Search Results (992)

This research is part of a broader investigation into innovative simulation-based approaches for improving traffic efficiency and road safety in roundabout corridors. These corridors, composed of successive roundabouts along arterials, present systemic challenges due to the dynamic interactions between adjacent intersections. While previous studies have addressed localized inefficiencies or proposed isolated interventions, this paper introduces possible replicable methodology based on a microsimulation and surrogate safety analysis to evaluate roundabout corridors as integrated systems. In this context, efficiency refers to the ability of a road corridor to maintain stable traffic conditions under a given demand scenario, with low delay times corresponding to acceptable levels of service. Safety is interpreted as the minimization of vehicle conflicts and critical interactions, evaluated through surrogate measures derived from simulated vehicle trajectories. The proposed approach—implemented through Aimsun Next and the SSAM tool—is tested on two real-world corridors: Via Aurelia Nord in Pisa (Italy) and Route de Marseille in Avignon (France), assessing multiple intersection configurations that combine roundabouts and signal-controlled junctions. Results show how certain layouts can produce unexpected performance outcomes, underlining the importance of system-wide evaluations. The proposed framework aims to support engineers and planners in identifying optimal corridor configurations under realistic operating conditions. Full article

Advanced three-dimensional extended reality (XR) technologies are highly suitable for cultural heritage research and education. XR tools enable the creation of realistic virtual or augmented reality applications for curating and disseminating information about cultural artifacts and sites. Developing XR applications for cultural heritage requires interdisciplinary collaboration involving strong teamwork and soft skills to manage user requirements, system specifications, and design cycles. Given the diverse end-users, achieving high precision, accuracy, and efficiency in information management and user experience is crucial. Human–computer interaction (HCI) design and evaluation methods are essential for ensuring usability and return on investment. This article presents ten case studies of cultural heritage software projects, illustrating the interdisciplinary work between computer science and HCI design. Students from institutions such as the State University of New York (USA), Glasgow School of Art (UK), University of Granada (Spain), University of Málaga (Spain), Duy Tan University (Vietnam), Imperial College London (UK), Research University Institute of Communication & Computer Systems (Greece), Technical University of Košice (Slovakia), and Indiana University (USA) contributed to creating, assessing, and improving the usability of these diverse cultural heritage applications. The results include a structured typology of CH XR application scenarios, detailed insights into design and evaluation practices across ten international use cases, and a development framework that supports interdisciplinary collaboration and stakeholder integration in phygital cultural heritage projects. Full article

Cardiomyopathies represent a heterogeneous group of myocardial disorders, traditionally classified by phenotype into hypertrophic, dilated, and arrhythmogenic. Historically, these conditions have been attributed to high-penetrance rare variants in key structural genes, consistent with a classical Mendelian pattern of inheritance. However, emerging evidence suggests that this model does not fully capture the full spectrum and complexity of disease expression. Many patients do not harbor identifiable pathogenic variants, while others carrying well-known disease-causing variants remain unaffected. This highlights the role of incomplete penetrance, likely modulated by additional genetic modifiers. Recent advances in genomics have revealed a broader view of the genetic basis of cardiomyopathies, introducing new players such as common genetic variants identified as risk alleles, as well as intermediate-effect variants. This continuum of genetic risk, reflecting an overall genetic influence, interacts further with environmental and lifestyle factors, likely contributing together to the observed variability in clinical presentation. This model offers a more realistic framework for understanding genetic inheritance and helps provide a clearer picture of disease expression and penetrance. This review explores the evolving genetic architecture of cardiomyopathies, spanning from a monogenic foundation to intermediate-risk variants and complex polygenic contribution. Recognizing this continuum is essential for enhancing diagnostic accuracy, guiding family screening strategies, and enabling personalized patient management. Full article

I argue that the universally accepted assumption that in realist fiction a character narrator’s narration contributes to their characterization needs to be complicated. Working with a conception of narrative as rhetoric that highlights readerly interest in the author’s handling of the mimetic, thematic, and synthetic components of narrative, I suggest that the question about narration as characterization is one about the relation between the mimetic (character as possible person) and synthetic (character as invented construct) components. In addition, understanding the mimetic-synthetic relation requires attention to issues at the macro and micro levels of such narratives. At the macro level, I note the importance of (1) the tacit knowledge, shared by both authors and audiences, of the fictionality of character narration, which means authors write and readers read with an interest in its payoffs; and of (2) the recognition that character narration functions simultaneously along two tracks of communication: that between the character narrator and their narratee, and that between the author and their audience. These macro level matters then provide a frame within which authors and readers understand what happens at the micro level. At that level, I identify seven features of a character’s telling that have the potential to be used for characterization—voice, occasion, un/reliability, authority, self-consciousness, narrative control, and aesthetics. I also note that these features have their counterparts in the author’s telling. Finally, I propose that characterization via narration results from the interaction between the salient features of the character’s telling and their counterparts in the author’s telling. I develop these points through the analysis of four diverse case studies: Mark Twain’s Huckleberry Finn, Robert Browning’s “My Last Duchess,” Nadine Gordimer’s “Homage,” and Ernest Hemingway’s A Farewell to Arms. Full article

This study theoretically investigates the potential of a polyacrylamide copolymerized with amylose, a primary component of starch, to evaluate its efficiency in removing heavy metals from industrial wastewater. This material concept seeks to combine the high adsorption capacity of polyacrylamide with the low cost and biodegradability of starch, ultimately aiming to offer an economical, efficient, and sustainable alternative for wastewater treatment. To this end, a computational model based on density functional theory (DFT) was developed, utilizing the B3LYP functional with the 6-311++G(d,p) basis set, a widely recognized combination that strikes a balance between accuracy and computational cost. The interactions between an acrylamide-amylose (AM/Amy) polymer matrix, as well as the individual polymers (AM and Amy), and the metal ions Pb, Hg, and Cd in their hexahydrated form (M·6H₂O) were analyzed. This modeling approach, where M represents any of these metals, simulates a realistic aqueous environment around the metal ion. Molecular geometries were optimized, and key parameters such as total energy, dipole moment, frontier molecular orbital (HOMO-LUMO) energy levels, and Density of States (DOS) graphs were calculated to characterize the stability and electronic reactivity of the molecules. The results indicate that this proposed copolymer, through its favorable electronic properties, exhibits a high adsorption capacity for metal ions such as Pb and Cd, positioning it as a promising material for environmental applications. Full article

Background/Objectives: Dental cavity preparation is a critical procedure in restorative dentistry that involves the removal of decayed tissue while preserving a healthy tooth structure. Excessive stress during tooth preparation leads to enamel cracking, dentin damage, and long term compressive pulp health. This study employed finite element analysis (FEA) to investigate the stress distribution in dental structures during cavity preparation using round diamond burs of varying diameters and depths of cut (DOC). Methods: A three-dimensional human maxillary first molar was generated from computed tomography (CT) scan data using 3D Slicer, Fusion 360, and ANSYS Space Claim 2024 R-2. Finite element analysis (FEA) was conducted using ANSYS Workbench 2024. Round diamond burs with diameters of 1, 2, and 3 mm were modeled. Cutting simulations were performed for DOC of 1 mm and 2 mm. The burs were treated as rigid bodies, whereas the dental structures were modeled as deformable bodies using the Cowper–Symonds model. Results: The simulations revealed that larger bur diameters and deeper cuts led to higher stress magnitudes, particularly in the enamel and dentin. The maximum von Mises stress was reached at 136.98 MPa, and dentin 140.33 MPa. Smaller burs (≤2 mm) and lower depths of cut (≤1 mm) produced lower stress values and were optimal for minimizing dental structural damage. Pulpal stress remained low but showed an increasing trend with increased DOC and bur size. Conclusions: This study provides clinically relevant guidance for reducing mechanical damage during cavity preparation by recommending the use of smaller burs and controlled cutting depths. The originality of this study lies in its integration of CT-based anatomy with dynamic FEA modeling, enabling a realistic simulation of tool–tissue interaction in dentistry. These insights can inform bur selection, cutting protocols, and future experimental validations. Full article

Earthwork construction presents significant challenges due to its unique characteristics, including irregular topography, inhomogeneous geotechnical properties, dynamic operations involving heavy equipment, and continuous terrain updates over time. Existing methods often fail to accurately capture these complexities, support semantic attributes, simulate realistic equipment–environment interactions, and update the model dynamically during construction. Moreover, most current digital solutions lack an integrated framework capable of linking geotechnical semantics with construction progress in a continuously evolving terrain. This study introduces a novel, voxel-based digital twin framework tailored for earthwork construction. Unlike previous studies that relied on surface, mesh, or layer-based representations, our approach leverages semantically enriched voxelization to encode spatial, material, and behavioral attributes at a high resolution. The proposed framework connects the physical and digital representations of the earthwork environment and is structured into five modules. The data acquisition module gathers terrain, geotechnical, design, and construction data. Virtual models are created for the earthwork in as-planned and as-built models. The digital twin core module utilizes voxels to create a realistic earthwork environment that integrates the as-planned and as-built models, facilitating model–equipment interaction and updating models for progress monitoring. The visualization and simulation module enables model–equipment interaction based on evolving as-built conditions. Finally, the monitoring and analysis module provides volumetric progress insights, semantic material information, and excavation tracking. The key innovation of this framework lies in multi-resolution voxel modeling, semantic mapping of geotechnical properties, and supporting dynamic updates during ongoing construction, enabling model–equipment interaction and material-specific construction progress monitoring. The framework is validated through real-world case studies, demonstrating its effectiveness in providing realistic representations, model–equipment interactions, and supporting progress information and operational insights. Full article

Reinforcement learning (RL), a branch of artificial intelligence (AI), is becoming more popular in a variety of application fields such as games, workplaces, and behavioral analysis, due to its ability to model complex decision-making through interaction and feedback. Traditional systems for personality and behavior assessment often rely on self-reported questionnaires, which are prone to bias and manipulation. RL offers a compelling alternative by generating diverse, objective behavioral data through agent–environment interactions. In this paper, we propose a Reinforcement Learning-based framework in a game environment, where agents simulate personality-driven behavior using context-aware policies and exhibit a wide range of realistic actions. Our method, which is based on the OCEAN Five personality model—openness, conscientiousness, extroversion, agreeableness, and neuroticism—relates psychological profiles to in-game decision-making patterns. The agents are allowed to operate in numerous environments, observe behaviors that were modeled using another simulation system (HiDAC) and develop the skills needed to navigate and complete tasks. As a result, we are able to identify the personality types and team configurations that have the greatest effects on task performance and collaboration effectiveness. Using interaction data derived from self-play, we investigate the relationships between behaviors motivated by the personalities of the agents, communication styles, and team outcomes. The results demonstrate that in addition to having an effect on performance, personality-aware agents provide a solid methodology for producing realistic behavioral data, developing adaptive NPCs, and evaluating team-based scenarios in challenging settings. Full article

Blindness and low vision are increasing serious public health issues that affect a significant percentage of the population worldwide. Vision plays a crucial role in spatial navigation and daily activities. Its reduction or loss creates numerous challenges for an individual. Assistive technology can enhance mobility and navigation in outdoor environments. In the field of orientation and mobility training, technologies with haptic interaction can assist individuals with visual impairments in learning how to navigate safely and effectively using the sense of touch. This paper presents a virtual reality platform designed to support the development of navigation techniques within a safe yet realistic environment, expanding upon existing research in the field. Following extensive optimization, we present a visual representation that accurately simulates various 3D tile textures using graphics replicating real tactile surfaces. We conducted a user interaction study in a virtual environment consisting of 3D navigation tiles enhanced with tactile textures, placed appropriately for a real-world scenario, to assess user performance and experience. This study also assess the usability and user experience of the platform. We hope that the findings will contribute to the development of new universal navigation techniques for people with visual impairments. Full article

Epoxy adhesives derived from bisphenol A diglycidyl ether (BADGE) are widely utilized in segmental construction—particularly in precast concrete structures—and in building structural strengthening, owing to their outstanding adhesion properties and long-term durability. These materials constitute a significant class of polymeric adhesives in structural engineering applications. However, BADGE-based epoxy adhesives are susceptible to aging under service conditions, primarily due to environmental stressors such as thermal cycling, oxygen exposure, moisture ingress, ultraviolet radiation, and interaction with corrosive media. These aging processes lead to irreversible physicochemical changes, manifested as degradation of microstructure, mechanical properties, and dynamic mechanical properties to varying degrees, with performance deterioration becoming increasingly significant over time. Notably, for the mechanical properties of concern, the decline can exceed 40% in accelerated aging tests. A comprehensive understanding of the aging behavior of BADGE-based epoxy resin under realistic environmental conditions is essential for predicting long-term performance and ensuring structural safety. This paper provides a critical review of existing studies on the aging behavior of BADGE-based epoxy resins. This paper summarizes the findings of various aging tests involving different influencing factors, identifies the main degradation mechanisms, and evaluates current methods for predicting long-term durability (such as the Arrhenius method, Eyring model, etc.). Furthermore, this review provides recommendations for future research, including investigating multifactorial aging, conducting natural exposure tests, and establishing correlations between laboratory-based accelerated aging and field-exposed conditions. These recommendations aim to advance the understanding of long-term aging mechanisms and enhance the reliability of BADGE-based epoxy resins in structural applications. Full article

Authentic learning opportunities that simulate full-scale design and construction using real materials provide valuable experiential learning environments for construction and civil engineering students by challenging students to apply building concepts in practical settings. These activities challenge students to apply theoretical concepts in a realistic, hands-on context. However, the excessive cost of real building materials required for this mode of education limits access to the vast majority of students. As a result, educational researchers have explored potential alternatives to provide cost-effective experiential learning through activities using mock-up materials (e.g., plastic straws and popsicle sticks) and a simulation of experiences using immersive technologies (e.g., virtual reality or augmented reality). While some of these alternatives approximate the environment and others provide physical interaction with mock-up materials, the lack of authenticity in the building materials used introduces some apparent differences between the “authentic” learning environments and their cost-effective approximations. Therefore, this research aims to identify the learning processes reported by students and faculty who participated in authentic learning experiences to understand the ways in which this mode of education offers unique value to construction education. Their interview responses illustrated characteristics of authentic learning experiences that were believed to be critical to the learning process, some of which included working in groups; interdisciplinary participants; and the use of real construction materials. Although some of these characteristics are intrinsically linked to the use of real materials, others do not explicitly refer to interaction with real materials. This may point to specific aspects of authentic learning that educational researchers can replicate or enhance to provide cost-effective learning environments, such as virtual or augmented reality. The contribution of this paper is in identifying the characteristics of authentic learning experiences that may guide educational investment and research innovations that aim to replicate some of these learning experiences through more accessible learning environments. Full article

This study presents a computational fluid dynamics (CFDs) investigation of the hydrodynamic behavior of surface-textured lateral bushings in external gear pumps (EGPs), emphasizing the effects of combined sliding and squeezing motions within the lubrication gap. A comprehensive numerical model was developed to analyze how surface texturing implemented through different dimple shapes and texture densities influences pressure distribution and load-carrying capacity under transient lubrication conditions. The analysis demonstrates that the interaction between shear-driven flow and squeeze-film compression significantly amplifies pressure, particularly when optimal dimple configurations are applied. Results indicate that dimple geometry, depth, and arrangement critically influence hydrodynamic performance, while excessive texturing reduces effectiveness due to increased average gap height. Cavitation was intentionally not modeled in the early single dimple evaluations to allow clear comparison between configurations. The findings offer a design guideline for employing surface textures to enhance tribological performance and efficiency in EGP applications under realistic dynamic conditions. Full article

Background: Data accessibility remains a significant barrier in healthcare AI due to privacy constraints and logistical challenges. Synthetic data, which mimics real patient information while remaining both realistic and non-identifiable, offers a promising solution. Large Language Models (LLMs) create new opportunities to generate high-fidelity clinical conversations between patients and physicians. However, the value of this synthetic data depends on careful evaluation of its realism, accuracy, and practical relevance. Objective: To assess the performance of four leading LLMs: ChatGPT 4.5, ChatGPT 4o, Claude 3.7 Sonnet, and Gemini Pro 2.5 in generating synthetic transcripts of patient–physician interactions in plastic surgery scenarios. Methods: Each model generated transcripts for ten plastic surgery scenarios. Transcripts were independently evaluated by three clinically trained raters using a seven-criterion rubric: Medical Accuracy, Realism, Persona Consistency, Fidelity, Empathy, Relevancy, and Usability. Raters were blinded to the model identity to reduce bias. Each was rated on a 5-point Likert scale, yielding 840 total evaluations. Descriptive statistics were computed, and a two-way repeated measures ANOVA was used to test for differences across models and metrics. In addition, transcripts were analyzed using automated linguistic and content-based metrics. Results: All models achieved strong performance, with mean ratings exceeding 4.5 across all criteria. Gemini 2.5 Pro received mean scores (5.00 ± 0.00) in Medical Accuracy, Realism, Persona Consistency, Relevancy, and Usability. Claude 3.7 Sonnet matched the scores in Persona Consistency and Relevancy and led in Empathy (4.96 ± 0.18). ChatGPT 4.5 also achieved perfect scores in Relevancy, with high scores in Empathy (4.93 ± 0.25) and Usability (4.96 ± 0.18). ChatGPT 4o demonstrated consistently strong but slightly lower performance across most dimensions. ANOVA revealed no statistically significant differences across models (F(3, 6) = 0.85, p = 0.52). Automated analysis showed substantial variation in transcript length, style, and content richness: Gemini 2.5 Pro generated the longest and most emotionally expressive dialogues, while ChatGPT 4o produced the shortest and most concise outputs. Conclusions: Leading LLMs can generate medically accurate, emotionally appropriate synthetic dialogues suitable for educational and research use. Despite high performance, demographic homogeneity in generated patients highlights the need for improved diversity and bias mitigation in model outputs. These findings support the cautious, context-aware integration of LLM-generated dialogues into medical training, simulation, and research. Full article

Pedestrian flow simulations play a pivotal role in urban planning, transportation engineering, and disaster response by enabling the detailed analysis of crowd dynamics and walking behavior. While physical models such as the Social Force model and Boids have been widely used, they often struggle to replicate complex inter-agent interactions. On the other hand, reinforcement learning (RL) methods, although adaptive, suffer from limited interpretability due to their opaque policy structures. To address these limitations, this study proposes a pedestrian simulation framework based on the Anticipatory Classifier System 2 (ACS2), a rule-based evolutionary learning model capable of extracting explicit behavior rules through trial-and-error learning. The proposed model captures the interactions between agents and environmental features while preserving the interpretability of the acquired strategies. Simulation experiments demonstrate that the ACS2-based agents reproduce realistic pedestrian dynamics and achieve comparable adaptability to conventional reinforcement learning approaches such as tabular Q-learning. Moreover, the extracted behavior rules enable systematic analysis of movement patterns, including the effects of obstacles and crowd composition on flow efficiency and group alignment. The results suggest that the ACS2 provides a promising approach to constructing interpretable multi-agent simulations for real-world pedestrian environments. Full article

Rotating machinery is essential in various industrial fields, and growing demands for high performance under harsh operating conditions have heightened interest in fault diagnosis and prognostic technologies. However, a major challenge in fault diagnosis research lies in the scarcity of data, primarily due to the inability to deliberately introduce faults into machines during actual operation. In this study, a physical model is proposed to realistically simulate the system behavior of a ship’s turbo-rotating machinery by coupling the torsional and lateral vibrations of the rotor. While previous studies employed simplified single-shaft models, the proposed model adopted gear mesh interactions to reflect the coupling behavior between shafts. Furthermore, the time-domain response of the system is analyzed through state-space transformation. The proposed model was applied to simulate imbalance and gear teeth damage conditions that may occur in marine turbo-rotating systems and the results were compared with those under normal operating conditions. The analysis confirmed that the model effectively reproduces fault-induced dynamic characteristics. By enabling rapid implementation of various fault conditions and efficient data acquisition data, the proposed model is expected to contribute to enhancing the reliability of fault diagnosis and prognostic research. Full article