bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessing Pedestrian Trust in Autonomous Vehicles: a Comparative Study of Immersive Virtual Reality and Non-Immersive Video Media
Authors: Islam Marufa , She Jinjuan , Chagdes James 
Published: November 2024

Abstract
Trust is crucial for integrating autonomous vehicles (AVs) into society, with various studies examining pedestrian trust through methods like images, texts, videos, virtual reality (VR), and field studies. The impact of these representational methods on trust assessment remains uncertain. Our study conducted a 2 (media types: VR vs. video) by 7 (vehicle behavior trials) within-subject experiment with 50 adults to compare pedestrian trust, risk perception, and comprehensibility in immersive VR and non-immersive video settings. The results showed VR enhanced subjective judgments like trust and risk perception in general, even though participants’ understanding of vehicle behaviors is statistically the same. In addition, representing scenarios in an immersive VR or non-immersive video environment does not influence the relative effectiveness of vehicle behaviors on the trust assessment. The findings indicate the necessity of selecting appropriate media in trust studies depending on the research purpose (e.g., general assessment vs. comparative effectiveness) and validate the use of non-immersive video media to compare trust differences due to vehicle behavior variations. This research provides guidance for future AV-pedestrian trust studies in media type selection, particularly for meta-analyses aiming to consolidate findings from varied research by highlighting the critical role of media choice in trust assessments.
Dynamic Simulation and Collision Detection for Flexible Mechanical Systems With Contact Using the Floating Frame of Reference Formulation
Authors: Dai Xu , Kövecses József , Teichmann Marek 
Published: November 2024

Abstract
Contact simulation is essential in modelling mechanical systems. The contact models require accurate geometric information, which is determined through collision detection methods. When the mechanical system includes flexible bodies such as structural components, the dynamic formulation and collision detection can be more challenging, as the geometric boundaries of such components keep changing during the simulation. The floating frame of reference (FFR) formulation is suitable for flexible systems with small deformation. In this work, a stable and efficient dynamic simulation method is introduced for flexible systems with contact based on the FFR formulation. In addition, a curve-based collision detection method is proposed, which is more consistent with the dynamic formulation and more efficient than common existing collision detection methods. Case studies of flexible beams and multibody systems are employed to demonstrate the performance of the proposed dynamic simulation and collision detection methods.
Effects of Image Uncertainty on Blood Flow Simulation
Authors: Li Huiru , Yu Huidan , Wei Jie , Du Xiaoping 
Published: November 2024

Abstract
Image-based Computational Fluid Dynamics (ICFD) is widely applied for simulating and predicting patient-specific hemodynamics in human blood vessels. This study explores the impact of image input uncertainty from segmentation on ICFD simulation outputs for the iliac stenosis application for individual patients, using patients’ Computed Tomography Angiography imaging data. This investigation includes key components: first, we assess uncertainty in the patient’s three-dimensional (3D) vessel geometry extracted by three image segmentation platforms and multiple different operators, highlighting segmentation-induced variability. Secondly, ICFD simulations are performed to predict flow pressure fields based on random 3D shapes generated from segmentation, illustrating the impact of image uncertainty. To enable statistical image analysis, we develop an alignment method that computes the average 3D shape from a set of sample shapes from segmentation. By transforming complex 3D vessel shapes into coordinate vectors with automatic detection of correspondence points, this method enables the alignment and averaging of multiple 3D shapes. The resulting average 3D shape serves as input for ICFD simulation, generating a flow pressure field that can approximate the average prediction. The findings of this study demonstrate the effects of image uncertainty and the need for new methodologies to quantify these effects. The alignment method proves valuable for statistically analyzing random image data and 3D geometries, providing a useful tool for image statistical analysis.
Construction of 3D-Engineered Glioma Tissues Using the Acoustic Patterning Device
Authors: Du Yingshan , Li Jiali , Shen Liang , Pei Zhe , Cai Bowen , Li Teng , Qiu Chongpeng , Tian Zhenhua 
Published: November 2024

Abstract
Cell patterning techniques play a pivotal role in the development of three-dimensional (3D) engineered tissues, holding significant promise in regenerative medicine, drug screening, and disease research. Current techniques encompass various mechanisms, such as nanoscale topographic patterning, mechanical loading, chemical coating, 3D inkjet printing, electromagnetic fields, and acoustic waves. In this study, we introduce a unique standing bulk waves-based acoustic cell patterning device designed for constructing anisotropic-engineered glioma tissues containing acoustically patterned human glioblastoma cell U251. Our device features two orthogonal pairs of piezoelectric transducers securely mounted on a customized holder. The energy of standing bulk waves generated from these transducers can be transmitted into the medium in a Petri dish through its bottom wall. Cells in the medium can be directed to the local minima of Gor’kov potential fields and trapped by the resultant acoustic radiation force. Through proof-of-concept experiments, we validate the functionality of our acoustic patterning device and assess the morphology and differentiation of U251 cells within the engineered glioma tissues. Our findings reveal that cells can be arranged in different distributions, such as parallel-line-like and lattice-like patterns. Moreover, the aligned cells exhibit more obvious elongation along the cell alignment orientation compared to the result of a control group. We anticipate that this study will catalyze the advancement in contactless cell patterning technologies within tissue engineering, facilitating the development of engineered tissues for applications in regenerative medicine and disease research.
Hydraulic-Mechanical Coupled Simulation of 2D Micromotion in Variable Displacement Vane Pumps for Engine Lubrication
Authors: Rundo Massimo , Squarcini Raffaele 
Published: November 2024

Abstract
Variable displacement vane pumps are widely used for lubrication in internal combustion engines. A key aspect concerns the contact between the vane tip and the stator, both for wear issues and for potential leakages in case of detachment. The purpose of this study is to introduce a simulation methodology based on coupling the lumped parameter hydraulic model and the multibody mechanical model. The principle underlying the method consists of calculating the forces on the moving elements in the hydraulic model and letting the multibody model calculate the displacements of the bodies. The advantage of this approach lies in the assessment of 2D micromovements resulting from clearances of the vanes relative to other mechanical components of the pump core. It allows simulating the detachment of vanes from the stator track, their tilt in the rotor slots, as well as the mutual interaction between vanes through the underneath floating rings. Simulations revealed two distinct behaviors depending on the operating conditions. At low speeds, a strong interaction between the vanes and the rings was observed. In particular, the rings are maintained in an almost constant position by the thrust of the vanes. Conversely, at high speeds, all vanes remain in constant contact with the stator track, and the rings are completely floating. The model is well-suited for enhancing pump design, given its ability to calculate the forces exchanged by the mechanical parts more realistically.
Inspired by AI? A Novel Generative AI System to Assist Conceptual Automotive Design
Authors: Wang Ye , Damen Nicole B. , Gale Thomas , Seo Voho , Shayani Hooman 
Published: November 2024

Abstract
Design inspiration is crucial for establishing the direction of a design as well as evoking feelings and conveying meanings during the conceptual design process. Many practice designers use text-based searches on platforms like Pinterest to gather image ideas, followed by sketching on paper or using digital tools to develop concepts. Emerging generative AI techniques, such as diffusion models, offer a promising avenue to streamline these processes by swiftly generating design concepts based on text and image inspiration inputs, subsequently using the AI generated design concepts as fresh sources of inspiration for further concept development. However, applying these generative AI techniques directly within a design context has challenges. Firstly, generative AI tools may exhibit a bias towards particular styles, resulting in a lack of diversity of design outputs. Secondly, these tools may struggle to grasp the nuanced meanings of texts or images in a design context. Lastly, the lack of integration with established design processes within design teams can result in fragmented use scenarios. Focusing on these challenges, we conducted workshops, surveys, and data augmentation involving teams of experienced automotive designers to investigate their current practices in generating concepts inspired by texts and images, as well as their preferred interaction modes for generative AI systems to support the concept generation workflow. Finally, we developed a novel generative AI system based on diffusion models to assist conceptual automotive design.
Design, Development, and Testing of a Smart Hand Tool: Achieving Work Task Recognition Using Synthetic Data and Edge Intelligence
Authors: New Rachel , Salazar Carlos D. , Bendaña Jose , Sripada V. S. Sundar , Chinchali Sandeep , Fleischmann Kenneth R. , Longoria Raul G. 
Published: November 2024

Abstract
This paper describes research toward developing smart hand tools that leverage artificial intelligence (AI) and sensors for use by human workers. Smart hand tools can provide useful feedback that can benefit human workers, contribute to worker training, and broaden participation in the skilled trade workforce. Specifically, the paper focuses on task recognition. Given the challenges of producing enough training data for machine learning (ML) using data purely from human-based testing, this paper shows how data synthetically-generated by a robot can be leveraged in the ML training process. The paper also demonstrates how fine-tuning ML models for individual physical tasks and workers can significantly scale up the benefits of using ML to provide this feedback. Experimental results show the effectiveness and scalability of this approach, including comparing test data size versus accuracy. In order for smart hand tools of the type introduced here to operate in real-time task recognition, as well as providing analytics on efficient and safe tool usage and operation, ML models need to be deployed ‘on tool’. This paper demonstrates how this can be accomplished by using a tinyML implementation. This paper provides a proof-of-concept for using automated platforms to help train smart tools, which will be essential given the wide range of uses for smart hand tools.
3D Infill Path Generation on Quadric Surfaces for Additive Manufacturing
Authors: Wegner Isaac , Campbell Matthew I. 
Published: November 2024

Abstract
Conventional 3-axis 3D printing has limitations due to the need for supports for certain geometries and in weaknesses along layer lines. Layers for these printers are entirely planar, leading to difficulty in printing overhanging material. The planar structure also allows the weaker inter-layer adhesion to align with stresses, making components prone to failure under certain loading conditions. The recent advent of additional rotational axes on these printers has allowed for a departure from using strictly planar layers, but the software needed to generate these non-planar toolpaths is not nearly as robust as current planar slicers. This paper proposes a method to generate the infill paths for printing on arbitrary quadric surfaces as opposed to only planar surfaces. The method uses coordinate transforms of the layer surface to ensure that toolpaths conform to the interior of a layer perimeter without leaving empty regions within the perimeter interior. This includes proper exclusion of paths within any interior cavities within the outermost perimeters. Two common 3D infill patterns, cubic and gyroid, are implemented and tested. Examples of sliced single layers and complete models are showcased and their structures are compared with those comprised of only planar paths. The viability of extending the method to other patterns in the future is discussed.
Harvesting Energy From Low-Frequency Excitations Using a Rolling Ball With Frequency Up-Conversion
Authors: Kortam Zahwa , Arafa Mustafa 
Published: November 2024

Abstract
This paper presents the design and testing of an electromagnetic device that harvests energy from low-frequency, low-amplitude excitations. The concept is based on a steel ball that acts as an inertial mass that rolls along two parallel guides. Tilting the guides cyclically causes the ball to roll back and forth, magnetically plucking a cantilever beam that is fixed underneath it and carrying a tip magnet. Frequency up-conversion is achieved as the beam vibrates at its natural frequency, which is higher than the excitation frequency, and this is employed to harvest energy by electromagnetic induction as the tip magnet moves relative to a stationary coil. Experimental testing at frequencies ranging from 0.075 Hz – 1.125 Hz with tilt angles ranging from 1.8° – 5.7° demonstrate the effectiveness of the device in harvesting energy from low-frequency, low-amplitude tilting excitations. The experiments are used to gain insight into the frequency up-conversion mechanism and its characteristics. The direction of the ball’s rolling was observed to affect the output voltage magnitude, and the experiments were used to determine the most favorable frequencies, drive conditions as well as limitations of the proposed concept, as plucking the cantilever beam essentially draws from the kinetic energy of the ball. The device was also used to charge a capacitor through a rectifying circuit to validate the concept. Potential applications include utilizing the concept in low-frequency environments, such as human motion or the vibration of tree branches.
Analysis of Contact Points on Guidewires in Neurointerventional Surgery Using U-Net-Based Shape Reconstruction
Authors: Xu Yang , Rana Mahad , Zhou Maggie , Mangla Sundeep , Shi Yong 
Published: November 2024

Abstract
A study is currently underway to enhance a robotic system tailored for assisting surgeons during neuroendovascular interventions. This system relies on a guidewire to navigate through the vascular artery network. However, due to the guidewire’s high flexibility, locating the contact points on it poses a challenge, which will lead to safety concerns and imprecise navigation outcomes for the robotic system. To address this issue, this research proposes a shape-based force estimation method to precisely locate the contact points on the guidewire. This method uses the guidewire’s contour in the image as the shape information and the guidewire’s mechanical property to establish the relationship between shape change and applied loads. To extract the guidewire’s contour from the background, a convolutional neural network, specifically a U-Net model, was employed. Ninety-six images were used for the U-Net model training. The U-Net model achieved a Dice score of 82.8%, indicating its effectiveness in contour extraction. Based on the extracted contour, a thinning method was used to extract the mid-line of the guidewire, which can be used to calculate the curvature of the guidewire. By analyzing the curvature change, the localized maximum value of the curvature indicated the location of the applied load. An experimental validation of the proposed method was conducted. The relative error of the load location estimation was approximately 6.5%, demonstrating the accuracy of the proposed approach.
Mechanical Design of a Novel Robotic Gripper for Fixed Automation Applications
Authors: Jones Hunter , Slaboch Brian J. 
Published: November 2024

Abstract
This paper presents an industrial robotic gripper for high-volume, fixed automation applications. The gripper is unique in that it can both grasp an object and retract the object using a single electric motor. Once the object is grasped and retracted, it can also be extended and released by reversing the direction of the motor. The new gripper is driven by a single cam mechanism with two cam profiles that control both the extension and retraction of the mechanism as well as the opening and closing of the gripper fingers. The cam driven approach includes an extension spring that allows the gripper fingers to automatically grasp and release different sized objects. This paper presents the new robotic gripper, including a discussion of its design principles. The advantage of this mechanism is that it only needs one actuator to complete a high-volume fixed automation task that usually requires two actuators. Thus, the proposed gripper design has the potential to have lower power consumption and improved energy efficiency when compared to conventional approaches that use some combination of pneumatic and electric actuators.
Human-Artificial Intelligence Teaming for Scientific Information Extraction From Data-Driven Additive Manufacturing Research Using Large Language Models
Authors: Safdar Mutahar , Xie Jiarui , Mircea Andrei , Zhao Yaoyao Fiona 
Published: November 2024

Abstract
Data-driven research in Additive Manufacturing (AM) has gained significant success in recent years. This has led to a plethora of scientific literature to emerge. The knowledge in these works consists of AM and Artificial Intelligence (AI) contexts that haven’t been mined and formalized in an integrated way. It requires substantial effort and time to extract scientific information from these works. AM domain experts have contributed over two dozen review papers to summarize these works. However, information specific to AM and AI contexts still requires manual effort to extract. The recent success of foundation models such as BERT (Bidirectional Encoder Representations for Transformers) or GPT (Generative Pre-trained Transformers) on textual data has opened the possibility of expediting scientific information extraction. We propose a framework that enables collaboration between AM and AI experts to continuously extract scientific information from data-driven AM literature. A demonstration tool is implemented based on the proposed framework and a case study is conducted to extract information relevant to the datasets, modeling, sensing, and AM system categories. We show the ability of LLMs (Large Language Models) to expedite the extraction of relevant information from data-driven AM literature. In the future, the framework can be used to extract information from the broader design and manufacturing literature in the engineering discipline.
Enhancements to a Topology Optimization Interpreter for Features Common in Aircraft Structures
Authors: Myszka David H. , Studnicka Jack A. , Murray Andrew P. , Joo James J. 
Published: November 2024

Abstract
This paper describes enhancements made to a topology optimization interpreter to specifically address features that are common in aircraft structures. Structural topology optimization is a computational method that distributes material within a design domain to achieve a structurally efficient layout. Topology optimization is particularly attractive for the design of aircraft structures, where low weight and high stiffness are essential criteria. Yet, a barrier to the broad implementation of topology optimization is that the results are not typically in a form that is readily manufactured. Consistent with a well-known understanding that trusses are efficient structures, many topology optimization results resemble a spaceframe structure. To create a manufacturable design, an interpreter has previously been developed to automatically convert topology optimization results into a producible welded-tube spaceframe. That interpreter accepts the optimization results file, uses image processing techniques to decipher a matching spaceframe, and creates a computer-aided-design model.
This paper describes the enhancements made to the interpreter that were made to explicitly address characteristics that arise with air vehicle applications. Specifically, air frames support a curved outer skin that mostly requires a hollow core. Straight frame members are prone to violate the structure’s design domain. Additionally, aircraft often exhibit symmetric geometry and loading. To properly address these circumstances, the results interpreter is expanded to provide the capabilities to generate curved members and accommodate symmetry. The enhancements to the interpreter are demonstrated on the frame of an aircraft with a bio-inspired, rotating empennage.
Innovating Emitter Inlet Design for Enhanced Clog Resistance and Longevity in Drip Irrigation Systems
Authors: Huynh Christy , Molina De Jesus Andrea M. , Sams Sarah A. , Weizer Benjamin T. , Zaza Nadine , Ghodgaonkar Aditya A. , Judge Benjamin M. , Winter Amos G. 
Published: November 2024

Abstract
This study addresses the critical issue of emitter clogging in drip irrigation systems, a prevalent challenge affecting their efficiency and lifespan. The emitter inlet functions as the gateway between the drip line and the inner hydraulics of the emitter, making it an ideal location to filter out particles from the water flow-path. We developed a novel emitter inlet design focused on enhancing clog resistance, inspired by natural hydraulic and filtration processes such as those found in aquatic gills, wake formation, and nozzle principles. The design was analyzed using Computational Fluid Dynamics (CFD) in ANSYS Dynamics Simulation Software, enabling detailed modeling of fluid-surface interactions. Experimental clog testing was conducted to assess the inlet’s clog resistance. Our findings demonstrate a substantial improvement in clog resistance with our design, maintaining 85 ± 28% of initial flow rates after exposure to 150 micron diameter aluminum oxide grit, significantly better than a comparable industry-standard design. After exposure to 425 micron diameter grit and a system restart, our design maintained 91 ± 53% of the initial flow rate, showcasing enhanced recovery performance. This research offers a promising solution to the challenge of irrigation system clogging, with implications for better water conservation and more sustainable agricultural practices.