bluuebunny/crossref_metadata_2025_split huggingface dataset:
Existence and Uniqueness of Micropolar Elastic Stoneley Waves With Sliding Contact and Formulas for the Wave Slowness
Authors: Pham Giang Thi Ha , Pham Vinh Chi 
Published: November 2024

Abstract
The existence of Stoneley waves propagating in two micropolar isotropic elastic half-spaces with sliding contact was considered by Tajuddin (1995, Existence of Stoneley Waves at an Unbonded Interface Between Two Micropolar Elastic Half-Spaces, ASME J. Appl. Mech., 62, 255–257). However, the existence of Stoneley waves was proved only for the case when two half-spaces are incompressible or Poisson solids and their material properties are close to each other. In this paper, the authors investigate the existence of micropolar elastic Stoneley waves with sliding contact for the general case when two micropolar isotropic elastic half-spaces are arbitrary. By using the complex function method, the authors have established the necessary and sufficient conditions for a micropolar elastic Stoneley wave to exist and have proved that if a micropolar elastic Stoneley wave exists, it is unique. When the micropolarity is absent, the established existence result recovers the necessary and sufficient condition for the existence of an elastic Stoneley wave with a sliding contact that was found by Barnett and co-workers (1988, Slip Waves Along the Interface Between Two Anisotropicelastic Half-Spaces in Sliding Contact, Proc. R. Soc. London, Ser. A, 415, 389–419) by using the interface impedance matrix method. Explicit formulas for the slowness (the inverse of velocity) of micropolar elastic Stoneley waves have also been derived which will be of great interest in both theoretical and practical aspects.
LLM4CAD: Multimodal Large Language Models for Three-Dimensional Computer-Aided Design Generation
Authors: Li Xingang , Sun Yuewan , Sha Zhenghui 
Published: November 2024

Abstract
The evolution of multimodal large language models (LLMs) capable of processing diverse input modalities (e.g., text and images) holds new prospects for their application in engineering design, such as the generation of 3D computer-aided design (CAD) models. However, little is known about the ability of multimodal LLMs to generate 3D design objects, and there is a lack of quantitative assessment. In this study, we develop an approach to enable LLMs to generate 3D CAD models (i.e., LLM4CAD) and perform experiments to evaluate their efficacy where GPT-4 and GPT-4V were employed as examples. To address the challenge of data scarcity for multimodal LLM studies, we created a data synthesis pipeline to generate CAD models, sketches, and image data of typical mechanical components (e.g., gears and springs) and collect their natural language descriptions with dimensional information using Amazon Mechanical Turk. We positioned the CAD program (programming script for CAD design) as a bridge, facilitating the conversion of LLMs’ textual output into tangible CAD design objects. We focus on two critical capabilities: the generation of syntactically correct CAD programs (Cap1) and the accuracy of the parsed 3D shapes (Cap2) quantified by intersection over union. The results show that both GPT-4 and GPT-4V demonstrate great potential in 3D CAD generation by just leveraging their zero-shot learning ability. Specifically, on average, GPT-4V outperforms when processing only text-based input, exceeding the results obtained using multimodal inputs, such as text with image, for Cap 1 and Cap 2. However, when examining category-specific results of mechanical components, the prominence of multimodal inputs is increasingly evident for more complex geometries (e.g., springs and gears) in both Cap 1 and Cap 2. The potential of multimodal LLMs to improve 3D CAD generation is clear, but their application must be carefully calibrated to the complexity of the target CAD models to be generated.
Data and Model Synergy-Driven Rolling Bearings Remaining Useful Life Prediction Approach Based on Deep Neural Network and Wiener Process
Authors: Zhu Yonghuai , Zhou Xiaoya , Cheng Jiangfeng , Liu Zhifeng , Zou Xiaofu , Cheng Qiang , Xu Hui , Wang Yong , Tao Fei 
Published: November 2024

Abstract
Various remaining useful life (RUL) prediction methods, encompassing model-based, data-driven, and hybrid methods, have been developed and successfully applied to prognostics and health management for diverse rolling bearing. Hybrid methods that integrate the merits of model-based and data-driven methods have garnered significant attention. However, the effective integration of the two methods to address the randomness in rolling bearing full life cycle processes remains a significant challenge. To overcome the challenge, this paper proposes a data and model synergy-driven RUL prediction framework that includes two data and model synergy strategies. First, a convolutional stacked bidirectional long short-term memory network with temporal attention mechanism is established to construct Health Index (HI). The RUL prediction is achieved based on HI and polynomial model. Second, a three-phase degradation model based on the Wiener process is developed by considering the evolutionary pattern of different degradation phases. Then, two synergy strategies are designed. Strategy 1: HI is adopted as the observation value for online updating of physics degradation model parameters under Bayesian framework, and the RUL prediction results are obtained from the physics degradation model. Strategy 2: The RUL prediction results from the data-driven and physics-based model are weighted linearly combined to improve the overall prediction accuracy. The effectiveness of the proposed model is verified using two bearing full life cycle datasets. The results indicate that the proposed approach can accommodate both short-term and long-term RUL predictions, outperforming state-of-the-art single models.
Hybrid RANS-LES Simulation of Rotor–Stator Interaction in a Compressor Stage Using a Grid-Adaptive Simulation Method
Authors: Tang Yumeng , Wei Xindi , Liu Yangwei 
Published: November 2024

Abstract
Flows in compressors are extremely complex with various scales. Small-scale turbulence, middle-scale rotor–stator interaction (RSI), large-scale corner separation, and tip leakage flow should all be considered in the simulation of compressors. Recently, a new hybrid Reynolds-averaged Navier–Stokes-large eddy simulation (RANS-LES) strategy that modifies the turbulent viscosity equation based on the Kolmogorov energy spectrum, termed the grid-adaptive simulation (GAS) method, is proposed by our group to achieve high accuracy simulation using different grid resolutions. In this study, the GAS method with the shear stress transport (SST) turbulence model is employed to simulate the RSI just with RANS-like grid resolution in a single-stage transonic compressor TUDa-GLR open test case. Compared with experiments and other simulation methods (including RANS and delayed detached eddy simulation (DDES) methods), results show that the GAS method can significantly improve the prediction accuracy for stall margin and radial distribution of flow parameters. Then, the effect of RSI on the secondary flow structures is analyzed based on the unsteady flow field simulated by the GAS method. Results show that the incorrect prediction of rotor tip leakage vortex breakdown and the underestimation of mixing losses in the tip region of the rotor are blamed for the high prediction deviation of RANS. An intuitive total pressure fluctuation caused by wakes is observed in the stator inlet. The particle tracking shows that the wake from the suction surface of the rotor has a strong trend to transport into the tip region of the stator. Spectral proper orthogonal decomposition (SPOD) is also utilized. Unsteady temporal–spatial structures induced by local unsteadiness and RSI are distinguished by SPOD, which includes the reverse flow in the tip region of the rotor and the pressure wave generated from RSI. SPOD also found that the separation of the stator on the suction side exhibits an unsteady fluctuation with a frequency of 1.6 blade passing frequency (BPF).
Bearing Electrical Erosion Signal Detection Technique Based on Current Signal Analysis for Electric Motors
Authors: Hwang Ji-Min , Jeong Ji-Su , Kim Ji-Wook , Kim Dong-Hyuk , Ko Dae-Cheol , Kim Dong-Hwan , Jang Jin-Seok 
Published: November 2024

Abstract
In modern industries, enhancing the efficiency and performance of electric motors is a critical requirement. Pulse-width modulation (PWM) inverters utilized to enhance the energy efficiency of electric motors generate complex shaft voltages and bearing currents, leading to bearing electrical erosion. This study proposes a new method for detecting high-frequency (HF) circulating current and electric discharge machining (EDM) current signals in bearings for electric motors. The proposed method utilizes common mode voltage (CMV) and bearing current data, analyzing the relationship between these signals. Subsequently, it applies filtering techniques and differentiation for signal preprocessing. The interquartile range (IQR) method is used to detect outliers, classify HF circulating current and EDM current signals, and perform time-series data clustering to determine the occurrence frequency and timing of EDM signals. Finally, the implementation outcomes of the proposed method are validated, and its classification efficacy is evaluated and benchmarked against established methodologies through a comprehensive performance analysis. The proposed technique is anticipated to be applicable to the maintenance and prediction of modern electric motors in future developments, contributing to enhanced durability and reliability.
Theoretical Model Construction and Static Performance Analysis of Multi-Leaf Journal Foil Bearings
Authors: Hu Yang , Ding Pengjing 
Published: November 2024

Abstract
Multi-leaf journal foil bearing (MLJFB) structures are complex, making performance prediction challenging, and the accurate prediction of gas film thickness and pressure distributions is crucial. In this study, comsol finite-element software was used to establish an initial foil assembly and structural deformation simulation model for an MLJFB with bump foils. Using matlab, a compressible gas lubrication model was established and solved using the finite volume method. Real-time data transfer between comsol and matlab enabled a fully coupled fluid–structure interaction simulation. This model meticulously accounts for the pre-tension force in the multi-leaf bearing assembly, contact friction between adjacent foils, and the interaction between the top foil and gas film pressure. The effects of journal movement distance, direction, and top foil overlapping ratio on the bearing capacity as well as the effects of different external load directions on the journal equilibrium position were analyzed based on this model. The results indicate that increasing the top foil overlapping ratio enhances the bearing capacity of the MLJFB. Furthermore, compared to the direction of the top foil overlap position, external loads applied in the direction of the top foil midpoint are more beneficial for bearing operational stability.
Fluid-fluxed melting of juvenile lower crust traced by molybdenum isotopes
Authors: Xia You-Shan , Gao Peng , Sun Guo-Chao , Zhao Zi-Fu , Qin Lei , Wen Gang , Li Zhen-Xin , Dai Li-Qun 
Published: November 2024

Abstract
Fluid-fluxed melting is considered to be an important process in reworking continental crust. However, the process is challenging to identify because of complex magma differentiation processes. This study uses novel molybdenum (Mo) isotopes to address this issue because they are sensitive to fluid activity. Early Paleozoic adakitic rocks from the Dabie orogen, China, were analyzed for whole-rock Mo isotopes as well as elements and Sr–Nd isotopes to identify the magma sources and the influence of fluids during melting. The rocks are characterized by high and highly varied δ98Mo values (–0.13‰ to 0.74‰, median = 0.22‰), which cannot be explained by fractional crystallization (which can only cause a maximum of 0.06‰ fractionation). Sr–Nd isotopes indicate that these rocks probably originated from melting of juvenile basements, but the higher δ98Mo values relative to those of adjacent coeval mafic rocks and their positive correlation with Na2O/K2O ratios indicate the influx of external fluids with heavy Mo isotope compositions. Meanwhile, the correlation between δ98Mo values and Sr/Y ratios and fluid-immobile elements suggests that the Dabie adakitic rocks are more likely to have originated from fluid-fluxed melting induced by fluid addition. This conclusion is supported by Ti-in-zircon thermometry and phase equilibria modeling. Our work presents compelling evidence for fluid-fluxed melting in generating adakitic rocks and highlights that Mo isotopes can act as an effective tracer for fluid activity during crustal reworking.
Supplemental Material: Differential tectonic activity along the southern boundary of the Qilian Mountains: Insights from low-temperature thermochronology, seismic data, and fluvial geomorphology
Authors: YU Xiangjiang , al. et 
Published: November 2024

<p>Table S1. Apatite (U-Th)/He dating results for bedrock samples in this study. Table S2. Published AHe ages in the southern boundary of the Qilian Mountains. </p>
Round Splashes of a Viscous Liquid
Authors: Bazilevskii A. V. , Rozhkov A. N. 
Published: November 2024

Abstract
The splashes of a highly viscous fluid (glycerol) resulting from its pulsed displacement from a gap between two rapidly approaching disks are studied. It is found that, outside the disks, the splash has the form of a thin film bounded by an annular rim. A physical model of the splash is formulated, and analytical solutions describing its trajectory are given. The calculation results are compared with experimental data. The effects of fluid viscosity, surface tension, and film breakdown are analyzed. It is shown that the key influence on the splash development scenarios is exerted by surface tension of the film connecting the rim to the disks.
Nyctohemeral effects of topical beta-adrenoceptor blocking agents measured with an intraocular telemetry sensor
Authors: Mansouri Kaweh , Rao Harsha Laxmana , Weinreb Robert N 
Published: November 2024

ObjectiveTo evaluate nyctohemeral effects of topical beta-adrenoceptor blocking agents and their fixed combinations on intraocular pressure (IOP) in patients with primary open-angle glaucoma implanted with an ocular telemetry sensor.Methods22 patients who had previously been implanted with a sulcus-based IOP sensor (eyemate) were included in this prospective clinical trial. Three classes of medications were analysed: beta-blockers (BB), fixed combination of BB and carbonic anhydrase inhibitors (BB-CAI), and combinations of BB and prostaglandin analogues (BB-PGAs). SD and IQR of IOP were calculated and used as surrogates of nyctohemeral IOP variation.ResultsThe mean (±SD) age of patients was 67.8±6.8 years (36.4% female). A total of 50 920 IOP measurements over 4084 days from 10 eyes were included. The mean 24-hour IOP (±SE) with BB-PGA (17.3±1.1 mm Hg) was significantly (p<0.001) less than that with BB (18.8±1.1 mm Hg). Mean 24-hour IOP with BB-CAI (18.4±1.1 mm Hg) was similar (p>0.05) to that with the other two medications. Mean 24-hour SD of IOP with BB-PGA (1.9±0 .2 mm Hg) and BB-CAI (2.0±0.2 mm Hg) were significantly (p<0.05) less than that with BB (2.4±0.2 mm Hg). Mean 24-hour IQR of IOP with BB-PGA (2.3±0.4 mm Hg) was significantly less than that with both BB (3.8±0.4 mm Hg) and BB-CAI (3.2±0.4 mm Hg).ConclusionContinual IOP monitoring shows that combinations of BB-PGAs have more pronounced effects on lowering 24-hour IOP fluctuations than BB-CAIs and BBs alone.Trial registration numberNCT03651336.
Earlier preoperative percutaneous intercostal cryoanalgesia improves recovery after pectus excavatum surgery
Authors: Mariconti Stefano , Bronco Alfio , Pellicioli Isabella , Chiudinelli Lorenzo , Cattaneo Martina , Cheli Maurizio , Bonanomi Ezio 
Published: November 2024

Background and aimsMinimally invasive repair of pectus excavatum is associated with intense postoperative pain. We aimed to evaluate the effectiveness of percutaneous intercostal cryoanalgesia according to the different timing of its preoperative application. Outcome variables included pain, drug consumption, time to mobilization, and hospital length of stay.MethodsRetrospective data analyzed from a single pediatric institution, involving a series of consecutive patients who underwent pectus surgery. Group 1: 18 patients received thoracic epidural analgesia. Group 2: 11 patients underwent cryoanalgesia on the same day of surgery and epidural analgesia as a bridge to cryoanalgesia onset. Group 3: 13 patients underwent earlier preoperative intercostal cryoanalgesia with a median of 6 days (IQR 3–6) before surgery. All groups received rescue systemic analgesia.ResultsPain scores were similar among groups. The incidence of severe pain decreased in Group 3 versus Group 1 (38% vs 78%) (p<0.05). Ropivacaine epidural cumulative dose (mg/kg) decreased in Group 2 versus Group 1: 16 (IQR 12–23) versus 27 (IQR 22–35) (p<0.01). Cumulative oral morphine equivalent dose (mg/kg) decreased among groups: 12 (IQR 9.3–17), 8 (IQR 4.2–12), and 0.2 (IQR 0.0–0.3) (p<0.001) for Groups 1–3, respectively. Length of stay (days) also decreased among Groups 1–3, respectively: 7 (IQR 6–7), 6 (IQR 5–7), and 4 (IQR 4–5) (p<0.001).ConclusionsThis exploratory observational study suggests that the timing of cryoanalgesia may play a crucial role in its efficacy, with earlier placement being associated with improvements in pain, opioid utilization, and length of stay.
Topology and embeddedness of Lawson’s bipolar surfaces in the five-sphere
Authors: Mäder-Baumdicker Elena , Rothe Melanie 
Published: November 2024

In his seminal paper from 1970 [Complete minimal surfaces in [Formula: see text], Ann. Math. 92 (1970) 335–374], Lawson provided a construction of minimal surfaces with higher codimension in spheres. By the wedge product of any conformally parametrized minimal surface in the three-sphere and its Gauss map, he obtained a corresponding minimal surface in the five-sphere (contained in a three- or four-sphere in very few cases). The resulting minimal surface is called bipolar surface. We give a topological classification of Lawson’s bipolar minimal surfaces in the five-sphere for two of his families of minimal surfaces in the three-sphere, the [Formula: see text]- and [Formula: see text]-family. Therefrom, we deduce upper as well as lower bounds on the area of these surfaces and find that they are not embedded. Our proof is based on transporting the group structure of the original minimal surface arising by their definition via Schwarz reflections to the bipolar surface. We then study multiplicities of tangent planes of the bipolar surfaces at points corresponding to points in the initial minimal surface at certain vertices of their geodesic polygons.
Performance and Durability of Heavy-Duty Fuel Cell Systems with an Advanced Ordered Intermetallic ORR Alloy Catalyst and Novel Support
Authors: Ahluwalia Rajesh , Wang Xiaohua , Chen Kate , Wang Xiaojing , Spendelow Jacob S. 
Published: November 2024

Ordered PtCo intermetallic (OIM) catalyst (L10-PtCo/C) is a promising candidate as the oxygen reduction reaction (ORR) catalyst in hybrid fuel cell systems (FCS) for class-8 heavy duty (HD) trucks. Compared to a baseline annealed Pt on high surface area carbon (a-Pt/HSC) catalyst, its mass activity (MA) is 71% higher initially and 144% higher after 90,000 potential cycles in an accelerated stress test (AST). Analysis of the AST data indicates that the ORR kinetic constants do not change with aging and the degradation in the OIM catalyst activity is linearly proportional to the loss in the electrochemically active surface area (ECSA). Several operational strategies are investigated to mitigate catalyst degradation and achieve 25,000-h electrode lifetime and 2.5 kW g−1 Pt utilization on a HD truck duty cycle including load sharing with the hybrid battery, regulating the radiator fan power to maintain the coolant temperature close to 60 °C, clipping the maximum cell voltage below 850 mV, limiting the ECSA loss to 55%, and oversizing the active area of the membrane electrode assemblies by 20%. Drive cycle simulations indicate that the lifetime average voltage degradation rate is about 1.8 μV h−1 and the integrated stack and FCS drive cycle efficiencies decrease by 3.5 to 3.9%.
Predicting Suicidal Behaviors in Individuals With Diabetes Using Machine Learning Techniques
Authors: Mamun Mohammed A. , Al-Mamun Firoj , Hasan Md Emran , Roy Nitai , ALmerab Moneerah Mohammad , Muhit Mohammad , Moonajilin Mst. Sabrina 
Published: November 2024

Background: Diabetes management poses significant challenges worldwide, with individuals often facing increased risks of mental health issues, including suicidal behaviors. While traditional statistical methods have been used to assess risk factors, this study introduces machine learning techniques to predict suicidal behaviors among diabetic patients. Therefore, this study aims to investigate the prevalence and risk factors of suicidal behaviors among individuals with diabetes in Bangladesh using both traditional statistical methods and machine learning models.Methods: A cross‐sectional study was carried out with 390 diabetic patients, assessing demographic characteristics, diabetes‐related variables, mental health indicators, and suicidal behaviors. Statistical analyses including chi‐square tests and logistic regression, and various machine learning models, were employed to examine associations and predict risk factors.Results: This study found that 13.8% of the individuals with diabetes reported lifetime suicidal behavior and 9.0% reported past‐year suicidal behavior. Significant risk factors associated with higher rates of suicidal behaviors included younger age, marital status, lower monthly income, higher educational attainment, being insulin users, not having a family history of diabetes, and suffering from anxiety. Among the machine learning models tested, the CatBoost model outperformed others, obtaining low log loss values of 0.45 and 0.32 for lifetime and past‐year suicidal behaviors, respectively. CatBoost demonstrated excellent accuracy rates of 83.97% and 88.46%, significantly higher than those of traditional logistic regression models. The most significant predictors identified by machine learning were treatment type, followed by occupation, anxiety, age group, and having chronic diseases.Conclusion: The findings suggest the urgent need for comprehensive interventions and support systems for individuals managing diabetes, particularly those at higher risk of suicidal behaviors. Machine learning offers improved predictive power and the ability to identify complex patterns in risk factors. Collaborative efforts between healthcare providers, mental health professionals, and public health initiatives are essential for prioritizing mental well‐being in diabetes management.