331 research outputs found
An early fault feature extraction method for rolling bearings based on variational mode decomposition and random decrement technique
The early fault characteristics of rolling bearing are weak, and the background noise is so strong that it is difficult to diagnose. In order to solve the above problems, an early fault feature extraction method for rolling bearings based on variational mode decomposition and random decrement technique was proposed. The variational mode decomposition was used to decompose the collected vibration signals, and the component with the larger correlation coefficient was selected as the fault component. Then the fault component was processed by random decrement technology, and the Hilbert envelope spectrum of the fault component was made. According to the proposed method, the early fault characteristic of outer ring of rolling bearing was extracted. Compared with the method based on EMD, the proposed method is more effective in extracting the early fault characteristics of rolling bearings
A light Higgs scalar in the NMSSM confronted with the latest LHC Higgs data
In the Next-to-Minimal Supersymemtric Standard Model (NMSSM), one of the
neutral Higgs scalars (CP-even or CP-odd) may be lighter than half of the
SM-like Higgs boson. In this case, the SM-like Higgs boson h can decay into
such a light scalar pair and consequently the diphoton and ZZ signal rates at
the LHC will be suppressed. In this work, we examine the constraints of the
latest LHC Higgs data on such a possibility. We perform a comprehensive scan
over the parameter space of the NMSSM by considering various experimental
constraints and find that the LHC Higgs data can readily constrain the
parameter space and the properties of the light scalar, e.g., at 3
level this light scalar should be highly singlet dominant and the branching
ratio of the SM-like Higgs boson decay into the scalar pair should be less than
about 30%. Also we investigate the detection of this scalar at various
colliders. Through a detailed Monte Carlo simulation we find that under the
constraints of the current Higgs data this light scalar can be accessible at
the LHC-14 with an integrated luminosity over 300 fb.Comment: Accepted by JHE
nnDetection for Intracranial Aneurysms Detection and Localization
Intracranial aneurysms are a commonly occurring and life-threatening
condition, affecting approximately 3.2% of the general population.
Consequently, detecting these aneurysms plays a crucial role in their
management. Lesion detection involves the simultaneous localization and
categorization of abnormalities within medical images. In this study, we
employed the nnDetection framework, a self-configuring framework specifically
designed for 3D medical object detection, to detect and localize the 3D
coordinates of aneurysms effectively. To capture and extract diverse features
associated with aneurysms, we utilized TOF-MRA and structural MRI, both
obtained from the ADAM dataset. The performance of our proposed deep learning
model was assessed through the utilization of free-response receiver operative
characteristics for evaluation purposes. The model's weights and 3D prediction
of the bounding box of TOF-MRA are publicly available at
https://github.com/orouskhani/AneurysmDetection.Comment: 6 pages, 4 figure
Highly sensitive electrochemical sensing platform for the detection of L-dopa based on electropolymerizing glutathione disulfide and multi-walled carbon nanotube-modified electrodes
Afacile sensing platformfor the detection of L-dopa has been developed by electropolymerizing glutathione disulfide (PGSSG) on the surface of glass carbon electrodes (GCE) which were modified by multi-walled carbon nanotubes (MWCNTs). The electrochemical behaviour of the proposed electrodes were investigated via cyclic voltammetry (CV) and differential pulse voltammetry DPV). The morphology of the PGSSG and PGSSG/MWCNTs were characterized by scanning electron microscopy (SEM). Under the optimized experimental conditions, the sensing platform showed the linear response to L-Dopa in a range from 1.0 × 10–6 to 1.2 × 10–3 M with a detection limit of 3.3 × 10–7M (S/N = 3). Moreover, with the merits of high sensitivity and selectivity, good stability and reproducibility, the sensor was successfully applied for the determination of L-dopa in a real sample.Keywords: L-dopa, glutathione disulfide, multi-walled carbon nanotubes, electropolymerization, electrochemical determinatio
The cellular distribution of Na+/H+ exchanger regulatory factor 1 is determined by the PDZ-I domain and regulates the malignant progression of breast cancer
The oncogenic role of ectopic expression of Na+/H+ exchanger regulatory factor 1 (NHERF1) was recently suggested. Here, we show that NHERF1 was upregulated in high grades compared with low grades. Increased NHERF1 expression was correlated with poor prognosis and poor survival. NHERF1 expression was higher in the nucleus of cancer cells than in contiguous non- mammary epithelial cells. A novel mutation, namely NHERF1 Y24S, was identified in human breast cancer tissues and shown to correspond to a conserved residue in the PDZ-I domain of NHERF1. Truncation and mutation of the PDZ-I domain of NHERF1 increased the nuclear distribution of the NHERF1 protein, and this redistribution was associated with the malignant phenotype of breast cancer cells, including growth, migration, and adhesion. The present results suggest a role for NHERF1 in the progression of breast cancer mediated by the nuclear distribution of the NHERF1 protein, as determined by the truncation or key site mutation of the PDZ-I domain
Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity Recognition
Meta-learning methods have been widely used in few-shot named entity
recognition (NER), especially prototype-based methods. However, the Other(O)
class is difficult to be represented by a prototype vector because there are
generally a large number of samples in the class that have miscellaneous
semantics. To solve the problem, we propose MeTNet, which generates prototype
vectors for entity types only but not O-class. We design an improved triplet
network to map samples and prototype vectors into a low-dimensional space that
is easier to be classified and propose an adaptive margin for each entity type.
The margin plays as a radius and controls a region with adaptive size in the
low-dimensional space. Based on the regions, we propose a new inference
procedure to predict the label of a query instance. We conduct extensive
experiments in both in-domain and cross-domain settings to show the superiority
of MeTNet over other state-of-the-art methods. In particular, we release a
Chinese few-shot NER dataset FEW-COMM extracted from a well-known e-commerce
platform. To the best of our knowledge, this is the first Chinese few-shot NER
dataset. All the datasets and codes are provided at
https://github.com/hccngu/MeTNet
Exchanging-based Multimodal Fusion with Transformer
We study the problem of multimodal fusion in this paper. Recent
exchanging-based methods have been proposed for vision-vision fusion, which aim
to exchange embeddings learned from one modality to the other. However, most of
them project inputs of multimodalities into different low-dimensional spaces
and cannot be applied to the sequential input data. To solve these issues, in
this paper, we propose a novel exchanging-based multimodal fusion model MuSE
for text-vision fusion based on Transformer. We first use two encoders to
separately map multimodal inputs into different low-dimensional spaces. Then we
employ two decoders to regularize the embeddings and pull them into the same
space. The two decoders capture the correlations between texts and images with
the image captioning task and the text-to-image generation task, respectively.
Further, based on the regularized embeddings, we present CrossTransformer,
which uses two Transformer encoders with shared parameters as the backbone
model to exchange knowledge between multimodalities. Specifically,
CrossTransformer first learns the global contextual information of the inputs
in the shallow layers. After that, it performs inter-modal exchange by
selecting a proportion of tokens in one modality and replacing their embeddings
with the average of embeddings in the other modality. We conduct extensive
experiments to evaluate the performance of MuSE on the Multimodal Named Entity
Recognition task and the Multimodal Sentiment Analysis task. Our results show
the superiority of MuSE against other competitors. Our code and data are
provided at https://github.com/RecklessRonan/MuSE
Versatile Ratiometric Fluorescent Probe Based on the Two-Isophorone Fluorophore for Sensing Nitroxyl
Nitroxyl (HNO) is closely linked with numerous biological processes. Fluorescent probes provide a visual tool for determining HNO. Due to fluorescence quenching by HNO-responsive recognition groups, most of the current fluorescent probes exhibit an "off-on"fluorescence response. As such, the single fluorescence signal of these probes is easily affected by external factors such as the microenvironment, sensor concentration, and photobleaching. Herein, we have developed a ratiometric fluorescent probe (CHT-P) based on our previously developed two-isophorone fluorophore. CHT-P could be used to determine HNO through ratiometric signal readouts with high selectivity and sensitivity, ensuring the accurate quantitative detection of HNO. Additionally, the probe exhibited low cytotoxicity, was cell permeable, and could be used for ratiometric imaging of HNO in cells. Finally, CHT-P-coated portable test strips were used to determine HNO using the solid-state fluorescence signal readout. </p
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