bluuebunny/crossref_metadata_2025_split huggingface dataset:
A novel multi-scale gated convolutional neural network based on informer for predicting the remaining useful life of rotating machinery
Authors: Wu Chenbin , You Aogui , Ge Ming-Feng , Liu Jie , Zhang Jiachuan , Chen Qian 
Published: September 2024

Abstract
Recently, neural networks based on deep learning techniques have been employed for remaining useful life (RUL) prediction of rotating machinery. However, there are some limitations: (1) convolutional neural networks primarily use local receptive fields to extract features, which leads to a relative weakness in modeling long-term dependencies on a global scale; (2) transformers face difficulties when dealing with uncertainties such as anomalies in input sequences, missing data, or variations in sampling frequency. This paper introduces a informer-based multi-scale gated convolutional network. Initially, a multi-scale gated convolution module is constructed to effectively extract features across various levels, adeptly capturing temporal patterns and long-term dependencies within the dataset. Subsequently, the derived multi-scale degradation features are utilized in predicting the RUL through an informer network, thereby enhancing the efficiency of parallel computing. To validate the effectiveness and superiority of this method, comparative experiments were conducted using two publicly available bearing datasets and various model approaches.
MDSC-FSPPA-LCFF network for diagnosis of rolling bearing with multipoint fault
Authors: Huang Yuan , Yan Changfeng , Liu Bin , Zhao Yingjie , Wu Lixiao 
Published: September 2024

Abstract
Faults that occur in rolling bearings during operation are complex and variable. While extensive research has been conducted on compound faults involving multiple components, studies on multiple faults in single component are relatively scarce. However, the occurrence of multiple faults in single component is a common phenomenon. To address the issues of difficulty in feature extraction, numerous network parameters, and slow computational speed, a multi-scale dynamic snake convolution with fast spatial pyramid pooling attention (FSPPA) and lightweight comprehensive feature fusion (LCFF) network is proposed for multipoint fault diagnosis of rolling bearings. Firstly, multi-scale shallow feature extraction module is applied to extract the features from the original signals. Then, dynamic snake convolution (DSC) with FSPPA module is used to refine these features deeply. Subsequently, LCFF module is employed to reduce network parameters while still fully extracting fault features. Additionally, fault identification is obtained through the softmax function. Finally, the t-distributed stochastic neighbor embedding method is utilized to visually demonstrate the fault classification performance of the proposed method. The experimental evaluation conducted on bearing datasets indicates that the proposed network exhibits excellent performance of multipoint fault diagnosis in rolling bearings.
Position and orientation estimation method based on 3D digital morphology contour registration
Authors: Wang Kan , Zhu Wen-hao , Yang Li-ping , Gu Xiao-hua , Guo Li-xia 
Published: September 2024

Abstract
Accurately and quickly obtaining the positions and orientations of mechanical parts based on the digital morphologies of mechanical parts are the key to achieving efficient and accurate assembly of mechanical parts. However, due to poor robustness and compactness in extracting digital morphology contours of mechanical parts, the accuracy of assembly positions and orientations obtained by using digital morphology contours cannot meet the requirements of high-precision assembly. Therefore, this paper proposes a position and orientation estimation method based on 3D digital morphology contour registration. This method extracts and optimizes digital morphology contours of mechanical parts and obtains the assembly positions and orientations by using an improved iterative closest point method to register the extracted digital morphology contours with those of the mechanical parts in assembly targets with desired positions and orientations. Experiments are conducted using mechanical parts from the ABC dataset and inertial confinement fusion micro-target. From the experimental results, when using the assembly positions and orientations obtained through the method proposed in this paper to assemble mechanical parts, it can achieve translation absolute errors of 8 μm, 5 μm, and 9 μm along the X-, Y-, and Z-axes, respectively. Similarly, the angular absolute errors in rotations around the Z-, Y-, and X-axes can be less than or equal to 0.16°, 0.15°, and 0.11°, respectively. The results prove that the proposed method in this paper exhibits high computational efficiency and accuracy, providing an effective approach for digital assembly of mechanical parts.
VSLAM based on deep learning in low-textured scenes
Authors: Zhu Qiguang , Li Xiang , Zhao Yuchao , Chen WeiDong 
Published: September 2024

Abstract
Traditional visual simultaneous localization and mapping (VSLAM) algorithms often suffer from the low positioning accuracy and robustness of the traditional feature extraction methods. These methods typically require environments with clear features, perform poorly or even fail in low-textured scenes. To solve this problem, we propose an RGB-D VSLAM algorithm that integrates a deep learning feature extraction network. First, to solve the problem in which sufficient feature points cannot be extracted, the proposed algorithm uses the R2D2 feature extraction network to extract keypoints and generate descriptors. We also perform lightweight optimization on the R2D2 network, ensuring accuracy while doubling the inference speed. Subsequently, we employ the L2 distance to measure the distance between descriptors, thereby enhancing the accuracy of keypoint matching. Ultimately, we add a dense mapping thread to obtain a dense 3D point map generated by the RGB-D information and poses of the keyframes. The experimental results on the TUM dataset show that the average absolute trajectory error is reduced by 59% and the relative trajectory error is reduced by 56%. The results demonstrate that our algorithm significantly improves localization accuracy and system robustness in low-textured environments.
Adaptive threshold algorithm for detecting EEG-interburst intervals in extremely preterm neonates
Authors: Mader Johannes , Hartmann Manfred , Dressler Anastasia , Oberdorfer Lisa , Rona Zsofia , Glatter Sarah , Czaba-Hnizdo Christine , Herta Johannes , Kluge Tilmann , Werther Tobias , Berger Angelika , Koren Johannes , Klebermass-Schrehof Katrin , Giordano Vito 
Published: September 2024

Abstract

Objective. This study provides an adaptive threshold algorithm for burst detection in electroencephalograms (EEG) of preterm infantes and evaluates its performance using clinical real-world EEG data. Approach. We developed an adaptive threshold algorithm for burst detection in EEG recordings from preterm infants. To assess its applicability in the real-world, we tested the algorithm on a dataset of 30 clinical EEG recordings which were not preselected for good quality, to ensure a real-world scenario. Main results. Interrater agreement was substantial at a kappa of 0.73 (0.68–0.79 inter-quantile range). The performance of the algorithm showed a similar agreement with one clinical expert of 0.73 (0.67–0.76) and a sensitivity and specificity of 0.90 (0.82–0.94) and 0.95 (0.93–0.97), respectively. Significance. The adaptive threshold algorithm demonstrated robust performance in detecting burst patterns in clinical EEG data from preterm infants, highlighting its practical utility. The fine-tuned algorithm achieved similar performance to human raters. The algorithm proves to be a valuable tool for automated burst detection in the EEG of preterm infants.
A comparative study of fibre Bragg grating for spatially and temporally resolved gas temperature measurements in cold atmospheric pressure plasma jets
Authors: Billeau Jean-Baptiste , Thomas Jyothis , Kashyap Raman , Rosenzweig Derek , Reuter Stephan 
Published: September 2024

Abstract
Cold atmospheric pressure plasma jets (CAP-Jet) are successfully used in medical therapy for healing of chronic wounds and are widely researched in inactivation of pathogens and in assisting in cancer therapy. A crucial parameter for these plasma applications is that CAP-Jets operate at temperatures that are tolerable for biological tissues. While tools characterizing the plasma’s gas temperature are well developed, there are only a few methods that work with an agreeable limit of uncertainty, complexity and limited perturbation properties to accurately determine that the studied plasma jet operates at tissue tolerable temperatures at all times. In the current work, time resolved measurements of the gas temperature in the effluent of a CAP-Jet are performed using the innovative technique of a fibre Bragg grating (FBG), in which the temperature dynamics is measured by a shift of the FBGs resonant wavelength through its thermo-optic coefficient. Comparing with other temporal and spatial diagnostic tools such as thermocouple measurement, Schlieren imaging, and optical emission spectroscopy, we demonstrate reliable calorimetric measurements at different plasma duty cycles. The plasma source maintains tissue tolerable temperatures inside the plasma active zone with values below 35 °C at 1 cm distance from the jet nozzle. The calorimetric measurements have revealed that the heat power dissipation in comparison to electric energy of our plasma source is at least 50%.
Physics and technology of laser lightning control
Authors: Produit Thomas , Kasparian Jérôme , Rachidi Farhad , Rubinstein Marcos , Houard Aurélien , Wolf Jean-Pierre 
Published: September 2024

Abstract
The recent development of high average, high peak power lasers has revived the effort of using lasers as a potential tool to influence natural lightning. Although impressive, the current progress in laser lightning control (LLC) technology may only be the beginning of a new area involving a positive feedback between powerful laser development and atmospheric research. In this review paper, we critically evaluate the past, present and future of LLC, considering both its technological and scientific significance in atmospheric research.
Baseline offset correction technique for terahertz signals based on improved wavelet multiresolution analysis
Authors: Xiong Weihua , Jiang Yufei , Huang Xiaotong , Cao Lixian 
Published: September 2024

Abstract
During the terahertz nondestructive testing of bonded structures, the incomplete discharge of the capacitance in the photoconductive antenna within the terahertz time-domain spectroscopy system results in a shift of the terahertz baseline produced by the antenna. This baseline shift causes variations in the amplitude information of the detected signals. Consequently, when feature imaging of the detection waveforms is performed, the baseline shift can lead to erroneous detection results. In this study, an improved wavelet multiresolution analysis method was used to eliminate high-frequency noise and baseline offset in terahertz detection. The method is based on the frequency characteristics of the detection waveforms, setting thresholds and using similarity as a measurement standard to determine the number of decomposition layers. Ultimately, this achieves the correction of the baseline offset in terahertz signals. Compared with other baseline correction methods, the method presented in this paper achieves the lowest root mean square error of 0.57%, the highest signal-to-noise ratio of 12.64%, and a defect identification accuracy of 96.27% in two-dimensional visualization results.
Azimuthally modulating surface plasmon polaritons
Authors: Ibrahim Muhammad , Rahmatullah  , Qamar Sajid 
Published: September 2024

Abstract
We suggest a scheme for the lossless excitation of surface plasmon polaritons (SPPs) at a metal-dielectric interface using a Laguerre–Gaussian (LG) beam. We consider a three-layer structure consisting of a top transparent medium, a middle metal thin film, and a dielectric bottom layer. The dielectric medium contains three-level atoms exhibiting electromagnetically induced transparency (EIT). By applying a control field, a LG beam, and a microwave field, we induce azimuthally varying permittivity, which leads to the azimuthal modulation of SPPs. The propagation length of SPPs is increased when excited by a LG beam carrying optical angular momentum (OAM). Moreover, the group velocity of SPPs can be effectively controlled by adjusting the OAM and azimuthal angle of the LG beam, allowing for both slow and fast propagation of SPPs.
Analyzing the dynamics and optimal control of a vector-borne model with dual vertical transmission and multiple serotypes
Authors: Qin Yuyan , Yang Lixin , Li Jia 
Published: September 2024

Abstract
Transmission and contact patterns have an important influence on the intensity and trend of disease transmission. Taking into account two different serotypes and the vertical transmission of the disease in both humans and mosquitoes, this paper proposes a novel vector-borne disease model. In order to explore the evolution mechanisms of the model and the impacts of contact pattern on disease transmission. The basic regeneration number of the proposed model is obtained via the next-generation matrix approach. Subsequently, we analyze the conditions of the stabilities and existences of the equilibrium points. Further, the sensitivity of the parameters is studied using numerical simulations. It is observe that the increase of vertical transmission rate of mosquitoes leads to the increase of the basic reproduction number. In addition, we develop vaccination and insecticide control strategies and derive the existence and expression of the optimal solution. Our numerical results show that vaccination has a little effect on preventing the spread of disease among mosquitoes. However, insecticide spraying is effective in reducing the spread of disease between humans and mosquitoes.
Semiconductor-metal transition in TlPbF3 under isotropic pressure and its impact on optical, elastic and mechanical properties: a DFT computation with HSE06
Authors: Ahmad Bilal , Siddique Sofia , Zeba I , Zafar Sana , Shakil M , Gillani S S A 
Published: September 2024

Abstract
This research aims to comprehensively evaluate the structure, elasticity, mechanics, anisotropy, electrical properties, and optical attributes of TlPbF3 at pressures from 0 to 60 GPa. A cubic structure of the material remains same without any phase changes, but there is a reduction in the lattice parameters. The material is determined to be mechanically secure by doing calculations on a variety of mechanical and elastic characteristics, including bulk, shear, and Young’s modulus, stiff, and not particularly flexible. It also shows a significant resistance to shear force. The material’s ability to withstand high pressures, its metallic bond nature, and ductility have been shown by the Kleinman’s parameter, Poisson’s ratio, Cauchy pressure, and Pugh ratio. Anisotropy can be confirmed by activating specific anisotropy factors. When we consider the electronic band structure, we observe a transition from a broad gap in the band (3.719 eV) comparable with a small band distance (0.65 eV), and converts to a metal (0 eV). To explore this, we have estimated the total, partial and elemental partial density of states. Additionally, we have computed the real and imaginary functions of dielectric, absorption factor, refractive index, extinction coefficient, loss function, reflectivity and real/imaginary conductivity to assess the material’s applicability. As pressure is applied, the static values of

ε

1

(
ω
)

and

n
(
ω
)

increase. This material is also well-suited for optoelectronic devices due to its high conductivity, reflectivity, absorption, and refractive index.
Stress transfer paths and damage modes of layered coal-rock mass under static loading
Authors: Li Feng , Wang Bo 
Published: September 2024

Abstract
In order to explore the distribution characteristics of nonlinear mechanical behavior and the tendency of damage and failure of deep in situ coal-rock mass, a layered composite thin plate coal-rock mass model is established based on thin plate theory and finite element method. The continuity of interlayer stress and displacement is maintained by transfer matrix function, and the maximum shear stress is calculated by Mohr-Coulomb strength criterion. The distribution characteristics of stress, displacement, energy and maximum shear stress of coal-rock mass under static load are obtained. The analysis results are as follows : ① The torsional stress concentration areas of τxz and τyz are formed on the boundary of coal-rock interface, and the principal stress concentration areas of σx, σy and σz are formed in the center and center of the boundary, and the shear stress distribution of τxy is formed on the diagonal line. ② The maximum shear stress of the long axis has a strengthening effect on the shear failure, and each layer forms a stress state of alternating tension-shear-compression-shear. It first destroys in the middle of the coal-rock interface and boundary, and then destroys along the long side and short side. ③ The coal-rock mass presents a ′ X ′ -type shear zone along the radial and axial. The shear failure occurs first, followed by the principal stress compression failure. The cracks are preferentially developed in the direction perpendicular to the minimum principal stress, and the stepped ′ V ′ -shaped failure characteristics are formed under the action of shear and tensile stress.
Aero-engine gas path anomaly monitoring based on unsupervised structure and deep slow feature representation
Authors: Li Zhiqiang , Li Dewen , Cai Jing , Wei Jiashun , Li Yang , Zhang Ying 
Published: September 2024

Abstract
Gas path anomaly monitoring holds a crucial position in aero-engine health management due to the dynamic nature of gas path parameters, data imbalance, and the lack of labels, presenting significant challenges. To address these issues, this study proposes a novel method for dynamic anomaly monitoring in aero-engines utilizing Kernel Slow Feature Analysis (KSFA) and Deep Support Vector Data Description (Deep SVDD). In this approach, the original gas path parameter values undergo preprocessing using the KSFA algorithm to extract pertinent features indicative of gradual changes in gas path status. The Deep SVDD model, employing a one-dimensional Convolutional Neural Network (1D-CNN) fused with a feature attention mechanism, is iteratively trained to identify the optimal hypersphere. The Health Indicator (HI) is then determined by quantifying the distance between the test set and the hypersphere’s center, enabling a quantitative assessment of the aero-engine’s performance degradation. Experimental findings demonstrate that this method outperforms alternative evaluation techniques by effectively tracking the aero-engine’s degradation process and anticipating engine anomalies, showcasing its practical value in engineering applications.
The role of Rashba spin-orbit induced spin textures in the anomalous Josephson effect
Authors: Monaghan R D , Tettamanzi G C 
Published: September 2024

Abstract
This work reports the theoretical investigation into the mechanism underpinning the anomalous Josephson effect within ballistic systems; currently, there is no agreed-upon microscopic mechanism behind the origin of this effect. The prototypical system we study is a ballistic two-dimensional junction containing a two-dimensional Rashba spin-orbit interaction. In this paper, we demonstrate how this two-dimensional Rashba interaction mixes the spins of adjacent transverse subbands, leading to significant spin-asymmetry within the junction. Under an external magnetic field, applied perpendicular to both the axis of transport and the normal vector of the junction, the sinusoidal Josephson current can then experience an anomalous phase shift. The role of this spin mixing in the limit of a single sub-band is initially explored by deriving an analytical expression for the resulting anomalous phase shift. The analysis is then extended to systems with multiple occupied sub-bands; in this later section, starting from a microscopic model, we derive an analytic formula for the resulting anomalous phase shift indicating it is linear in both magnetic field and spin-orbit strength. We then verify and validate all findings by comparing them with numerical results evaluated by a tight-binding model.
Synthesis and characterizations of upconverting luminescent Er3+/ Yb3+: Gd2O3 uniform nanospheres for biomedical applications
Authors: Anh Tran Kim , Ha Vu Thi Thai , Huong Nguyen Thanh , Thao Do Thi , Nguyen Tien Dai , Thai Dang Van , Tomala Robert , Minh Le Quoc 
Published: September 2024

Abstract
We present the synthesis of Er3+/ Yb3+ co-doped Gd2O3 nanospheres using the wet-chemical method followed by a synergistic step-by-step calcination process, a novel approach in the field. The nanospheres exhibited red color upconversion luminescence (UCL) after multi-step annealing at temperatures ranging from 650 °C to 1150 °C, achieving a fully spherical morphology. The diameter and surface morphology of the nanospheres were significantly altered by the annealing process, decreasing from 382.77 ± 1.72 nm with a smooth surface to 245.7 ± 1.31 nm with a rougher surface. The UCL intensity increased with the annealing temperature. The influence of Er3+/ Yb3+ co-doped Gd2O3 nanospheres, excited by a 975 nm laser, was investigated, and the decay time for UCL samples was analyzed. The luminescence peak at 1026 nm was attributed to the 4I11/2 (Er3+) + 2F7/2 (Yb3+) → 4I15/2 (Er3+) + 2F5/2 (Yb3+) transition/ or 2F5/2 → 2F7/2 of the Yb3+ ions within the Gd2O3 matrix. Furthermore, the UCL properties of Er3+/ Yb3+ co-doped Gd2O3 nanophosphors were explored to detect the NTERA-2 cancer cells. These findings suggest that the rare earth co-doped Gd2O3 nanospheres might hold significant potential for biomedicine and imaging diagnostics applications, sparking new interest and possibilities in these fields.
Enhanced photocatalytic degradation of Cr (VI) and organic pollutants using novel 3D/2D MoS2/SnS2 heterostructures for water remediation
Authors: Kumari Ritu , Kumar Rakesh 
Published: September 2024

Abstract
In this study, 3D flower-like MoS2 nanostructures were synthesized using a hydrothermal technique to form heterostructures with 2D porous SnS2 nanosheets. The resulting 3D/2D MoS2/SnS2 heterostructures were evaluated for their photocatalytic abilities in removing Cr (VI), tetracycline (TC), and methylene blue (MB) under simulated solar irradiation. The results demonstrate that the MoS2/SnS2 heterostructures significantly outperformed pristine MoS2 and SnS2 in photocatalytic efficiency. Specifically, the MoS2/SnS2 photocatalysts achieved 99.9% degradation of Cr (VI) within 50 min, 96% degradation of TC in the same timeframe, and 99.9% elimination of MB in just 10 min. The reduction rate constant for Cr (VI) reduction by MoS2/SnS2 photocatalysts was 0.117 min−1, surpassing that of pure SnS2 (0.007 min−1) and MoS2 (0.0034 min−1) by 16 and 30 times, respectively. This outstanding performance is attributed to the heterojunction formation between SnS2 and MoS2, which suppresses the recombination of photogenerated charge carriers and provides abundant reactive sites due to their large specific surface area. The proposed photodegradation mechanism illustrates the facilitated migration of photogenerated charge carriers under light irradiation, enabled by the energy band alignment at the MoS2/SnS2 interface. These findings represent a significant advancement in the development of photocatalysts based on 3D flower-like MoS2 and porous SnS2 nanostructures, offering promise for applications in wastewater treatment and environmental remediation.