bluuebunny/crossref_metadata_2025_split huggingface dataset:
How do constructions with modal verbs develop in second language learners of English?
Authors: Römer-Barron Ute 
Published: August 2024

Abstract
The study reported on in this paper uses data from a large, pseudolongitudinal corpus of second language (L2) learner writing to investigate how L2 knowledge of verb-argument constructions (VACs) develops from low-beginner to upper-intermediate proficiency levels. The focus is on learners from two L1 backgrounds (L1 German and L1 Spanish) and on a subset of VACs that contain modal auxiliaries in combination with lexical verbs (e.g., subject-modal-verb, as in I can cook). For each VAC, L1, and proficiency level, frequency lists of verbs were created and compared across datasets. Reference data on the same constructions was extracted from a corpus of L1 writing. Results suggest that modal VACs become more frequent, less predictable, more productive, and more aligned with L1 production as learner proficiency increases. The findings of this study help to expand our understanding of the processes that underlie L2 construction acquisition.
Linguistic approaches to language acquisition
Authors: Meisel Jürgen M. 
Published: August 2024

Abstract
Acquisition research is an integral part of contemporary linguistic theorizing. 50 years ago, this
was not the case. Change came about following a theoretical reorientation that established linguistics as a cognitive
science, defining mental grammars as the prime object of study. Here I review an early proposal for developmental
psycholinguistics and show how it shaped subsequent research, inspired by grammatical and acquisition theory.
Summarizing analyses of German verb placement by L1 and L2 learners, I argue that this research of the 1970–80s
achieved important insights into properties of learner grammars, discovered acquisition orders and established
similarities as well as differences between L1 and L2 acquisition.
Gender assignment in German as a heritage language in an English-speaking context
Authors: Kupisch Tanja , Dressler Roswita 
Published: August 2024

Abstract
We present a case study of a heritage speaker of German, Luisa, who is growing up in an
English-speaking part of Canada, focussing on the acquisition of grammatical gender in German. While German has cues
to gender assignment, the acquisition of gender in this setting is compromised by the magnitude of gender cues and
form syncretism, and the absence of gender in English. We present longitudinal, naturalistic data from three periods:
age 1–2, age 4–5, and age 7. We ask whether Luisa develops grammatical gender akin to monolingual children or whether
there are indications of delay, stagnation, or attrition, as observed for heritage speakers of other languages. The
results show monolingual-like development despite a shift in dominance from German to English.
Isolated and combined effects of models and corrective feedback in the acquisition of the Turkish locative
morpheme
Authors: Yilmaz Yucel , Lee Senyung , Köylü Yılmaz 
Published: August 2024

Abstract
This study investigated the relative effects of models, corrective feedback, and a mixed treatment
including both models and corrective feedback in the development of the Turkish locative morpheme. Native speakers of
English were assigned to one of four conditions: models, corrective feedback, mixed, or control. Participants
performed one input-based and one output-based task with a native Turkish speaker and received treatment according to
their group assignments. Learners’ performance was measured through a multiple-choice task and an oral picture
description test once immediately after the treatment and once two weeks after the treatment. Results showed that the
mixed treatment in which learners received models first and then corrective feedback was the most effective
treatment.
First exposure to Russian word forms by adult English speakers
Authors: Pavlovskaya Natalia , Riches Nick , Young-Scholten Martha 
Published: August 2024

Abstract
How language learners segment (recognise and store words) in the speech stream has
typically been explored with children (Jusczyk 1997). Researchers have only
recently begun to examine how adults segment an unfamiliar natural language after first exposure without instruction
(Gullberg et al. 2010; Gullberg
et al. 2012; Carroll 2012, 2013, 2014; Shoemaker &
Rast 2013). We report on a study of how 28 English-speaking adults begin to segment words after hearing
them in fluent Russian during four sessions. The results showed that segmentation improved significantly over time.
Segmentation patterns reflected the influence of English phonotactics and sensitivity to weak-strong stress. We
conclude that beyond native language bias, adults deploy the segmentation mechanisms similar to those children
use.
Speech modifications and the Processability Theory hierarchy
Authors: Håkansson Gisela 
Published: August 2024

Abstract
This study explores to what extent there is a difference in input in L1 and L2 Swedish regarding
word order. Swedish is a V2 language with only one constituent preceding the tensed verb. This phenomenon is acquired
in different ways by L1 and L2 learners. L1 learners produce V2 without errors around the age of two years whereas L2
learners experience long-lasting problems with the postverbal placement of the subject. The reason for this difference
is not fully understood. This study set out to investigate the characteristics of the ambient language in the two
acquisition conditions. The results reveal a lot of variation but also differences in the input which suggest that
‘input simplification’ is not always helpful.
Varieties of DP recursion
Authors: Pérez-Leroux Ana T. , Roberge Yves , Massam Diane , Bejar Susana , Castilla-Earls Anny 
Published: August 2024

Abstract
Our objective is to shed light on recursion through an exploration of the L1 development of four
DP structures in English. Since recursion is in narrow syntax, there is no reason to expect asymmetries between
constructions but specific formal differences between structures might make acquisition of certain forms more
difficult, and there may be differences according to meaning classes. We report a study targeting possessives,
comitatives, locatives, and part-whole expressions, each with 2-level embedding. The results reveal statistically
significant effects of age and condition, with no interaction. Possessives and comitatives develop earlier than
locatives and relatives, and are also more productive for adults. These results help us to delineate the domain of
recursion and formulate a credible developmental scenario.
Drude’s lesser known error of a factor of two and Lorentz’s correction
Authors: Singh Navinder 
Published: August 2024

Abstract
As is well known, Paul Drude put forward the very first quantitative theory of electrical conduction in metals in 1900. He could successfully account for the Wiedemann–Franz law which states that the ratio of thermal to electrical conductivity divided by temperature is a constant called the Lorenz number. As it turns out, in Drude’s derivation there is a lucky cancellation of two errors. Drude’s underestimatation (by an order of 100) of the value of square of the average electron velocity compensated for his overestimatation of the electronic heat capacity (by the same order of 100). This compensation or cancellation of two errors lead to a value of the Lorenz number very close to its experimental value; which is well known. There is another error of a factor of two which Drude made when he calculated two different relaxation times for heat conductivity and electrical conductivity; in this article we highlight how and why this error occurred in Drude’s derivation and how it was removed 5 years later (in 1905) by Hendrik Lorentz when he used the Boltzmann equation and a single relaxation time. This article is of pedagogical value and may be useful to undergraduate/graduate students learning solid state physics.
The Casimir-like effect induced by active nematics
Authors: Pour Haddadan Fahimeh Karimi 
Published: August 2024

Abstract
We consider an active nematic phase and use hydrodynamical equations of it to model the activity as an internal field. The interaction of this field with the nematic director in a confined geometry is included in the Hamiltonian of the system. Based on this model Hamiltonian and the standard field theoretical approach, the Casimir-like force induced between the boundaries of such a confined film is discussed. The force depends on the geometrical shape and the dynamical character of the constituents of our active phase, as well as the anchoring conditions. For homeotropically aligned rod-like particles which in principle tend to align along a planar flow field, extensile activity enhances the attraction present in a thin nematic film. As the film thickness increases the force reduces. Beyond a critical thickness, a planar flow field instantaneous to a bend distortion sets in. Near but below the threshold of this activity-induced instability, the force crosses zero and repulsively diverges right at the critical threshold of this so-called flow instability. For contractile rods, in the same geometry as above, the structure is stable and the Casimir-like force diminishes by an exponential factor as a function of the film thickness. On the other side for a planar director alignment, rod-like contractile particles can induce opposite shear flows at the boundaries creating a splay distortion for the director between the plates. In this configuration, we obtain the same universal pretransitional behavior for the force as above. Vice versa, for extensile rod-like particles in this geometry, the director fluctuations become massive and the Casimir-like force diminishes again by an exponential factor as the film thickness increases. The effect of the active field on thermal fluctuations of the director and the fluctuation-induced Casimir force per area is derived through a ”semi”-dynamical approach as well. However, the results of the calculation due to a mathematical sum over the fluctuating modes do not lead to an approved closed form.
Fracture failure analysis of heavy-haul train couplers using convolutional neural network along with multilayer perceptron
Authors: Feng Qiang , Fei Jiyou , Bao Junhua , Liu Xiaodong , Chen Guofeng , Ma Jie , Zhao Xing , Yang Qi 
Published: August 2024

Abstract
Metal couplers are susceptible to unpredictable failure and fracture under long-term high-load conditions in heavy-haul railway transportation. The current mainstream manual inspection method has the disadvantages of high subjectivity and high a priori knowledge requirements, thus not meeting the rapid analysis requirements of production companies. Therefore, in this study, an automated failure analysis method is proposed for heavy-haul coupler fractures. First, a novel image segmentation method (PermuteNet) combining a visual multilayer perceptron and a convolutional neural network is designed to segment different failure patterns of fracture surfaces. The proposed method uses two newly proposed modules—permute attention module and context attention module—to improve the network’s ability to perceive weakly differentiated objects, thereby improving the recognition ability of the model for different failure patterns. In addition, a deep supervisory function is adopted to accelerate the convergence speed of the network. Finally, the proposed image segmentation method is deployed on a computer in conjunction with a developed client application to implement a single-click detection function for coupler fracture pattern analysis. Experiments are performed using the heavy-haul coupler fracture dataset established using on-site data; the proposed segmentation method achieves a mean intersection over union of 77.8%, which is considerably higher than that of other existing methods. By using the client software, the single-click detection function of the fracture area is realized. Thus, the proposed method provides a more convenient and accurate fracture identification solution for factory inspectors and has broad application prospects.
Automatic lockbolt detection method for riveting quality evaluation based on specific geometric feature descriptors of 3D point cloud
Authors: Guo Ronghui , Cui Haihua , Jiang Tao , Deng Yan , Wan Qing , Cheng Xiaosheng 
Published: August 2024

Abstract
Lockbolt is increasingly utilized in different fields due to its good performance in riveting and locking. However, the digital measurement and evaluation methods for the riveting quality of lockbolts still need to be explored. In this paper, we proposed a point cloud-based automatic lockbolt detection method, estimating the riveting quality via the protruding height of the lockbolt pin. The method involves three main stages: density computation for 3D point cloud, identification of real lockbolt regions, and segmentation for the top surface of the lockbolt. Firstly, a noise-weakened density computation model is proposed to suppress the density value of noise points. Secondly, the idea of principal component analysis is adopted, and a set of specific geometric feature descriptors is built to identify the real lockbolts from the candidate regions. Lockbolts can be robustly represented even if noise and missing in the point cloud. Thirdly, an efficient section-by-section segmenting algorithm is proposed to segment the top surface of the pin protrusion of the riveted lockbolt. Finally, the riveting quality of each lockbolt is evaluated based on the height from the top surface of the pin protrusion to the bottom plane of the lockbolt. Experiments on real data verified the accuracy, robustness, and efficiency of the proposed method.
Investigation of noise characteristics of electric vibrator utilizing acoustic camera and transfer path analysis
Authors: Xu Zhenyu , Chen Zhangwei 
Published: August 2024

Abstract
Mechanical equipment operation frequently results in noise generation, which can raise concerns regarding quality and safety. This study focuses on electric vibrators, essential electromechanical systems, with the goal of identifying the source and transfer path of noise. To achieve this objective, an acoustic camera positioning analysis was performed to approximate the distribution and characteristics of the noise. Building upon this foundation, a thorough evaluation of the transfer path and noise contribution was conducted. Subsequently, targeted noise suppression methods were implemented based on the properties of the noise. Primary noise sources were observed to be classified into wind turbulence and mechanical vibration based on operating conditions. Time-frequency analysis revealed the presence of low-frequency signals (around 50 Hz) and intermediate-frequency signals (around the resonant frequency), corresponding to wind-induced and mechanical response noise, respectively. Leveraging the characteristics of the noise and its transfer path, passive methods (Sound-absorbing rubber and Soundproof cotton) and active method (filtered-x least mean squares algorithm) were employed. The findings of this study contribute to a better understanding of noise mechanisms and provide insights for noise emission strategies.
A novel fault diagnosis method for bearing based on maximum average kurtosis morphological deconvolution
Authors: Lu Yixiang , Yao Zhiyi , Gao Qingwei , Zhu De , Zhao Dawei , Huang Darong 
Published: August 2024

Abstract
Maximum average kurtosis deconvolution (MAKD) effectively enhances periodic impulses in vibration signals. However, under conditions of random impulse interference, MAKD tends to amplify impulses within a single period. To address this problem, this paper proposes a maximum average kurtosis morphological deconvolution (MAKMD) method. First, on the basis of proposing a time-varying structural element more in line with the characteristics of vibration signals and constructing a new morphological gradient squared operator, an enhanced time-varying morphological filtering (ETVMF) is proposed. Then, ETVMF is introduced into MAKD to eliminate the effect of random impulse. Finally, the diagonal slice spectrum is utilized to detect the coupling frequency of the bearing, which makes the spectrum clearer and more convenient for bearing fault diagnosis. In MAKMD, the effect of random impulse is eliminated and the capability of fault feature extraction is enhanced. To demonstrate the method’s effectiveness and feasibility, experiments are conducted using simulated signals and measured bearing fault data, comparing results with existing deconvolution methods.
Local fusion generative adversarial network with dual-discriminator and parallel multipath and its application in machinery fault diagnosis with imbalanced data
Authors: Ju Miao , Ding Chuancang , Huang Weiguo , Zhu Zhongkui , Shen Changqing , Shi Juanjuan 
Published: August 2024

Abstract
Diagnosing faults in critical machinery components is imperative for effective condition monitoring and real-world datasets often suffer from data imbalance. To address this issue, numerous data generation methods have been developed, such as improved local fusion generative adversarial network (ILoFGAN), variational autoencoding GAN (VAEGAN), etc. However, the existing data generation methods primarily concentrate on global and single-scale features and often ignore local or multi-scale features, which leads to the omission of key features or nuances in the generated data. Therefore, a novel approach called the local fusion generative adversarial network with dual-discriminator and parallel multipath (LoFGAN-DP) is designed to enhance the fault diagnosis performance in the context of imbalanced data. The LoFGAN-DP features a parallel multi-path (PMP) module along with a dual-discriminator scheme, in which the multipath module facilitates feature extraction at various scales through convolution across paths of diverse sizes, and the dual-discriminator scheme can better improve the quality and diversity of the samples generated by the generator. The PMP module and dual-discriminator scheme enhance the proposed method’s robustness against variations in input data. After generating data by LoFGAN-DP, a two-dimensional capsule network is further used to achieve the efficient recognition of fault features. To validate the proposed LoFGAN-DP in the machinery fault diagnosis with imbalanced data, the gear dataset and the self-constructed bearing dataset were utilized. Experimental results show that LoFGAN-DP significantly improves structural similarity index, Fréchet inception distance, and fault classification accuracy compared to several advanced methods.
CNN-ELMNet: fault diagnosis of induction motor bearing based on cross-modal vector fusion
Authors: Yi Lingzhi , Huang Yi , Zhan Jun , Wang Yahui , Sun Tao , Long Jiao , Liu Jiangyong , Chen Biao 
Published: August 2024

Abstract
As the primary driving equipment in industrial, accurate fault diagnosis and condition monitoring of induction motor is crucial for ensuring operational safety. This paper focuses on the bearing faults of induction motors, which have a substantial impact on both the mechanical and electromagnetic systems of the motors. However, in diagnostic tasks, we are faced with the challenges of multi-source, multi-modal data, significant influence from environmental noise, and minimal differentiation between fault data. This paper proposed a novel cross-modal vector fusion fault diagnosis and classification model (CNN-ELMNet), which includes a cross-modal vector fusion network (VF) based on D-S evidence theory, feature extraction layer (FE) and classification layer (CL). Specifically, the VF prioritizes the integration of diagnostic results from individual vibration signals or stator current signals within convolutional neural networks with the features of the input implicit vectors as decision-making evidence, followed by weighted vector fusion through D-S evidence theory at the decision level. The FE focuses on retaining the convolutional, pooling, and fully connected layers of the convolutional network and freezing the final fully connected layer, thus preserving training parameters and fully utilizing the network’s powerful FE capabilities. The CL includes an Extreme Learning Machine optimized for random hyperparameters using the snow ablation optimizer (SAO) algorithm, which offers rapid convergence and high classification recognition rates. The CNN-ELMNet model combines a convolutional network with an extreme learning machine optimized by the SAO algorithm, which not only preserves the model’s FE capability but also enhances the convergence speed and classification recognition rate of the model. Experimental results on real datasets demonstrate that the proposed model exhibits strong stability, generalization, and high accuracy in fault diagnosis, achieving accuracy rate of 99.29% and 98.75%. This provides a more feasible solution for the bearing fault diagnosis of induction motors and holds promising prospects for practical applications.
Hybrid TiO2–RGO nanocomposite as high specific capacitance electrode for supercapacitor
Authors: Jadoon Jamil K , Pham Phuong V 
Published: August 2024

Abstract
This study describes the fabrication of composite electrodes comprising TiO2 and reduced graphene oxide layers using a moderate-temperature hydrothermal method. The morphology, crystalline structure, chemical composition, and optical features of the prepared composites were analyzed by FE-SEM, x-ray diffraction, FTIR, and UV–visible spectroscopy. The cyclic voltammetry (CV) and Nyquist plots were used to assess the electrochemical and impedance responses of the composite electrodes, respectively. The analysis revealed that the incorporation of RGO reduced the TiO2 bandgap to 3.87 eV 3.02 eV and improved the specific capacitance, enhancing the TiO2-RGO electrode’s supercapacitive performance. CV studies highlight that the TiO2-RGO composite has a high specific capacitance of 152 F g−1 at a substantially faster scan rate of 25 mV s−1 in a 1.0 M-KOH dilute electrolyte. These findings confirmed the applicability of the fabricated electrodes as prospective supercapacitor electrodes.
An inverse problem for the minimal surface equation in the presence of a riemannian metric
Authors: Nurminen Janne 
Published: August 2024

Abstract
In this work we study an inverse problem for the minimal surface equation on a Riemannian manifold

(

R

n

,
g
)

where the metric is of the form

g
(
x
)
=
c
(
x
)
(

g
^

⊕
e
)

. Here

g
^

is a simple Riemannian metric on

R

n
−
1

, e is the Euclidean metric on

R

and c a smooth positive function. We show that if the associated Dirichlet-to-Neumann maps corresponding to metrics g and

c
~

g

agree, then the Taylor series of the conformal factor

c
~

at

x
n

=
0

is equal to a positive constant. We also show a partial data result when n = 3.