Shandong University
  • Jinan, Shandong, China
Recent publications
Surface defect detection (SDD) plays an extremely important role in the manufacturing stage of products. However, this is a fundamental yet challenging task, mainly because the intraclass defects have large differences in shape and distribution, and low contrast between the object regions and background, and it is difficult to adapt to other materials. To address this problem, we propose a complementary adversarial network-driven SDD (CASDD) framework to automatically and accurately identify various types of texture defects. Specifically, CASDD consists of an encoding–decoding segmentation module with a specially designed loss measurement and a novel complementary discriminator mechanism. In addition, to model the defect boundaries and enhance the feature representation, the dilated convolutional (DC) layers with different rates and edge detection (ED) blocks are also incorporated into CASDD. Moreover, a complementary discrimination strategy is proposed, which employs two independent yet complementary discriminator modules to optimize the segmentation module more effectively. One discriminator identifies contextual features of the object regions in the input defect images, while the other discriminator focuses on edge detail differences between the ground truth and the segmented image. To obtain more edge information during training, a new composite loss measurement containing edge information and structural features is designed. Experimental results show that CASDD can be suitable for defect detection on four real-world and one artificial defect database, and its detection accuracy is significantly better than the state-of-the-art deep learning methods.
Sulfate radical AOPs (SR-AOP) were successfully utilized in degradation of antibiotics in water and wastewater treatment. The review discusses details on SR-AOPs mechanisms and applications for antibiotics degradation. The progress in this field was discussed, highlighting the most promising developments and remaining challenges. The applicability of SR-AOPs was summarized revealing the most susceptible and persistent to oxidation groups of pharmaceuticals. Highest effectiveness was reported for degradation of pharmaceuticals on ppb level. Systems revealed a scavenging effect in case of oxidant dose 0.7mM of the PS and 2mM of PMS. Future development demands simple persulfates activation systems for real matrix treatment.
In this paper, a real-time state estimation platform for distribution grids monitored by Phasor Measurement Units (PMUs) is developed, tested, and validated using Real Time Digital Simulator (RTDS). The developed platform serves as a proof-of-concept for potential implementation in an existing 50 kV ring network of the Dutch distribution utility Stedin medium voltage distribution grid located in the southwest (Zeeland area) of the Netherlands. To catch up with the fast sampling rates of PMUs, the platform incorporates computationally efficient techniques for state estimation and detection, discrimination and identification of anomalies like bad data and sudden load changes. Forecasting Aided State Estimation has been utilized to enable measurement innovations needed for fast anomaly detection, discrimination, and identification, whilst the Extended Kalman Filter (EKF) algorithm is selected to provide fast state forecasting and filtering. The platform has been tested under various normal and abnormal operating conditions considering different statistical properties of measurement noise as well as different bad data and sudden load change scenarios. To demonstrate advantages and disadvantages for embedding EKF into the platform, EKF is compared with Unscented Kalman Filter (UKF) in terms of estimation accuracy, computational efficiency, and compatibility with the module for anomaly detection, discrimination, and identification. The results of extensive simulations provide good hints about the feasibility of PMU-based real-time state estimation for the Stedin distribution grid.
The proliferation of advanced metering devices such as phasor measurement units (PMUs) along with communication systems readiness has opened new horizons for centralized protection and control of transmission systems. Wide-area event identification (WAEI) is considered an indispensable enabling block to these advanced applications. This paper is aimed at scrutinizing existing WAEI methods and discussing their prospects and shortcomings in improving the situational awareness of complex transmission systems. The disturbances of interest are those that significantly impact system operation and stability, namely short-circuit faults, line outages, and generation outages. The reluctance of system operators to entrust WAEI methods is discussed and linked to the inability of existing methods to deal with real-world challenges such as communication latencies, temporarily incomplete network observability, and the loss of the time synchronization signal. The superimposed-circuit concept is detailed and promoted as a powerful methodology with great unleashed potential for addressing these problems. The paper ends with remarks on the remaining research gaps that need to be addressed to fulfill the needs of power system operators, thus facilitating the uptake of WAEI methods in practice.
The microbial corrosion of marine structural steels (09CrCuSb low alloy steel (LAS) and Q235 carbon steel (CS)) in Desulfovibrio vulgaris medium and Pseudomonas aeruginosa medium based on seawater was investigated. In the D. vulgaris medium, the weight loss and maximum pit depth of 09CrCuSb LAS were 0.59 and 0.56 times as much as those of Q235 CS, respectively. Meanwhile, in the P. aeruginosa medium, the values were 0.53 and 0.67 times, respectively. Compared to Q235 CS, 09CrCuSb LAS contains more alloy elements (Cr, Ni, Cu, Al and Sb), which led to obvious inhibition of sessile bacteria growth but had no effect on planktonic bacteria. The number of live sessile cells on the 09CrCuSb LAS surface was 23.4 % and 26.9 % of that on the Q235 CS surface in the D. vulgaris medium and P. aeruginosa medium, respectively. Fewer sessile cells on the steel surface led to a lower extracellular electron transfer (EET) rate so that less corrosion occurred. In addition, the combined effect of alloying elements on grain refinement and passive film formation also improved the anti-corrosion property of the steels.
Given the high abundance of water in the atmosphere, the reaction of Criegee intermediates (CIs) with (H2O)2 is considered to be the predominant removal pathway for CIs. However, recent experimental findings reported that the reactions of CIs with organic acids and carbonyls are faster than expected. At the same time, the interface behavior between CIs and carbonyls has not been reported so far. Here, the gas-phase and air-water interface behavior between Criegee intermediates and HCHO were explored by adopting high-level quantum chemical calculations and Born-Oppenheimer molecular dynamics (BOMD) simulations. Quantum chemical calculations evidence that the gas-phase reactions of CIs + HCHO are submerged energy or low energy barriers processes. The rate ratios speculate that the HCHO could be not only a significant tropospheric scavenger of CIs, but also an inhibitor in the oxidizing ability of CIs on SOx in dry and highly polluted areas with abundant HCHO concentration. The reactions of CH2OO with HCHO at the droplet's surface follow a loop structure mechanism to produce i) SOZ (), ii) BHMP (HOCH2OOCH2OH), and iii) HMHP (HOCH2OOH). Considering the harsh reaction conditions between CIs and HCHO at the interface (i.e., the two molecules must be sufficiently close to each other), the hydration of CIs is still their main atmospheric loss pathway. These results could help us get a better interpretation of the underlying CIs-aldehydes chemical processes in the global polluted urban atmospheres.
Stem cell transplantation has been proved a promising therapeutic instrument in intervertebral disc degeneration (IVDD). However, the elevation of oxidative stress in the degenerated region impairs the efficiency of mesenchymal stem cells (BMSCs) transplantation treatment via exaggeration of mitochondrial ROS and promotion of BMSCs apoptosis. Herein, we applied an emulsion-confined assembly method to encapsulate Coenzyme Q10 (Co-Q10), a promising hydrophobic antioxidant which targets mitochondria ROS, into the lecithin micelles, which renders the insoluble Co-Q10 dispersible in water as stable colloids. These micelles are injectable, which displayed efficient ability to facilitate Co-Q10 to get into BMSCs in vitro, and exhibited prolonged release of Co-Q10 in intervertebral disc tissue of animal models. Compared to mere use of Co-Q10, the Co-Q10 loaded micelle possessed better bioactivities, which elevated the viability, restored mitochondrial structure as well as function, and enhanced production of ECM components in rat BMSCs. Moreover, it is demonstrated that the injection of this micelle with BMSCs retained disc height and alleviated IVDD in a rat needle puncture model. Therefore, these Co-Q10 loaded micelles play a protective role in cell survival and differentiation through antagonizing mitochondrial ROS, and might be a potential therapeutic agent for IVDD.
The joint optimization in distribution networks considering the uncertainties in wind power or photovoltaic (PV) outputs is a larger scale stochastic mixed integer nonlinear programming (MINLP) problem. However, how to efficient and accurate solve the problem under uncertainties is still a challenge. To handle the uncertainties, a scenario based chance constrained programming model is established. To improve the accuracy, the network reconfiguration and capacitor control are simultaneously performed by optimizing serious voltage and shunt current sources in the model by using equivalent network transformation. To improve calculation efficiency in dealing with chance constraints, a cluster of scenarios satisfying the confidence level is formed for optimization. To avoid introducing plenty of binary variables for the radial constraint and improving calculation efficiency, a heuristic method opening loops one by one is developed. The numerical simulations on distribution networks show the efficiency and accuracy of the proposed algorithm over the existing methods.
This paper designs an event-trigger rolling horizon optimization framework to alleviate electricity congestion caused by peer-to-peer (P2P) energy transactions among microgrids (MGs) under uncertainty. This framework incorporating abounded time dimensions includes three processes: first, a discrete-time linear programming model for the P2P energy trading among MGs with real-time data updating for reaction to underling uncertainties, are formulated to derive initial schemes in prediction time windows; second, a modified optimal power flow model is used to verify congestion in control time windows, with congestion occurrence defined as an event; third, the power rescheduling of MGs is triggered by this event for reducing periodic calculation burden and executed to generate new trading schemes for congestion alleviation in control time windows. We develop an online distributed optimization algorithm to realize independent decisions of MGs and guarantee rolling updates of system parameters through embedding alternating direction method of multipliers into every time window. Finally, simulations in different cases are implemented to illustrate the reasonability and effectiveness of the proposed optimization framework. Results show that congestion is reasonably managed while ensuring the diversity of P2P energy transactions. Compared with the period-driven rolling horizon optimization framework, the computational time is significantly reduced in the proposed framework.
This study analyzes the impact of biomass energy, financial development, and economic growth on environmental quality using the novel Fourier autoregressive distributed lag (ARDL) approach on annual data for the period 1965–2018 in the United States (USA). The study analyzes the impact of related variables on the load capacity factor (LCF) as well as on indicators of environmental degradation such as carbon dioxide emissions and ecological footprint. The LCF is one of the most comprehensive environmental indicators to date, encompassing both biocapacity and ecological footprint. In this regard, this study contributes to the environmental economics literature by examining, for the first time, the impact of biomass energy on the LCF. The results of the cointegration test show that there is only a long-run relationship between the LCF and the independent variables. According to the Fourier ARDL results, biomass energy improves the environmental quality, while financial development has no effect on the LCF. Moreover, the increase in per capita income reduces the LCF. Furthermore, since the income elasticity is larger in the long run than in the short-run, the environmental Kuznets curve is validated. Therefore, the United States government should encourage the use of biomass and investment in this form of energy.
The sudden outbreak of COVID-19 has dramatically altered the state of the global economy, and the stock market has become more volatile and even fallen sharply as a result of its negative impact, heightening investors' apprehension regarding the correlation between unexpected events and stock market volatility. Additionally, internal and external characteristics coexist in the stock market. Existing research has struggled to extract more effective stock market features during the COVID-19 outbreak using a single time-series neural network model. This paper presents a framework for multitasking learning-based stock market forecasting (COVID-19-MLSF), which can extract the internal and external features of the stock market and their relationships effectively during COVID-19.The innovation comprises three components: designing a new market sentiment index (NMSI) and COVID-19 index to represent the external characteristics of the stock market during the COVID-19 pandemic. Besides, it introduces a multi-task learning framework to extract global and local features of the stock market. Moreover, a temporal convolutional neural network with a multi-scale attention mechanism is designed (MA-TCN) alongside a Multi-View Convolutional-Bidirectional Recurrent Neural Network with Temporal Attention (MVCNN-BiLSTM-Att), adjusting the model to account for the changing status of COVID-19 and its impact on the stock market. Experiments indicate that our model achieves superior performance both in terms of predicting the accuracy of the China CSI 300 Index during the COVID-19 period and in terms of sing market trading.
Modulating the structure and morphology is essential in fabricating high-performance electromagnetic absorbing materials. Herein, we obtained porous Fe3O4/carbon hollow microspheres and porous Fe4N/carbon hollow microspheres derived from Fe-glycerol hollow microspheres. Through structure and morphology analysis, we proved the existence of porous and hollow features. By comparison, it can be found that the porous Fe4N/carbon hollow microspheres have electromagnetic wave absorption performance superior to that of porous Fe3O4/carbon hollow microspheres. The reflection loss value of porous Fe4N/carbon hollow microspheres reaches -42.2 dB at a matching thickness of merely 1.4 mm, and its effective absorbing bandwidth approaches 4.5 GHz, whereas the reflection loss of porous Fe3O4/carbon hollow microspheres in the 2-18 GHz range is over -10 dB. Reasons for the better electromagnetic wave absorption performance are revealed to be that the magnetic Fe4N has higher complex permittivity and complex permeability, and the porous hollow microspherical structure increases the multiple scattering and reflection of electromagnetic waves. Meanwhile, the impedance matching and attenuation constant are optimized together through the synergy of dielectric and magnetic loss. This research can provide instructive findings for thin-thickness electromagnetic wave absorbing materials based on Fe4N with an appropriate microstructure.
The intracellular viscosity is an important parameter of the microenvironment and SO2 is a vital gas signal molecule. At present, some dual-response fluorescence probes for simultaneous measurements of viscosity and SO2 derivatives (HSO3-/SO32-) possessed poor water solubility. In this work, we developed a water-soluble fluorescence probe CIJ (0.0864 g/100 mL of water at 20 °C) for simultaneous measurements of viscosity and SO2 derivatives. CIJ exhibited a sensitive fluorescence enhancement to environmental viscosity from 0.97 to 28.04 cP based on a twisted intramolecular charge transfer mechanism and was applied to effective measurement of viscosity in vitro and in vivo. CIJ could also respond to SO2 derivatives with a low detection limit (44 nM) and a fast response time (5 min) based on the nucleophilic addition reaction. Furthermore, CIJ was applied to monitor SO2 derivatives in ratiometric response manner in living cells.
This study explores how virtual reality (VR) interventions mitigate daily negative mood spillover among hotel frontline employees through a daily dairy study. A within-subject field experiment was conducted to collect data from 87 hotel employees over ten consecutive workdays (846 daily responses). The multilevel analysis supports daily negative mood spillover by revealing positive relationships between negative moods before work and midday negative moods, and between midday negative moods and turnover intentions. Exposure to virtual natural scenes alleviates these daily positive relationships. Employees with high (vs. low) levels of trait mindfulness are less likely to be influenced by their negative moods before work when exposed to the VR intervention. This study advances our knowledge by integrating spillover theory, stress recovery theory, and mindfulness through a multilevel framework of employees’ daily emotional fluctuations moderated by VR interventions. The study findings provide hotel professionals with meaningful information regarding workplace stress management.
This work proposes a novel adaptive global optimization algorithm called Disturbance Inspired Equilibrium Optimizer. The purpose of this study is to enhance the exploitation ability of the newly developed Equilibrium Optimizer, and to address the issue of getting trapped in local minima. The proposed algorithm is benefited from the novel disturbance-based hybrid initialization strategy, the new form of time factor, and the new update rule of particle's position. In addition, a novel boundary check strategy and an adaptive global position disturbance mechanism are proposed and installed into our algorithm. Based on the disturbance-inspired modifications, the exploration and exploitation ability of the standard Equilibrium Optimizer are significantly improved. The performance of the proposed algorithm is evaluated using representative different benchmark functions, consisting of three well-known mathematical benchmark functions, six complex composite functions, and four challenge functions proposed on 2017 IEEE Congress on Evolutionary Computation. Also, the proposed algorithm is conducted to optimize three engineering designs to examine its applicability in constrained real-world problems. In all experiments, the developed algorithm is compared with six other state-of-the-art metaheuristics. Experimental results and the average rank of Friedman test show that our algorithm provides promising results in solving mathematical problems and constrained real-world engineering optimization problems. Therefore, the proposed algorithm is competitive compared to the other state-of-the-art metaheuristic algorithms and is an effective solution to real-world engineering problems.
It is a challenge to enhance the catalytic activity of the oxidation of volatile organic compounds (VOCs) and the poison-tolerance capacity in the practical application. Here, we report the construction of Pt/Ni-CeO2 catalyst via Ni doping, which exhibited the excellent toluene catalytic performance, as well as remarkably improved water-resistance and SO2-tolerance. The electron energy loss spectroscopy and density functional theory calculations demonstrated that the doped Ni species induced the generation of abundant oxygen vacancies from bulk to the surface, improving the redox property, activation of oxygen species, and adsorption capacity of toluene molecules. Moreover, the Pt-NiO interfacial structure was formed by the thermal-driven Ni species to the adjacent Pt species, which could modify the electronic and chemical properties of Pt, thus restraining the adsorption of water and SO2 molecules. This investigation provides new insights into the activation of oxygen species via oxygen vacancies, and anti-poison activity via surface modification engineering for catalyst development in practical applications.
In this paper, we study an entropy-regularized continuous-time linear-quadratic two-person zero-sum stochastic differential game problem from the perspective of reinforcement learning (RL). By the solvability of a discounted algebraic Riccati equation, we construct a Gaussian closed-loop optimal control pair for the problem, which achieves the best tradeoff between exploration and exploitation. Then, in this exploratory framework, we propose an RL algorithm that relies on only partial system information to solve a stochastic H∞ control problem. The corresponding convergence analysis and simulation examples are also provided to verify the efficiency of the proposed algorithm.
Although the effect of Cu2+ on antibiotic removal during photocatalytic reaction has been studied in depth, there is less known about the effect of antibiotics on Cu2+ removal. In this study, we report for the first time that, during the photocatalytic purification of sulfamerazine (SMZ) and Cu2+ combined pollution, Cu2+ concentration showed an obvious five-stage fluctuation, which was completely different from the simple promotion or inhibition reported in previous studies. By employing HPLC-MS analysis and density functional theory (DFT) calculation, the repeated fluctuation of Cu2+ concentration was found to be closely related to the SMZ degradation process, mainly resulting from solution pH drop and formation of Cu-containing intermediates which acted as sacrificial agents for Cu2+ reduction. In addition, compared with the SMZ-free system, the presence of SMZ can greatly enhance the deep removal of Cu2+ (minimum Cu2+ concentration was only 0.17 mg/L vs. 1.28 mg/L without SMZ), and there was a wide time interval to ensure the efficient recovery of Cu metal. More interestingly, the in-situ obtained Cu-decorated TiO2 photocatalyst performed well in water splitting, nitrogen fixation and bacterial sterilization. Results of this study confirmed the great potential of photocatalytic technology in purifying antibiotic-heavy metal combined pollution.
Carbon dioxide (CO2) emitted by human activities not only brings about a serious greenhouse effect but also accelerates global climate change. This has resulted in extreme climate hazards that can obstruct human development in the near future. Hence, there is an urgent need to achieve carbon neutrality by increasing negative emissions. The ocean plays a vital role in absorbing and sequestering CO2. Current research on marine carbon storage and sink enhancement mainly focuses on biological carbon sequestration using carbon sinks (macroalgae, shellfish, and fisheries). However, seawater inorganic carbon accounts for more than 95 % of the total carbon in marine carbon storage. Increasing total alkalinity at a constant dissolved inorganic carbon shifts the balance of existing seawater carbonate system and prompts a greater absorption of atmospheric CO2, thereby increasing the ocean's "carbon sink". This review explores two main mechanisms (i.e., enhanced weathering and ocean alkalinization) and materials (e.g., silicate rocks, metal oxides, and metal hydroxides) that regulate marine chemical carbon sink (MCCS). This work also compares MCCS with other terrestrial and marine carbon sinks and discusses the implementation of MCCS, including the following aspects: chemical reaction rate, cost, and possible ecological and environmental impacts.
Three-dimensional (3D) deformable image registration is a fundamental technique in medical image analysis tasks. Although it has been extensively investigated, current deep-learning-based registration models may face the challenges posed by deformations with various degrees of complexity. This paper proposes an adaptive multi-level registration network (AMNet) to retain the continuity of the deformation field and to achieve high-performance registration for 3D brain MR images. First, we design a lightweight registration network with an adaptive growth strategy to learn deformation field from multi-level wavelet sub-bands, which facilitates both global and local optimization and achieves registration with high performance. Second, our AMNet is designed for image-wise registration, which adapts the local importance of a region in accordance with the complexity degrees of its deformation, and thereafter improves the registration efficiency and maintains the continuity of the deformation field. Experimental results from five publicly-available brain MR datasets and a synthetic brain MR dataset show that our method achieves superior performance against state-of-the-art medical image registration approaches.
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12,085 members
Baibiao Huang
  • State Key Laboratory for Crystal Materials
Minyong Li
  • School of Pharmaceutical Sciences
Yuanhua Sang
  • State Key Laboratory for Crystal Materials
Hong Liu
  • Institute for Crystal Materials
27 Shanda Nanlu, 250100, Jinan, Shandong, China
Head of institution
Liming Fan