bluuebunny/crossref_metadata_2025_split huggingface dataset:
Temporal channel reconfiguration multi‐graph convolution network for skeleton‐based action recognition
Authors: Lei Siyue , Tang Bin , Chen Yanhua , Zhao Mingfu , Xu Yifei , Long Zourong 
Published: April 2024

AbstractSkeleton‐based action recognition has received much attention and achieved remarkable achievements in the field of human action recognition. In time series action prediction for different scales, existing methods mainly focus on attention mechanisms to enhance modelling capabilities in spatial dimensions. However, this approach strongly depends on the local information of a single input feature and fails to facilitate the flow of information between channels. To address these issues, the authors propose a novel Temporal Channel Reconfiguration Multi‐Graph Convolution Network (TRMGCN). In the temporal convolution part, the authors designed a module called Temporal Channel Fusion with Guidance (TCFG) to capture important temporal information within channels at different scales and avoid ignoring cross‐spatio‐temporal dependencies among joints. In the graph convolution part, the authors propose Top‐Down Attention Multi‐graph Independent Convolution (TD‐MIG), which uses multi‐graph independent convolution to learn the topological graph feature for different length time series. Top‐down attention is introduced for spatial and channel modulation to facilitate information flow in channels that do not establish topological relationships. Experimental results on the large‐scale datasets NTU‐RGB + D60 and 120, as well as UAV‐Human, demonstrate that TRMGCN exhibits advanced performance and capabilities. Furthermore, experiments on the smaller dataset NW‐UCLA have indicated that the authors’ model possesses strong generalisation abilities.
WR‐3.4 multi‐mode waveguide module with single‐chip‐integrated array circuits
Authors: Koh Yunkyeong , Jeon Sanggeun , Kim Moonil 
Published: April 2024

AbstractThis paper introduces a WR‐3.4 waveguide module that contains an array of circuits fabricated on a single chip with an area of 3 × 0.75 mm2. The array comprises ten elements, each containing a straight differential thru line connecting input and output waveguide transitions. Modifications to the edge element design are intended to produce perfect cell‐to‐cell symmetry by providing amplitude and phase balances for all array elements. The measurements show that the array module possesses an insertion loss of around 3.5 dB in the frequency range of 220–300 GHz. This result represents an increase of 2.5 dB compared to an empty waveguide module, displaying similarity to the performance of single‐element modules.
Study on the evaluation method of polymerisation degree based on Raman spectroscopic plot
Authors: Yang Zhuang , Chen Weigen , Yin Zhixian , Yang Dingkun 
Published: April 2024

AbstractIn order to achieve accurate quantitative evaluation of the ageing state of oil paper insulation, an evaluation method of polymerisation degree (DP) of oil–paper insulation based on Raman spectroscopic plot is presented. Samples covering the full ageing stage of oil–paper insulation were obtained by accelerated thermal ageing experiments. The samples were tested by Raman spectra, and the Raman spectra were converted into Raman spectroscopic plot using Gramian angular field. Feature extraction was performed on the images using local binary pattern (LBP), and 28 feature variables were selected by competitive adaptive reweighted sampling and uninformative variable elimination. Finally, the DP prediction model was constructed by long short‐term memory neural network (LSTM). The training accuracy exceeded 90%, and the mean absolute deviation of DP prediction for 45 test samples was only 40.70. A new visual detection method for transformer ageing diagnosis is presented.
Video object segmentation via couple streams and feature memory
Authors: Liang Yun , Xiao Xinjie , Qiu Shaojian , Zhang Yuqing , Su Zhuo 
Published: April 2024

AbstractIn recent years, most video segmentation methods use deep CNN to process the input image, but they did not fully mine the rich intermediate predictions in spatio‐temporal space. And, the segmentation challenges such as occlusion, severe deformation and illumination have not been well solved so far. To alleviate these problems, this paper focuses on constructing multi module network structures that represent multi semantics and proposes a video object segmentation network via coupled‐stream architecture with feature memory mechanism. This network first extracts high‐level semantic features, edge features, long‐term and short‐term stable depth features of the target, and then decode them into the segmentation mask of target. In addition, negative skeleton inhibition and frame interpolation are used to prevent the interference of similar objects and motion blur, respectively. The method has a low GPU memory usage, regardless of the number of object in video. And performs 86.5%and 62.4% in J&F measure on DAVIS 2016 and DAVIS 2017 validation set, without fine‐tuning and online training.
Local feature‐based video captioning with multiple classifier and CARU‐attention
Authors: Im Sio‐Kei , Chan Ka‐Hou 
Published: April 2024

AbstractVideo captioning aims to identify multiple objects and their behaviours in a video event and generate captions for the current scene. This task aims to generate a detailed description of the current video in real‐time using natural language, which requires deep learning to analyze and determine the relationships between interesting objects in the frame sequence. In practice, existing methods typically involve detecting objects in the frame sequence and then generating captions based on features extracted through object coverage locations. Therefore, the results of caption generation are highly dependent on the performance of object detection and identification. This work proposes an advanced video captioning approach that works in adaptively and effectively addresses the interdependence between event proposals and captions. Additionally, an attention‐based multimodel framework is introduced to capture the main context from the frame and sound in the video scene. Also, an intermediate model is presented to collect the hidden states captured from the input sequence, which performs to extract the main features and implicitly produce multiple event proposals. For caption prediction, the proposed method employs the CARU layer with attention consideration as the primary RNN layer for decoding. Experimental results showed that the proposed work achieves improvements compared to the baseline method and also better performance compared to other state‐of‐the‐art models on the ActivityNet dataset, presenting competitive results in the tasks of video captioning.
Beyond Circular Eclipsers (BeyonCE) light curve modelling
Authors: van Dam Dirk M. , Kenworthy Matthew A. 
Published: April 2024

Context. Time series photometry offers astronomers the tools to study time-dependent astrophysical phenomena, from stellar activity to fast radio bursts and exoplanet transits. Transit events, in particular, are focussed primarily on planetary transits and eclipsing binaries with eclipse geometries that can be parameterised with a few variables. However, more complex light curves caused by the substructure within the transiting object would require a customised analysis code.
Aims. We present Beyond Circular Eclipsers (BeyonCE), which reduces the parameter space encompassed by the transit of circumsec-ondary disc (CSD) systems with azimuthally symmetric, non-uniform optical-depth profiles. By rejecting disc geometries that are not able to reproduce the measured gradients within their light curves, we can constrain the size and orientation of discs with a complex sub-structure.
Methods. We mapped out all the possible geometries of a disc and calculated the gradients for rings crossing the star. We then rejected those configurations where the measured gradient of the light curve is greater than the theoretical gradient from the given disc orientation.
Results. We present the fitting code BeyonCE and demonstrate its effectiveness in considerably reducing the parameter space of discs that contain an azimuthally symmetric structure. We used the code to analyse the light curves seen towards J1407 and PDS 110, attributed to CSD transits.
A fast-rotating blue straggler star in the tidal tail of the open cluster NGC 752
Authors: Li Chunyan , Zhong Jing , Qin Songmei , Jiang Dengkai , Shan Xingmei , Chen Li 
Published: April 2024

Context.NGC 752 is a well-known Galactic open cluster of intermediate age. In recent works, a very long and asymmetric tail was revealed. A blue straggler star (BSS) at the periphery of the tidal tail of the cluster was subsequently identified.Aims.We aim to perform a detailed analysis of the newly detected BSS based on the available comprehensive spectroscopic and photometric data. We also explored this BSS’s possible formation pathway and age limitation based on the collected spectroscopic and photometric data.Methods.We reconfirmed the membership of the newly determined BSS of NGC 752, supplemented byGaiaDR3 radial velocity data. Moreover, we also estimated the projected rotational velocity and the mass of the BSS from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope low-resolution spectra and multiband photometric data from various catalogs, respectively.Results.The newly discovered BSS is confirmed as a genuine member of NGC 752. The lack of ultraviolet excess in the spectral energy distribution and no significant variations in the light curve imply that this BSS is likely a single star (mass = 1.86−0.94+3.62 M⊙) formed through stellar mergers. The fast rotation velocity (v sin i = 206.9 ± 4.9 km s−1) of the BSS may provide constraints on its age (less than a hundred million years), but more formation details require further investigation.
The Effectiveness of Blood Flow Restriction Training on Upper
Extremity Muscle Strength: A Systematic Review
Authors: Yazgan Elif Aleyna , Atli Ecenur , Razak Ozdincler Arzu 
Published: April 2024

Abstract
Objective This systematic review aims to investigate the effect of blood
flow restriction training (BFRT) on upper extremity muscle strength in
pathological conditions of the upper extremity musculoskeletal system or in
healthy individuals.
Materials and Methods This study was conducted in accordance with the
PRISMA guideline statement. The randomized controlled studies which published
from January 2000 to May 2022 were searched in the PubMed, Web of Science,
MEDLINE, Scopus, and Cochrane Library databases. Inclusion criteria were healthy
clinical population or musculoskeletal pathology related to the upper extremity,
participants aged 18 and above, application of blood flow restriction to the
arm, a randomized controlled study design, and publication in English.
Additionally, the presentation of upper extremity muscle strength as an outcome
measurement was required. The quality of the studies was evaluated using The
Physiotherapy Evidence Database (PEDro) scale. Articles that were
non-randomized, inaccessible in full text, and scored 4 or below on the PEDro
scale were excluded.
Results Five studies were included with 219 participants. The included
studies had PEDro scores ranging from 5 to 8, with an average score of 6.4. This
review demonstrated a positive or neutral effect of BFRT on grip strength and
shoulder strength.
Conclusion Although some studies have reported positive effects of BFRT on
upper extremity muscle strength, there is no conclusive evidence regarding the
protocol to be used for increasing upper extremity muscle strength.
Electrochemical Oxidative Cross-Coupling for the Construction of C(sp3)–C(sp3) Bonds
Authors: Chang Xi-Hao , Guo Chang , Wen Kang-Min 
Published: April 2024

AbstractA highly effective oxidation cross-coupling method involving para-cresol derivatives and malononitrile derivatives has been developed utilizing undivided electrolytic conditions. This electrochemical approach offers a robust route for synthesizing diverse malononitrile derivatives featuring quaternary carbon centers and incorporating para-phenol groups. Notably, the direct electrooxidation of the C(sp3)–H bond in the para-cresol derivative plays a crucial role in this process under electrolytic conditions. Various para-cresol derivatives and malononitrile derivatives with different substituents are readily compatible with this electrochemical transformation, affording coupling compounds in up to 99% yield.
Cation doping and oxygen vacancies in the orthorhombic FeNbO4 material for solid oxide fuel cell applications: A density functional theory study
Authors: Wang Xingyu , Santos-Carballal David , de Leeuw Nora H. 
Published: April 2024

The orthorhombic phase of FeNbO4, a promising anode material for solid oxide fuel cells (SOFCs), exhibits good catalytic activity toward hydrogen oxidation. However, the low electronic conductivity of the material specifically in the pure structure without defects or dopants limits its practical applications as an SOFC anode. In this study, we have employed density functional theory (DFT + U) calculations to explore the bulk and electronic properties of two types of doped structures, Fe0.9375A0.0625NbO4 and FeNb0.9375B0.0625O4 (A, B = Ti, V, Cr, Mn, Co, Ni) and the oxygen-deficient structures Fe0.9375A0.0625NbO3.9375 and FeNb0.9375B0.0625O3.9375, where the dopant is positioned in the first nearest neighbor site to the oxygen vacancy. Our DFT simulations have revealed that doping in the Fe sites is energetically favorable compared to doping in the Nb site, resulting in significant volume expansion. The doping process generally requires less energy when the O-vacancy is surrounded by one Fe and two Nb ions. The simulated projected density of states of the oxygen-deficient structures indicates that doping in the Fe site, particularly with Ti and V, considerably narrows the bandgap to ∼0.5 eV, whereas doping with Co at the Nb sites generates acceptor levels close to 0 eV. Both doping schemes, therefore, enhance electron conduction during SOFC operation.
Development of Taenia asiatica cysticerci to infective stage and adult stage in Mongolian gerbils
Authors: Chang S.L. , Ooi H.K. , Nonaka N. , Kamiya M. , Oku Y. 
Published: April 2024

AbstractThe development of metacestodes and adult worms of Taenia asiatica in Mongolian gerbils (Meriones unguiculatus) were observed. Cysticerci were recovered from gerbils subcutaneously injected with hatched oncospheres. The recovery rate ranged from 0.1 to 3.2%. No cysticerci were recovered from the orally inoculated gerbils. The infectivity of the cysticerci recovered at 48 weeks post-infection was evaluated. Tapeworms were recovered on day 14 postinfection from the small intestine of 5 of 11 gerbils, with a recovery rate of 27% (6 worms recovered/22 worms inoculated). Three and four adult worms were recovered from two human volunteers who ingested five cysticerci after 4 months post-infection. In worms recovered from gerbils, segmentation and genital primordia in the posterior proglottids and hooklets in the residual rostellum were observed. The results indicate that gerbils can serve as an alternative intermediate host and that partial development of the adult worm stage occurs in gerbils.
The relationship between haematological indices, serum gamma-glutamyl transferase and glutamate dehydrogenase, visual hepatic damage and worm burden in cattle infected with Fasciola gigantica
Authors: Molina E.C. , Lozano S.P. , Barraca A.P. 
Published: April 2024

AbstractThe association between visual hepatic damage, burden of Fasciola gigantica, serum levels of gamma glutamyl transferase (GGT) and glutamate dehydrogenase (GLDH) is described from an abattoir study of 70 cattle in the Philippines. In another abattoir study of 60 cattle, the relationship between burden of F. gigantica and haematological indices was investigated. The degree of visual hepatic damage and burden of F. gigantica were significantly positively related to levels of GGT and GLDH. Red blood cell counts and packed cell volume were significantly inversely related to worm burden, but animals compensated for reduced numbers of red blood cells by increasing red cell haemoglobin content.
A direct sampling method for time-fractional diffusion equation
Authors: Qiu Lingyun , Sim Jiwoon 
Published: April 2024

Abstract
This paper introduces a direct sampling method tailored for identifying the location of the source term within a time-fractional diffusion equation (TFDE). The key aspect of our approach involves the utilization of a versatile family of index functions, which can be chosen according to the specific characteristics of the source term. Recognizing the key role of the TFDE’s fundamental solution within the index function, we further enhance our method by deriving its asymptotic expansions. This advancement not only enhances the accuracy, but also significantly improves the computational efficiency of our method. To validate the effectiveness and robustness of the proposed sampling method, we conduct a series of comprehensive numerical experiments.
Adaptive Kalman filter with LSTM network assistance for abnormal measurements
Authors: Yin Shu , Li Peng , Gu Xinxing , Yang Xusheng , Yu Li 
Published: April 2024

Abstract
A classic state estimation method, the Kalman filter integrates prior information, system dynamics models, and measurement data to achieve posterior state estimations. However, measurements often encounter various unknown disturbances, leading to abnormal or inaccurate measurements and subsequently impacting the performance of Kalman filtering. In response to this challenge, this paper introduces a novel adaptive Kalman filter approach aided by posterior state estimations using the Long short-term memory (LSTM) networks. The proposed method begins by utilizing prior residuals to construct a chi-square distribution model, which facilitates the detection of abnormal measurements within data. Upon identifying abnormal measurements, an LSTM network is employed to generate alternative predictive measurements, replacing the original inaccurate measurement. This approach enhances the model’s capability to handle complex relationship and measurement uncertainties and improves filtering estimation accuracy. A noise covariance adjustment method is introduced in extreme cases where alternative predictive measurements alone are insufficient for filter requirements. This method mitigates the adverse effects of abnormal measurements on posterior estimation. Throughout the entire adaptive assistance process involving the LSTM network, deep learning maintains a stable structure of the filter while enhancing adaptability. This strategy ensures the filter’s resilience in dynamic environments with unknown factors by providing predicted measurements conforming to the chi-square distribution and corresponding measurement noise covariance. Ultimately, the efficacy of the proposed algorithm is validated through simulations and experiments involving vehicle positioning with inertial sensors.
Tuneable quantised conductance memory states in TiO2 based resistive switching devices in crossbar geometry for high density memory applications
Authors: Sahu Vikas Kumar , Das Amit Kumar , Ajimsha R S , Singh Rashmi , Misra Pankaj 
Published: April 2024

Abstract
The tunability and controllability of conductance quantization mediated multilevel resistive switching (RS) memory devices, fabricated in crossbar geometry can be a promising alternative for boosting storage density. Here, we report fabrication of Cu/TiO2/Pt based RS devices in 8 × 8 crossbar geometry, which showed reliable bipolar RS operations. The crossbar devices showed excellent spatial and temporal variability, time retention and low switching voltage (<1 V) and current (∼100 μA). Furthermore, during the reset switching, highly repeatable and reliable integral and half-integral quantized conductance (QC) was observed. The observed QC phenomenon was attributed to the two dimensional confinement of electrons as lateral width of the conducting filament (CF) matches the fermi wavelength. The magnitude and number of the QC steps were found to increase from ∼2.5 to 12.5 and from 5 to 18, respectively by increasing the compliance current (I
C) from 50 to 800 μA which also increased the diameter of the CF from ∼1.2 to 3.3 nm. The enhancement in both number and magnitude of QC states was explained using electrochemical dissolution mechanism of CF of varying diameter. A thicker CF, formed at higher I
C, undergoes a gradual rupture during reset process yielding a greater number of QC steps compared to a thinner CF. The realisation of QC states in the crossbar Cu/TiO2/Pt device as well as I
C mediated tunability of their magnitude and number may find applications in high-density resistive memory storage devices and neuromorphic computing.
ERSegDiff: a diffusion-based model for edge reshaping in medical image segmentation
Authors: Chen Baijing , Wang Junxia , Zheng Yuanjie 
Published: April 2024

Abstract
Medical image segmentation is a crucial field of computer vision. Obtaining correct pathological areas can help clinicians analyze patient conditions more precisely. We have observed that both CNN-based and attention-based neural networks often produce rough segmentation results around the edges of the regions of interest. This significantly impacts the accuracy of obtaining the pathological areas. Without altering the original data and model architecture, further refining the initial segmentation outcomes can effectively address this issue and lead to more satisfactory results. Recently, diffusion models have demonstrated outstanding results in image generation, showcasing their powerful ability to model distributions. We believe that this ability can greatly enhance the accuracy of the reshaping results. This research proposes ERSegDiff, a neural network based on the diffusion model for reshaping segmentation borders. The diffusion model is trained to fit the distribution of the target edge area and is then used to modify the segmentation edge to produce more accurate segmentation results. By incorporating prior knowledge into the diffusion model, we can help it more accurately simulate the edge probability distribution of the samples. Moreover, we introduce the edge concern module, which leverages attention mechanisms to produce feature weights and further refine the segmentation outcomes. To validate our approach, we employed the COVID-19 and ISIC-2018 datasets for lung segmentation and skin cancer segmentation tasks, respectively. Compared with the baseline model, ERSegDiff improved the dice score by 3%–4% and 2%–4%, respectively, and achieved state-of-the-art scores compared to several mainstream neural networks, such as swinUNETR.