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The performance of heat exchangers in the pressurized water test loop is a critical factor in ensuring the achievement of irradiation parameters for fuel assemblies and the safety of experimental operations. The effect of the heat exchange area margin on the heat exchangers in the pressurized water test loop for the fuel assembly during the steady-state irradiation is analyzed. Additionally, optimization methods for determining the margin of heat exchange area and corresponding design strategies are further investigated. It shows that the effect of the heat exchange area margin on the heat exchange power is less affected by the inlet temperature of the primary water and is primarily influenced by the flow rate of the primary water. A decrease in the flow rate of the primary water reduces the compensatory effect of the cooling section on power and enhances the weakening effect of the regeneration section on power. Meanwhile, the correspondence between the margin of the regeneration section and the cooling section, established based on design conditions, can be applicable when there are changes in the inlet temperature of the primary water, but it is not suitable when there are changes in the flow rate of the primary water. When the flow rate of the primary water decreases, the cooling section margin required to compensate for the decrease in power caused by the regeneration section margin will increase significantly. In addition, short-circuiting the heat exchange tubes in the regeneration section can effectively enhance the heat transfer capability. Furthermore, setting the heat exchange area margins of the regeneration and cooling sections to zero can serve as a termination condition for iterative calculations in the verification of regenerative heat exchangers under off-design conditions.

The numerical modeling and centrifuge test are very effective tools in demonstrating the actual behavior of deep excavation, but the preciseness in verification is crucial. This study proposes a Response Surface (RS) based technique for updating a deep-braced excavation seismic numerical model based on a small-scale centrifuge test. The proposed method involves establishing a relationship between soil properties and seismic characteristics of the system and creating an RS model to predict both. The RS model is made via a series of Eigenvalue analyses using the Design of Experiments (DOE) approach. The constructed RS model shows excellent goodness of fit. The seismic characteristics of the model in the centrifuge test are estimated using the Frequency Domain Decomposition (FDD) approach. By setting that test result as a target in the RS model, soil properties are updated through back analysis, and minimum error with the numerical response is achieved. The established RS model predicts seismic characteristics with less than a 1.5 % difference with numerical responses. Thus, an efficient three-dimensional (3D) numerical model on a prototype scale is made. The efficiency of the seismic response of the updated model is checked by acceleration time history analysis, and it shows trends similar to those of the centrifuge test.

Accurately predicting the thermal characteristics and heat transfer distribution of the rotating detonation engine (RDE) and acquiring a clear understanding of the performance and mechanism of the rotating detonation are of great significance for achieving the safe and reliable long-duration operation of RDEs. Using RP-3 as fuel, a long-duration experimental study is performed on a 220 mm-diameter RDC to investigate the details with respect to the thermal environment. The heat flux at the typical location and the average heat flux of both the inner and outer cylinders are measured, respectively. Meanwhile, the peak pressure of the rotating detonation wave (RDW) and specific thrust are analyzed. When the ER is between 0.5 and 1 (oxidizer 2 kg/s), the stable rotating detonation mode is obtained, and the detonation duration is set as 40 s to accurately calculate the heat released by the detonation combustion. The heat flux in the upstream region of the RDW location ranges from 2.40 × 105 W/m2 to 3.17 × 105 W/m2, and the heat flux in the downstream area of the RDW location ranges from 1.05 × 106 W/m2 to 1.28 × 106 W/m2. The results demonstrate the important role of the detonation combustion zone, and the thrust performance of RDC can be improved by making the RDW move forward along the RDC axis, which is the optimal direction of detonation combustion. Through a comparison of average heat flux under different conditions, it is found that the heat released by the RDC is directly related to its thrust. In addition, the average heat flux of the inner cylinder is about three times that of the outer cylinder for the two-phase RDC with a Tesla valve intake structure, indicating that the high-temperature combustion product is closer to the inner wall. Therefore, more thermal protection should be allocated to the inner cylinder, and a more systematic analysis of the two-phase flow field distribution in the annular combustion chamber should be carried out to improve the thrust performance. In this paper, the average heat flux of the inner and outer cylinders of the RDC as well as the typical local heat flux of the outer cylinders is quantitatively measured by means of experiments, which not only deepens the understanding of RDC flow field distribution, but also provides quantitative boundary conditions for the thermal protection design of RDCs.

Article Highlights Environmental factors had different effects on different forms of phosphorus transformation. The submerged macrophytes affected the migration and transformation of phosphorus by increasing pH, oxidation-redox potential and dissolved oxygen in the overlying water. H. verticillata and P. crispus acted on the different forms of phosphorus through environmental factors.

Alleviating excessive inflammation while accelerating chronic wound healing to prevent wound infection has remained challenging, especially during the coronavirus disease 2019 (COVID-19) pandemic caused by SARS-CoV-2 when patients experienced difficulties with receive appropriate healthcare. We addressed this issue by developing handheld electrospun aloe-nanofiber membranes (ANFMs) with convenient, environmentally friendly properties and a therapeutic capacity for wound closure. Our results showed that ANFMs fabricated with high molecular weight polyvinyl alcohol (PVA) to form fibers during electrospinning had uniform fibrous architecture and a porous structure. Given the value of aloe gel in accelerating wound healing, liquid extracts from ANFMs significantly downregulated the expression of the pro-inflammatory genes, interleukin-6 (IL-6) and inducible nitric oxide synthase (iNOS), and markedly suppress the generation of reactive oxygen species (ROS) induced by lipopolysaccharide in RAW264.7 macrophages. These results indicated the excellent antioxidant and anti-inflammatory effects of ANFMs. After implantation into a mouse diabetic wound model for 12 days in situ, ANFMs notably expedited chronic wound healing via promoting angiogenesis and enhancing cell viability. Our ANFMs generated by handheld electrospinning in situ healed chronic wounds offer a convenient and promising alternative for patients to heal their own wounds under variable conditions.

The role of self-regulated learning in language learning has been widely acknowledged, and there is a growing number of studies on technology-enhanced self-regulated language learning (SRLL). This systematic review aims to provide a holistic picture of existing studies in this area by identifying the characteristics of published studies, the research methods used to evaluate SRLL effectiveness and the role of technology in SRLL. The review covered 34 empirical studies focusing on SRLL that were published from 2011 to 2020. The results showed varied characteristics of technology-enhanced SRLL studies, dominance of the use of quantitative methods, greater focus on examining students’ SRLL outcomes instead of their processes, and the role of technology in supporting the performance phase of students’ SRLL instead of the entire SRLL process. These findings have implications for using technologies to facilitate and examine the holistic process of students’ SRLL.

Soft robots, especially soft robotic hands, possess prominent potential for applications in close proximity and direct contact interaction with humans due to their softness and compliant nature. The safety perception of users during interactions with soft robots plays a crucial role in influencing trust, adaptability, and overall interaction outcomes in human–robot interaction (HRI). Although soft robots have been claimed to be safe for over a decade, research addressing the perceived safety of soft robots still needs to be undertaken. The current safety guidelines for rigid robots in HRI are unsuitable for soft robots. In this paper, we highlight the distinctive safety issues associated with soft robots and propose a framework for evaluating the perceived safety in human–soft robot interaction (HSRI). User experiments were conducted, employing a combination of quantitative and qualitative methods, to assess the perceived safety of 15 interactive motions executed by a soft humanoid robotic hand. We analyzed the characteristics of safe interactive motions, the primary factors influencing user safety assessments, and the impact of motion semantic clarity, user technical acceptance, and risk tolerance level on safety perception. Based on the analyzed characteristics, we summarize vital insights to provide valuable guidelines for designing safe, interactive motions in HSRI. The current results may pave the way for developing future soft machines that can safely interact with humans and their surroundings.

Abstract Bismuth-based materials (e.g., metallic, oxides and subcarbonate) are emerged as promising electrocatalysts for converting CO2 to formate. However, Bio-based electrocatalysts possess high overpotentials, while bismuth oxides and subcarbonate encounter stability issues. This work is designated to exemplify that the operando synthesis can be an effective means to enhance the stability of electrocatalysts under operando CO2RR conditions. A synthetic approach is developed to electrochemically convert BiOCl into Cl-containing subcarbonate (Bi2O2(CO3) x Cl y ) under operando CO2RR conditions. The systematic operando spectroscopic studies depict that BiOCl is converted to Bi2O2(CO3) x Cl y via a cathodic potential-promoted anion-exchange process. The operando synthesized Bi2O2(CO3) x Cl y can tolerate − 1.0 V versus RHE, while for the wet-chemistry synthesized pure Bi2O2CO3, the formation of metallic Bio occurs at − 0.6 V versus RHE. At − 0.8 V versus RHE, Bi2O2(CO3) x Cl y can readily attain a FEHCOO- of 97.9%, much higher than that of the pure Bi2O2CO3 (81.3%). DFT calculations indicate that differing from the pure Bi2O2CO3-catalyzed CO2RR, where formate is formed via a *OCHO intermediate step that requires a high energy input energy of 2.69 eV to proceed, the formation of HCOO− over Bi2O2(CO3) x Cl y has proceeded via a *COOH intermediate step that only requires low energy input of 2.56 eV.

In the realm of building demolition, ensuring the uniform distribution of energy from multiple sources is of paramount significance for the systematic deconstruction of large structures. This study presents an integrated methodology that combines genetic optimization and potential energy balance to determine the most suitable locations for multiple energy release points, thereby enhancing the efficiency and reliability of the demolition process. We initiate our approach by randomly selecting energy release points within a building model and subsequently simulate energy dispersion utilizing a potential function until reaching stable boundaries. In instances where the discrepancy in the area between the regions with maximum and minimum energy dispersion exceeds a predefined threshold, we instigate an optimization process employing genetic algorithms. This optimization process involves genetic crossover and mutation operations, followed by subsequent energy balance calculations. The result is not only an improvement in demolition efficiency but also an assurance of even energy coverage throughout the target area.

Transition metal dichalcogenides (TMDs) with a 1T′ layer structure have recently received intense interest due to their outstanding physical and chemical properties. While the physicochemical behaviors of 1T′ TMD monolayers have been widely investigated, the corresponding properties of layered 1T′ TMD crystals have rarely been studied. As TMD monolayers do not have interlayer interactions, their physicochemical properties will differ from those of layered TMD materials. In this study, the electronic and mechanical characteristics of a range of 1T′ TMDs are systematically examined by means of density functional theory (DFT) calculations. Our results reveal that the properties of 1T′ TMDs are mainly affected by their anions. The disulfides are stiffer and more rigid, diselenides are more brittle. In addition, the 1T′ polytype is softer than 2H TMDs. Comparison with the properties of the monolayers shows that the interlayer van der Waals forces can slightly weaken the TM–X covalent bonding strength, which can further influence the mechanical properties. These insights revealed by our theoretical studies may boost more applications of 1T′ TMD materials.

Domestic wastewater, when collected and evaluated appropriately, can provide valuable health-related information for a community. As a relatively unbiased and non-invasive approach, wastewater surveillance may complement current practices towards mitigating risks and protecting population health. Spurred by the COVID-19 pandemic, wastewater programs are now widely implemented to monitor viral infection trends in sewersheds and inform public health decision-making. This review summarizes recent developments in wastewater-based epidemiology for detecting and monitoring communicable infectious diseases, dissemination of antimicrobial resistance, and illicit drug consumption. Wastewater surveillance, a quickly advancing Frontier in environmental science, is becoming a new tool to enhance public health, improve disease prevention, and respond to future epidemics and pandemics.

Highlights The processing methods of functional polymer composites (FPCs) are systematically summarized in “Toolbox”. The relationship of processing method-structure-property is discussed and the selection and combination of tools in processing among different FPCs are analyzed. A promising prospect is provided regarding the design principle for high performance FPCs for further investigation. Abstract Functional polymer composites (FPCs) have attracted increasing attention in recent decades due to their great potential in delivering a wide range of functionalities. These functionalities are largely determined by functional fillers and their network morphology in polymer matrix. In recent years, a large number of studies on morphology control and interfacial modification have been reported, where numerous preparation methods and exciting performance of FPCs have been reported. Despite the fact that these FPCs have many similarities because they are all consisting of functional inorganic fillers and polymer matrices, review on the overall progress of FPCs is still missing, and especially the overall processing strategy for these composites is urgently needed. Herein, a “Toolbox” for the processing of FPCs is proposed to summarize and analyze the overall processing strategies and corresponding morphology evolution for FPCs. From this perspective, the morphological control methods already utilized for various FPCs are systematically reviewed, so that guidelines or even predictions on the processing strategies of various FPCs as well as multi-functional polymer composites could be given. This review should be able to provide interesting insights for the field of FPCs and boost future intelligent design of various FPCs.

Decidual natural killer cells (dNK cells) are an essential component of the immune cells present at the maternal–foetal interface during early pregnancy, and they play a vital role in various physiological processes. Abnormalities in the ratio or function of dNK cells have been linked to recurrent miscarriages. CD96 has been previously shown to regulate NK cell function in the tumour microenvironment; however, its role and mechanism at the maternal–foetal interface remains unclear. The present study aimed to investigate the immunomodulatory role of CD96 in dNK cells and its function at the maternal–foetal interface. Immunofluorescence staining and flow cytometry were used to detect the expression of cellular markers such as CD96. Furthermore, the secretory function, adhesion-function-related molecules, and cell proliferation markers of CD96+ and CD96− dNK cells were detected using flow cytometry. In addition, we performed cell culture experiments via the magnetic bead sorting of NK cells to detect changes in the expression of the aforementioned functional molecules in dNK cells after the CD96 blockade. Furthermore, we examined the functional characteristics of dNK cells after palmitic acid treatment at a concentration of 10 μM. We also examined the changes in dNK cell function when subjected to the combined effect of palmitic acid and CD96 antagonists. The results indicated that CD96, TIGIT, CD155, and CD112 were highly expressed at the maternal–foetal interface, with dNK cells predominantly expressing CD96, whereas TIGIT was mainly expressed on T cells, and CD155 and CD112 were mainly present in metaphase stromal and trophoblast cells. CD96+ dNK cells displayed low cytotoxic activity and a high adhesion phenotype, which mediated the immunosuppressive effect on dNK cells at the maternal–foetal interface. Palmitic acid upregulated CD96 expression on the surface of dNK cells in the coculture system, inhibiting dNK cell activity and increasing their adhesion molecule expression. CD96 antagonist treatment blocked the inhibitory effect of trophoblasts on dNK cells, resulting in enhanced cytokine secretion and reduced adhesion. The results of this study provide valuable insight into the immunomodulatory role of CD96 in dNK cells and its mechanism at the maternal–foetal interface, particularly in metaphase NK cells. This study sheds light on the mechanisms of immune regulation at the maternal–foetal interface and their implications for the study of recurrent miscarriages of unknown origin.

The study of the human visual system not only helps to understand the mechanism of the visual system but also helps to develop visual aid systems to help the visually impaired. As the systematic study of neural signal processing mechanisms in early biological vision continues, the hierarchical structure of the visual system is gradually being dissected, bringing the possibility of building brain-like computational models from a bionic perspective. In this paper, we follow the objective facts of neurobiology and propose a parallel distributed processing computational model of primary visual cortex orientation selection with reference to the complex process of visual signal processing and transmission between the retina to the primary visual cortex, the hierarchical receptive field structure between cells in each layer, and the very fine-grained parallel distributed characteristics of cortical visual computation, which allow for high speed and efficiency. We approach the design from a brain-like chip perspective, map our network model on the field programmable gate array (FPGA), and perform simulation experiments. The results verify the possibility of implementing our proposed model with programmable devices, which can be applied to small wearable devices with low power consumption and low latency.

The performance of long-wave infrared (LWIR) quantum well (QWIP) detection systems is seriously affected by the dark current of the detectors. Tiny variations in the focal-plane temperature of the devices cause fluctuations in the dark current, which in turn generate temporal noise. It is difficult to measure the dark current accurately after the detector assembly is packaged. To address the above problems, a QWIP dark current measurement method based on focal-plane temperature is proposed, as well as a method to reduce dark current noise. First, the response model of the LWIR QWIP detection system was established, and the dark current model was introduced. Then, the detection system components were introduced, chiller calibration experiments were carried out, and the dark current values of the QWIP at different temperatures were measured by combining the system design and parameters. Next, the dark current noise correction method was proposed, the target data were collected, and experiments were carried out to correct them. Finally, after the calculation, the temporal noise was reduced by 57.69% after the correction, which is proof of a significant effect. This method can obtain the real-time dark current value by collecting the focal-plane temperature data, and reduce the dark current temporal noise (difficult to eliminate using conventional methods), which is beneficial for promoting the application of QWIPs in LWIR remote sensing detection.

This paper provides an H∞ robust control strategy for a permanent magnet synchronous generator (PMSG)-based wind farm to realize subsynchronous resonance suppression (SSR) subject to uncertain system distortions and parameter perturbation. Firstly, an eighth-order state space mathematical model of a PMSG-based wind farm is established, including the grid-side converter (GSC), GSC controller, and phase-locked loop (PLL) model. Secondly, the SSR characteristics of a PMSG-based wind farm are analyzed through eigenvalue analysis. Thirdly, a robust subsynchronous damping controller is designed based on eigenvalue analysis of SSR. Finally, the designed robust subsynchronous damping controller is validated with case studies of wind farms. The results show that the controller can increase the stability of PMSG-based wind farm systems and restrain SSR.

Background and Objective: Dementia is a broad term for a complex range of conditions that affect the brain, such as Alzheimer’s disease (AD). Dementia affects a lot of people in the elderly community, hence there is a huge demand to better understand this condition by using cost effective and quick methods, such as neuropsychological tests, since pathological assessments are invasive and demand expensive resources. One of the promising initiatives that deals with dementia and Mild Cognitive Impairment (MCI) is the Alzheimer’s Disease Neuroimaging Initiative (ADNI), which includes cognitive tests, such as Clinical Dementia Rating (CDR) scores. The aim of this research is to investigate non-invasive dementia indicators, such as cognitive features, that are typically diagnosed by clinical assessment within ADNI’s data to understand their effect on dementia. Methods: To achieve the aim, machine learning techniques have been utilized to classify patients into Cognitively Normal (CN), MCI, or having dementia, based on the sum of CDR scores (CDR-SB) besides demographic variables. Particularly, the performance of Support Vector Machine (SVM), K-nearest neighbors (KNN), Decision Trees (C4.5), Probabilistic Naïve Bayes (NB), and Rule Induction (RIPPER) is measured with respect to different evaluation measures, including specificity, sensitivity, and harmonic mean (F-measure), among others, on a large number of cases and controls from the ADNI dataset. Results: The results indicate competitive performance when classifying subjects from the baseline selected variables using machine learning technology. Though we observed fairly good results across all machine learning algorithms utilized, there was still variation in the performance ability, indicating that some algorithms, such as NB and C4.5, are better suited to the task of classifying dementia status based on our baseline data. Conclusions: Using cognitive tests, such as CDR-SB scores, with demographic attributes to pinpoint to dementia using machine learning can be seen a less invasive approach that could be good for clinical use to aid in the diagnosis of dementia. This study gives an indication that a comprehensive assessment tool, such as CDR, may be adequate in assessing and assigning a dementia class to patients, upon their visit, in order to speed further clinical procedures.

Traffic infrastructure safety is a core topic in traffic construction and development. As the impact of global climate change becomes more and more significant, extreme weather brings more and more safety issues to the normal operation of subway systems. Therefore, it is an urgent issue in the construction of subway systems to fully prepare for extreme weather and improve system resilience under external disturbances. The resilience of a complex system generally refers to its ability to adapt to external disturbances and return to a functional state. As one of several key infrastructure systems in large cities, a subway system needs to be highly resilient to cope with various risks, and it needs to recover quickly under uncertain weather conditions and other external damage events. In order to achieve the goal of conducting a real-time resilience assessment of a subway system, this study adopts the Bayesian network and the traditional failure mode and effect analysis (FMEA) method to realize resilience assessment with multiple performance indicators. Combined with the risk matrix method from FMEA, multiple important indicators of a subway system under the influence of extreme weather are obtained. These important indicators are integrated into the resilience assessment of the subway system within a Bayesian method. In this paper, the feasibility and applicability of the proposed method are verified by taking the Changping Line of the Beijing subway under extreme rainfall weather (>10 mm) as a case.

Periodic noise is a well-known problem in seismic exploration, caused by power lines, pump jacks, engine operation, or other interferences. It contaminates seismic data and affects subsequent processing and interpretation. The conventional methods to attenuate periodic noise are notch filtering and some model-based methods. However, these methods either simultaneously attenuate noise and seismic events around the same frequencies, or need expensive computation time. In this work, a new method is proposed to attenuate periodic noise based on sparse representation. We use a noise dictionary to sparsely represent periodic noise. The noise dictionary is constructed based on ambient noise. An advantage of our method is that it can automatically suppress monochromatic periodic noise, multitoned periodic noise and even periodic noise with complex waveforms without pre-known noise frequencies. In addition, the method does not result in any notches in the spectrum. Synthetic and field examples demonstrate that our method can effectively subtract periodic noise from raw seismic data without damaging the useful seismic signal.

In this paper, the concept of a distributed wireless system of weigh-in-motion is proposed. A wireless sensor for weigh-in-motion (WIM) based on piezoelectric materials is designed. The corresponding prototype for indoor testing is made according to the design. It aims to realize the main functions of dynamic pressure sensing, charge signal amplification and conversion, wireless signal transmission and reception, etc. The material properties of the mechanical properties and the piezoelectric properties in the analyses are provided in detail. Through the indoor test platform, the feasibility of the wireless sensor for WIM designed in this paper is preliminarily verified, which provides a basic tool for the realization of the distributed self-powered wireless system of WIM in the next step.

With the rapid growth of the aviation fields, the remaining useful life (RUL) estimation of aero-engine has become the focus of the industry. Due to the shortage of existing prediction methods, life prediction is stuck in a bottleneck. Aiming at the low efficiency of traditional estimation algorithms, a more efficient neural network is proposed by using Convolutional Neural Networks (CNN) to replace Long-Short Term Memory (LSTM). Firstly, multi-sensor degenerate information fusion coding is realized with the convolutional autoencoder (CAE). Then, the temporal convolutional network (TCN) is applied to achieve efficient prediction with the obtained degradation code. It does not depend on the iteration along time, but learning the causality through a mask. Moreover, the data processing is improved to further improve the application efficiency of the algorithm. ExtraTreesClassifier is applied to recognize when the failure first develops. This step can not only assist labelling, but also realize feature filtering combined with tree model interpretation. For multiple operation conditions, new features are clustered by K-means++ to encode historical condition information. Finally, an experiment is carried out to evaluate the effectiveness on the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) datasets provided by the National Aeronautics and Space Administration (NASA). The results show that the proposed algorithm can ensure high-precision prediction and effectively improve the efficiency.

Centrifugal tests provide an efficacious experimental process to predict the behavior of deep excavations, and numerical models are indispensable for demonstrating the test results and analyzing the engineering demand parameters. Uncertainty in material properties can cause simulations to differ from tests; therefore, updating the model becomes inevitable. This study presents a response-surface-based model updating technique for the nonlinear three-dimensional simulation of the centrifugal testing model of strutted deep excavation in sand. An overview of the fundamentals of the response-surface model is provided, including selecting uncertain parameters as input factors, creating a design order for training the model, building a second-order polynomial surface, and updating the input factors through targeted centrifugal results. The bending strains of diaphragm wall panels at multiple points along the depth are used to form the multiobjective function. Response-surface model predictions were well-matched with actual numerical responses, with less than a 0.5% difference. Parametric analyses could be conducted utilizing this updated strutted deep excavation model.

This study aimed to reveal the anisotropic permeabilities of tight oil reservoirs impacted by heterogeneous minerals. SEM imaging, image processing, fractal calculation, microscopic reservoir modeling, and visual flow simulation were carried out to investigate the above problems. Results show that the variation coefficient of two-dimensional permeability for the studied tight reservoir samples ranges from 0.09 to 0.95, with an average value of 0.68. The penetration coefficient ranges from 1.16 to 2.64, with an average value of 2.13. The ratio of maximum to minimum permeability is between 1.25 and 7.67, with an average value of 5.62. The fluid flow in tight reservoirs has significant anisotropy comprising dominant flow through conductive channels. Flow in tight oil reservoirs tends to involve minor hydraulic fracturing with no proppant.

The classification of hidden dangers in urban gas pipelines plays a vital role in the smooth operation of urban gas pipelines and in solving the problem of hidden safety dangers in urban gas pipelines. In recent years, the number and proportion of polyethylene (PE) pipelines in urban gas pipelines are increasing day by day, but the current classification of hidden dangers in urban gas pipelines is still based on steel pipelines, and the classification method is highly subjective. Therefore, this paper proposes an improved PLC method that integrates the use of a risk matrix and compensation coefficient to solve the problem of grading the hidden dangers of PE pipelines of urban gas. The improved PLC method is based on the failure database of urban gas PE pipelines to obtain the vulnerability and severity of consequences when determining the initial level of hidden dangers, and the compensation coefficient is modified according to regional vulnerability, ease of rectification, condition around the pipeline, positioning technology, leak detection technology, and emergency ability, which can effectively reduce the subjectivity of hidden danger classification. Using the improved PLC method to classify urban gas pipelines for hidden dangers can provide pipeline operating companies with a basis for decision making in the process of hidden danger disposal and effectively reduce pipeline safety risks.

The underground energy geostructure represented by the energy pile is one of the key paths for the cooperative development of underground space and geothermal energy. Because of its advantages of low cost, high efficiency and no extra occupation of underground space, it has become a feasible alternative to the borehole heat exchanger. The change in the temperature field of the energy pile and its surrounding ground not only affects the geological environment but also influences the thermomechanical performance and the durability of the structure. However, the temporal and spatial unsteady-state temperature distribution of piles and surrounding rock under typical intermittent and unbalanced thermal load conditions is still unclear. In this paper, a finite element model was applied to analyze the unsteady-state temperature distribution, and the thermomechanical behavior of the energy pile group was developed and verified. The temperature field distribution of pile and surrounding rock under typical intermittent working and unbalanced thermal load conditions were determined. Moreover, the thermomechanical behavior characteristics of the energy pile group were investigated. Finally, the influences of pile layout on the thermomechanical behavior of the energy pile group were identified by designing six different scenarios. The results indicate that under typical intermittent operation conditions, the temperature of the energy pile and surrounding ground near the heat exchange pipe varies periodically. For areas with unbalanced cooling and heating loads, long-term operation of energy piles leads to thermal accumulation, and the maximum temperature of energy piles occurs in the first daily cycle. In summer/winter working conditions, the increase/decrease in pile temperature induces axial compression/tensile stress. When the pile group is partially used as the energy pile, the non-energy pile acts as the “anchor pile”, and it generates the added tensile stress.

The multivehicle routing problem (MVRP) is a variation of the classical vehicle routing problem (VRP). The MVRP is to find a set of routes by multiple vehicles that serve multiple customers at a minimal total cost while the travelling-time delay due to traffic congestion is tolerated. It is an NP problem and is conventionally solved by metaheuristics such as evolutionary algorithms. For the MVRP in a distribution network, we propose an optimal distribution path optimization method that is composed of a distribution sequence search stage and a distribution path search stage that exploits a divide-and-conquer strategy, inspired by the idea of dynamic programming. Several optimization objectives subject to constraints are defined. The search for the optimal solution of the number of distribution vehicles, distribution sequence, and path is implemented by using an improved genetic algorithm (GA), which is characterized by an operation for preprocessing infeasible solutions, an elitist’s strategy, a sequence-related two-point crossover operator, and a reversion mutation operator. The improved GA outperforms the simple GA in terms of total cost, route topology, and route feasibility. The proposed method can help to reduce costs and increase efficiency for logistics and transportation enterprises and can also be used for flow-shop scheduling by manufacturing enterprises.

Large grain UO2 is considered as an accident tolerant fuel with great application potential due to its competitive advantage of good fission gas retention. In this paper, the influence of preparation parameters such as sintering atmosphere, mixing process, powder pretreatment and grain growth additives on the grain size of UO2 is systematically studied. The result shows that the factors mentioned above have different effects on the grain size of UO2. The grain growth of UO2 pellet sintered in oxidizing atmosphere is better than those in reducing atmosphere. The wet mixing process has a significant advantage over the dry mixing process. In addition, the powder pretreatment has little effect on grain growth while the influence of additives plays the main role. Large grain UO2 pellets with uniform grain size up to 150 μm are successfully prepared. Finally, the thermo-physical properties of the pellets are investigated.

A variety of modulation strategies for suppressing output common-mode voltage (CMV) of indirect matrix converters (IMC) have been proposed, but most of them mainly reduce the peak of CMV by 42.3%. The frequency-domain characteristics of CMV have not been deeply analyzed. In order to improve the theory of frequency-domain characteristics of output common-mode voltage of IMC, this paper proposed a common-mode frequency domain analysis of five common-mode voltage peak suppression modulation methods for IMC. The common-mode voltage spectrum corresponding to the five modulation methods is obtained by the triple Fourier series, and the variation laws of the output common-mode components of the five modulation methods under different modulation indexes are compared and analyzed. By comparing and analyzing the low-frequency amplitude characteristics and high-frequency amplitude characteristics of five modulation methods, the suppression performance of the five modulation methods is evaluated. Finally, experiments verify the correctness of the theoretical analysis of the frequency domain characteristics of the output common-mode voltage of the indirect matrix converter.

In recent years, the hybrid AC-DC microgrid has been well accepted as it combines the advantages of both AC and DC systems. As the microgrid contains both DC sub-grids and AC sub-grids, interlinking DC-AC converters are essential. Meanwhile, considering the nonlinear AC loads may deteriorate the voltage quality of the AC bus, embedding an ancillary harmonic compensation function to the interlinking converters is promising. However, the conventional harmonic control methods used for active power filters (APFs) may not be suitable for the interlinking converters due to the main purpose of it is to exchange real and reactive power between the DC and AC sub-grids. The switching frequency is preferred to be lower than the APFs when the capacity of the microgrid is large. At low switching frequency, harmonic compensation performance or even the system stability may be affected. In this paper, a harmonic compensation approach suitable for hybrid AC-DC interlinking converters at low switching frequency is proposed. Through feeding the PWM reference signal with the harmonic compensation component directly to avoid the multi-loop control path of the fundamental component, the proposed method can achieve the effective harmonics compensation without being limited by the closed-loop control bandwidth. The proposed method, modeling approaches, stability analysis, as well as detailed virtual impedance design are presented. Experimental verification is also provided.

Under the assumptions of linear elasticity and small deformation in traditional elastodynamics, the anisotropy of the medium has a significant effect on rotations observed during earthquakes. Based on the basic theory of the first-order velocity-stress elastic wave equation, this paper simulates the seismic wave propagation of the translational and rotational motions in two-dimensional isotropic and VTI (transverse isotropic media with a vertical axis of symmetry) media under different source mechanisms with the staggered-grid finite-difference method with respect to nine different seismological models. Through comparing the similarities and differences between the translational and rotational components of the wave fields, this paper focuses on the influence of anisotropic parameters on the amplitude and phase characteristics of the rotations. We verify that the energy of S waves in the rotational components is significantly stronger than that of P waves, and the response of rotations to the anisotropic parameters is more sensitive. There is more abundant information in the high-frequency band of the rotational components. With the increase of Thomsen anisotropic parameters ε and δ, the energy of the rotations increases gradually, which means that the rotational component observation may be helpful to the study of anisotropic parameters.

Synthetic biology allows the re-engineering of biological systems and promotes the development of bioengineering to a whole new level, showing great potential in biomanufacturing. Here, in order to make the heterologous lycopene biosynthesis pathway compatible with the host strain YSy 200, we evolved YSy200 using a unique Synthetic Chromosome Rearrangement and Modification by LoxP-mediated Evolution (SCRaMbLE) system that is built in the Sc2.0 synthetic yeast. By inducing SCRaMbLE, we successfully identified a host strain YSy201 that can be served as a suitable host to maintain the heterologous lycopene biosynthesis pathway. Then, we optimized the lycopene biosynthesis pathway and further integrated into the rDNA arrays of YSy201 to increase its copy number. In combination with culturing condition optimization, we successfully screened out the final yeast strain YSy222, which showed a 129.5-fold increase of lycopene yield in comparison with its parental strain. Our work shows that, the strategy of combining the engineering efforts on both the lycopene biosynthesis pathway and the host strain can improve the compatibility between the heterologous pathway and the host strain, which can further effectively increase the yield of the target product.

In this paper, the cyber-security of smart microgrids is thoroughly discussed. In smart grids, the cyber system and physical process are tightly coupled. Due to the cyber system’s vulnerabilities, any cyber incidents can have economic and physical impacts on their operations. In power electronics-intensive smart microgrids, cyber-attacks can have much more harmful and devastating effects on their operation and stability due to low inertia, especially in islanded operation. In this paper, the cyber–physical systems in smart microgrids are briefly studied. Then, the cyber-attacks on data availability, integrity, and confidentiality are discussed. Since a false data injection (FDI) attack that compromises the data integrity in the cyber/communication network is one of the most challenging threats for smart microgrids, it is investigated in detail in this paper. Such FDI attacks can target state estimation, voltage and frequency control, and smart microgrids’ protection systems. The economic and physical/technical impacts of the FDI attacks on smart microgrids are also reviewed in this paper. The defensive strategies against FDI attacks are classified into protection strategies, in which selected meter measurements are protected, and detection/mitigation strategies, based on either static or dynamic detection. In this paper, implementation examples of FDI attacks’ construction and detection/mitigation in smart microgrids are provided. Samples of recent cyber-security projects in the world, and critical cyber-security standards of smart grids, are presented. Finally, future trends of cyber-security in smart microgrids are discussed.

“Thin-bed” reservoirs have become important targets of seismic exploration and exploitation. However, traditional amplitude versus offset/amplitude versus angle (AVO/AVA) technologies, for example, those based on Zoeppritz equations and their approximations for a single interface, are not sufficiently accurate for thin-bed stratigraphy. Analytic solutions of thin-bed reflectivity may become practical for thin-bed AVO analysis and inversion. Therefore, a linear analytic approximation of thin-bed P-wave reflectivity is developed under small-incidence and thin-bed assumptions. Numerical simulations show that the amplitude approximation errors are usually smaller than 10% for incidence angles less than 20 degrees, and the thin-bed thicknesses are less than one-tenth of the P-wave wavelength. Based on the least-squares approach, the inversion strategy is proposed using the approximate formula. A synthetic data test shows that the proposed inversion method can produce more accurate thin-bed properties than that based on the Zoeppritz equations, which reveals the potential of the inversion method based on the linear analytic approximate formula in the fine characterization of thin reservoirs.

The existing research problem is that the hypergraph-based method does not comprehensively consider the power loss factors of the mechanical transmission system, especially the existing windage power loss. In the power flow diagram, it is difficult to express and calculate the power loss separately, based on the power loss mechanism. Furthermore, the hypergraph-based methods purposed by some researches are not suitable for high-speed and heavy-load conditions. This paper presents a new hypergraph-based method for analyzing the transmission efficiency of complex planetary gear trains. In addition, a formula for calculating transmission efficiency is derived. The power loss model is established for a high-speed heavy-load herringbone planetary transmission pair considering several sources of power losses such as gear meshing friction, windage, and bearing friction. Then, the efficiency of a two-stage herringbone planetary transmission system is calculated using the proposed method. Furthermore, the influence of input speed, input power, and lubrication state on transmission efficiency is investigated. Finally, the proposed method is verified by comparing calculated values with values published in the literature.

Residual stress can be easily generated during material processing and affect the performance of structural components. Phase stress distribution in austenitic-ferritic duplex stainless steels (DSSs) is complicated due to the different material properties between the two phases. In this study, residual phase stress distribution along the thickness direction of centrifugally cast DSS hollow cylinder was measured by pulsed neutron diffraction with the time-of-flight (TOF) method. The triaxial phase stress distribution along the thickness direction shows that the phase stress of austenitic phase is generally in tension and higher than that of ferrite phase. From the outer surface to the inner surface, the macro-stress distributes from −400 MPa to 200 MPa. The mechanism of macro-stress formation was deduced by taking into consideration the thermal shrinkage behavior during the cooling process of water quench after the solution heat treatment. Furthermore, the lattice strain and phase stress evolution under the uniaxial tensile loading was evaluated by in-situ neutron diffraction measurement. The results indicated that the magnitude of phase stress could be affected by plastic working as well. All these measurements were conducted at Japan Proton Accelerator Research Complex (J-PARC).

The emerging applications of deep learning in solving geophysical problems have attracted increasing attention. In particular, it is of significance to enhance the computational efficiency of the computationally intensive geophysical algorithms. In this paper, we accelerate deabsorption prestack time migration (QPSTM), which can yield higher-resolution seismic imaging by compensating absorption and correcting dispersion through deep learning. This is implemented by training a neural network with pairs of small-sized patches of the stacked migrated results obtained by conventional PSTM and deabsorption QPSTM and then yielding the high-resolution imaging volume by prediction with the migrated results of conventional PSTM. We use an encoder-decoder network to highlight the features related to high-resolution migrated results in a high-order dimension space. The training data set of small-sized patches not only reduces the required high-resolution migrated result (for instance, only several inline is required) but leads to a fast convergence in training. The proposed deep-learning approach accelerates the high-resolution imaging by more than 100 times. Field data is used to demonstrate the effectiveness of the proposed method.

This paper presents a fast coordinated control scheme of the rotor side converter (RSC), the Direct Current (DC) chopper and the grid side converter (GSC) of doubly fed induction generator (DFIG) wind turbine generators (WTGs) to improve the low voltage ride through (LVRT) and high voltage ride through (HVRT) capability of the DFIG WTGs. The characteristics of DFIG WTGs under voltage sags and swells were studied focusing on the DFIG WTG stator flux and rotor voltages during the transient periods of grid voltage changes. The protection schemes of the rotor crowbar circuit and the DC chopper circuit were proposed considering the characteristics of the DFIG WTGs during voltage changes. The fast coordinated control of RSC and GSC were developed based on the characteristic analysis in order to realize efficient LVRT and HVRT of the DFIG WTGs. The proposed fast coordinated control schemes were verified by time domain simulations using Matlab-Simulink.

This paper presents the day-ahead energy planning of passenger cars with 100% electric vehicle (EV) penetration in the Nordic region by 2050. EVs will play an important role in the future energy systems which can both reduce the greenhouse gas (GHG) emissions from the transport sector and provide the demand side flexibility required by smart grids. On the other hand, the EVs will increase the electricity consumption. In order to quantify the electricity consumption increase due to the 100% EV penetration in the Nordic region to facilitate the power system planning studies, the day-ahead energy planning of EVs has been investigated with different EV charging scenarios. Five EV charging scenarios have been considered in the energy planning analysis which are: uncontrolled charging all day, uncontrolled charging at home, timed charging, spot price based charging all day and spot price based charging at home. The demand profiles of the five charging analysis show that timed charging is the least favorable charging option and the spot priced based EV charging might induce high peak demands. The EV charging demand will have a considerable share of the energy consumption in the future Nordic power system.

Multi-switching combination synchronization of three fractional-order delayed systems is investigated. This is a generalization of previous multi-switching combination synchronization of fractional-order systems by introducing time-delays. Based on the stability theory of linear fractional-order systems with multiple time-delays, we propose appropriate controllers to obtain multi-switching combination synchronization of three non-identical fractional-order delayed systems. In addition, the results of our numerical simulations show that they are in accordance with the theoretical analysis.

Multi-component seismic data contain a great deal of vector field information that reflects the situation of the underground medium. However, the processing methods used for multi-component seismic data are still being developed, and effectively retaining and using this information is the difficulty and the focus of the task. Currently, the main-stream processing techniques of multi-component seismic data treat the individual components independently as a scalar field; in this way, they do not excavate the vector features of the wavefield, thus restricting the potential utilities of the effective information. Research into processing methods that are suitable for use with the vector field, which can better retain and use the orientations and the relative amplitude relationship between multi-component seismic data, is urgently needed and represent an important direction for the current development of multi-component seismic data processing techniques. In this paper, we introduce and summarize several existing vector pre-processing techniques, including polarization filtering, de-noising using vector order statistics, group sparse representation, and vector separation of compressional waves and shear waves, to help scholars develop more effective vector field processing methods and to promote the development of vector processing techniques for multi-component seismic data.

The study of thin-bed seismic phenomena is important in crustal, exploration and engineering seismology. Presently, seismic reflectivity theories based on single-interface assumption are widely used though they are only suitable for thick deposits. Thin-bed reflectivity theories are established on complex propagator matrices and are difficult to be applied to reveal thin-bed properties directly. Therefore, an approximation of thin-bed PP-wave reflection coefficients (RPP) is derived in this paper. First, the relationship between thin-bed RPP and incidence angles is analyzed through series expansion method. For PP-wave, its reflection coefficients are even power series functions of sine incidence angles. Then, for small incidence, RPP of the thin bed is further simplified into a second-order series approximation with respect to the sine incidence angles. Simulations and accuracy analyses of the approximate formula show that approximation errors are smaller than 5% as the incidence angles smaller than 20 degrees. Based on this approximate formula, an approach is given for estimating thin-bed properties including P-wave impedance ratios and thickness. The estimation approach is applied in properties estimation of a thin bed model. Perfect performances of the model example show the future potentiality of the approximate formula in thin-bed Amplitude-Versus-Offset (AVO) analysis and inversion.

It is essential to extract high-fidelity surface waves in surface-wave surveys. Because reflections usually interfere with surface waves on X components in multicomponent seismic exploration, it is difficult to extract dispersion curves of surface waves. To make matters worse, the frequencies and velocities of higher-mode surface waves are close to those of PS-waves. A method for surface-wave extraction is proposed based on the morphological differences between surface waves and reflections. Frequency-domain high-resolution linear Radon transform (LRT) and time-domain high-resolution hyperbolic Radon transform (HRT) are used to represent surface waves and reflections, respectively. Then, a sparse representation problem based on morphological component analysis (MCA) is built and optimally solved to obtain high-fidelity surface waves. An advantage of our method is its ability to extract surface waves when their frequencies and velocities are close to those of reflections. Furthermore, the results of synthetic and field examples confirm that the proposed method can attenuate the distortion of surface-wave dispersive energy caused by reflections, which contributes to extraction of accurate dispersion curves.

Since its first application toward displacement measurements in the early-1960s, laser feedback interferometry has become a fast-developing precision measurement modality with many kinds of lasers. By employing the frequency-shifted optical feedback, microchip laser feedback interferometry has been widely researched due to its advantages of high sensitivity, simple structure, and easy alignment. More recently, the laser confocal feedback tomography has been proposed, which combines the high sensitivity of laser frequency-shifted feedback effect and the axial positioning ability of confocal microscopy. In this paper, the principles of a laser frequency-shifted optical feedback interferometer and laser confocal feedback tomography are briefly introduced. Then we describe their applications in various kinds of metrology regarding displacement measurement, vibration measurement, physical quantities measurement, imaging, profilometry, microstructure measurement, and so on. Finally, the existing challenges and promising future directions are discussed.

In opportunistic networks such as delay tolerant network, a message is delivered to a final destination node using the opportunistic routing protocol since there is no guaranteed routing path from a sending node to a receiving node and most of the connections between nodes are temporary. In opportunistic routing, a message is delivered using a ‘store-carry-forward’ strategy, where a message is stored in the buffer of a node, a node carries the message while moving, and the message is forwarded to another node when a contact occurs. In this paper, we propose an efficient opportunistic routing protocol using the history of delivery predictability of mobile nodes. In the proposed routing protocol, if a node receives a message from another node, the value of the delivery predictability of the receiving node to the destination node for the message is managed, which is defined as the previous delivery predictability. Then, when two nodes contact, a message is forwarded only if the delivery predictability of the other node is higher than both the delivery predictability and previous delivery predictability of the sending node. Performance analysis results show that the proposed protocol performs best, in terms of delivery ratio, overhead ratio, and delivery latency for varying buffer size, message generation interval, and the number of nodes.

An experimental study was conducted to evaluate the anti-icing performance of NS-DBD plasma actuator under the conditions of airflow speed U = 65 m/s, ambient temperature T = −10 °C, liquid water content LWC = 0.5 g/m3, mean-volume diameter MVD = 25 μm, mainly to clarify the effect of pulse frequency and voltage amplitude of actuation on anti-icing performance. A NACA0012 airfoil model with a chord length of c = 280 mm was used in the tests. The NS-DBD plasma actuator was mounted at the front part of the airfoil. A FLIR infrared (IR) imager and CCD camera were used to record the anti-icing process of the NS-DBD plasma actuator. Two typical discharge conditions were selected for the anti-icing experiments. The first was HV-LF discharge, corresponding to discharge under higher voltage amplitude with lower pulse frequency; the second was LV-HF discharge, corresponding to discharge under lower voltage amplitude with higher pulse frequency. Results reveal that NS-DBD is a very promising method for anti-icing. With the same power consumption, the LV-HF discharge shows a better anti-icing performance compared to HV-LF discharge under the same icing conditions. In view of pulse duration and duty circle, combined with heat dissipation, it is suggested that there is a threshold frequency, corresponding to the voltage amplitude of electric actuation signal and the incoming flow condition, to achieve effective anti-icing performance.

Coupled with the multichannel discharge model and plasma synthetic jet actuator (PSJA) aerodynamic model, an analytical model to predict the performance of the PSJA array is put forward. The multichannel discharge model takes these factors into consideration, the delay time in the breakdown process, the electrical transformation of the discharge channel from a capacitor to a resistor induced by the air breakdown, and the varying plasma resistance in the discharge process. The PSJA aerodynamic model is developed based on the conservation equations of mass, momentum, energy, and the lumped capacitance method. The multichannel discharge model can simulate the multichannel discharge process and give the discharge energy in the plasma channel. With a constant heating efficiency, the time-independent heating energy deposition power in a discharge channel is obtained. Importing the heating energy, the PSJA aerodynamic model presents the evolution process of the jet. Simulation results show that the jet strength induced by a single actuator decreases with the number of actuators in the PSJA array. When the actuator number increases from 1 to 20, the weakening extent of mass ejected, peak jet velocity, and jet duration time is 62%, 54%, and 33%, respectively. The discharge efficiency increases with the actuator number, while the thermodynamic efficiency decreases with the actuator number. As a result, the total energy efficiency doesn’t always increase with an increase in the number of actuators. When the discharge efficiency of a conventional one channel discharge has been a relatively large value, the total energy efficiency actually decreases with the growth of actuator number.

Finding a new ignition strategy for ignition enhancement in a lean-burn combustor has always been the biggest challenge for high-altitude, long-endurance unmanned aerial vehicles (UAVs). It is of great importance for the development of high-altitude, long-endurance aircraft to improve the secondary ignition ability of the aero-engine at high altitude where the ignition capability of the aero-engine igniter rapidly declines. An innovative ignition mode is therefore urgently needed. A novel plasma-assisted ignition method based on a multichannel discharge jet-enhanced spark (MDJS) was proposed in this study. Compared to the conventional spark igniter (SI), the arc discharge energy of the MDJS was increased by 13.6% at 0.12 bar and by 14.7% at 0.26 bar. Furthermore, the spark plasma penetration depth of the MDJS was increased by 49% and 103% at 0.12 bar and 0.26 bar, respectively. The CH* radicals showed that the MDJS obtained a larger initial spark kernel and reached a higher spark plasma penetration depth, which helped accelerate the burning velocity. Ignition tests in a model swirl combustor showed that the lean ignition limit was extended 24% from 0.034 to 0.026 at 25 m/s with 20 °C kerosene and 17% from 0.075 to 0.062 at 12 m/s with −30 °C kerosene maximally. The MDJS was a unique plasma-assisted ignition method, activated by the custom ignition power supply instead of a special power supply with an extra gas source. The objective of this study was to provide a novel multichannel discharge jet-enhanced spark ignition strategy which would help to increase the arc discharge energy, the spark plasma penetration depth and the activated area without changing the power supply system and to improve the safety and performance of aero-engines.

Delay tolerant network (DTN) protocol was proposed for a network where connectivity is not available. In DTN, a message is delivered to a destination node via store-carry-forward approach while using opportunistic contacts. Probabilistic routing protocol for intermittently connected networks (PRoPHET) is one of the widely studied DTN protocols. In PRoPHET, a message is forwarded to a contact node, if the contact node has a higher delivery predictability to the destination node of the message. In this paper, we propose an improved opportunistic routing protocol, where context information of average distance travelled and average time elapsed from the reception of a message to the delivery of the message to the destination node is used. In the proposed protocol, the average distance and average time are updated whenever a message is delivered to a destination node. Then, both average distance and average time as well as delivery predictability of PRoPHET protocol are used to decide a message forwarding. The performance of the proposed protocol is analyzed and compared with that of PRoPHET and reachable probability centrality (RPC) protocol, which is one of the latest protocols using the contact history information of a mobile node. Simulation results show that the proposed protocol has better performance than both PRoPHET and RPC, from the aspect of delivery ratio, overhead ratio, and delivery latency for varying buffer size, message generation interval, and the number of nodes.

Separation of downgoing and upgoing P-waves and S-waves in an offset 2D/3D vertical seismic profile (VSP) record is a basic step in VSP data processing. Radon transform and median filtering methods are commonly used to separate wavefields. However, with the former, it is hard to strike a balance between the resolution and amplitude preservation, while the latter ignores the variation in the slowness (apparent velocity) of events. In order to solve these problems, we propose a method based on a modified linear Radon transform and dynamic time shifting. We derive the slowness spectrum of an input VSP record through the modified Radon transform. Based on the optimal slope values of the events derived from the slowness spectrum, we align the wavefields by dynamic time shifting. Then, the target wavefields are separated using a median filtering constrained by the linear Radon transform. Our wavefield separation process is iterated until the four kinds of wavefields are separated according to a priority ranking based on the order of the energy spectrum from strong to weak. The results were found to be better than those derived from wave separation using the traditional median filter or those derived using the high-resolution Radon transform method.

Polarization filtering has been widely used to enhance signal-to-noise ratios for multicomponent seismic data. Polarization filters routinely depend on the ellipticity and directionality of spatial particle motions. However, factors such as noise and formation heterogeneity often make the polarization characteristics of body waves hard to distinguish. Here, we introduce a technique in the time domain for the separation of valid body waves and noise based on wave vector characteristics. First, we characterise the ground-roll polarization by the median wave vectors derived in large-scale moving time windows. For the suppression of ground roll, we fit the particle trajectory of ground roll by the least square method using all components simultaneously. Second, we apply three-stage smoothing to the ground-roll-removed multicomponent records. In each stage, we use mean or median vectors derived in small-scale moving-time or moving-trace windows to attenuate random noise and other non-ground-roll related coherent noise. The filter in the proposed method is not devised according to ellipticity and directionality. Instead, we use the wave vector decomposition to distinguish between noise and valid signals. Synthetic data and field data examples confirm that the proposed method can effectively suppress noise without damaging the high and low frequencies of a valid signal.