MECHANICAL BEHAVIOR OF HORIZONTAL SWIVEL SYSTEM WITH UHPC SPHERICAL HINGE UNDER SEISMIC ACTION

In the process of rotation, the total weight of the bridge structure is jointly supported by the spherical hinge and the supporting structure, and its lateral stability is poor. It is easy to lose stability under the action of dynamic loads such as seismic action effect. The present paper takes a 10,000-ton continuous rigid frame swivel bridge as the re-search object, analyzes the dynamic response of the seismic action to the horizontal swivel system, and establishes several structure simulation models. Eighteen seismic waves in three directions that meet the calculation requirements are screened for time history analysis and compared with the response spectrum method. Finally, an optimization algorithm for the seismic response of the bridge under horizontal swivel system is proposed based on the mode superposition method. The UHPC spherical hinge bears all the vertical forces and 20% of the bending moment caused by the seismic action, the support structure bearing the remaining 80% of the bending moment. The optimization algorithm proposed in this paper features high accuracy

EXPERIMENTAL RESEARCH ON THE VIBRATION CHARACTERISTICS OF BRIDGE'S HORIZONTAL ROTATION SYSTEM

As a new construction method, the bridge horizontal rotation construction method can reduce the impact of traffic under the bridge. During the horizontal rotation of the bridge, the overall structure will inevitably lead to a vibration response due to the construction error of the contact surface of the spherical hinge. Due to the large weight of the structure and the longer cantilever of the superstructure, the vibration at the spherical hinge will be amplified at the girder end, which will adversely affect the stability of the structure. Taking a 10,000-ton rotating bridge as a reference, a scaled model was made to test the vibration of the girder during the rotating process of the horizontal rotating system.And by analyzing the frequency domain curve of girder vibration and the results of simulation calculation, it is found that the vertical vibration displacement response is related to the first three modes of longitudinal bending of the girder structure, but has nothing to do with the higher modes or other modes. Applying the harmonic response analysis module in ANSYS software method, it is proposed that the structural vibration effect will reach the smallest by controlling the rotating speed in order to control the excitation frequency within the first-order mode frequency of girder. Also in this research, the expression of the relationship between the vertical vibration velocity and acceleration of the girder end of the horizontal rotation system and the vibration frequency of the girder is established. Based on that, it is proposed that the stability of the horizontal rotation can be predicted by monitoring the vertical velocity and acceleration of the cantilever girder end during the horizontal rotation

Inequalities for Permanents and Permanental Minors of Row Substochastic Matrices

In this paper, some inequalities for permanents and permanental minors of row substochastic matrices are proved. The convexity of the permanent function on the interval between the identity matrix and an arbitrary row substochastic matrix is also proved. In addition, a conjecture about the permanent and permanental minors of square row substochastic matrices with fixed row and column sums is formulated

Synthesis of N/Fe Comodified TiO 2

To improve the efficiency of TiO2 as a photocatalyst for contaminant degradation, a novel nanocomposite catalyst of (N, Fe) modified TiO2 nanoparticles loaded on bentonite (B-N/Fe-TiO2) was successfully prepared for the first time by sol-gel method. The synthesized B-N/Fe-TiO2 catalyst composites were characterized by multiple techniques, including scanning electron microscope (SEM), energy dispersive spectrometry (EDS), X-ray diffraction (XRD), Fourier transform infrared spectra (FT-IR), X-ray fluorescence (XRF), nitrogen adsorption/desorption, UV-Vis diffuse reflectance spectra (DRS), and electron paramagnetic resonance (EPR). The results showed that bentonite significantly enhanced the dispersion of TiO2 nanoparticles and increased the specific surface area of the catalysts. Compared with nondoped TiO2, single element doped TiO2, or unloaded TiO2 nanoparticles, B-N/Fe-TiO2 had the highest absorption in UV-visible region. The photocatalytic activity of B-N/Fe-TiO2 was also the highest, based on the degradation of methyl blue (MB) at room temperature under UV and visible light irradiation. In particular, the synthesized B-N/Fe-TiO2 showed much greater photocatalytic efficiency than N/Fe-TiO2 under visible light, the newly synthesized B-N/Fe-TiO2 is going to significantly increase the photocatalytic efficiency of the catalyst using sun light

Contrastive Cross-Domain Sequential Recommendation

Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions based on user's historical sequential interactions from multiple domains. Generally, a key challenge of CDSR is how to mine precise cross-domain user preference based on the intra-sequence and inter-sequence item interactions. Existing works first learn single-domain user preference only with intra-sequence item interactions, and then build a transferring module to obtain cross-domain user preference. However, such a pipeline and implicit solution can be severely limited by the bottleneck of the designed transferring module, and ignores to consider inter-sequence item relationships. In this paper, we propose C^2DSR to tackle the above problems to capture precise user preferences. The main idea is to simultaneously leverage the intra- and inter- sequence item relationships, and jointly learn the single- and cross- domain user preferences. Specifically, we first utilize a graph neural network to mine inter-sequence item collaborative relationship, and then exploit sequential attentive encoder to capture intra-sequence item sequential relationship. Based on them, we devise two different sequential training objectives to obtain user single-domain and cross-domain representations. Furthermore, we present a novel contrastive cross-domain infomax objective to enhance the correlation between single- and cross- domain user representations by maximizing their mutual information. To validate the effectiveness of C^2DSR, we first re-split four e-comerce datasets, and then conduct extensive experiments to demonstrate the effectiveness of our approach C^2DSR.Comment: This paper has been accepted by CIKM 202

DMP_MI: an effective diabetes mellitus classification algorithm on imbalanced data with missing values

© 2019 Institute of Electrical and Electronics Engineers Inc.. All rights reserved. As a widely known chronic disease, diabetes mellitus is called a silent killer. It makes the body produce less insulin and causes increased blood sugar, which leads to many complications and affects the normal functioning of various organs, such as eyes, kidneys, and nerves. Although diabetes has attracted high attention in research, due to the existence of missing values and class imbalance in the data, the overall performance of diabetes classification using machine learning is relatively low. In this paper, we propose an effective Prediction algorithm for Diabetes Mellitus classification on Imbalanced data with Missing values (DMP_MI). First, the missing values are compensated by the Naïve Bayes (NB) method for data normalization. Then, an adaptive synthetic sampling method (ADASYN) is adopted to reduce the influence of class imbalance on the prediction performance. Finally, a random forest (RF) classifier is used to generate predictions and evaluated using comprehensive set of evaluation indicators. Experiments performed on Pima Indians diabetes dataset from the University of California at Irvine, Irvine (UCI) Repository, have demonstrated the effectiveness and superiority of our proposed DMP_MI

The Prospects for Immigration Amendments

Obg proteins are a family of P-loop GTPases, conserved from bacteria to human. The Obg protein in Escherichia coli (ObgE) has been implicated in many diverse cellular functions, with proposed molecular roles in two global processes, ribosome assembly and stringent response. Here, using pre-steady state fast kinetics we demonstrate that ObgE is an anti-association factor, which prevents ribosomal subunit association and downstream steps in translation by binding to the 50S subunit. ObgE is a ribosome dependent GTPase; however, upon binding to guanosine tetraphosphate (ppGpp), the global regulator of stringent response, ObgE exhibits an enhanced interaction with the 50S subunit, resulting in increased equilibrium dissociation of the 70S ribosome into subunits. Furthermore, our cryo-electron microscopy (cryo-EM) structure of the 50S? ObgE? GMPPNP complex indicates that the evolutionarily conserved N-terminal domain (NTD) of ObgE is a tRNA structural mimic, with specific interactions with peptidyl-transferase center, displaying a marked resemblance to Class I release factors. These structural data might define ObgE as a specialized translation factor related to stress responses, and provide a framework towards future elucidation of functional interplay between ObgE and ribosome-associated (p) ppGpp regulators. Together with published data, our results suggest that ObgE might act as a checkpoint in final stages of the 50S subunit assembly under normal growth conditions. And more importantly, ObgE, as a (p) ppGpp effector, might also have a regulatory role in the production of the 50S subunit and its participation in translation under certain stressed conditions. Thus, our findings might have uncovered an under-recognized mechanism of translation control by environmental cues