental and numerical research of debonding defects detection in fiber metal laminates using low-power ultrasonic-induced thermography

Debonding defects in fiber metal laminates (FMLs) pose a significant threat to structural reliability, necessitating efficient and non-destructive inspection methods. This study explores the use of low-power ultrasonic-induced thermography (LUIT) for rapid visualization of debonding defects in FMLs through combined experimental and numerical investigations. An inspection system was developed, incorporating bispectral analysis for the determination of optimized excitation frequencies, thereby enhancing the heat generated at defect locations to achieve improved detection performance. Infrared thermography was employed to monitor transient temperature evolution, and a contrast-based time-slice selection strategy was introduced to enhance defect visibility. Furthermore, a comprehensive numerical simulation framework integrating modal analysis, implicit dynamic simulation, and thermo-mechanical coupling was proposed to reveal the underlying heating mechanisms, focusing on frictional dissipation, viscoelastic damping, and plastic deformation. The combined results demonstrate the capability of LUIT to selectively heat debonding defects without damaging the material, with defect detectability strongly influenced by defect size, depth, and excitation timing. The findings demonstrate that LUIT offers a fast, safe, and non-destructive approach for reliable debonding defect detection in FML structures

Generative AI as a New Assistive Technology for Web Interaction

For users who are unfamiliar with technology or rely on assistive tools such as screen readers, interacting with a web page can be challenging. Ensuring a seamless experience requires a well-designed user interface (UI) that prioritizes accessibility and usability. However, achieving this target demands specialized expertise from developers and can involve significant effort. In this context, Generative Artificial Intelligence (GAI) has become a valuable aid for improving access to information and facilitating interaction with web interfaces. To effectively enhance user interaction---such as accessing services or specific functionalities---AI-driven tools must first be capable of understanding the structure and content of a web page. This study investigates if GAIs can be exploited to assist the user when navigating through a website, describing the site contents, explaining the interface structure and interactive elements, and suggesting actions or procedures to follow to perform a certain task or accomplish a specific goal. This kind of assistive technology can benefit not only visually impaired people but also persons with cognitive impairment and, more generally, people that are not ``skilled'' with modern web applications, like seniors. Specifically, thirteen popular websites were analyzed by asking Copilot one hundred questions. Results suggest that GAIs have the potential to assist people in web tasks. However, limitations have still been detected, with 20{\%} of completely erroneous answers received from the navigation and interaction questions and 15{\%} for those related to structure, mainly detected in pages having scarce accessibility and sites having a complex HTML structure, respectively

Headache diagnosis in children and adolescents: validation of the Italian version of the HARDSHIP questionnaire

Background: Headache disorders are common in children and adolescents, significantly affecting quality of life and academic performance. Despite their high prevalence, they remain underdiagnosed and undertreated. The Child and Adolescents HARDSHIP questionnaire, developed by the Global Campaign Against Headache, has been widely used in adult epidemiological studies but lacks validation in the Italian pediatric population. Methods: The study was conducted within the Epidemiologic Registry of Child and Adolescents Headaches - Registro Epidemiologico delle Cefalee in età evolutiva (REPICEF), a population-based registry on pediatric headache epidemiology in L'Aquila, Italy. The Italian version of the HARDSHIP questionnaire was adapted using the TRAPD method, ensuring linguistic and conceptual accuracy. Validation was performed by comparing questionnaire-based diagnoses with clinical diagnoses attributed by expert neurologists assumed as gold standard. We computed the sensitivity, specificity, positive and negative predictive values. Results: Out of 858 screened children and adolescents, the first 535 (62.4%) were selected for the validation questionnaire. Based on the HARDSHIP questionnaire, the most common diagnoses were probable Migraine (pMig) in 175 (20.7%), probable Tension-Type Headache (pTTH) in 144 (16.7%), Undifferentiated Headache (UDH) in 105 (12.2%), Tension-Type Headache (TTH) in 86 (10.0%), migraine in 68 (7.9%) children and adolescents. The agreement between questionnaire-based and clinical diagnoses was for migraine k = 0.432, for TTH k = 0.327, for pMOH k = 0.214. Conclusions: The validated Italian version of the HARDSHIP questionnaire provides a useful instrument for epidemiological research and for the improvement in diagnosis of pediatric headache disorders

Coupling biomass hydrothermal carbonization and green solvent extraction

This paper merges two leading-edge practices of sustainable waste valorization chains – biomass hydrothermal carbonization (HTC) and green solvent extraction – into an integrated process scheme. The innovation is to intersperse two hydrothermal carbonization steps with solvent extraction to recover valuable products from the reaction process water before completing the carbonization. The study uses rice husk as the waste biomass, hydrophobic deep eutectic decanoic acid/thymol (DES) as the green solvent, and furfural and 5-hydroxymethylfurfural as the target chemicals. HTC went batch-wise (230 °C, 1/4 solid/liquid ratio, total reaction time 2 h). DES extraction recovered up to 91 % of the chemicals from the process water. Although only 2 % of the original biomass converts to platform chemicals, the process illustrates a new methodology that is tailorable for other optimized productions. A flowsheet scheme helps quantify mass balances. The many degrees of freedom in the operational parameters allow for the intensification of the industrial-scale process. The new method paves the way for further developments, applying the combined process to other biomasses, solvents, and target chemicals

Design, Modeling and Numerical Investigation of Multi-Scale Materials

Mechanical metamaterials are designed to provide desired behaviors for engineering and scientific researches using multi-scale modeling techniques. The proliferation of new technological facilities emphasize the advancements of engineering design and mechanical testing methodologies. As a matter of fact, these modeling techniques play an effective role in the investigation of complex of diverse materials, mechanical metamaterials. Renewed metamaterial types inspired by materials found in nature are becoming a popular concept. Therefore, additive manufacturing, namely 3D printing, is used to fabricate the complex microstructures and reveal internal structural pattern contribution. The layer-upon-layer approach in additive manufacturing, along with open or closed cells in polymeric or metallic foams, involves an intrinsic microstructure tailored to the underlying applications. With developed mathematical models, higher-order theories and approaches are utilized to identify extraordinary material responses by homogenizing such architectured materials. In fact, classical models and processes with high computational efficiency are being replaced by systematic frameworks based on new methods and principles, particularly when the microstructure’s characteristic length is comparable to the length scale of the structure. While classical homogenization approaches applied to heterogeneous materials are suitable for cases where scale separation is evident, they fail to be accurate when the effective continuum’s length scale approaches the characteristic length of the material’s microstructure. In such cases, highergradient theories can be employed to enable multi-scale material modeling of complex structures, both in terms of geometry and material properties. Therefore, second-order modeling in mechanics is used to study and determine the additional constitutive parameters that arise from strain-gradient theor

Ethics-based Automated Negotiation in the Decision-making of Autonomous Systems

The presence of autonomous decision-making systems is growing rapidly and affecting many aspects of our daily lives. Designed to learn and act independently, they are capable of autonomous decision-making without human assistance. Although these systems promise many advantages, their increased degree of freedom raises concerns about their ethical behavior during decision-making. The demand for ethically aware AI is also driven by laws and regulations, such as the AI Act, which emphasizes responsible and transparent AI development, reinforcing the need to align autonomous systems with ethical values. As of today, however, human values and ethics are mostly not considered by autonomous systems that make decisions for them. This presents the challenge of incorporating user ethical preferences into the decision-making of autonomous systems, ensuring that they take into account the user's morals and beliefs, which may vary across contexts and are unique to each individual in society. Indeed, considering ethics in the decision-making process raises another challenge of how systems may interact should their ethical preferences differ. The absence of universal ethics implies that they need to reach an ethical agreement to interact with each other. Negotiation is a possible way to reach such an agreement and automated negotiation is the process through which multiple autonomous systems communicate by automatically exchanging bids, dialogues, and offers for that purpose. However, when considering ethics in negotiation, the challenge lies in quantifying user ethical preferences and formalizing negotiation rules, as systems require measurable parameters to assess the ethical implications of their decisions and ensure alignment with the ethical values of the users they represent. This raises another challenge of systematically capturing user ethical preferences and generating ethical profiles that encapsulate users' preferences for effective integration into decision-making. To address these challenges, we propose an ethics-based automated negotiation approach in which autonomous systems utilize users' ethical profiles together with contextual factors to control their autonomy while collaboratively negotiating with each other to reach an ethical agreement that satisfies the ethical beliefs of all parties involved. It is not realistic to expect that the agreement is reached once and for all; rather, it is situational in that it relates to or depends on (i) environmental factors (e.g., the location and surroundings of a place, social circumstances, weather conditions) and (ii) user status, which is a set of physical, social, or emotional conditions that (possibly) apply to the user in a given context at a particular time (e.g., specific health conditions or state of affairs such as elderly, injured, and crowd anxiety). Accordingly, the contributions of this thesis are as follows: i) providing the research community with a comprehensive overview of the current literature on automated negotiation through a systematic mapping study; (ii) proposing a novel approach that provides a domain-agnostic template solution for architecting autonomous systems that leverages the ethical profiles of end users and their contextual status to regulate their autonomous behavior and support decision-making through ethics-based negotiation; (iii) implementing the proposed approach to validate it and demonstrate the feasibility and effectiveness of the system in realizing ethics-based negotiation in real-life scenarios; and (iv) proposing an approach to develop a tool that facilitates the automated generation of ethical profile-building questionnaires to further generate user ethical profiles through their responses

Numerical-experimental solar loading thermography method and chemical-physical techniques: A Tandem for the preservation of ancient books

In recent decades, the need to preliminarily study works of art with non-destructive and portable techniques has given rise to the figure of the conservation scientist for applications in the field of cultural heritage. This study applies solar loading thermography to detect surface and subsurface defects in an ancient book (1861), examining both natural degradation and fabricated defects. The latter were generated by the natural and inevitable degradation process to which the organic components of the book (for example cellulose, lignin, etc.) are subjected, and voluntarily introduced inside the book cover to determine the sensitivity and the feasibility of the technique. Thermal imaging analysis, supported by numerical simulation, revealed humidity damage and adhesive residues. Two experimental conditions were tested using or not clips to optimize cover-to-page adhesion. Four circoular dowels of different compositions assessed technique sensitivity. Complementary analyses (UV fluorescence, XRF spectroscopy, optical microscopy) validated surface anomal detection and material characterization