bluuebunny/crossref_metadata_2025_split huggingface dataset:
Understanding the User Perception Gap: Older Adults and Sit-to-Stand Assistance
Authors: Stansfield Stephan , Schelhaas Booker , Hogan Neville , Yang Maria 
Published: November 2023

Abstract
There is an urgent need to provide ways to help a fast-growing older adult population to maintain daily mobility. A great deal of work exists in medical devices and robotics to generate effective assistive solutions, yet at the same time, limits have been observed in the adoption of such systems. In this paper we explore possible factors in adoption from a user-centered design perspective. We investigated user needs surrounding the act of standing up from a seated position and older users’ attitudes toward assistive device prototypes.
Older adults completed a standard timed-up-and-go mobility assessment, rated their own difficulty standing, participated in interviews, and shared responses to “look-and-feel” prototypes, all in an effort to uncover latent needs. A licensed physical therapist rated videos of the subjects while standing up and the two ratings were compared. While the physical therapist’s rating of difficulty increased as subjects’ performance on the clinical mobility assessment worsened, subjects’ self-ratings did not significantly correlate with mobility performance, even when timed-up-and-go performance indicated a risk of falling.
Subjects expressed preferences for potential assistive devices that were more discreet, lightweight, and flexible over devices that were bulkier, heavier, and rigid. In general, subjects’ attitudes toward assistive devices for their own sit-to-stand use were similar regardless of their demonstrated need. The results highlight the challenges designers may face when creating products for older adult users and underline the importance of a user-centered design process. Implications for assistive technology design are discussed.
Design and Optimization of Quasi-Periodic Volumetric Microstructures by Aperiodic Tiling and Implicit Function
Authors: Wang Jun , Rai Rahul 
Published: November 2023

Abstract
Materials with intricate microstructures exhibit unique properties, including a large surface area and a highly distributed load path. This makes them very effective in engineering applications where weight reduction and structural strength are critical. In this paper, we propose two different frameworks to model quasi-periodic microstructures. The quasi-periodic structure can be formed following aperiodic tiling rules and isoparametric transformation, which infills single-type unit cells into the aperiodic hexahedral tiles. For the other method, the proposed implicit function-based representation provides compact, precise, and arbitrarily parameterized models of coherent microstructures, which contain more than one type of unit cell that follows the cubic periodic repeating manner. Compared to periodic structures that possess perfect regularity and non-periodic structures in a disordered random manner, a quasi-periodic structure shows a perfect long-range order with short-range variability, which overcomes the shortcomings of periodic structures with limited design space and non-periodic structures with unmanageable random style. This paper also demonstrates the practicability of the proposed quasi-periodic structures by utilizing a surrogate model-based optimization scheme to design lightweight but high-strength functional parts with the optimized quasi-periodic structures filled internally.
Design of Self-Organizing Systems Using Multi-Agent Reinforcement Learning and the Compromise Decision Support Problem Construct
Authors: Jiang Mingfei , Ming Zhenjun , Li Chuanhao , Mistree Farrokh , Allen Janet K. 
Published: November 2023

Abstract
How can multi-robot self-organizing systems be designed so that they show the desired behaviors and are able to perform tasks specified by designers?
Multi-robot self-organizing systems, e.g., swarm robots, have great potential for adapting when performing complex tasks in a changing environment. However, such systems are difficult to design due to the stochasticity of the system performance and the non-linearity between the local actions/interaction and the desired global behavior. In order to address this, in this paper we propose a framework for designing self-organizing systems using Multi-Agent Reinforcement Learning (MARL) and the compromise Decision-Support Problem (cDSP) construct.
The design framework consists of two stages — preliminary design and design improvement. In the preliminary design stage, MARL is used to help designers train the robots so that they show stable group behavior for performing the task. In the design improvement stage, the cDSP construct is used to enable designers to explore the design space and identify satisfactory solutions considering several performance indicators based on the trained system established in the previous stage. Between the two stages, surrogate models are used to map the relationship between local parameters and global performance indicators utilizing the data generated in preliminary design. The surrogate models represent the goal functions in the cDSP. A multi-robot box-pushing problem is used as an example to test the efficacy of the proposed framework. The framework is general and can be extended to design other multi-robot self-organizing systems. Our focus in this paper is in describing the framework.
Transfer Reinforcement Learning: Feature Transferability in Ship Collision Avoidance
Authors: Wang Xinrui , Jin Yan 
Published: November 2023

Abstract
The integration of artificial intelligence into engineering work has become increasingly prevalent. Engineering work processes can be highly complex, and learning from scratch requires large computation resources. Transfer learning has emerged as a promising technique for improving learning efficiency by leveraging knowledge gained from related tasks to the target task. To achieve optimal performance, one of the key challenges is to figure out how transferrable the features are among different work processes and within training networks. Simulation-based ship collision avoidance is used for case studies due to its inherent complexity and diversity. Two transfer reinforcement learning methods, feature extraction, and finetuning, are implemented and evaluated against the baseline. Instead of introducing large-scaled pre-trained models as the backbone, a light CNN model pre-trained in a related base case has been proven to transfer essential features to target cases. Simplified ship dynamics is introduced into the training process to make it more realistic and applicable, and the delay caused by the large moment of inertia is addressed by modifying the model-environment interaction mechanism. Work process features for the ship collision avoidance process are concluded from crucial aspects. The effects on transferability are displayed by experimental results discussed from the feature category and similarity perspective.
Mechanism Designs of a Robot for Charging Multiple Electric Vehicles
Authors: Gao Dalong 
Published: November 2023

Abstract
Automobile industry is experiencing a major transformation into the era of electric vehicles. General Motors Global Research and Development Center developed a concept robotic system to charge multiple electric vehicles. This work demonstrated the process of designing the optimal robot mechanisms that can fulfill the functionalities of this system.
Due to the application specific constraints, this system demands a robot that can cover a very large workspace, enter narrow gaps between parked vehicles, and reach vehicle inlets at a large height span at the same time. To navigate among a large number of potential mechanism options and to identify the optimal designs without the overuse of actuators, a Key DOF was proposed for this application and the design process was unfolded based on the different realizations of the Key DOF. Our results showed that the system might have limited applications when using a 5 degree-of-freedom robot. Redundancies were evaluated for their benefits in this application. The first redundant joint could be used to achieve the long-range travel in the parking lot drive aisle direction. This redundancy enabled many designs and two were recommended for the application towards different volumed products. Additional redundancy in the robot mechanism didn’t bring in enough benefits for the application to justify the increase in cost and complexity.
Experimental Verification of Sobol Sensitivity Indices in a Space Latching Mechanism
Authors: Johnson Nathan , Roberts Christopher , Slaboch Brian , Voglewede Philip 
Published: November 2023

Abstract
Effectively and efficiently designing latching mechanisms is important for meeting precise design requirements of these mechanisms. This paper attempts to verify the use of a particular sensitivity measure, Sobol indices, to design latching mechanisms more systematically by applying it to a space mechanism. The latch studied was adapted from the Schmidt latch and was to be finely adjustable to test for the effects of small changes in the latch design. A simulation of the latch system yielded a set of Sobol indices which were compared to experimental results. The results were inconclusive due to larger than anticipated error when measuring the potential energy of the torsion springs in the latch, which made the effects of small design changes indistinguishable from noise in the system.
Using Machine Learning to Predict the Adoption of Building Electrification Technologies in US Households
Authors: Majowicz Andrew , Odonkor Philip 
Published: November 2023

Abstract
This paper explores the use of machine learning to predict the adoption of building electrification technologies within US households. This is important due to the increasing prevalence of building electrification as a pathway to addressing climate change, which inadvertently poses a threat to the energy resilience of households during power outages. A non-intrusive, data-driven means of predicting the level of technology adoption can help guide mitigation and adaptation strategies aimed at minimizing the risks vulnerable households may face when power outages are compounded by extreme weather events. This study develops machine learning models based on the energy consumption dynamics of US households to predict the presence of critical electric appliances, including furnace, water heater, induction stove, cooling system, and solar panels. The models are trained using a large dataset of building end-use load consumption for buildings located in New Jersey. The results show that the models are reasonably accurate in predicting the presence of appliances in homes, although there is still significant potential for improvement in model accuracy.
Evaluation of CAE Design Change Updates – A Case Study on Gas Turbine Airflow Disruptors
Authors: Procopio David , Morris John , Wagner John 
Published: November 2023

Abstract
The rapid growth of digitalization in design and advanced manufacturing, characterized by computer tools, has emphasized software tools throughout the process. To compete in the global marketplace, companies need to embrace virtual design and digital manufacturing methods. Computer-aided design and computer-aided engineering embody the workflow process and tools to bring forth products from conception to design, where detailed changes can be made to fabricate the product efficiently in the manufacturing phase. This paper examines three CAD/CAE patterning features used in Siemens NX on the design of gas turbine turbulators — internal turbine blade surface discontinuities that enhance the heat transfer between the cooling air and blades on whose outside surface are subject to hot combustion gases. Each patterning feature is used to produce a pattern with a high number of instances on a variety of surfaces. The simulations’ execution speed, memory usage, versatility, and integrity of the produced model is evaluated to quantify the performance of the three features. The global pandemic and supply chain challenges has shown the need for virtual modeling and engineering design platforms to efficiently complete complex projects and patterning methods applicable when replicating elements.
Effects of Gas Flow Rates and Sample Position on Carbon-Infiltrated Carbon Nanotube Growth
Authors: Ogilvie Grant , Harriman Isaac , Bowden Anton , Bowden Jared , Jensen Brian D. 
Published: November 2023

Abstract
Carbon nanotubes have been investigated for their antimicrobial properties. The goal of this experiment was to better characterize the effects of gas flow rate and sample position on carbon-infiltrated carbon nanotubes (CICNTs) grown via chemical vapor deposition (CVD) on a silicon substrate to allow for improved sample preparation for antimicrobial experiments. 18 batches of 3 samples each (52 samples) were prepared by CVD coating silicon wafer substrates with alumina (400 nm) and iron (6 nm). Standard processes were generated for creating CICNT samples, with carbon nanotube (CNT) growth at 750 C and subsequent carbon infiltration at 900 C. In each batch, samples were positioned linearly with samples located centrally, downstream, and upstream in the furnace. Three batches had −20% gas flows (both hydrogen and ethylene), three batches had standard gas flows, and three batches had +20% gas flows. The experiment was then repeated with hydrogen flow held constant (only the ethylene was varied). Results showed that gas flow rates and sample position had negligible effects on nanotube height and diameter. This means that minor changes in gas flow rates and sample position do not have outsize effects on CICNT synthesis, which indicates that appropriate nanotube coatings can be prepared over a wide range of growth conditions.
A Design Knowledge Guided Position Encoding Methodology for Implicit Need Identification From User Reviews
Authors: Han Yi , Moghaddam Mohsen 
Published: November 2023

Abstract
Aspect-based sentiment analysis (ABSA) enables a systematic identification of user opinions on particular aspects, thus enhancing the idea creation process in the initial stages of product/service design. Attention-based large language models (LLMs) like BERT and T5 have proven powerful in ABSA tasks. Yet, several key limitations remain, both regarding the ABSA task and the capabilities of attention-based models. First, existing research mainly focuses on relatively simpler ABSA tasks such as aspect-based sentiment analysis, while the task of extracting aspect, opinion, and sentiment in a unified model remains largely unaddressed. Second, current ABSA tasks overlook implicit opinions and sentiments. Third, most attention-based LLMs like BERT use position encoding in a linear projected manner or through split-position relations in word distance schemes, which could lead to relation biases during the training process. This article addresses these gaps by (1) creating a new annotated dataset with five types of labels, including aspect, category, opinion, sentiment, and implicit indicator (ACOSI), (2) developing a unified model capable of extracting all five types of labels simultaneously in a generative manner, and (3) designing a new position encoding method in the attention-based model. The numerical experiments conducted on a manually labeled dataset scraped from three major e-Commerce retail stores for apparel and footwear products demonstrate the performance, scalability, and potential of the framework developed. The article concludes with recommendations for future research on automated need finding and sentiment analysis for user-centered design.
An Advanced Method to Investigate and Manage the Stability of Autonomous Utility Trucks With the Morphing Boom Equipment Under Crosswind
Authors: Patel Parth Y. , Koomullil Roy , Vantsevich Vladimir 
Published: November 2023

Abstract
During natural disasters, such as hurricanes, tornadoes, earthquakes, and floods, utility trucks with boom equipment, also known as bucket trucks, are often the first responders in assisting in the rebuilding and recovery process in affected areas. Crew safety is critical while driving utility trucks in hazardous weather conditions to ensure efficient and effective service delivery to affected communities. Therefore, the utility truck’s stability while driving in extreme weather conditions is a crucial aspect of the safety of the rescue crew. This paper proposes an advanced novel method to investigate and manage the stability of autonomous utility trucks equipped with morphing booms under crosswind. The proposed method involves developing mathematical models to analyze the truck statically, simulating various crosswind scenarios to identify the stability issues, and implementing a solution to mitigate those issues by morphing strategies. The mathematical model considers crosswind effects on the truck, including the aerodynamic forces and moments, the lateral displacement, and the roll angle. The model then simulates different crosswind scenarios (varying wind speed and direction) and evaluates the truck’s stability and performance under different weather conditions. This study offers valuable insights for improving the stability and safety of an autonomous utility truck under diverse and challenging crosswind conditions.
Lock-In Control and Mitigation of Vortex-Induced Vibrations in Aircraft Wings Using a Conserved-Mass Nonlinear Vibration Absorber
Authors: Basta Ehab , Gupta Sunit K. , Barry Oumar R. 
Published: November 2023

Abstract
The impact of the vibration absorber on the synchronization region during vortex-induced vibration of turbine blades is investigated. This work is based on a 3DOF model, including a coupled plunge-pitch airfoil motion and a van der Pol oscillator to the fluid-structure interaction caused by the vortex shedding of the incoming flow. The aeroelastic system is increased by a degree of freedom, namely, the vibration absorber. Linear and nonlinear vibration absorbers are used in this investigation to analyze the effectiveness of the vibration absorber. To demonstrate the effect of the resonator on the lock-in, the coupled natural frequencies, numerical frequency responses, and time histories are plotted. The study reveals the promising capability of the absorber to reduce the lock-in region and mitigate the VIV amplitudes within these regions. For the current application, however, the nonlinear absorber response was indifferent compared to its linear counterpart for the given values of coupling coefficients. This observation indicates that a linear absorber efficiently shrinks the lock-in regions and mitigates the VIV in turbomachinery.
Recent Developments in Ontology Standards and Their Applicability to Biomanufacturing
Authors: Drobnjakovic Milos , Kulvatunyou Boonserm , Frechette Simon , Srinivasan Vijay 
Published: November 2023

Abstract
ISO and IEC have jointly initiated, and recently issued, a series of standards (the ISO/IEC 21838 series) for top-level ontologies. These standards have been used by industrial consortia to develop and disseminate standards for mid-level ontologies to ease the development of ontologies in specific domains and applications. This paper traces the motivation for these concerted efforts to the successful application of ontologies in biological sciences. It then analyzes these recently released international standards for top-level and mid-level ontologies, and shows how they can be used for domain-level and application-level ontologies. Finally, it provides a rationale for applying these standards to the important and emerging field of biomanufacturing.
A Functional Perspective on the Emergence of Dominant Designs
Authors: Robinson Myles , Taube-Adams Bailey , Kang Samantha , Dong Andy 
Published: November 2023

Abstract
Models of long-term product innovation depict the trajectory of products through an evolutionary selection metaphor in which product designs converge toward a dominant design. The product innovation literature favors trajectory descriptions based on the physical manifestations of products while neglecting to account for solution principles. This paper offers a new way to explain the life-cycle of product innovation through the identification of motifs that describe the functions of a product. Functional motifs are recurrent function blocks across multiple generations of designs for a product. A collection of functional motifs defines the functional architecture of the product. Using some key examples from innovations in sewing machines, the paper illustrates the occurrence of motifs as the basis for detecting the emergence of a dominant design. Patents related to the sewing machine over 177 years are analyzed to identify functional motifs characterizing the evolution and convergence toward a dominant design. Results show that motifs do not change over long periods once a dominant design emerges even though components continue to change. This observation confirms a view of dominant designs as a technological frame but refutes the notion that design no longer matters in the era of incremental change. These motifs refine our understanding of how designs evolve along a particular path over the course of product innovation.
The Impact of Robotics Expertise on Iterative Robot Design Decisions and Vulnerability to Design Fixation
Authors: Wilson Cristina G. , Brown Kallahan , Sung Cynthia 
Published: November 2023

Abstract
Robot design is a complex cognitive activity that requires the designer to iteratively navigate multiple engineering disciplines and the relations between them. In this paper, we explore how people approach robot design and how trends in design strategy vary with the level of expertise of the designer. Using our interactive Build-a-Bot software tool, we recruited 39 participants from the 2022 IEEE International Conference on Robotics and Automation. These participants varied in age from 19 to 56 years, and had between 0 and 17 years of robotics experience. We tracked the participants’ design decisions over the course of a 15 min. task of designing a ground robot to cross an uneven environment. Our results showed that participants engaged in iterative testing and modification of their designs, but unlike previous studies, there was no statistically significant effect of participant’s expertise on the frequency of iterations. We additionally found that, across levels of expertise, participants were vulnerable to design fixation, in which they latched onto an initial design concept and insufficiently adjusted the design, even when confronted with difficulties developing the concept into a satisfactory solution. The results raise interesting questions for how future engineers can avoid fixation and how design tools can assist in both efficient assessment and optimization of design workflow for complex design tasks.
Design Factors for the Dimensional Synthesis of Cylindrical Developable Mechanisms
Authors: Vennard Henry , Greenwood Jacob , Butler Jared 
Published: November 2023

Abstract
Developable mechanisms provide unparalleled compactness and deployability. This paper explores the kinematic behavior of developable mechanisms that conform to regular cylindrical surfaces. Equations that define these mechanisms within mechanism synthesis are provided and compared with path and motion synthesis tasks for traditional four-bar mechanisms. The design implications, limitations, and opportunities associated with regular cylindrical developable mechanisms are discussed through the lens of both an analytical and graphical method.