Site Preference of Alloying Elements in DO22-Ni3V Phase: Phase-Field and First-Principles Study

Ding-Ni Zhang,Qian-Qian Shangguan,Fu Liu,Ming-Yi Zhang 대한금속·재료학회 2015 METALS AND MATERIALS International Vol.21 No.4

Site preference of alloying elements in DO22-Ni3V phase was investigated using phase-field and first-principles method. The concentrations of alloying elements on sublattices of DO22-Ni3V phase were quantitatively studied using phase-field model based on microscopic diffusion equations. The phase-field computation results demonstrate that the concentration differences of alloying elements on the NiI and NiII site are attributed to the coordination environment difference. Host atoms Ni and substitutional ternary additions Al prefer to occupy NiI site. Antisite atoms V show site preference on the NiII site. Further reason of site preference of alloying elements on the two different Ni sites were studied using first-principles method to calculate the electronic structure of DO22-Ni3V phase. Calculation of density of states, orbitals population and charge population of the optimized Ni3V structure found that the electronic structures of NiI and NiII sites are different. Electronic structure difference, which is caused by coordination environment difference, is the essential reason for site selectivity behaviors of alloying elements on NiI and NiII sites.

Structural damage alarming and localization of cable-supported bridges using multi-novelty indices: a feasibility study

Yi-Qing Ni,Junfang Wang,Tommy H.T. Chan 국제구조공학회 2015 Structural Engineering and Mechanics, An Int'l Jou Vol.54 No.2

This paper presents a feasibility study on structural damage alarming and localization of longspancable-supported bridges using multi-novelty indices formulated by monitoring-derived modalparameters. The proposed method which requires neither structural model nor damage model is applicable tostructures of arbitrary complexity. With the intention to enhance the tolerance to measurementnoise/uncertainty and the sensitivity to structural damage, an improved novelty index is formulated in termsof auto-associative neural networks (ANNs) where the output vector is designated to differ from the inputvector while the training of the ANNs needs only the measured modal properties of the intact structure underin-service conditions. After validating the enhanced capability of the improved novelty index for structuraldamage alarming over the commonly configured novelty index, the performance of the improved noveltyindex for damage occurrence detection of large-scale bridges is examined through numerical simulationstudies of the suspension Tsing Ma Bridge (TMB) and the cable-stayed Ting Kau Bridge (TKB) incurredwith different types of structural damage. Then the improved novelty index is extended to formulate multinoveltyindices in terms of the measured modal frequencies and incomplete modeshape components fordamage region identification. The capability of the formulated multi-novelty indices for damage regionidentification is also examined through numerical simulations of the TMB and TKB.

Serum Amyloid A is a Novel Prognostic Biomarker in Hepatocellular Carcinoma

Ni, Xiao-Chun,Yi, Yong,Fu, Yi-Peng,He, Hong-Wei,Cai, Xiao-Yan,Wang, Jia-Xing,Zhou, Jian,Fan, Jia,Qiu, Shuang-Jian Asian Pacific Journal of Cancer Prevention 2014 Asian Pacific journal of cancer prevention Vol.15 No.24

Purpose: To investigate the prognostic value of serum amyloid A (SAA) in patients with hepatocellular carcinoma (HCC) undergoing surgery. Materials and Methods: Preoperative serum samples of 328 patients with HCC who underwent curative resection and of 47 patients with benign liver lesion were assayed. Serum levels of SAA were measured by enzyme-linked immunosorbent assay and its correlations with clinicopathological characteristics and survival were explored. Results: Levels of SAA were significantly higher in patients with HCC than those with benign liver lesion. There were strong correlations between preoperative serum SAA level and tumor size and more advanced BCLC stage. On univariate analysis, elevated SAA was associated with reduced disease-free survival and overall survival (p=0.001 and 0.03, respectively). Multivariate analyses showed that serum SAA level was an independent prognostic factor for overall survival (hazard ratio 2.80, p=0.01). Conclusions: High SAA serum level is a novel biomarker for the prognosis of HCC patients.

Multiple damage detection of maglev rail joints using time-frequency spectrogram and convolutional neural network

Yi-Qing Ni,Su-Mei Wang,Gao-Feng Jiang,Yang Lu,Guobin Lin,Hong-Liang Pan,Junqi Xu,Shuo Hao 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.29 No.4

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-loosenesscaused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFSCNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.

A vision-based system for long-distance remote monitoring of dynamic displacement: experimental verification on a supertall structure

Yi-Qing Ni,You-Wu Wang,Wei-Yang Liao,Wei-Huan Chen 국제구조공학회 2019 Smart Structures and Systems, An International Jou Vol.24 No.6

Dynamic displacement response of civil structures is an important index for in-construction and in-service structural condition assessment. However, accurately measuring the displacement of large-scale civil structures such as high-rise buildings still remains as a challenging task. In order to cope with this problem, a vision-based system with the use of industrial digital camera and image processing has been developed for long-distance, remote, and real-time monitoring of dynamic displacement of supertall structures. Instead of acquiring image signals, the proposed system traces only the coordinates of the target points, therefore enabling real-time monitoring and display of displacement responses in a relatively high sampling rate. This study addresses the in-situ experimental verification of the developed vision-based system on the Canton Tower of 600 m high. To facilitate the verification, a GPS system is used to calibrate/verify the structural displacement responses measured by the vision-based system. Meanwhile, an accelerometer deployed in the vicinity of the target point also provides frequency-domain information for comparison. Special attention has been given on understanding the influence of the surrounding light on the monitoring results. For this purpose, the experimental tests are conducted in daytime and nighttime through placing the vision-based system outside the tower (in a brilliant environment) and inside the tower (in a dark environment), respectively. The results indicate that the displacement response time histories monitored by the vision-based system not only match well with those acquired by the GPS receiver, but also have higher fidelity and are less noise-corrupted. In addition, the low-order modal frequencies of the building identified with use of the data obtained from the vision-based system are all in good agreement with those obtained from the accelerometer, the GPS receiver and an elaborate finite element model. Especially, the vision-based system placed at the bottom of the enclosed elevator shaft offers better monitoring data compared with the system placed outside the tower. Based on a wavelet filtering technique, the displacement response time histories obtained by the vision-based system are easily decomposed into two parts: a quasi-static ingredient primarily resulting from temperature variation and a dynamic component mainly caused by fluctuating wind load.

Structural damage alarming and localization of cable-supported bridges using multi-novelty indices: a feasibility study

Ni, Yi-Qing,Wang, Junfang,Chan, Tommy H.T. Techno-Press 2015 Structural Engineering and Mechanics, An Int'l Jou Vol.54 No.2

This paper presents a feasibility study on structural damage alarming and localization of long-span cable-supported bridges using multi-novelty indices formulated by monitoring-derived modal parameters. The proposed method which requires neither structural model nor damage model is applicable to structures of arbitrary complexity. With the intention to enhance the tolerance to measurement noise/uncertainty and the sensitivity to structural damage, an improved novelty index is formulated in terms of auto-associative neural networks (ANNs) where the output vector is designated to differ from the input vector while the training of the ANNs needs only the measured modal properties of the intact structure under in-service conditions. After validating the enhanced capability of the improved novelty index for structural damage alarming over the commonly configured novelty index, the performance of the improved novelty index for damage occurrence detection of large-scale bridges is examined through numerical simulation studies of the suspension Tsing Ma Bridge (TMB) and the cable-stayed Ting Kau Bridge (TKB) incurred with different types of structural damage. Then the improved novelty index is extended to formulate multi-novelty indices in terms of the measured modal frequencies and incomplete modeshape components for damage region identification. The capability of the formulated multi-novelty indices for damage region identification is also examined through numerical simulations of the TMB and TKB.

A novel computer vision-based vibration measurement and coarse-to-fine damage assessment method for truss bridges

Yi-Qing Ni,Wen-Qiang Liu,En-Ze Rui,Lei Yuan,Si-Yi Chen,You-Liang Zheng 국제구조공학회 2023 Smart Structures and Systems, An International Jou Vol.31 No.4

To assess structural condition in a non-destructive manner, computer vision-based structural health monitoring (SHM) has become a focus. Compared to traditional contact-type sensors, the advantages of computer vision-based measurement systems include lower installation costs and broader measurement areas. In this study, we propose a novel computer vision-based vibration measurement and coarse-to-fine damage assessment method for truss bridges. First, a deep learning model FairMOT is introduced to track the regions of interest (ROIs) that include joints to enhance the automation performance compared with traditional target tracking algorithms. To calculate the displacement of the tracked ROIs accurately, a normalized cross-correlation method is adopted to fine-tune the offset, while the Harris corner matching is utilized to correct the vibration displacement errors caused by the non-parallel between the truss plane and the image plane. Then, based on the advantages of the stochastic damage locating vector (SDLV) and Bayesian inference-based stochastic model updating (BISMU), they are combined to achieve the coarse-to-fine localization of the truss bridge's damaged elements. Finally, the severity quantification of the damaged components is performed by the BI-SMU. The experiment results show that the proposed method can accurately recognize the vibration displacement and evaluate the structural damage.

Online railway wheel defect detection under varying running-speed conditions by multi-kernel relevance vector machine

Yi-Qing Ni,Yuan-Hao Wei,You-Wu Wang 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.30 No.3

The degradation of wheel tread may result in serious hazards in the railway operation system. Therefore, timely wheel defect diagnosis of in-service trains to avoid tragic events is of particular importance. The focus of this study is to develop a novel wheel defect detection approach based on the relevance vector machine (RVM) which enables online detection of potentially defective wheels with trackside monitoring data acquired under different running-speed conditions. With the dynamic strain responses collected by a trackside monitoring system, the cumulative Fourier amplitudes (CFA) characterizing the effect of individual wheels are extracted to formulate multiple probabilistic regression models (MPRMs) in terms of multi-kernel RVM, which accommodate both variables of vibration frequency and running speed. Compared with the general single-kernel RVM-based model, the proposed multi-kernel MPRM approach bears better local and global representation ability and generalization performance, which are prerequisite for reliable wheel defect detection by means of data acquired under different running-speed conditions. After formulating the MPRMs, we adopt a Bayesian null hypothesis indicator for wheel defect identification and quantification, and the proposed method is demonstrated by utilizing real-world monitoring data acquired by an FBG-based trackside monitoring system deployed on a high-speed trial railway. The results testify the validity of the proposed method for wheel defect detection under different running-speed conditions.

Research on the Choice of Knowledge Trading Pattern among Enterprises in Online Innovation Environment

Yi Li,Yuanjie Ni,Wei Liu,Wenxing Yan 보안공학연구지원센터(IJUNESST) 2015 International Journal of u- and e- Service, Scienc Vol.8 No.1

In the network innovation environment, the knowledge trading between enterprises could be divided into traditional pattern (TP) and the E-commerce pattern (ECP). The two both have their advantages and disadvantages, so enterprises would face the problem. This paper generalizes the operation characteristics and the influence factors of two patterns. The mathematical model is used to analyze the selection mechanism of choosing two patterns, and the result illustrated that when the agency fee, the risk cost of ECP and the trading potential get bigger, the possibility that enterprises choose TP would get bigger, too; and when the explicitness of knowledge, cost of searching, trading incentive, the risk cost of TP and the reserve cost of ECP increase, the possibility that enterprises choose ECP would increase. When the reserve cost of ECP becomes big enough, it would make enterprises be more willing to choose hybrid pattern.

The dopamine D1–D2DR complex in the rat spinal cord promotes neuropathic pain by increasing neuronal excitability after chronic constriction injury

Bao Yi-Ni,Dai Wen-Ling,Fan Ji-Fa,Ma Bin,Li Shan-Shan,Zhao Wan-Li,Yu Bo-Yang,Liu Ji-Hua 생화학분자생물학회 2021 Experimental and molecular medicine Vol.53 No.-

Dopamine D1 receptor (D1DR) and D2 receptor (D2DR) are closely associated with pain modulation, but their exact effects on neuropathic pain and the underlying mechanisms remain to be identified. Our research revealed that intrathecal administration of D1DR and D2DR antagonists inhibited D1–D2DR complex formation and ameliorated mechanical and thermal hypersensitivity in chronic constriction injury (CCI) rats. The D1–D2DR complex was formed in the rat spinal cord, and the antinociceptive effects of D1DR and D2DR antagonists could be reversed by D1DR, D2DR, and D1–D2DR agonists. Gαq, PLC, and IP3 inhibitors also alleviated CCI-induced neuropathic pain. D1DR, D2DR, and D1–D2DR complex agonists all increased the intracellular calcium concentration in primary cultured spinal neurons, and this increase could be reversed by D1DR, D2DR antagonists and Gαq, IP3, PLC inhibitors. D1DR and D2DR antagonists significantly reduced the expression of p-PKC γ, p-CaMKII, p-CREB, and p-MAPKs. Levo -corydalmine ( l -CDL), a monomeric compound in Corydalis yanhusuo W.T. Wang, was found to obviously suppress the formation of the spinal D1–D2DR complex to alleviate neuropathic pain in CCI rats and to decrease the intracellular calcium concentration in spinal neurons. l- CDL-induced inhibition of p-PKC γ, p-MAPKs, p-CREB, and p-CaMKII was also reversed by D1DR, D2DR, and D1–D2DR complex agonists. In conclusion, these results indicate that D1DR and D2DR form a complex and in turn couple with the Gαq protein to increase neuronal excitability via PKC γ, CaMKII, MAPK, and CREB signaling in the spinal cords of CCI rats; thus, they may serve as potential drug targets for neuropathic pain therapy.