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Superconducting materials have extremely high application value in our life. At present, the discovery of new superconducting materials relies on the experience of experts through a large number of "trial and error" experiments. Obtaining the properties of superconductors also requires a large number of experiments. In this paper, we propose to use the XGBoost model to identify superconductors, reaching 0.986 in accuracy. We apply the deep forest algorithm to predict the critical temperature of superconductors and the coefficient of determination reaches 0.944. We propose to apply the same algorithm to predict the forbidden band width of materials and the coefficient of determination reaches 0.917. A new sub-network structure is constructed to predict the Fermi level of materials and the coefficient of determination reaches 0.984. All of these algorithms have state-of-the-art performance. Finally, the model is tested with a publicly available dataset to identify 50 candidate superconducting materials with a critical temperature greater than 90K.

Laryngeal squamous cell carcinoma (LSCC) is the most common malignant head and neck cancer, with a 40% recurrence rate in the first 3 years after radical treatment. Recurrence of LSCC mostly comprises lymphogenous metastasis, hematogenic metastasis, and locoregional recurrence, while LSCC seeding is rarest: there are only 4 cases reported in PubMed, and none of them is one of subcutaneous seeding. We report a case with post-surgery subcutaneous seeding of LSCC. The final biopsy demonstrated that the subcutaneous seeding of the LSCC was 2 cm away from the primary lesion, with no recurrent foci observed in the larynx and tracheostoma and little relation to the primary lesion. Thus, we drew the conclusion that LSCC surgeries should stick to the principle of the non-tumor technique to prevent subcutaneous seeding.

Background and aim: Small ubiquitin–like modifier (SUMO)-specific protease (SENP)3 is a protease molecule that responds to reactive oxygen species (ROS) with high sensitivity. However, the role of ROS and SENP3 in the formation of nasal polyps (NPs) remains unclear. This study aimed to explore how SENP3 influenced the outcome of chronic rhinosinusitis (CRS) by altering macrophage function, that is, the formation of NPs. Methods: The alternative activation of macrophage (M2) was detected with CD68+CD206+ in humans and CD206+ in mice. The nasal mucosa of patients with CRS was tested using flow cytometry (CD68, CD80, and CD206) and triple-color immunofluorescence staining (CD68, CD206, and SENP3). The bone marrow–derived macrophages from SENP3 knockout and control mice were stimulated with interleukin (IL)-4 and IL-13 to analyze alternative macrophage polarization in vitro. An animal model of eosinophil infiltration rhinitis was constructed using SENP3 knockout mice. CD206 was detected by immunofluorescence staining. The thickening of eosinophil-infiltrated mucosa was detected by Luna staining. Results: The number of CD68+ CD206+ M2 increased in the nasal mucosa of patients with CRS with NP (CRSwNP) compared with patients with CRS without NP (CRSsNP), but with no significant difference between the groups. SENP3 knockout increased the polarization of F4/80+CD206+M2. Meanwhile, the number of CD206+M2 significantly increased in the osinophil infiltration rhinitis model constructed using SENP3 knockout mice and controls, with a more obvious proliferation of the nasal mucosa. Conclusion: The downregulation of the expression of macrophage SENP3 in the nasal mucosa in osinophil infiltration rhinitis promoted the formation of NPs. Trial registration: This study was retrospective and did not involve any health interventions for human participants.

In order to make full use of the complementary information of multichannel vibration signals, this paper proposes a method of channel selection based on the similarity ratio and constructs a multichannel deep belief network (MDBN) for condition analysis of a high-speed train. First, fast Fourier transform (FFT) coefficients of the signals of all channels are extracted. Then, the similarity ratio of FFT coefficients of each channel is calculated, and a number of channels with a large similarity ratio are selected. Finally, the MDBN model is constructed to learn the features of the selected multichannel data and recognize conditions, and the feature fusion of multichannel data is realized in the common layer. The experimental results show that the similarity ratio is effective to select the channels with rich feature information, the feature learning ability of MDBN is better than DBN, and the condition recognition rate of MDBN is higher than other state-of-the-art methods.

To solve the problem of determining the fault damage of rolling bearings, a fault diagnosis method for intelligent classification of vibration signals with different fault locations and different damage degrees is proposed. First, the research object is the laboratory dataset. By transforming into spectrograms, this can preserve the original information of the time-domain signal to a greater extent. Then, we use a deep, fully convolutional neural network to train the dataset. It has a rapid convergence and the accuracy is up to 100%. Second, in order to verify the correctness of the model, we take the service data on the real line as the research object, and the accuracy rate is as high as 99.22%. Compared with some other machine learning algorithms, our method boasts better generalization capability and accuracy and could be applied to practical engineering.

Monitoring the operating status of a High-Speed Train (HST) at any moment is necessary to ensure its security. Multi-channel vibration signals are collected by sensors installed on bogies and beneficial information are extracted to determine the running condition. Based on multi-view clustering and considering different views of complementary information, this study proposes a Multi-view Kernel Fuzzy C-Means (MvKFCM) model for condition recognition of the HST bogie. First, fast Fourier transform coefficients of HST vibration signals of all channels are extracted. Then, the fuzzy classification coefficient of every channel is calculated after clustering to select the appropriate channels. Finally, the selected channels are used to cluster by MvKFCM and the conditions of HST are determined. Experimental results show that the selection is effective to maintain rich feature information and remove redundancy. Furthermore, the condition recognition rate of MvKFCM is higher than that of single-view and four other multiple-view clustering algorithms.

Based on the multi-source and heterogeneous characteristics of massive state sensing data of bridges, this paper establishes a multi-source heterogeneous bridge state sensing data feature extraction and analysis method. A dynamic evaluation model of bridge technology based on multi-source heterogeneous data fusion is proposed. For the first time, the relationship between multi-source perceptual data and bridge infrastructure technology is established, the knowledge graph of bridge infrastructure technology is constructed, and the relationship between various state-aware data and bridge technology is explicitly expressed by the map. This collaborative association is used to dynamically evaluate and predict bridge technology.

This paper proposes a method to deal with long-term robust object tracking in unconstrained environment. The approach exploits both target and background information on the fly automatically. It builds the structural constraint using active context learning to enhance the adaptability for variation of the target and stability of tracking. An optical-flow-based motion region extraction method is integrated into the context learning framework to address the problem of fast target motion or abrupt camera motion. Experimental results on challenging real-world video sequences demonstrate the effectiveness and robustness of our approach. Comparisons with several state-of-the-art methods are provided.

The aim of this study was to assess morphological features of intact adipose tissue (AT) ex vivo from both subcutaneous (s.c.) abdominal and gluteal areas using a novel approach of multiphoton autofluorescence microscopy (MPAM) combined with second harmonic generation microscopy (SHGM), and to assess the relationship between morphological features in the two AT sites and insulin resistance to peripheral glucose disposal.This study was a cross-sectional evaluation of AT morphology feature and peripheral insulin resistance.Fourteen overweight/obese premenopausal women underwent body composition studies, hyperinsulinemic-euglycemic clamps, and needle biopsy of both the s.c. abdominal and gluteal AT areas. MPAM combined with SHGM was used to measure adipocyte maximal diameter and collagen fiber bundle thickness within a sampled image volume after three-dimensional visualization.Higher body mass index (BMI) was associated with larger adipocyte diameter in s.c. abdominal, but not gluteal, AT. Higher adipocyte diameter was associated with higher pericellular collagen thickness. Adipocyte diameter in s.c. abdominal, but not gluteal, AT was associated positively with leptin and negatively with adiponectin plasma levels and peripheral glucose disposal rate. The latter correlation was no longer significant after adjustment for collagen thickness.In overweight/obese premenopausal women, larger adipocyte diameter in s.c. abdominal, but not gluteal, AT associates with low plasma adiponectin and systemic insulin resistance, and suggests that increased collagen thickness (obesity-related scarring) could contribute to these findings.