bluuebunny/crossref_metadata_2025_split huggingface dataset:
Where Should Additional City Benches be Installed to Reduce Continuous Walking Distance? Comparison of Incrementally and Simultaneously Added Installations
Authors: Usui Hiroyuki 
Published: November 2024

ABSTRACTThe present locations of city benches show the required continuous walking distance, which can be defined as the distance from a city bench to the furthest neighboring one, called the furthest neighbor distance (FND). However, we lack a method for identifying where additional city benches should be installed incrementally to reduce the FND. The aim of this paper is thus (1) to develop a new method for determining the priority of the locations of new benches, subject to the number of city benches; and (2) to investigate the relationship between the priority and the reduction of the FND. The reduction of the FND is quantified based on the probability of an FND greater than 100 m. The empirical study around Tokyo Central Station showed that if reducing the probability of the FND being greater than 100 m is the top priority, additional city benches should be installed simultaneously.
Ethical Implications of Social Science Research on Xenotransplantation
Authors: Kögel Johannes , Ernst Paulina , Sauermeister Jochen , Marckmann Georg 
Published: November 2024

ABSTRACTSocial science research has generated extensive knowledge on xenotransplantation, encompassing the perspectives of actual and potential patients, other stakeholders, public opinion and debate, human‐animal relationships, animal production and husbandry, bioeconomy, as well as biotechnology governance and regulation. We therefore convened social science researchers to discuss the latest developments in xenotransplantation research and practice in late 2023. Based on a brief workshop report, we aim to highlight the various ethical implications of this debate. After outlining the role of social science research in the ethical evaluation of xenotransplantation, we elaborate three critical points that may become pivotal in the future evolution of xenotransplantation: the framing of xenotransplantation in the clinical setting and in the public, the potential impact of religious beliefs on patients' transplant choices, and the consequences for the allotransplantation system if xenotransplantation becomes clinically established, including the allocation of allo‐ and xeno‐organs.
Numerical Investigation of Unsteady Flow Characteristics of Planar Plug Nozzle in Overexpanded Conditions
Authors: Trivedi Rahul , Bhaduri Sreejita , Sugarno Mohammed Ibrahim , De Ashoke 
Published: November 2024

Abstract
Plug nozzles are frequently regarded as a strong contender for futuristic single-stage-to-orbit (SSTO) space missions due to their inherent altitude-compensating properties. Optimizing the design of these nozzles to achieve peak performance during the initial ascent phase is of pre-eminent importance. The current research aims to deepen our comprehension of the unsteady behavior of plug nozzles during this ascent phase, commonly referred to as the overexpanded flow regime. It utilizes a full-spike plug nozzle and employs a finite volume-based solver within the OpenFOAM framework to investigate flow features across nozzle pressure ratios (NPRs) ranging from 3 to 8. Additionally, the Ffowcs Williams Hawkings (FW-H) analogy is employed to assess the influence of acoustics in the nozzle's flow field at different NPRs. Spectral analysis and flow visualizations are employed to study the frequency content and the respective flow features of the system. It is observed that the flow exhibits separation beyond NPR 4 and is highly unsteady at NPR 6, which is inferred from the prominence of the recirculation bubble. However, beyond NPR 6, the flow tends to reattach the plug surface, dampening the disturbance.
Selective Deposition and Fusion of AISI 316L: An Additive Manufacturing Process for Space Environments via Direct Ink Writing and Laser Processing
Authors: Hoffmann Miguel , Ye Jiahui , Elwany Alaa 
Published: November 2024

Abstract
Unlocking the potential of additive manufacturing (AM) for space exploration hinges on overcoming key challenges, notably the ability to manufacture or repair parts on-site during exploration missions with consideration of quality, feedstock utilization, and challenges involved in microgravity environments. While there are multiple efforts to investigate the use of existing metal AM processes such as powder bed fusion (PBF), directed energy deposition (DED), and filament-based material extrusion, each process comes with a different set of challenges in space environments. Here, we introduce a new AM method that integrates the benefits of direct ink writing (DIW) to selectively deposit metallic pastes with laser-based processing to locally debind and subsequently melt and fuse metal powder, layer by layer, enabling the manufacturing of AISI 316L samples with densities exceeding 99.0%. The impact of process parameters on single-track dimensions, surface morphology, and porosity was characterized. The efficacy of laser debinding was assessed via secondary-ion mass spectrometry, permitting the carbon content to be estimated at 0.0152%, which is safely below the acceptable limit (0.03 wt%) for AISI 316L.
Modeling Elastic Cable-Surface Friction for Soft Robots
Authors: Matte Christopher-Denny , Kwok Tsz Ho 
Published: November 2024

Abstract
Due to inherent compliance and non-linear behaviour, modelling soft robots is highly complex. While the elasticity of their materials provides the robots with adaptability and resilience, it also causes undesirable effects. Cable-driven soft robots are particularly affected by frictional forces, which can significantly alter their deformed shape. Unfortunately, most existing cable-friction models have been developed for rigid surfaces and do not account for the interactions between elastic cables and surfaces. As a result, research on cable-driven soft robots often neglects friction due to the difficulty of acquiring data and implementing the mathematical models. This paper introduces a novel friction model that considers the asperity behaviour of both the cable and the friction surface. We propose a new methodology that accurately replicates friction interaction in soft robots, which requires as few as nine data points. The methodology is assessed on four distinct material interactions, comprising two cables with different diameters and materials, and two 3D-printed surfaces made from PLA and TPU. By accurately estimating the nonuniform distribution of the joint deformation caused by friction, the new friction formulation achieves a 2.8% error in predicting a soft gripper’s tip locations, while the current state-of-the-art model shows a 16.1% error. We also demonstrate that, with an accurate friction model, it is possible to optimize the cable routing points to achieve the desired grasping strategy for a soft gripper.
A Design for Remanufacturing Framework Incorporating Identification, Evaluation, and Validation: A Case Study of Hydraulic Manifold
Authors: Liu Xinyang , Chung In-Bum , Behtash Mohammad , Davied Matthew , Thompson Todd , Lopez Richard , Lee Michael , Bishop William , Wang Pingfeng , Hu Chao 
Published: November 2024

Abstract
In recent years, academic researchers, industrial designers, and remanufacturers have widely recognized the imperative to integrate remanufacturing considerations into the initial phases of product design to advance sustainability objectives, acknowledging the importance of Design for Remanufacturing (DfRem). However, existing DfRem guidelines and tools largely rely upon experiential insights and qualitative assessments, leaving a gap in the ability to assess the economic and environmental impacts of design choices quantitatively. To bridge this gap, we present a framework for DfRem that facilitates informed design decisions while accounting for potential remanufacturing options. We rigorously assess our framework through a meticulous examination of the design and remanufacturing processes associated with a hydraulic manifold in heavy-duty tractors’ transmissions. Through this industry-relevant case study, we showcase the practical utility of our framework. Conducting analyses guided by our proposed framework, we identify and validate design modifications that could reduce life cycle costs, energy consumption, and emissions. These modifications are tested with plans for integration into future iterations of the hydraulic manifold design, ensuring sustained improvements in sustainability metrics while maintaining performance and reliability standards.
Sustaining Creatively? Investigating the Effects of Creativity and Sustainability-Focused Evaluation Criteria in Design Tasks
Authors: Alsager Alzayed Mohammad , Starkey Elizabeth M. , Ritter Sarah C. , Prabhu Rohan 
Published: November 2024

Abstract
As the need for environmentally sustainable solutions grows, there has emerged an ongoing effort to integrate considerations for the environment throughout the engineering design process. Towards these efforts, several researchers have employed creativity-invoking methods such as alternate uses tasks to encourage the generation of environmentally sustainable solutions. Moreover, these efforts span the design process, ranging from concept generation to concept selection and evaluation. Prior research suggests that external evaluations play an important role in regulating designers’ motivation, and consequently, the outcomes of their design and creative processes. However, little research has investigated how the evaluation criteria used in sustainable design tasks influence designers’ performance. Our aim in this research is to investigate this research gap through an experimental study with novice designers. Specifically, we compared the solutions generated by designers when given a design problem with either creativity-focused evaluation or sustainability-focused evaluation. From the results of our experiment, we see that the evaluation criteria used in the design problem did not impact either the creativity or the environmental sustainability of the participants’ solutions. This finding calls for a deeper investigation into the role of creativity in the generation of environmentally sustainable solutions. This finding also calls for a further study of the potentially conflicting tradeoffs involved in the design of environmentally sustainable solutions.
Prototype2Code: End-to-End Front-End Code Generation From UI Design Prototypes
Authors: Xiao Shuhong , Chen Yunnong , Li Jiazhi , Chen Liuqing , Sun Lingyun , Zhou Tingting 
Published: November 2024

Abstract
UI-to-code technology has streamlined the front-end development process, reducing repetitive tasks for engineers. prior research mainly use design prototypes as inputs, with the effectiveness of the generated code heavily dependent on these prototypes’ quality, leading to compromised robustness. Moreover, these approaches also exhibit shortcomings in code quality, including issues such as disorganized UI structures and the inability to support responsive layouts. To address these challenges, we introduce Prototype2Code, which achieves end-to-end front-end code generation with business demands. For Prototype2Code, we incorporate design linting into the workflow, addressing the detection of fragmented elements and perceptual groups, enhancing the robustness of the generated outcomes. By optimizing the hierarchical structure and intelligently recognizing UI element types, Prototype2Code generates code that is more readable and structurally clearer. To meet responsive design requirements, Prototype2Code primarily supports flexbox layout model, ensuring code compatibility across various device sizes. To validate the efficacy, we compare Prototype2Code with the commercial code generation platform CodeFun and Screenshot-to-code based on GPT-4 with vision. Employing structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and mean squared error (MSE) for visual similarity assessment, Prototype2Code’s rendered UI effects align most closely with the design prototypes, exhibiting the minimal errors. We also conduct a user study with five experienced front-end engineers, inviting them to review and revise code generated by the three methods. As a result, Prototype2Code surpasses other methods in readability, usability, and maintainability, better meeting the business needs of industrial development.
Design of Lightweight Structures by Reinforcing Thin-Walled Cylinders
Authors: Sun Ruoyu , Mankame Nilesh , Krishnan Girish 
Published: November 2024

Abstract
There is an enduring need for structures that are strong, stiff, and lightweight. In this work, we explore a rational approach to the design of such structures by building on the work of Wegst and Ashby [1]. Wegst and Ashby proposed partitioning the design space of thin-walled cylinders subject to pure bending loads based on the dominant failure mode to guide the selection of circular cross section shapes that maximize strength per unit mass. We extend this work by using the same framework to guide the selection of discrete internal reinforcements for these tubes. The partitioned design space, referred to as the Failure Mode Space (FMS), is generated for a given base tube using finite element analysis. Each branch of the failure envelope in the FMS is associated with a unique failure mode. We investigate three different reinforcement strategies: a) structural foam lining, b) a longitudinal rib and c) a ring stiffener to improve the mass-specific strength of a given base tube. Mass equivalence between the reinforced and un-reinforced tubes is used to project the reinforced tubes onto the FMS generated for the un-reinforced tubes. FE analysis of the reinforced tubes reveals that different reinforcement strategies behave differently in different partitions of the FMS based on the associated failure modes. The approach demonstrated in this preliminary study can be extended to more complex structures under generalized loading conditions.
Three-Dimensional Visualization of Energy Consumption Data in Additive Manufacturing
Authors: Hansen Morten Hejlskov , Vernica Teodor , Mansour Rami , Ramanujan Devarajan 
Published: November 2024

Abstract
This paper addresses the growing interest in design for sustainable manufacturing, focusing particularly on Additive Manufacturing (AM). While AM offers advantages like customized geometries and reduced material usage, there’s a need to address energy consumption in 3D printing beyond the conventional emphasis on material reduction. While previous research has explored various techniques for predicting and reducing the energy consumption of AM processes, there is limited work on guiding designers to generate more energy-efficient designs via localized geometric changes. This paper introduces a method to visualize energy consumption directly on 3D objects, aiming to guide designers in optimizing object shapes for sustainability. The methodology presented in the paper involves several steps: data collection, interpolation of positional points, classification of objects, support structures, and rapid movements. Following this, support and rapid movement energy are allocated to the printed object. Finally, the energy required for printing the object is directly visualized on a 3D model of the object using a mesh interpolation scheme. A use case demonstrating the potential application of the visualization method for reducing printing energy consumption is presented, through introducing a local shape modification to an axle bracket based on the visualization information. This modification resulted in a 7.5% energy reduction in this analyzed region compared to the original design. Thus, results showcase the ability of the proposed visualization method to support sustainability-oriented design for AM, through incorporating local shape modifications.
Exploring Human-Machine Interaction in Level 4 AVS: A Study on Take-Over Maneuver in a Roundabout Scenario
Authors: Boscaro Linda , Fossati Andrea , De Guglielmo Veronica , Galbiati Andrea , Gobbi Massimiliano , Mastinu Giampiero , Previati Giorgio , Sabbioni Edoardo , Signorini Maria Gabriella , Somma Antonella , Subitoni Luca , Uccello Lorenzo 
Published: November 2024

Abstract
Currently, one of the most important challenges in the field of road vehicles concerns the reduction of road accidents and fatalities. The development of new advanced driver assistance or automated driving technologies may contribute to this goal in the future. This research delves into the analysis of Level 4 (L4) automated vehicles (AVs), specifically focusing on the take-over manoeuvre when the AV exits its Operational Design Domain (ODD). It aims to analyse how the take-over changes, considering factors such as the human driver’s state, the machine’s operation, and the output devices used to alert the driver. To carry out the preliminary tests, an urban road network, including a mini-roundabout, was replicated in the VI-WorldSim environment, and real human drivers experienced the simulation through a dynamic driving simulator. Drivers were assessed qualitatively, using questionnaires to analyse their perception of L4 AVs, and quantitatively, examining the take-over time needed and their heart rate to investigate the physiological response. Preliminary results show that participants perceived the simulation as realistic and immersive, and they reported an ease of use of AVs. Furthermore, physiological data highlighted an increase in heart rate upon the start of take-over, validating the cognitive load of the individual. The study is part of the HL4IT project aimed at supporting the development of Level 4 AVs in an Italian traffic environment.
Developing Heuristics for Resource Allocation and Utilization in Systems Design: A Hierarchical Reinforcement Learning Approach
Authors: Gadi Vikranth S. , Szajnfarber Zoe , Panchal Jitesh H. 
Published: November 2024

Abstract
This paper presents an approach that integrates systems engineering principles with reinforcement learning (RL) to solve complex design problems under resource constraints. Product design processes often involve decomposing a large system into interconnected subsystems, with resources allocated across teams responsible for optimizing each subsystem. We formulate this process as a hierarchical multi-armed bandit (MAB) problem, where decisions are made at both the system level (allocating budget across subsystems) and subsystem level (selecting heuristics for sequential information acquisition). We employ Thompson Sampling, a Bayesian RL technique, to concurrently learn effective decision-making policies at each level. This hierarchical RL agent aims to maximize the overall system performance while adhering to fixed budgets for design evaluations. To demonstrate the approach, we present an illustrative example of a race car optimization in The Open Racing Car Simulator (TORCS) environment. Results show that the RL agent can learn to allocate resources strategically, prioritize the most impactful subsystems, and identify effective information acquisition heuristics for each subsystem team. The results also indicate that our method converges to high-performing car configurations with greater efficiency when compared to Bayesian Optimization.
Inverse Design With Conditional Cascaded Diffusion Models
Authors: Habibi Milad , Fuge Mark 
Published: November 2024

Abstract
Adjoint-based design optimizations are usually computationally expensive and those costs scale with resolution. To address this, researchers have proposed machine learning approaches for inverse design that can predict higher-resolution solutions from lower cost/resolution ones. Due to the recent success of diffusion models over traditional generative models, we extend the use of diffusion models for multi-resolution tasks by proposing the conditional cascaded diffusion model (cCDM). Compared to GANs, cCDM is more stable to train, and each diffusion model within the cCDM can be trained independently, thus each model’s parameters can be tuned separately to maximize the performance of the pipeline. Our study compares cCDM against a cGAN model with transfer learning.
Our results demonstrate that the cCDM excels in capturing finer details, preserving volume fraction constraints, and minimizing compliance errors in multi-resolution tasks when a sufficient amount of high-resolution training data (more than 102 designs) is available. Furthermore, we explore the impact of training data size on the performance of both models. While both models show decreased performance with reduced high-resolution training data, the cCDM loses its superiority to the cGAN model with transfer learning when training data is limited (less than 102), and we show the break-even point for this transition. Also, we highlight that while the diffusion model may achieve better pixel-wise performance in both low-resolution and high-resolution scenarios, this does not necessarily guarantee that the model produces optimal compliance error or constraint satisfaction.
Metamodels and Vision Obstruction: A New Lens on Driver Visibility
Authors: Firouzi M. Amin , Bhaskaran Vignesh , Demirel H. Onan , Hoyle Christopher 
Published: November 2024

Abstract
Driver visibility studies, particularly focusing on the impact of A-pillar designs and cutout modifications, are a critical challenge in the automotive industry. Utilizing Gaussian Process Regression (GPR) modeling, we comprehensively analyze how different A-pillar cutout sizes influence visibility in various traffic scenarios. Our research compares two vehicle types, SUVs and sedans, across a spectrum of real-world situation, including interactions with traffic lights, bicycles, and pedestrian crossings. The uniqueness of our approach lies in the application of digital human modeling data to construct surrogate models, which facilitates a nuanced understanding of the relationship between driver anthropometry, visibility, and cutout design. The results reveal a paradox in A-pillar design, where increasing the number of cutouts does not linearly correlate with enhanced visibility, thus challenging conventional design practices. Additionally, the study highlights the multimodal nature of visibility across different driver heights, suggesting the need for a tailored approach in automotive design. Looking forward, our research sets the foundation for future work that aims to optimize cutout designs using techniques like Bayesian Optimization, with a particular focus on improving visibility for drivers of smaller stature. This study contributes significantly to the evolution of automotive design, advocating for a shift towards more inclusive and ergonomic vehicles that cater to the diverse needs of drivers, thereby enhancing overall road safety.