Single-runway systems constitute a significant share of global airport infrastructure, particularly in high-density regional and urban settings. Given their inherently limited configuration, the capacity of such systems is highly sensitive to fluctuations in demand, meteorological scenarios, and the effectiveness of air traffic management strategies. A robust understanding and quantification of these sensitivities are essential for evaluating the current performance of airport infrastructure, supporting capital investment and infrastructure planning decisions, and enhancing flight scheduling and air traffic flow management strategies. The primary objective of this study is to develop and apply a simulation model for the analysis and evaluation of the operational capacity of a single-runway airport. We develop a novel hybrid architecture that seamlessly integrates System Dynamics (SD) and Discrete Event Simulation (DES) paradigms to model the complex operational behavior enabling a realistic representation of the interactions between arrival and departure flows, accounting for key operational constraints such as separation minima, sequencing logic, runway occupancy times, and varying weather conditions. It has been applied to Venice Airport (VCE) in Italy.

Understanding the factors associated with children’s pro-environmental intentions is important for developing theory and informing future research on sustainable behavior early in life. At the same time, research with young children often involves small and hard-to-reach samples, making confirmatory modeling difficult. In this exploratory pilot study, we applied psychometric network analysis to examine the pattern of conditional associations among pro-environmental behavior, injunctive norms, descriptive norms, attitude, and intention in 30 Italian 9-year-old children. Because preliminary analyses indicated gender differences in intention and a weaker trend toward gender differences in attitude, these variables were residualized with respect to gender in the main network, and a non-residualized network was estimated as a sensitivity analysis. Overall, the non-residualized and gender-adjusted networks showed a similar core structure. The regularized gender-adjusted network showed its strongest positive edges between descriptive and injunctive norms, between injunctive norms and attitude, and between attitude and intention. Pro-environmental behavior showed only weak direct connections to the rest of the network. Strength and expected influence were both highest for injunctive norms, followed by descriptive norms and attitude. Although these findings are provisional and hypothesis-generating, they illustrate how psychometric network analysis can be applied in small pilot samples to map patterns of conditional associations and inform future confirmatory research.

Green building is a practical pathway for meeting the European Green Deal objectives through lower life cycle impacts, healthier indoor environments, responsible material use, and improved resource efficiency across construction and renovation. This paper develops and characterises a competence framework for green building derived from the GreenSCENT competence framework materials. The framework is organised into four competence areas and twelve competences, each articulated through sets of knowledge, skills, and attitudes and mapped across European Qualifications Framework levels. The resulting framework contains 276 statements distributed across knowledge, skills, and attitudes, enabling curriculum design, formative assessment, and micro credential development for learners ranging from introductory to expert levels. Quantitative profiling highlights uneven density across competences, with project management and energy saving in buildings carrying the largest statement sets, indicating strong cross cutting requirements in governance and operational performance. The framework supports education and training that connects building design, material stewardship, technology selection, circular practices, and economic decision, making in a single competence logic aligned with Green Deal policy directions.

The urgency of addressing climate change necessitates innovative tools to facilitate informed policymaking. This study introduces a knowledge management tool leveraging a knowledge graph to enhance the accessibility and usability of the IPCC’s sixth assessment report (AR6) Summary for Policymakers (SPM) documents. The tool addresses the challenges policymakers face in navigating vast amounts of information by structuring the SPM content into an interconnected network of nodes and links, based on the taxonomies defined by the IPCC authors. Using Obsidian software, the tool organizes key concepts into hierarchical notes with both strong and weak connections, enabling seamless exploration of related themes. Results demonstrate that this cognitive artifact not only preserves the integrity of the original documents but also reveals thematic overlaps, providing a comprehensive view of climate issues. Policymakers can efficiently retrieve critical data, supporting timely and evidence-based decision-making. AR6 unequivocally attributes climate change to human activities, highlighting the need for immediate action. This tool empowers policymakers to implement scientifically sound strategies, countering climate change denial and focusing efforts on mitigating its impacts, ultimately contributing to global sustainability and resilience. The integration of this tool into the field of industrial engineering can enhance the efficiency and effectiveness of climate-related decision-making processes.

Green chemistry is built on twelve guiding principles intended to reduce waste, energy use, and hazardous substances in chemical manufacturing. These principles have inspired more sustainable practices, yet their implementation in real-world industrial contexts reveals significant limitations and internal contradictions. This position paper critically examines each principle's practical challenges, with an emphasis on Principle 6 (design for energy efficiency) and its relationship to process complexity and resource intensity. Using concepts from complexity theory-notably simplexity and complixity-the analysis highlights how chemical production systems behave as complex adaptive networks, where straightforward "green" solutions can trigger emergent trade-offs. Industrial case studies from pharmaceuticals, microelectronics, and large chemical producers (e.g., BASF and Dow) illustrate successes and setbacks in applying green chemistry: catalytic routes that improve yields but rely on scarce elements, solvent recovery systems that save waste at the cost of energy and capital, and integrated processes that achieve remarkable efficiency gains while introducing control complexity. These examples underscore that the principles cannot be treated as isolated absolutes; instead, a holistic, systems-thinking approach is required. The discussion calls for expanding the Green Chemistry framework with new or revised principles that account for lifecycle complexities, adaptive process design, and socio-technical factors. By confronting the gaps between the idealized principles and industrial reality, this analysis offers insight into how green chemistry can evolve-guided by both scientific rigor and practical pragmatism-to better meet the sustainability challenges of modern chemical production. The novelty of this work resides in its systems oriented analysis of the Twelve Principles of Green Chemistry when applied to complex industrial processes. By integrating industrial examples with concepts from complexity theory, the manuscript clarifies limitations and trade offs that are not evident in principle based or metric focused approaches.

Carbonate ramp systems present significant seismic interpretation challenges due to their pronounced facies heterogeneity, which frequently results in chaotic seismic outputs that obscure the underlying geological structures. The Porto Badisco Calcarenite in Salento, southern Italy, an Oligocene carbonate ramp, serves as the case study for this research, offering an analogue for understanding similar geological systems. By integrating fieldwork, laboratory analysis and MATLAB modelling, this study pioneers the use of detailed petrophysical data to construct innovative velocity models based on the velocity ranges of the different lithofacies analysed. These models distinctly illustrate the impact of facies heterogeneity on seismic velocities, providing fresh insights into acoustic impedance and variable propagation velocities across different facies constituting the carbonate ramp. Through advanced high-resolution synthetic seismic modelling conducted on carefully fine-tuned unmigrated stack sections, the research demonstrates how variations in petrophysical characteristics within measured ranges reflecting carbonate textures can dramatically alter seismic imaging. The innovative models, based on propagation velocity ranges, not only deepen the understanding of the seismic representation of lithofacies but also act as a potent tool for probing the subsurface architecture of complex carbonate systems, providing an interpretative key for the analysis of seismic images. This approach signifies a substantial advancement in seismic modelling that is aimed at refining interpretations and enhancing exploration strategies in carbonate ramp environments globally.

This article presents a novel encoding scheme for the Functional Resonance Analysis Method (FRAM) to address its ambiguity about the time concept in sociotechnical systems analysis. The scheme introduced is a tensor-based encoding that allows for the dynamic temporal dimension to be natively incorporated into the FRAM model, thereby overcoming the method’s traditional limitation of static representation. By integrating tensors for single instantiations and evolutionary pathways of sociotechnical systems—namely, emergent pathways, the framework enhances the descriptive power of FRAM, enabling a deeper understanding of system behaviour over time. The proposed approach reframes the entire FRAM as a tool depicting sociotechnical systems by enriching the description of emergent and calculated instantiations, and suggests potential applications based on this underlying encoding scheme, thereby expanding the method’s applicability in understanding and managing complex sociotechnical systems.

Natural frequencies change in the presence of localized damage, yet such alterations are often minimal because these frequencies represent global properties. The Lekszycki method amplifies these small by introducing a mass at various positions in the structure before and after damage occurs. The frequency change is most pronounced when the mass is near the damaged area. To ensure the method's effectiveness, a sufficiently large number of natural frequencies must be monitored. However, the presence of dissipation such as viscous damping complicates measurement, as higher order modes are more heavily attenuated, potentially undermining damage identification. This paper is the first to incorporate viscous damping into the analysis, offering a parametric study on different dissipation levels. Additionally, it presents the method under the name "Lekszycki method", honoring Tomesz Lekszycki, the Polish scientist originally conceived it and contributed to M&MoCS, the international research center associated with journal.

This communication examines the interplay between linguistic mediation and knowledge conversion in cyber-sociotechnical systems (CSTSs) via the WAx framework, which outlines various work representations and eight key conversion activities. Grounded in enactivist principles, we argue that language is a dynamic mechanism that shapes, and is shaped by, human–machine interactions, enhancing system resilience and adaptability. By integrating the concepts of simplexity, complixity, and complexity compression, we illustrate how complex cognitive and operational processes can be selectively condensed into efficient outcomes. A case study of a chatbot-based customer support system demonstrates how the phases of socialization, introspection, externalization, combination, internalization, conceptualization, reification, and influence collaboratively drive the evolution of resilient CSTS designs. Our findings indicate that natural language serves as a bridging tool for effective sense-making, adaptive coordination, and continuous learning, offering novel insights into designing technologically advanced, socially grounded, and evolving sociotechnical systems.

It is well known from the literature that the phase velocity of waves is directly correlated with the stiffness of the material; however, experimental practice shows that this velocity changes significantly with varying frequencies, despite the fact that the elastic modulus of the material is, by definition, a material constant. We explore the dependence on the frequency of longitudinal ultrasonic plane waves velocity in construction materials, both from experimental and modeling points of view. For the sake of simplicity, the dispersive features are modeled by considering the case of a 1D medium, and two different kinds of mechanical models capable of describing wave dispersion phenomena are employed: a non-dissipative strain-gradient elastic model, and a dissipative viscoelastic one. In both cases, by using the extended Rayleigh-Hamilton principle, we derive the governing equations for 1D bulk waves propagation; in particular, in the case of the dissipative viscoelastic model either classical linear damping or Kelvin-Voigt damping is considered. The comparison of theoretical results with experimental findings obtained by ultrasonic tests on natural (sandstone) and artificial (concrete) construction materials shows that both theoretical models can satisfactorily describe the experimental behavior. These results encourage further experimental investigations for a clear and quantitative identification of the model that can be better used for engineering purposes.

This study investigates the effectiveness of citric acid as a salt crystallization inhibitor aimed at improving the durability and mechanical performance of concrete exposed to marine environments. The goal is to evaluate whether the addition of citric acid can mitigate the deterioration of concrete caused by salt crystallization during wet–dry cycles and simulated wave impacts. The novelty of this work lies in the experimental demonstration that a simple and environmentally friendly organic compound can effectively reduce salt-induced damage in marine-exposed concrete. Concrete samples were subjected to repeated wet–dry cycles and simulated marine wave impacts to assess changes in their physical and elastic properties. Variations in P-wave and S-wave velocities, Young’s modulus, and the effects of salt crystallization within the concrete matrix were evaluated through acoustic measurements. Results show that citric acid significantly reduces internal cracking, stiffness loss, and salt accumulation, leading to enhanced structural integrity and greater resistance to environmental stressors. These findings highlight the potential of citric acid as a sustainable additive for improving the long-term durability and mechanical stability of concrete structures in marine environments.

Scientific disinformation has emerged as a critical challenge at the interface of science and society. This paper examines how false or misleading scientific content proliferates across both social media and traditional media and evaluates strategies to counteract its spread. We conducted a comprehensive literature review of research on scientific misinformation across disciplines and regions, with particular focus on climate change and public health as exemplars. Our findings indicate that social media algorithms and user dynamics can amplify false scientific claims, as seen in case studies of viral misinformation campaigns on vaccines and climate change. Traditional media, meanwhile, are not immune to spreading inaccuracies—journalistic practices such as sensationalism or “false balance” in reporting have at times distorted scientific facts, impacting public understanding. We review efforts to fight disinformation, including technological tools for detection, the application of inoculation theory and prebunking techniques, and collaborative approaches that bridge scientists and journalists. To empower individuals, we propose practical guidelines for critically evaluating scientific information sources and emphasize the importance of digital and scientific literacy. Finally, we discuss methods to quantify the prevalence and impact of scientific disinformation—ranging from social network analysis to surveys of public belief—and compare trends across regions and scientific domains. Our results underscore that combating scientific disinformation requires an interdisciplinary, multi-pronged approach, combining improvements in science communication, education, and policy. We conducted a scoping review of 85 open-access studies focused on climate-related misinformation and disinformation, selected through a systematic screening process based on PRISMA criteria. This approach was chosen to address the lack of comprehensive mappings that synthesize key themes and identify research gaps in this fast-growing field. The analysis classified the literature into 17 thematic clusters, highlighting key trends, gaps, and emerging challenges in the field. Our results reveal a strong dominance of studies centered on social media amplification, political denialism, and cognitive inoculation strategies, while underlining a lack of research on fact-checking mechanisms and non-Western contexts. We conclude with recommendations for strengthening the resilience of both the public and information ecosystems against the spread of false scientific claims.

This paper presents a novel method for measuring organizational resilience by integrating the Rasch model into the Resilience Analysis Grid (RAG), providing a robust and objective tool for cross-sectional resilience studies. By treating the four cornerstones of resilience as abilities, Rasch’s model allows for an assessment that positions both the difficulty of the items and the organizations’ ability along a common scale. The requirement is the availability of a number of different organizations to be assessed. We employ a dataset generated through an artificial simulation and analyzed in a controlled environment, demonstrating the potential of Rasch-based resilience assessments to provide accurate, comparable, and scalable results in different organizational contexts. The traditional RAG is designed without a normative reference group, which makes it challenging to evaluate its results. The proposed model overcomes this limitation by offering a measurement scale on which different organizations can be placed without the need to use a normative group, facilitating the more consistent and timely monitoring of systems. This novel approach to quantifying resilience potentials highlights the transformative role of digital technologies in improving workplace safety and resilience. It advances resilience engineering and occupational health and safety practices in complex environments like manufacturing and industrial sectors.

Industrial production processes require effective management of operational variability to ensure safety and maintain stringent quality standards. In aluminum rolling plants, the grinding of rolling cylinders is not a traditional maintenance task but a continuous, integrated activity essential for preserving the surface precision needed to meet demanding product requirements. This study applies the Functional Random Walker (FRW) approach – an extension of the Functional Resonance Analysis Method (FRAM) enhanced by network theory – to analyze the propagation of variability within the grinding operations of rolling cylinders. By modeling these activities, the FRW identifies how specific operational loops can amplify uncertainty (such as those related to coolant management), while operator routines (such as systematic grinding wheel inspections) can effectively dampen variability. The findings demonstrate the practical value of the FRW in enhancing decision-making and proactive resilience management in complex production environments, particularly where the continuous restoration of rolling cylinder surfaces is vital to maintaining high process quality. This work supports the adoption of system-theoretic approaches for the management of industrial plants where production and maintenance are deeply interwoven.

Introduction: Competency-based teaching is the preferred approach for anaesthesia training, however, limited data exists on Portuguese residents' exposure to essential competencies. This study aimed to evaluate their daily exposure to seven selected competencies from the 2022 European Training Requirements (ETR). Methods: A cross-sectional survey was conducted amongst 350 Portuguese anaesthesia residents, throughout a 10 working day period, using a questionnaire with 170 questions. Participants were on either anaesthesia or intensive care unit rotation. Demographic data and scores of exposures to selected competencies were gathered. Statistical analyses included descriptive statistics, comparison of means and a Linear Mixed Model using the restricted maximum likelihood estimation method. The significance threshold was set at p < 0.05. Results: Regarding ETR competency exposure, no statistical differences were found based on gender. Residents reported statistically significant higher levels of exposure to competencies while in anaesthesia rotations, except for lung, cardiac and Point-of Care ultrasound. Apart from ultrasound and academic research activities, the maximum exposure level was attained only during anaesthesia rotations. There was no reported exposure to airway ultrasound in any rotation. Exposure to academic research activities, in a scale from 0 to 5, was on average below one. The average reported values for direct patient communication were the highest. As expected, the fifth-year residents reported overall higher scores. Residents from the North reported lower scores for general anaesthesia maintenance, peripheral regional anaesthesia, airway intubation and ventilation management, but higher scores of exposures to academic research activities. Discussion: Adopting a national logbook, formative regular assessment, supporting the trainers as well as strategies to improve competencies in academic research activities and ultrasound training are recommendations to improve the Portuguese training curricula. Conclusion: Addressing the gaps between expected and monitored competencies contributes to the advancement of anaesthesiology training. The survey drew attention to the ETR among the residents.

In engineering applications, it is standard to consider modal parameters (e.g. natural frequencies and mode shapes), to obtain measurable changes induced by damage events. However, such changes are often too subtle to detect and interpret accurately. In order to overcome this limitation and enhance the efficacy of damage detection, this study introduces an innovative technique inspired by Prof. Lekszycki, outlined prior to his untimely departure. This technique is designed to highlight the influence of structural damage on natural frequencies. In other words, we add an external mass in a general point of a structure and calculate the set of natural frequencies before and after the occurrence of a damage event. We, therefore, repeat this procedure for all the points of the structure. The hypothesis we have verified in this paper is that if the mass is posed in the neighborhood of the place where the damage is considered, then it interacts with the undamaged and with the damaged structure in a different way. In particular, the changes of the natural frequencies between the undamaged and the damaged structure are emphasized. We verify the correctness of this idea for selected and simple examples in both 2D and 3D environments.

This paper introduces a novel generative artificial intelligence workbench specifically tailored to the field of safety sciences. Utilizing large language models (LLMs), this innovative approach significantly diverges from traditional methods by enabling the rapid development, refinement, and preliminary testing of new safety methodologies. Traditional techniques in this field typically depend on slow, iterative cycles of empirical data collection and analysis, which can be both time-intensive and costly. In contrast, our LLM-based workbench leverages synthetic data generation and advanced prompt engineering to simulate complex safety scenarios and generate diverse, realistic data sets on demand. This capability allows for more flexible and accelerated experimentation, enhancing the efficiency and scalability of safety science research. By detailing an application case, we demonstrate the practical implementation and advantages of our framework, such as its ability to adapt quickly to evolving safety requirements and its potential to significantly cut down development time and resources. The introduction of this workbench represents a paradigm shift in safety methodology development, offering a potent tool that combines the theoretical rigor of traditional methods with the agility of modern AI technologies.