A Novel Bird Sound Recognition Method Based on Multifeature Fusion and a Transformer Encoder.

Birds play a vital role in the study of ecosystems and biodiversity. Accurate bird identification helps monitor biodiversity, understand the functions of ecosystems, and develop effective conservation strategies. However, previous bird sound recognition methods often relied on single features and overlooked the spatial information associated with these features, leading to low accuracy. Recognizing this gap, the present study proposed a bird sound recognition method that employs multiple convolutional neural-based networks and a transformer encoder to provide a reliable solution for identifying and classifying birds based on their unique sounds. We manually extracted various acoustic features as model inputs, and feature fusion was applied to obtain the final set of feature vectors. Feature fusion combines the deep features extracted by various networks, resulting in a more comprehensive feature set, thereby improving recognition accuracy. The multiple integrated acoustic features, such as mel frequency cepstral coefficients (MFCC), chroma features (Chroma) and Tonnetz features, were encoded by a transformer encoder. The transformer encoder effectively extracted the positional relationships between bird sound features, resulting in enhanced recognition accuracy. The experimental results demonstrated the exceptional performance of our method with an accuracy of 97.99%, a recall of 96.14%, an F1 score of 96.88% and a precision of 97.97% on the Birdsdata dataset. Furthermore, our method achieved an accuracy of 93.18%, a recall of 92.43%, an F1 score of 93.14% and a precision of 93.25% on the Cornell Bird Challenge 2020 (CBC) dataset.

Dynamic Electromagnetic Scattering Simulation of Tilt-Rotor Aircraft in Multiple Modes.

To study the electromagnetic scattering of tilt-rotor aircraft during multi-mode continuous flight, a dynamic simulation approach is presented. A time-varying mesh method is established to characterize the dynamic rotation and tilting of tilt-rotor aircraft. Shooting and bouncing rays and the uniform theory of diffraction are used to calculate the multi-mode radar cross-section (RCS). And the scattering mechanisms of tilt-rotor aircraft are investigated by extracting the micro-Doppler and inverse synthetic aperture radar images. The results show that the dynamic RCS of tilt-rotor aircraft in helicopter and airplane mode exhibits obvious periodicity, and the transition mode leads to a strong specular reflection on the rotor's upper surface, which increases the RCS with a maximum increase of about 36 dB. The maximum micro-Doppler shift has functional relationships with flight time, tilt speed, and wave incident direction. By analyzing the change patterns of maximum shift, the real-time flight state and mode can be identified. There are some significant scattering sources on the body of tilt-rotor aircraft that are distributed in a planar or point-like manner, and the importance of different scattering sources varies in different flight modes. The pre-studies on the key scattering areas can provide effective help for the stealth design of the target.

Recognition of Ellipsoid-like Herbaceous Tibetan Medicinal Materials Using DenseNet with Attention and ILBP-Encoded Gabor Features.

Tibetan medicinal materials play a significant role in Tibetan culture. However, some types of Tibetan medicinal materials share similar shapes and colors, but possess different medicinal properties and functions. The incorrect use of such medicinal materials may lead to poisoning, delayed treatment, and potentially severe consequences for patients. Historically, the identification of ellipsoid-like herbaceous Tibetan medicinal materials has relied on manual identification methods, including observation, touching, tasting, and nasal smell, which heavily rely on the technicians' accumulated experience and are prone to errors. In this paper, we propose an image-recognition method for ellipsoid-like herbaceous Tibetan medicinal materials that combines texture feature extraction and a deep-learning network. We created an image dataset consisting of 3200 images of 18 types of ellipsoid-like Tibetan medicinal materials. Due to the complex background and high similarity in the shape and color of the ellipsoid-like herbaceous Tibetan medicinal materials in the images, we conducted a multi-feature fusion experiment on the shape, color, and texture features of these materials. To leverage the importance of texture features, we utilized an improved LBP (local binary pattern) algorithm to encode the texture features extracted by the Gabor algorithm. We inputted the final features into the DenseNet network to recognize the images of the ellipsoid-like herbaceous Tibetan medicinal materials. Our approach focuses on extracting important texture information while ignoring irrelevant information such as background clutter to eliminate interference and improve recognition performance. The experimental results show that our proposed method achieved a recognition accuracy of 93.67% on the original dataset and 95.11% on the augmented dataset. In conclusion, our proposed method could aid in the identification and authentication of ellipsoid-like herbaceous Tibetan medicinal materials, reducing errors and ensuring the safe use of Tibetan medicinal materials in healthcare.

Wind-tunnel tests of synthetic jet control effects on airfoil flow separation.

To find out the comprehensive principles of synthetic jet control effects on airfoil stall characteristics, fundamental contrastive wind-tunnel tests were conducted systematically. By using six-component balance, Particle Image Velocimetry (PIV) technology and boundary layer probe, the measurements of model aerodynamic forces, the whole velocity field over airfoil and velocity profiles in the boundary layer were conducted, respectively. Based upon the experimentally parametric analyses of synthetic jet control on airfoil, it is concluded that the incline angle of synthetic jet has an important impact on both the flow in boundary layer and aerodynamic forces of airfoil. A tangential jet inject energy and accelerate the velocity of flow in inner boundary layer thus has a better control effects on delay stall of airfoil when the momentum coefficient of jet is relatively large, and the normal jet helps to enlarge the thickness of boundary layer which is proved to have better control effects on flow separation control and improving the aerodynamic characteristics of airfoil when the momentum coefficient of a jet is relatively small.

Machine-learning-based risk stratification for probability of dying in patients with basal ganglia hemorrhage.

To confirm whether machine learning algorithms (MLA) can achieve an effective risk stratification of dying within 7 days after basal ganglia hemorrhage (BGH). We collected patients with BGH admitted to Sichuan Provincial People's Hospital between August 2005 and August 2021. We developed standard ML-supervised models and fusion models to assess the prognostic risk of patients with BGH and compared them with the classical logistic regression model. We also use the SHAP algorithm to provide clinical interpretability. 1383 patients with BGH were included and divided into the conservative treatment group (CTG) and surgical treatment group (STG). In CTG, the Stack model has the highest sensitivity (78.5%). In STG, Weight-Stack model achieves 58.6% sensitivity and 85.1% specificity, and XGBoost achieves 61.4% sensitivity and 82.4% specificity. The SHAP algorithm shows that the predicted preferred characteristics of the CTG are consciousness, hemorrhage volume, prehospital time, break into ventricles, brain herniation, intraoperative blood loss, and hsCRP were also added to the STG. XGBoost, Stack, and Weight-Stack models combined with easily available clinical data enable risk stratification of BGH patients with high performance. These ML classifiers could assist clinicians and families to identify risk states timely when emergency admission and offer medical care and nursing information.

Multisystem inflammatory syndrome (MIS-C) with SARS-CoV-2 omicron variant BA.2.38 in a four-year-old Chinese girl: A case report.

We report a severe COVID-19 complicated with MIS-C in a girl treated by the author in China, and discuss the current research status and progress in the diagnosis and therapy of MIS-C in children. The patient was a 4-year-old child previously healthy who was referred to the hospital with a complaint of fever, finally, Multisystem inflammatory syndrome was diagnosed with COVID-19.

Event-Triggered Fractional-Order Tracking Control for an Uncertain Nonlinear System With Output Saturation and Disturbances.

In this article, an event-triggered (ET) fractional-order adaptive tracking control scheme (ATCS) is studied for the uncertain nonlinear system with the output saturation and the external disturbances by using the nonlinear disturbance observer (NDO) and the neural networks (NNs). Based on NNs, the system uncertainties are approximated. An NN-based NDO is designed to estimate the bounded disturbances. Combining the NNs, the output of the designed NDO, the fractional-order theory, and the ET mechanism, an ATCS is proposed under the output saturation. According to the stability analysis, all the closed-loop signals are semiglobally uniformly ultimately bounded based on the investigative ATCS. The simulation results and the comparative experiment verifications are shown to indicate the viability of the developed control scheme.

Automatic Recognition of Giant Panda Attributes from Their Vocalizations Based on Squeeze-and-Excitation Network.

The giant panda (Ailuropoda melanoleuca) has long attracted the attention of conservationists as a flagship and umbrella species. Collecting attribute information on the age structure and sex ratio of the wild giant panda populations can support our understanding of their status and the design of more effective conservation schemes. In view of the shortcomings of traditional methods, which cannot automatically recognize the age and sex of giant pandas, we designed a SENet (Squeeze-and-Excitation Network)-based model to automatically recognize the attributes of giant pandas from their vocalizations. We focused on the recognition of age groups (juvenile and adult) and sex of giant pandas. The reason for using vocalizations is that among the modes of animal communication, sound has the advantages of long transmission distances, strong penetrating power, and rich information. We collected a dataset of calls from 28 captive giant panda individuals, with a total duration of 1298.02 s of recordings. We used MFCC (Mel-frequency Cepstral Coefficients), which is an acoustic feature, as inputs for the SENet. Considering that small datasets are not conducive to convergence in the training process, we increased the size of the training data via SpecAugment. In addition, we used focal loss to reduce the impact of data imbalance. Our results showed that the F1 scores of our method for recognizing age group and sex reached 96.46% ± 5.71% and 85.85% ± 7.99%, respectively, demonstrating that the automatic recognition of giant panda attributes based on their vocalizations is feasible and effective. This more convenient, quick, timesaving, and laborsaving attribute recognition method can be used in the investigation of wild giant pandas in the future.

3-D Convolutional Neural Networks for RGB-D Salient Object Detection and Beyond.

RGB-depth (RGB-D) salient object detection (SOD) recently has attracted increasing research interest, and many deep learning methods based on encoder-decoder architectures have emerged. However, most existing RGB-D SOD models conduct explicit and controllable cross-modal feature fusion either in the single encoder or decoder stage, which hardly guarantees sufficient cross-modal fusion ability. To this end, we make the first attempt in addressing RGB-D SOD through 3-D convolutional neural networks. The proposed model, named, aims at prefusion in the encoder stage and in-depth fusion in the decoder stage to effectively promote the full integration of RGB and depth streams. Specifically, first conducts prefusion across RGB and depth modalities through a 3-D encoder obtained by inflating 2-D ResNet and later provides in-depth feature fusion by designing a 3-D decoder equipped with rich back-projection paths (RBPPs) for leveraging the extensive aggregation ability of 3-D convolutions. Toward an improved model, we propose to disentangle the conventional 3-D convolution into successive spatial and temporal convolutions and, meanwhile, discard unnecessary zero padding. This eventually results in a 2-D convolutional equivalence that facilitates optimization and reduces parameters and computation costs. Thanks to such a progressive-fusion strategy involving both the encoder and the decoder, effective and thorough interactions between the two modalities can be exploited and boost detection accuracy. As an additional boost, we also introduce channel-modality attention and its variant after each path of RBPP to attend to important features. Extensive experiments on seven widely used benchmark datasets demonstrate that and perform favorably against 14 state-of-the-art RGB-D SOD approaches in terms of five key evaluation metrics. Our code will be made publicly available at https://github.com/PPOLYpubki/RD3D.

Assessment of Clinical Diagnostic Efficacy of Pulmonary Function Test Based on DBN-SVM of Pediatric Asthma and Cough Variant Asthma.

The diagnosis of asthma depends on the unprejudiced proof of the varying airflow obstruction. The pulmonary function tests are carried out to evaluate the clinical value of different types of respiratory diseases in children or infants. This study is focused on the clinical evaluation of the pulmonary function tests in the diagnosis of pediatric asthma and cough variant asthma. A differential diagnosis method for chronic obstructive pulmonary disease (COPD) and asthma-COPD overlap with complementary diagnostic value is proposed. For the pulmonary function tests, the COPD gene dataset was selected and feature selection was performed using the DBN-SVM scoring method. For analysis and comparison, the differential diagnosis models were built using ROC curves for the accuracy of the deep belief network model and the support vector machine model. The sensitive features associated with COPD and ACO classification using the deep belief network model were found to be in good agreement with known clinical diagnostic strategies. The clinical diagnosis tests for pulmonary pediatric asthma and cough variant asthma were conducted on two groups of children, with both groups containing a basis of comparison. 80 cases of pediatric asthma and cough variant asthma were admitted from 2013 to 2014 and 80 cases of children with a healthy physical examination. The results of the two groups were compared. The results showed that the levels of FEV1, PEF, and FVC were significantly lower (P < 0.05), in healthy children, and FEV1/FVC%, RV, and RV/TCL% were significantly higher (P < 0.05) in children with asthma and cough variant asthma during acute exacerbation and chronic persistence. There were no statistically significant differences in the duration of clinical remission (P > 0.05). Thus, the study suggests that confirmed cases of the diagnosis of pediatric asthma and cough variant asthma by pulmonary function tests were significantly higher than those of conventional tests (P < 0.05). From this study, we can conclude that pulmonary function tests can accurately diagnose pediatric asthma and cough variant asthma, and also accurately reflect the development of the child's disease, which is of high clinical value.

Multicenter analysis of gastrointestinal stromal tumors in inner Mongolia of China: A study of 804 cases.

INTRODUCTION: To analyze the clinicopathological characteristics, immunohistochemistry, genotyping and prognosis of patients in the multicenter GIST data in Inner Mongolia, China. METHODS: Retrospective analysis was performed on GIST data from January 2013 to January 2018 in Inner Mongolia. Descriptive statistics were used to analyze the clinical characteristics of GIST patients. The Chi-square test was performed on the modified NIH criteria by age distribution, and Kaplan-Merie method was used for survival analysis. RESULTS: A total of 804 patients were included in the GIST database in Inner Mongolia, with a male to female ratio of 1.1102:1. The most common location was the gastric (465). Mitotic count ≤5/50HPFs was found in 67.3 % patients. There were 276 patients with tumor diameter of 2-5 cm and 354 patients with tumor diameter of 5.1-10 cm.The modified NIH criteria was mainly of intermediate risk (210) and high risk (342). The recurrence and metastasis of patients were related to the tumor location, mitotic index, tumor size, and modified NIH criteria. All patients were followed up for 1-10 years, in which 63.1 % of them were followed up for at least three years. The 3-year survival rates of patients with modified NIH criteria of very low risk, low risk, intermediate risk, and high risk were 100 %, 100 %, 100 %, and 96.3 %, respectively. CONCLUSIONS: The incidence of GIST in middle-aged and elder people in Inner Mongolia is high, and the long-term prognosis of patients after surgical treatment is good, which can objectively reflect the incidence, diagnosis and treatment of GIST in Inner Mongolia.

The Antifungal Effects of Citral on Magnaporthe oryzae Occur via Modulation of Chitin Content as Revealed by RNA-Seq Analysis.

The natural product citral has previously been demonstrated to possess antifungal activity against Magnaporthe oryzae. The purpose of this study was to screen and annotate genes that were differentially expressed (DEGs) in M. oryzae after treatment with citral using RNA sequencing (RNA-seq). Thereafter, samples were reprepared for quantitative real-time PCR (RT-qPCR) analysis verification of RNA-seq data. The results showed that 649 DEGs in M. oryzae were significantly affected after treatment with citral (100 µg/mL) for 24 h. Kyoto Encyclopedia of Genes and Genomes (KEGG) and a gene ontology (GO) analysis showed that DEGs were mainly enriched in amino sugar and nucleotide sugar metabolic pathways, including the chitin synthesis pathway and UDP sugar synthesis pathway. The results of the RT-qPCR analysis also showed that the chitin present in M. oryzae might be degraded to chitosan, chitobiose, N-acetyl-D-glucosamine, and ß-D-fructose-6-phosphate following treatment with citral. Chitin degradation was indicated by damaged cell-wall integrity. Moreover, the UDP glucose synthesis pathway was involved in glycolysis and gluconeogenesis, providing precursors for the synthesis of polysaccharides. Galactose-1-phosphate uridylyltransferase, which is involved in the regulation of UDP-α-D-galactose and α-D-galactose-1-phosphate, was downregulated. This would result in the inhibition of UDP glucose (UDP-Glc) synthesis, a reduction in cell-wall glucan content, and the destruction of cell-wall integrity.

Proteomic analysis reveals that naturally produced citral can significantly disturb physiological and metabolic processes in the rice blast fungus Magnaporthe oryzae.

Rice blast (Magnaporthe oryzae), a major fungal disease in rice producing areas all over the world as well as in China, seriously affects the safety of rice production. Citral, a mixture of Z/E and trans isomers, is a natural acycloid monoterpene compound with good bacteriostatic effect on rice blast. To further investigate the underlying molecular mechanism, a comparative proteomics analysis was conducted between citral-treated and non-treated M. oryzae spores through two-dimensional gel electrophoresis and MALDI-TOF mass spectrometry. Our analysis identified 1600-1800 proteins from M. oryzae ZB15, of which 147 were differentially expressed in 100 µg/mL citral-treated samples relative to the control group. Among these differentially expressed proteins (DEPs), 40 proteins showed significantly different expression. GO enrichment and NCBI conserved domains database analysis showed that the main groups of the cellular component were cytoplasm (23.33%), and the major molecular function categories were ion binding (31.37%), and the major categories of biological processes included small molecule metabolic process (22.22%) and transport (13.89%). Further analysis found that down-regulated proteins included the tubulin α chain, ATP synthase subunit ß and malate dehydrogenase, while the tubulin ß, enolase were upregulated. These DEPs could possibly limit the availability of energy required for many cellular processes and result in various physiological adaptions of M. oryzae. This study represents the first proteomic analysis of M. oryzae treated by citral and will help to uncover the mode-of-action of this biologically active compound against M. oryzae. These findings have practical implications with respect to the use of citral for fungal disease control.

Siamese Network for RGB-D Salient Object Detection and Beyond.

Existing RGB-D salient object detection (SOD) models usually treat RGB and depth as independent information and design separate networks for feature extraction from each. Such schemes can easily be constrained by a limited amount of training data or over-reliance on an elaborately designed training process. Inspired by the observation that RGB and depth modalities actually present certain commonality in distinguishing salient objects, a novel joint learning and densely cooperative fusion (JL-DCF) architecture is designed to learn from both RGB and depth inputs through a shared network backbone, known as the Siamese architecture. In this paper, we propose two effective components: joint learning (JL), and densely cooperative fusion (DCF). The JL module provides robust saliency feature learning by exploiting cross-modal commonality via a Siamese network, while the DCF module is introduced for complementary feature discovery. Comprehensive experiments using 5 popular metrics show that the designed framework yields a robust RGB-D saliency detector with good generalization. As a result, JL-DCF significantly advances the SOTAs by an average of ~2.0% (F-measure) across 7 challenging datasets. In addition, we show that JL-DCF is readily applicable to other related multi-modal detection tasks, including RGB-T SOD and video SOD, achieving comparable or better performance.

Evaluation of cuff deflation and inflation rates on a deep learning-based automatic blood pressure measurement method: a pilot evaluation study.

OBJECTIVE: The aim of this study was to evaluate the performance of using a deep learning-based method for measuring SBPs and DBPs and the effects of cuff inflation and deflation rates on the deep learning-based blood pressure (BP) measurement (in comparison with the manual auscultatory method). METHODS: Forty healthy subjects were recruited. SBP and DBP were measured under four conditions (i.e. standard deflation, fast deflation, slow inflation and fast inflation) using both our newly developed deep learning-based method and the reference manual auscultatory method. The BPs measured under each condition were compared between the two methods. The performance of using the deep learning-based method to measure BP changes was also evaluated. RESULTS: There were no significant BP differences between the two methods (P > 0.05), except for the DBPs measured during the slow and fast inflation conditions. By applying the deep learning-based method, SBPs measured from fast deflation, slow inflation and fast inflation decreased significantly by 3.0, 3.5 and 4.7 mmHg (all P < 0.05), respectively, in comparison with the standard deflation condition. Whereas, corresponding DBPs measured from the slow and fast inflation conditions increased significantly by 5.0 and 6.8 mmHg, respectively (both P < 0.05). There were no significant differences in BP changes measured by the two methods in most cases (all P > 0.05, except for DBP change in the slow and fast inflation conditions). CONCLUSION: This study demonstrated that the deep learning-based method can achieve accurate BP measurement under the deflation and inflation conditions with different rates.

Joint Face Alignment and 3D Face Reconstruction with Application to Face Recognition.

Face alignment and 3D face reconstruction are traditionally accomplished as separated tasks. By exploring the strong correlation between 2D landmarks and 3D shapes, in contrast, we propose a joint face alignment and 3D face reconstruction method to simultaneously solve these two problems for 2D face images of arbitrary poses and expressions. This method, based on a summation model of 3D faces and cascaded regression in 2D and 3D shape spaces, iteratively and alternately applies two cascaded regressors, one for updating 2D landmarks and the other for 3D shape. The 3D shape and the landmarks are correlated via a 3D-to-2D mapping matrix, which is updated in each iteration to refine the location and visibility of 2D landmarks. Unlike existing methods, the proposed method can fully automatically generate both pose-and-expression-normalized (PEN) and expressive 3D faces and localize both visible and invisible 2D landmarks. Based on the PEN 3D faces, we devise a method to enhance face recognition accuracy across poses and expressions. Both linear and nonlinear implementations of the proposed method are presented and evaluated in this paper. Extensive experiments show that the proposed method can achieve the state-of-the-art accuracy in both face alignment and 3D face reconstruction, and benefit face recognition owing to its reconstructed PEN 3D face.

Deep learning-based automatic blood pressure measurement: evaluation of the effect of deep breathing, talking and arm movement.

Objectives: It is clinically important to evaluate the performance of a newly developed blood pressure (BP) measurement method under different measurement conditions. This study aims to evaluate the performance of using deep learning-based method to measure BPs and BP change under non-resting conditions.Materials and methods: Forty healthy subjects were studied. Systolic and diastolic BPs (SBPs and DBPs) were measured under four conditions using deep learning and manual auscultatory method. The agreement between BPs determined by the two methods were analysed under different conditions. The performance of using deep learning-based method to measure BP changes was finally evaluated.Results: There were no significant BPs differences between two methods under all measurement conditions (all p > .1). SBP and DBP measured by deep learning method changed significantly in comparison with the resting condition: decreased by 2.3 and 4.2 mmHg with deeper breathing (both p < .05), increased by 3.6 and 6.4 mmHg with talking, and increased by 5.9 and 5.8 mmHg with arm movement (all p < .05). There were no significant differences in BP changes measured by two methods (all p > .4, except for SBP change with deeper breathing).Conclusion: This study demonstrated that the deep learning method could achieve accurate BP measurement under both resting and non-resting conditions.Key messagesAccurate and reliable blood pressure measurement is clinically important. We evaluated the performance of our developed deep learning-based blood pressure measurement method under resting and non-resting measurement conditions.The deep learning-based method could achieve accurate BP measurement under both resting and non-resting measurement conditions.

Boosting Depth-Based Face Recognition from a Quality Perspective.

Face recognition using depth data has attracted increasing attention from both academia and industry in the past five years. Previous works show a huge performance gap between high-quality and low-quality depth data. Due to the lack of databases and reasonable evaluations on data quality, very few researchers have focused on boosting depth-based face recognition by enhancing data quality or feature representation. In the paper, we carefully collect a new database including high-quality 3D shapes, low-quality depth images and the corresponding color images of the faces of 902 subjects, which have long been missing in the area. With the database, we make a standard evaluation protocol and propose three strategies to train low-quality depth-based face recognition models with the help of high-quality depth data. Our training strategies could serve as baselines for future research, and their feasibility of boosting low-quality depth-based face recognition is validated by extensive experiments.