A fault prediction method for catenary of high-speed rails based on meteorological conditions

Fault frequency of catenary is related to meteorological conditions. In this work, based on the historical data, catenary fault frequency and weather-related fault rate are introduced to analyse the correlation between catenary faults and meteorological conditions, and further the effect of meteorological conditions on catenary operation. Moreover, machine learning is used for catenary fault prediction. As with the single decision tree, only a small number of training samples can be classified correctly by each weak classifier, the AdaBoost algorithm is adopted to adjust the weights of misclassified samples and weak classifiers, and train multiple weak classifiers. Finally, the weak classifiers are combined to construct a strong classifier, with which the final prediction result is obtained. In order to validate the prediction method, an example is provided based on the historical data from a railway bureau of China. The result shows that the mapping relation between meteorological conditions and catenary faults can be established accurately by AdaBoost algorithm. The AdaBoost algorithm can accurately predict a catenary fault if the meteorological conditions are provided. Document type: Articl

Diagenesis of the first member of Canglangpu Formation of the Cambrian Terreneuvian in northern part of the central Sichuan Basin and its influence on porosity

In this paper, taking the first Member of the Canglangpu Formation of the Cambrian Terreneuvian in the northern central Sichuan Basin as an example, the diagenesis and its influence on porosity are systemically studied based on the observations and identifications of cores, casts and cathodoluminescence thin sections. The results show that the rock types of the first member of Canglangpu Formation are various, including mixed rocks, carbonate rocks and clastic rocks. The specific lithology is dominated by sand-bearing oolitic dolomite, sandy oolitic dolomite, sparry oolotic dolomite and fine-grained detrital sandstone. At the same time, the Cang 1 Member has experienced five types of diagenetic environments, including seawater, meteoric water, evaporative seawater, shallow burial, and medium-deep burial diagenetic environments. Moreover, the main diagenetic processes under different diagenetic environments include cementation, dissolution, compaction, chemical compaction, dolomitization and structural fractures. According to the analysis, fabric-selective dissolution in meteoric water diagenetic environment, dolomitization in evaporative seawater environment, and non-fabric-selective dissolution, dolomitization and structural fractures in buried diagenetic environment are beneficial to the development of pores. However, cementation, compaction and chemical compaction in medium and deep burial environments, are unfavorable for the development of pores

DataSheet_1_The causal relationship between gut microbiota and inflammatory dermatoses: a Mendelian randomization study.docx

BackgroundObservational studies have shown that gut microbiota is closely associated with inflammatory dermatoses such as psoriasis, rosacea, and atopic dermatitis (AD). However, the causal relationship between gut microbiota and inflammatory dermatosis remains unclear.MethodsBased on Maximum Likelihood (ML), MR-Egger regression, Inverse Variance Weighted (IVW), MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO), Weighted Mode, and Weighted Median Estimator (WME) methods, we performed a bidirectional two-sample Mendelian randomization (MR) analysis to explore the causal relationship between gut microbiota and inflammatory dermatosis. The genome-wide association study (GWAS) summary data of gut microbiota came from the MiBioGen consortium, while the GWAS summary data of inflammatory dermatosis (including psoriasis, AD, rosacea, vitiligo, acne, and eczema) came from the FinnGen consortium and IEU Open GWAS project. Cochran’s IVW Q test tested the heterogeneity among instrumental variables (IVs). The horizontal pleiotropy was tested by MR-Egger regression intercept analysis and MR-PRESSO analysis.ResultsEventually, the results indicated that 5, 16, 17, 11, 15, and 12 gut microbiota had significant causal effects on psoriasis, rosacea, AD, vitiligo, acne, and eczema, respectively, including 42 protective and 34 risk causal relationships. Especially, Lactobacilli and Bifidobacteria at the Family and Genus Level, as common probiotics, were identified as protective factors for the corresponding inflammatory dermatoses. The results of reverse MR analysis suggested a bidirectional causal effect between AD and genus Eubacterium brachy group, vitiligo and genus Ruminococcaceae UCG004. The causal relationship between gut microbiota and psoriasis, rosacea, acne, and eczema is unidirectional. There was no significant heterogeneity among these IVs. In conclusion, this bidirectional two-sample MR study identified 76 causal relationships between the gut microbiome and six inflammatory dermatoses, which may be helpful for the clinical prevention and treatment of inflammatory dermatoses.</p

Research Progress on Slip Behavior of α-Ti under Quasi-Static Loading: A Review

This paper reviews the dislocation slip behavior of α phase in α, near α and α + β titanium alloys dominated by α-Ti deformation under quasi-static loading. The relation of slip activity, slip transfer, slip blocking, twinning and crack initiation is discussed, mainly combined with in situ tensile technology. The slip behavior in Ti-alloys is analyzed in detail from the aspects of critical resolved shear stress (CRSS), grain orientation distribution and geometric compatibility factor m′. In addition, slip blocking is an important factor of the formation of twins and micro-cracks. The interaction of slip behavior and interfaces is clarified systematically. Finally, the insufficiency of current research, future research directions and key difficulties of study are also discussed

Visual Analytic Method for Students’ Association via Modularity Optimization

Students spend most of their time living and studying on campus, especially in Asia, and they form various types of associations in addition to those with classmates and roommates. It is necessary for university authorities to master these types of associations, so as to provide appropriate services, such as psychological guidance and academic advice. With the rapid development of the &ldquo;smart campus,&rdquo; many kinds of student behavior data are recorded, which provides an unprecedented opportunity to deeply analyze students&rsquo; associations. In this paper, we propose a visual analytic method to construct students&rsquo; association networks by computing the similarity of their behavior data. We discover student communities using the popular Louvain (or BGLL) algorithm, which can extract community structures based on modularity optimization. Using various visualization charts, we visualized associations among students so as to intuitively express them. We evaluated our method using the real behavior data of undergraduates in a university in Beijing. The experimental results indicate that this method is effective and intuitive for student association analysis

A cytotoxic T cell inspired oncolytic nanosystem promotes lytic cell death by lipid peroxidation and elicits antitumor immune responses

Abstract Lytic cell death triggers an antitumour immune response. However, cancer cells evade lytic cell death by several mechanisms. Moreover, a prolonged and uncontrolled immune response conversely leads to T-cell exhaustion. Therefore, an oncolytic system capable of eliciting an immune response by killing cancer cells in a controlled manner is needed. Here, we establish a micro-scale cytotoxic T-cell-inspired oncolytic system (TIOs) to precisely lyse cancer cells by NIR-light-controlled lipid peroxidation. Our TIOs present antigen-based cell recognition, tumour-targeting and catalytic cell-lysis ability; thus, the TIOs induce oncolysis in vivo. We apply TIOs to preclinical cancer models, showing anti-tumor activity with negligible side-effects. Tumour regression is correlated with a T-cell based anti-tumour immune response and TIOs also improve responses to anti-PD-1 therapy or STING activation. Our study provides insights to design oncolytic systems for antitumour immunity. Moreover, activation of STING can reverse T-cell exhaustion in oncolysis

Table_1_The causal relationship between gut microbiota and inflammatory dermatoses: a Mendelian randomization study.xlsx

BackgroundObservational studies have shown that gut microbiota is closely associated with inflammatory dermatoses such as psoriasis, rosacea, and atopic dermatitis (AD). However, the causal relationship between gut microbiota and inflammatory dermatosis remains unclear.MethodsBased on Maximum Likelihood (ML), MR-Egger regression, Inverse Variance Weighted (IVW), MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO), Weighted Mode, and Weighted Median Estimator (WME) methods, we performed a bidirectional two-sample Mendelian randomization (MR) analysis to explore the causal relationship between gut microbiota and inflammatory dermatosis. The genome-wide association study (GWAS) summary data of gut microbiota came from the MiBioGen consortium, while the GWAS summary data of inflammatory dermatosis (including psoriasis, AD, rosacea, vitiligo, acne, and eczema) came from the FinnGen consortium and IEU Open GWAS project. Cochran’s IVW Q test tested the heterogeneity among instrumental variables (IVs). The horizontal pleiotropy was tested by MR-Egger regression intercept analysis and MR-PRESSO analysis.ResultsEventually, the results indicated that 5, 16, 17, 11, 15, and 12 gut microbiota had significant causal effects on psoriasis, rosacea, AD, vitiligo, acne, and eczema, respectively, including 42 protective and 34 risk causal relationships. Especially, Lactobacilli and Bifidobacteria at the Family and Genus Level, as common probiotics, were identified as protective factors for the corresponding inflammatory dermatoses. The results of reverse MR analysis suggested a bidirectional causal effect between AD and genus Eubacterium brachy group, vitiligo and genus Ruminococcaceae UCG004. The causal relationship between gut microbiota and psoriasis, rosacea, acne, and eczema is unidirectional. There was no significant heterogeneity among these IVs. In conclusion, this bidirectional two-sample MR study identified 76 causal relationships between the gut microbiome and six inflammatory dermatoses, which may be helpful for the clinical prevention and treatment of inflammatory dermatoses.</p

Strengthening effects of Al element on strength and impact toughness in titanium alloy

Pure Ti and binary Ti–6Al alloy have been employed as the investigated targets of our research. The strengthening effects of Al element on strength and impact toughness in titanium alloy were systematically investigated. The experimental results indicated that the addition of Al element significantly improved the tensile strength while deteriorating the plasticity and impact toughness. Analysis of deformation mechanisms indicated that addition of Al element strongly inhibited the dislocation movement and deformation twinning in titanium alloy. The theoretical results demonstrated that the dissolution of Al atoms reset the atomic bond configurations and electronic structures of the α-Ti lattice. Therefore, the lattice resistance to dislocation nucleation and dislocation gliding was significantly improved which led to the strong strengthening effect of Al element in α-Ti. The lattice resistance to the shearing atomic motion of deformation twinning was also improved due to the dissolution of Al atoms. Moreover, the strengthening of Al element was revealed at the electronic level by employing the empirical electron theory (EET) of solids and molecules. The strengthening effects of Al element in titanium was also quantitatively evaluated according to the valence electron structure (VES) parameters. A prediction model for the tensile strength of α-type Ti-xAl alloys was proposed based on the quantitative strengthening of Al element. The high accuracy of the prediction model for strength of Ti-xAl alloy was verified by the average error (3.58%) between the computational strength and experimental results

Microstructure induced duplex Hall-Petch effect and its strengthening/toughening mechanisms in SiC@TC4 composites prepared by spark plasma sintering

Microstructure induced duplex Hall-Petch effect and its strengthening/toughening mechanisms in SiC@TC4 composites prepared by spark plasma sintering were systematically investigated in the current study. The microstructure characterization demonstrated that SiC additions significantly tailored the microstructure of SiC@TC4 composites. The SiC additions distinctly refined the grain size of SiC@TC4 composites. Increasing SiC addition also changed the microstructure of SiC@TC4 composites from the Widmanstätten type to the duplex type. Large SiC addition almost inhibited the formation of continuous αGB phase in SiC@TC4 composites. The growth of α phase was promoted due to the energy storage effect inside the TC4 alloy powders during the sintering process. SiC addition distinctly improved the tensile strength while seriously deteriorating the plasticity of SiC@TC4 composites compared to that of TC4 alloy. However, SiC@TC4 composites with various SiC additions exhibited the similar plasticity. SiC additions also resulted in different tribological properties of SiC@TC4 composites. The TC4-0.1SiC exhibited the worse tribological properties than that of TC4 alloy due to the softened matrix and low SiC addition. TC4-0.3SiC and TC4-0.5SiC composites exhibited the more excellent tribological properties due to the more SiC addition. A duplex Hall-Petch model was proposed to evaluate the multiple strengthening and toughening mechanisms in the SiC@TC4 composites

X3DFast model for classifying dairy cow behaviors based on a two-pathway architecture

Abstract Behavior is one of the important factors reflecting the health status of dairy cows, and when dairy cows encounter health problems, they exhibit different behavioral characteristics. Therefore, identifying dairy cow behavior not only helps in assessing their physiological health and disease treatment but also improves cow welfare, which is very important for the development of animal husbandry. The method of relying on human eyes to observe the behavior of dairy cows has problems such as high labor costs, high labor intensity, and high fatigue rates. Therefore, it is necessary to explore more effective technical means to identify cow behaviors more quickly and accurately and improve the intelligence level of dairy cow farming. Automatic recognition of dairy cow behavior has become a key technology for diagnosing dairy cow diseases, improving farm economic benefits and reducing animal elimination rates. Recently, deep learning for automated dairy cow behavior identification has become a research focus. However, in complex farming environments, dairy cow behaviors are characterized by multiscale features due to large scenes and long data collection distances. Traditional behavior recognition models cannot accurately recognize similar behavior features of dairy cows, such as those with similar visual characteristics, i.e., standing and walking. The behavior recognition method based on 3D convolution solves the problem of small visual feature differences in behavior recognition. However, due to the large number of model parameters, long inference time, and simple data background, it cannot meet the demand for real-time recognition of dairy cow behaviors in complex breeding environments. To address this, we developed an effective yet lightweight model for fast and accurate dairy cow behavior feature learning from video data. We focused on four common behaviors: standing, walking, lying, and mounting. We recorded videos of dairy cow behaviors at a dairy farm containing over one hundred cows using surveillance cameras. A robust model was built using a complex background dataset. We proposed a two-pathway X3DFast model based on spatiotemporal behavior features. The X3D and fast pathways were laterally connected to integrate spatial and temporal features. The X3D pathway extracted spatial features. The fast pathway with R(2 + 1)D convolution decomposed spatiotemporal features and transferred effective spatial features to the X3D pathway. An action model further enhanced X3D spatial modeling. Experiments showed that X3DFast achieved 98.49% top-1 accuracy, outperforming similar methods in identifying the four behaviors. The method we proposed can effectively identify similar dairy cow behaviors while improving inference speed, providing technical support for subsequent dairy cow behavior recognition and daily behavior statistics