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In this paper, the Zn0.95Cu0.05O/p-Si heterojunction with lateral photovoltaic effect (LPE) is prepared that has the tuning effect and switching behavior of sunlight field. The dependence of the lateral photovoltage (LPV) on the laser position shows high sensitivity (20 mV mm−1) and strict linearity. Interestingly, the LPV eventually decreased to 0 mV as the sunlight intensity increased to 48 mW, under the constant laser intensity and position, indicating the application potential of a photo-switch. This light field effect results from the irradiation of sunlight, which changes the position of quasi-Fermi level, reduces the drive power for carriers by the space charge region in the heterojunction, and increases the concentration of carriers resulting in increased the scattering between carriers to decrease the lifetime of carriers produced by the laser. This work can expand the application of LPE in the field of multifunctional devices such as photo-switches, position sensors and self-powered devices.

The emergence of 5G represents a pivotal step in merging mobile communication networks with the Industrial Internet of Things. Despite the numerous advantages of 5G, the presence of unknown obstacles can adversely affect user signals. Although mitigating signal pressures can be achieved by increasing base station density, it often involves bulky equipment and high costs. To address this, we propose a deep learning-based method for controlling tunable transmissive metasurfaces and validate their scattering control capabilities in the presence of obstacles. By constructing a network model to analyze the mapping relationship between metasurface arrays and far-field scattering, rapid control of scattering characteristics is achieved. AI-driven high-performance tunable metasurfaces exhibit vast potential applications in intelligent communication, offering a universal solution for intelligent control in complex signal environments.

This study focuses on optimizing the lateral jet efficiency of THAAD-like (Terminal High Altitude Area Defense) missiles operating under hypersonic rarefied flow conditions. We employ the DSMC-QK algorithm to simulate the three-dimensional lateral jet flow field, accounting for thermochemical non-equilibrium effects. The analysis investigates how the force/momentum amplification coefficient varies with the angle of attack, jet pressure ratio, jet Mach number, and jet gas composition. Subsequently, we develop an artificial neural network (ANN) proxy model using the pyrenn toolbox, achieving an average prediction error of 0.866% and a maximum error of 1.60%. Utilizing this ANN model, we perform single- and multi-objective optimizations with a genetic algorithm to determine the optimal jet parameters. The results reveal that in multi-objective optimization, the proportion of helium in the jet gas composition increases, leading to a slight reduction in the force amplification coefficient but a substantial 61.4% decrease in the mass flow rate. This demonstrates that a judicious selection of jet gas composition can significantly reduce mass flow while maintaining high jet efficiency, thus achieving efficient lateral jet control.

This paper presents a structure for designing Junction Termination Extension (JTE) in Silicon Carbide (SiC) power devices, particularly for Schottky Barrier Diodes (SBD). The P-type island composite multi-step JTE configuration has been developed by optimizing the number, length, and thickness of JTE steps. The JTE with step number of 4 and step length of 8 μm (SBD-n4L8) achieved a breakdown voltage of 1626 V at the same drift layer thickness of 10 μm, outperforming other step-based JTE structures. At reverse voltage of 1200 V, the SBD-n4L8 demonstrates a decrease in reverse leakage current with a uniform step-like current distribution profile. Subsequent analysis of the underlying mechanisms showed that, with step thickness of 2 μm, the SBD-n5L8 located at the corner and edge of the terminal region significantly reduced the peak electric field by more than 12.6 %.

Due to the high efficiency and high performance of permanent magnet synchronous motors are used in various industries, such as aerospace. The main problem of achieving optimal performance control of PM synchronous motors is the heavy burden of control algorithms and large current pulsation. Due to the advantages of simple model predictive control algorithm and no need to set any control parameters, it is applied to the performance control of PM synchronous motors. The mid-point clamped three-level inverter is widely used because of its simple structure and versatility. However, the PM synchronous motor is susceptible to external load disturbances and internal parameter disturbances with large variations, and current harmonics and rotational arterial distance are difficult to control. In order to effectively solve the problem of disturbance disturbances, this paper proposes a functional disturbance observer (FDOB) based on the disturbance observer (DOB), which has the advantages of low order and handling more kinds of disturbances. In this paper, model predictive control and midpoint clamped three-level inverter are used. Compared with the traditional PI control method, the proposed functional disturbance observer plus model prediction control strategy can effectively reduce the disturbance perturbation of the motor, perform disturbance compensation, improve the motor speed response speed, improve the output current quality, enhance the system robustness and other advantages.

Gravel soil faces significant engineering challenges such as leakage erosion and soil flow due to its complex composition and susceptibility to groundwater effects. This study integrates the entire machine learning process, including pre- and post-processing of images, WGAN implementation, and validation of hydraulic and morphological properties. Obtaining intact gravel soil samples is difficult and costly due to their erodible nature in the Li River, China. A μ-CT scanning series is employed to capture detailed images with three microstructural characteristics of gravel soil, forming the basis for training datasets using WGANs. This approach allows the generation of similar 3D realizations that replicate the microstructural characteristics and hydraulic behaviors of a prototype of gravel soils. Through computational fluid dynamics (CFD) simulations, the effectiveness of the realizations in hydraulic behavior within reconstructed porous structures is verified. This process indirectly validates the consistency between the realization′s microstructure and the prototype. This integrated methodology not only enhances understanding but also aids in the optimization of engineering designs and applications in geotechnical and materials science disciplines.

Both NH3 and H2 are considered to be carbon-free fuels, and their mixed combustion has excellent performance. Considering the laminar burning velocity as a key characteristic of fuels, accurately predicting the laminar burning velocity of NH3/H2/Air is crucial for its combustion applications. The study made improvements to the XGBoost model and developed NH3/H2/Air Laminar Burning Velocity Net (NHLBVNet), which adopts a composite hierarchical structure to connect the functions of feature extraction, feature combination, and model prediction. The dataset consists of 487 sets of experimental data after the exclusion of outliers. The correlation coefficient (R2 > 0.99) of NHLBVNet is higher than that of the XGBoost model (R2 > 0.93). Robustness experiment results indicate that this model can obtain more accurate prediction results than other models even under small sample datasets.

Currently, the primary method for controlling red tides in the ocean involves spraying water solutions with special chemicals as solutes. High-pressure spraying results in the formation of typical jet structures. In this study, numerical simulation methods are employed to investigate the velocity variations, turbulent characteristics, and gas content distribution of jet flow fields under different initial jet flow pressures, cone angles, and nozzle diameters. Based on practical application scenarios, cluster analysis is used to explore the similarities and differences in jet equivalent diameters under different parameter conditions. The research findings indicate the following. (1) The difference of jet velocity distribution at the far field exit will be enlarged with the increase in the nozzle cone angle. When the nozzle cone angle is 4 mm, the velocity uniformity at the outlet is the best. (2) The TKE of the flow field has no consistent change law along the central axis. At the jet exit, the TKE shows an obvious multi-peak structure. (3) The gas content demonstrates a typical “double-valley” feature at the jet outlet cross-section. Increasing the initial pressure leads to a decrease in the gas content within the jet due to reduced entrainment, while the entrainment range remains largely constant. (4) Cluster analysis reveals that the similarity of jet flow width when it reaches the water surface is minimal compared to other operating conditions when the initial pressure is 0.36 MPa, the cone angle is 115°, and the nozzle diameter is 2 mm. All conditions can be categorized into two or three groups to ensure jet effectiveness. The study results provide scientific guidance for selecting spray devices for controlling red tides in the ocean.

Neurological disorders, particularly stroke, pose significant public health challenges, prompting a comprehensive investigation into the intricate interplay between ferroptosis and immune responses. This study aims to uncover the molecular mechanisms of stroke, emphasizing the connection between ferroptosis and immune regulation. Objectives include identifying differentially expressed genes in stroke patients, assessing immune cell subtype activation, employing advanced predictive modeling to pinpoint key candidate genes such as STMN1 and ATG16L1, exploring associations with immune cell subtypes, and revealing critical pathways, such as matrix degradation and inflammatory responses, activated in stroke through Gene Set Variation Analysis (GSVA). A comprehensive approach was adopted, involving differential gene expression analysis, functional enrichment analysis, weighted gene co-expression network analysis, Lasso regularization, random forest models, and the CIBERSORT (https://cibersortx.stanford.edu) algorithm, to assess immune cell subtypes. The study pinpointed genes with significant expression differences in stroke patients, highlighting the activation of Th17 cells and the NF-κ B signaling pathway. Key candidate genes, including STMN1 and ATG16L1, were identified using advanced predictive modeling. The relative abundance of immune cell subtypes, particularly natural killer cells (NK cells) and T cell subgroups, was associated with stroke through CIBERSORT. GSVA uncovered the activation of critical biological pathways such as matrix degradation and inflammatory responses. The analysis pinpointed STMN1 and ATG16L1 as robust predictors, emphasizing their potential as therapeutic targets. Th17 cells and the NF-κ B signaling pathway emerged as pivotal contributors to stroke pathogenesis. Notably, NK cells and specific T cell subgroups demonstrated significant associations with stroke. GSVA further illuminated the activation of key pathways, providing deeper insights into the biological processes underlying stroke. This study sheds light on crucial genes and pathways implicated in stroke, emphasizing the roles of ferroptosis and immune cell subtypes. The identified genes and pathways offer promising avenues for targeted therapeutic interventions in stroke, with a broader implication for advancing neurological disorder treatments through innovative perspectives on ferroptosis and immune regulation.

This paper presents an analytical study of the dynamic responses in the vehicle–pavement–foundation system, where the vehicle is simplified to a two-degree-of-freedom system, the pavement is modeled using both Euler–Bernoulli (E-B) beam and Timoshenko beam with consideration of pavement roughness, and the subgrade is simulated with a Winkler foundation model featuring cubic nonlinear stiffness. The focus is on using approximate analytical solutions of pavement response to discuss the impact of nonlinear stiffness under various parameter conditions. In previous analytical studies of vehicle–pavement–foundation systems, vehicles were typically simplified to a constant moving force, leading to the conclusion that when the applied force is small, the impact of nonlinear stiffness on the pavement’s dynamic response is minimal; whereas when the force is large, the pavement response increases with the increase in nonlinear stiffness. In this study, the force exerted by the vehicle on the pavement is harmonic, and the impact of nonlinear stiffness on the pavement response is different and much more complex. The research finds that there is a critical value for nonlinear stiffness under the given vehicle parameter conditions: when the nonlinear stiffness is less than the critical value, it has almost no effect on the pavement response; when it exceeds the critical value, the pavement’s response first decreases and then increases with the increase in nonlinear stiffness. The critical value of nonlinear stiffness is not fixed and increases as the vehicle velocity and foundation damping. Moreover, an increase in nonlinear stiffness also causes an increase in the offset between the wheel position and the position of maximum pavement deformation. Under the same parameter conditions, the offset in the E-B beam is significantly greater than that in the Timoshenko beam. Our study’s results enhance the understanding of the nonlinear dynamics within the vehicle–pavement interaction.

Enhanced dewatering remains a major challenge in sludge disposal. In this paper, a thermal solidification treatment under low temperatures was first and successfully employed to improve the dewaterability of sludge from water-based ink wastewater. A total of 95.1% of dewatering ratio, 36.6% of moisture content, and 91.9% of sludge volume reduction could be acquired at 75 °C and 30 min setting time by the reported approach. The DSC results indicated that a large amount of bound water trapped in sludge could be released into the bulk solution to become free water after thermal solidification treatment. The bound water content of sludge can be reduced from 3.40 to 0.20 g g−1 dry solid. The reported thermal solidification treatment is a thermophysical process that does not involve the solubilization of solid substrates and does not cause an increase in the COD value of dehydrated water. The obtained sludge after cooling was a clot of compacted cake with a uniform coralline-like structure. The softening and shrinking of acrylic resin within the sludge during heating contributed to the formation of compacted cake and thus enhanced the dewaterability. This study offers an economical and efficient treatment of sludge for the water-based ink printing industry.

The prediction of land subsidence is of significant value for the early warning and prevention of geological disasters. Although numerous land subsidence prediction methods are currently available, two obstacles still exist: (i) spatio-temporal heterogeneity of land subsidence is not well considered, and (ii) the prediction performance of individual models is unsatisfactory when the data do not meet their assumptions. To address these issues, we developed a spatio-temporal heterogeneous ensemble learning method for predicting land subsidence. Firstly, a two-stage hybrid spatio-temporal clustering method was proposed to divide the dataset into internally homogeneous spatio-temporal clusters. Secondly, within each spatio-temporal cluster, an ensemble learning strategy was employed to combine one time series prediction model and three spatio-temporal prediction models to reduce the prediction uncertainty of an individual model. Experiments on a land subsidence dataset from Cangzhou, China, show that the prediction accuracy of the proposed method is significantly higher than that of four individual prediction models.

Recently, convolutional neural networks (CNNs) and self-attention mechanisms have been widely applied in plant disease identification tasks, yielding significant successes. Currently, the majority of research models for tomato leaf disease recognition rely solely on traditional convolutional models or Transformer architectures and fail to capture both local and global features simultaneously. This limitation may result in biases in the model’s focus, consequently impacting the accuracy of disease recognition. Consequently, models capable of extracting local features while attending to global information have emerged as a novel research direction. To address these challenges, we propose an Eff-Swin model that integrates the enhanced features of the EfficientNetV2 and Swin Transformer networks, aiming to harness the local feature extraction capability of CNNs and the global modeling ability of Transformers. Comparative experiments demonstrate that the enhanced model has achieved a further increase in training accuracy, reaching an accuracy rate of 99.70% on the tomato leaf disease dataset, which is 0.49~3.68% higher than that of individual network models and 0.8~1.15% higher than that of existing state-of-the-art combined approaches. The results show that integrating attention mechanisms into convolutional models can significantly enhance the accuracy of tomato leaf disease recognition while also offering the great potential of the Eff-Swin backbone with self-attention in plant disease identification.

Framing theory is a widely accepted theoretical framework in the field of news communication studies, frequently employed to analyze the content of news reports. This paper innovatively introduces framing theory into the text summarization task and proposes a news text summarization method based on framing theory to address the global context of rapidly increasing speed and scale of information dissemination. Traditional text summarization methods often overlook the implicit deep-level semantic content and situational frames in news texts, and the method proposed in this paper aims to fill this gap. Our deep learning-based news frame identification module can automatically identify frame elements in the text and predict the dominant frame of the text. The frame-aware summarization generation model (FrameSum) can incorporate the identified frame feature into the text representation and attention mechanism, ensuring that the generated summary focuses on the core content of the news report while maintaining high information coverage, readability, and objectivity. Through empirical studies on the standard CNN/Daily Mail dataset, we found that this method performs significantly better in improving summary quality and maintaining the accuracy of news facts.

The pollution caused by printing and dyeing wastewater is increasingly severe, posing significant harm to aquatic plants and animals. In this study, porous carbon was synthesized via the high-temperature pyrolysis of light and heavy organic matter present in oily sludge, utilizing low oil content sludge as the raw material and zinc chloride as a chemical activator. The results exhibited a significant increase in the specific surface area of the oily sludge-based porous carbon, from 4.95 m²/g to 10.95 m²/g. The effects of various parameters such as pH, amount of sorbent, dye concentration, temperature, and contact time on dye removal have been studied. The results showed that the oil sludge-based porous carbon exhibited high efficiency in removing Malachite Green from aqueous solutions, which has low polarity and remains consistently above 97%. The removal rate of Crystal Violet, which is more polar, was as low as 24.14%. The corresponding adsorption capacities were 33.41 mg/g for Malachite Green, 16.41 mg/g for Crystal Violet, and 13.56 mg/g for Methylene Blue. The adsorption capacity of OSC700 for three types of dyes was characterized by monolayer adsorption, primarily driven by chemical adsorption, with significant contributions from electrostatic and hydrophobic effects. The adsorption process was spontaneous, exothermic, and accompanied by an increase in entropy. For less polar substances, the adsorption on oily sludge-based porous carbon is primarily driven by aromatic functional groups on the carbon surface, hydrophobicity, π-π electron-donor-acceptor (π-π EDA) interactions, and surface hydrogen bond formation. In contrast, for more polar dyes, electrostatic and hydrophobic interactions dominate, with electrostatic adsorption being the predominant mechanism and minimal hydrogen bond formation during adsorption.

Due to their exceptional electronic and optical properties, two-dimensional materials have emerged as one of the most promising candidates for future optoelectronic detection. However, optoelectronic detectors based on two-dimensional transition metal materials still face challenges due to factors such as limited absorption coefficients and carrier recombination. In this study, we combine two-dimensional Bi2O2Se with PbS quantum dots to prepare a hybrid heterojunction, effectively broadening the detection range and significantly enhancing the photoresponse rate. The hybrid photodetector exhibited a remarkable photoresponsivity of 14.89 A/W at 450 nm and demonstrated broadband detection capabilities from visible (405 nm) to near-infrared (1350 nm) light illumination. Moreover, the hybrid device showed reduced photocurrent response and recovery times, highlighting its improved performance over bare Bi2O2Se photodetectors. This work underscores the potential of hybrid heterojunctions for enhancing optoelectronic detection capabilities, paving the way for advanced applications in various fields.

Abstract The quasi-1D chiral edge states in a quantum anomalous Hall insulator are dissipationless, while the 2D helical surface states in a topological insulator are insensitive to spin-independent scatterings due to the topological protection. Both serve as essential ingredients for topological electronics. Here, we integrate these states into a single device using selective area epitaxy based on the molecular beam epitaxy technique. The chiral edge state comes from the quantum anomalous Hall insulator Cr:(Bi,Sb)2Te3, while the helical surface state comes from the intrinsic topological insulator (Bi,Sb)2Te3 which only interfaces with a partial edge of the former, forming a selective-area heterostructure. At the heterointerface, the chiral state in Cr:(Bi,Sb)2Te3 is allowed to be scattered into (Bi,Sb)2Te3 so that the incoming current will be redistributed according to the coordination between the chirality and helicity. Our device enables the collaboration between chiral and helical states for low-dissipative transport with tunable current dimension.

Ultrashort laser pulses can serve as fast probes to record instant events. The isolated attosecond pulses (IAPs) generated from high-order harmonic generation (HHG) have been shortened down to about 2 atomic units in time, empowering us to study quantum behaviors of electrons in atoms, molecules, and solids with unprecedented time resolution. Following the cutoff energy law of HHG, the shortest IAP reported so far is driven with short-wavelength infrared (SWIR) pulses, which require additional broadband frequency conversion techniques and raise the bar for attosecond researches. Here, we show that with few-cycle near-infrared (NIR) laser pulses, IAP with pulse duration of 51 ± 4 as is generated during 1-fs linear polarization gate formed by generalized double optical gating (GDOG) technique. The characterization is done with attosecond streak camera, and phase reconstruction is performed with quick phase retrieval by omega oscillation filtering (qPROOF). Furthermore, we show that the IAP generation favors certain carrier envelope phases (CEPs) in the narrow gate, i.e., IAP is only efficiently produced for certain CEPs, which eliminates the requirement of CEP stabilization. The demonstrated scheme for IAP generation in principle has much higher conversion efficiency than the long-wave driver scheme according to the wavelength scaling law of HHG. Our work suggests an alternative way to generate ultrashort IAPs by applying GDOG on few-cycle free-CEP NIR driving pulses, and is thereby of great importance to facilitate the development of attosecond science and technology.

Dynamic multi-objective big data optimization problems (DMBDOPs) are challenging because of the difficulty of dealing with large-scale decision variables and continuous problem changes. In contrast to classical multi-objective optimization problems, DMBDOPs are still not intensively explored by researchers in the optimization field. At the same time, there is lacking a software framework to provide algorithmic examples to solve DMBDOPs and categorize benchmarks for relevant studies. This paper presents a metaheuristic software framework for DMBDOPs to remedy these issues. The proposed framework has a lightweight architecture and a decoupled design between modules, ensuring that the framework is easy to use and has enough flexibility to be extended and modified. Specifically, the framework now integrates four basic dynamic metaheuristic algorithms, eight test suites of different types of optimization problems, as well as some performance indicators and data visualization tools. In addition, we have proposed an experience reuse method, speeding up the algorithm’s convergence. Moreover, we have implemented parallel computing with Apache Spark to enhance computing efficiency. In the experiments, algorithms integrated into the framework are tested on the test suites for DMBDOPs on an Apache Hadoop cluster with three nodes. The experience reuse method is compared to two restart strategies for dynamic metaheuristics.

Ground Penetrating Radar (GPR) is a non-destructive geophysical technique utilizing electromagnetic pulses to detect subsurface material properties. The analysis of regions of interest (ROIs) in GPR images often entails the identification of hyperbolic reflection regions of underground targets through accurate segmentation, a crucial preprocessing step. Currently, this represents a research gap. In the hyperbolic reflection region, manual segmentation not only demands professional expertise but is also time-consuming and error-prone. Automatic segmentation can aid in accurately determining the location and depth of the reflection region, thereby enhancing data interpretation and analysis. This study presents a deep residual Convolutional Neural Network (Res-CNN) that integrates skip connections within an encoder-decoder framework for ROI-binarized hyperbolic segmentation. The proposed framework includes designed downsampling and upsampling modules that facilitate feature computation sharing between these two modules through skip connections within network blocks. In the evaluation of both simple and complex models, our method attained PSNR, SSIM, and FSIM values of 57.1894, 0.9933, and 0.9336, and 58.4759, 0.9958, and 0.9677, respectively. Compared to traditional segmentation methods, the proposed approach demonstrated clearer segmentation results, enabling intelligent and effective identification of the ROI region containing abnormal hyperbolic reflection waves in GPR images.

In this paper, the initiation of the fracture of a segment caused by the pressure of the jack and other factors during shield construction is discussed. Based on the Rots model in the finite element software Diana 10.4, the fracture width is solved. Combined with in situ measurements, the mechanisms of concrete fracturing of a segment under external loads, such as the jack thrust deflection angle and uneven jack thrust caused by the changes in the segment due to the upward buoyancy and shield attitude, are studied; additionally, the occurrence conditions and engineering control measures for segment fracture are summarized. The results show that when the attitudes of the shield and segment are identical, the total thrust of the shield is recommended not to exceed 21,000 kN, and is strictly limited to 24,000 kN. When the attitude inclination angle between the shield machine and the segment is less than 1°, the impact on the segment quality is small. When the inclination angle reaches 2°, the total thrust of the shield is recommended not to exceed 16,000 kN, and is strictly limited to 18,000 kN. When the inclination reaches 3°, a fracture is easily produced. When the total thrust is 19,000 kN, it is recommended that the loading increase or decrease in the left and right four grippers should not exceed 20%, and they are prohibited to exceed 30%. The fracture width increases exponentially with the increase in misalignment between adjacent segment rings. These research results provide a theoretical basis for jack pressure control during shield construction.

Key errors of machine tools have a significant impact on their accuracy, however accurately and quickly measuring the geometric errors of machine tools is essential for key error identification. Fortunately, a quick and direct laser measurement method and system for 21 geometric errors of three linear axes of machine tools were proposed previously, which enables the measurement of all 21 geometric errors via a one-step installation and a three-step automated measurement process. Based on this, to efficiently identify the key error factors, this paper first utilizes the 21 geometric errors obtained from the proposed measurement system to evaluate the contribution of each error to the volumetric errors of machine tools, leading to the building of a 21-geometric-error sensitivity analysis model. Then, experiments are carried out on the vertical machining tool TH5656, and all 21 geometric errors are obtained in 5 min. After this, the volumetric error distribution in the machining workspace is mapped according to the relationship between the geometric errors and the machining errors, and the key error factors affecting the manufacturing and machining accuracy of the TH5656 are ultimately determined. Thus, this new method provides a way to quickly identify key errors of the three linear axes of machine tools, and offers guidance for the machine tool configuration design, machining technology determination, and geometric error compensation.

Molecular orbital tomography (MOT) through high-order harmonic generation (HHG) is a crucial method for probing individual molecular orbitals. Previous studies mainly focused on linear molecules such as N2, and CO, where valence orbitals were imaged by detecting harmonic emission at various alignments. In this study, we investigate the HHG from the water molecules, with the aim of exploring MOT for nonlinear molecules. By employing the time-dependent density functional theory, we obtain HHG spectra for different angles between the laser polarization and molecular symmetry axis. By manipulating the scanning planes (planes perpendicular to the laser propagation direction and contain the field polarization direction), specific orbitals can be selectively imaged. Particularly, a well-reconstructed highest occupied molecular orbital (HOMO) is obtained when the laser scanning plane approximately aligns with the nodal plane of HOMO-1. However, when the laser polarization deviates from the nodal plane, multiple orbitals contribute to the HHG process, and we visualize the orbital distortions resulting from such effects. Furthermore, HOMO-1, can be reconstructed when the laser scanning plane aligns with the nodal plane of HOMO. This research significantly advances the development of MOT and opens up intriguing possibilities for reconstructing orbitals of more complex molecules.

The aerodynamic optimization of a Gurney flap attached to the NACA 0012 airfoil in rarefied gas flow is studied in this study. In order to investigate the effects of Gurney flap geometry (height, mounting angle, and mounting location) and angle of attack on the aerodynamic characteristics of the airfoil in the rarefied gas flow, the flow fields around NACA 0012 airfoils with Gurney flap attached are simulated by using the direct simulation Monte Carlo method. Furthermore, the multi-objective aerodynamic optimization of the airfoil with Gurney flap is performed at the single design point, and the Pareto front is obtained. Based on the benchmark configuration (obtained from the Pareto front), aerodynamic optimization by controlling the mounting angle of the Gurney flap and the angle of attack is investigated over a wide range of Mach numbers (ranging from 2 to 4) and Knudsen numbers (ranging from 0.024 to 0.24) by constructing an artificial neural network model. The results show that through optimization, the lift-to-drag ratio of the airfoil is increased by up to 29.25% under the combination of different Mach numbers and Knudsen numbers, which significantly improves the aerodynamic characteristics.

This study employs the power spectrum inversion method to generate the phase screens of atmospheric turbulence over the sea, the theoretical model of the vortex beam array emitted by a focusing system through anisotropic non-Kolmogorov maritime atmospheric turbulence was built. The propagation characteristics of the vortex beam array were investigated theoretically and compared to those of the Gaussian beam array. The results show that the vortex beam array is less affected by turbulence and exhibits stronger resistance to the turbulence. On this basis, using the evaluation parameters including the relative beam width and the beam wander, the influences of various parameters of the vortex beam array and atmospheric turbulence, such as topological charge, beamlet width, number of beamlets, anisotropy factor, and power law, on its propagation characteristics through anisotropic non-Kolmogorov maritime atmospheric turbulence were studied extensively. The results can provide a useful reference for the applications of the vortex beam array in optical communication through the maritime atmosphere.

We propose a description of the gluon scattering amplitudes as the inverse Mellin transforms of the conformal correlators of light operators in two-dimensional Liouville theory tensored with WZW-like chiral currents on the celestial sphere. The dimensions of operators are Mellin dual to gluon light cone energies while their positions are determined by the gluon momentum directions. Tree-level approximation in Yang-Mills theory corresponds to the semiclassical limit of Liouville theory. By comparing subleading corrections, we find b2=(8π2)−1β0g2(M), where b is the Liouville coupling constant, g(M) is the Yang Mills coupling at the renormalization scale M and β0 is the one-loop coefficient of the Yang-Mills beta function.

In order to solve the problem that the transient response of ZPW-2000A track circuit cannot be calculated directly in time domain, the modified nodal admittance method (MNA) combined with Q-D method is proposed to analyze the transient response of ZPW-2000A track circuit. Firstly, the hybrid model of traction network lines and ZPW-2000A track circuit is established. Secondly, using this model and Kirchhoff’s law, the MNA time domain equation is obtained. Through Laplace transform and decoupling, the complex frequency domain solution of the rail surface voltage at the receiving end of ZPW-2000A track circuit is obtained. Then, the numerical time domain solution of rail surface voltage is obtained by using Fourier transform and Q-D algorithm, and a simulation model is built to verify the correctness of the proposed method. Furthermore, the variation laws of the rail surface voltage at the receiving end affected by frequency, transmission distance and ballast resistance are analyzed. Finally, when the train enters and leaves the track section, there is a transient process, which can provide a reference for the train occupancy inspection and when the air core coil is in fault state, its transient response is different from that in normal state, which provides a reference for fault detection of track circuit.

In order to break the limitation of plasma nitriding technology, which can only be applied to a few non-metallic gaseous elements, the “double glow discharge phenomenon” was defined and then the “double glow plasma surface metallurgy technology” was invented. This double glow plasma surface metallurgy technology can use any element of the Periodic Table for surface alloying of metallic materials. Countless surface alloys with special physical and chemical properties have been produced on the surfaces of conductive materials. By using the double glow discharge phenomenon, a series of new double glow plasma technologies have been developed to apply to surface metallurgy, graphene technology, brazing, sintering, arc plasma surface alloying, nanotechnology, cleaning, carburizing without hydrogen, and so on. This article briefly introduces the basic principles, functions, and characteristics of each technology. The application prospects and development directions of plasma in metallic materials and the machinery manufacturing industry will also be discussed.

In recent years, with the use of electric drilling rig, electric fracturing pump, and other equipment, the harmonic resonance problem of oilfield power grid is becoming more and more serious. In view of the high time and space complexity and long calculation time of the traditional harmonic resonance analysis method, the improved power iteration method is combined with the traditional modal analysis method, and the harmonic resonance problem of the oilfield power grid is analyzed by the fast modal analysis method. At the same time, a new set of harmonic resonance amplification index is derived and verified, and the harmonic content amplification times in the resonant influence region and different busbars are revealed. And then a passive filter is used to suppress harmonic resonance, and the effectiveness of the scheme is verified by field tests. The results show that, in the frequency range of 0–1000 Hz, the resonant frequency points in the oil field are mainly related to the rectifier pulse number of the drilling rig and the frequency conversion device for fracturing. The proposed fast modal analysis method has the advantages of fast convergence speed and memory saving, which is helpful to accurately detect the harmonic resonance region caused by the multi-pulse frequency conversion device, and the harmonic resonance can be effectively suppressed by installing a filter at the main excitation bus of the resonant frequency.

The determination of rate coefficient parameters in detailed chemical kinetic mechanisms through experiments often suffers from avoidable aleatory uncertainty, while the use of reduced mechanisms, based on various reduction methods, introduces epistemic uncertainty. Both sources of uncertainty pose significant challenges for modeling and numerical simulation, highlighting the need for calibrating these uncertain parameters to achieve robust chemical mechanisms in the next generation. However, the high-dimensional parameter space of chemical kinetic mechanisms remains a significant obstacle in uncertainty computing. The Anisotropic Sparse Grid (ASG) technique has been successful in dealing with high-dimensional uncertainty problems, but it lacks prior knowledge of anisotropy. To address this issue, we propose the Local Sensitivity-Informed Anisotropic Sparse Grid (LSIASG) method, which utilizes local sensitivity as prior information for the ASG method, thereby accelerating the entire uncertainty quantification process. We test the LSIASG method on a theoretical model, a detailed hydrogen kinetic mechanism, and a methane mechanism and demonstrate that it can capture high-dimensional uncertainty characteristics, including expectation and deviation.

Recently, wearable sensors for human motion posture and medical diagnosis have received widespread attention. However, most wearable sensors rely on a power supply, and their preparation technology still faces limitations. Here, we used eyebrow powder to fabricate a triboelectric nanogenerator (E-TENG) for bio-mechanical energy harvesting and gait monitoring of patients with osteoarthritis. Under a maximum separation distance (5 mm) and a maximum motion frequency (6 Hz), the E-TENG device can attain a open-circuit voltage (Voc) of 169 V and a short-circuit current (Isc) of 5.5 µA. Meanwhile, the maximum output power of the E-TENG can arrive at 175 µW (load resistance: 20 MΩ). The E-TENG can detect human gait patterns (walking, running, and jumping), finger motion, and elbow joint movements. Further research has shown that the E-TENG can be used for gait recognition and monitoring in patients with osteoarthritis, providing reference data for osteoarthritis prevention and treatment. This research can promote the application of TENG devices based on cosmetic materials in medical diagnosis and adjuvant treatment.

Providing accurate information about bus travel times can help passengers plan their itinerary and reduce waiting time. However, due to various uncertainty factors and the sparsity of single-route data, traditional travel time predictions cannot accurately describe the credibility of the prediction results, which is not conducive to passengers waiting based on the predicted results. To address the above issues, this paper proposes a bus travel time prediction intervals model based on shared road segments, multiple routes’ driving style similarity, and the bootstrap method. The model first divides the predicted route into segments, dividing adjacent stations shared by multiple routes into one section. Then, the hierarchical clustering algorithm is used to group all drivers in multiple bus routes in this section according to their driving styles. Finally, the bootstrap method is used to construct a bus travel time prediction interval for different categories of drivers. The travel time data sets of Shenyang 239, 134, and New Area Line 1 were selected for experimental verification. The experimental results indicate that the quality of the prediction interval constructed using a data set fused with multiple routes is better than that constructed using a single-route data set. In the two cases studied, the MPIW of the three time periods decreased by 101.04 s, 151.72 s, 33.87 s, and 126.58 s, 127.47 s, 17.06 s, respectively.

Northwest China (NWC), is characterized by its arid and semi-arid environment, and exhibits high sensitivity to precipitation variations. Recent research indicates a wetting tendency over NWC, yet quantifying its moisture source remains challenging. Here, employing a 40 year simulation with Community Atmosphere Model version 5.1 (CAM5.1) coupled to an atmospheric water tracer algorithm, we ascertain that the dominant source of summer moisture over NWC are from terrestrial sources (82% of vapor and 77% of precipitation), i.e. from local evaporation, North Asia (NA), Europe (EUP), the southern Tibetan Plateau (STP), and southeastern China (SEC), rather than oceanic sources. Favorable synoptic patterns over NWC enhance the precipitation-conversion efficiency from the southeasterly airflow transport (STP and SEC) compared to northwesterly airflow (NA and EUP). We also find that the fluctuations in precipitation over NWC, manifesting as alternating dry and wet summers, are primarily driven by increased moisture contributions from direct inputs from NA and re-evaporation transport from STP. Our study indicates that moisture variability in inland is predominantly driven by nearby terrestrial sources and underscores the complex mechanisms of terrestrial moisture transport.

Vegetation impacts on cloud physical properties and climate. The rapid greening in China in the past two decades, which contributed the most to global greening, may influence clouds in the region. However, due to the influence of a rapid concomitant decline in aerosol levels in China and of global warming, such influence of vegetation change is yet to be clarified. By utilizing observation data from satellite, we explored the impact of vegetation change on summertime low-level cloud cover in China from 2003 to 2022. After excluding the influence of changing aerosol and temperature on clouds, we revealed a significant positive correlation between vegetation changes and low-level cloud cover. Moreover, we explored the underlying mechanisms through which vegetation exerts its influence on clouds. We found that such influence is mediated through enhancing surface water vapor content by vegetation as well as altering net surface radiation and sensible heat flux.

The dynamics of lateral nutrient fluxes through hydrological movements are crucial for understanding ecological functions related to the flow of energy, materials, and organisms across various spatiotemporal scales. To investigate the connectivity of multiple spatial flow processes, we conducted a one-year field study to measure lateral hydrologic carbon (C) and nitrogen (N) fluxes across the continental shelf in the Yangtze estuary. We observed a significant correlation between the differences in remote sensing-based estimates of gross primary production (GPP) (ΔGPP _MODIS ) and the differences in eddy covariance (EC) tower-based GPP (ΔGPP _EC ) at both high-elevation and low-elevation sites. Our findings indicate that the saltmarsh acts as a net source of dissolved total C while serving as a net sink for dissolved total N. Furthermore, there was a significant correlation in the total dissolved stoichiometry of the C/N ratio between imports from and exports to adjacent aquatic systems. These findings highlight the importance of integrating ecological stoichiometric principles to improve our understanding of the complex relationships among physical, chemical, and ecological processes, particularly within the context of the meta-ecosystem framework. Additionally, when reciprocal hydrological lateral C and N flows are considered, a single ecosystem can function as both a source and sink within the meta-ecosystem framework.

Valley topological phononic crystals (PCs) have attracted wide attention due to the topological properties of their edge states. In general, valley interface states can exist in the interfaces that are constructed by opposite valley topological phases. Here we study the anti-scattering propagation properties of edge states in a single valley PC. We present that the edge states can exist in different boundary terminations with different band dispersions. The boundary transport behaviors of acoustic waves along the two designed PCs are demonstrated numerically. The results show that the chiral edge states are immune against additional scatterers that preserve the valley pseudospins, but the backscattering can happen when intervalley scattering is included. Nevertheless, the anti-scattering propagation in complex multiple-bend structures can be realized by the smooth transition between the edge states and the valley interface states. Similar to the designed frequency-selective device, more prospective applications can be anticipated in the manipulation of acoustic wave propagation.

Low carbon transition strategies of power plants are crucial to meet China’s ‘Dual Carbon’ targets. While the Beijing–Tianjin–Hebei (BTH) region, the ‘capital economic circles’ of China, is suffering from serious air pollution, air quality co-benefits of low carbon transition policies in electricity system in BTH area remain unclear. Here, we estimate the impacts of low carbon transition policies, including one BAU, six single and five combined scenarios, in electricity system in BTH area on installed power capacity, CO _2 emissions, air quality and human health through 2060 based on open source energy modeling system and extend response surface model ERSM models. Results show that the total installed capacity under single and combined scenarios (except RE and Tech single scenarios) fluctuates around the BAU level of 310.5 GW in 2060. While all single and combined scenarios would generally achieve ‘carbon peak’ in BTH electricity system before 2030, only S4 (combining technological progress, renewable energy development and CCUS) and S5 (in additional to S4, including gas-fired power generation instead of coal-fired power generation) scenarios have the potential to realize carbon neutrality by 2060. The magnitude of reductions in air pollutant emissions and improvement in air quality in BTH area from the BAU level in 2060 under combined scenarios, especially S4 and S5, generally exceed that of six single scenarios. Importantly, S5 in 2060 contributes to about 8528 avoided premature mortalities in BTH area, which are more effective than any other scenarios. The results suggest that S5 is a promising low carbon transition policy to achieve environmental improvement and produce health benefits.

Terrestrial evapotranspiration plays a critical role in drought monitoring and water resource management. Changes in evapotranspiration are significantly influenced by cloud-related precipitation and radiation effects. However, the impact of cloud amount (CA) on evapotranspiration through its influence on precipitation remains uncertain, especially in the transition zone affected by the East Asian summer monsoon (EASM), which limits the understanding of the water cycle. Therefore, this study deeply explores the impact of CA on evapotranspiration and its potential physical mechanisms in Northwest China. The results show that the correlation between 31-year average evapotranspiration and CA is negative only in the semi-arid region and is positive in other climatic regions of Northwest China. This unique negative correlation is related to the change of precipitation pattern in the semi-arid region caused by the weak EASM. Smaller CA in weak monsoons results in more short-wave radiation reaching the surface, larger sensible heat, and weaker convective inhibition. Consequently, the proportion of convective clouds (CCs) increases and precipitation from these CCs enhances evapotranspiration. Less CA increases evapotranspiration and potentially exacerbates aridity in the semi-arid region of Northwest China. These results emphasize the role of cloud type in evapotranspiration. It is well known that global warming can change cloud type with more CCs. Therefore, this study sheds new light on evapotranspiration change under global warming.

Exploring the realm of Dark Matter research, Light DM, which has a mass in the range of 1 MeV to 1 GeV, is a fascinating topic both theoretically and experimentally. We assume that the light dark matter is composed of complex scalars and produced from semi-annihilation, which is close to the scale of the MeV Gamma-ray satellite, allowing us to explore the implications of this hypothesis. The experimental data we used to constrain the scenario is from five different sources: the COMPTEL, EGRET, INTEGRAL, Fermi Gamma-ray Space Telescope, and the e-ASTROGAM future reach. We use the analytical formula to measure the X-ray spectra allowing us to determine the annihilation cross-section bounds from 10−28cm3/s to 10−22cm3/s for different combinations of dark matter and mediator masses. We found that the MeV gamma-ray provides valuable insight into the structure of the semi-annihilation dark matter, where EGRET contributes to the stringent constraint to the semi-annihilation, and the e-ASTROGAM future reach could probe the whole parameter space of the model.

When rockbursts and coal and gas outbursts simultaneously occur in a coal mine, changes in gas adsorption (concentration of ambient methane) and displacement of coal and rock must occur. The co-associated minerals in coal reservoirs can affect the mechanical properties and methane adsorption capacity, which are commonly disregarded. It is important to construct compound molecular structure models of coal and rock and conduct molecular dynamic simulations to gain a microscopic understanding of underground disasters. In this work, the molecular structure models of anthracite and coking coal–rock compound models containing different contents of calcite and kaolinite were constructed, and the methane adsorption amount and mechanical properties considering temperature, pressure, and mineral contents were simulated and analysed. The results showed that the methane adsorption amount of the compound models increased rapidly, then increased moderately, and stabilized eventually with increasing adsorption pressure, and the Langmuir fitting findings were good. The saturation adsorption amount of methane in the coal models linearly decreased with increasing temperature, while the methane adsorption heat increased. The presence of minerals adsorbed a certain amount of methane, and the methane adsorption amount increased with increasing mineral contents. The mechanical properties of coal molecules changed when mineral molecules such as calcite and kaolinite were present, which had opposite contribution effects. The addition of kaolinite minerals to the coal molecular model always increased the bulk modulus and shear modulus, while the addition of calcite decreased the bulk modulus of the anthracite, causing an increase in the brittleness of the models. The results of the study further explain the adsorption behaviour and mechanical properties of methane in coal and minerals.

Ship resistance has a very important value in the determination of ship power and the design of emission standards. In this paper, a ship resistance model with different displacement, speed, and attachment under the condition of a fixed scale ratio is tested by means of experimental research, which is used to analyze the change law of ship resistance under the condition of a single factor. The coupling effects of multiple factors on the actual ship power are studied after the establishment of a mathematical relationship between the actual ship power and resistance on the basis of the response surface method. The research results show that: (1) there is an obvious positive correlation between ship resistance and speed, which matches the change law of the exponential equation. Compared with ship appendages, displacement and speed have the greatest influence on resistance. (2) According to the correlation analysis, the maximum correlation coefficient between ship speed/resistance and power is 0.99, and the correlation coefficients between displacement/resistance and power are 0.93 and 0.88, respectively. However, the correlation coefficients between ship appendages and resistance and power are only 0.23 and 0.14, respectively. (3) The actual ship power and speed, displacement, and appendages form a quadratic polynomial relationship. The multi-factor interaction analysis results show that speed and displacement have the greatest influence on the actual ship power. The research results have a certain guiding significance for ship design.

DNA extraction for downstream molecular diagnostic applications can be an expensive, time-consuming process. We devised a method to quickly extract total bacterial DNA from environmental samples based on the sodium hydroxide lysis of cells with or without capture by magnetic beads for subsequent PCR or quantitative PCR. The final DNA extraction method using NaOH is extremely low-cost and can be completed in as little as 10 min at room temperature with dilution, or the DNA can be further purified using silica-coated paramagnetic beads. NaOH extraction was effective for Gram-negative and Gram-positive bacteria in samples from air, soil, sewage, food, laboratory surfaces, and chicken cloacal swabs. The NaOH extraction method was comparable to commercial kits for extraction of DNA from pig fecal samples for 16S amplicon sequencing analyses. We demonstrated that an impinger and portable pump can efficiently capture bacteria from the air in poultry facilities for rapid NaOH extraction to quantify total bacteria and for detection of specific species using qPCR. The air sampling and NaOH extraction procedures are well suited for routine, high-throughput screening and for metagenomic analyses for specific pathogens, even in resource-limited situations.

As the most common geological disaster problem in mines, slope geological disasters have become a focus of research, along with the difficulty of mine safety and ecological environment protection together with the ecological restoration of open-pit mines. At present, a large number of slope-retaining wall structures lack research on safety monitoring, real-time acquisition, and intelligent early warning. Therefore, this paper combines cement-modified loess with gravity retaining wall structures and puts forward a new type of greening gravity retaining wall structure. From the perspective of “the Internet of Things + construction”, a video monitoring system is established to monitor the retaining wall structure in real time. Finally, based on video image processing technology, the deformation of the retaining wall surface is identified and the inclination angle of the wall surface is calculated, so as to improve the real-time and intelligent monitoring of the new greening gravity retaining wall. The results show that the new greening gravity retaining wall based on video monitoring proposed in this paper has the characteristics of a gravity retaining wall and ecological retaining wall, which are conducive to improving the real-time and intelligent monitoring of the new greening gravity retaining wall. Cement-modified loess is used as the planting matrix, and the cement mixing ratio should not exceed 10%. Considering the requirements of economy and shear strength, the cement mixing ratio should be selected from 5% to 12%.

Laser absorption spectroscopy tomography is an effective combustion diagnostic method for obtaining simultaneous two-dimensional distribution measurements of temperature and gas molar concentrations. For the reconstruction process of complex combustion flames, a new algorithm named ‘multi-iterative adaptive optimization regularization’ (MIARO) is proposed. This algorithm is a further development of another algorithm known as the ‘modified adaptive algebraic reconstruction technique’ (MAART) with the improvement of the initial value and adaptive regularization parameter selections. In MIARO, the problem of the MAART’s initial value sensitivity is compensated for, and in addition, reconstruction parameters are also introduced into the regularization so that both the quality of reconstruction and the convergence of regularization are guaranteed. In butane burner experiments, an average relative error of 1.82% was achieved with MIARO, compared to 2.44% with MAART, which is a significant reduction of 25.1%. The simulation and experimental results clearly demonstrate that the MIARO algorithm can be used to reconstruct dynamic combustion fields and eliminate boundary artifacts with improved measurement accuracy and robustness.

The growing utilization of web-based search engines for learning purposes has led to increased studies on searching as learning (SAL). In order to achieve the desired learning outcomes, web learners have to carefully plan their learning objectives. Previous SAL research has proposed the significant influence of task planning quality on learning outcomes. Therefore, accurately predicting web-based learners’ task planning abilities, particularly in the context of SAL, is of paramount importance for both web-based search engines and recommendation systems. To solve this problem, this paper proposes a method for predicting the ability of task planning for web learners. Specifically, we first introduced a tree-based representation method to capture how learners plan their learning tasks. Subsequently, we proposed a method based on the deep learning technique to accurately predict the SAL task planning ability for web learners. Experimental results indicate that, compared to baseline approaches, our proposed method can provide a more effective representation of learners’ task planning and deliver more accurate predictions of learners’ task planning abilities in SAL.

At present, bridge crane accidents occur frequently, resulting in significant losses and casualties; to ensure the safe use of in-service bridge cranes, it is necessary to predict the residual fatigue life of in-service bridge cranes. Firstly, a static analysis of the most dangerous working conditions of in-service bridge cranes is carried out to find the fatigue failure point. Subsequently, a three-parameter Weibull distribution model is established for the characteristic parameters affecting the acquisition of the stress spectrum. Latin hypercubic sampling is applied to randomly sample the characteristic parameters to produce a random sample set of characteristic parameters for use in obtaining the stress–time history. The amplitude and mean values of the stress spectra are obtained by cycle counting using the rainflow counting method. Finally, Forman’s formula and Miner’s continuous damage accumulation theory were used to derive the remaining life prediction equations for constant and variable amplitude loads. Comparing the remaining life obtained from the simulation and test, the error is about 9.145%, which proves that the remaining life obtained from the simulation is more accurate. The results show that the combined method of simulation and testing is feasible and can predict the remaining life more accurately. In the past, the prediction of residual life was performed with either testing or simulation, which is long and costly. Simulation is low-cost and takes a short time, but the accuracy is not high. In this paper, the combination method of testing and simulation improves the efficiency of production and reduces the cost of use.

The hazard of highly combustible marine oil leakage greatly increases fishing vessel operation risks. This research integrates an experiment to explore the coupling mechanism of a typical heated surface of an engine room as a source to ignite marine oil. A numerical model is established that depicts the dynamic process of and variations in the combined effects regarding multiple factors of oil ignition under actual experiment. The leaked marine oil is ignited with a heated surface, relevant models are applied to reproduce the results, and the influences of specific parameters of a fishing vessel’s engine room are analyzed. The results indicate that the leaked oil boils violently on the heated surface, and a vapor film forms on the oil surface. Increased heated-surface temperatures lead to a significant difference in the initial ignition occurrences of marine oil, and the distance between the ignition height and oil is closely related to the engine room environment. The ignition probability of marine oil shows a gradually increasing trend with elevated heated-surface temperatures. The ignition height presents a downward trend with the increase in the heated-surface temperature, while the engine room’s humidity in air inhibits the upward transfer of heat; however, the degree of inhibition is limited accordingly. The results evidence that this comparative work can be an effective approach to reveal the impacts of marine oil, heat source, ventilation velocity, and humidity on initial ignition characteristics. Additionally, this work provides a basis for setting up emergency planning with appropriate monitoring equipment and further preventing vessel fires due to oil–thermal ignition.

This paper deals with a problem that we found when directly implementing the scalar MUSCL scheme to spectral coefficient equations of the gPC-Galerkin method. The order degradation at extrema of spectral coefficients and non-uniform reconstruction process lead to a distortion of standard deviation at some extrema, especially where the peak consists of several extrema of coefficients. From the perspective of probability space, a spectral MUSCL scheme is proposed based on the generalized minmod limiter to avoid the defects mentioned above when applying the scalar MUSCL. In this paper, we present some properties of the new scheme related to total variation and demonstrate them by the uncertain linear scalar conservation law. Finally, we employ this scheme to compressible Euler equations, and a good correlation of standard deviation is obtained. The details of implementation of the gPC-Galerkin method for Euler equations are also discussed, and the approximate Jacobian matrix is adopted.