bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploring perturbative constraints in higher-order curvature gravity theories
Authors: Molano Daniel , Bargueño Pedro 
Published: December 2024

Abstract
In the realm of general relativity (GR) and extended theories of gravity, obtaining solutions for scenarios of physical interest is a highly intricate challenge. By employing the formalism of mathematical perturbation theory within the GR framework, we demonstrate that, for a significant class of vacuum

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theories, the corresponding solutions do not yield additional effects beyond those predicted by GR’s perturbation theory. However, models characterized by terms of the form

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exhibit distinctive contributions not present in GR. We assert that fundamental limitations exist, explaining why solutions of certain

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models can deviate from their GR counterparts, indicating non-connected solutions or non-analytic behavior. Conversely, in the models

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, the solutions seamlessly connect with those of GR. This distinction highlights the nuanced interplay between higher-order curvature terms and their impact on gravitational dynamics, offering new insights into the landscape of modified gravity theories.
A smooth surface measurement method by flexible contact using multiple fingers device
Authors: Gong Jiale , Li Ziyang , Wang Hong , Zhang Cuiping , Guan Jinling , Li Jin 
Published: December 2024

Abstract
Contact measurement technology is commonly used for analysis and modeling of three-dimensional (3D) object. This paper presents a contact measurement method, using a claw type probe based on a set of rotary encoders that can flexibly contact objects with efficiently multi-lines scanning. In this paper, a 3D measurement system by flexible contact of multiple fingers was built. The claw probe was set on a coordinate measurement machine. By moving the position of the claw for interpolation detection to improve the sampling rate, higher precision 3D reconstruction was achieved. Also, a compensation algorithm was proposed for this type detector to improve measuring accuracy. In the experiments, two surface models were produced by 3D printing as the test objects. By comparing the designed surface with the reconstructed surface, it is demonstrated that this system can effectively and softly measure smooth surface, like the back of human body, which is valuable for further study and use in rehabilitation massage, apparel design, and etc.
Neural network-based method for structured light phase-shifted fringe denoising and phase-to-height calibrations
Authors: Li Chengcheng , Mao Junjie , Zhang Xinna 
Published: December 2024

Abstract
The monocular structured light measurement system is widely applied across various fields due to its simple structure, low cost, and extensive measurement range. However, measurement accuracy can be affected by noise, non-linear intensity variations, variations in object surface reflectance, and calibration precision, leading to unstable or inaccurate results. Traditional filtering methods are limited in effectively addressing complex noise and non-linear issues, and phase-to-height calibration often depends on high-precision motion platforms, which increases system costs, complexity, and calibration uncertainty. To address these challenges, this paper proposes a neural network-based approach for monocular structured light measurement. An iterative denoising algorithm based on a denoising autoencoder (DAE) is developed for phase-shift fringe images. By optimizing the iteration count, denoised phase-shift fringes are regenerated to de noise to reduce image noise and enhance phase computation accuracy effectively. Additionally, leveraging the high-precision absolute phase calculated from the denoised phase-shift fringe images, a multilayer feedforward neural network (FNN) algorithm is proposed for absolute phase-to-height calibration. This method directly maps the phase-to-height relationship, integrating both intrinsic and extrinsic camera parameters to achieve high-precision calibration without requiring a high-precision motion platform. Consequently, it significantly mitigates errors related to the motion platform and reduces operational errors during calibration. The DAE enhances the quality of phase images, providing more precise input for the FNN calibration and further improving measurement accuracy. Experimental results demonstrate that the proposed method achieves effective 3D reconstruction from low-quality phase-shift fringes and maintains robust performance when measuring objects with varying reflectances.
Natural gas production process fault localization based on direct transfer entropy with adaptive lag
Authors: Chen Jiexue , Liang Bing , Nie Ke , Cui Yue , Wang Yuanyuan , Ren Hongji , Yin Aijun 
Published: December 2024

Abstract
Natural gas production equipment has complex structures, numerous monitoring parameters, and intricate correlation relationships, making it challenging to trace the source of faults for abnormal events. This paper proposes a fault localization method for natural gas production processes based on direct transfer entropy with adaptive lag (AL-DTE). Firstly, a variable set is screened based on the contribution plot. To enhance the adaptability to multiple operating conditions and reduce the reliance on experience, this paper integrates the adaptively to DTE and proposed AL-DTE. A causal analysis is then constructed and the causality graph is utilized to visualize the abnormal propagation path and identify the root cause of the fault. The proposed method can locate abnormal and faulty sub-equipment without relying on historical experience, enabling rapid localization of unknown faults. The effectiveness of the method is verified through simulation data and real faults of a triethylene glycol dehydration equipment.
Research status and prospects of intelligent logging lithology identification
Authors: Jin Huang , Yutong Ci , Xuan Liu 
Published: December 2024

Abstract
With the increasing of petroleum exploration and development, accurate lithology identification is of crucial. Machine learning (ML) plays a key role in logging lithology identification. By introducing traditional logging lithology identification methods, we review the application of ML in logging lithology identification from the perspectives of bibliometrics and ML classification in this paper. The applications of supervised learning, semi-supervised learning, unsupervised learning, ensemble learning, and deep learning algorithms in logging lithology identification are introduced in detail. Multiple ML algorithms have achieved remarkable results in different scenarios. For example, support vector machine, random forest, eXtreme gradient boosting, and convolutional neural network perform well in logging lithology identification and obtain relatively high identification accuracy. However, ML for logging lithology identification also faces challenges such as data quality, data imbalance, model generalization, and model interpretability. Future research should focus on algorithm optimization and innovation, improvements in data quality and quantity, deep multidisciplinary integration and practical application to enhance the accuracy and reliability of lithology identification. These findings provide strong support for oil and gas exploration and development.
An adaptive feature refinement network for pixel-level segmentation of surface defect
Authors: Zhang Yuzhong , Ge Wenhao , Liu Shiyang , Wang Jiazheng , Hu Haibing , Dong Jingtao , Zhang Tengda 
Published: December 2024

Abstract
In the field of industrial production, automatic surface defect segmentation plays a crucial role in improving product quality and production efficiency. However, due to the interference of complex backgrounds and uneven illumination, efficient and accurate segmentation of surface defect remains a significant challenge. To address these issues, this work proposes an adaptive feature refinement (AFR) U-shaped network (U-Net) for pixel-level segmentation of surface defect. The proposed framework draws inspiration from the classic U-Net architecture, and the pre-trained EfficientNet-B0 is selected as the encoder part to extract the multi-scale context information. Then, a novel AFR module, which further learns the global channel-wise dependencies and captures cross-channel local spatial patterns from multi-level features, is introduced to enhance the robustness of feature representation. Finally, these enhanced encoder features are up-sampled, and are further fed into the decoder part to obtain pixel-level defect segmentation results. To assess the validity and accuracy of the proposed model, this network is validated through extensive experiments on four different types of defect datasets. Experimental results show that the proposed model performs well on these datasets, with the maximum mean intersection over union (MIoU) of 91.63%. Meanwhile, the model is compared with some U-Net-based models and other state-of-the-art segmentation models, and it can also achieve competitive performance on MIoU scores. With the acceleration of the graphics processing unit, the proposed network can achieve a detection speed of 82 fps, which can meet the needs of real-time detection in the industrial fields.
Effect of Mn2+ ions and TOP molecules on the luminescence properties of oleic acid caped CsPbI3 nanocrystals
Authors: Huang Xiaozong , Li Wen , Kong Linghang , Lu Xiaoyan , Yang Chengzhi , Zou Bingsuo 
Published: December 2024

Abstract
Due to the easy transformation to the non-luminous yellow δ-CsPbI3 phase in air, α-CsPbI3 nanocrystalline materials with red light emission find limited applications. Lifting its structural stability is a challenge in its quantum dot (QD) lighting field. Here we studied the doping of Mn2+ ions (5.0%) and processing by a small amount (0.315 ml) of TOP molecules on the OA-capped CsPbI3 nanocrystals. It is found that after the successful introduction of Mn2+ into the CsPbI3 nanocrystal, the grain size reduces, which leads to a stronger quantum confinement effect than the undoped QDs, which leads to the blue shift of PL and absorption spectra. The incorporation of Mn2+ simultaneously reduced defects and lifted the luminescence efficiency and lifetimes of QDs, the cause for the above optical behavior is due to the formation of excitonic magnetic polaron (EMP) excitons near the bandedge. On the other hand, the treated TOP molecule on the OA-capped NCs did not significantly affect their room-temperature luminescence. However, it improved the low-temperature emission performances of QDs significantly. Moreover, the TOP-treated QDs fixed in the PMMA film can transform into rod-like shapes in acidic environments, giving strong stability for emission, especially for the Mn-doped CsPbI3 QDs. This work has been done to improve the stability and emission efficiency of related QDs through the EMP formation near band-edge and surface modification of CsPbI3 NCs, favoring their potential applications in display and low-temperature light-emitting devices.
Streamer propagation in CO2 and N2/CO2 mixtures at atmospheric pressure
Authors: Omori Kento , Ono Ryo , Komuro Atsushi 
Published: December 2024

Abstract
This study investigated streamer discharge in CO2 and N2CO2 mixtures. Experiments were conducted at atmospheric pressure and room temperature conditions by using a point-to-plane electrode configuration. A repetitive pulse discharge at 50 Hz was used to stabilise the discharge in CO2. Under these conditions, the discharge current and intensified charge-coupled device camera images were systematically analysed during streamer evolution in CO2. The results revealed that streamer propagation and electrical properties were considerably influenced by the N2 concentration in CO2. Analysis of the emission of secondary streamers indicated that the emission ratio of primary streamers and secondary streamers in CO2 differed considerably from that in air. The onset delay time of streamer discharge was measured and statistically processed based on previous studies of streamer onset processes. Findings indicated an increase in the discharge delay time and its dispersion when N2 was added to CO2, which indicated that variations in the initial electron content affected the inception process of streamer discharge.
Multi-material single-vat dual-wavelength DLP 4D printing of shape memory polymers
Authors: Kröger Finn , Schulte Lennard , Spiegel Christoph A , Vazquez-Martel Clara , Blasco Eva 
Published: December 2024

Abstract
Shape memory polymers are a fascinating class of responsive materials with potential in various fields, especially when combined with precise structuring by three-dimensional (3D) printing strategies such as digital light processing (DLP). However, such 3D printing techniques are usually limited to homogeneous single material four-dimensional (4D) structures restricting potential applicability. In this study we present a dual-wavelength multi-material DLP-based strategy for the fabrication of 4D multi-material structures with a spatial controllable shape memory effect from a single ink formulation. To achieve this, we designed an ink system, allowing generation of static as well as responsive parts depending on the applied curing wavelength with a high spatial control. Specifically, the multi-material ink formulations are composed of epoxide-based, (meth)acrylate-based monomers and a radical photoinitiator—providing appropriate selectivity of the polymerization mechanism. A germanium-based derivative has been selected as a suitable radical photoinitiator active in the blue wavelength regime (460 nm), enabling the exclusive formation of poly(meth)acrylate-based networks exhibiting shape memory properties. When printing with UV light (365 nm), simultaneous formation of epoxy and polymethacrylate networks results in a non-responsive material. Finally, by exploiting these capabilities, the fabrication of multi-material 4D structures with spatially controllable shape memory properties is successfully demonstrated.
Investigation into vibration filtering for reducing friction loss of ultrasonic motors through a numerical approach
Authors: Liao Xinxin , Perriard Yves 
Published: December 2024

Abstract
Ultrasonic motors (UMs) have emerged as a leading solution for actuators in biomedical equipment, aerospace applications, and other fields, owing to their distinct advantages, such as compact structures and self-locking ability. The operating principle of nearly all UMs heavily relies on friction coupling. Excluding material fatigue, friction loss is the primary factor affecting the UMs’ service life. To address this challenge, we propose, for the first time, the integration of vibration filtering into UMs to minimize friction loss during operation. The core innovation of this study involves partitioning the mover into two components: the primary mover and the secondary mover, connected by a flexible mechanism. The lightweight secondary mover remains in contact with the vibrator, while the primary mover contains the majority of the mover’s mass. The flexible mechanism serves as a filter, effectively preventing the transmission of harmonic vibrations, which are known to contribute to friction loss, from the secondary mover to the primary mover. By developing a generalized kinematic model for UMs, we systematically examined the feasibility of our approach with different parameters. Our findings demonstrate that, under a preload of 100 N, friction loss can be reduced by approximately 5 times. Although this technique introduces additional structural complexity, we contend that it holds heuristic significance for enhancing the efficiency of UMs, despite still being at the proof-of-concept level.
Deep learning bimodal frequency-selective digital holography by using a confocal dual-beam setup
Authors: Zhang Wanbin , Ren Yanchen , Feng Yijian , Sun Xiangdong , Hu Jiangnan , Xing Weiyong , Zhang Zhanjun 
Published: December 2024

Abstract
This paper proposes a bimodal digital holography technique based on deep learning, marking the first application of neural networks in frequency-selective holographic reconstruction. The method achieves dual-channel mixed-mode recording and super-resolution separation reconstruction, enabling simultaneous multimodal holography and enhancing wavefront acquisition efficiency. Direct current and conjugate terms are effectively suppressed, allowing coherent and incoherent holography integration. Experiments in overlapping and non-overlapping modes with confocal dual viewpoints confirm the method’s fidelity in isolating target wavefront spectra and demonstrate improved resolution after deep learning reconstruction. This technique offers broad potential in multimodal compressed holography, super-resolution, and extended field-of-view imaging.
Adsorption behavior of toxic nitrogen-containing gas molecules on GeC monolayer for the sensitive and reusable gas sensors: first-principles calculations
Authors: Thajitr W , Busayaporn W , Sukkabot W 
Published: December 2024

Abstract
Using the first-principles calculations, the gas sensing properties of GeC monolayer are analyzed to explore the possibilities in the toxic nitrogen-containing molecular sensors to detect NH3, NO2 and NO molecules. The adsorption behavior is computed under different stable adsorption configurations. NH3 is physically adsorbed on GeC monolayer with modest adsorption energies (Eads = −0.487 eV). NO2 is chemisorbed on GeC monolayer with Eads of −0.770 eV. NO is either physisorbed or chemisorbed on GeC monolayer with Eads of −0.437 eV or −0.605 eV depending on the stable adsorption configurations. NO2 and NO molecule dramatically change the electronic properties of GeC monolayer, while NH3 molecule barely modifies those of GeC monolayer. Because of the change in the electric conductivity, the descending order of the sensitivity is NO2 > NO > NH3. Finally, the quick recovery times are found for all molecules which determine the worth of reusability of a sensing material.
Topological characterization of [n]-triangulenes through degree-based molecular descriptors with the prediction to π-electron energy
Authors: K Jyothish , Roy S 
Published: December 2024

Abstract
Triangulene and its π-extended homologues are a family of polycyclic aromatic hydrocarbons with a peculiar chemical structure. They are recognized for their intricate structural configurations and electrical properties, which make them a promising material for potential uses in spintronics. They are built of benzenoid rings fused in a triangular manner. Topological indices are widely utilized as graph theoretical measures for evaluating the physicochemical properties of polycyclic aromatic hydrocarbons by analyzing their molecular structures, makes them hold a significant position in the domain of mathematical and computational chemistry. In this study, a mathematical exploration of topological indices of [n]-triangulenes has been done to establish a comprehensive understanding of their applications and significance. Generalized expressions for topological indices have been computed, and their predictive power for various physicochemical properties has been studied using statistical methods. Also, a quantitative structure-property relationship analysis of [n]-triangulene's energetic characteristics has been performed. Moreover, a generalized algebraic expression to predict the π-electron energy of [n]-triangulene structure has been derived.
Analysis of air and vacuum annealing on microstructural, optical and electrical characteristics of In2O3 layers for gas sensors
Authors: Suthar Kamlesh , Suthar D. , Sharma Ritika , Panwar S. , Himanshu Himanshu , Purohit L.P. , Dhaka M.S. 
Published: December 2024

Abstract
With consideration of significant potential of metal oxides in the effective gas sensing applications, herein, an assessment of air and vacuum annealing to the physical characteristics of In2O3 (Indium Oxide) layers is meticulously unveiled. Development of 550 nm thin In2O3 films on glass and ITO (Indium doped Tin oxide) coated substrates is accomplished by means of electron beam evaporation technique. An annealing treatment on grown In2O3 layers is executed at 300 °C, 375 °C and 450 °C for one hour in air and vacuum conditions followed by their subsequent characterizations using X-ray diffractometer, UV-Vis spectrophotometer, source meter, scanning electron microscope, and energy dispersive spectroscope tools. The crystallite size corresponding to predominant cubic peak (222) is obtained in 23-33 nm range for both types of films and observed least at 450 °C. Optical analysis exhibited that In2O3 films showed maximum transmittance of ~90% in visible region whereas band gap is measured within the range 2.20-3.50 eV. The electrical measurements unveiled Ohmic nature of films contacts where resistivity of In2O3 layers is found to be enhanced with air and vacuum annealing temperature. The morphological micrographs indicate uniform In2O3 films deposition with nanospherical grains and EDS patterns exhibited In and O peaks ensuring successful deposition of n-type In2O3 films with In richness and O vacancies. The gas sensor fabricated by using In2O3 films annealed in air at 450 °C is assessed for detection of H2S gas which showed gas response and response time of 1.08 and 38 seconds, respectively.
RSP-UV: real-time sampling-based path planning method for unmanned vehicles
Authors: Zhou Rui , Zhang Chuanwei , Zhao Ruiqi , Zhang Tianle 
Published: December 2024

Abstract
The Rapidly-exploring Random Trees Star (RRT*) is widely used in unmanned vehicle path planning. However, it suffers from excessive sampling randomness, long search time and not capable of dynamic obstacle avoidance. This is not only fatal to safety, but also detrimental to smooth driving. We propose a real-time sampling-based path planning method for unmanned vehicles (RSP-UV) that systematically solves these problems. To address the problem of excessive sampling randomness, we formulate a partitioned sampling strategy and limit the density of sampling points. It reduces the sampling time to a certain extent. Additionally, we design a fuzzy controller to adjust the scale factor of the Dynamic Window Approach (DWA) evaluation function to optimise the local path and improve the dynamic obstacle avoidance efficiency. Fusing the improved RRT* and the improved DWA, the global path output from the improved RRT* is used as the bootstrap path for the improved DWA. Besides, the return path set rule is made to avoid the backtracking phenomenon. This improves the reliability and asymptotic optimality of the final path. Simulation experiments show that the trajectories planned by RSP-UV outperform the three compared methods, the similar method, ORRT*-DWA and MTAR. Real experiments show that the average path length of RSP-UV is reduced by 2.26%, 3.03%, and 3.33% compared to the other three methods, respectively. The average planning time of RSP-UV is reduced by 7.16%, 2.15% and 8.78% compared to the other three methods, respectively. RSP-UV not only ensures the success of path planning but also excels in real-time and adaptability. The paths formed by RSP-UV are cheaper and safer.