Tongji University
  • Shanghai, China
Recent publications
The dilation angle is the most commonly used parameter to study nonlinear post-peak dilatancy (PPD) behavior and simulate surrounding rock deformation; however, simplified or constant dilatancy models are often used in numerical calculations owing to their simple mathematical forms. This study developed a PPD model for rocks (rock masses) based on the Alejano–Alonso (A–A) dilatancy model. The developed model comprehensively reflects the influences of confining pressure (σ3) and plastic shear strain (γp), with the advantages of a simple mathematical form, while requiring fewer parameters and demonstrating a clear physical significance. The overall fitting accuracy of the PPD model for 11 different rocks was found to be higher than that of the A–A model, particularly for Witwatersrand quartzite and jointed granite. The applicability and reliability of the PPD model to jointed granites and different scaled Moura coals were also investigated, and the model was found to be more suitable for the soft and large-scale rocks, e.g. deep rock mass. The PPD model was also successfully applied in studying the mechanical response of a circular tunnel excavated in strain-softening rock mass, and the developed semi-analytical solution was compared and verified with existing analytical solutions. The sensitivities of the rock dilatancy to γp and σ3 showed significant spatial variabilities along the radial direction of the surrounding rock, and the dilation angle did not exhibit a monotonical increasing or decreasing law from the elastic–plastic boundary to the tunnel wall, thereby presenting the σ3-or γp-dominated differential effects of rock dilatancy. Tunnel deformation parabolically or exponentially increased with increasing in situ stress (buried depth). The developed PPD model is promising to conduct refined numerical and analytical analyses for deep tunneling, which produces extensive plastic deformation and exhibits significant nonlinear post-peak behavior.
According to the organizational support theory, leaders' words and deeds are not only the products of their own will but also a reflection of organizations' standpoints. We thus focus on leader apology in the case of organizational transgressions and predict that leaders' apologetic acts are likely to influence employees' organization-oriented attitudes and behaviors. Specifically, leader apology is hypothesized to positively influence employees' perception of organizational support, which in turn, is positively associated with employees' helping and risk taking behavior. Furthermore, drawing upon the organizational support theory that delineates the discretion and value perceived in the employee-organization relationship, we further propose that employees' perceived leader competence and power distance belief serve as two contingencies that influence the relationship between leader apology and employees' perceived organizational support. In particular, this relationship is stronger when employees perceive higher leader competence or hold stronger power distance beliefs. Two multi-wave data collected from hospitality employees support these hypotheses. The findings provide a new perspective to comprehending leader apology within the employee-organization relationship wherein leaders are considered as organizational agents. This research extends the existing literature on leader apology that largely focuses on leader apology following leaders’ transgressions and leader-oriented outcomes.
Injectable bone biomaterials like bone cement should be designed and fabricated with certain biological criteria, which include: 1) recruitment and polarization of the macrophages from M1 (pro-inflammatory) to M2 (anti-inflammatory) phenotype, 2) enhance vascularization, and 3) activate osteogenic differentiation of bone marrow-derived stem cells to promote bone healing. So far, no injectable biomaterials could spontaneously regulate the entire bone healing process that involves inflammation, angiogenesis, and osteogenesis. Therefore, in this study, we designed bone cement comprised of strontium and copper-incorporated borosilicate glass (Sr/Cu-BSG) in the liquid phase of chitosan to modulate bone healing. In vitro studies showed that the controlled release of Sr and Cu ions up-regulated anti-inflammatory genes(IL-1Ra and TGF-β1) while down-regulating pro-inflammatory genes(IL-1β and IL-6) in macrophages at 3 days. Sr and Cu ions also increased the expressions of angiogenic genes (VEGF and bFGF) in HUVECs at 5 days and osteogenic genes (Runx-2, OCN, and OPN) in hBMSCs at 7, 14, and 21 days. 5Sr3Cu-BSG bone cement exhibited the best anti-inflammatory, angiogenic, and osteogenic properties among the bone cement groups with different Sr and Cu ratios. Short-term and long-term implantation of Sr/Cu-BSGs in femoral condylar bone defects of rats and rabbits confirmed the in vitro results, where the degradation rate of Sr/Cu-BSG matched the bone healing rate. Similar to in vitro, the 5Sr3Cu-BSG group also showed the highest bone formation in vivo. Excellent physical and chemical properties, along with its bone repairing ability, make the Sr/Cu-BSG bone cement a good candidate biomaterial for treating bone defects.
Purpose This review aims to explore the history, research hotspots, and emerging trends of drug-eluting stents(DES)in the last two decades from the perspective of structural and temporal dynamics. Methods Publications on DES were retrieved from WoSCC. The bibliometric tools including CiteSpace and HistCite were used to identify the historical features, the evolution of active topics, and emerging trends on the DES field. Results In the last 20 years, the field of DES is still in the hot phase and there is a wide range of extensive scientific collaborations. In addition, active topics emerge in different periods, as evidenced by a total of 41 disciplines, 511 keywords, and 1377 papers with citation bursts. Keyword clustering anchored five emerging research subfields, namely #0 dual antiplatelet therapy, #3 drug-coated balloon, #4 bifurcation, 5# rotational atherectomy, and 6# quantitative flow ratio. The keyword alluvial map shows that the most persistent research concepts in this field are thrombosis, restenosis, etc., and the emerging keywords are paclitaxel eluting balloon, coated balloon, drug-eluting balloon, etc. There are 7 recent research subfields anchored by reference clustering, namely #2 dual antiplatelet therapy, #4 drug-coated balloon, #5 peripheral artery disease, #8 fractional flow reserve, #10 bioresorbable vascular scaffold, # 13 intravascular ultrasound, #14 biodegradable polymer. Conclusion The findings based on the bibliometric studies provide the current status and trends in DES research and may help researchers to identify hot topics and explore new research directions in this field.
Chronic low back pain and dyskinesia caused by intervertebral disc degeneration (IDD) are seriously aggravated and become more prevalent with age. Current clinical treatments do not restore the biological structure and inherent function of the disc. The emergence of tissue engineering and regenerative medicine has provided new insights into the treatment of IDD. We synthesized biocompatible layered double hydroxide (LDH) nanoparticles and optimized their ion elemental compositions to promote chondrogenic differentiation of human umbilical cord mesenchymal stem cells (hUC-MSCs). The chondrogenic differentiation of LDH-treated MSCs was validated using Alcian blue staining, qPCR, and immunofluorescence analyses. LDH-pretreated hUC-MSCs were differentiated prior to transplantation into the degenerative site of a needle puncture IDD rat model. Repair and regeneration evaluated using X-ray, magnetic resonance imaging, and tissue immunostaining 4–12 weeks after transplantation showed recovery of the disc space height and integrated tissue structure. Transcriptome sequencing revealed significant regulatory roles of the extracellular matrix (ECM) and integrin receptors of focal adhesion signaling pathway in enhancing chondrogenic differentiation and thus prompting tissue regeneration. The construction of ion-specific LDH nanomaterials for in situ intervertebral disc regeneration through the focal adhesion signaling pathway provides theoretical basis for clinical transformation in IDD treatment.
Maglev trains are strongly nonlinear, open-loop unstable, and flexible track systems. Vehicle-guideway self-excited vibration, which occurs mainly due to the deflection of the flexible guideway, may take place when a train is levitated on the guideway, even if the train is stationary. In this research, maglev vehicle-guideway coupling vibration is studied by employing the Hopf bifurcation criterion. First, the nonlinear dynamic model is presented with consideration of the flexibility of the guideway. To avoid the massive computations required to solve all the eigenvalues when analyzing system stability, the stability criterion of the Hopf bifurcation for the maglev system is proposed and proven with strict mathematical analysis. Based on the proposed lemma, the bifurcation points of the maglev system are calculated, and the critical points of the coupling vibration are found. Next, the influence law of the parameters of the guideway on the vehicle-guideway coupling vibration is analyzed with theoretical and numerical simulation. Then the influence law and the system stability range of the control parameters are analyzed to suppress the coupled vibration. Finally, the effectiveness of the proposed theory is verified by experiments.
Gearbox has a compact structure, a stable transmission capability, and a high transmission efficiency. Thus, it is widely applied as a power transmission system in various applications, such as wind turbines, industrial machinery, aircraft, space vehicles, and land vehicles. The gearbox usually operates in harsh and non-stationary working environments, expediting the degradation process of the gear surface. The degradation process may lead to severe gear failures, such as tooth breakage and root crack, which could damage the gear transmission system. Therefore, it is essential to assess the progression of gear surface degradation in order to ensure a reliable operation. The digital twin is an emerging technology for machine health management. A high-fidelity digital twin model can help reflect the operation status of the gearbox and reveal the corresponding degradation mechanism, which could benefit the remaining useful life (RUL) prediction and the predictive maintenance-based decision-making framework. This paper develops a digital twin-driven intelligent health management method to monitor and assess the gear surface degradation progression. The developed method can effectively reveal the gear wear propagation characteristics and predict the RUL accurately. Furthermore, the knowledge learned from digital twin models can be well transferred to the surface wear assessment of the physical gearbox in wide industrial applications, which is of great practical significance. Two endurance tests with different dominant degradation mechanisms were conducted to validate the effectiveness of the proposed methodology for gear wear assessment.
This paper examines individuals' choice of in-store and online grocery shopping channels using stated preference (SP) choice experiments. The study uses 1,391 records from a stated preference choice experiment in the Greater Toronto Area (GTA), Canada. It applies a Semi-Compensatory Independent Availability Logit (SCIAL) Model with latent variables. The methodology accounts for semi-compensatory choice behaviour through probabilistic choice set formation considering effects from socioeconomic and psychological variables. This study demonstrates the advantage of considering probabilistic choice set formation and semi-compensatory behaviour in modelling the adoption of innovative products. Empirical results reveal that shoppers demonstrated similar myopic behaviours once they firmly considered in-store grocery and subscribed free delivery services in their choice sets. They are equally likely to choose both channels without careful comparison to alternative channels once they firmly consider both channels in the choice set. However, considering the latter in choice sets is much costlier than in-store shopping. Therefore, in-store grocery shopping will still dominate the grocery shopping channel unless all home delivery services become free. Moreover, grocery shoppers value same-day delivery service. For typical delivery services charged between $4 and $20 in the GTA, Canada, grocery shoppers are willing to pay between $3.91 and $8.44 for same-day delivery. The latent variable describing shoppers’ perceived pandemic fear significantly contributes to the choice set inclusion probability of in-store grocery pick-up services, but the effect is not significant for other home delivery channels. This highlights heterogeneity in grocery shoppers' choice behaviour within the online channel.
Objectives To develop and validate a deep learning (DL) model based on multi-scale features of Lung ultrasound (LUS) and attention mechanism to detect A-line, B-line, pulmonary consolidation, and pleural effusion caused by pulmonary gas–liquid ratio variations. Methods A total of 6000 LUS images were prospectively collected from 3966 patients, of which 5545 images were selected. All the images were randomly divided into the training set (4,436 images) and the testing set (1,109 images) with a ratio of 4:1. Faced on multi-scale features of LUS, an end-to-end deep learning model based on multi-scale split attention and Mish function was proposed to automatically identify the four LUS features. Results The overall prediction AUC, accuracy, specificity, and sensitivity of the independent test set were 99.76%, 98.20%, 99.41%, and 98.27%, respectively, and achieved significant and consistent improvement as compared to other deep learning baselines. Conclusions Our proposed model could interpret the four important LUS features intelligently and be adopted as a support system in the routine diagnosis of an emergency clinician. Significance This study can not only assist clinicians in recognizing common lung lesions but also provide a new method for the realization of high-quality intelligent diagnosis.
Due to the indispensable role of electric vehicles (EVs) in achieving carbon neutrality, lithium-ion batteries (LIBs) for EVs have attracted considerable attention in the context of a widely distributed raw material supply and cross-border LIB production. Most previous studies have focused on only one specific LIB-related commodity supply, ignoring the intricate dependent relationships among mineral resources, intermediate components, and finished products. To fill this gap, this study employs a multilayer network model to construct the global EV-LIB supply network from 1990 to 2020 and explores critical risk sources from static and dynamic network perspectives. From the static perspective, the results based on the MultiRank algorithm reveal the critical position of countries, which are covered by single-layer-based indicators. The EV-LIB industry is shifting from upstream mineral resources to intermediate components and finished products (EV-LIBs and anodes). From the dynamic perspective, the impacts of risk sources and their risk transmission paths are revealed by the proposed dynamic shock propagation models under two realistic scenarios, i.e., supply restrictions on a specific commodity and blocked export channels. Some unremarkable shocks to a specific upstream commodity are revealed to have a substantial influence on downstream processes. Different effects of improving a country's anti-risk capacity on strengthening the robustness of the trade system are shown. The findings provide anti-risk support for policymakers seeking to hedge supply risks, adjust industrial planning, and ensure industrial safety.
First-passage probability estimation of high-dimensional nonlinear stochastic dynamic systems is a significant task to be solved in many science and engineering fields, but remains still an open challenge. The present paper develops a novel approach, termed ‘fractional moments-based mixture distribution’, to address such challenge. This approach is implemented by capturing the extreme value distribution (EVD) of the system response with the concepts of fractional moment and mixture distribution. In our context, the fractional moment itself is by definition a high-dimensional integral with a complicated integrand. To efficiently compute the fractional moments, a parallel adaptive sampling scheme that allows for sample size extension is developed using the refined Latinized stratified sampling (RLSS). In this manner, both variance reduction and parallel computing are possible for evaluating the fractional moments. From the knowledge of low-order fractional moments, the EVD of interest is then expected to be reconstructed. Based on introducing an extended inverse Gaussian distribution and a log extended skew-normal distribution, one flexible mixture distribution model is proposed, where its fractional moments are derived in analytic form. By fitting a set of fractional moments, the EVD can be recovered via the proposed mixture model. Accordingly, the first-passage probabilities under different thresholds can be obtained from the recovered EVD straightforwardly. The performance of the proposed method is verified by three examples consisting of two test examples and one engineering problem.
In this study, a modified continuous-flow nitrifying reactor was successfully operated for rapid cultivation of micro-granules and achieving robust nitritation. Results showed that sludge granulation with mean size of ca. 100 µm was achieved within three weeks by gradually increasing settling velocity-based selection pressure from 0.48 to 0.9 m/hr. Though Nitrospira like nitrite-oxidizing bacteria (NOB) were enriched in the micro-granules with a ratio between ammonia-oxidizing bacteria (AOB) and NOB of 5.7%/6.5% on day 21, fast nitritation was achieved within one-week by gradually increasing of influent ammonium concentration (from 50 to 200 mg/L). Maintaining ammonium in-excess was the key for repressing NOB in the micro-granules. Interestingly, when the influent ammonium concentration switched back to 50 mg/L still with the residual ammonium of 15–25 mg/L, the nitrite accumulation efficiency increased from 90% to 98%. Experimental results suggested that the NOB repression was intensified by both oxygen and nitrite unavailability in the inner layers of micro-granules. Unexpectedly, continuous operation with ammonium in excess resulted in overproduction of extracellular polysaccharides and overgrowth of some bacteria (e.g., Nitrosomonas, Arenimonas, and Flavobacterium), which deteriorated the micro-granule stability and drove the micro-granules aggregation into larger ones with irregular morphology. However, efficient nitritation was stably maintained with extremely high ammonium oxidation potential (> 50 mg/g VSS/hr) and nearly complete washout of NOB was obtained. This suggested that smooth and spherical granule was not a prerequisite for achieving NOB wash-out and maintaining effective nitritation in the granular reactor. Overall, the micro-granules exhibited a great practical potential for high-rate nitritation.
In this study, the effects of soluble readily biodegradable COD (sCOD) and particulate slowly biodegradable COD (pCOD) on anammox process were investigated. The results of the long-term experiment indicated that a low sCOD/N ratio of 0.5 could accelerate the anammox and denitrification activity, to reach as high as 84.9%±2.8% TN removal efficiency. Partial denitrification-anammox (PDN/anammox) and denitrification were proposed as the major pathways for nitrogen removal, accounting for 91.3% and 8.7% of the TN removal, respectively. Anammox bacteria could remain active with high abundance of anammox genes to maintain its dominance. Candidatus Kuenenia and Thauera were the predominant genera in the presence of organic matter. Compared with sCOD, batch experiments showed that the introduction of pCOD had a negative effect on nitrogen removal. The contribution of denitrification to nitrogen removal decreased from approximately 14% to 3% with increasing percentage of pCOD. In addition, the analysis result of the process data using an optimized ASM1 model indicated that high percentage of pCOD resulted in serious N2O emission (the peak value up to 0.25 mg N/L), which was likely due to limited mass diffusion and insufficient available carbon sources for denitrification. However, a high sCOD/N ratio was beneficial for alleviating N2O accumulation.
This paper deals with the issue on metamodelling (a.k.a. surrogate modelling) of nonlinear stochastic dynamical systems, which are often with multiple input uncertainties Θ∈Rn, viz., the dimension n may range from low to high (e.g., n≥10). In this paper, to circumvent the problem of “curse of dimensionality” of high-dimensional input uncertainties, the feature spaces of outputs and inputs are firstly extracted from the original output and input spaces, and thus a feature mapping strategy is proposed. To form the feature output space, the nonlinear autoregressive with exogenous inputs (NARX) and the proper orthogonal decomposition (POD) are adopted, while the feature input space is detected by the active subspace method (ASM). It is found that the dimension of feature input (output) space may be much less than the one of original input (output) space, thus the applicability of many metamodelling methods can be naturally enhanced. On the constructed input–output feature space, the procedure of metamodelling is completed by the polynomial chaos expansion (PCE) combined with Kriging, which can capture global behaviours as well as local characteristics of the computational model. Two techniques are introduced to accelerate the proposed feature mapping strategy, consisting of the GF-discrepancy minimization algorithm for the design of experiments (DoEs), and the manifold optimization technique for the parameter identification of ASM. Four benchmarks, including a mathematical function (n=2), a dynamical quarter car model (n=10), a Bouc–Wen nonlinear oscillator subjected to earthquake ground motions (n=30), and the first sub-system (as a black box) of the NASA UQ Challenge 2019 (n=100), are studied to demonstrate the accuracy and efficiency of the proposed method. Some problems to be further studied are also outlined.
The coupled system of the car body and the under-chassis equipment is a significant part of the railway vehicle and directly influences the vehicle dynamics. A continuous modal parameter tracking method is proposed to analyse the frequency veering phenomenon occurring between a car body and under-chassis equipment. Veering determination methods based on the modal assurance criterion, mode shape similarity and phase difference were proposed. Results show that altering the natural frequency of the under-chassis equipment causes frequency veering between the first-order vertical bending of the car body and vertical motion of the under-chassis equipment. For the car body with a single under-chassis equipment, frequency veering occurs when the natural frequency of the under-chassis equipment approach the frequency of the first-order vertical bending mode of the car body without under-chassis equipment, and the modal damping of the veering modes changes significantly in the veering zone and intersect at the veering point. The proportions of the car body bending vector magnitudes associated with the natural frequency of the equipment on each veering mode before veering are interchanged during the veering. Furthermore, when the natural frequency of the under-chassis equipment is equal to that at the veering point, the vibration transmissibility near the car body first-order bending motion is the lowest. For the car body with multiple under-chassis equipment, a frequency locus may veer more than once due to different types of modal coupling between the car body vertical bending and the under-chassis equipment vertical vibrations. When the natural frequency of the under-chassis equipment is equal to that of the modal damping intersection point, the vibration transmissibility near the car body first-order bending motion is minimal.
This paper investigates a resources-limited situation in the event-triggered model predictive control (ETMPC) for continuous-time nonlinear system with first-order hold fashion. In consideration of limited bandwidth in data transmission through wireless network under actual operation, our strategy divides the prediction horizon, and applies linear interpolation instead of zero-order hold fashion to obtain a better system performance, so that the reduction of resources and the optimization of strategy can be guaranteed. Furthermore, in actual industry processes, quadratic cost function cannot be implemented in all operations, then general cost function is adopted in this paper. Based on the first-order hold method and general cost function, the feasibility of the ETMPC algorithm and the stability of dynamical systems are analyzed. At last, a practical example is given to show the advantages of our method.
Modal identification is an important step to evaluate the basic modal properties of structures using measured data. In this process, the existence of modeling error and measurement noise will inevitably lead to uncertainty in modal identification. A Bayesian framework was established to determine the optimal values of modal parameters efficiently based on the structural response under earthquake excitations. In this paper, in the same framework, a new method is presented to determine the analytical formulation for carrying out uncertainty evaluation of modal parameters utilizing seismic structural responses. Based on Bayes’ Theorem, the covariance matrix can be calculated based on the Hessian matrix determined using the negative log-likelihood function (NLLF). In this work, for determining the Hessian matrix, a series of formulations were derived analytically. Simulated data of a six-story building were generated to investigate the new formulations. The noise effects on the posterior uncertainty were investigated. After the verification, applications were carried out using the data in a shaking table test model under laboratory conditions and a real building from a field test. The modal properties and their uncertainty obtained by the proposed method were studied under different earthquake excitations.
Helicopter detection is an important part of the safety and security during flight. Most of the aerodynamic noise of modern helicopters comes from the main rotor, the main rotor aerodynamic noise thus can be used for helicopter detection. However, the Signal-to-Noise Ratio (SNR) of the measurement noise has always been quite small while the helicopter is flying over long distance away from the microphone. The current passive detection methods are not robust enough in the presence of substantial interference. A robust passive sound detection method is proposed to detect the far-field helicopter based on the cyclostationarity of the main rotor noise. The Sparsity-Enhanced Spectral Coherence (SESC) is derived from the spectral coherence decomposition to improve the detection robustness at far distances and low SNR. Furthermore, a global detector is constructed to detect helicopters adaptively by fusing the detection of multiple orders of BPF, which can be performed on the calculated SESC. More accurate and robust detection of the far-field helicopter can be obtained by the proposed SESC detector. The effectiveness of the proposed detection approach is demonstrated and contrasted by using simulation and far-field flight test measurements of the ROBINSON R22 helicopter.
Institution pages aggregate content on ResearchGate related to an institution. The members listed on this page have self-identified as being affiliated with this institution. Publications listed on this page were identified by our algorithms as relating to this institution. This page was not created or approved by the institution. If you represent an institution and have questions about these pages or wish to report inaccurate content, you can contact us here.
18,899 members
Zhanju Liu
  • Shanghai No.10 People's Hospital
Zhe Liang
  • Department of Management Science and Engineering
Qinghui Huang
  • Department of Environmental Science
Wenxin Niu
  • Medical School
Fang Liu
  • College of Civil Engineering
Shanghai, China