In hyperspectral image (HSI) classification, each pixel sample is assigned to a land-cover category. In the recent past, convolutional neural network (CNN)-based HSI classification methods have ...greatly improved performance due to their superior ability to represent features. However, these methods have limited ability to obtain deep semantic features, and as the layer's number increases, computational costs rise significantly. The transformer framework can represent high-level semantic features well. In this article, a spectral-spatial feature tokenization transformer (SSFTT) method is proposed to capture spectral-spatial features and high-level semantic features. First, a spectral-spatial feature extraction module is built to extract low-level features. This module is composed of a 3-D convolution layer and a 2-D convolution layer, which are used to extract the shallow spectral and spatial features. Second, a Gaussian weighted feature tokenizer is introduced for features transformation. Third, the transformed features are input into the transformer encoder module for feature representation and learning. Finally, a linear layer is used to identify the first learnable token to obtain the sample label. Using three standard datasets, experimental analysis confirms that the computation time is less than other deep learning methods and the performance of the classification outperforms several current state-of-the-art methods. The code of this work is available at https://github.com/zgr6010/HSI_SSFTT for the sake of reproducibility.
In order to overcome the disadvantages of convolution neural network (CNN) in the current hyperspectral image (HSI) classification/segmentation methods, such as the inability to recognize the ...rotation of spatial objects, the difficulty to capture the fine spatial features and the problem that principal component analysis (PCA) ignores some important information when it retains few components, in this paper, an HSI segmentation model based on extended multi-morphological attribute profile (EMAP) features and cubic capsule network (EMAP–Cubic-Caps) was proposed. EMAP features can effectively extract various attributes profile features of entities in HSI, and the cubic capsule neural network can effectively capture complex spatial features with more details. Firstly, EMAP algorithm is introduced to extract the morphological attribute profile features of the principal components extracted by PCA, and the EMAP feature map is used as the input of the network. Then, the spectral and spatial low-layer information of the HSI is extracted by a cubic convolution network, and the high-layer information of HSI is extracted by the capsule module, which consists of an initial capsule layer and a digital capsule layer. Through the experimental comparison on three well-known HSI datasets, the superiority of the proposed algorithm in semantic segmentation is validated.
Hyperspectral image classification (HSIC) has garnered increasing attention among researchers. While classical networks like convolution neural networks (CNNs) have achieved satisfactory results with ...the advent of deep learning, they are confined to processing local information. Vision transformers, despite being effective at establishing long-distance dependencies, face challenges in extracting high-representation features for high-dimensional images. In this paper, we present the multiscale efficient attention with enhanced feature transformer (MEA-EFFormer), which is designed for the efficient extraction of spectral–spatial features, leading to effective classification. MEA-EFFormer employs a multiscale efficient attention feature extraction module to initially extract 3D convolution features and applies effective channel attention to refine spectral information. Following this, 2D convolution features are extracted and integrated with local binary pattern (LBP) spatial information to augment their representation. Then, the processed features are fed into a spectral–spatial enhancement attention (SSEA) module that facilitates interactive enhancement of spectral–spatial information across the three dimensions. Finally, these features undergo classification through a transformer encoder. We evaluate MEA-EFFormer against several state-of-the-art methods on three datasets and demonstrate its outstanding HSIC performance.
The curative effect of Cuscutae Semen (CS) could be adjusted after stir-frying, and it is difficult to distinguish between the raw CS and stir-fried processed Cuscutae Semen (SFCS) in clinical ...practice.
The paper aims to establish the fingerprints of raw and fried Cuscutae Semen products by high-performance liquid chromatography (HPLC) so that the compounds in raw and stir-fried products could be analyzed qualitatively and quantitatively. At the same time, the chemometrics method was used to evaluate the difference between CS and SFCS to provide reference for the research of traditional Chinese medicine (TCM) CS.
The raw and SFCS products' related substances were separated on a C18 column (250 mm × 4.6 mm, 5 µm) maintained at 30 °C (column temperature). The mobile phase comprised 0.1% formic acid aqueous solution (A) and acetonitrile (B), and a detection wavelength of 328 nm. The data were imported into statistical software for chemometric analysis.
The developed HPLC method exhibits good linearity and has good systematic applicability. The content of these 12 compounds in the samples was further determined and the data analyzed by chemometrics. The results showed that the composition of CS changed on heating, and HCA showed that CS and SFCS could be clearly distinguished. PCA showed that six components caused differences, namely neochlorogenic acid, cryptophyllogenic acid, caffeic acid, quercetin, isorhamnetin, and kaempferol.
This study unequivocally establishes a chromatographic fingerprint method intended for the extensive analysis of raw and stir-fried processed CS, which could substantially enhance the quality control of CS and the rational development and utilization of TCM resources.
This method for the simultaneous quantification of multiple compounds in CS and SFCS revealed the components responsible for the differences between raw and processed products. This will provide support for quality control of this herbal medicine.
Convolutional neural network (CNN)-based and Transformer-based methods for hyperspectral image (HSI) classification have rapidly advanced due to their unique characterization capabilities. However, ...the fixed kernel sizes in convolutional layers limit the comprehensive utilization of multi-scale features in HSI land cover analysis, while the Transformer’s multi-head self-attention (MHSA) mechanism faces challenges in effectively encoding feature information across various dimensions. To tackle this issue, this article introduces an HSI classification method, based on multi-scale convolutional features and multi-attention mechanisms (i.e., MSCF-MAM). Firstly, the model employs a multi-scale convolutional module to capture features across different scales in HSIs. Secondly, to enhance the integration of local and global channel features and establish long-range dependencies, a feature enhancement module based on pyramid squeeze attention (PSA) is employed. Lastly, the model leverages a classical Transformer Encoder (TE) and linear layers to encode and classify the transformed spatial–spectral features. The proposed method is evaluated on three publicly available datasets—Salina Valley (SV), WHU-Hi-HanChuan (HC), and WHU-Hi-HongHu (HH). Extensive experimental results have demonstrated that the MSCF-MAM method outperforms several representative methods in terms of classification performance.
Background. Prolonged disorders of consciousness (pDOC) are common in neurology and place a heavy burden on families and society. This study is aimed at investigating the characteristics of brain ...connectivity in patients with pDOC based on quantitative EEG (qEEG) and extending a new direction for the evaluation of pDOC. Methods. Participants were divided into a control group (CG) and a DOC group by the presence or absence of pDOC. Participants underwent magnetic resonance imaging (MRI) T1 three-dimensional magnetization with a prepared rapid acquisition gradient echo (3D-T1-MPRAGE) sequence, and video EEG data were collected. After calculating the power spectrum by EEG data analysis tool, DTABR (δ+θ/α+β ratio), Pearson’s correlation coefficient (Pearson r), Granger’s causality, and phase transfer entropy (PTE), we performed statistical analysis between two groups. Finally, receiver operating characteristic (ROC) curves of connectivity metrics were made. Results. The proportion of power in frontal, central, parietal, and temporal regions in the DOC group was lower than that in the CG. The percentage of delta power in the DOC group was significantly higher than that in the CG, the DTABR in the DOC group was higher than that in the CG, and the value was inverted. The Pearson r of the DOC group was higher than that of CG. The Pearson r of the delta band (Z=−6.71, P<0.01), theta band (Z=−15.06, P<0.01), and alpha band (Z=−28.45, P<0.01) were statistically significant. Granger causality showed that the intensity of directed connections between the two hemispheres in the DOC group at the same threshold was significantly reduced (Z=−82.43, P<0.01). The PTE of each frequency band in the DOC group was lower than that in the CG. The PTE of the delta band (Z=−42.68, P<0.01), theta band (Z=−56.79, P<0.01), the alpha band (Z=−35.11, P<0.01), and beta band (Z=−63.74, P<0.01) had statistical significance. Conclusion. Brain connectivity analysis based on EEG has the advantages of being noninvasive, convenient, and bedside. The Pearson r of DTABR, delta, theta, and alpha bands, Granger’s causality, and PTE of the delta, theta, alpha, and beta bands can be used as biological markers to distinguish between pDOC and healthy people, especially when behavior evaluation is difficult or ambiguous; it can supplement clinical diagnosis.
A quench boiler is the key equipment in ethylene production for the rapid cooling of high-temperature cracking gas. In the boiler, heat transfer is occurs between the hot cracking gas passing through ...the inner heat exchange tubes with an average temperature of 385 °C and cold water (or boiler water) passing through the inner heat exchange tubes with an average temperature of 350 °C. Required for double-pipe heat transfer, special tubesheets formed by welding flat-round tubes side by side are difficult to design, as no suitable design code is available. The thermal expansion difference between the inner heat exchange tubes and the jacketed tubes could lead to high thermal stress on the tubesheet. In this study, we investigated the effects of pretension or prestretching of the heat exchange tubes on stress distribution and strength assessment of the flat-round tubesheet in a quench boiler under two dangerous load conditions. Results show that without prestretching the heat exchange tubes, the flat-round tubesheet cannot pass the strength assessment. Prestretching the heat exchange tubes is necessary, and a pretension of 9 mm is most suitable. The magnitude of the pretension of the heat exchange tubes should be determined based on the thermal expansion difference between the inner heat exchange tubes and the jacketed tubes, with consideration of the strength improvement of the flat-round tubesheet.
The joint use of multisource remote-sensing (RS) data for Earth observation missions has drawn much attention. Although the fusion of several data sources can improve the accuracy of land-cover ...identification, many technical obstacles, such as disparate data structures, irrelevant physical characteristics, and a lack of training data, exist. In this article, a novel dual-branch method, consisting of a hierarchical convolutional neural network (CNN) and a transformer network, is proposed for fusing multisource heterogeneous information and improving joint classification performance. First, by combining the CNN with a transformer, the proposed dual-branch network can significantly capture and learn spectral-spatial features from hyperspectral image (HSI) data and elevation features from light detection and ranging (LiDAR) data. Then, to fuse these two sets of data features, a cross-token attention (CTA) fusion encoder is designed in a specialty. The well-designed deep hierarchical architecture takes full advantage of the powerful spatial context information extraction ability of the CNN and the strong long-range dependency modeling ability of the transformer network based on the self-attention (SA) mechanism. Four standard datasets are used in experiments to verify the effectiveness of the approach. The experimental results reveal that the proposed framework can perform noticeably better than state-of-the-art methods. The source code of the proposed method will be available publicly at https://github.com/zgr6010/Fusion_HCT.git .
To analyze the factors related to the efficacy of consciousness-regaining therapy (CRT) for prolonged disorder of consciousness.
A retrospective analysis was conducted on the case data of 114 ...patients with prolonged disorder of consciousness (pDOC) admitted to the Department of Functional Neurosurgery of Tianjin Huanhu Hospital from January 2019 to January 2022 to explore the relevant factors that affect the efficacy of CRT for pDOC. Next, basic information on the cases, data on pDOC disease assessment, CRT methods, and efficacy evaluation were collected.
These 114 patients were grouped, and a comparative analysis was done based on the efficacy at the end of treatment. Of these, 61 cases were allotted to the ineffective group and 53 cases to the effective group. There was a lack of statistical difference (P > 0.05) between the 2 groups based on gender, age, etiology, acute cerebral herniation, emergency craniotomy surgery, emergency decompressive craniectomy, time from onset to start of CRT, and CRT duration (P > 0.05). However, secondary hydrocephalus, CRT methods, JFK Coma Recovery Scale-Revised grading before treatment, and extended Glasgow Outcome Scale score at six months after treatment were found to be statistically different. The results of binary logistic regression analysis showed that the type of therapy (OR = 0.169, 95% CI: 0.057–0.508) affected the efficacy of CRT (P < 0.05).
Personalized awakening therapy using various invasive CRT methods could improve the efficacy of therapy for pDOC compared with noninvasive therapy.
Background
In advanced Parkinson’s disease (PD), axial symptoms are common and can be debilitating. Although deep brain stimulation (DBS) significantly improves motor symptoms, conventional ...high-frequency stimulation (HFS) has limited effectiveness in improving axial symptoms. In this study, we investigated the effects on multiple axial symptoms after DBS surgery with three different frequency programming paradigms comprising HFS, low-frequency stimulation (LFS), and variable-frequency stimulation (VFS).
Methods
This study involved PD patients who had significant preoperative axial symptoms and underwent bilateral subthalamic nucleus (STN) DBS. Axial symptoms, motor symptoms, medications, and quality of life were evaluated preoperatively (baseline). One month after surgery, HFS was applied. At 6 months post-surgery, HFS assessments were performed, and HFS was switched to LFS. A further month later, we conducted LFS assessments and switched LFS to VFS. At 8 months after surgery, VFS assessments were performed.
Results
Of the 21 PD patients initially enrolled, 16 patients were ultimately included in this study. Regarding HFS, all axial symptoms except for the Berg Balance Scale (
p
< 0.0001) did not improve compared with the baseline (all
p
> 0.05). As for LFS and VFS, all axial symptoms improved significantly compared with both the baseline and HFS (all
p
< 0.05). Moreover, motor symptoms and medications were significantly better than the baseline (all
p
< 0.05) after using LFS and VFS. Additionally, the quality of life of the PD patients after receiving LFS and VFS was significantly better than at the baseline and with HFS (all
p
< 0.0001).
Conclusion
Our findings indicate that HFS is ineffective at improving the majority of axial symptoms in advanced PD. However, both the LFS and VFS programming paradigms exhibit significant improvements in various axial symptoms.
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