Search Results (26,700)

The article deals with the evaluation of dimensional deformations of a building element manufactured additively from a cement mixture. The study follows up on previous research within the 3DStar project and expands the methodology for monitoring deformations over time. The aim is to contribute to the development of more accurate simulation models for predicting the behaviour of printed structures, especially in the early stages after printing. For the analysis, an experimental ‘L’-shaped element was designed and printed, whose deformations were monitored using repeated 3D scanning and dimensional changes were evaluated for up to 93 days. The results show that the most significant deformations occur in the first hours after printing due to gravitational loading and mixture curing, while later changes are mainly due to shrinkage. The element’s geometry and the walls’ thickness also play a role. The analysis confirms the effectiveness of the ‘Caliper’ measurement method and outlines the potential for future use of photogrammetry as a method for online deformation monitoring. The data obtained will be used to optimise printing parameters and calibrate material parameters in the developed simulation software for non-linear numerical simulations in additive manufacturing using cement mixtures. Full article

In this study, we report the fabrication and multi-technique characterization of pure and rare-earth (RE)-doped ZnO thin films using nanostructured microclusters synthesized via electrospinning followed by calcination. Lanthanum (La), erbium (Er), and samarium (Sm) were each incorporated at five concentrations (0.1–5 at.%) into ZnO, and the resulting powders were drop-cast as thin films on glass substrates. This approach enables the transfer of pre-engineered nanoscale morphologies into the final thin-film architecture. The morphological analysis by scanning electron microscopy (SEM) revealed a predominance of spherical nanoparticles and nanorods, with distinct variations in size and aspect ratio depending on dopant type and concentration. X-ray diffraction (XRD) and Rietveld analysis confirmed the wurtzite ZnO structure with increasing evidence of secondary phase formation at high dopant levels (e.g., Er₂O₃, Sm₂O₃, and La(OH)₃). Raman spectroscopy showed peak shifts, broadening, and defect-related vibrational modes induced by RE incorporation, in agreement with the lattice strain and crystallinity variations observed in XRD. Elemental mapping (EDX) confirmed uniform dopant distribution. Optical transmittance exceeded 70% for all films, with Tauc analysis revealing slight bandgap narrowing (Eg = 2.93–2.97 eV) compared to pure ZnO. This study demonstrates that rare-earth doping via electrospun nanocluster precursors is a viable route to engineer ZnO thin films with tunable structural and optical properties. Despite current limitations in film-substrate adhesion, the method offers a promising pathway for future transparent optoelectronic, sensing, or UV detection applications, where further interface engineering could unlock their full potential. Full article

3D bioprinting has emerged as a key tool in tissue engineering by facilitating the creation of customized scaffolds with properties tailored to specific needs. Among the design parameters, porosity stands out as a determining factor, as it directly influences critical mechanical and biological properties such as nutrient diffusion, cell adhesion and structural integrity. This review comprehensively analyses the state of the art in scaffold design, emphasizing how porosity-related parameters such as pore size, geometry, distribution and interconnectivity affect cellular behavior and mechanical performance. It also addresses advances in manufacturing methods, such as additive manufacturing and computer-aided design (CAD), which allow the development of scaffolds with hierarchical structures and controlled porosity. In addition, the use of computational modelling, in particular finite element analysis (FEA), as an essential predictive tool to optimize the design of scaffolds under physiological conditions is highlighted. This narrative review analyzed 112 core articles retrieved primarily from Scopus (2014–2025) to provide a comprehensive and up-to-date synthesis. Despite recent progress, significant challenges persist, including the lack of standardized methodologies for characterizing and comparing porosity parameters across different studies. This review identifies these gaps and suggests future research directions, such as the development of unified characterization and classification systems and the enhancement of nanoscale resolution in bioprinting technologies. By integrating structural design with biological functionality, this review underscores the transformative potential of porosity research applied to 3D bioprinting, positioning it as a key strategy to meet current clinical needs in tissue engineering. Full article

Tool structure design methodologies predominantly rely on trial-and-error approaches or single-objective optimization but fail to achieve coordinated enhancement of multiple performance metrics while lacking thorough investigation into complex cutting coupling mechanisms. This study proposes a multi-objective optimization framework integrating joint simulation approaches. First, a finite element model for orthogonal turning was developed, incorporating the hyperbolic tangent (TANH) constitutive model and variable coefficient friction model. The cutting performance of four micro-groove configurations is comparatively analyzed. Subsequently, parametric modeling coupled with simulation–data interaction enables multi-objective optimization targeting minimized cutting force, reduced cutting temperature, and decreased wear rate. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) explores Pareto-optimized solutions for arc micro-groove geometric parameters. Finally, optimized tools manufactured via powder metallurgy undergo experimental validation. The results demonstrate that the optimized tool achieves significant improvements: a 19.3% reduction in cutting force, a 14.2% decrease in cutting temperature, and tool life extended by 33.3% compared to baseline tools. Enhanced chip control is evidenced by an 11.4% reduction in chip curl radius, accompanied by diminished oxidation/adhesive wear and superior surface finish. This multi-objective optimization methodology effectively overcomes the constraints of conventional single-parameter optimization, substantially improving comprehensive tool performance while establishing a reference paradigm for cutting tool design under complex operational conditions. Full article

This study provides an in-depth structural analysis of UNESCO World Heritage Apulian trulli, considering the three-layer dry-stone structure of their characteristic conical roofs. An integrated approach involving laser scanning, ground-penetrating radar, endoscopic investigation, and laboratory materials testing is used to identify and characterize the multi-wythe masonry system. A detailed finite element model is created in ANSYS to analyze seismic performance on Italian building codes. The model is validated through ambient vibration testing using accelerometric measurements. The diagnostic survey identified a three-layer system including exterior stone wythe, interior wythe, and rubble core, with compressive strength of stone averaging 2.5 MPa and mortar strength of 0.8 MPa. The seismic assessment will allow the examination of displacement patterns and stress distribution under design load conditions (ag = 0.15 g). The structural analysis demonstrates adequate performance under design loading conditions, with maximum stress levels remaining within acceptable limits for historic masonry construction. The experimental validation confirmed the finite element model predictions, with good correlation between numerical and experimental frequencies. The improvement of the overall seismic performance with the multi-wythe configuration and the role of wall thickness and geometric proportions will be taken into account. The methodology aims to provide technical evidence supporting the continued use of vernacular buildings while contributing to scientifically informed conservation practices throughout the region. Full article

Local variations in mechanical properties are commonly observed in engineering structures, driven by complex manufacturing histories and harsh service environments. The evaluation of mechanical properties accurately constitutes a fundamental requirement for structural integrity assessment. The Instrumented Indentation Technique (IIT) can play a critical role in the in-site testing of local properties. However, it could be often a challenge to correlate indentation characteristics with uniaxial stress–strain relationships. In this study, we investigated quantitatively the connection between the indentation responses of commonly used metals and their plastic properties using the finite element inversion method. Materials typically exhibit plastic deformation mechanisms characterized by either linear or power-law hardening behaviors. Consequently, conventional prediction methods based on a single constitutive model may no longer be universally applicable. Hence, this study developed methods for acquiring mechanical properties suitable for either the power-law and linear hardening model, or combined, respectively, based on analyses of microstructures of materials exhibiting different hardening behaviors. We proposed a novel integrated IIT incorporating microstructures and material-specific constitutive models. Moreover, the inter-dependency between microstructural evolution and hardening behaviors was systematically investigated. The proposed method was validated on representative engineering steels, including austenitic stainless steel, structural steel, and low-alloy steel. The predicted deviations in yield strength and strain hardening exponent are broadly within 10%, with the maximum error at 12%. This study is expected to provide a fundamental framework for the advancement of IIT and structural integrity assessment. Full article

Composite materials are widely used in various fields due to their superior properties. Given their complex internal structures, they are often modeled as homogeneous materials in engineering applications to simplify temperature distribution analysis. The key parameter in this approach is effective thermal conductivity (ETC). Conventional ETC models, based on Fourier’s law or the effective field approach, tend to underestimate temperatures when applied to composites containing internal heat sources, such as nuclear fuels. Preliminary studies have been conducted on ETC models for composite plates and particle-dispersed spheres with internal heat sources, using average temperature as the conserved quantity instead of the heat flux. This study focuses on dispersed disks containing internal heat sources. The finite element method is used to calculate its average-temperature-based ETC. The influence of filler size, filling fraction, and component thermal conductivities on the ETC is analyzed. Additionally, the impact of internal heat sources on ETC is discussed based on the theoretical model for the ETC of a one-dimensional composite plate. This research enhances understanding of ETC in composites with internal heat sources, reveals the connection between conventional and temperature-based ETC models, and provides insights for developing an ETC model for dispersed disks. Full article

Driven by the new wave of technological revolution and industrial transformation, firms are accelerating strategic change to gain new competitive advantages. Situated within a complex adaptive system, firms must adapt to highly dynamic and uncertain external environments by adjusting executive cognitive structures, reconfiguring resources and capabilities, and strengthening collaboration with industrial ecosystem elements; hence, digital strategic change is characterized by continuous evolution. Using a sample of Chinese A-share listed firms from 2015 to 2023, this study develops a “cognition–capability–strategy” pathway model grounded in upper echelons theory and dynamic capabilities theory to examine how executive cognitive styles, i.e., cognitive flexibility and cognitive complexity, drive digital strategic change via absorptive capacity and how environmental dynamism moderates these relationships. The findings show that executive cognition, as a decision node in strategic change, can dynamically adjust firms’ strategic paths by activating absorptive capacity in rapidly changing external information environments; environmental dynamism differentially affects the two cognitive styles. Heterogeneity tests further indicate that the role of executive cognition varies significantly with regional digital economy development levels, firm life cycle, and industry factor intensities. The study reveals how firms can respond to high environmental uncertainty through cognition–strategy alignment and resource capability reconfiguration in a complex adaptive system, providing theoretical references and practical insights for emerging economies to advance digital transformation and enhance competitiveness. Full article

This article presents the results of a rockfall analysis conducted for the limestone walls of a former quarry that is now used as an urban park. The performed simulations (2D statistical analysis using Rigid Body Impact Mechanics—RBIM and Discrete Element Modelling—DEM) enabled the determination of the maximum displacement range during the ballistic phase and the maximum rebound height at the slope base, which facilitated the delineation of a safe land-use zone. A hazard zone was also identified, within which public access must be strictly prohibited due to the risk posed by flying debris. Based on slope stability assessments (safety factor values and rockfall trajectories), recommendations were formulated for slope reinforcement measures and appropriate management actions for designated sections to ensure safe operation of the site. Three mitigation strategies were proposed: (1) no protective measures, (2) no structural reinforcements but with installation of a rockfall barrier, and (3) full-scale stabilisation to allow unrestricted access to the quarry walls. The first option—leaving slopes unsecured with only designated safety buffers—is not recommended. Full article

Background/Objectives: Fenbendazole is a potential cancer treatment and a proven antiparasitic in veterinary applications. However, its poor water solubility limits its application. In this study, potential fenbendazole solubility enhancement was investigated through size reduction methods. The effect of the presence of Soluplus^® on solubility was investigated as well. Methods: Solubility enhancement was explored using microfluidization and ultrasonication techniques. These techniques were applied to fenbendazole alone and in combination with Soluplus^®. UV–Vis spectroscopy was used to determine solubility. Possible chemical reactions were checked using Fourier transform infrared spectroscopy (FT-IR). Differential scanning calorimetry (DSC) was conducted to analyze the physical structure and crystallinity of the samples. Scanning electron microscopy (SEM) was also utilized for characterization of the effect of the treated formulations and the size reduction method on morphology. The elements present in samples were identified with energy-dispersive X-ray spectroscopy (EDX) combined with SEM. A comparison of crystalline structure between the products was performed via X-ray powder diffraction (XRPD). Dynamic light scattering (DLS) was also used to measure the samples’ average particle size at different stages. Results: Both ultrasonication and microfluidization led to marginal increases in the solubility of neat fenbendazole. In contrast, formulations processed in the presence of Soluplus^® demonstrated a greater enhancement in solubility. However, solubility improvement was not retained in the dried samples. The post-drying samples, irrespective of the presence of Soluplus^®, showed nearly the same solubility as neat fenbendazole. Conclusions: Size-reduction methods, particularly when combined with Soluplus^®, improved the solubility of fenbendazole. However, drying appeared to reverse these gains, regardless of the method used. Full article

This study presents a systematic review of the literature on teaching techniques that enhance student motivation and academic performance across basic, secondary, and higher education levels. The review is grounded in the distinction between intrinsic and extrinsic motivation, highlighting their decisive roles in engagement and achievement. The analysis focuses on active learning methodologies such as project-based learning, collaborative learning, gamification, and flipped classrooms. It identifies the mechanisms by which each approach fosters students’ interest, sense of competence, and persistence. Four international databases were consulted, and studies published between 2000 and 2024 reporting quantitative measures of motivation and/or performance were selected. Five investigations met all eligibility criteria and were assessed for methodological quality. The results indicate moderate motivational effects, especially when interventions last at least eight weeks, provide frequent feedback, and place students at the center of authentic problem-solving. Greater gains were also observed in STEM disciplines and in contexts that encourage peer collaboration. Based on these findings, practical recommendations are proposed for educators: structure interdisciplinary projects, incorporate playful elements in the initial stages of formal education, combine autonomous work with small-group discussions, and use data analysis tools to deliver personalized feedback. The study concludes that adopting diverse, student-centered pedagogical practices enhances motivation and academic achievement, leading to deeper and more lasting learning outcomes. Full article

The integration of large language models (LLMs) into structural engineering workflows presents both a transformative opportunity and a critical challenge. While LLMs enable intuitive, natural language interactions with complex data, their limited arithmetic reasoning, contextual fragility, and lack of verifiability constrain their application in safety-critical domains. This study introduces a novel automation pipeline that couples generative AI with finite element modelling through the Model Context Protocol (MCP)—a modular, context-aware architecture that complements language interpretation with structural computation. By interfacing GPT-4 with OpenSeesPy via MCP (JSON schemas, API interfaces, communication standards), the system allows engineers to specify and evaluate 3D frame structures using conversational prompts, while ensuring computational fidelity and code compliance. Across four case studies, the GPT+MCP framework demonstrated predictive accuracy for key structural parameters, with deviations under 1.5% compared to reference solutions produced using conventional finite element analysis workflows. In contrast, unconstrained LLM use produces deviations exceeding 400%. The architecture supports reproducibility, traceability, and rapid analysis cycles (6–12 s), enabling real-time feedback for both design and education. This work establishes a reproducible framework for trustworthy AI-assisted analysis in engineering, offering a scalable foundation for future developments in optimisation and regulatory automation. Full article

The development of generative AI Large Language Models (LLMs) raised the alarm regarding the identification of content produced by generative AI vs. humans. In one case, issues arise when students heavily rely on such tools in a manner that can affect the development of their writing or coding skills. Other issues of plagiarism also apply. This study aims to support efforts to detect and identify textual content generated using LLM tools. We hypothesize that LLM-generated text is detectable by machine learning (ML) and investigate ML models that can recognize and differentiate between texts generated by humans and multiple LLM tools. We used a dataset of student-written text in comparison with LLM-written text. We leveraged several ML and Deep Learning (DL) algorithms, such as Random Forest (RF) and Recurrent Neural Networks (RNNs) and utilized Explainable Artificial Intelligence (XAI) to understand the important features in attribution. Our method is divided into (1) binary classification to differentiate between human-written and AI-generated text and (2) multi-classification to differentiate between human-written text and text generated by five different LLM tools (ChatGPT, LLaMA, Google Bard, Claude, and Perplexity). Results show high accuracy in multi- and binary classification. Our model outperformed GPTZero (78.3%), with an accuracy of 98.5%. Notably, GPTZero was unable to recognize about 4.2% of the observations, but our model was able to recognize the complete test dataset. XAI results showed that understanding feature importance across different classes enables detailed author/source profiles, aiding in attribution and supporting plagiarism detection by highlighting unique stylistic and structural elements, thereby ensuring robust verification of content originality. Full article

The linear rolling guide (LRG) is widely used in the computer numerical control machine tool industry and other industries. To accurately evaluate the performance of LRGs, a multi-parameter test bench was developed to measure motion accuracy, preload drag force (PDF), vibration, temperature rise, and fatigue life. The mechanical structure and measurement and control system of the test bench were designed based on established principles and methods. ANSYS 19.0 software was used for static analysis of the gantry, modal analysis of the upper bed, and simulation of the impact of loading block thickness on load distribution uniformity. At the same time, we used an impact hammer modal test to verify the correctness of the finite element analysis of the upper bed. The analysis results validated the structural design. To verify the test bench’s repeatability, comparative experiments were conducted with the Hilectro LGD35-type LRGs, focusing on motion accuracy, PDF, and fatigue life. The experimental results confirmed the test bench’s high repeatability and validated the derived equations for measuring motion accuracy. Full article

Inconel 718 is a nickel-based superalloy that has a wide range of applications in the industries that require corrosion resistance or high-temperature resistance. It is well known that parts display internal stresses, anisotropy, and alloying element segregation after the selective laser melting (SLM) process. A homogenization heat treatment, which reduces internal stresses and homogenizes the material structure, can resolve these shortcomings. The present study focuses on the impact of this heat treatment on the microstructure of the Inconel 718 material produced by SLM. The research results indicate that this heat treatment improves both the material microstructure and mechanical performance by lessening the microstructural inhomogeneities, dissolving the Laves phases, and promoting grain coarsening. The findings of this study can contribute to the optimization of post-fabrication strategies for Inconel 718 parts fabricated by SLM. Full article