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Long-span cable-supported bridge is one of crucial transportation infrastructures. Structural health monitoring (SHM) system that is used to assess structural service conditions and detect early damage has drawn a growing attention in engineering structural society. It provides a real-time monitoring of various structural changes under operational and extreme conditions. Yangluo Bridge over Yangtze River in Wuhan city of China is a 1280 m central span suspension bridge with an orthotropic steel box girder. Taking the SHM system of Yangluo Yangtze River Bridge as an example, this paper presents an implementation of SHM system on the bridge in terms of sensor layout, data acquisition strategy, data transmission, data processing, and abnormal alarm framework. With such an SHM system, the typical monitoring results are demonstrated such as operational environments, loadings, structural deformation, strains, dynamic properties, and structural responses. The SHM system of Yangluo Yangtze River Bridge is continuously gathering data and monitoring structural behavior. It is anticipated that it will provide either scientific or technological supports to the service comfort and safety of the bridge.

The Qiupu River Bridge is the first suspension bridge with the application of a novel anchorage of root pile foundation. The static behaviors of the bridge were studied in this paper. Before the bridge was opened to traffic, the statically loaded field tests of the bridge have been carried out to check its load-carrying capacity. Then, a finite element (FE) model of the bridge was developed and calibrated to the measured initial equilibrium configuration. The efficiency of the FE model has been verified by comparing the calculated results of the model to the measured ones under the live loads, which indicates that calibration of the FE model to the initial equilibrium configuration is crucial to the construction of a baseline FE model. Based on the verified FE model, the influences of anchorage displacements on the bridge loading behavior were studied. Furthermore, a simplified analytical method was proposed to analyze the mechanism of structural responses under the anchorage horizontal displacements. The comparison of analytical results with FE results revealed a good agreement. It is demonstrated that the horizontal displacement of the anchorage of suspension bridges affects the structural responses more obviously than the vertical displacement, while the pylon is sensitive to the anchorage displacement. The deflection of the bridge deck results from the pylon displacement, which is caused by the unbalanced main cable force between the main span and side span. It is reasonable to take the internal force of pylons as the controlling condition of anchorage displacements. Finally, based on the analytical method, the parametric studies reveal that increasing the sag-to-span ratio and side-to-center span ratio can reduce the displacement and internal force of pylons caused by the anchorage displacement.

Background The gut microbiota plays an essential role in maintaining gut homeostasis and improving performance, with the composition of microbial communities visibly differing across different laying stages in hens and significantly correlating with egg production. To gain further insights into the association between microbial community characteristics and laying periods in Hy-Line variety brown and Isa brown laying hens, we conducted a 16S rRNA amplicon sequencing survey. Results Our result revealed the diversity of bacteria in the early laying period was commonly higher than peak, and in Hy-Line variety brown laying hens were generally higher than Isa brown. Principal coordinate analysis (PCoA) and permutational multivariate analysis of variance (PERMANOVA) revealed that the structure and composition of the gut microbiota of laying hens exhibited significant differences among different groups. Phylum Firmicutes, Bacteroidota, Proteobacteria, and Fusobacteriota were found that dominant in the host’s feces. Therein, the abundance of Fusobacteriota was higher in the peak period than in the early period, while the abundance of Cyanobacteria in the early period was higher in two breeds of hens. Furthermore, random forest based on machine learning showed that there were several distinctly abundant genera, which can be used as potential biomarkers to differentiate the different groups of laying periods and breeds. In addition, the prediction of biological function indicated the existing discrepancy in microbial function among the microbiota of four groups. Conclusions Our findings offer new insights into the bacterial diversity and intestinal flora composition of different strains of laying hens during various laying periods, contributing significantly to the improvement of production performance and the prevention of chicken diseases.

Background Hepatocellular carcinoma (HCC) is a highly vascularized tumor in which abnormal blood vessels contribute to poor treatment efficacy and prognosis. In this study, we assessed the efficacy, safety, and potential ability of bevacizumab to normalize tumor vascularity in patients with advanced HCC. Methods Patients with histologically or clinically confirmed advanced HCC that were refractory to conventional transarterial chemoembolization (c-TACE) received a transarterial infusion of bevacizumab (5 mg/kg), followed by c-TACE (named as BEVA-TACE). The primary endpoint was overall survival (OS), which was defined as the time from a patient identified as TACE refractory to the occurrence of death. The secondary endpoints included progression-free survival (PFS) and the disease control rate (DCR). Results From January 2014 to December 2017, 20 patients with Barcelona Clinic Liver Cancer (BCLC) staging scores C (80.0%) or D (20.0%) received BEVA-TACE. The median OS time was 9.2 months [95% confidence interval (CI): 2.1-22.6 months]. The median PFS time was 6.3 months (95% CI: 1.0-10.5 months). Despite the late stage, 1 patient (5.0%) had a complete response (CR), 6 patients (30.0%) had a partial response (PR), and 10 patients (50.0%) had stable disease (SD) [overall response rate (ORR) 30.0%; DCR 85.0%]. The most common adverse events (AEs) were postembolic syndrome (25%), hyperbilirubinemia (10.0%), and melena (10.0%). Severe III-IV oral mucositis and hypertension were observed in only 1 patient (5.0%) during the follow-up period. Conclusions BEVA-TACE showed clinical efficacy, and patients with TACE-refractory HCC had acceptable AE rates. A low dose of targeted localized vessel bevacizumab infusion may normalize the condition of tumor blood vessels in patients with advanced HCC.

The performance benefits of passive vibration suppression with network configurations consisting of stiffness, damping and inertance elements have been demonstrated for a wide range of mechanical systems. Considering physical implementations of these beneficial network configurations, hydraulic realisations have the advantages of durability and simplicity for integration with existing hydraulic dampers. Such designs are exemplified by fluid inerters and fluid-inerter-damper devices. However, in contrast to the convenience of realising inertance and damping elements, realising ‘embedded’ stiffness is very challenging. We use ‘embedded’ to refer to a network element, which is not purely in series or in parallel with the remainder of the network but instead lies within the network layout. In this work, a setup using a rubber membrane to realise such embedded stiffness is proposed, together with a procedure for hydraulic implementations of any stiffness-damping-inertance configurations. The nonlinear properties of the embedded stiffness due to rubber membrane properties are then investigated both theoretically and experimentally. In addition, the effectiveness of both the membrane setup and the design procedure are demonstrated via a case study of suspension design for passenger vehicle ride comfort enhancement.

Frequency variations are normally employed for damage detection as frequencies possess the advantages of the high accuracy and high anti-noise capability. However, the validity and applicability of such methods are restricted due to the limited order of frequencies. A damage localization and quantification approach is proposed for beam bridges by using the frequency variation of parked vehicle-bridge systems. Firstly, the phenomenon of frequency variation of parked vehicle-bridge systems is briefly illustrated. Secondly, a damage location index (DLI) is defined by the frequency change rate (FCR) of the vehicle-bridge systems with the vehicle parked at different positions before and after damage. Then the relationship between the frequency-parameter change rate (FPCR) and damage severity is established via finite element model (FEM) and further employed to formulate a damage equation(s) for severity estimation. The dimension of coefficient matrix in damage equations is reduced to large extent as the damage locations are determined by DLI in advance, which is meaningful in enhancing the effectiveness of severity estimation. Results of numerical and experimental examples with different damage scenarios indicate that the proposed approach can localize and quantify damages with high accuracy and efficiency by using frequency information only. The effects of measurement noise and FEM error are discussed to examine the robustness of the proposed approach. The cases when damage occurs in neighbor elements are analyzed to investigate the feasibility and robustness of the proposed approach.

Root pile is a new type of pile that improves the load carrying capacity by roots penetrating into soils. To carry out the nonlinear settlement analysis of such a root pile in multilayered soils, the hyperbolicity-based load transfer model is in this paper reformulated to account for the discontinuities between the segments with and without roots. The procedure to determine the model parameters for root piles is presented accordingly. The feasibility and reliability of such a proposed modified hyperbolic model for nonlinear settlement analysis of root piles in multilayered soils are verified by a numerical case and two real experimental cases. The numerical case study shows that the root pile does increase the pile load carrying capacity to some extent. In a parametric study based on this numerical case, it can be found that the bearing capacity of root piles increases along with the increase in the root number, size, depth and the elastic modulus of the surrounding soil. The loading test results on two real root piles sited in Chizhou Yangtze River Bridge, China, are used to further verify the proposed method. Comparing with other analytical methods, it is demonstrated that the proposed method incorporated with the proposed modified hyperbolic model can achieve a better agreement with the measured ones especially in a large loading stage.

To investigate the structural dynamics of a container subjected to a vented hydrogen explosion, 48 field tests were conducted in a 40-foot container with roof vents and an end vent. The effects of the hydrogen concentration, ignition position, and obstacles on the evolution of the dynamic responses were investigated. Three stages were generally observed for displacements: (1) At the stage of the vent rupture, the displacement could be approximated as a quasi-static response, and there was a linear relationship between the peaks of positive overpressure and displacement. (2) Structural deformation appeared as reciprocating vibration at the stage of Helmholtz oscillation. (3) The structure exhibited relatively weak irregular fluctuation when high-frequency acoustic oscillation occurred. Two types of the structural acceleration with low and high amplitudes resulting from Helmholtz oscillation and acoustic oscillation, respectively, were clearly observed. For the end-vented explosion, multiple peaks were observed for the displacement at the quasi-static stage due to the rupture, discharge, and external explosion. Moreover, the displacement was sensitive to hydrogen concentration, whereas the number of obstacles and the ignition position had significant influences on the peak acceleration for roof venting. This work conducted the fundamental explanation for the evolution law of structural responses induced by vented hydrogen explosions from the perspective of structural dynamics and enriched the experimental accumulation in a large-scale container with congestion in this field.

Central to structural health monitoring (SHM) is data modeling, manipulation, and interpretation on the basis of a sophisticated SHM system. Despite continuous evolution of SHM technology, the precise modeling and forecasting of SHM measurements under various uncertainties to extract structural condition-relevant knowledge remains a challenge. Aiming to resolve this problem, a novel application of a fully probabilistic and high-precision data modeling method was proposed in the context of an improved Sparse Bayesian Learning (iSBL) scheme. The proposed iSBL data modeling framework features the following merits. It can remove the need to specify the number of terms in the data-fitting function, and automatize sparsity of the Bayesian model based on the features of SHM monitoring data, which will enhance the generalization ability and then improve the data prediction accuracy. Embedded in a Bayesian framework which exhibits built-in protection against over-fitting problems, the proposed iSBL scheme has high robustness to data noise, especially for data forecasting. The model is verified to be effective on SHM vibration field monitoring data collected from a real-world large-scale cable-stayed bridge. The recorded acceleration data with two different vibration patterns, that is, stationary ambient vibration data and non-stationary decay vibration data, are investigated, returning accurate probabilistic predictions in both the time and frequency domains.

A new physical parameter identification method with a name of EKPF-LS is proposed for shear frame structures under limited inputs and outputs by a combination of extended Kalman particle filter (EKPF) and least square (LS) algorithm. The basic principle of EKPF-LS is to establish the proposed distribution function of the particle filter through EKF-LS. In this method, EKPF is introduced to get rid of the restriction of Gaussian white noise model and reduce the linearization error caused by EKF. Meanwhile, LS is utilized to address the problem of unmeasured excitation estimations. The effectiveness and accuracy of the proposed EKPF-LS method is verified by a numerical example of a four-story hysteretic shear frame under an earthquake excitation and an experimental test of a four-story shear type frame using Gaussian white noise and sine sweep signal as excitations, respectively. Gaussian colored noises are then added to the solved and measured response signals in the numerical example and experimental test, respectively. The results demonstrate that the proposed method can identify the stiffness of shear frame structures effectively and is superior to the existed EKF-LS approach when the structural system is nonlinear structural system or Colored noise model.

This study focuses on the acoustic emission wave classification for the sake of more accurate and comprehensive rail crack monitoring in the field typically with complex cracking conditions, high-operational noise, and mass data. There are mainly three types of acoustic emission waves induced by operational noise, impact, and crack propagation, respectively. Synchrosqueezed wavelet transform was introduced to represent intrinsic characteristics of acoustic emission waves more clearly in the time–frequency domain, where acoustic emission waves induced by different mechanisms were found to show various patterns of energy distribution. Then, a multi-branch convolutional neural network model with two branches was developed to automatically classify the three types of acoustic emission waves by taking into account their synchrosqueezed wavelet transform plots in various time–frequency scales. Training, validation, and test data sets were constructed using acoustic emission waves collected through a series of field and laboratory tests with various noise levels and loading conditions. The transfer learning was used to train the model faster, and the Bayesian optimization algorithm was applied to tune the hyperparameters. Finally, the multi-branch convolutional neural network model achieved higher accuracy and robustness than the traditional convolutional neural network model of single branch in identifying different acoustic emission mechanisms. The proposed acoustic emission wave classification method based on synchrosqueezed wavelet transform and multi-branch convolutional neural network is able to detect not only surface rail cracks, where both impact-induced and crack propagation-induced acoustic emission waves would be identified, but also internal rail cracks where only crack propagation-induced acoustic emission waves would be captured.

Groundwater heat pumps (GWHP) are an efficient utilisation of shallow geothermal energy technology and of great significance in terms of promoting energy conservation and reducing emissions. However, recharge clogging has been a key problem restricting the continuous operation of GWHP. In this study, a simulation test device for sand column was designed with the aim of addressing chemical clogging induced by heat pump reinjection in a porous saline aquifer in the Huaibei Plain, China. The trend in the variation of the permeability coefficient was studied based on the detection of the sand sample composition, recharge water quality, and sand layer temperature, and the cause of formation was analysed using the saturation index (SI) and ion ratio method. The results indicated that the permeability coefficient in the sand column decreased exponentially, with a maximum and minimum decrease of 8.14% and 71.65% of the original coefficient, respectively, found in sections P2-P3 and P8-P9. Therefore, the clogging effect of the aquifer at approximately 200-400 mm from the recharge well was significant. Water-rock interactions predominantly involved the dissolution of halite, albite, chlorite, anhydrite, and dolomite and the precipitation of calcite, as well as the exchange adsorption of Ca

Previous seismic events have shown that bridges are more susceptible to severe damage when subjected to near-fault ground motions. Near-fault ground motions usually possess obvious pulse-l...

Frequency-domain operational vibration responses have been widely applied in structural health monitoring (SHM). Characterizing and quantifying their uncertainties are of fundamental importance for enhancing the robustness of SHM technologies. The classic complex Gaussian distribution is being increasingly used to model the distributions of fast Fourier transform (FFT) coefficients due to its elegant and convenient mathematical nature. However, the field-test data analysis of engineering structures under operational vibrations in this study emphasize the possibility of non-Gaussianity of some observations. The higher peaks and heavier tails than those of a Gaussian distribution emerge as observable features. As a member of the general family of elliptically symmetric distributions, the t distribution has been widely recognized as a useful extension of the Gaussian distribution for the robust statistical modeling of data sets with heavier-than-normal tails. In this paper, we consider using the complex-valued t distribution to characterize the FFT coefficients with high kurtosis and heavy tails. The probability density function (PDF) of multivariate proper complex t random variables are derived based on the equivalent counterparts in the real-valued domain. The marginal PDFs of the real part, the imaginary part, the magnitude, and the phase of a univariate complex-valued t random variable are also derived analytically based on advanced integration techniques. The field test data of different engineering structures provide an illustration of the performance of complex Gaussian and complex t probabilistic models at different frequencies evaluated via the K-S test, goodness-of-fit test, and probability plots.

Horse meat and milk are an important source of nutrition for many Chinese. Previous studies have shown a fairly high prevalence of Toxoplasma gondii infection in horses. However, there is no data concerning the molecular characterization of T. gondii in horses in China. The present study tested 231 cervical lymph node samples of horses from northern China (97 from Jilin, 54 from Liaoning, and 80 from Xinjiang) for the presence of the T. gondii B1 gene by seminested PCR. The B1-positive samples were genotyped at nine nuclear loci, that is, SAG1, alternative SAG2, SAG3, BTUB, c22-8, GRA6, c29-2, PK1, and an apicoplast locus, using PCR-restriction fragment length polymorphism technique. A total of 14 (6.06%) out of 231 samples were T. gondii positive. The overall prevalence of T. gondii in the horses was 5.15% (5/97) for Jilin Province, 5.55% (3/54) for Liaoning Province, and 7.50% (6/80) for Xinjiang Uygur Autonomous Region. Of the 14 positive samples, only 2 were successfully genotyped at all loci, 5 were successfully genotyped at 5-8 loci, and all typed samples belong to ToxoDB genotype no. 9. To our knowledge, this is the first molecular characterization of the T. gondii isolates from horses in China.

Civil engineering structures such as high-rise buildings and long-span cable-supported bridges usually exhibit low-frequency characteristics and the resultant response signals may be non-asymptotic (the amplitude change rate is higher than its phase change rate) and even have closely-spaced frequency components. The identification of satisfactory instantaneous frequencies of such response signals is a challenge faced by standard synchrosqueezing wavelet transform. The paper aims to propose a combined method to address this challenge. The proposed method combines an extended analytical mode decomposition (AMD) method, a recursive Hilbert transform and a zoom synchrosqueezing wavelet transform (consisting of frequency-shift operation and partial zoom synchrosqueezing operation). In the method, a multi-component signal, which may consist of closely spaced frequency components, is firstly decomposed into several mono-component signals by the extended AMD, and the extracted mono-components are then demodulated into asymptotic signals by recursively using the Hilbert transform. After that, a frequency-shift operation is employed to improve time resolution and a partial zoom synchrosqueezing operation is then applied to improve the frequency resolution in the narrow low frequency range of interest. Two numerical examples, an experiment on an aluminum cantilever beam with abrupt mass reduction and an experiment on a cable with time-varying tension forces are provided to illustrate the effectiveness, accuracy and robustness of the method.

Mechanical joints usually affect structural dynamic responses, leading to local nonlinear stiffness and damping. In this paper, hysteresis Iwan model is discussed in detail to describe nonlinear mechanical behaviors of contact surface. Based on adjusted Iwan model, the derivation process of force-displacement relationship is presented for Iwan beam element. Meanwhile, relevant experimental study on the dynamic behaviors of a cantilever beam with a bolted joint is performed. Linear beam element and Iwan beam element are used to simulate the bolted cantilever beam structure, and the corresponding structural dynamic responses are calculated by forward incremental displacement center difference method. Then, combined with the nonlinear structural model updating method based on the instantaneous characteristics of principal dynamic response components, the bolted cantilever beam structure is updated and the parameters of Iwan beam element are obtained. The updated results are in good agreement with the experimental results, that is, the feasibility and effectiveness of the proposed nonlinear structural model updating method are verified by comparing the updated responses and measured data.

It is still an issue to identify the vehicle weight for bridges with multi-T-girders which is essential in bridge operation management. In this study, a vehicle weight identification method is proposed for bridges with multi-T-girders. To consider the load transverse distribution effect, first the separation variable method is adopted to separate the spatial influence surface function of vehicle load into the product of two single-valued functions. Then the procedures of the vehicle weight identification method are proposed using the measured strain responses of the T-girders caused by the passing vehicle accordingly. A numerical example of a bridge with five T-girders and an experimental example of a bridge with two T-girders are conducted to verify the feasibility and effectiveness of the proposed method. Furthermore, the performance of the proposed method is compared with the typical existing method in which the load transverse distribution effect is not considered.

This paper proposes a Bayesian based nonlinear model updating approach using the instantaneous amplitudes of the decomposed dynamic responses. Uncertainty quantification of the model updating results due to the measurement noise is conducted. The residual of the instantaneous amplitudes of the decomposed structural dynamic responses between the test structure and the analytical nonlinear model is used to construct the maximum likelihood function. Since nonlinear model parameters and simulated error variances of the instantaneous parameters are all unknown, the extended maximum likelihood estimation method is used to update these parameters. The uncertainty in the updated nonlinear model parameters can be evaluated by using the Cram-Rao lower bound theorem with the exact Fisher Information matrix. A numerical study on a three-storey building structure model under earthquake excitation is performed to verify the accuracy and performance of the proposed approach. An experimental verification on a high voltage switch structure under harmonic excitation is conducted to investigate the accuracy of using the proposed approach for nonlinear model updating. Both numerical and experimental results demonstrate that the proposed approach is reliable and accurate for nonlinear model updating, with the capacity of considering the uncertain noise effect in the measurements.

This paper proposes a damage detection method with two phases, namely, localization and quantification, for beam structures subjected to moving load and successfully validates it via a laboratory experiment. Firstly, the discrete wavelet transform (DWT) is applied to decompose the displacement response change induced by a moving vehicle and locate potential structural damages. Then adaptive-scale wavelet finite element model (WFEM) updating is employed to estimate the damage severity in the identified damage regions in a progressive fashion. The elemental scales of WFEM are adaptively changed according to not only the moving vehicle position but also the progressively identified damage regions. Such a method can effectively minimize the number of modeling degree-of-freedoms (DOFs) and updating parameters during optimization. The feasibility and effectiveness of the proposed method is examined through a laboratory experiment with different damage scenarios. The results indicate the proposed method can achieve good consistency between structural modeling, damage scenarios, and load conditions, as well as an optimal tradeoff between damage detection accuracy and efficiency.

The difficulty of achieving controlled input has led to the development of new output-only structural health monitoring (SHM) approaches. Without measuring the input or assuming a specific input model, a transmissibility function (TF) as a mathematical representation of the output-to-output relationship has proven to be an attractive tool in SHM. The state of the art and challenges to TF analysis that cast SHM in the context of a system identification (SI) paradigm are reviewed and discussed in this study. This review starts with an overview of the fundamentals of TFs by illustrating its categories, connections with frequency response functions (FRFs), and basic properties. By categorizing TFs as local and global, this literature review limits the practice of various methodologies to the following key aspects: modal analysis, damage detection, and model updating. A brief treatment of the basic ideas is presented while a comprehensive and critical explanation of their contents is not attempted. Based on the review, existing studies are discussed, highlighting gaps requiring additional work and possible future trends for TF-based system identification.

Current probabilistic seismic risk assessment framework does not fully address the impact of the spatial variability of ground motions on long bridges. However, recent studies have highlighted that long-span bridges are more vulnerable to damage due to spatial varying ground motions. To achieve this increased vulnerability, this research presents a reliable risk assessment tool for long-span bridges subjected to spatial varying ground motions. The proposed method includes the following tasks: (i) the simulation of spatially variable ground motions, (ii) creation of numerical bridge model, (iii) generation of probabilistic seismic demand models, (iv) development of component and system fragility curves, (v) estimation of annual exceedance of damage through site-compatible hazard curves, and (vi) computation of repair time. As an illustration, a cable-stayed bridge is selected to highlight the effect of spatial variability of ground motions on the seismic risk and repair time of individual bridge components and bridge system in comparison to uniform excitations. Additionally, a sensitivity analysis is performed to examine the effect of spatial variability parameters and hazard curve-related parameters on the seismic risk of the bridge.

Cable force estimation is essential for security assessment of cable-stayed bridges. Cable force estimation methods based on the relationship between cable force and frequency have been extensively studied and used during both construction phase and service phase. However, the effect induced by inclination angle of the cable is not included in the establishment of frequency-cable force relationship as horizontal cable model is normally employed. This study aims to investigate the influence of the inclination angle on vibration based cable force estimation and provide practical formulas accordingly. Firstly numerical examples of fixed-fixed and hinged-hinged cables are simulated to illustrate the necessity of considering the inclination angle effect on the modal parameters and cable force estimation for inclined cables with small sag. Then practical formulas considering the inclination angle effect to estimate the cable force of fixed-fixed and hinged-hinged cables via the fundamental frequency are established accordingly. For the inclined cables with unknown boundary conditions, the coefficients reflecting boundary condition are predicted via the practical formulas for fixed-fixed and hinged-hinged cables. And the cable force considering the influence of inclination angle and unknown boundary conditions is obtained by iteration method. Finally, numerical examples are presented to demonstrate the effectiveness of the proposed method.

This paper proposes a Markov Chain Monte Carlo (MCMC)-based Bayesian method for nonlinear stochastic model updating by using the instantaneous characteristics of the structural dynamic responses. According to the discrete analytical mode decomposed method and Hilbert transform, the instantaneous characteristics of the mono-components are firstly extracted from the structural dynamic response and applied to the calculation of likelihood function. Then, the posterior probability density function associated with Bayesian theorem is derived under the assumption of Gaussian prior distribution by using instantaneous characteristics. Afterwards, to calculate the posterior probability density function and improve the sampling efficiency, the delayed rejection adaptive Metropolis-Hastings (DRAM) algorithm is implemented with the advantages of strong adaptive and fast convergence. In the process of Bayesian inference, the posterior samples generated by DRAM require vast quantities of finite element analysis to guarantee the accuracy. For reducing the computational cost, the response surface model is constructed to establish the mathematical regression model between the structural parameters and the theoretical dynamic responses. To validate the effectiveness and applicability of the proposed method, the numerical cases on a three-story nonlinear structure under earthquake excitation considering various noise level effects and an Iwan beam model with two types of excitations are simulated. In addition, an experimental validation on a ¼ scale, 3-story steel frame structure subjected to a series of earthquake excitations in the laboratory is also performed to further verify the proposed method. Both the numerical and experimental results demonstrate that the DRAM-based Bayesian method can be effectively used to update nonlinear stochastic models with a high accuracy.

Background: Primary liver cancer, around 90% are hepatocellular carcinoma in China, is the fourth most common malignancy and the second leading cause of tumor-related death, thereby posing a significant threat to the life and health of the Chinese people. Summary: Since the publication of Guidelines for Diagnosis and Treatment of Primary Liver Cancer (2017 Edition) in 2018, additional high-quality evidence has emerged with relevance to the diagnosis, staging, and treatment of liver cancer in and outside China that requires the guidelines to be updated. The new edition (2019 Edition) was written by more than 70 experts in the field of liver cancer in China. They reflect the real-world situation in China regarding diagnosing and treating liver cancer in recent years. Key Messages: Most importantly, the new guidelines were endorsed and promulgated by the Bureau of Medical Administration of the National Health Commission of the People’s Republic of China in December 2019.