Single-crystal cathode materials for lithium-ion batteries have attracted increasing interest in providing greater capacity retention than their polycrystalline counterparts. However, after being ...cycled at high voltages, these single-crystal materials exhibit severe structural instability and capacity fade. Understanding how the surface structural changes determine the performance degradation over cycling is crucial, but remains elusive. Here, we investigate the correlation of the surface structure, internal strain, and capacity deterioration by using operando X-ray spectroscopy imaging and nano-tomography. We directly observe a close correlation between surface chemistry and phase distribution from homogeneity to heterogeneity, which induces heterogeneous internal strain within the particle and the resulting structural/performance degradation during cycling. We also discover that surface chemistry can significantly enhance the cyclic performance. Our modified process effectively regulates the performance fade issue of single-crystal cathode and provides new insights for improved design of high-capacity battery materials.
Abstract
The survival rate of breast cancer patients is closely related to the pathological stage of cancer. The earlier the pathological stage, the higher the survival rate. Breast ultrasound is a ...commonly used breast cancer screening or diagnosis method, with simple operation, no ionizing radiation, and real-time imaging. However, ultrasound also has the disadvantages of high noise, strong artifacts, low contrast between tissue structures, which affect the effective screening of breast cancer. Therefore, we propose a deep learning based breast ultrasound detection system to assist doctors in the diagnosis of breast cancer. The system implements the automatic localization of breast cancer lesions and the diagnosis of benign and malignant lesions. The method consists of two steps: 1. Contrast enhancement of breast ultrasound images using segmentation-based enhancement methods. 2. An anchor-free network was used to detect and classify breast lesions. Our proposed method achieves a mean average precision (mAP) of 0.902 on the datasets used in our experiment. In detecting benign and malignant tumors, precision is 0.917 and 0.888, and recall is 0.980 and 0.963, respectively. Our proposed method outperforms other image enhancement methods and an anchor-based detection method. We propose a breast ultrasound image detection system for breast cancer detection. The system can locate and diagnose benign and malignant breast lesions. The test results on single dataset and mixed dataset show that the proposed method has good performance.
State-of-the-art halide perovskite solar cells have bandgaps larger than 1.45 eV, which restricts their potential for realizing the Shockley-Queisser limit. Previous search for low-bandgap (1.2 to ...1.4 eV) halide perovskites has resulted in several candidates, but all are hybrid organic-inorganic compositions, raising potential concern regarding device stability. Here we show the promise of an inorganic low-bandgap (1.38 eV) CsPb
Sn
I
perovskite stabilized via interface functionalization. Device efficiency up to 13.37% is demonstrated. The device shows high operational stability under one-sun-intensity illumination, with T
and T
lifetimes of 653 h and 1045 h, respectively (T
and T
represent efficiency decays to 80% and 70% of the initial value, respectively), and long-term shelf stability under nitrogen atmosphere. Controlled exposure of the device to ambient atmosphere during a long-term (1000 h) test does not degrade the efficiency. These findings point to a promising direction for achieving low-bandgap perovskite solar cells with high stability.
A computer-aided diagnosis (CAD) system based on mammograms enables early breast cancer detection, diagnosis, and treatment. However, the accuracy of the existing CAD systems remains unsatisfactory. ...This paper explores a breast CAD method based on feature fusion with convolutional neural network (CNN) deep features. First, we propose a mass detection method based on CNN deep features and unsupervised extreme learning machine (ELM) clustering. Second, we build a feature set fusing deep features, morphological features, texture features, and density features. Third, an ELM classifier is developed using the fused feature set to classify benign and malignant breast masses. Extensive experiments demonstrate the accuracy and efficiency of our proposed mass detection and breast cancer classification method.
Secondary structure plays an important role in determining the function of noncoding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a ...mainstream approach for predicting RNA secondary structure. Unfortunately, even though new methods have been proposed over the past 40 years, the performance of computational prediction methods has stagnated in the last decade. Recently, with the increasing availability of RNA structure data, new methods based on machine learning (ML) technologies, especially deep learning, have alleviated the issue. In this review, we provide a comprehensive overview of RNA secondary structure prediction methods based on ML technologies and a tabularized summary of the most important methods in this field. The current pending challenges in the field of RNA secondary structure prediction and future trends are also discussed.
Microorganisms such as bacteria and fungi play essential roles in many application fields, like biotechnique, medical technique and industrial domain. Microorganism counting techniques are crucial in ...microorganism analysis, helping biologists and related researchers quantitatively analyze the microorganisms and calculate their characteristics, such as biomass concentration and biological activity. However, traditional microorganism manual counting methods, such as plate counting method, hemocytometry and turbidimetry, are time-consuming, subjective and need complex operations, which are difficult to be applied in large-scale applications. In order to improve this situation, image analysis is applied for microorganism counting since the 1980s, which consists of digital image processing, image segmentation, image classification and suchlike. Image analysis-based microorganism counting methods are efficient comparing with traditional plate counting methods. In this article, we have studied the development of microorganism counting methods using digital image analysis. Firstly, the microorganisms are grouped as bacteria and other microorganisms. Then, the related articles are summarized based on image segmentation methods. Each part of the article is reviewed by methodologies. Moreover, commonly used image processing methods for microorganism counting are summarized and analyzed to find common technological points. More than 144 papers are outlined in this article. In conclusion, this paper provides new ideas for the future development trend of microorganism counting, and provides systematic suggestions for implementing integrated microorganism counting systems in the future. Researchers in other fields can refer to the techniques analyzed in this paper.
Direct sequence spread spectrum (DSSS) communications are highly significant in military and civilian wireless communications because of its ability to resist narrowband interference, multipath ...interference and high security. However, under low signal-to-noise ratio (SNR), the detection of DSSS signals becomes very difficult under non-cooperative communication. Therefore, in order to improve the detection performance of DSSS signals in fading channels with low SNR, a DSSS signal detection method based on an eigenvalues local binary patterns residual network (EL-ResNet) is proposed with an unknown spread spectrum sequence. Firstly, according to the characteristics of DSSS signals, the eigenvalues of the sample covariance matrix of the DSSS signals are used to construct the signal eigenvalue histograms. This method uses the strong contrast of eigenvalue energy and gradient change to enlarge the discrimination of images with or without DSSS signals. Then, EL-ResNet is built to distinguish between eigenvalue histograms with or without DSSS signals. The local binary patterns (LBPs) are introduced to constrain the loss function of the network, increasing the differentiation of the two types of eigenvalue histograms, and improving the DSSS signal detection performance of the network. Finally, the experimental results show that the performance of the DSSS signal detection method based on EL-ResNet is larger than fast Fourier transform (FFT), FFT-deep neural network (FFT-DNN), covariance matrix-convolutional neural network (CM-CNN) detection methods.
The carotid artery is one of the most important blood vessels that supply blood to the brain. If thrombus occurs, it may cause cerebral ischemic stroke and endanger life. Carotid intima-media ...thickness and stability of carotid plaque are essential indicators for predicting stroke, which can be measured through medical image segmentation. Therefore, automatic and accurate carotid artery image segmentation and measurement of carotid intima-media thickness and the area and volume of carotid plaque are of great significance for stroke risk prediction and treatment. However, due to the complex shape of the carotid artery and the characteristics of carotid artery imaging, the traditional methods (such as threshold methods, region growth methods) can not segment the carotid artery very well. In recent years, researchers have taken artificial intelligence (traditional machine learning and deep learning) as a critical research method for carotid artery segmentation and extensive research has been performed with satisfactory results. In this paper, we present a comprehensive review of carotid artery segmentation using artificial intelligence methods. We first briefly introduce medical image processing methods and artificial intelligence methods. And then, review and summarize the application of artificial intelligence segmentation methods in carotid artery segmentation (including carotid lumen, media-adventitia, lumen-intima, and plaques). Finally, the challenges of current artificial intelligence methods in carotid artery segmentation are analyzed.
Lung segmentation constitutes a critical procedure for any clinical-decision supporting system aimed to improve the early diagnosis and treatment of lung diseases. Abnormal lungs mainly include lung ...parenchyma with commonalities on CT images across subjects, diseases and CT scanners, and lung lesions presenting various appearances. Segmentation of lung parenchyma can help locate and analyze the neighboring lesions, but is not well studied in the framework of machine learning.
We proposed to segment lung parenchyma using a convolutional neural network (CNN) model. To reduce the workload of manually preparing the dataset for training the CNN, one clustering algorithm based method is proposed firstly. Specifically, after splitting CT slices into image patches, the k-means clustering algorithm with two categories is performed twice using the mean and minimum intensity of image patch, respectively. A cross-shaped verification, a volume intersection, a connected component analysis and a patch expansion are followed to generate final dataset. Secondly, we design a CNN architecture consisting of only one convolutional layer with six kernels, followed by one maximum pooling layer and two fully connected layers. Using the generated dataset, a variety of CNN models are trained and optimized, and their performances are evaluated by eightfold cross-validation. A separate validation experiment is further conducted using a dataset of 201 subjects (4.62 billion patches) with lung cancer or chronic obstructive pulmonary disease, scanned by CT or PET/CT. The segmentation results by our method are compared with those yielded by manual segmentation and some available methods.
A total of 121,728 patches are generated to train and validate the CNN models. After the parameter optimization, our CNN model achieves an average F-score of 0.9917 and an area of curve up to 0.9991 for classification of lung parenchyma and non-lung-parenchyma. The obtain model can segment the lung parenchyma accurately for 201 subjects with heterogeneous lung diseases and CT scanners. The overlap ratio between the manual segmentation and the one by our method reaches 0.96.
The results demonstrated that the proposed clustering algorithm based method can generate the training dataset for CNN models. The obtained CNN model can segment lung parenchyma with very satisfactory performance and have the potential to locate and analyze lung lesions.
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