bluuebunny/crossref_metadata_2025_split huggingface dataset:
Non-Contact Measurement of Spiral Bevel Gears Using the Laser Profiler on the Five-Axis Machine
Authors: Shih Yi-Pei , Sheen Bor-Tyng , Wu Wun-Kai , Wei Bo-Lin 
Published: November 2023

Abstract
Nowadays, the measuring machines for gears, such as the P series of Klingelnberg and the GMS series of Gleason, are all four-axis structures. They have a three-dimensional contact scanning probe system and high measurement accuracy. This measurement has two main disadvantages: (1) it is time-consuming, and (2) difficult to measure small-size gears with less than a 0.5 mm module. Due to the above drawbacks, non-contact measurement has become more popular recently.
This research aims to develop a non-contact measurement system of spiral bevel gears using a laser profiler. We establish the coordinate systems of the measuring machine. This machine has five axes, four for measuring movement and one for the orientation of the laser profiler. The five coordinates of pitch and tooth flank measurements are derived according to theoretical gear flank points. The NC codes are programmed for measuring the pitch and tooth flank of bevel gears according to those coordinates. After that, we propose a method based on DIN 3965-2 to evaluate the pitch and tooth flank errors. Here, a double-rotary-table type five-axis machine is adopted as an experiment machine. The results are compared to the report of the Klingelnberg P40 gear measuring center. Their pitch and the maximum tooth flank deviations are within 0.004 and 0.05 mm, respectively. Those results verify the correctness of the proposed method.
Evaluating Heart-Rate Variability Metrics As a Measure of Stress During Unsedated Medical Procedures
Authors: Cameron Chris J. , Pretty Christopher G. , Bagshaw Phil , Aramowicz Jaana , Hayes Michael P. 
Published: November 2023

Abstract
Heart rate variability (HRV) has been proposed as a measure of psychophysiological stress, however validations often focus on complex stressors that may cause non-stress arousal. We investigate the efficacy of a range of HRV metrics as an objective measure of stress during an unsedated flexible sigmoidoscopy procedure on 47 patients. HRV metrics were calculated at several epochs across the procedure. Acceleration and deceleration capacity, the standard deviation of normal-normal intervals, high-frequency power, and the α1 parameter of detrended fluctuation analysis all showed a significant difference between stressed and unstressed states, to p &lt; 0.0167. Low-frequency power and the low:high frequency ratio both exhibited trends in an unexpected direction, casting doubt on their suitability. The root-mean-square of successive differences and sample entropy failed to differentiate between stressed and unstressed states.
Measured effect sizes and standard deviations, combined with population norm values, were used to calculate sample sizes for future studies. Acceleration and deceleration capacities, the standard deviation of normal-normal intervals, and α1 provided the highest power with low sample sizes, allowing well-powered tests a n &lt; 100 for a reduction in stress of 75% or greater. Other metrics which successfully identified stress had large variances that ruled out well-powered trials in small samples.
GradeS: An AI-Driven Graphic Design Support System for Design Style Analysis
Authors: Song Jinyu , You Weitao , Shi Shuhui , Tu Ziwei , Ji Juntao , Han Kaixin , Sun Lingyun 
Published: November 2023

Abstract
The style of graphic design is an important design factor that influences the memorability of designs. Graphic designers routinely analyze the latest designs, capture the style trends, and create designs that match the style trends to appeal to a larger audience. Nonetheless, the lack of quantitative style analysis techniques can lead to an inefficient analysis process and introduce subjectivity and bias. To expedite designers’ understanding of design style trends and make the analysis more objective, we propose GradeS, an AI-driven graphic design support system that facilitates multifaceted quantitative analysis of graphic design style. The system was designed and developed in collaboration with designers and comprises four primary interfaces: GradeS:S, GradeS:Q, GradeS:C, and GradeS:T, each serving specific needs identified through interviews with designers. We leveraged the Vision Transformer to model the one-to-many relationship between designs and styles and implemented all interfaces based on the quantitative style representation learned by the model. To train the model, we built a graphic design dataset with carefully designed coarse-grained style labels. We have released the dataset to the community to promote research in data-driven design. To demonstrate the effectiveness of our study, we evaluated both the model and the system. Our model exhibits superior performance in style classification compared to CLIP. Through a user study involving six designers, our system’s effectiveness in supporting designers in analyzing style quantitatively, capturing style trends comprehensively and quickly, and further stimulating creative thinking was demonstrated.
A Compliant Hinge Joint Driven by the PneuNets Bending Actuator for Enhancing the Performance of Soft Robots
Authors: Jin Yi , Su Hai-Jun 
Published: November 2023

Abstract
While soft robots enjoy the benefits of high adaptability and safety, their inherent flexibility makes them suffer from lower load-carrying capacity and low motion precision, which limits their applications to a broader range of fields. In this paper, we propose an active hinge joint with a hard backbone combined with soft pneumatic actuators to overcome this challenge. We detail the design, theoretical modeling, and experimental testing of a novel compliant hinge joint driven by two pneumatic networks (PneuNets) bending actuators. The joint is constructed by attaching the two actuators to a cross-spring pivot’s two flexible leaf springs. Pressurizing one of the actuators can drive the joint to rotate clockwise or counterclockwise about the central axis. A Pseudo-rigid-body model is developed to analyze the kinematics and statics of the joint. FEA simulation and experimental experiments have been developed to validate the model. The results show that the joint can achieve ± 43° under 0.276 MPa, and the pressure-displacement curve is close to linear. Experiments are also conducted to measure the joint’s in-axis and the other three off-axis stiffness. After calculating the stiffness ratio, the results show that the three off-axis stiffness (Kθx, KY, KZ) are 16.74, 471.75, and 627.63 times the in-axis stiffness (Kθz), respectively. It means our hinge joint can carry a high payload off-axis while maintaining the in-axis flexibility.
Interpretable Neural Network Analyses for Understanding Complex Physical Interactions in Engineering Design
Authors: Dolar Tuba , Lee Doksoo , Chen Wei 
Published: November 2023

Abstract
In engineering design, global sensitivity analysis (GSA) is used for analyzing the effects of the system inputs on the model response. Common GSA methods use analytical or surrogate models to study the relationships between model inputs and outputs. However, the accuracy of such system models depends on the complete conformance to all model assumptions. Even so, they are not flexible and fail to capture nonlinear behaviors in complex systems. Besides these GSA approaches, interpretable machine learning would also identify the relationships between system variables, eliminating the disadvantages of common GSA implementations. Apart from studying the independent variables individually, the evaluation of groups of them is likewise valuable. One example motivation in engineering design for performing GSA with groups of input variables would be managing the design space complexity in programmable material systems (PMS) development. In this article, we employ a flexible, interpretable artificial neural network model to uncover individual as well as grouped global sensitivity indices for understanding complex physical interactions in engineering design. The employed model allows the investigation of the feature importance of the main effects and pairwise interaction effects in GSA according to functional analysis of variance (ANOVA) decomposition. To draw a higher-level understanding, we further use a subset decomposition method to analyze the significance of the groups of input variables. Using PMS as an example, we demonstrate the use of our approach for understanding the impact of material, architecture, and stimulus variables as well as their interactions for a programmable photonic metasurface system. This information lays the foundation for deriving design guidelines for PMS development.
Multiphysics Missing Data Synthesis (MiDaS): A Machine-Learning Approach for Mitigating Data Gaps and Artifacts
Authors: Steuben John C. , Geltmacher Andrew B. , Rodriguez Steven N. , Graber Benjamin D. , Iliopoulos Athanasios P. , Michopoulos John G. 
Published: November 2023

Abstract
The presence of gaps and spurious non-physical artifacts in datasets is a nearly ubiquitous problem in many scientific and engineering domains. In the context of multiphysics numerical models, data gaps may arise from lack of coordination between modeling elements and limitations of the discretization and solver schemes employed. In the case of data derived from physical experiments, the limitations of sensing and data acquisition technologies, as well as myriad sources of experimental noise, may result in the generation of data gaps and artifacts. In the present work we develop and demonstrate a machine learning (ML) meta-framework for repairing such gaps in multiphysics datasets. A unique “cross-training” methodology is used to ensure that the ML models capture the underlying multiphysics of the input datasets, without requiring training on datasets free of gaps/artifacts. The general utility of this approach is demonstrated by the repair of gaps in a multiphysics dataset taken from hypervelocity impact simulations. Subsequently, we examine the problem of removing scan artifacts from X-ray computed micro-tomographic (XCMT) datasets. A unique experimental methodology for acquiring XCMT data, wherein articles are scanned multiple times under different conditions, enables the ready identification of artifacts, their removal from the datasets, and the filling of the resulting gaps using the ML framework. This work concludes with observations regarding the unique features of the developed methodology, and a discussion of potential future developments and applications for this technology.
When Is it Actually Worth Learning Inverse Design?
Authors: Habibi Milad , Wang Jun , Fuge Mark 
Published: November 2023

Abstract
Design optimization, and particularly adjoint-based multi-physics shape and topology optimization, is time-consuming and often requires expensive iterations to converge to desired designs. In response, researchers have developed Machine Learning (ML) approaches — often referred to as Inverse Design methods — to either replace or accelerate tools like Topology optimization (TO). However, these methods have their own hidden, non-trivial costs including that of data generation, training, and refinement of ML-produced designs. This begs the question: when is it actually worth learning Inverse Design, compared to just optimizing designs without ML assistance?
This paper quantitatively addresses this question by comparing the costs and benefits of three different Inverse Design ML model families on a Topology Optimization (TO) task, compared to just running the optimizer by itself. We explore the relationship between the size of training data and the predictive power of each ML model, as well as the computational and training costs of the models and the extent to which they accelerate or hinder TO convergence. The results demonstrate that simpler models, such as K-Nearest Neighbors and Random Forests, are more effective for TO warmstarting with limited training data, while more complex models, such as Deconvolutional Neural Networks, are preferable with more data. We also emphasize the need to balance the benefits of using larger training sets with the costs of data generation when selecting the appropriate ID model. Finally, the paper addresses some challenges that arise when using ML predictions to warmstart optimization, and provides some suggestions for budget and resource management.
The Presence of Chaos in a Viscoelastic Harmonically Forced Von Mises Truss
Authors: Ghoshal Pritam , Gibert James , Bajaj Anil K. 
Published: November 2023

Abstract
This paper examines the effect of viscoelasticity on the dynamic behavior of a bistable dome-shaped structure represented as a lumped parameter viscoelastic von Mises truss. The viscoelastic system is governed by a third order jerk equation. The presence of viscoelasticity also introduces additional time scales and degrees of freedom into the problem when compared to their viscous counterparts, thus making the study of these systems in the presence of harmonic loading even more interesting. It is highly likely that the system would exhibit non-regular behavior for some combination of forcing frequency and forcing amplitude. With this motivation, we start by studying the dynamics of a harmonically forced von Mises truss in the presence of viscous damping only. This leads to a Duffing type equation with an additional quadratic non-linearity. We demonstrate some of the rich dynamic behavior that this system exhibits in some parameter ranges. This provides useful insight into the possible behavior of the viscoelastic system. The viscous damper is then replaced by a viscoelastic unit. We show that the system can exhibit both regular as well as chaotic behavior. The threshold limit for the chaotic motion has been determined using Melnikov’s criteria and verified through numerical simulations using the largest Lyapunov exponent.
Data-Driven Control Co-Design for Indirect Liquid Cooling Plate With Microchannels for Battery Thermal Management
Authors: Liu Zheng , Xu Yanwen , Wu Hao , Wang Pingfeng , Li Yumeng 
Published: November 2023

Abstract
The demand for high-performance electric vehicles keeps increasing with the booming electric vehicles market. Thus, battery cooling is significant in enabling the battery to work under harsh discharge process. Thanks to its high efficiency and low cost, indirect liquid cooling is a widely used cooling method for batteries. Researchers are trying to optimize the plant or control design separately for a better cooling effect. However, they can only produce suboptimal results with low efficiency. Motivated by the imperfections of existing battery cooling systems, we aim to lower the cost of indirect liquid cooling for batteries considering the plant design and control design using data-driven co-design optimization. First, a finite element model of the battery was built to predict the temperature and validate our findings against experimental data. Then, a Gaussian process-based surrogate model combined with Monte Carlo simulation extended the prediction to many scenarios under harsh discharge process. Finally, the surrogate model obtained the optimal plant and control designs. The finite element model validated the optimal design, which lowered the cost by 10%.
Topology Optimization Using Neural Networks With Conditioning Field Initialization for Improved Efficiency
Authors: Chen Hongrui , Joglekar Aditya , Burak Kara Levent 
Published: November 2023

Abstract
We propose conditioning field initialization for neural network based topology optimization. In this work, we focus on (1) improving upon existing neural network based topology optimization, (2) demonstrating that by using a prior initial field on the unoptimized domain, the efficiency of neural network based topology optimization can be further improved. Our approach consists of a topology neural network that is trained on a case by case basis to represent the geometry for a single topology optimization problem. It takes in domain coordinates as input to represent the density at each coordinate where the topology is represented by a continuous density field. The displacement is solved through a finite element solver. We employ the strain energy field calculated on the initial design domain as an additional conditioning field input to the neural network throughout the optimization. The addition of the strain energy field input improves the convergence speed compared to standalone neural network based topology optimization.
Physics-Informed Neural Networks for Degradation Diagnostics of Lithium-Ion Batteries
Authors: Navidi Sina , Thelen Adam , Li Tingkai , Hu Chao 
Published: November 2023

Abstract
Quantifying the extent of degradation in a lithium-ion battery cell can provide valuable insights into the cell’s state of health. However, physics-based methods capable of diagnosing component-level degradation typically require long-term aging data and are computationally expensive to deploy. This work investigates combining physics-based modeling and data-driven machine learning to retain high diagnostic accuracy while mitigating the need for long-term degradation data. We develop a physics-informed neural network (PINN) algorithm to diagnose cell health in the late aging stage without needing long-term data. Using a small set of early-life experimental data from an aging experiment, we train a neural network using a physics-informed loss function which penalizes discrepancies between the neural network’s and the physics-based model’s estimates of four cell health parameters. The trained network can then estimate cell capacity and diagnose three primary degradation modes in the late-aging stage. The proposed method is evaluated and compared with other machine learning algorithms using data from a long-term (3.5 years) cycling experiment of 16 implantable-grade lithium-ion cells. Cross-validation results show that the proposed PINN algorithm can improve the estimation accuracy of the health parameters compared to a purely data-driven approach and has comparable accuracy to the other two physics-informed machine learning methods.
Comparison of next generation technologies and bioinformatics pipelines for capsular typing of
Streptococcus pneumoniae
Authors: Henares Desiree , Lo Stephanie W. , Perez-Argüello Amaresh , Redin Alba , Ciruela Pilar , Garcia-Garcia Juan Jose , Brotons Pedro , Yuste Jose , Sá-Leão Raquel , Muñoz-Almagro Carmen 
Published: November 2023

ABSTRACT

Whole genome sequencing (WGS)-based approaches for pneumococcal capsular typing have become an alternative to serological methods.
In silico
serotyping from WGS has not yet been applied to long-read sequences produced by third-generation technologies. The objective of the study was to determine the capsular types of pneumococci causing invasive disease in Catalonia (Spain) using serological typing and WGS and to compare the performance of different bioinformatics pipelines using short- and long-read data from WGS. All invasive pneumococcal pediatric isolates collected in Hospital Sant Joan de Déu (Barcelona) from 2013 to 2019 were included. Isolates were assigned a capsular type by serological testing based on anticapsular antisera and by different WGS-based pipelines: Illumina sequencing followed by serotyping with PneumoCaT, SeroBA, and Pathogenwatch vs MinION-ONT sequencing coupled with serotyping by Pathogenwatch from pneumococcal assembled genomes. A total of 119 out of 121 pneumococcal isolates were available for sequencing. Twenty-nine different serotypes were identified by serological typing, with 24F (
n
= 17; 14.3%), 14 (
n
= 10; 8.4%), and 15B/C (
n
= 8; 6.7%) being the most common serotypes. WGS-based pipelines showed initial concordance with serological typing (>91% of accuracy). The main discrepant results were found at the serotype level within a serogroup: 6A/B, 6C/D, 9A/V, 11A/D, and 18B/C. Only one discrepancy at the serogroup level was observed: serotype 29 by serological testing and serotype 35B/D by all WGS-based pipelines. Thus, bioinformatics WGS-based pipelines, including those using third-generation sequencing, are useful for pneumococcal capsular assignment. Possible discrepancies between serological typing and WGS-based approaches should be considered in pneumococcal capsular-type surveillance studies.
Reaction of 3-(Pyridin-2-yl)-1,2,4-triazine-5-carbonitriles with 2-Amino-4-aryl-1,3-oxazoles in Anhydrous Medium
Authors: Rammohan A. , Krinochkin A. P. , Kopchuk D. S. , Shtaitz Ya. K. , Sharafieva E. R. , Gaviko V. S. , Zyryanov G. V. , Chupakhin O. N. 
Published: November 2023

Abstract
We previously reported the solvent-free reaction of 5-aryl-3-(pyridin-2-yl)-1,2,4-triazine-5-carbo­nitriles with 2-amino-4-aryl-1,3-oxazoles, which afforded 4,5-diaryl-3-hydroxy-2,2′-bipyridine-6-carbonitriles. Similar reaction in anhydrous medium led to the formation of two products, previously described 4,5-diaryl-3-hydroxy-2,2′-bipyridine-6-carbonitriles (up to 44%) and 4,5-diaryl-2,2′-bipyridine-6-carbonitriles (up to 32%).
Comparative survey among paediatricians, nurses and health information technicians on ethics implementation knowledge of and attitude towards social experiments based on medical artificial intelligence at children’s hospitals in Shanghai: a cross-sectional study
Authors: Wang Yingwen , Fu Weijia , Gu Ying , Fang Weihan , Zhang Yuejie , Jin Cheng , Yin Jie , Wang Weibing , Xu Hong , Ge Xiaoling , Ye Chengjie , Tang Liangfeng , Fang Jinwu , Wang Daoyang , Su Ling , Wang Jiayu , Zhang Xiaobo , Feng Rui 
Published: November 2023

ObjectivesImplementing ethics is crucial to prevent harm and promote widespread benefits in social experiments based on medical artificial intelligence (MAI). However, insufficient information is available concerning this within the paediatric healthcare sector. We aimed to conduct a comparative survey among paediatricians, nurses and health information technicians regarding ethics implementation knowledge of and attitude towards MAI social experiments at children’s hospitals in Shanghai.Design and settingA cross-sectional electronic questionnaire was administered from 1 July 2022 to 31 July 2022, at tertiary children’s hospitals in Shanghai.ParticipantsAll the eligible individuals were recruited. The inclusion criteria were as follows: (1) should be a paediatrician, nurse and health information technician, (2) should have been engaged in or currently participating in social experiments based on MAI, and (3) voluntary participation in the survey.Primary outcomeEthics implementation knowledge of and attitude to MAI social experiments among paediatricians, nurses and health information technicians.ResultsThere were 137 paediatricians, 135 nurses and 60 health information technicians who responded to the questionnaire at tertiary children’s hospitals. 2.4–9.6% of participants were familiar with ethics implementation knowledge of MAI social experiments. 31.9–86.1% of participants held an ‘agree’ ethics implementation attitude. Health information technicians accounted for the highest proportion of the participants who were familiar with the knowledge of implementing ethics, and paediatricians or nurses accounted for the highest proportion among those who held ‘agree’ attitudes.ConclusionsThere is a significant knowledge gap and variations in attitudes among paediatricians, nurses and health information technicians, which underscore the urgent need for individualised education and training programmes to enhance MAI ethics implementation in paediatric healthcare.