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研发T形方颈单边螺栓的初衷是为解决闭口截面钢构件的螺栓连接难题，尤其是钢梁-钢管柱连接。目前已有较多针对T形方颈单边螺栓连接钢梁-钢管柱节点的研究报道，但是相关研究并未建立系统全面的节点承载力计算模型，这在一定程度上限制了此类连接的推广。在国内外相关研究成果的基础上应用组件分析法和薄板塑性铰线理论提出了T形方颈单边螺栓连接钢梁-钢管柱节点的抗弯承载力计算方法，可用于此类连接的结构性能预测。本文主要结果：（1）应用薄板屈服线理论，提出无肋端板连接下钢管柱连接面的屈服线分布模式并推导得到钢管柱壁强度控制下节点抗弯承载力。（2）提出栓孔V型分布和H型分布下的有效冲切面积折减系数，分别取值0.65和0.60。（3）本文所提T形方颈单边螺栓连接钢梁-钢管柱节点的抗弯承载力计算方法准确可靠，其所得节点抗弯承载力与试验值之间的误差保持在15%之内。

T形方颈单边螺栓是一类新型的单侧安装紧固件，可用于闭口截面钢构件连接，其构造简单、安装便利、无需额外的安装辅助工具，能适应现场粗放的施工方式，具有较大的应用潜力。对4个T形方颈单边螺栓连接钢梁-方钢管柱节点和1个传统螺栓连接节点开展了单调荷载下的静力性能试验，探讨了长圆形螺栓孔布置方案以及端板加劲肋和钢管柱内混凝土两种加强措施对节点结构响应的影响。研究了T形方颈单边螺栓连接节点的破坏模式、转角-弯矩关系、关键部位转角-应变关系、各部位屈服顺序等，揭示了此类节点的工作及破坏机理。结果表明此类连接具有与传统螺栓连接节点相似的破坏模式、相近的初始刚度，以及不低于85%的全生命周期内承载力。

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Intolerance of uncertainty（IU） is a cognitive process related to anxiety disorder. High IU levels predict increased anxiety and reflect low tolerance for uncertain events. IU is associated with many emotional disorders, and psychotherapy which focused on IU could be used to help patients with mood disorder improve mood regulation strategies. This article summarizes the theory and efficacy of IU based psychotherapy, indicating IU is related to emotional disorder and affects emotional regulation, and could be considered as a transdiagnostic factor of psychotherapy. [ABSTRACT FROM AUTHOR]

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Plants leaves are the main organ of gas exchange between plants and environment. The differences of δ13C in plant blades are mainly affected by plant types, temperature, precipitation, hydrological environments, and organic matter content. These factors induce different degrees of carbon isotope fractionation of organic matter in plants. Carbon stabilization isotope of amino acids(δ13CAAS) which is one of the basic units of natural life in leaves can reflect the environmental transformation of plant growth and its own metabolic law, and single amino acid can record more precise and accurate environmental information. To this end, this study is based on the experimental method of extracting amino acids in plant leaves. Therefore, this study selected northern and southern areas of Qinling Mountains as the research site, and extracted free amino acids from higher plants leaves. The content of specific amino acids and corresponding δ13C values in leaves of two groups of plants of different species in the same area and the same species in different areas were investigated, get the following conclusions: (1) Plant growth rate was slow and δ13CAAS were high in relatively cold climate; δ13CAAS decreased when the temperature increased, and δ13CAAS were negatively correlated with temperature; (2) Under normal plant growth, the δ13C values of single amino acids in leaves are: δ13CIle＞δ13CVal,δ13CIle＞δ13CLeu,δ13CTyr＞δ13CPhe; (3) Under the influence of environmental stress, the content of Phe in leaves decreased, while the content of other amino acids increased; δ13CTyr was relatively depleted, and δ13C of other amino acids were relatively enriched. [ABSTRACT FROM AUTHOR]

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The Wushi Sag has entered the stage of targeting thick and thin interaction reservoirs with different reservoir scales and strong heterogeneity. The sedimentary origins of the Liushagang Formation are diverse, and the superimposed styles of sand bodies are different. The L2 Formation is in the form of thin sand-mud inter-reservoir, and the L3 Formation is dominated by thick layers, with some thin layers in between. However, the faults in the study area are relatively developed, the effective frequency bandwidth of seismic data is 5 ~ 60 Hz, and the main frequency is only 18 Hz. Relying on the conventional impedance inversion body is not enough to effectively identify the macroscopic lithological characteristics of the reservoir in the study area. At the same time, how to break through the constraints of seismic resolution on thin sand-mud inter-reservoir is a major difficulty to be solved urgently. Therefore, two seismic inversion methods were introduced on the basis of seismic rock physics response analysis and fitting of acoustic curve construction. The identification efficiency of different scale reservoirs in the study area is improved, and the quantitative characterization of the Eocene thick and thin interaction reservoirs in the Wushi Sag is realized. According to the principle of sparse pulse deconvolution, a seismic inversion method of quasi-wave impedance was established, which combined the acoustic wave curve reflecting the wave impedance and the Gamma curve reflecting the sand and mud content in the study area. According to this method, the macroscopic distribution characteristics of sand body and favorable sand body development zones were characterized. In the study area, the geostatistical inversion with seismic coordinated multi-well constraints was used for the first time. Using the statistical inversion method based on Markov chain Monte Carlo, the sand body of the L2 Formation was engraved, and the sand body quantitative knowledge base was constructed. The research results provide important parameter guidance for sand body comparison in the study area. [ABSTRACT FROM AUTHOR]

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OBJECTIVE: To summarize the various biological functions of Sema3A, focusing on the role of Sema3A in regulating the proliferation, differentiation, and inflammatory environment of oral tissue-derived mesenchymal stem cells and the research progress of related signaling pathways so as to provide a feasible solution for Sema3A to treat oral diseases and repair oral soft and hard tissue defects. METHODS: Articles related to Sema3A and oral mesenchymal stem cells included in CNKI and PubMed databases from January 1993 to March 2022 were searched. A total of 70 articles were included for review. RESULTS AND CONCLUSION: (1) Sema3A has a great potential in the field of tissue engineering and is a potential therapeutic factor for various diseases. (2) At present, there are relatively few studies on Sema3A on oral tissue-derived mesenchymal stem cells, but it is basically confirmed that Sema3A can play an active role in the regulation of oral tissue-derived mesenchymal stem cells. (3) The inflammatory environment has a significant inhibitory effect on the proliferation and differentiation of stem cells, but studies have found that Sema3A can effectively regulate the proliferation and differentiation of a variety of oral mesenchymal stem cells in an inflammatory environment, thus playing an important role in inflammatory diseases. (4) Because Sema3A itself has the biological function of bone protection and Sema3A can effectively enhance the stem cell characteristics of oral tissue-derived mesenchymal stem cells and induce their proliferation and differentiation, it has unique advantages on regulating the differentiation of oral tissue-derived mesenchymal stem cells into bone to treat bone defect. (5) Studying that Sema3A regulates the proliferation and differentiation of oral tissue-derived mesenchymal stem cells can become a new idea for the treatment of oral diseases, and make breakthroughs in the field of soft and hard tissue repair and regeneration in the oral cavity. [ABSTRACT FROM AUTHOR]

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It is important to judge the density of pear flower accurately for automatic thinning. In order to judge the density of pear flowers precisely and achieve the purpose of automatic flower thinning, a density classification of pear flower images method based on improved density peak clustering algorithm was proposed in this study. The pear flower image samples used in the experiment were taken from the research base of Hebei Agricultural University, Niugang Village, Yi County, Baoding City, Hebei Province, and the variety was three-year-old Qiuyue. In this study, the images of pear flowers in natural environment were collected on April 18, 2021, when the pear flowers were in full bloom. The pear flower recognition images with high detection accuracy were obtained by using the depth learning model to detect the pear flower. The central coordinates of the target detection frame were extracted from the recognition images to obtain the data points that needed to be clustered. Secondly, in order to achieve the density classification of pear flower images, the method of selecting the cluster center was improved according to the shortcomings of the original density peak clustering algorithm in the density classification of pear flower and the requirements of pear flower density classification. The decision graph was divided into four parts by four groups of local density and center offset distance segmentation thresholds to select the cluster centers. These four parts corresponded to four grades of high, medium and low density and no thinning treatment. The accurate classification of pear flower images with reasonable density was realized. Finally, to solve the problem of inaccurate clustering classification of pear flower distribution with only cluster distribution, sparse distribution and large-scale close-up, the calculation method of dij parameter of distance between two points was improved. The pear flower scale and density grading standard were unified, and reasonable density grading could be achieved for all distribution types of pear flower images. The experimental results showed that the proposed algorithm could adapt to pear flower images of different scales. The accuracy of scale prediction was 94.89%, and the accuracy of density classification was 94.29%. Compared with the existing methods, the proposed method could achieve the density classification of local flower clusters in natural environment. the algorithm in this paper was compared with K-means algorithm and DBSCAN algorithm, and the accuracy of density classification was 94.29% and 68.57% respectively. Although the density classification accuracy of K-Means clustering algorithm was high, it lacked the algorithm steps to automatically select the clustering centers of different density classification, and was too sensitive to noise points. The density calculation method of DBSCAN algorithm lacked continuity, and the division of density grade was not precise enough. Based on the comprehensive analysis of density classification accuracy and cluster center selection method, the improved density peak clustering algorithm in this paper had the best effect. The clustering centers of different density grade could be automatically selected and the error of random initialization of clustering centers could be reduced. The proposed algorithm was robust to noise points and could eliminate noise points effectively. The proposed algorithm adopted soft statistics method for density calculation, which had continuity and was more accurate for density classification. It provides technical support for analyzing the density of pear flower and machine intelligent flower thinning. [ABSTRACT FROM AUTHOR]

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