bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Study on the Efficacy of Thrombectomy in Patients with Atherosclerotic and Cardioembolic Basilar Artery Occlusion
Authors: Chen Fulei , Dai Linzhi , Dong Jiangtao , Zhu Licang , Li Yang , Zhang Lei , Zhao Dong 
Published: March 2023

Abstract
Background Studies on basilar artery occlusion are relatively few compared with those of anterior circulation stroke. The aim of the present study was to compare the efficacy of endovascular therapy (EVT) in patients with basilar artery occlusion classified as large artery atherosclerosis (LAA) and cardioembolism (CE), and to analyze the independent risk factors affecting the prognosis of EVT.
Methods A total of 123 people were assigned to the LAA and CE groups (97 to the LAA and 26 to the CE). The primary outcome was a modified Rankin Scale (mRS) score of 2 or lower at 90 days. The primary safety outcome was mortality at 90 days. Secondary safety endpoints included the rates of symptomatic intracranial hemorrhage and reinfarction. Multiple logistic regression was used to screen out independent risk factors for EVT prognosis of the LAA and CE groups.
Results In the analysis, the patients with LAA stroke had better collateral circulation (American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology [SIR] score of 2–4; 61.9 vs. 19.2%, p = 0.000), and higher angioplasty rate (32.0 vs. 3.8%, p = 0.002). The proportions of patients with a 90-day mRS score of 0 to 2 and 90-day mortality were not found to be statistically significant between the two groups. Multivariate logistic regression analysis indicated that age, SIR, white blood cell, blood glucose, and modified thrombolysis in cerebral infarction were independent risk factors for the poor prognosis of EVT in the LAA group.
Conclusion Although there were differences in clinical characteristics and imaging features between LAA and CE, there was no evidence of a significant difference in prognosis after EVT. In addition, the National Institutes of Health Stroke Scale score was not among the independent risk factors affecting the prognosis of the LAA group.
L2 learning and religious communities of practice in study
abroad
Authors: Edwards Laura C. 
Published: March 2023

Abstract
The “social turn” in ongoing efforts to understand L2
acquisition in study abroad has brought about valuable additions to the
literature, encouraging researchers to consider notions of identity in study
abroad such as gender, class, nationality, race, and sex. This study focuses on
an under-researched aspect of identity abroad: religiosity, and the benefit of
pre-existing beliefs to find, create, and become a member of religious
communities of practice for personal support – during what can be a difficult
time of adjustment and homesickness – as well as for practice and learning in
the L2. Findings indicate that joining religious communities of practice while
abroad can encourage L2 learning while supporting critical notions of the L2
learner’s identity.
“You just picked it up”
Authors: Zhou Siyang , Baffoe-Djan Jessica Briggs 
Published: March 2023

Abstract
Phrasal verbs are highly common in informal discourse among native English speakers, yet they pose extraordinary
difficulty to second language (L2) English learners. Informed by usage-based theory, this mixed-methods study attempted to
determine the relationship between the amount of out-of-class English exposure in study abroad and the phrasal verb knowledge of
international adult students in the United Kingdom. It also brought foundation program students (i.e., students in a preparation
course for university degrees), a previously under-researched study-abroad population, under the spotlight. One hundred and
eighteen foundation program students at a British university completed a modified Language Contact Profile and a productive
phrasal verb test. Nine students were interviewed to further unravel how they acquired phrasal verbs via informal language
contact. Analyses revealed a significant positive relationship between speaking English with international friends and phrasal
verb knowledge. Hierarchical regression identified that overall English proficiency, spoken input contact, and non-interactive
language contact were significant predictors of phrasal verb scores. This study points to the importance of having meaningful,
emotionally enjoyable, and regular L2 contact to formulaic language competence during study abroad.
Early decoding of walking tasks with minimal set of EMG channels
Authors: Barberi Federica , Iberite Francesco , Anselmino Eugenio , Randi Pericle , Sacchetti Rinaldo , Gruppioni Emanuele , Mazzoni Alberto , Micera Silvestro 
Published: March 2023

Abstract

Objective. Powered lower-limb prostheses relying on decoding motor intentions from non-invasive sensors, like electromyographic (EMG) signals, can significantly improve the quality of life of amputee subjects. However, the optimal combination of high decoding performance and minimal set-up burden is yet to be determined. Here we propose an efficient decoding approach obtaining high decoding performance by observing only a fraction of the gait duration with a limited number of recording sites. Approach. Thirteen transfemoral amputee subjects performed five motor tasks while recording EMG signals from four muscles and inertial signals from the prosthesis. A support-vector-machine-based algorithm decoded the gait modality selected by the patient from a finite set. We investigated the trade-off between the robustness of the classifier’s accuracy and the minimization of (i) the duration of the observation window, (ii) the number of EMG recording sites, (iii) the computational load of the procedure, measured the complexity of the algorithm. Main results. When including pre-foot-strike data in the decoding, the combination of three EMG recording sites and the inertial signals led to correct rates above 94% at the 20% of the gait cycle, showing the best trade-off between invasiveness of the setup and accuracy of the classifier. The complexity of the algorithm proved to be significantly higher when applying a polynomial kernel compared to a linear one, while the correct rate of the classifier generally showed no differences between the two approaches. The proposed algorithm led to high performance with a minimal EMG set-up and using only a fraction of the gait duration. Significance. These results pave the way for efficient control of powered lower-limb prostheses with minimal set-up burden and a rapid classification output.
Multiple Attitude Estimation Models based on a Pressure Sensor Array
Authors: Wang Chongyang , Yu Huapeng 
Published: March 2023

Abstract
Autonomous navigation is critical for autonomous underwater vehicle to complete underwater tasks, and attitude is an essential parameter of autonomous navigation. Inspired by the biomimicry studies, three attitude estimation models based on a pressure sensor array are constructed, which are the direct method, the multi-layer perception model and the attitude estimation model based on physical knowledge respectively. The field tests are carried out to verify the effectiveness of the proposed methods, and the advantages and disadvantages of the three methods are compared. It turns out that the attitude estimation model based on physical knowledge achieves the optimal results, and the estimated error of pitch is lower than 1.9133°, and the estimated error of roll is lower than 2.6719°. Further, there is no cumulative attitude error, which complies that the attitude estimation model based on physical knowledge has a high potential for applications in autonomous navigation systems.
Image Measurement Attribute Extraction Method under Large Error Condition
Authors: Li Feng , Du Xuehui , Liu Aodi , Zhang Liu 
Published: March 2023

Abstract
In recent years, the rapid development of image processing based on deep learning has had a wide impact on industrial production and social life. In the field of information security, image content description as a kind of attribute has been used in authentication, access control and other technologies. In addition to describing what is in an image, fine-grained image content properties should also be able to express the relative size and position relationships between objects. Due to the strong fuzziness of the relative scale between objects in the image, it is not suitable for precise definition. This paper proposes a method to extract measurement attributes under large error to describe the relative size of two parts in an image. Based on image classification and object detection, the error range is estimated by statistical method, and a fuzzy set is constructed as the length unit. Then the contradictory critical points of measurement are used to determine the comparable scale range and similar regions. Finally, the measurement attribute of image content is given. Experiments show that the size description obtained by this method accords with people’s subjective feelings, and can effectively extract the fine-grained attributes of the image.
Metamorphic Robustness Testing for DeepL Translation
Authors: Li Zilong , Xiao Lei , Lin Ruijin , Xiao Zhengxinchao 
Published: March 2023

Abstract
Due to the complexity and flexibility of natural languages, such robustness testing involving the ability of machine translation software to handle incorrect or unintended input are extremely challenging, especially if there is no human oracle or reference translation. In this paper, using metamorphic robustness testing methods, we compare the translation of the primitive input with the translation of a subsequent input that has different types of very small changes and may cause large changes in the translation results. The lack of robustness of the DeepL translation is shown by our experiments, these preliminary results show that metamorphic testing has potential in the field of natural language processing.
Feature Pyramid Network Based on Skip Connect for Vehicle Prediction
Authors: Li Peixin , Liu Jiaqi , Yang Lu 
Published: March 2023

Abstract
The trajectory prediction of the target vehicle can improve the perception ability of autonomous driving vehicles in the open world environment and is an essential technology for realizing high-level fully automatic driving. The traditional vehicle trajectory prediction model based on image input is sensitive to the image style of the dataset and has weak generalization ability. The prediction model based on intermediate features can overcome the problem of different dataset styles. However, there are relatively few explorations on building models based on intermediate semantic features. Inspired by the feature pyramid in image processing, this paper proposes an FPSP (Feature Pyramid network based on Skip connect for vehicle Prediction) network. By adding the pyramid feature branch, the original network is improved. The initial coarse information is hierarchically introduced into corresponding abstract high-dimensional features. Experiments on the KITTI dataset prove that FPSP achieves considerable improvement with only a slight increase in time consumption.
Abnormal Behavior Recognition Model of Power Grid Based on Multi-Scale Feature Fusion
Authors: Kong Qingyu , Zhang Yi , Du Zexu , Zhu Chun , Xiao Bin 
Published: March 2023

Abstract
Artificial intelligence technology is applied to power grid transmission and operation status identification to improve the intelligence level of power inspection. However, the existing grid anomalous behavior recognition model has the problem of unsatisfactory accuracy for small target detection. To address this problem, this paper proposes an abnormal behavior recognition model of power grid based on multi-scale feature fusion. First, the texture feature extraction network with a multi-scale mechanism is constructed to obtain object features. Secondly, the graph relation network between target and background is constructed. Finally, the grid abnormal behavior recognition model based on multi-scale feature fusion is constructed. The model proposed in this article is compared with the existing object recognition model in the simulation of the different datasets. In terms of the evaluation accuracy index, the proposed model is 93.27, which is improved by 2.18%.
Deep Reinforcement Learning Based Three-dimensional Path Tracking Control of An Underwater Robot
Authors: Liang Zhenyu , Feng Dongliang , Qu Xingru 
Published: March 2023

Abstract
This paper presents a deep reinforcement learning (DRL)-based three-dimensional path tracking control algorithm of an underwater robot to learn the path-tracking capability by interacting with the environment. To be specific, a hybrid path tracking guidance and controller based on three-dimensional line-of-sight (3D LOS) guidance and twin delayed deep deterministic policy gradient (TD3) algorithm is applied to complete kinematics and dynamics controller design. The reference angle is obtained by LOS algorithm, and TD3 algorithm is used to output the control laws. Aiming at the chattering problem in the output of the reinforcement learning controller, the commands filter and chattering penalty term are designed respectively. The tracking experiment of ten waypoints proves the feasibility of the algorithm proposed in this paper.
Dynamic Parameter Identification for Intelligent PID Control
Authors: Zhen Yan , Hou Mingzhe , Hao Mingrui 
Published: March 2023

Abstract
In this paper, an intelligent PID control structure using dynamic parameter is designed to solve the problem that the parameters of the aircraft model change, which results in controller performance degradation in real physical systems. The system dynamics parameters are identified by the deep neural network, and the parameters of the PID controller are adaptively scheduled based on the parameter identification results, so that the control system has the best matching with the system dynamics and the control performance is the best.
Fuzzy PI Control Active Power Filter Based on Improved Particle Swarm Algorithm
Authors: Yang Shengxin , Sun Xianbo , Zhang Yi 
Published: March 2023

Abstract
Electric vehicle generates a large amount of harmonic when charged, and yet active power filter (APF) can have some reduction in harmonic. Based on APF as the research object, this paper introduces a control system combining particle swarm algorithm (PSO) and fuzzy proportional integral (PI) control for the problems of large overshoot and poor robustness of traditional PI control APF, and further boosts the accuracy of harmonic suppression by improving the weight value of PSO algorithm. MATLAB/SIMULINK simulation experiments show that the harmonic distortion rate of traditional PI control in APF, total harmonic distortion (THD) of can be reduced with traditional PI control decreased by 18.25%, conventional PI control 21.2% decreased by and the control of improved PSO algorithm combined with fuzzy PI control decreased by 22.68%. The simulation results show that the proposed method in this paper can effectively suppress the harmonic fluctuations on the grid side.
Online Weapon-target Assignment based on Distributed Auction Mechanism
Authors: Si JiaShuai , Hao MingRui 
Published: March 2023

Abstract
To solve the problem of online weapon-target assignment (OWTA) in the integration of large-scale search and attack in unknown environment, an OWTA algorithm based on distributed auction mechanism is presented. Aiming at the problem that the traditional combinatorial optimization algorithm needs to set up the global battlefield situation in advance, considering the consumability of resources in the attack process, the integrated search and attack task flow is established. Considering the communication restricted environment, the unmanned aerial vehicles (uavs) are grouped, with centralized architecture within the group and distributed structure between the groups, and the corresponding distributed auction mechanism is constructed to achieve OWTA within the communication range limited. In order to solve the problem that it is difficult to ensure the time consistency of the coordinated attack target, a dubins cooperative path planning based on cooperative particle swarm optimization (CPSO) algorithm is proposed. Particle swarm optimization algorithm is used to adjust the radius of the dubins path of each bomb, so that the uav in the same group can hit the target simultaneously without collision and have the shortest flight range. The simulation results show that the designed distributed auction algorithm takes into account the consumption of attack resources, and quickly redistributes the firepower to the new targets in the dynamic uncertain environment, which ensures the maximization of the execution efficiency of the multi-machine cluster fire allocation task.
Satellite-derived forest canopy greenness shows differential drought vulnerability of secondary forests compared to primary forests in Peru
Authors: Zutta Brian R , Salinas Norma , Cosio Eric G , Tito Richard , Aragón Susan , Nina-Quispe Alex , Roman-Cuesta Rosa Maria 
Published: March 2023

Abstract
Understanding tropical secondary forest canopy greenness and responses to climatic conditions is important for climate change mitigation, particularly in the tropics where secondary forest growth is a substantial carbon sink and a promoted natural climate solution. We here test three hypotheses: (a) forest canopy greenness is higher in younger, secondary forests than in older, primary or mature forests, (b) secondary forests are more vulnerable to climatic pressures and (c) there are significant differences between forest types regarding primary–secondary canopy greenness and their differential responses to drought anomalies. To explore these relationships, we monitored wet and dry seasonal greenness from 2001 to 2020, estimated through the enhanced vegetation index (EVI), of Peruvian tropical dry, montane and lowland secondary forests and compared it to nearby primary forests. We developed predictive models of seasonal EVI using remotely sensed variables, including land surface temperature (LST), evapotranspiration (ET), potential evapotranspiration (PET), ratio of ET and PET (ETn), and the standard precipitation index (SPI). Overall, there was a higher change in annual and seasonal EVI for secondary forests compared to primary forests. However, primary forests maintained relatively stable EVI levels during the wet season despite drought anomalies. When decoupling forest type canopy greenness and drought response, primary forest greenness in dry and lowland ecosystems were temporally more stable. Secondary montane had a lower increase in greenness when drought anomalies held during different seasons. Stepwise multiple linear regression models indicated that LST and ETn, a plant water use index, were the most significant factors to predict greening fluctuations in dry and montane forest types. ET and SPI mostly drove wet season mean EVI across all forest types. Predictors of dry season mean EVI varied, but mostly including water availability. Our results suggest that tropical secondary forests are more productive overall yet more vulnerable to prolonged drought.
Sundman transformation and alternative tangent structures
Authors: Cariñena J F , Martínez Eduardo , Muñoz-Lecanda Miguel C 
Published: March 2023

Abstract
A geometric approach to Sundman transformation defined by basic functions for systems of second-order differential equations is developed and the necessity of a change of the tangent structure by means of the function defining the Sundman transformation is shown. Among other applications of such theory we study the linearisability of a system of second-order differential equations and in particular the simplest case of a second-order differential equation. The theory is illustrated with several examples.
Computational approaches for detecting disease-associated alternative splicing events
Authors: Liu Jiashu , Lin Cui-Xiang , Zhang Xiaoqi , Li Zongxuan , Huang Wenkui , Liu Jin , Guan Yuanfang , Li Hong-Dong 
Published: March 2023

Abstract
Alternative splicing (AS) is a key transcriptional regulation pathway. Recent studies have shown that AS events are associated with the occurrence of complex diseases. Various computational approaches have been developed for the detection of disease-associated AS events. In this review, we first describe the metrics used for quantitative characterization of AS events. Second, we review and discuss the three types of methods for detecting disease-associated splicing events, which are differential splicing analysis, aberrant splicing detection and splicing-related network analysis. Third, to further exploit the genetic mechanism of disease-associated AS events, we describe the methods for detecting genetic variants that potentially regulate splicing. For each type of methods, we conducted experimental comparison to illustrate their performance. Finally, we discuss the limitations of these methods and point out potential ways to address them. We anticipate that this review provides a systematic understanding of computational approaches for the analysis of disease-associated splicing.
Aposematic coloration of Pacific newts (Taricha) provides a qualitatively but not quantitatively honest signal to predators
Authors: Moniz Haley A , Kruleski Sage M , Risbud Amar D , Louden Samuel J H , Hanlon Roger T , Stokes Amber N , Palmer Stephanie E , Feldman Chris R 
Published: March 2023

Abstract
Colourful displays are used by diverse taxa to warn predators of dangerous defences (aposematism). Aposematic coloration is especially widespread among amphibians, which are often protected by harmful toxins. Pacific newts (Taricha) are considered a model of aposematism because when threatened, they arch the head and tail upwards to expose a vivid orange ventrum against a dark dorsum. Given that newts are defended by tetrodotoxin (TTX), a lethal neurotoxin, this signal is assumed to warn predators that an attack would be risky. However, colours have not been quantified in Taricha, and it remains unknown whether coloration provides qualitatively honest (signalling toxic defence) or quantitatively honest (signalling toxin level) warnings. We used two colour quantification methods (spectrometry and hyperspectral imaging) to measure chromatic (hue) and achromatic (brightness) qualities of ventral and dorsal coloration in two newt species (Taricha granulosa and Taricha sierrae). We assessed qualitative honesty using visual models of potential predators (snakes, birds and mammals). Next, we evaluated quantitative honesty by measuring TTX in newts and examining the potential correlation between defence level (amount of TTX) and colorimetrics. We found support for qualitative but not quantitative honesty. Selective pressures and evolutionary constraints might impede the evolution of honest quantitative signalling in this system.