bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impacts of dielectric screening on the luminescence of monolayer WSe2
Authors: Costa Fábio J R , Brito Thiago G-L , Barcelos Ingrid D , Zagonel Luiz Fernando 
Published: May 2023

Abstract
Single layers of transition metal dichalcogenides (TMDCs), such as WSe2 have gathered increasing attention due to their intense electron–hole interactions, being considered promising candidates for developing novel optical applications. Within the few-layer regime, these systems become highly sensitive to the surrounding environment, enabling the possibility of using a proper substrate to tune desired aspects of these atomically-thin semiconductors. In this scenario, the dielectric environment provided by the substrates exerts significant influence on electronic and optical properties of these layered materials, affecting the electronic band-gap and the exciton binding energy. However, the corresponding effect on the luminescence of TMDCs is still under discussion. To elucidate these impacts, we used a broad set of materials as substrates for single-layers of WSe2, enabling the observation of these effects over a wide range of electrical permittivities. Our results demonstrate that an increasing permittivity induces a systematic red-shift of the optical band-gap of WSe2, intrinsically related to a considerable reduction of the luminescence intensity. Moreover, we annealed the samples to ensure a tight coupling between WSe2 and its substrates, reducing the effect of undesired adsorbates trapped in the interface. Ultimately, our findings reveal how critical the annealing temperature can be, indicating that above a certain threshold, the heating treatment can induce adverse impacts on the luminescence. Furthermore, our conclusions highlight the influence the dielectric properties of the substrate have on the luminescence of WSe2, showing that a low electrical permittivity favours preserving the native properties of the adjacent monolayer.
Investigations on mechanical properties and stacking sequence of Kevlar/banana fiber reinforced nano graphene oxide hybrid composites
Authors: Shubham S Kartik , Pandey Ajay , Purohit Rajesh 
Published: May 2023

Abstract
This paper focuses on the fabrication of a new hybrid polymer composite laminate (HPCL) using biodegradable materials to avoid dumping of waste and has all the desired characteristics as compared to the conventional matrix composites used in various applications such as aerospace, railways cabin, structures, sports equipment, medical field, etc. Utilizing the hand lay-up method and compression molding machine, six layers of Kevlar fiber, banana fiber, and an epoxy-based matrix reinforced with graphene oxide (GO) were fabricated. By altering the stacking order of fibers in which the HPCL were stacked and embedding GO of various weight percentages (0 wt%, 0.25 wt%, 0.50 wt%, 0.75 wt%, and 1 wt%). As a result, it was seen that the best mechanical characteristics were found to be 42.23 MPa interlaminar shear strength, flexural strength, 300.39 MPa tensile strength, and 85.68 hardness were obtained at set C-2 i.e. 0.5 wt% of GO embedded in KKBBKK stacking order. The 0.25 wt% of GO of HPCL of set C-1 yielded the impact strength with the greatest value of 771.6 J m−1. Field emission scanning electron microscopy, energy dispersive x-ray mapping and x-ray diffraction tests were also conducted for validating the homogeneity of the material. All the mechanical properties were enhanced by embedding GO and incorporating stacking order in HPCL. Therefore, HPCL can be used where lightweight material with proper mechanical strength and biodegradability is an important condition for sustainable development.
Enhanced optoelectronic activity of lead-free halide perovskites KMBr3 (M = Ge, Sn) under hydrostatic pressure
Authors: Sultana Aldina , Saiduzzaman Md , Hossain Khandaker Monower , Ahmed Tanjun , Alam Safin , Biswas Arpon , Molla Riaz , Ahmad Sohail , Mitro S K 
Published: May 2023

Abstract
The density functional theory was used to investigate lead-free tin- and germanium-based halide perovskites KMBr3 (M = Sn, Ge) under pressure (0 to 10 GPa). The structural, electronic, optical, and mechanical properties are inquired to determine their potentiality as future photovoltaic materials. The structure shows high accuracy in terms of lattice parameters, which goodly comply with previously reported data. The estimated band gap demonstrates the compounds’ semiconducting nature at zero pressure condition. But the increment of pressure lowers the band gap, improving their conductivity. Furthermore, charge density differences between K-Br and Sn(Ge)-Br are used to determine whether the bonds are ionic or covalent. Besides, the bond length consistently decreases, resulting in stronger bonding under pressure. In addition, the optical functions are improved by pressure, suggesting that these materials could be used in multiple optoelectronic devices operating in the visible and ultraviolet spectrums. Furthermore, the hydrostatic pressure has a prominent effect on the mechanical properties while maintaining stability. The ductile natures as well as the anisotropic behavior get more intensive under applied pressure.
Odd-parity perturbations of the wormhole-like geometries and quasi-normal modes in Einstein-Æther theory
Authors: Zhang Chao , Wang Anzhong , Zhu Tao 
Published: May 2023

Abstract
The Einstein-Æther theory has drawn a lot of attentions in recent years. As a
representative case of gravitational theories that break the Lorentz symmetry, it plays an
important role in testing the Lorentz-violating effects and shedding light on the attempts to
construct quantum gravity. Since the first detection to the gravitational wave, the event
GW150914, a brand new window has been opened to testing the theory of gravity with gravitational
wave observations. At the same time, the study of gravitational waves itself also provides us a
serendipity of accessing the nature of a theory. In this paper, we focus on the odd-parity
gravitational perturbations to a background that describes a wormhole-like geometry under the
Einstein-Æther theory. Taking advantage of this set of analytic background solutions, we are
able to simplify the Lagrangian and construct a set of coupled single-parameter dependent master
equations, from which we solve for the quasi-normal modes that carry the physical information of
the emitted gravitational waves. Basically, the results reflect a consistency between
Einstein-Æther theory and general relativity. More importantly, as long as the no-ghost
condition and the latest observational constraints are concerned, we notice that the resultant
quasi-normal mode solutions intimate a kind of dynamical instability. Thus, the
solutions are ruled out based on their stability against small linear perturbations.
Multi-angular radiation of electromagnetic waves acting on the site of targeted therapy based on high-bit metamaterials
Authors: Yang Qianke , Jing Xufeng 
Published: May 2023

Abstract
Encoding metasurface refers to a two-dimensional ultrathin planar array structure in which the unit structure is arranged in a periodic or aperiodic sequence, which can freely manipulate the amplitude, phase and polarization direction of the electromagnetic beam. Based on the generalized Snell’s theorem and the far-field scattering theorem, several reflective coding particles are designed to construct high-bit encoding metasurface sequences in the terahertz band. In order to realize the free regulation of the far-field scattering angle of the encoding metasurface, based on the Fourier convolution principle in digital signal processing, we performs addition and subtraction convolution operations on two different reflective encoding metasurface sequences to obtain a new encoding sequence. This coding sequence enables flexible regulation and continuous change of the scattering angle of the terahertz beam.
Broadband cross circularly polarized carpet invisibility cloak based on Pancharatnam–Berry phase reflective metasurface
Authors: Zhang Peng , Dong Yanyan 
Published: May 2023

Abstract
We present a broadband circularly polarized reflective metasurface carpet cloak. The metasurface unit cell is composed of two crossed H-shaped metal patterns. When the incident wave is left-handed circularly polarization (LCP), the metasurface can achieve high-efficiency cross-polarization conversion in the wide frequency range. The cross-polarization reflection efficiency is higher than 94%. By using the proposed cross-polarized reflection unit cell structure, the phase gradient metasurfaces based on the Pancharatnam–Berry phase principle is proposed, and a broadband reflective metasurface invisibility cloak is constructed. The simulation results show that when the LCP light is incident vertically, the proposed carpet cloak can significantly suppress the scattering and reconstruct the wavefront in the broadband frequency range.
Spatial dependent of the new generated light via Fano-interference in a quantum well nanostructure
Authors: Li Qiang , Yang Zhao 
Published: May 2023

Abstract
In this letter, we will study the spatially dependent of the new generated light via Fano interference in a quantum well nanostructure. We will show analytically that by Fano-type interference the new light can be generated when the weak probe light propagates through the medium. We will study the efficiency of the new generated light for different strengths of the Fano-interference. We will also consider the condition when the probe light becomes optical vortex light which can carry the orbital angular momentum (OAM). By controlling the OAM of light, we will study the spatial dependence of the absorption spectrum of the generated light. Our proposed model may have potential application in the future quantum information science based on semiconductor quantum well nanostructures.
Characterization of the collisional frequency shift in an atomic fountain clock using absorption imaging
Authors: Beattie Scott , Jian Bin 
Published: May 2023

Abstract
The collisional frequency shift is a dominant contribution to the uncertainty of many caesium fountain clocks. Minimizing the effect can be difficult, as lowering the atomic density comes at the cost of a reduced signal-to-noise ratio. Also, it is typically not atomic density, but total atom number that is measured in the experiment, which can lead to a potential measurement bias. In this paper, we describe a direct measurement of the atomic density using absorption imaging of the atomic cloud. For the caesium fountain clock, NRC-FCs2, at the National Research Council Canada, these measurements have led to a reduction in the uncertainty due to the collisional shift by a factor of 10.
Validated computational models predict vagus nerve stimulation thresholds in preclinical animals and humans
Authors: Musselman Eric D , Pelot Nicole A , Grill Warren M 
Published: May 2023

Abstract

Objective. We demonstrated how automated simulations to characterize electrical nerve thresholds, a recently published open-source software for modeling stimulation of peripheral nerves, can be applied to simulate accurately nerve responses to electrical stimulation. Approach. We simulated vagus nerve stimulation (VNS) for humans, pigs, and rats. We informed our models using histology from sample-specific or representative nerves, device design features (i.e. cuff, waveform), published material and tissue conductivities, and realistic fiber models. Main results. Despite large differences in nerve size, cuff geometry, and stimulation waveform, the models predicted accurate activation thresholds across species and myelinated fiber types. However, our C fiber model thresholds overestimated thresholds across pulse widths, suggesting that improved models of unmyelinated nerve fibers are needed. Our models of human VNS yielded accurate thresholds to activate laryngeal motor fibers and captured the inter-individual variability for both acute and chronic implants. For B fibers, our small-diameter fiber model underestimated threshold and saturation for pulse widths >0.25 ms. Our models of pig VNS consistently captured the range of in vivo thresholds across all measured nerve and physiological responses (i.e. heart rate, Aδ/B fibers, Aγ fibers, electromyography, and Aα fibers). In rats, our smallest diameter myelinated fibers accurately predicted fast fiber thresholds across short and intermediate pulse widths; slow unmyelinated fiber thresholds overestimated thresholds across shorter pulse widths, but there was overlap for pulse widths >0.3 ms. Significance. We elevated standards for models of peripheral nerve stimulation in populations of models across species, which enabled us to model accurately nerve responses, demonstrate that individual-specific differences in nerve morphology produce variability in neural and physiological responses, and predict mechanisms of VNS therapeutic and side effects.
A Path Planning Algorithm for Mobile Robot Based on MR-QL in the Unknown Scene
Authors: Fu Zongli , Liu Zhandong , Hu Chuangye , Ding Nan 
Published: May 2023

Abstract
The Q-Learning (QL) algorithm is widely used for path planning. As the scene in which the mobile robot is located becomes complex, the algorithm suffers from the limitations of low convergence speed and long exploration paths. Therefore, a Max Reward-Q-learning (MR-QL) path planning algorithm based on maximum reward is proposed for complex unknown scenarios. The original algorithm’s discrete reward function and action selection strategy are improved, and a new reward function is designed to dynamically adjust the reward mechanism to heuristically guide the robot motion. The action selection strategy is also optimized to avoid invalid exploration and improve the algorithm’s convergence. Finally, three experimental environments with different complexity are constructed to demonstrate the feasibility of the proposed algorithm. The simulation results show that the MR-QL algorithm is about 50% of the original algorithm in terms of exploration step length and training time, and the convergence speed of the algorithm is better than the original algorithm.
Document-level Relation Extraction Based on Heterogeneous Graph Convolutional Network and Local Semantic Fusion
Authors: Qu Siyu , Chen Duanbing , Xu Dengyou 
Published: May 2023

Abstract
Document-level relation extraction is a fundamental task of many downstream applications such as knowledge graph and has gained improvement through document graph and sequence models. These methods always utilize the whole document as an essential global feature while ignoring the discrimination of entity representation. Focusing on local semantic feature, a novel model named GCNLEF based on graph convolutional network is proposed in this paper. In the presented method, a heterogeneous graph containing mention nodes and sentence group nodes is constructed first. Then multi-hop path reasoning is presented to infer the relations between entities. Experimental results on DocRED show that the proposed model can achieve 61.45 F
1 score with 90 epochs, improved by 0.15 F
1 score compared with the state-of-the-art method ATLOP.
Nested Deep Feature Attention Module for Underwater Image Enhancement
Authors: Xiao Yeqing , Wang Qiang , Li Yupeng , Tang Yandong 
Published: May 2023

Abstract
The captured underwater images always suffer degradations because of absorption and light scattering in water. Thus, underwater image enhancement becomes indispensable as a precondition to carry out underwater tasks. Thus, we put forward an end-to-end structure to solve the problem of underwater image degradation. Our network uses continuously stacked deep-layer feature extraction modules to exploit more significant features. For purpose of enhancing the image contrast, we recover high-frequency information by hiring the nested residual attention groups in feature extraction modules, which can adaptively extract more deeper features. Moreover, channel attention module is hired in residual groups to emphasize important information. Comparing with other state-of-the-art approaches, our network performs best on quantitative and qualitative evaluations at datasets EUVP and UIEBD.
Document-level Event Extraction - A Survey of Methods and Applications
Authors: Liu Qi , Luan Zhen , Wang Kunlong , Zou Ye , Liu Bing , Zhang Yang 
Published: May 2023

Abstract
Event extraction aims to detect occurrences of specified types and extract corresponding event arguments from unstructured data input, which is an integral part of information extraction [1]. Many downstream tasks, such as text summarization, causal relationship identification, and event reasoning, make extensive use of event extraction. However, most of the existing research on event extraction remains at the sentence level. Document-level event extraction is still under exploration and lacks relevant review. Focusing on the task of document-level event extraction, this survey first divides the existing technologies into three categories and introduces the most representative models; Then, we list some commonly used datasets for document-level event extraction with their usage messages; Finally, we analyse the current research gaps of document-level event extraction and future research trends.
Novel Robust Span-based Model for Event Detection
Authors: Zhang Hang , Yuan Xiaoguang , Song Yingyi , Yang Yuting , Wang Rui , Liu Bing , Han Cuihong 
Published: May 2023

Abstract
Event detection (ED) aims to identify events and their types from the given text, which is the vital subtask of event extraction. However, poor quality training corpus can seriously affect the validity of current dominating deep learning-based techniques. In order to improve the robustness of the model, this paper converts the ED task into the named entity recognition (NER) task, and uses the span-based NER framework to identify and classify event triggers. In addition, we ameliorate the framework through data cleaning strategy, hierarchical learning rate strategy, sentence-level loss function and model ensemble strategy. The experimental results suggest that our approach achieves state-of-the-art performance on the widely used Chinese dataset. Moreover, the deep analysis has confirmed the effectiveness of the improvement strategy in resisting data pollution.
Densely stacked rebar counting based on image semantic segmentation in wide scene
Authors: Wang Yi , Chen Duanbing , Li Wenrui , Xue Yu , Deng Xiang , Zhou Junlin , Ren Junsong 
Published: May 2023

Abstract
To accurately count densely stacked rebars belongs to semantic segmentation in image. Various methods have been proposed to segment the objects in single image. However, widely and densely stacking scene such as warehouse or vehicle stacking of rebars is always faced. For these scenes, clear image of end faces of rebars cannot be obtained through a single camera. Generally, multiple cameras are often used to take clear images to count rebars. One strategy is to stitch multiple images into a single image at pixel level first, and then count rebars in this stitched image. Various methods have been proposed to stitch images, but it is extremely difficult to stitch two images at pixel level accurately for parallax distortion or partially hidden. A topological stitching strategy is proposed in this paper to count the rebars in whole scene. In the method, rebars in each image are identified and counted first, and then the identification and counting results in the adjacent images are stitched so as to esitmate the total number of rebars. The perfomance of the method is evaluated on real datasets, and the counting accuracy exceeds 99%. Experiments show that the proposed method is effective on widely and densely stacked rebars identification.
Object Detection from Point Clouds for Driverless Vehicles
Authors: Wu Sidong , Zhong Zhuonan , Jiang Tao , Ren Liuquan , Yuan Jianying , Duan Cuiping 
Published: May 2023

Abstract
Point clouds offer the advantage of providing direct access to 3D data. So, utilizing solely point clouds, we present an object detection technique for driverless vehicles in this study. In this method, a graph is first constructed using the k-nearest neighbour (KNN) method, and then a graph neural network module is proposed for local information extraction. Then, depending on the coordinates of points, we utilize a pillar-based projection approach to project the locally informative feature into bird’s-eye-view (BEV). After that, residual-based networks with attention mechanisms are used for the BEV features processing. The attention system is capable of assigning appropriate weights to several nearby points, hence improving detection performance. The proposed work achieves 58.49 percent and 67.90 percent mAP (mean Average Precision) for 3D Object detection and BEV detection, respectively, on the KITTI 3D object detection benchmark. We also tested the proposed method on our driverless vehicle in our campus, and we compared it to the prior method we utilized. The experimental results reveal that the proposed method outperforms the existing methods (about 2% higher than PointPillars in BEV detection) and gives more accurate information.
2-D Omnidirectional Human Body Attribute Fusion Recognition System
Authors: Bu Deqing , Jiang Niande , Yang Yang , Gao Xiaohui , Hu Min , Zhu Hongqin , Wang Yaobin 
Published: May 2023

Abstract
Traditional face or body attribute recognition can solely train and understand the specific attributes marked according to the marked content of the dataset. Most recognition systems on the market integrate specific attribute recognition algorithms. The lack of corresponding attribute data fusion leads to the problem of high diversity but poor recognition accuracy. This paper designs a recognition system based on data fusion and attribute fusion of two-dimensional images, which can fuse subtle data during training, and fuse the recognition results of overlapping attributes, to achieve multi-directional recognition of human subtle attributes, make up for the shortcomings of single recognition algorithm, and obtain more comprehensive and accurate recognition results. The experimental results show that the recognition system. Firstly, the overall recognition accuracy is above 83.5%. Secondly, it can make up for the problems of high accuracy but poor diversity of single recognition algorithm and poor accuracy of multi-feature recognition algorithm.
Optical Image Change Detection Method Based On Depth Convolution Difference Map Fusion
Authors: Li Yujing , Wang Longchao , Chen Xi , Li Chuan , Xiang Haibing 
Published: May 2023

Abstract
Aiming at the problem of low change detection accuracy caused by the difference in the brightness of optical remote sensing images, an optical This method borrows the idea of convolution learning features in deep learning, performs convolution operations on two images, and obtains the convolutional map fusion. features in deep learning, performs convolution operations on two images, and obtains the convolutional difference map and logarithmic ratio map; and then performs multi-layer convolution on the two difference maps respectively. The feature maps after each layer of convolution are combined to obtain a The feature maps after each layer of convolution are combined to obtain a deep convolution difference map; two deep convolution difference maps are fused to obtain a deep convolution difference fusion map; finally the maximum This article uses LANDSAT satellite images to verify and compares the maximum variance automatic threshold between classes is used for segmentation to obtain change detection result graph. The experimental results show that the difference map fusion enhances the detailed information of the In terms of the detection effect, the anti-noise performance is enhanced. The detection accuracy is improved.