Search Results (173)

This study investigates the mechanical properties of ABS parts fabricated via used deposition modeling (FDM) through integrated experimental and numerical approaches. ABS resin was used as the experimental material, and tensile tests were conducted using a universal testing machine. Finite element analysis (FEA) was performed via ANSYS 2021 to simulate stress deformation behavior, with key parameters including a gauge length of 10 mm (pre-stretching) and printing temperature gradients. The results show that the specimen exhibited a maximum tensile force of 7.3 kN, upper yield force of 3.7 kN, and lower yield force of 3.2 kN, demonstrating high strength and toughness. The non-proportional elongation reached 0.06 (6%), and the quantified enhancement multiple of AM relative to traditional manufacturing was 1.1, falling within the reasonable range for glass fiber-reinforced or specially formulated ABS. FEA results validated the experimental data, showing that the material underwent 15 mm of plastic deformation before fracture, consistent with ABS’s ductile characteristics. Full article

Background/Objectives: Mobile healthcare units (MHUs) comprise flexible, ambulatory healthcare teams that deliver community care services, particularly in underserved or remote areas. In Greece, MHUs were pivotal in epidemiological surveillance during the COVID-19 pandemic and are now evolving into a sustainable and integrated service for much-needed community-based healthcare. To support this expanded role, targeted, competency-based training is essential; however, this can pose challenges, especially in coordinating synchronous learning across geographically dispersed teams and in ensuring engagement using an online format. Methods: A nationwide, online training program was developed to improve the knowledge of the personnel members of the Hellenic National Public Health Organization’s MHUs. This program was structured focusing on four core themes: (i) prevention–health promotion; (ii) provision of care; (iii) social welfare and solidarity initiatives; and (iv) digital health skill enhancement. The program was implemented by the University of Crete’s Center for Training and Lifelong Learning from 16 January to 24 February 2025. A multidisciplinary team of 64 experts delivered 250 h of live and on-demand educational content, including health screenings, vaccination protocols, biomarker monitoring, chronic disease management, treatment adherence, organ donation awareness, counseling on social violence, and eHealth applications. Knowledge acquisition was assessed through a pre- and post-training multiple-choice test related to the core themes. Trainees’ and trainers’ qualitative feedback was evaluated using a 0–10 numerical rating scale (Likert-type). Results: A total of 873 MHU members participated in the study, including both healthcare professionals and administrative staff. The attendance rate was consistently above 90% on a daily basis. The average assessment score increased from 52.8% (pre-training) to 69.8% (post-training), indicating 17% knowledge acquisition. The paired t-test analysis demonstrated that this improvement was statistically significant (t = −8.52, p < 0.001), confirming the program’s effectiveness in enhancing knowledge. As part of the evaluation of qualitative feedback, the program was positively evaluated, with 75–80% of trainees rating key components such as content, structure, and trainer effectiveness as “Very Good” or “Excellent.” In addition, using a 0–10 scale, trainers rated the program relative to organization (9.4/10), content (8.8), and trainee engagement (8.9), confirming the program’s strength and scalability in primary care education. Conclusions: This initiative highlights the effectiveness of a structured, online training program in enhancing MHU knowledge, ensuring standardized, high-quality education that supports current primary healthcare needs. Future studies evaluating whether the increase in knowledge acquisition may also result in an improvement in the personnel’s competencies, and clinical practice will further contribute to assessing whether additional training programs may be helpful. Full article

This paper presents the implementation of a vision system for a collaborative robot equipped with a web camera and a Python-based control algorithm for automated object-sorting tasks. The vision system aims to detect, classify, and manipulate objects within the robot’s workspace using only 2D camera images. The vision system was integrated with the Universal Robots UR5 cobot and designed for object sorting based on shape recognition. The software stack includes OpenCV for image processing, NumPy for numerical operations, and scikit-learn for multilayer perceptron (MLP) models. The paper outlines the calibration process, including lens distortion correction and camera-to-robot calibration in a hand-in-eye configuration to establish the spatial relationship between the camera and the cobot. Object localization relied on a virtual plane aligned with the robot’s workspace. Object classification was conducted using contour similarity with Hu moments, SIFT-based descriptors with FLANN matching, and MLP-based neural models trained on preprocessed images. Conducted performance evaluations encompassed accuracy metrics for used identification methods (MLP classifier, contour similarity, and feature descriptor matching) and the effectiveness of the vision system in controlling the cobot for sorting tasks. The evaluation focused on classification accuracy and sorting effectiveness, using sensitivity, specificity, precision, accuracy, and F1-score metrics. Results showed that neural network-based methods outperformed traditional methods in all categories, concurrently offering more straightforward implementation. Full article

Background: Eating disorders (EDs) are complex conditions with significant psychological, physical, and economic impacts, prompting national calls to prioritize ED prevention. Despite numerous prevention programs being implemented in Australian schools, no review to date has systematically mapped their scope, design, and outcomes. Aims: This scoping review aimed to map the current landscape of school-based ED prevention programs conducted in Australia. The review focused on their methodological features, participant and school characteristics, data collected, and key findings. Method: Four electronic databases (MEDLINE, PsycINFO, EMBASE, and Scopus) were searched for relevant papers published from 2010 to February 2025. Studies were included if they reported on a school-based ED prevention program targeting Australian students. Data were extracted and narratively synthesized. Results: A total of 23 studies were identified, representing a range of universal and selective prevention programs. Programs varied in design, delivery, and target populations, with most focusing on students in Grades 7–8. Universal media literacy programs like Media Smart showed good outcomes for boys and girls, while several selective programs demonstrated improvements in body image for girls. Interventions targeting boys or using mindfulness approaches often lacked effectiveness or caused unintended harm. Major gaps in the literature include a lack of qualitative research, limited long-term follow-up, and minimal focus on protective factors. Conclusion: While a range of ED prevention programs have been trialed in Australian schools, few have been rigorously evaluated or demonstrated sustained effectiveness. There is a need for developmentally appropriate, gender-sensitive, and culturally inclusive prevention efforts in schools. Future research should use diverse methods, include underrepresented groups, assess long-term outcomes, integrate broader sociocultural factors shaping students’ environment, and consider enhancing protective factors. Full article

Objectives: Ex vivo nasal mucosa models provide physiologically relevant platforms for evaluating nasal drug permeability; however, their application is often limited by high experimental variability and the absence of standardized methodologies. This study aimed to improve experimental design by addressing two major limitations: the confounding effects of mucosal thickness and the questionable reliability of fluorescein sodium (Flu-Na) as an integrity marker for chemically induced mucosal injury. Methods: Permeability experiments were conducted using porcine nasal tissues mounted in Franz diffusion cells, with melatonin and Flu-Na as model compounds. Tissues of varying thickness were collected from both intra- and inter-individual sources, and a numerical simulation-based method was employed to normalize apparent permeability coefficients (Papp) to a standardized mucosal thickness of 0.80 mm. The effects of thickness normalization and chemically induced damage were systematically evaluated. Results: Thickness normalization substantially reduced variability in melatonin Papp, particularly within same-animal comparisons, thereby improving statistical power and data reliability. In contrast, Flu-Na exhibited inconsistent correlations across different pigs and failed to reflect the expected increase in permeability following isopropyl alcohol (IPA)-induced epithelial damage. These results suggest that the relationship between epithelial injury and paracellular transport may be non-linear and not universally applicable under ex vivo conditions, limiting the suitability of Flu-Na as a standalone marker of mucosal integrity. Conclusions: The findings highlight the importance of integrating mucosal thickness correction into standardized experimental protocols and call for a critical reassessment of Flu-Na in nasal drug delivery research. Full article

High penetration of green energy sources presents substantial challenges to grid stability and resilience, primarily due to inherent voltage and frequency variability, which worsens during critical events. This study proposes an integrated framework for stability and resilience enhancement in renewable-dense power grids by designing optimized universal droop controllers (UDCs) tailored for grid-forming operations under high-impact contingencies. The UDC incorporates fault localization functionality via grid-forming inverters embedded with phasor measuring capabilities (phase voltage magnitude and angle) to facilitate real-time fault detection and response, thus augmenting operational reliability. Leveraging integrated solution environments, the developed framework employs numerical optimization routines for resource allocation, load prioritization, economic dispatch of distributed energy resources (DERs), and adaptive network reconfiguration under constrained conditions and during critical events that may necessitate decentralized network configurations in the wake of main grid failures. Validation conducted on the IEEE 123-node distribution network indicates that the optimized UDC framework achieves superior voltage and frequency regulation compared to conventional droop-based methods, ensuring optimal resource distribution and sustained load support. Full article

A significant portion of the academic community, including students, teachers, and researchers, has incorporated generative artificial intelligence (GenAI) tools into their everyday tasks. Alongside increased productivity and numerous benefits, specific challenges that are fundamental to maintaining academic integrity and excellence must be addressed. This paper examines whether ethical implications related to copyrights and authorship, transparency, responsibility, and academic integrity influence the usage of GenAI tools in higher education, with emphasis on differences across academic segments. The findings, based on a survey of 883 students, teachers, and researchers at University North in Croatia, reveal significant differences in ethical awareness across academic roles, gender, and experience with GenAI tools. Teachers and researchers demonstrated the highest awareness of ethical principles, personal responsibility, and potential negative consequences, while students—particularly undergraduates—showed lower levels, likely due to limited exposure to structured ethical training. Gender differences were also significant, with females consistently demonstrating higher awareness across all ethical dimensions compared to males. Longer experience with GenAI tools was associated with greater ethical awareness, emphasizing the role of familiarity in fostering understanding. Although strong correlations were observed between ethical dimensions, their connection to future adoption was weaker, highlighting the need to integrate ethical education with practical strategies for responsible GenAI tool use. Full article

To address the challenges of unclear thermo-mechanical coupling mechanisms and unpredictable multi-field synergistic effects in segmented tire molds during vulcanization, this study focuses on segmented tire molds and proposes a multi-physics coupling numerical model. This model integrates fluid flow dynamics into heat transfer mechanisms. It systematically reveals molds’ heat transfer characteristics, stress distribution and deformation behavior under combined high-temperature and mechanical loading. Based on a fluid-solid-thermal coupling framework and experimental validations, simulations indicate that the internal temperature field of the mold is highly uniform. The global temperature difference is less than 0.13%. The temperature load has a significant dominant effect on the deformation of key components such as the guide ring and installation ring. Molding forces play a secondary role in total stress. The error between multi-field coupling simulation results and experimental results is controlled within 6%, verifying the model’s reliability. This research not only provides a universally applicable multi-field coupling analysis method for complex mold design but also highlights the critical role of temperature fields in stress distribution and deformation analysis. This lays a theoretical foundation for the intelligent design and process optimization of high-temperature, high-pressure forming equipment. Full article

As the transition to clean energy accelerates, wind energy plays a crucial role in power generation, particularly in remote onshore and offshore locations. The integration of hybrid AC/DC networks with multi-terminal high-voltage direct current (MTHVDC) systems enhances power transfer capability and reliability. However, maintaining stable operation under both normal and disturbed conditions remains challenging. This paper applies the Flexible Universal Branch Model (FUBM) to hybrid AC/DC networks incorporating MTHVDC, providing a unified framework for power flow analysis. Unlike conventional methods that separately analyze AC and DC systems, the FUBM enables simultaneous modeling of both, improving computational efficiency and accuracy. Additionally, the paper introduces advanced control strategies to regulate active power transfer from offshore wind farms to onshore grids while maintaining voltage stability. The proposed approach is validated under steady-state and disturbance scenarios, including converter outages, within the CIGRE B4 system, which is a complex multi-terminal network interconnected with numerous converters. The results demonstrate the effectiveness of the FUBM in ensuring stable operation, offering new insights into unified power flow modeling. This study lays the groundwork for future advancements in AC/DC power systems with MTHVDC integration. Full article

From the perspective of the talent supply chain, this paper employs evolutionary game theory to study the decision-making behaviors of university graduates’ employment-related participants, establishes a tripartite evolutionary game model of enterprises, graduates, and universities based on prospect theory, and analyzes the main factors affecting the system game strategy by combining numerical simulation. The evolutionary game theory is a theory that integrates game theory with the analysis of dynamic evolutionary processes, studying the strategy selection and dynamic equilibrium of bounded rational participants in complex environments. The findings are as follows: (1) The decision-makers influence and promote each other, and universities play a very important role in promoting the employment of graduates. (2) In the case of random initial probability, when the additional profit of each decision-maker is greater than their cost, enterprises, graduates, and universities can realize the ideal model of “recruitment, participation in recruitment, active employment assistance”. The higher the initial probability, the faster the system reaches a steady state. (3) Enhancing the risk perception of enterprises, graduates, and universities has a dual effect on the employment ecosystem. (4) The behavioral strategies of enterprises, graduates, and universities are affected by many factors, such as the initial probability, loss aversion degree, profit and loss sensitivity degree, talent loss risk, cost, and unemployment risk. Full article

The Euler-Type Universal Numerical Integrator (E-TUNI) is a discrete numerical structure that couples a first-order Euler-type numerical integrator with some feed-forward neural network architecture. Thus, E-TUNI can be used to model non-linear dynamic systems when the real-world plant’s analytical model is unknown. From the discrete solution provided by E-TUNI, the integration process can be either forward or backward. Thus, in this article, we intend to use E-TUNI in a backward integration framework to model autonomous non-linear dynamic systems. Three case studies, including the dynamics of the non-linear inverted pendulum, were developed to verify the computational and numerical validation of the proposed model. Full article

HKUST-1 (HKUST = Hong Kong University of Science and Technology) is one of the most recognized metal-organic frameworks (MOFs) based on copper and trimesate, extensively studied for a variety of applications, such as gas storage, separation, adsorption, electrocatalysis, drug delivery, sensor and photodegradation, etc. In this work, we introduce a novel nanofused HKUST-1, referred to as N-CuBTC (BTC = trimesate), which has been synthesized with the hydrothermal method at room temperature (typical synthesis temperature is from 80~120 °C). The resulting N-CuBTC features an irregular particle morphology, with numerous crystals clustering together and edges that have fused, creating a hierarchical pore structure. In contrast to the traditional micro-sized octahedral HKUST-1 (named as M-CuBTC), N-CuBTC displays a unique clumped morphology, where the HKUST-1 crystals are seamlessly integrated into a cohesive structure. This innovative formation significantly enhances mass transfer capabilities and porosity accessibility. Consequently, N-CuBTC demonstrates markedly improved catalytic performance in the cyanosilylation of aldehydes. Full article

The management of public institutions is focused not only on providing and improving public services but also on managing the physical infrastructure that these institutions use—buildings for provision of such services. The focus of this paper is on decision support to the management of individual buildings and the set of such buildings (portfolio) during the planning phase. More precisely, it is directed towards support towards both the decision-maker (DM) and decision-making process (DMP) when planning construction activities/projects such as maintenance, renovation, reconstruction, extension, construction, design/preparation of project-technical documentation, etc. The aforementioned DMP includes the processing of a large amount of diverse data (technical, economic, social, etc.) expressed differently—numerically or descriptively, as well as in different units of measurement, simultaneously taking into account the different wishes and attitudes of stakeholders (consequently meeting their often conflicting goals and criteria). The above indicates that it is a complex and ill-defined multi-criteria problem faced by the DM/planner. On top of that, and knowing that the DM usually does not have all the necessary knowledge and skills, this paper proposes how to overcome these issues by supporting the DM within the DMP during such a planning process. The proposed concept promotes an integral (considering relevant aspects of this management problem) and inclusive (taking into account the views of relevant stakeholders) approach to managing complex construction projects and their portfolios. It is methodologically based on the logic of decision support systems and multi-criteria analysis. The multi-criteria methods used include the Preference Ranking Organization METhod for Enrichment Evaluation (PROMETHEE) for the evaluation and comparison of alternatives in an integral manner, as well as the Analytic Hierarchy Process (AHP) for determining the weights of criteria and achieving an inclusive and consistent approach to relevant stakeholders (based on the goal tree approach). The concept was tested on the planning of infrastructure management at a university in the Republic of Croatia, and it was proven to be useful because it provided the DM with a basis for decision making. The usefulness of the concept was confirmed by the concordance of the plan obtained using the concept and the activities/projects actually realized. Full article

Urban heat islands (UHIs) increase urban warming and reduce outdoor thermal comfort due to changing surface characteristics and climate change. This study investigates the role of green walls (GWs) in mitigating UHI, improving outdoor thermal comfort, and reducing carbon emissions under current and future (2050) scenarios. Focusing on Via della Consolata, Turin, Italy, the study combines remote sensing for UHI detection and numerical simulations for thermal analysis during seasonal extremes. The results show that GWs slightly reduce air temperatures, with a maximum decrease of 1.6 °C in winter (2050), and have cooling effects on mean radiant temperature (up to 2.27 °C) during peak summer solar radiation. GWs also improve outdoor comfort, reducing the Universal Thermal Climate Index by 0.55 °C in the summer of 2050. The energy analysis shows that summer carbon emission intensity is reduced by 31%, despite winter heating demand increasing emissions by 45%. The study highlights the potential of GWs in urban climate adaptation, particularly in dense urban environments with low sky view factors. Seasonal optimization is crucial to balance cooling and heating energy demand. As cities face rising temperatures and heat waves, the integration of GWs offers a sustainable strategy to improve microclimate, reduce carbon emissions, and mitigate the effects of UHI. Full article

Machine learning has become indispensable across various domains, yet understanding its theoretical underpinnings remains challenging for many practitioners and researchers. Despite the availability of numerous resources, there is a need for a cohesive tutorial that integrates foundational principles with state-of-the-art theories. This paper addresses the fundamental concepts and theories of machine learning, with an emphasis on neural networks, serving as both a foundational exploration and a tutorial. It begins by introducing essential concepts in machine learning, including various learning and inference methods, followed by criterion functions, robust learning, discussions on learning and generalization, model selection, bias–variance trade-off, and the role of neural networks as universal approximators. Subsequently, the paper delves into computational learning theory, with probably approximately correct (PAC) learning theory forming its cornerstone. Key concepts such as the VC-dimension, Rademacher complexity, and empirical risk minimization principle are introduced as tools for establishing generalization error bounds in trained models. The fundamental theorem of learning theory establishes the relationship between PAC learnability, Vapnik–Chervonenkis (VC)-dimension, and the empirical risk minimization principle. Additionally, the paper discusses the no-free-lunch theorem, another pivotal result in computational learning theory. By laying a rigorous theoretical foundation, this paper provides a comprehensive tutorial for understanding the principles underpinning machine learning. Full article