bluuebunny/crossref_metadata_2025_split huggingface dataset:
The effect of bending angle on a flexible electrode DBD plasma under sinusoidal excitation
Authors: Li Jiayin , Zhou Desheng , Rebrov Evgeny , Tang Xin , Kim Minkwan 
Published: June 2024

Abstract
There is a critical demand for sophisticated surface disinfection and sterilization devices accessible to the public by using cold atmospheric pressure air plasmas. A flexible printed circuit design of a dielectric barrier discharge reactor under non-bending and two bending configurations with an angle of 120° and 180° was studied. The characteristics of power consumption, the optical emission spectrum, dynamic process, electrode temperature and ozone concentration are evaluated. The non-bending configuration produces more O3, as compared to the bending configuration at the same applied voltage. The 180° configuration has a maximum concentration of excited species at the expense of higher electrode temperature. Both bending configurations demonstrated the propagation of filaments to bending axis where the continues luminescence is observed due to the high electrical field. The energy efficiency for plasma-generated reactive species reaches to 40% for non-bending configuration and decreases with the increase of bending angle. This research provides a new strategy for perspective into the plasma generated reactive species in biomedical and environmental applications.
Search for charged lepton flavor violation of vector mesons in the U(1)
X
SSM
Authors: Dong Xing-Xing , Zhao Shu-Min , Dong Bo-Wei , Huo Jia-Peng , Guo Cai , Feng Tai-Fu 
Published: June 2024

Abstract
Charged lepton flavor violation (CLFV) represents a clear new physics (NP) signal beyond the standard model (SM). In this work, we investigate the CLFV decays of vector mesons

V
→

l

i

±

l

j

∓

with V ∈ {ϕ, J/Ψ, ϒ(1S), ϒ(2S), ϒ(3S)} in the U(1)
X
SSM. Considering the SM-like Higgs boson mass within 3σ region and the experimental upper bounds of Br(l

j
→ l

i

γ), we discuss the corresponding constraints on the relevant parameter space of the model. From the numerical analyses, the CLFV sources originated from the non-diagonal elements correspond to the initial and final generations of the leptons. And the branching ratios to these CLFV processes can easily reach the present experimental upper bounds, which indicate that this model can produce significant contributions to such CLFV decays. Therefore, searching for CLFV processes of vector mesons may be an effective channel to study NP.
Research on features and localization of AE signals from the mechanical body of industrial robots based on WD-EMD
Authors: Dong Shishang , You Jun , Li Ming , Gong Shi , Zhang Hao , Qiao Shuai , Zhang Xin , Guo Li , Yuan Xuechao 
Published: June 2024

Abstract
In order to reveal the propagation characteristics of acoustic emission (AE) signals in the body of industrial machinery, the characteristic frequencies and wave speeds of AE signals propagated on the interior and exterior surfaces (IS and ES) of the body were extracted. Subsequently, an algorithm utilizing characteristic frequency and time difference of arrival (TDOA) is proposed for the identification and localization of AE sources. Initially, an AE source is induced on the IS and ES of the body using the pencil-lead break method in accordance to ASTM standards, and the AE signal is captured by two piezoelectric sensors at a sampling frequency of 3 MHz. Then, to avoid the limitations of wavelet decomposition using self-selected wavelet scale and the problem that a single indicator cannot properly evaluate the correlation between independent mode functions (IMFs) with the original signal, this paper will conduct 4-layer wavelet decomposition of the original signal according to the response frequency range of the sensor, and select the wavelet details within the stable range of the response frequency of the sensor for preliminary reconstruction,and then the empirical mode decomposition (EMD) method is used to decompose the de-noised AE signal into 7 IMFs, and the AE waveform is reconstructed by the combined information of correlation coefficient and variance accounted for. Secondly, the reconstruction method combined with EMD analysis and a single index is compared with the proposed method in this paper to verify the reliability of the proposed method. In addition, the frequency domain characteristics of AE signal propagation process on the IS and ES of the body are extracted. Finally, based on the TDOA principle, the propagation speed of AE signal on the IS and ES of the body is calculated. Based on the geometric relationship between the AE source and two sensors, an algorithm for the location of the AE source is proposed. The results show that the proposed signal reconstruction method can effectively extract the features of AE signals, and the average positioning accuracy of the localization algorithm based on characteristic frequency and TDOA reaches 0.86%.
A high sensitivity digital giant magneto-impedance (GMI) sensor for magnetic communication
Authors: Xu Miao , Traore Papa Silly , Asfour Aktham 
Published: June 2024

Abstract
A high sensitivity Magneto-Impedance (GMI) sensor for magnetic communication systems in harsh environments is presented. In this system, the receiving coil of a conventional magnetic communication system is replaced by a high sensitivity GMI sensor and its digital electronic conditioning as well as digital signal processing. The whole architecture of the sensor is described in detail. It achieves two digital amplitude demodulations to recover, in real time, the binary sequence of the message sent by the transmitter. The demonstration and the validation of the reliable functioning of the system are illustrated through the transmission of messages using On-Off-Keying modulation method. The measured equivalent magnetic noise of the sensor was about noise

2

p
T

/

Hz

at 60 kHz, which is largely lower than the noise of other potential types of magnetic sensors such as Anisotropic Magneto-Resistive sensors for this application. This noise level suggests a potential higher communication range. When compared with an inductive coil, the developed sensor also presented an almost flat and sufficiently wide bandwidth compatible with magnetic communication. This bandwidth could potentially be extended according to the application.
DRLSTM-DA: a novel aeroengine remaining useful life prediction method based on deep optimization of multiple sensors information
Authors: Suo Shiwei , Fu Song , Lin Lin , Zhang Sihao , Liu Yikun , Hu Guolei , Hu Changsheng 
Published: June 2024

Abstract
One of the crucial tasks in Remaining useful life prediction is to effectively extract key sensor information from numerous sensor signals. In this article, a novel dual-attention enhanced deep residual LSTM (DRLSTM-DA) is developed to deeply optimize multidimensional sensor signals. First, two LSTM layers are designed to compress and reconstruct representative degradation information from input multidimensional time series data, to generate a new feature space. Second, a novel channel adaptive soft threshold module is designed to assign different weights according to the importance of different sensor information, and simultaneously eliminate the noise information in the signal. Thirdly, a temporal attention mechanism is designed to automatically highlight moments containing important decay information while suppressing unimportant moments. Finally, compared with other SOTA methods, our architecture achieves RMSE of 11.55, 13.74, 11.25, and 14.19 on four sub-datasets of the C-MAPSS dataset, with scores of 234.24, 465.49, 202.23, and 537.66, respectively. Meanwhile, in real aeroengine operation dataset, our architecture achieved the smallest RMSE (8.62). These results all validate the good predictive performance of our model.
Iterative Laplace of Gaussian filter and improved Teager energy operator for bearing fault detection in gearboxes
Authors: Yang Na , Liu Yie , Wei Yu , Feng Kezhu 
Published: June 2024

Abstract
The presence of cyclic impulses is considered to be a significant indicator of bearing defects. The extraction of fault-related information from gearboxes, however, poses a significant challenge due to the complex working conditions in gear transmission systems. These conditions often lead to fault-related impulse features being masked by inevitable background noise and vibration components. To address this issue, a novel iterative Laplace of Gaussian (LoG) filtering technique is proposed for the fault diagnosis field of bearings in gearboxes. An objective function incorporating Kurtosis statistics is used to iteratively update LoG coefficients instead of relying on fixed coefficients as in the original version, making the iterative LoG filtering technique better suited for handling fault-induced impact signals. Furthermore, an improved Teager energy operator (TEO) is developed to reduce the susceptibility of the original TEO to noise. The improvement involves incorporating a multi-resolution symmetric difference sequence into the discrete expression of TEO. This symmetric difference acts as a translation-invariant, sliding-window filter that, when combined with an energy operator, enables both energy detection and frequency filtering. The robustness against noise and sensitivity to frequency can be further improved by adjusting the multi-resolution parameter. The results of the experiments confirm the effectiveness of the proposed approach in identifying weak bearing fault features for gearboxes. Besides, the proposed method is also compared with the original LoG filter-based method and some competing methods, demonstrating its superior performance.
Deformation mechanism of the dual thermo-sensitive hydrogel bilayer structure
Authors: Jiang Huilong , Lei Jincheng , Liu Zishun 
Published: June 2024

Abstract
Thermo-sensitive hydrogel is a smart soft material that undergoes significant volume deformation in response to temperature changes, making it highly applicable in soft smart actuators. However, traditional thermo-sensitive hydrogel bilayer structures are often characterized by slow response rates and limited unidirectional bending capabilities. To overcome these limitations, a new thermo-sensitive hydrogel bilayer structure with faster response and bidirectional deformation is proposed in this work. This structure consists of two active thermo-sensitive hydrogel layers with different thermo-sensitive effect, in which one shrinks and the other swells when the temperature changes. The hydrogels with the fastest temperature response are identified by optimizing the monomer fraction and used to create the bilayer structure. The deformation states of the dual thermo-sensitive hydrogel bilayer structure are controlled by regulating the phase state of the both layers, resulting in different deformation patterns under varied temperature in experiments. We have established a model to describe the deformation of the bilayer structure. Finally, the capability of the bilayer structure to mimic human body movements and the blooming and wilting of flowers is demonstrated. This work reveals the deformation mechanism for a novel dual thermo-sensitive hydrogel bilayer structure, which holds great significance for the advancement of soft smart actuators.
Influence of the preparation method (thermal quench, ball-milling, dehydration and freeze-drying) on the dynamical properties of glassy trehalose
Authors: Decressain R , Carpentier L 
Published: June 2024

Abstract
Dielectric relaxation measurements have been performed on the glassy states of trehalose reached using different routes of amorphisation: thermal quench of the liquid state, milling of the anhydrous crystalline form, freeze-drying and dehydration of the dihydrate crystalline form. This study has revealed that all the glassy states are characterized by two relaxation processes respectively attributed to the slow Johari-Goldstein mode and to fast secondary intramolecular relaxations. These sub-Tg secondary relaxations are however strikingly different in the four glassy states revealing different energy landscape topologies.
Spatial transcriptomics at the brain-electrode interface in rat motor cortex and the relationship to recording quality
Authors: Whitsitt Quentin , Saxena Akash , Patel Bella , Evans Blake M , Hunt Bradley , Purcell Erin K 
Published: June 2024

Abstract
Study of the foreign body reaction to implanted electrodes in the brain is an important area of research for the future development of neuroprostheses and experimental electrophysiology. After electrode implantation in the brain, microglial activation, reactive astrogliosis, and neuronal cell death create an environment immediately surrounding the electrode that is significantly altered from its homeostatic state. Objective. To uncover physiological changes potentially affecting device function and longevity, spatial transcriptomics (ST) was implemented to identify changes in gene expression driven by electrode implantation and compare this differential gene expression to traditional metrics of glial reactivity, neuronal loss, and electrophysiological recording quality. Approach. For these experiments, rats were chronically implanted with functional Michigan-style microelectrode arrays, from which electrophysiological recordings (multi-unit activity, local field potential) were taken over a six-week time course. Brain tissue cryosections surrounding each electrode were then mounted for ST processing. The tissue was immunolabeled for neurons and astrocytes, which provided both a spatial reference for ST and a quantitative measure of glial fibrillary acidic protein and neuronal nuclei immunolabeling surrounding each implant. Main results. Results from rat motor cortex within 300 µm of the implanted electrodes at 24 h, 1 week, and 6 weeks post-implantation showed up to 553 significantly differentially expressed (DE) genes between implanted and non-implanted tissue sections. Regression on the significant DE genes identified the 6–7 genes that had the strongest relationship to histological and electrophysiological metrics, revealing potential candidate biomarkers of recording quality and the tissue response to implanted electrodes. Significance. Our analysis has shed new light onto the potential mechanisms involved in the tissue response to implanted electrodes while generating hypotheses regarding potential biomarkers related to recorded signal quality. A new approach has been developed to understand the tissue response to electrodes implanted in the brain using genes identified through transcriptomics, and to screen those results for potential relationships with functional outcomes.
Cracking modes and force dynamics in the insertion of neural probes into hydrogel brain phantom
Authors: Li Gen , Jang Dongyeol , Shin Yieljae , Qiang Yi , Qi Yongli , Wang Shuodao , Fang Hui 
Published: June 2024

Abstract

Objective. The insertion of penetrating neural probes into the brain is crucial for advancing neuroscience, yet it involves various inherent risks. Prototype probes are typically inserted into hydrogel-based brain phantoms and the mechanical responses are analyzed in order to inform the insertion mechanics during in vivo implantation. However, the underlying mechanism of the insertion dynamics of neural probes in hydrogel brain phantoms, particularly the phenomenon of cracking, remains insufficiently understood. This knowledge gap leads to misinterpretations and discrepancies when comparing results obtained from phantom studies to those observed under the in vivo conditions. This study aims to elucidate the impact of probe sharpness and dimensions on the cracking mechanisms and insertion dynamics characterized during the insertion of probes in hydrogel phantoms. Approach. The insertion of dummy probes with different shank shapes defined by the tip angle, width, and thickness is systematically studied. The insertion-induced cracks in the transparent hydrogel were accentuated by an immiscible dye, tracked by in situ imaging, and the corresponding insertion force was recorded. Three-dimensional finite element analysis models were developed to obtain the contact stress between the probe tip and the phantom. Main results. The findings reveal a dual pattern: for sharp, slender probes, the insertion forces remain consistently low during the insertion process, owing to continuously propagating straight cracks that align with the insertion direction. In contrast, blunt, thick probes induce large forces that increase rapidly with escalating insertion depth, mainly due to the formation of branched crack with a conical cracking surface, and the subsequent internal compression. This interpretation challenges the traditional understanding that neglects the difference in the cracking modes and regards increased frictional force as the sole factor contributing to higher insertion forces. The critical probe sharpness factors separating straight and branched cracking is identified experimentally, and a preliminary explanation of the transition between the two cracking modes is derived from three-dimensional finite element analysis. Significance. This study presents, for the first time, the mechanism underlying two distinct cracking modes during the insertion of neural probes into hydrogel brain phantoms. The correlations between the cracking modes and the insertion force dynamics, as well as the effects of the probe sharpness were established, offering insights into the design of neural probes via phantom studies and informing future investigations into cracking phenomena in brain tissue during probe implantations.
Seizure onset zone (SOZ) identification using effective brain connectivity of epileptogenic networks
Authors: Balaji Sai Sanjay , Parhi Keshab K 
Published: June 2024

Abstract

Objective. To demonstrate the capability of utilizing graph feature-based supervised machine learning (ML) algorithm on intracranial electroencephalogram recordings for the identification of seizure onset zones (SOZs) in individuals with drug-resistant epilepsy. Approach. Utilizing three model-free measures of effective connectivity (EC)-directed information, mutual information-guided Granger causality index (MI-GCI), and frequency-domain convergent cross-mapping (FD-CCM) - directed graphs are generated. Graph centrality measures at different sparsity are used as the classifier’s features. Main results. The centrality features achieve high accuracies exceeding 90% in distinguishing SOZ electrodes from non-SOZ electrodes. Notably, a sparse graph representation with just ten features and simple ML models effectively achieves such performance. The study identifies FD-CCM centrality measures as particularly significant, with a mean AUC of 0.93, outperforming prior literature. The FD-CCM-based graph modeling also highlights elevated centrality measures among SOZ electrodes, emphasizing heightened activity relative to non-SOZ electrodes during ictogenesis. Significance. This research not only underscores the efficacy of automated SOZ identification but also illuminates the potential of specific EC measures in enhancing discriminative power within the context of epilepsy research.
A cross-dataset adaptive domain selection transfer learning framework for motor imagery-based brain-computer interfaces
Authors: Jin Jing , Bai Guanglian , Xu Ren , Qin Ke , Sun Hao , Wang Xingyu , Cichocki Andrzej 
Published: June 2024

Abstract

Objective. In brain-computer interfaces (BCIs) that utilize motor imagery (MI), minimizing calibration time has become increasingly critical for real-world applications. Recently, transfer learning (TL) has been shown to effectively reduce the calibration time in MI-BCIs. However, variations in data distribution among subjects can significantly influence the performance of TL in MI-BCIs. Approach. We propose a cross-dataset adaptive domain selection transfer learning framework that integrates domain selection, data alignment, and an enhanced common spatial pattern (CSP) algorithm. Our approach uses a huge dataset of 109 subjects as the source domain. We begin by identifying non-BCI illiterate subjects from this huge dataset, then determine the source domain subjects most closely aligned with the target subjects using maximum mean discrepancy. After undergoing Euclidean alignment processing, features are extracted by multiple composite CSP. The final classification is carried out using the support vector machine. Main results. Our findings indicate that the proposed technique outperforms existing methods, achieving classification accuracies of 75.05% and 76.82% in two cross-dataset experiments, respectively. Significance. By reducing the need for extensive training data, yet maintaining high accuracy, our method optimizes the practical implementation of MI-BCIs.
Retrieving and reconstructing conceptually similar images from fMRI with latent diffusion models and a neuro-inspired brain decoding model
Authors: Ferrante Matteo , Boccato Tommaso , Passamonti Luca , Toschi Nicola 
Published: June 2024

Abstract

Objective. Brain decoding is a field of computational neuroscience that aims to infer mental states or internal representations of perceptual inputs from measurable brain activity. This study proposes a novel approach to brain decoding that relies on semantic and contextual similarity. Approach. We use several functional magnetic resonance imaging (fMRI) datasets of natural images as stimuli and create a deep learning decoding pipeline inspired by the bottom-up and top-down processes in human vision. Our pipeline includes a linear brain-to-feature model that maps fMRI activity to semantic visual stimuli features. We assume that the brain projects visual information onto a space that is homeomorphic to the latent space of last layer of a pretrained neural network, which summarizes and highlights similarities and differences between concepts. These features are categorized in the latent space using a nearest-neighbor strategy, and the results are used to retrieve images or condition a generative latent diffusion model to create novel images. Main results. We demonstrate semantic classification and image retrieval on three different fMRI datasets: Generic Object Decoding (vision perception and imagination), BOLD5000, and NSD. In all cases, a simple mapping between fMRI and a deep semantic representation of the visual stimulus resulted in meaningful classification and retrieved or generated images. We assessed quality using quantitative metrics and a human evaluation experiment that reproduces the multiplicity of conscious and unconscious criteria that humans use to evaluate image similarity. Our method achieved correct evaluation in over 80% of the test set. Significance. Our study proposes a novel approach to brain decoding that relies on semantic and contextual similarity. The results demonstrate that measurable neural correlates can be linearly mapped onto the latent space of a neural network to synthesize images that match the original content. These findings have implications for both cognitive neuroscience and artificial intelligence.