Structural model updating based on metamodel using modal frequencies

Modal frequencies are often used in structural model updating based on the finite element model, and metamodel technique is often applied to the corresponding optimization process. In this work, the Kriging model is used as the metamodel. Firstly, the influence of different correlation functions of Kriging model is inspected, and then the approximate capability of Kriging model is investigated via inspecting the approximate accuracy of nonlinear functions. Secondly, a model updating procedure is proposed based on the Kriging model, and the samples for constructing Kriging model are generated via the method of Optimal Latin Hypercube. Finally, a typical frame structure is taken as a case study and demonstrates the feasibility and efficiency of the proposed approach. The results show the Kriging model can match the target functions very well, and the finite element model can achieve accurate frequencies and can reliably predict the frequencies after model updating

Synthesis and Biological Activity of Organothiophosphoryl Polyoxotungstates

Organothiophosphoryl polyoxotungstates R∋XW∞∞O∋∃/- , R∋ P∋W∞,O∞/-, R∋PW∃O∋ Δ-(X = P, Si, Ge, B or Ga; R = PhP(S), C6H11P(S)) have been prepared from lacunary polyoxoanions and PhP(S). The products were characterized by elemental analysis, IR, and NMR spectroscopy. According to spectroscopic observations, the hybrid anions consist of a lacunary anion framework on which are grafted two equivalent or groups through P-O-W bridges. Some of the title compounds showed the antigerm activity

Negative differential conductance effect and electrical anisotropy of 2D ZrB2 monolayers

Two-dimensional (2D) metal-diboride ZrB2 monolayers was predicted theoretically as a stable new electronic material [A. Lopez-Bezanilla, Phys. Rev. Mater., 2018, 2, 011002 (R)]. Here, we investigate its electronic transport properties along the zigzag (z-ZrB2) and armchair (a-ZrB2) directions, using the density functional theory and non-equilibrium Green's function methods. Under low biases, the 2D ZrB2 shows a similar electrical transport along zigzag and armchair directions as electric current propagates mostly via the metallic Zr-Zr bonds. However, it shows an electrical anistropy under high biases, and its I-V curves along zigzag and armchair directions diverge as the bias voltage is higher than 1.4 V, as more directional B-B transmission channels are opened. Importantly, both z-ZrB2 and a-ZrB2 show a pronounced negative differential conductance (NDC) effect and hence they can be promising for the use in NDC-based nanodevices

Community structure and plant diversity under different degrees of restored grassland in mining areas of the Qilian Mountains, Northwestern China

Background: Mining activities are known to exert significant effects on the structure and function of grassland ecosystems. However, the role of mining grasslands restoration in altering the plant community and soil quality remains poorly understood, especially in alpine regions. Here, we investigated species diversity in grasslands with dynamic changes and different restoration levels in the Tianzhu alpine mining area locating in the Qilian Mountains.Methods: The plant community structure and species composition of the grasslands with different restoration levels were analyzed by the sample method. We used five different restoration levels: very low recovered degree (VLRD), low recovered degree (LRD), medium recovered degree (MRD), and high recovered degree (HRD), and selected natural grassland (NGL, CK) as the control.Results: Plant community structure and species composition were significantly higher than those under the VLRD in the Tianzhu alpine mining area (p < 0.05), with HRD > MRD > LRD > VLRD. There were 11 families, 18 genera, and 17 species of plants, mainly in the families of Leguminosae, Asteraceae, Gramineae, Rosaceae, and Salicaceae; among them, Salicaceae and Gramineae played a decisive role in the stability of the community. The ecotype community showed that perennial herbaceous plants were the most dominant, with annual herbaceous plants being the least dominant, and no tree and shrub layers were observed; the dominance index was the highest in VLRD at 0.32, the richness index was the highest in HRD at 2.73, the diversity of HRD was higher at 1.93, soil pH and EC showed a decreasing trend, and SMC, SOC, TN, NO3-N, NH4-N, AN, TP, and AP content showed an increasing trend with the increase of grassland restoration.Conclusion: In summary, with the increase of restored grassland in the Tianzhu alpine mining area, plant diversity gradually increased and plant community structure gradually diversified, which was close to the plant diversity of NGL. The protection of partially VLRD and LRD grasslands in the mining area should be emphasized, and the mine grassland should be used rationally and scientifically restored

Grid text classification method based on DNN neural network

With the rapid development of network technology, the electric power Internet of Things needs to face a large number of electronic texts and a large number of distributed data access and analysis requirements. If the system wants to complete accurate and efficient data analysis and build an existing data and service standard system covering the entire chain of energy and power business on the existing basis, it must implement massive electronic text retrieval, information extraction and classification in the power grid system. In order to achieve this purpose, a DNN neural network classification model is constructed to classify the text information of the power grid, and the effectiveness of the method is verified by experiments based on data from the substation information system

Grid text classification method based on DNN neural network

With the rapid development of network technology, the electric power Internet of Things needs to face a large number of electronic texts and a large number of distributed data access and analysis requirements. If the system wants to complete accurate and efficient data analysis and build an existing data and service standard system covering the entire chain of energy and power business on the existing basis, it must implement massive electronic text retrieval, information extraction and classification in the power grid system. In order to achieve this purpose, a DNN neural network classification model is constructed to classify the text information of the power grid, and the effectiveness of the method is verified by experiments based on data from the substation information system