Search Results (454)

Current airway stents, including silicone and metallic devices, remain associated with important complications such as migration, restenosis, mucus retention and the need for repeated interventions. Biodegradable stents offer a promising alternative by providing temporary mechanical support while avoiding the long-term presence of a permanent implant. However, the influence of stent geometry and material properties on their mechanical performance and interaction with airway tissue is still not fully understood. This study presents a computational framework integrating computer-aided design and finite element analysis to investigate the mechanical behaviour of biodegradable tracheobronchial stents. Two stent architectures (X-pattern and W-pattern) were analysed over a range of wire thicknesses using two biodegradable materials: a PLA/PCL; 70/30 wt.% blend and AZ31 magnesium alloy. Radial compression, diameter recovery after radial compression and stent–tissue interaction simulations were performed to evaluate the influence of geometry, material selection and design parameters on device performance. The results suggested that both stent geometry and material properties strongly influence the mechanical behaviour of biodegradable airway stents, although they affect different aspects of the stent–tissue interaction. The X-pattern consistently exhibited greater resistance to radial compression, lower elastic diameter recovery after radial compression and improved maintenance of the expanded lumen compared with the W-pattern. Material properties primarily affected the magnitude of the mechanical response, as further confirmed by the quantitative contact-pressure analysis, with AZ31 providing greater radial support, while the spatial distributions of stress and strain within the tracheal wall were mainly governed by the stent architecture. Based on the computational analyses, X-pattern stents manufactured from the PLA/PCL; 70/30 wt.% blend were selected for in vivo evaluation in a rabbit model. Endoscopic observations revealed tissue features that were qualitatively consistent with the mechanical patterns predicted by the numerical simulations, although no direct causal relationship can be established from the available observations. These findings support the ability of the proposed framework to represent the principal aspects of stent–tissue interaction. The proposed computational framework provides a practical tool for the rational design and mechanical assessment of biodegradable airway stents and may facilitate the future development of customised airway prostheses. Full article

In recent years, Additive Manufacturing (AM) has transformed the development of new products, enabling more efficient, sustainable, and creative solutions across multiple sectors. Accordingly, this research explores the integration of Selective Laser Melting (SLM) with advanced Computer-Aided (CAx) tools, specifically Generative Design (GD) and Topology Optimisation (TO), to rethink an everyday product. The developed concept, an SLM water tap, highlights the seamless synergy between design and product engineering. Reverse Engineering (RE) was applied to analyse the conventional internal mechanism, which was redesigned in accordance with Design for Additive Manufacturing (DfAM) principles. This approach enabled the integration of the internal cartridge architecture into the tap body as a single metal component, reducing system complexity and part count. TO was applied to key components, achieving a 35% mass reduction without compromising the simulated structural performance of the system. GD was employed to generate optimised internal flow channels, resulting in a numerically simulated flow rate of 4.69 L/min. Integrating CAx tools enabled a customisable product with varied surface textures. This work contributes to the deconstruction of traditional manufacturing paradigms and advances the understanding of AM for functionally relevant product design. Full article

With the increasing importance of green design in the business landscape, designers are compelled to shift towards eco-design practices. However, existing methodologies face challenges related to resource requirements, abstract concepts, and industry specificity. To address these challenges and stimulate innovation, this study proposes a green design methodology that integrates TRIZ concepts and is anchored in TRIZ evolutionary trends. The methodology includes function and attribute analysis, the introduction of green features, the identification of TRIZ trends through a two-stage process, and the use of a developed system to improve calculation efficiency. Detailed design solutions are generated by combining green features, TRIZ trends, and inventive principles. A case study validates the methodology, showcasing its value in promoting sustainable development. By leveraging the evolutionary potential of products and incorporating TRIZ, the methodology offers a promising approach to address sustainability challenges and drive innovation. This research serves as a starting point for a practical and efficient design methodology that utilizes TRIZ concepts and a computer-aided application tool. Future steps involve stress-testing the methodology and exploring its application in different domains. Full article

Background: Clear aligners fabricated via computer-aided design and manufacturing are increasingly used in orthodontics and may also serve as carriers for peroxide-based bleaching agents. However, exposure to bleaching agents may affect the physical and surface properties of aligner polymers, which could influence their clinical performance. Objective: This systematic review aims to evaluate the available evidence on the use of clear aligners as carriers for tooth bleaching agents, with a focus on bleaching efficacy and reported effects on aligner materials based on the identified literature. Methods: A systematic search was conducted in PubMed, Web of Science, Scopus, Scielo, and Embase for studies published up to January 2026. Eligible clinical and in vitro studies investigated bleaching procedures using clear aligners or conventional trays and reported color change outcomes and/or changes in material properties such as hardness, surface integrity, or mechanical performance. Risk of bias was assessed using the Cochrane Risk of Bias tools and standardized criteria for in vitro and clinical studies. Results: Six studies (three clinical and three in vitro) met the inclusion criteria. Clinical evidence indicated that bleaching delivered through clear aligners achieved similar whitening outcomes to conventional tray-based systems. In vitro studies reported changes in surface hardness and mechanical properties of polymer-based aligner materials after peroxide exposure; however, no major structural degradation was observed. Clinical studies were generally at high risk of bias, while in vitro studies showed low to moderate risk. Conclusions: Within the limitations of this systematic review, clear aligners may represent a potential carrier for tooth bleaching agents with outcomes comparable to conventional trays. However, the available evidence is limited and heterogeneous. Well-designed randomized controlled trials are needed to confirm clinical effectiveness and long-term material safety. Full article

Factor Xa (FXa) remains a clinically validated and chemically tractable anticoagulant target despite the therapeutic role of direct oral FXa inhibitors. Contemporary FXa inhibitor literature, however, is heterogeneous in scaffold design, endpoint reporting, assay consistency, translational depth, and suitability for computer-aided drug design (CADD). This review evaluates published series of small-molecule FXa inhibitors through a framework that combines translational structure–activity relationships (SARs), assay-aware data quality, and QSAR-readiness. A structured narrative synthesis focused mainly on post-2014 studies reporting discrete small-molecule or semisynthetic FXa inhibitors. Eligible series were classified as fully synthetic or natural-product-derived/semisynthetic chemotypes, and extraction covered scaffold architecture, potency endpoints, assay context, selectivity, clotting or antithrombotic readouts, PK/ADME, structural clarity, translational context, and extraction confidence. QSAR-readiness was assessed using analog density, congenericity, endpoint quality, assay comparability, activity range, structural interpretability, and curation burden. Fully synthetic chemotypes, particularly anthranilamide-derived and related scaffolds, provided the most coherent and modellable FXa datasets, whereas natural-product-derived and semisynthetic series expanded structural diversity. Many exploratory series, however, were limited by small analog sets, heterogeneous endpoints, incomplete translational characterization, narrow activity ranges, or higher curation burden. The practical value of published FXa inhibitor series, therefore, depends not only on potency but also on whether chemical and biological information can be reconstructed with confidence for reproducible SAR interpretation, local QSAR modeling, AI/ML-enabled CADD reuse, and clinical benchmark-aware prioritization. The QSAR-readiness framework is a critical triage tool, not a substitute for formal validation, distinguishing datasets suitable for curated local modeling from those better suited to qualitative SAR, scaffold inspiration, or translational hypotheses. Full article

Artificial intelligence (AI) is reshaping the field of diagnostic radiology; however, its applications in interventional radiology and pediatric interventional radiology (PIR) remain limited despite clear clinical needs and the rich multimodal data environment characteristic of pediatric procedural care. In this narrative review, I summarize the current state of AI technologies relevant to PIR and outline future perspectives for their clinical integration. Peer-reviewed literature and position statements identified through MEDLINE/PubMed, Embase, Scopus, and major society publications up to the first quarter of 2026 are synthesized, focusing on AI applications across the PIR care pathway, including dose-sparing image acquisition and reconstruction, automated image interpretation and computer-aided diagnosis, data-driven procedural planning and navigation, and post-procedural risk prediction and monitoring. After briefly introducing core machine learning and deep learning concepts, pediatric-specific challenges are discussed, including radiation sensitivity, growth-related anatomical variability, regulatory constraints, and the scarcity of large, annotated datasets, as well as existing and emerging applications along the PIR care pathway: AI-assisted dose reduction and image reconstruction, automated image interpretation, segmentation, and computer-aided diagnosis; data-driven procedural planning, including three-dimensional modelling, augmented reality, AI-enabled/AI-adjacent robotics, and AI-directed procedural navigation; and post-procedural risk prediction and outcome monitoring. Finally, emerging paradigms, including explainable AI, federated learning, and multimodal integration, are highlighted, and research priorities, collaborative frameworks, and governance principles required to ensure safe, equitable, and effective AI deployment in PIR are outlined. In doing so, this review delineates the current evidence gaps and priority directions for clinically meaningful AI adoption in PIR. Although AI has the potential to improve patient care, it has not yet been specifically designed, validated, or deployed in children. Existing work demonstrates feasibility across the PIR workflow, but most tools remain weakly linked to pediatric clinical endpoints. Full article

Background/Objectives: Hysteroscopy is the gold standard for visualization and treatment of intrauterine pathology. Because hysteroscopic interpretation remains operator-dependent, artificial intelligence (AI) has been evaluated as a tool to improve consistency, lesion recognition, and decision support. We aimed to systematically review AI, machine learning (ML), deep learning (DL), or computer-aided diagnosis (CAD) applications in hysteroscopy. Methods: A systematic search of PubMed/MEDLINE and EBSCOhost was performed from database inception to 8 March 2026, supplemented by targeted searches. Risk of bias was assessed using QUADAS-2 (diagnostic), PROBAST (prognostic), RoB2, and structured technical quality domains. Results: Nineteen primary studies were included, covering five areas: diagnostic classification and object detection (n = 8), real-time lesion detection and localization (n = 4), segmentation and visual-field support (n = 3), operative guidance (n = 1), and prognostic or decision-support applications (n = 3). Performance was highest in narrowly defined binary tasks and in large multicenter systems (e.g., ECCADx: AUC 0.979 internal, 0.975 external) and in prognostic fertility-prediction models after hysteroscopic adhesiolysis (AUC up to 0.992). Broader multiclass classification of heterogeneous lesions showed uneven and lower performance. Most studies were single-center, retrospective, and lacked external validation. Only one randomized study linked AI support to measurable procedural outcomes. Conclusions: The available studies indicate good technical performance in selected hysteroscopic tasks, particularly binary classification, focal lesion detection, and postoperative fertility stratification. Current evidence, however, remains limited by retrospective design, operator-dependent image acquisition, inconsistent validation, and scarce outcome-based clinical testing. In the short term, the most likely role of these systems is to support image interpretation, improve visual quality control, highlight suspicious lesions, and integrate hysteroscopic findings with complementary clinical data. Full article

This study proposes and validates a structured methodology based on Design for Six Sigma (DFSS) for sustainable product design, addressing the lack of standardization in the integration of design tools and the need to simultaneously consider qualitative, quantitative, and sustainability-related variables. The methodology integrates Voice of the Customer (VOC), Quality Function Deployment (QFD), Theory of Inventive Problem Solving (TRIZ), computer-aided design and engineering (CAD/CAE), and Design of Experiments (DOE) within a ten-stage framework combining the stages from DMADV (Define, Measure, Analyze, Design, Verify) and IDOV (Identify, Design, Optimize, Validate) approaches. The proposed method was applied to the design of a structural concrete block, considering performance variables such as weight, factor of safety, displacement, energy consumption, and carbon emissions. The results show that the integration of QFD enabled prioritization of customer requirements, while DOE and regression models identified significant factors and interactions. Multi-response optimization using desirability functions achieved a balanced solution, improving structural performance and sustainability indicators. In particular, a significant reduction in carbon emissions was achieved. Validation through simulation confirmed the consistency between predicted and observed results. The findings demonstrate that the proposed methodology provides a systematic and replicable approach for product design, improving decision-making and supporting the development of more sustainable products. Full article

Radial Basis Functions (RBFs), since their inception in the 1960s, have emerged as a key tool in digital engineering applications. As interpolators in multidimensional spaces, RBFs play a crucial role both in generic data science problems and in 3D space manipulation. Their ability to represent large 3D datasets in a mesh-free manner has established them as the standard approach for data mapping and mesh deformation. A fast implementation of RBFs is essential to fully exploit this mathematical approach in digital engineering applications. This paper provides an overview of fast RBF methods in digital engineering and presents practical applications in the field of Computer-Aided Engineering (CAE), highlighting the role of RBFs in the development of a digital twin capable of real-time interaction with 3D structural components; after detailing the workflow for a simple plate with a hole, the method is demonstrated for the structural redesign of a scooter engine connecting rod and for the interactive conceptual design of a CubeSat. Full article

Establishing long-term human settlements in deep space presents significant challenges. Environmental conditions, such as extreme temperature fluctuations, micrometeorite impacts, seismic activity, and exposure to solar and cosmic radiation, pose obstacles to the design and operation of habitat systems. Prolonged mission duration and vast distances from Earth introduce further complications in the form of delayed communication and limited resources, making Earth independence through appropriate autonomous management systems especially desirable. Enabling the modeling and simulation of the consequences of disruptions and faults, and their propagation through the various habitat subsystems, is critically needed for the development of resilience-based design frameworks and methods for autonomous operation. While existing simulation tools can assist in modeling isolated aspects of damage, the integration of damage propagation and the capacity to enable detection and repair are rarely considered in a computational model. This paper introduces and demonstrates an architecture designed specifically to enable the modeling and integration of faults and damage, as well as their cascading effects. By combining physics-based and phenomenological models, our approach balances computational efficiency with model fidelity. After describing the modeling approach and corresponding architecture, we demonstrate its application within HabSim, a system-level space habitat model developed by the NASA-funded Resilient Extraterrestrial Habitat Institute (RETHi), as a simulation-based design aid suited to early-phase trade studies. Fire hazard propagation within a lunar habitat is used as an illustrative example of how the architecture supports modeling of disruption consequences, propagation, detection, and repair, and of how HabSim can be leveraged for stochastic simulations to support resilience assessment. Resilience-focused studies that apply this architecture can quantify and compare design alternatives. Full article

This study proposes and validates a framework that integrates Grey Wolf Optimization (GWO) with Extreme Gradient Boosting (XGBoost) for estimating the Poisson’s ratio of auxetic structures. First, for 320 models derived from Computer-Aided Design-based (CAD-based) unit-cell designs, a systematic sweep of diameter and cellular dimensions was conducted to obtain porosity coverage in the 45–85% range. Subsequently, elastic modulus and Poisson’s ratio were computed via finite element analysis (FEA) at three mesh resolutions (0.20/0.25/0.30 mm), and relationships between design variables and outputs were examined using correlation heatmaps and Locally Weighted Scatterplot Smoothing (LOWESS) curves. GWO optimized the XGBoost hyperparameters through a multi-band narrowed search strategy; performance was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Coefficient of Determination ($R^2$) metrics, as well as residual diagnostics and Ground Truth–Prediction alignments for Poisson’s ratio. Across all configurations, $R^2\ge 0.994$ and absolute errors are on the order of ∼${10}^{-3}$; the 0.25 mm mesh stands out in terms of overall balance with the lowest squared-error profile and the highest $R^2$, the 0.30 mm mesh is practically equivalent in terms of MAE, and the 0.20 mm mesh is comparatively weaker. Residual diagnostics—comprising a pattern-free cloud around zero, slight right-skewness, and limited heteroskedasticity—indicate low bias and no substantive model-specification issues. The findings align with physical insight, confirming that Poisson’s ratio shifts toward more negative values as porosity increases and toward less negative values as diameter increases. The proposed GWO–XGBoost framework provides a reliable pre-screening tool for rapid design exploration and Poisson’s-ratio-targeted optimization, with the potential to reduce the need for additional FEA simulations and experimental iterations during early-stage design. Full article

A novel 3D detector with a semi-spherical electrode detector structure is proposed in this study. The semi-spherical electrode is formed by concentric deep circular-type trenches of varying depths. These concentric trenches can be simultaneously deep-etched using DRIE (Deep Reactive-Ion Etching) depths obtained from our calculations for a certain time at a given aspect ratio. The focus of this work is the conceptualization, design considerations, 3D modeling, and electrical simulation of the proposed 3D detector. The detector’s electrical properties, including electric potential distribution, electric field distribution, electron concentration distribution, full depletion voltage, leakage current, and capacitance, were simulated using a technology computer-aided design (TCAD) tool. Simulation and analysis of the detector’s performance post-irradiation were also conducted. The small capacitance of our semi-spherical electrode detector renders it highly suitable for applications in photon sciences (e.g., X-ray). Full article

Cycle-to-cycle variability of switching parameters inherent to memristive devices introduces significant problems in the design of neuromorphic systems and non-volatile memory. This study investigates the dynamics of a second-order memristive system incorporating capacitive effects that model parasitic charge within individual memristors, addressing both the technical need for accurate analysis of complex regimes and the demand for exploratory environments. Simulations were performed using CUDAynamics, an interactive software suite developed by the authors, which utilizes parallel computing, primarily via NVIDIA Compute Unified Device Architecture (CUDA). It integrates multiple analysis tools for dynamical systems, including bifurcation diagrams, the largest Lyapunov exponent and periodicity mapping, and interactive navigation in multidimensional parameter spaces. The memristive system was discretized applying multiple integration methods with a fixed time step and various waveforms of the input signal. Analysis tools revealed well-defined regions of chaotic dynamics in the memristor resistance parameter space as functions of input signal properties. Sinusoidal and triangular waveforms produced topologically similar distributions of dynamical regimes, whereas the square waveform, mimicking digital inputs, generated distinct dynamical patterns while still preserving chaotic trajectories under specific conditions. Interactive visualization capabilities of CUDAynamics effectively demonstrate attractor evolution and hysteresis deformation, providing immediate visual feedback that significantly enhances conceptual comprehension of nonlinear feedback mechanisms. Beyond its practical implications for the design of analog and digital memristive devices, CUDAynamics offers a scalable, open-source toolkit to aid researchers and engineers in exploring complex dynamical phenomena. Full article

This paper presents a novel platform for automated self-paced learning in Computer-Aided Design (CAD) courses within engineering education. The platform fully automates the entire learning cycle, including exercise generation, submission, scheduling, test design, and grading. The central hypothesis posits that complete automation reduces repetitive tasks for instructors, allowing them to dedicate more time to individualized student support. The system also provides key advantages: it generates unique exercises for each student to prevent plagiarism while maintaining comparable complexity; it delivers instantaneous grading and feedback to enhance motivation; and it enables students to work with almost any CAD software, as the evaluation relies on physical properties rather than commercial tools. After several years of successive testing and refinement, the tool can generate and accurately grade frequent activities in large student cohorts, providing abundant data points. Their statistical analysis, via multiple approaches, confirms that the system reliably produces individualized exercises, reduces grading errors, and offers prompt, consistent feedback, thereby supporting a more efficient and engaging learning process. Full article

Background: Finite element analysis (FEA) has become an important tool in restorative dentistry for investigating stress distribution in teeth and dental restorations. However, the accuracy of such analyses strongly depends on the anatomical fidelity of the underlying tooth models, which is often limited in simplified geometries. The objective of this study was to develop an anatomically accurate three-dimensional tooth model based on micro-computed tomography (micro-CT) data and to evaluate the biomechanical behaviour of sound and composite-restored teeth under clinically relevant loading conditions. Methods: A human tooth was scanned using high-resolution micro-CT imaging. Enamel, dentin, and pulp were segmented and reconstructed into three-dimensional geometries, which were further refined using computer-aided design (CAD) tools. The resulting models were imported into a finite element environment for mechanical simulation. Static loading conditions were applied to both sound and composite-restored tooth models, including a vertical load of 200 N and an oblique load of 200 N applied at a 45° angle to the tooth crown. Von Mises stress distributions were evaluated to characterize stress concentration patterns. Results: Finite element simulations revealed maximum von Mises stresses of approximately 140 MPa, predominantly localized in the coronal regions of the tooth. Oblique loading produced increased and more asymmetric stress concentrations than vertical loading, particularly in the anterior and posterior crown regions. While overall stress distributions were comparable between sound and composite-restored teeth, locally increased stress levels were observed in restored models under oblique loading. Conclusions: Anatomically accurate, micro-CT-based finite element tooth models provide a robust framework for biomechanical analysis in restorative dentistry. The presented workflow enables detailed evaluation of stress distribution in composite-restored teeth and may contribute to improved understanding and optimization of restorative materials and treatment strategies. Full article