bluuebunny/crossref_metadata_2025 huggingface dataset:
CONHyperKGE: Using Contrastive Learning in Hyperbolic Space for Knowledge Graph Embedding
Authors: Gao Mandeng , Tian Shengwei , Yu Long
Published: March 2024

The embedding of Knowledge Graphs (KGs) in hyperbolic space has recently received great attention in the field of deep learning because it can provide more accurate and concise representations of hierarchical structures compared to Euclidean spaces and complex spaces. Although hyperbolic space embeddings have shown significant improvements over Euclidean spaces and complex space embeddings in handling the task of KG embedding, they still face challenges related to the uneven distribution and insufficient alignment of high-dimensional sparse data. To address this issue, we propose the CONHyperKGE model, which leverages contrastive learning to optimize the embedding distribution in hyperbolic space. This approach enables better capture of hierarchical structures, improved handling of symmetry, and enhanced treatment of sparse matrices. Our proposed method is evaluated on four standard KG Embedding (KGE) datasets: WN18RR, FB15k-237, Kinship, and UMLS. After extensive experimental verification, our method has improved its performance on all four datasets. Notably, on the low-dimensional Kinship dataset, our method achieves an average Mean Reciprocal Rank (MRR) improvement of 2% over the original method, while on the high-dimensional WN18RR dataset, an average MRR improvement of 1% is observed compared to the original method.
Altered Handwritten Text Detection in Document Images Using Deep Learning
Authors: Patil Gayatri , Palaiahnakote Shivakumara , Gornale Shivanand S. , Lopresti Daniel P.
Published: March 2024

Handwritten documents possess immense significance in domains such as law, history, and administration. However, they are vulnerable to forgery, which can undermine their credibility and reliability. This paper aims to establish a dependable technique for identifying altered text in handwritten document images, even in scenarios with high levels of noise and blur. Our study investigates 10 distinct categories of handwritten text that have been altered through various forgery operations. The suggested approach employs the deep neural architectures VGG16 and Resnet50 as feature extractors. The architecture comprises three parts: Feature extraction using individual models, a feature fusion layer, and a classification layer. Initially, we optimize the training process and feature extraction using VGG16 and ResNet50. The feature vectors obtained from both models are then fused together in the feature fusion layer and input into the classification layer for the classification task. Experiments are conducted on a custom-created dataset as well as benchmark datasets including ICPR FDC, IMEI Forged Number, and Kundu to demonstrate that the proposed method is superior to existing approaches.
Advancing Handwritten Musical Notation Recognition Using Deep Learning: A Convolutional Neural Network-Based Approach with Improved Accuracy
Authors: Kheng Ee Hern , Liew Chia Pao , Lan Tianhao , Tan Kim Geok
Published: March 2024

The use of computers to read musical scores is referred to as optical music recognition (OMR). The recent advancements in artificial intelligence and big data have led to the development of deep learning approaches for recognizing musical notes. Previous research has shown that there is a lot of room for improvement in handwritten musical notation recognition systems due to differences in writing styles and the complex structure of musical symbols. The research described here aims to develop a deep learning-based system for recognizing handwritten musical notation. The system uses a convolutional neural network (CNN) to extract and learn pixel features of musical symbols and achieve a recognition accuracy of over 90%. The CNN model was trained using image samples from the HOMUS dataset and fine-tuned to minimize the loss function and reduce classification errors. The CNN model achieved an accuracy of 96.95% on the test samples, which is a significant improvement over the 86.0% accuracy from previous studies. The performance of the CNN model was also compared to five state-of-the-art deep learning methods, namely, quantum gray Wolf optimization (QGWO) algorithm, nonfully connected network (NFC-Net) classifier, nearest neighbor classifier, data augmentation and ensemble learning, and the CNN model outperformed four of them. However, the CNN model occasionally misclassified musical symbols with similar shapes, indicating that there is still room for improvement in the system’s performance. Future research could focus on improving the model’s performance on similar-shaped symbols. Overall, the research demonstrates the effectiveness of using a CNN model for handwritten musical notation recognition and highlights the potential of deep learning approaches in this area.
Intelligent Classification of Metallographic Based on Improved Deep Residual Efficiency Networks
Authors: Huang Xiaohong , Liu Yanping , Qi Xueqian , Song Yue
Published: March 2024

The recognition of steel microstructure images plays a crucial role in the metallographic analysis process. Although some progress has been made through the application of artificial intelligence algorithms, several challenges remain. First, existing algorithms exhibit weak nonlinear feature extraction capabilities and noticeable limitations. Second, they overlook the intrinsic noise and redundant interference present in microscopic images. To address these issues, this paper investigates the automatic recognition of metallographic tissues by leveraging residual structures in deep neural networks. An enhanced residual network model based on transfer learning is proposed, which utilizes the pre-trained weights from the ImageNet dataset to facilitate learning with small sample data. This network offers higher classification accuracy and higher F1 scores. In addition, a deep residual shrinkage network model based on an attention mechanism is proposed. This model incorporates an attention sub-network into the original residual module and employs a soft threshold function to eliminate redundant features, including noise. The proposed algorithms are evaluated against various convolutional neural networks using 20 types of metallographic test sets. The experimental results showed that both methods have high accuracy rates of 95% and 94.44%, respectively, and F1 scores of 0.9464 and 0.9419. While maintaining the complexity of the model, there has been a significant improvement in accuracy, and the models exhibit strong generalization capabilities. Our research contributes to enhancing production efficiency, strengthening quality control, and improving material performance through computer vision technology.
Assessment of Power System Stability During Transients Using Deep Residual Shrinkage Network and CBAM Integration
Authors: Wang Qiang , Nie Zifan , Liu Hongwei
Published: March 2024

Aiming at the noise that may exist in the data collection and transmission of power system synchronous phasor measurement unit (PMU), as well as the imbalance between stable and unstable samples, making the model likely to tilt to most kinds of samples, thus affecting the evaluation results of power system transient stability model, a transient stability assessment method integrating Convolutional Block Attention Module (CBAM) and deep residual shrinkage network is proposed. First, the mapping relationship between the input values and the labels of the stability results is established, and the electrical quantities are transformed into the form of convolutional feature maps to be input into the model. Second, channel attention module and spatial attention module are introduced, and soft threshold function is used to automatically learn the noise threshold and reduce the interference of the noise. As the model tends to favor the majority of the samples during training, gradient harmonized mechanism (GHM) loss function is introduced to improve the attention of the minority of samples as well as the training effect and evaluation performance of the model. Finally, the model is tested by simulation on the IEEE39 node system.
Products of traceless and semi-traceless matrices over division rings and their applications
Authors: Danchev Peter V. , Dung Truong Huu , Son Tran Nam
Published: March 2024

In this paper, we prove that every matrix over a division ring is representable as a product of at most 10 traceless matrices as well as a product of at most four semi-traceless matrices. By applying this result and the obtained so far other results, we show that elements of some algebras possess some rather interesting and nontrivial decompositions into products of images of non-commutative polynomials.
Cosmological variation of the proton-to-electron mass ratio constrained by strong gravitational fields
Authors: Le T. D.
Published: March 2024

Exploring the cosmological variations of fundamental dimensionless constants, such as the fine-structure constant, [Formula: see text], the proton-to-electron mass ratio, [Formula: see text], and the gravitational constant, G, is essential for testing new phenomena beyond the standard cosmological models. This study focuses on investigating the potential variation of constants by analyzing strong gravitational fields associated with white dwarf stars. Utilizing the observed spectrum of G191-B2B, we present a new constraint on the cosmological variation of [Formula: see text] over extended time scales, [Formula: see text], incorporating the gravitational redshift [Formula: see text]. This finding represents a new tool for checking the parameters for Grand Unified Theories (GUTs).
Cosmological effects on f(R̄,T̄) gravity through a non-standard theory
Authors: Panda Arijit , Ray Saibal , Manna Goutam , Das Surajit , Ranjit Chayan
Published: March 2024

This study aims to investigate the impact of dark energy in cosmological scenarios by exploiting [Formula: see text] gravity within the framework of a nonstandard theory, called K-essence theory, where [Formula: see text] represents the Ricci scalar and [Formula: see text] denotes the trace of the energy–momentum tensor associated with the K-essence geometry. The Dirac–Born–Infeld (DBI) nonstandard Lagrangian has been employed to generate the emergent gravity metric [Formula: see text] associated with the K-essence. This metric is distinct from the usual gravitational metric [Formula: see text]. It has been shown that under a flat Friedmann–Lemaître–Robertson–Walker (FLRW) background gravitational metric, the modified field equations and the Friedmann equations of the [Formula: see text] gravity are distinct from the usual ones. In order to get the equation of state (EoS) parameter [Formula: see text], we have solved the Friedmann equations by taking into account the function [Formula: see text], where [Formula: see text] represents a parameter within the model. We have found a relationship between [Formula: see text] and time for different kinds of [Formula: see text] by treating the kinetic energy of the K-essence scalar field ([Formula: see text]) as the dark energy density which fluctuates with time. Surprisingly, this result meets the condition of the restriction on [Formula: see text]. By presenting graphical representations of the EoS parameter with time, we show that our model is consistent with the data of SNIa[Formula: see text][Formula: see text][Formula: see text]BAO[Formula: see text][Formula: see text][Formula: see text][Formula: see text] within a certain temporal interval.
Study of Support Motion of a Finite Bar with a Boundary Damper and a Spring Using Analytical Approaches and FEM
Authors: Chen Jeng-Tzong , Kao Hao-Chen , Lee Jia-Wei , Lee Ying-Te
Published: March 2024

In this paper, we solve the vibration problem of a finite bar with a viscously damped boundary in conjunction with a spring on the same side subject to the support motion on the other side. To avoid the computation of complex modes and the difficulty of orthogonal conditions for the partial differential equation (PDE) of a continuous system, two alternatives are employed. One is the analytical derivation by using the diamond rule of the method of characteristics. The advantage is that this method can yield an analytical solution. However, the disadvantage is that the method can be only applied to some simple problems such as a linear system and the one-dimensional (1D) wave equation. The other numerical method, finite element method (FEM), is incorporated into a general-purpose program to solve this problem. The advantage of the FEM is that this methodology can be applied to solve various problems such as different PDEs. Therefore, the solution obtained by using the FEM is compared with that of the analytical solution. Two special cases, only a spring and a damper alone, are also considered by using the FEM. Interestingly, the same silent area is captured in the displacement profile by using the FEM for the three cases. We also find that the displacement profiles have slope discontinuity which only occurs on the characteristic line. After the wave front arrives at the boundary, various responses appear due to different boundaries, spring, damper and both. It is found that the displacement amplitude of the general case is smaller than the other two special cases. This result matches the application of engineering practice.
Progressive Collapse Analysis of Single-Layer Latticed Domes Under Seismic Loading Using Enhanced Fiber-Based Approach
Authors: Cai Yuxiang , Zheng Yanfeng , Tang Jingzhe , Luo Yaozhi , Yang Chao
Published: March 2024

This paper presents a novel computational framework based on the Finite Particle Method (FPM) for the progressive collapse analysis of single-layer latticed domes subjected to seismic loading. The framework integrates a fiber-based approach to simulate the nonlinear behavior of steel structural members, including yielding, cyclic buckling, and fracture. For structural seismic analysis, the fiber-based approach is enhanced by incorporating a dynamic increase factor, which accounts for the impact of high strain rates during seismic events. Furthermore, the framework extends the pseudo-dynamical stiffness of the particle to consider Rayleigh damping, surpassing the limitations of the original mass damping model. The computational efficiency of the proposed framework is optimized by implementing it on a generic parallelized GPU platform. The computational framework has the advantage of elaborately simulating the response of individual structural members while efficiently capturing the complex behavior of the overall structure. Subsequently, the effectiveness of the enhanced framework is demonstrated through the analysis of the collapse behavior of a representative single-layer latticed dome under seismic loading. The study conducts a parametric analysis to evaluate the influence of two key factors, namely, rise-to-span ratios and imperfections, on the collapse behaviors. The findings contribute to a better understanding of the complex behaviors of single-layer latticed domes during seismic events, providing knowledge for improving structural safety and resilience in public buildings.
Enhancing Cable Vibration Measurement at Long Distances Through Super-Resolution Reconstruction and Target Foreground Segmentation
Authors: Cui Depeng , Wang Weidong , Peng Jun , Zhang Yukun , Zhao Yida , Chen Bin , Liu Yan , Wang Jin
Published: March 2024

This paper presents a comprehensive non-contact computer vision-based system for monitoring cable vibrations in cable-supported bridges, addressing challenges related to low image resolution and feature extraction difficulties at long distances. The proposed system utilizes deep learning techniques to enhance cable vibration recognition accuracy and offers a practical solution for cable monitoring without the need for target assistance. The core of the system is a novel two-stage model, which combines a super-resolution (SR) video reconstruction algorithm with state-of-the-art Resnet-34 and Swin-B models for precise target foreground segmentation. This approach significantly improves the recognition of target details and enhances the accuracy of cable vibration data in monitoring videos. Furthermore, a phase-based motion estimation (PME) algorithm is employed for precise cable vibration measurement. Field tests conducted on two cable-supported bridges validate the effectiveness of the system. The results demonstrate superior accuracy and noise immunity compared to traditional methods, achieving sub-pixel level precision with a maximum error rate below 2%. This system represents a significant advancement in non-contact structural health monitoring for long-distance cable vibration monitoring in cable-supported bridges.
Vibration Mitigation Using 3D-PSTMD for the Offshore Wind Turbine Considering Self-Excitation Behaviors
Authors: Liu Gang , Lei Zhenbo , Liang Yujia , Tang Wei
Published: March 2024

The self-excitation behaviors can be triggered by the blade rotation of the wind turbine. To mitigate the excessive vibration of offshore wind turbine (OWT) tower under this self-excitation addition with wind–wave loadings, a three-dimensional prestressed TMD (3D-PSTMD) is extended in this study. First, based on the Lagrange equation, the analytical formulations of 1P (rotor rotational speed) and 3P (blade passing frequency) loads from self-excitations are deduced, and more factors under real operational conditions of the OWT are further analyzed using theoretical derivation and numerical simulation. Then, structural aero-hydrodynamic loadings are modelled, and self-resonance phenomenon of the uncontrolled OWT and resonance dissipation capability of 3D-PSTMD are discussed at startup–operation–shutdown state. Finally, the energy harvesting competence of 3D-PSTMD is quantitatively calculated via the classical Wilson-[Formula: see text] algorithm under self-excitations with wind–wave loadings. Results indicate that this dashpot is remarkably able to mitigate the structural resonance responses over 61.3% and 40.5% respectively when under the 1P and 3P loadings, and it can also effectively reduce the bi-directionally horizontal vibrations of OWT under aero-hydrodynamic loadings.
Cohomology and the controlling algebra of crossed homomorphisms on 3-Lie algebras
Authors: Hou Shuai , Hu Meiyan , Song Lina , Zhou Yanqiu
Published: March 2024

In this paper, first we give the notion of a crossed homomorphism on a [Formula: see text]-Lie algebra with respect to an action on another [Formula: see text]-Lie algebra, and characterize it using a homomorphism from a 3-Lie algebra to the semidirect product 3-Lie algebra. We also establish the relationship between crossed homomorphisms and relative Rota–Baxter operators of weight [Formula: see text] on 3-Lie algebras. Next we construct a cohomology theory for a crossed homomorphism on [Formula: see text]-Lie algebras and classify infinitesimal deformations of crossed homomorphisms using the second cohomology group. Finally, using the higher derived brackets, we construct an [Formula: see text]-algebra whose Maurer–Cartan elements are crossed homomorphisms. Consequently, we obtain the twisted [Formula: see text]-algebra that controls deformations of a given crossed homomorphism on [Formula: see text]-Lie algebras.
THE ANALYSIS OF THE FLOW OF BLOOD IN A STENOSED ARTERY THROUGH SIMULATION: A COMPARISON AMONG VARIOUS NON-NEWTONIAN MODELS
Authors: SHANKAR NARAYAN S. , BHATTACHARJEE ANURADHA , SAHA SUNANDA , PUNEETH VENKATESH , SINGHAL ABHINAV , SAYFUTDINOVNA ABDULLAEVA BARNO
Published: March 2024

This paper focuses on the dual quality of blood, Newtonian and non-Newtonian, in particular by exploring the energy curves. Careful investigation of the dual property of blood has been made by considering two different geometries to represent a stenosed arterial segment. We present a cautious assessment of non-Newtonian blood rheology impacts in arterial stream simulations by coupling the Newtonian and non-Newtonian models. The flow of energy through the two flow dimensions is meticulously investigated using velocity (kinetic energy), pressure, and wall shear stress (pressure energy). Besides, the proper implementation of an interface boundary condition (IBC) was emphasized to ensure consistency with the flow conditions downstream of a backward-facing step. The integration of the Newtonian and non-Newtonian models adjoins the novelty of the current research. The energy curves are obtained by implementing five different non-Newtonian models to designate a suitable non-Newtonian model for blood flow investigations. The combination of the non-Newtonian models enforced in this research is novel and particular attention is paid to the energy curves obtained. The conclusion was to elect the Carreau model as a suitable non-Newtonian rheological model for the blood flow study. This study was able to finalize the fact that the coupling of Newtonian and non-Newtonian models is necessary to obtain accurate results. For the sinusoidal waveform considered for the velocity, Carreau and the Power law models yield better results, eliminating the other non-Newtonian models from the list. With a better inlet condition imposed in the form of the Fourier series for pressure and velocity, the Carreau model yields the best results.
A revisit to classical and quantum aspects of Raychaudhuri equation and possible resolution of singularity
Authors: Chakraborty Subenoy , Chakraborty Madhukrishna
Published: March 2024

In this review, we provide a concrete overview of the Raychaudhuri equation, Focusing Theorem and convergence conditions in a plethora of backgrounds and discuss the consequences. We also present various classical and quantum approaches suggested in the literature that could potentially mitigate the initial big-bang singularity and the black-hole singularity.
Some characterizations of Quasi-Einstein and doubly product manifold
Authors: Elsayied H. K. , Tawfiq A. M. , Elsharkawy A.
Published: March 2024

This research paper explores the properties of quasi-Einstein manifolds with a unit concircular generator vector field within doubly warped product structures. The paper begins by investigating the characteristics of quasi-Einstein manifolds that possess a unit concircular generator vector field. Subsequently, it analyzes the behavior of the Hessian, Riemann, and Ricci vector fields in the context of a doubly warped product. Moreover, the impact of the quasi-Einstein condition on both the doubly warped product and its factor manifolds is studied. Finally, conditions are derived to determine whether a doubly warped product satisfying the quasi-Einstein condition can be classified as a warped or a direct product. By presenting these findings, this study contributes to the comprehension of quasi-Einstein manifolds and their interactions within doubly warped product structures.
Behaviors of black holes and black strings in M-theory on Calabi–Yau manifolds
Authors: Belhaj Adil , Bouhouch Abderrahim
Published: March 2024

In this work, we reconsider the study of black holes and black strings in the compactification of M-theory on a Calabi–Yau three-fold, considered as a complete intersection of hypersurfaces in a product of weighted projective spaces given by [Formula: see text]. Using the [Formula: see text] supergravity formalism in five dimensions, we examine the BPS and non-BPSsolutions by wrapping M-branes on appropriate cycles in such a Calabi–Yau geometry. For the black hole case, we compute certain thermodynamical quantities. In particular, we calculate the entropy taking a maximal value corresponding to the ordinary projective space [Formula: see text] with [Formula: see text]. Using extended black hole entropies, we evaluate the temperature involving a minimal value for [Formula: see text]. Then, we approach the stability of the non-BPS black holes via the recombination factor. In the allowed electric charge regions, we show that such states are unstable. For the black string solutions, we calculate the tension taking a minimal value corresponding to [Formula: see text]. Computing the recombination factor, we show that the associated non-BPS black string states are stable in the allowed magnetic charge regions of the moduli space.
Generalized variational structures of the (3+1)-dimensional Zakharov–Kuznetsov–Burgers equation in dusty plasma
Authors: Wang Kang-Jia , Li Shuai , Shi Feng
Published: March 2024

The center of this paper is to establish the generalized variational structure (GVS) of the [Formula: see text]-dimensional Zakharov–Kuznetsov–Burgers equation (ZKBe) by taking advantage of the Semi-inverse method (SIM). Two different GVSs are extracted and the derivation process is presented in detail. The extracted GVSs reveal the energy conservation law and can offer some new insights on the study of the variational method.
Flux ruled surfaces and the magnetic curves obtained from the curvature theory
Authors: Şener Çağla Gizem , Güler Fatma
Published: March 2024

In this paper, our aim is to research the change in the magnetic field using the curvature theory of ruled surfaces. For this, we examine magnetic curves of the curve whose existence is guaranteed from the derivative formulas of the rotation frame. The Killing vector fields and Lorentz forces of these magnetic curves are calculated. Additionally, correlations between curvatures are obtained. Flux ruled surfaces are obtained and the conditions for developable are achieved. Finally, examples of the magnetic flux developable ruled surfaces are given.
Optical properties of a class of generalized conics
Authors: Guiot Eric
Published: March 2024

The optical and geometric properties of a class of generalized conics are presented. The study makes it possible to find in an original way several results of classical geometry and to connect numerous curves by a common law. Applications in geometric optics are envisaged, with the production of diopters, lenses or mirrors.
RBC aggregation, deformation and adhesion to endothelium: Role of nitric oxide derived from L-Arginine and sodium nitroprusside
Authors: Maksimov M. K. , Ermolinskiy P. B. , Scheglovitova O. N. , Sklyankina N. N. , Muravyov A. V. , Lugovtsov A. E. , Priezzhev A. V.
Published: March 2024

Red blood cells (RBCs) are the most abundant human blood cells. RBC aggregation and deformation strongly determine blood viscosity which impacts hemorheology and microcirculation. In turn, RBC properties depend on different endogenous and exogenous factors. One such factor is nitric oxide (NO), which is mainly produced by endothelial cells (EC) from L-arginine amino acid in the circulatory system. Since the mechanisms of the RBC-endothelium interplay are not clear up to date and considering its possible clinical importance, the aims of this study are to investigate in vitro: (1) The effect of L-arginine induced NO on RBC aggregation and adhesion to endothelium; (2) the NO effect on RBC aggregation and deformation induced by L-arginine and sodium nitroprusside without the presence of endothelium in the samples. The RBC aggregation and adhesion to a monolayer of EC were studied using optical tweezers (OT). The RBC deformability and aggregation without endothelium in the samples were studied using the flow chamber method and Myrenne aggregometer. We confirmed that NO increases deformability and decreases aggregation of RBCs. We showed that the soluble guanylate cyclase pathway appears to be the only NO signaling pathway involved. In the samples with the endothelium, the “bell-shaped” dependence of RBC aggregation force on L-arginine concentration was observed, which improves our knowledge about the process of NO production by endothelium. Additionally, data related to L-arginine accumulation by endothelium were obtained: Necessity of the presence of extracellular L-arginine stated by other authors was put under question. In our study, NO decreased the RBC-endothelium adhesion, however, the tendency appeared to be weak and was not confirmed in another set of experiments. To our knowledge, this is the first attempt to measure the forces of RBC adhesion to endothelium monolayer with OT.
On propagation behavior of shear wave in piezoelectric-sandwiched structure
Authors: Saroj Pradeep Kumar
Published: March 2024

This paper delves into the analysis of shear wave propagation with a sandwiched structure comprising a piezoelectric layer and an elastic layer, with a transversely isotropic layer in between. The frequency equation has been derived following Biot’s theory. The dimensionless phase velocities numerical values are computed and visually depicted to demonstrate their dependencies on anisotropy, piezoelectricity, initial stress and porosity in a comparative manner. The explicit demonstration of the relationship between each parameter and the geometry has been presented. The observation shows that as porosity in the medium increases, the phase velocity also increases. Furthermore, the existence of medium anisotropy results in a decrease in the phase velocity of shear waves. Moreover, a correlation is observed where higher tensile initial stress within the medium leads to a corresponding reduction in the phase velocity of shear waves. We conclude that considered parameters (viz. piezoelectricity, anisotropy, porosity, initial stress and thickness of layers) affect the velocity profile of the shear waves significantly. This study holds practical significance in the development of innovative-layered composites, surface acoustic wave (SAW) devices and sensors utilizing intelligent piezoelectric devices for engineering purposes.
Atomic resolution microstructure study of Bi-doped SrTiO3
Authors: Li Chao , Zhang Yijun , Dong Guohua , Hu Guangliang , Yang Guang , Liu Chaoqiang , Chen Houwen , Wei Xiaoyong
Published: March 2024

Intrinsic SrTiO3 is a quantum paraelectric, but moderately Bi-doped SrTiO3 exhibits dielectric frequency dispersion similar to relaxor ferroelectrics. In this paper, detailed electron microscopic studies of the microstructures of Bi-doped SrTiO3 samples were presented. It was found that the Sr sites were replaced by off-central Bi, resulting in tensile strain in the strontium titanate (STO) lattice. In the Bi-doped SrTiO3 samples, the valence of titanium mainly showed the Ti[Formula: see text] characteristic. According to the dielectric behavior and microstructure analysis, the polar nanoregions (PNRs) composed of strained SrTiO3 nanoclusters should be responsible for the ferroelectric relaxor behavior in samples with moderate Bi content.
Adjusting photocatalytic hydrogen production of TiO2 hollow nanospheres through nitrogen-induced shallow trapping levels
Authors: Ren Qingmiao , Chen Peng , Sun Na , Yue Miao , Zhang Qi , Kong Weihu , Ren Gaofei , Guo Wen , Fu Yongming , Ma Jie
Published: March 2024

N-doped titanium dioxide (TiO[Formula: see text] hollow nanospheres with abundant oxygen vacancies were successfully synthesized by coupling urea treatment and annealing in an N2 atmosphere. The pristine TiO2 hollow nanospheres exhibit a shallow donor level for electron trapping, while the urea treatment generates a N 2p acceptor level for hole trapping. After annealing in N2, the sufficient N atoms generate abundant oxygen vacancies for trapping electrons, resulting in further improved charge separation efficiency. The N-doped TiO2 exhibits the highest H2 evolution rate, reaching 2867[Formula: see text][Formula: see text]mol[Formula: see text]g[Formula: see text][Formula: see text]h[Formula: see text], which is six times higher than that of pristine TiO2 hollow nanospheres. The introduction of oxygen vacancies by interstitial N provides a promising way to improve the photocatalysis activity of photocatalysts.
LiDAR-Based Determination of Spring Constant Using Smartphones
Authors: Pal Sanjoy Kumar , Sarkar Soumen , Panchadhyayee Pradipta
Published: March 2024

A novel use of the LiDAR sensor of a smartphone spring in introductory physics experiments is discussed in this paper. We have determined the spring constant for various combinations of springs using the LiDAR sensor of a smartphone through the phyphox application. An electrical heater coil is used as a spring, and the period of oscillation of a vertical spring–mass system is measured using a LiDAR sensor. The experimental values of spring constants agree with the theoretical values. A high school student can perform this simple experiment in a smart way at home.
Analysis of General Cases of Sliding Ladder on Frictionless Surfaces Without Calculus
Authors: Yusmantoro
Published: March 2024

Theoretical analysis of sliding ladder on a stationary and freely moving wall is presented. The equation of motion and kinematic variables have been derived using Newton’s law and conservation of mechanical energy without calculus. We also plot several kinematic variables versus the angle formed by the ladder and wall. Moreover, we determine the last contact angle at which the ladder loses contact with the wall. This paper investigates four cases of sliding ladder which will be useful for undergraduate students in understanding rotational dynamics.
Persistent Magnitude for the Quantitative Analysis of the Structure and Stability of Carboranes
Authors: Bi Wanying , Fu Xin , Li Jingyan , Wu Jie
Published: March 2024

Magnitude, similar to concepts like volume, cardinality or Euler characteristic, has become a key focus in combinatorics and topology. Recent advancements in topological data analysis and persistent homology have emphasized its importance. Persistent magnitude, a newly highlighted invariant introduced by Govc and Hepworth, has emerged as a notable subject of interest. In this work, we apply persistent magnitude to analyze and predict the stability of closo-carborane structures. First, we assess the stability of carboranes by employing cross-validation with different magnitude features. The Pearson correlation coefficients for stability predictions using three distinct magnitude features are 0.900, 0.882 and 0.883, respectively. These results are comparable to the Pearson correlation coefficient of 0.881 obtained when using a single feature based on persistent homology. Second, the utilization of magnitude features to predict the HOMO, LUMO and HOMO–LUMO gaps of carboranes involves conducting eight gradient boosting regressions for each scenario. The lowest correlation coefficients observed are 0.9056, 0.9385 and 0.9427, respectively. These findings highlight the promising performance of persistent magnitude features in the analysis of material structure and stability.
Structural Insights into the Binding Mode of a BLU-945 Analog with Wild-Type and Mutant EGFRs
Authors: Liu Changying , Chen Huihui , Liu Yuqiang , Jin Jing , Meng Fancui
Published: March 2024

Overcoming the acquired C797S mutation in nonsmall cell lung cancer (NSCLC) represents a great challenge for EGFR tyrosine kinase inhibitors (TKIs). BLU-945 is a promising fourth-generation EGFR TKI undergoing clinical trials, with different binding conformation from other known EGFR TKIs. In this study, we explored the binding mode of a BLU-945 analog (BLU) with wild-type and mutant EGFRs using [Formula: see text]s-scale molecular dynamics. The results show that Met793 at the hinge region is the critical binding site for BLU as observed in other known EGFR inhibitors. The occupancy of hydrogen bonds with Met793 is lower in wild-type EGFRs than in mutant EGFRs. Meanwhile, Thr854 contributes largely to the hydrogen bond formation for triple mutant EGFR compared to wild-type EGFR. Energy decomposition reveals that Leu718 and Leu844 dominate the energy contribution. T790M mutation plays an important role in BLU binding with wild-type and mutant EGFRs. Dynamic network analysis indicates that Arg841 behaves differently in triple mutant EGFR compared to wild-type, single and double mutant EGFRs, confirmed by further analysis of salt-bridge formation. Our study may provide beneficial information for developing fourth-generation EGFR TKIs.
Transfer Learning-Based Bhutanese Currency Recognition
Authors: Jamtsho Yonten , Yangden Pema , Wangmo Sonam , Dema Nima
Published: March 2024

Currency recognition is an important field in the area of pattern recognition. Recent improvements in the field of deep learning have increased its ability to recognize complex features from images. The goal of this research was to create a first-ever Bhutanese dataset and train using transfer learning techniques. This paper proposes three different pre-trained models to train the Bhutanese currency datasets. A parameter fine-tuning was applied to get better accuracy from the custom dataset. The currency images were gathered using crowdsourcing techniques and then the data augmentation was performed to generate 1000 images per class. The datasets were split into the train, test and validation sets with the ratio of 70:20:10. The train and test sets were used while training the model and after the training, it was validated using the validation set. After training the model for certain epochs, VGG16 architecture outperformed other models with a training accuracy of 99.82%, a test accuracy of 99.12% and a validation accuracy of 95.5%. In the future, a greater number of images need to be included in the datasets and trained using other pre-trained models.
The Spatial Relationship between Cities and Mountains in Jiangnan Region in the Qing Dynasty Based on Historical Texts
Authors: Fan Zhixiang , Song Tianrui , Tang Xiaolan
Published: March 2024

The Jiangnan region of China boasts a superior natural environment, and the combination of cities and mountains that form its historical landscape give it unique attributes. Local chronicles serve as an abundant source of information for visualizing this historic environment. This study aims to achieve two objectives: (1) to use digital humanities methods to process textual information in local chronicles, and (2) to visualize the historical relationships formed by the mountains and cities. In this study, we chose Suzhou, Zhenjiang, Jiaxing, Huzhou, and Songjiang in the Jiangnan region during the Qing Dynasty as our research sites. Through textual analysis and data visualization, we classified and reconstructed the historical spatial patterns of cities and mountains. As a result, our study provides a reproduction of the past spatial relationship between cities and mountains in the Jiangnan region during the Qing Dynasty. The research findings indicate that natural mountains play multiple roles in shaping the historical urban environment, particularly in terms of cultural images, notably the symbolic function they hold. The preservation of the urban natural environment, as seen through the vantage point of the historical landscape, is a critical component in contemporary urban development. The digital humanities methodologies utilized within this article can be extended to other cultural heritage research, based on pre-existing local historical literature.
Continuous Image Outpainting with Neural ODE
Authors: Gao Penglei , Yang Xi , Zhang Rui , Huang Kaizhu
Published: March 2024

Generalised image outpainting is an important and active research topic in computer vision, which aims to extend appealing content all-side around a given image. Existing state-of-the-art outpainting methods often rely on discrete extrapolation to extend the feature map in the bottleneck. They thus suffer from content unsmoothness, especially in circumstances where the outlines of objects in the extrapolated regions are incoherent with the input sub-images. To mitigate this issue, we design a novel bottleneck with Neural ODEs to make continuous extrapolation in latent space, which could be a plug-in for many deep learning frameworks. Our ODE-based network continuously transforms the state and makes accurate predictions by learning the incremental relationship among latent points, leading to both smooth and structured feature representation. Experimental results on three real-world datasets both applied on transformer-based and CNN-based frameworks show that our methods could generate more realistic and coherent images against the state-of-the-art image outpainting approaches. Our code is available at
https://github.com/PengleiGao/Continuous-Image-Outpainting-with-Neural-ODE
.
Two-dimensional Search Space for Extracting Broadside Tests from Functional Test Sequences
Authors: Pomeranz Irith
Published: March 2024

Testing for delay faults after chip manufacturing is critical to correct chip operation. Tests for delay faults are applied using scan chains that provide access to internal memory elements. As a result, a circuit may operate under non-functional operation conditions during test application. This may lead to overtesting. The extraction of broadside tests from functional test sequences ensures that the tests create functional operation conditions. When
N
functional test sequences of length
L
+1 are available, the number of broadside tests that can be extracted is
N
·
L
. Depending on the source of the functional test sequences, the value of
N
·
L
may be large. In this case, it is important to select a subset of
n
≤
N
sequences and consider only the first
l
≤
L
clock cycles of every sequence for the extraction of
n
·
l
≪
N
·
L
broadside tests. The two-dimensional
N
×
L
search space for broadside tests is the subject of this article. Using a static procedure that considers fixed values of
N
and
l
, the article demonstrates that, for the same value of
N
·
L
, different circuits benefit from different values of
N
and
l
. It also describes a dynamic procedure that matches the parameters
N
and
l
to the circuit. The discussion is supported by experimental results for transition faults in benchmark circuits.
HMM‐Based Method for Aircraft Environmental Control System Turbofan Rolling Bearing Fault Diagnosis
Authors: Yang Gang , Wang Yu , Qin Dezhao , Zhu Rui , Han Qingpeng
Published: March 2024

In response to the high‐noise, nonlinear, and nonstationary characteristics of vibration signals from aircraft environmental control system (ECS) turbofan rolling bearings, this paper proposes a diagnostic method for the degree of ECS turbofan bearing faults based on the Hidden Markov Model (HMM). Experimental results demonstrate that HMM can accurately diagnose and predict faults in ECS turbofan rolling bearings. The HMM method enhances diagnostic accuracy, and its effectiveness and feasibility in fault diagnosis based on different rolling bearing fault instances are elaborated. By employing the HMM model to establish precise models from decomposed dynamic data, it successfully identifies faults such as the fracture of the bearing cage under biased load conditions, although its performance in recognizing overheating faults is suboptimal.
Pharmacokinetics of Ziyuglycoside I and Ziyuglycoside II in Rat Plasma by UPLC-MS/MS
Authors: Shen Xiuwei , Wang Ziyue , Li Wenting , Mei Shenshen , Ma Shunjun , Wang Xianqin , Wen Congcong , Chen Fan , Zheng Guojun
Published: March 2024

Ziyuglycoside I and ziyuglycoside II are important active components of Sanguisorba officinalis L., which have excellent pharmacological effects, such as antioxidant and anticancer effects. However, the bioavailability of ziyuglycoside I and ziyuglycoside II has not been reported. This work aims to establish a UPLC-MS/MS method to study the pharmacokinetics of ziyuglycoside I and ziyuglycoside II in rats under different administration routes (intragastric and intravenous administration) and to calculate the bioavailability. The concentration of ziyuglycoside I and ziyuglycoside II in rat plasma in the range of 2–2000 ng/mL showed a good linear relationship (r > 0.99). The intra-day accuracies of ziyuglycoside I and ziyuglycoside II ranged from 87% to 110%, and the inter-day accuracies ranged from 97% to 109%. The intra-day precision was less than 15% and the inter-day precision was less than 14%. The matrix effects ranged from 88% to 113%. The recoveries were all above 84%. The developed UPLC-MS/MS method for the determination of ziyuglycoside I and ziyuglycoside II in rat plasma was applied to pharmacokinetics. The bioavailability of ziyuglycoside I and ziyuglycoside II was measured at 2.6% and 4.6%, respectively.
Entre telas, jogos e plataformas digitais: estudo de caso do SBT (Sistema Brasileiro de Televisão)
Authors: Gomes Ana Claudia Fernandes
Published: March 2024

Este artigo tem como objetivo a descrição do processo de multiplataformização do SBT em um cenário de transformações do mercado televisivo brasileiro, a partir da interação entre emissora e YouTube, iniciada em 2008. Nesse estudo de caso, a “cultura da convergência” (Jenkins, 2009) foi impulsionada pelas telenovelas infantojuvenis disponibilizadas na TV aberta e nos canais digitais, possibilitando o mapeamento das narrativas transmídia criadas. Metodologicamente, para esta apresentação, optou-se por um recorte da pesquisa etnográfica desenvolvida e privilegiou-se as informações divulgadas por veículos especializados e por plataformas digitais. Entre a tradição e a modernização, o SBT elucida fluxos e refluxos comunicacionais da sociedade contemporânea hiperconectada, expressos em múltiplas telas e jogos de sociabilidade.
Por que as baratas morrem com as patas para cima? - Resultado da tradução automática de textos escritos em aplicativos para Língua Brasileira de Sinais
Authors: Martins Tânia Aparecida
Published: March 2024

No Brasil, os aplicativos e as diferentes plataformas online que transformam informações escritas da língua portuguesa para a Línguas Brasileira de Sinais (Libras) vêm aumentando gradativamente e parecem ser um meio facilitador para acesso a diferentes conteúdos essenciais, tais como sites do Ministério da Educação, do Departamento Estadual de Trânsito, dentre outros. É incontestável que a tradução de textos escritos para línguas de sinais desempenha um papel importante na quebra de barreiras de comunicação dos surdos e deficientes auditivos, ou para os não surdos que, de alguma maneira, utilizam desses recursos, seja como suporte na aprendizagem da Libras ou como um meio de comunicação. Em vista disso, o objetivo deste trabalho é apresentar os resultados obtidos com a tradução automática de textos da língua portuguesa para Libras, utilizando-se dois aplicativos gratuitos. Baseando-se em pesquisas que exploram o léxico e a semântica lexical, com especial atenção à ambiguidade lexical em Libras, realizo uma reflexão sobre o uso desses aplicativos por estudantes em estágios iniciais de aprendizado da Libras. Com o intuito de analisar o processo de tradução automática do português para Libras, apresento resultados oriundos do emprego desses aplicativos em ambientes de sala de aula. Como suporte teórico, recorro à Teoria da Semântica Lexical de Cruse (1996) e Geeraerts (2010), além de incorporar alguns princípios do Processamento da Linguagem Natural. Os resultados revelam que, embora a tradução automática seja uma ferramenta valiosa, ainda há desafios a serem superados. A ambiguidade lexical em Libras, por exemplo, apresenta-se como um obstáculo que requer aprimoramentos nos algoritmos de tradução. Além disso, a pesquisa destaca a importância de considerar o contexto e a semântica para uma tradução mais precisa.
Propostas metodológicas para a organização de obras terminográficas em língua de sinais baseadas em corpus
Authors: Souza Joyce Cristina , Stumpf Marianne Rossi , Tuxi Patricia
Published: March 2024

No campo dos Estudos do Léxico, a Terminologia tem sido destaque nos últimos anos, se tornando objeto de estudo para muitos pesquisadores que trabalham com a Língua Brasileira de Sinais (Libras). Da mesma forma, é grande o investimento na produção de obras terminográficas em diferentes áreas de conhecimento envolvendo esta mesma língua. Sob a justificativa da inexistência de sinais que representem os termos e/ou os conceitos desejados, é comum a prática metodológica guiada pelo processo de criação seguido da validação de sinais-termos. Fundamentados nos pressupostos da Linguística de Corpus, apresentamos neste trabalho cinco propostas metodológicas para a realização de pesquisas e organização de instrumentos linguísticos (glossários e dicionários) dedicados às terminologias em Libra, tendo como princípio a valorização do uso que o falante faz da língua em contextos reais de comunicação enquanto corpus linguístico autêntico.