bluuebunny/crossref_metadata_2025_split huggingface dataset:
A retrospective two-center cohort study of the bidirectional relationship between depression and tinnitus-related distress
Authors: Lukas Cosima F. , Mazurek Birgit , Brueggemann Petra , Junghöfer Markus , Guntinas–Lichius Orlando , Dobel Christian 
Published: November 2024

Abstract
Background
Tinnitus can cause considerable psychological distress among patients, particularly if comorbidities occur. Despite a strong relationship between tinnitus-related distress and depression, the underlying mechanisms represent a long-standing question. By investigating the co-development of tinnitus-related distress and depressiveness throughout therapy, we capture the dynamic interplay of both conditions and uncover underlying common features mediating their link.

Methods
Large datasets from two different day clinics in Germany have been analyzed using a regularization method for predictor selection (analysis 1) and latent growth curve modeling (LCM; analysis 2). Tinnitus-related distress was assessed using the Tinnitus Questionnaire (TQ). All patients have been experiencing chronic subjective tinnitus with a minimum mean severity level of TQ grade 2. Treatment at both day clinics involved tinnitus management according to clinical guidelines with minor idiosyncratic differences. Analysis 1 was performed on a dataset of 500 patients who received the Jena Interdisciplinary Treatment for Tinnitus (JITT) for 5 consecutive days between 2013 and 2017. Analysis 2 was performed on a second dataset, which included 1016 patients treated at the Tinnitus Center of the Charité Universitätsmedizin Berlin for 7 days between 2011 and 2015.

Results
Here, we show a substantial bidirectional relationship between tinnitus-related distress and depression severity while emphasizing the role of somatic symptoms and perceived stress in the experience and maintenance of tinnitus awareness. The LCM provides adequate model fit (CFI = 0.993, SRMR = 0.016).

Conclusions
Our results indicate enhanced therapy success in depression when tinnitus-related distress is addressed and vice versa. The combined treatment of tinnitus and depression is proposed for future treatment strategies.
Effects of stock rebuilding – A computable general equilibrium analysis for a mackerel fishery in Korea
Authors: Seung Chang K. , Kim Do-Hoon , Choi Ji-Hoon 
Published: November 2024

AbstractWe evaluate the effects of rebuilding a mackerel fishery in Korea using a bioeconomic computable general equilibrium (CGE) model. The effects include three different measures of the benefits of the stock rebuilding—the fishing sector’s rent, its value-added, and the aggregate regional welfare. In the baseline simulation, we run 20 scenarios over 30 years, depending on the magnitude of the annual percentage reduction in the harvest required to recover the stock to the target level (Bmsy). We find that the percentage reductions that yield the largest increase in the three measures of the benefits are 70% (yielding the largest rent increase), 35% (yielding the largest value-added increase), and 20% (yielding the largest aggregate welfare gain), respectively, to rebuild the stock to the target level. Results shed some light on which measure the policymakers should use when implementing a stock rebuilding policy.
Frame analysis of the semantics of mental verbs of the Kazakh language
Authors: Mamadiyarova Nazira , Kassym Balkiya , Yessenova Kalbike , Abisheva Nurziya 
Published: November 2024

Abstract
The study of mental verbs is relevant at the present stage of development of linguistic science, since there is still no clear classification of these lexical units in the Kazakh language. The purpose of this study was formed — the representation of the lexical-semantic categories of verbs associated with the processes of perception, cognition and affectivity, using frame model. To implement the tasks set in the study, the methods of frame, semantic, contextual and structural analysis, dictionary and thesaurus approaches used in combination were used. The article examined the main lexico-semantic categories of verbs expressing actions with mental meanings, focused on the formation of frame structures and substructures, evaluated the linguistic capabilities of the three main lexico-semantic categories (perception, cognition, affectivity), formed semantic links between functional-semantic fields each individual group representing different aspects of mental activity. Attention was focused on the fact that the processes of semantic changes are determined by the worldview of the people, their spiritual and cultural experience, and the discursive nature of the language. An idea was formed about the system of mental verbs, representing all types of knowledge, on the basis of the corpus of the Kazakh language and artistic material. This study showed that the Kazakh language is characterised by a high degree of figurativeness, metaphorisation and anthropomorphisation, determined the semantic boundaries of the main groups of mental verbs, so it can be used in the future to form frame models in the field of Kazakh phraseology, linguoculturology, cognitive and communicative linguistics.
“I am not populist”
Authors: Filardo-Llamas Laura , De Cock Barbara , Hambye Philippe , Shchinova Nadezda 
Published: November 2024

Abstract
Situated within studies on discourses about populism (De Cleen et al.,
2018), this paper zooms in on the use, meanings, and role of the word populist in contemporary socio-political debates
and, more specifically, on social media. This paper examines populist as stigma term (Kranert,
2020) and seeks to determine how people negotiate their categorisation as (non-)populist — and hence the meaning of
this category — on Twitter. Based on the analysis of 139 tweets including the phrase “I am not populist” in four different
languages (Dutch, French, English, Spanish), we propose that two patterns can be identified for the renegotiation of users’
identities as populist: denial and self-categorisation. This analysis confirms that populist as a category can refer to a variety
of (political) attitudes and orientations and shows the consequences of the polysemous nature of populist while proving that, in
certain contexts, populist refers to some specific and stable categories.
Bulk induced photo-current in topological insulating materials (Bi2Se3/Si) heterojunction for highly responsive photodetectors
Authors: Gautam Vidushi , Gautam Sudhanshu , Sharma Sanjay , Verma Sandeep Kumar , Maurya Gyanendra , Kushvaha Sunil Singh , Kumar Pramod 
Published: November 2024

Abstract
Studies of quintuple layers (QLs) dependent transport can shed light on the bulk and surface states because bulk and surface transport parameters have different QL dependencies. The QL dependency of material properties in Bi2Se3 reported in this study demonstrates that the QL is a parameter that may be adjusted to control and possibly optimise the bulk properties of Bi2Se3 thin films. We have explored the transport properties of the topological insulator Bi2Se3 thin films as a function of thickness. The resistivity-temperature curve reveals that as the thickness increases, the amount of bulk contribution to the conduction increases, indicating that thicker QL materials exhibit a superior photocurrent. The electronic properties have also been incorporated in support of the experimental findings, which were calculated using Quantum Espresso codes for the density functional theory. Using Bi2Se3/Si heterostructure, a photodetector with a robust response to 1100 nm irradiance is fabricated, revealing better optoelectronic properties for thicker QL.
A novel late-stage oscillating discharge current for plasma regulation and its effect in material removal in electrical discharge machining
Authors: Gao Qiang , Zhang Ya-Ou , Liao Hua-Lin 
Published: November 2024

Abstract
Improving the material removal rate (MRR) has recently become one of the most important issues in electrical discharge machining. During the discharge process, a large portion of molten material cannot be sufficiently expelled from the molten pool but re-solidifies, ultimately resulting in low energy utilization and machining efficiency. Unlike existing methods that primarily focus on optimizing general discharge parameters, this study aims to enhance molten material expulsion and MRR through discharge plasma regulation by employing a redesigned late-stage oscillating discharge current. During a single-pulse discharge process, this kind of discharge current firstly remains constant to ensure stable heat transfer from the plasma to the workpiece, then transitions to periodic oscillations to enhance plasma movement and facilitate molten material expulsion. High-speed plasma observations and heat-flow coupling simulations are conducted to analyze the effects of the discharge currents on material removal, and the optimal oscillation start time is obtained. Experimental results in machining stainless steel demonstrate that the use of the late-stage oscillating discharge current, in comparison to the conventional rectangular discharge current, results in a 74% increase in material removal volume per unit of energy and a 56% in average recast layer thickness.
Design of a microwave-induced atmospheric-pressure plasma source based on cylindrical resonant cavity TM010 mode
Authors: Liu Tao , Zhang Xianyu , Wu Li , Tao Junwu , Zhang Wencong 
Published: November 2024

Abstract
A microwave-induced low-temperature atmospheric-pressure source based on cylindrical resonant cavity TM010 mode was designed. The microwave power was fed into the cavity from a rectangular waveguide on top of it through a coupling hole. A metal pin with adjustable insertion depth was added to the cavity to tune its resonant frequency. The fed waveguide was connected by a sliding short. By tuning the sliding short, the energy transfer efficiency from the waveguide to the cavity was changed. Experiments showed that it could induce an argon discharge in the resonant cavity at atmospheric pressure with as low as 30 W incident wave power without any extra trigger. The plasma length reached 50 mm when the incident wave power was 200 W. By exciting the argon with an extra ignitor in the feeding waveguide, the length of the plasma plume could be extended to 260 mm when the incident wave power was 800 W. The plasma generated by this device was filamentous for both cases. The emission spectrum proved the uniformity of the plasma along its length. This work will be helpful in providing a new alternative microwave plasma device for waste gas treatment or chemical reactions that require plasma catalysis.
Simultaneous determination of refined values of mobility, channel and contact resistance in organic field-effect transistors utilizing the four- and three-probe methods
Authors: Saikh Samayun , Mukherjee Ayash Kanto 
Published: November 2024

Abstract
Accurate determination of charge carrier mobility is a critical aspect in an organic field-effect transistor (OFET) as it is tightly correlated to the development of new materials, better device designs and improved fabrication methods. The presence of high contact resistance at the metal–organic semiconductor interface introduces an intrinsic error in the extraction of charge carrier mobility. In this report, electrical circuit analysis for the determination of the refined channel mobility, total contact resistance and channel resistance values is showcased for a poly(3-hexylthiophene) OFET with bottom-gate bottom-contact architecture. The process of determination is based on the combination of the conventional four-probe and three-probe measurement techniques. These two techniques are used in conjunction to assess refined mobility values in the channel region. This is followed by the determination of total contact resistance and channel resistances. The results show that, at a fixed drain current, mobility increases with the gate voltage while both the contact and channel resistances decrease.
The finite volume effects of the Nambu–Jona–Lasinio model with the running coupling constant
Authors: Su Shou-Zheng , Zhao Ye-Yin , Wen Xin-Jian 
Published: November 2024

Abstract
With Schwinger’s proper-time formalism of the Nambu–Jona–Lasinio model, we investigate the finite volume effects with the anti-periodic boundary condition in the presence of magnetic fields. The model is solved with a running coupling constant G(B), which is properly fitted by the lattice average (Σ
u
+ Σ
d
)/2 and the difference Σ
u
− Σ
d
. For the model in a finite or infinite volume, the magnetic fields can increase the constituent quark mass M while the temperatures can decrease it. M is close to the infinite volume limit when the box length L is appropriately large. For a sufficiently small value of L, M is close to the chiral limit. The finite volume effects behave intensely in the narrow ranges of L where the partial derivative ∂M/∂L is greater than zero. These narrow ranges can be reduced by stronger magnetic fields and by higher temperatures. In addition, the chiral limit can be restored by a sufficiently small finite volume and be broken by sufficiently strong magnetic fields. Finally, we discuss the thermal susceptibility and the crossover phase transition depending on the temperature at finite volume in the presence of magnetic fields.
A novel end-to-end anomaly detection framework for spacecraft using MINE and LSTM-VAE with attention mechanism
Authors: Yu Bing , Yu Yang , Yang Zhiming , Xiang Gang 
Published: November 2024

Abstract
Anomaly detection in spacecraft plays a pivotal role in ensuring the safety and reliability of aerospace missions. As contemporary spacecraft systems become intricate with the integration of numerous subsystems and components, it is crucial to have effective methods to detect anomalies to maintain the normal operation of the spacecraft and prevent potential failures. However, due to the complexity of the space environment and the high level of integration of systems, conventional anomaly detection methods are often inadequate in dealing with multi-source, high-dimensional, and nonlinear data within spacecraft. To address these problems, this paper proposes a spacecraft end-to-end anomaly detection framework based on Mutual Information Neural Estimation (MINE) and long short-term memory networks-variational autoencoder (LSTM-VAE). This framework employs MINE to capture complex dependencies between features and utilizes LSTM-VAE for temporal modeling and anomaly detection, while an attention mechanism is also incorporated to enhance model interpretability. Specifically, MINE is used to learn nonlinear, high-order dependencies among variables in the aerospace system. LSTM-VAE manages to blend the temporal modeling capability of recurrent neural networks and the advantaged latent representation learning of variational autoencoders, providing deeply integrated modeling of the telemetric data dynamics and latent structure. The introduction of the attention mechanism elevates the model’s interpretability, making it possible to trace anomaly detection results back to specific time points and related features. The research conducted comparative experiments on two novel aerospace telemetry datasets. The results indicate that the proposed framework significantly outperforms existing methods in terms of anomaly detection accuracy, precision, recall rate, and F1 score. Additionally, through case study analysis, we demonstrate this framework’s application in actual spacecraft anomaly detection tasks, thereby illustrating its potential in aiding anomaly source tracing in spacecraft.
A wideband MUSIC algorithm using an improved empirical wavelet transform
Authors: Hu Yi-Ding , Deng Wei-Yao , Wu Gu-Xin , Yang Dong 
Published: November 2024

Abstract
The traditional multiple signal classification (MUSIC) algorithm is only suitable for narrowband array signals, however for wideband signals, sub-band division is required, most of which are based on preset filters or time–frequency analysis. These methods usually require manual selection of the parameters of the filters, basis functions, etc., according to different circumstances, which leads to reduced applicability. This paper proposes a wideband MUSIC algorithm using an improved empirical wavelet transform (EWT), which resolves the sub-band division issue by adaptively constructing a series of empirical wavelet functions to decompose the array received signals. In order to adapt EWT to array signal processing, the spectral division method of EWT is improved by utilizing the spectral mean value and order statistical filter of the array element signals, enabling EWT to uniformly decompose the received signal of each array element. Theoretical research and simulation analysis demonstrate that the wideband MUSIC algorithm using the improved EWT enables effective estimation of arrival angles of wideband signals. Due to the utilization of a more refined adaptive uniform sub-band division technique for array signal, the proposed algorithm has the lowest angle estimation error compared to the MUSIC algorithms based on short-time Fourier transform, auditory filter, and continuous wavelet transform.
A lightweight road traffic sign detection algorithm based on adaptive sparse channel pruning
Authors: Zheng Xiaolong , Guan Zhiwei , Chen Qiang , Wen Guoqiang , Lu Xiaofeng 
Published: November 2024

Abstract
The development of traffic sign recognition (TSR) has become increasingly important for enhancing the safety and convenience of assisted driving. To achieve high accuracy, faster inference speed, and a lightweight model, an improved lightweight TSR network, termed YOLOv8-ALWP, has been proposed. This network incorporates adaptive downsampling to replace the original convolution module in YOLOv8. By employing multiple pooling and convolution operations, it reduces the spatial resolution to extract additional feature information. To accommodate the varying scale characteristics of different traffic signs, large separable kernel attention is introduced to enhance spatial pyramid pooling-fast. Furthermore, the complete intersection over union loss has been improved, and a new Wise-Focaler-EIoU Loss has been proposed to accelerate model convergence and enhance generalization capabilities. Finally, layer-adaptive sparsity for magnitude-based pruning is employed to reduce the model’s parameters, decrease computational complexity, and improve inference speed. Experiments were conducted using the TT100K, Roadsign, CCTSDB, and GTSDB datasets. In the TT100K dataset, compared to the baseline model, the improved algorithm significantly reduced parameters by 64.67%, FLOPs by 44.44%, and increased mAP by 1.7%, precision by 5.5%, and FPS from 70.3 to 81.7, respectively. Under four specific conditions, the improved algorithm effectively addressed the shortcomings of the baseline model, such as missed detections and reduced accuracy. These experimental results indicate that the YOLOv8-ALWP algorithm achieves model lightweighting while enhancing detection accuracy.
Conception and calibration of low-cost diagnostic tool for the evaluation of the thermal, acoustic, and visual comforts in buildings
Authors: Cormier Pierre-Antoine , Laporte-Chabasse Quentin , Berton Julien , Penot Jean-Daniel 
Published: November 2024

Abstract
The comfort of indoor occupants is the primary factor influencing the energy consumption in a building. Even if it is a highly subjective notion, a better understanding of its links with the environmental parameters represents a promising way to optimize building energy consumption. The environmental physical parameters can be easily measured but usually require multiple and/or expensive systems. In this work, we present the conception and the calibration of an inexpensive and straightforward homemade diagnostic tool named VisAcTh, dedicated to measuring the environmental parameters related to indoor thermal, visual, and acoustic comforts. We paid particular attention to the simplicity of the different methods and concepts used in the conception, operation, and calibration. The VisAcTh tool comprises six inexpensive sensors controlled by an ESP32 microcontroller integrated into a 3D-printed PLA box. It allows measuring air temperature, air velocity, mean radiant temperature, relative humidity, atmospheric pressure, noise level, and illuminance. Three sensors require a calibration step to operate in specific indoor conditions, i.e. the homemade globe thermometer, the noise level, and the air velocity sensors. We designed protocols based on a homemade experimental setup that can be easily built using machines available in most fablabs and by scientists with limited knowledge. The last part of the paper is dedicated to evaluating the reliability and performance of the device. Two sets of experiments are performed, during which we monitor the noise level, the illuminance, and the well-known predicted mean vote, which depends on thermal physical parameters. The first one aims to evaluate the reliability of the VisAcTh device by comparing it with reference sensors over a period of a few hours in three spaces of an education building (a classroom, a lobby, and an office). A good agreement was found between these measurements and similar ones obtained from commercial and calibrated sensors. Second, we aim to evaluate the monitoring capability of the device during an extended period. Thus, four new VisAcTh are built, calibrated, and placed in four rooms of a modular education building for four months. The results are in line with the environmental conditions.
Gradient adaptive data augmentation and locally compensated feature fusion network for strip steel surface defect classification based on long tailed distribution
Authors: Zeng Liang , Feng Zheng , Chang Xinyu , Chen Jia , Wang Shanshan 
Published: November 2024

Abstract
Defect classification is a crucial aspect of strip production. The existing methods for strip steel surface defect classification still require improvement due to the multi-category nature and complexity of defects, as well as the long-tailed distribution characteristics of the defect dataset. In this paper, a gradient-adaptive data augmentation and locally compensated feature fusion network for strip steel surface defect classification is proposed. Firstly, we put forward a gradient-adaptive data augmentation method based on the number of category samples to solve the long-tail distribution problem, which uses gradient-adaptive sample expansion to avoid increasing inter-class variation and preserves the probabilistic information contained in the category distribution through an average-based undersampling operation of the category samples. Secondly, a locally compensated feature fusion network is proposed, which is the first method to increase the multi-scale perception capability of the network using local compensation, mimicking the human behaviour of observing complex image patterns, which is compensating the feature information of the global image with the help of the feature information from the local image. The method not only improves the model’s ability to perceive defects, but also mitigates the negative effects of image compression. The experimental results on one public dataset and three private datasets demonstrate that the proposed method achieves significant classification performance improvement. Thus, it is founded that our method provides a promising solution for the strip surface defect classification problem.