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Advancements have been achieved in the optimization of waverider designs with the aid of machine learning to expedite the design process. However, these approaches are hampered by the need for extensive sample sizes and susceptibility to becoming ensnared in local optima. This study undertakes a parametric design based on the wedge-derived, power-law-shaped waverider, increasing configuration diversity and creating a dataset with limited samples by calculating waverider geometry and aerodynamic parameters. At a Mach number of 10, a multi-objective optimization design is implemented using the Young's double-slit experiment-least squares support vector regression (YDSE-LSSVR) surrogate model in conjunction with improved congestion distance multi-objective particle swarm optimization algorithm, focusing on maximizing the lift-to-drag ratio and volumetric efficiency as much as possible. The results indicated that, under conditions of limited samples, the YDSE-LSSVR model outperforms standard models such as support vector regression, LSSVR, Kriging, and Polynomial Chaos Expansions-Kriging regarding prediction accuracy. The Pareto solutions for both concave and convex waveriders, obtained through multi-objective optimization, improve the lift-to-drag ratio by 17.36% and 21.70%, respectively, and increase the volumetric efficiency by 88.89% and 105.56%, in comparison to baseline configurations. In addition, the research examines the impact of various design parameters on the Pareto solutions. Finally, the study applies the K-means method to conduct a cluster analysis of the Pareto solutions, generating three-dimensional waverider configurations based on distinguished solutions from different clusters. [ABSTRACT FROM AUTHOR]

Gully erosion poses a significant global concern due to its role in land degradation and soil erosion, particularly pronounced in Northeast China's diverse agro-geomorphic regions. However, there is a lack of comprehensive studies on gully characteristics, development rates, and the topographic threshold of gully formation in these areas. To address this gap, we selected three different agro-geomorphic watersheds, named HL (Hailun), ML (Muling), and YKS (Yakeshi), with areas of 30.88 km2, 31.53 km2, and 21.98 km2, respectively. Utilizing high-resolution (2.1 m, 2 m) remote sensing imagery (ZY-3, GF-1), we analyzed morphological parameters (length, width, area, perimeter, etc.) and land use changes for all permanent gullies between 2013 and 2023. Approximately 30% of gullies were selected for detailed study of the upstream drainage area and gully head slopes to establish the topographic threshold for gully formation (S = a·A−b). In HL, ML, and YKS, average gully lengths were 526.22 m, 208.64 m, and 614.20 m, respectively, with corresponding widths of 13.28 m, 8.45 m, and 9.32 m. The gully number densities in the three areas were 3.14, 25.18, and 0.82/km2, respectively, with a gully density of 1.65, 5.25, and 0.50 km km−2, and 3%, 5%, and 1% of the land has disappeared due to gully erosion, respectively. YKS exhibited the highest gully head retreat rate at 17.50 m yr−1, significantly surpassing HL (12.24 m yr−1) and ML (7.11 m yr−1). Areal erosion rates were highest in HL (277.79 m2 yr−1) and lowest in YKS (105.22 m2 yr−1), with ML intermediate at 243.36 m2 yr−1. However, there was no significant difference in gully expansion rate (0.37–0.42 m yr−1) among the three areas (p > 0.05). Differences in gully development dynamics among the three regions were influenced by land use, slope, and topographic factors. The topographic threshold (S = a·A−b) for gully formation varied: HL emphasized drainage area (a = 0.052, b = 0.52), YKS highlighted soil resistance (a = 0.12, b = 0.36), and the parameters a and b of ML fell within the range between these of HL and YKS (a = 0.044, b = 0.27). This study has enriched the scope and database of global gully erosion research, providing a scientific basis for gully erosion prevention and control planning in Northeast China. [ABSTRACT FROM AUTHOR]

Nonconventional luminophores have received increasing attention, owing to their fundamental importance, advantages in outstanding biocompatibility, easy preparation, environmental friendliness, and potential applications in sensing, imaging, and encryption. Purely organic molecules with outstanding fluorescence and room‐temperature phosphorescence (RTP) have emerged as a new library of benign afterglow agents. However, the cost, toxicity, high reactivity, and poor stability of materials also limit their practical applications. Therefore, some natural products, synthetic compounds, and biomolecules have entered horizons of people. The as‐designed exhibits sky blue and green fluorescence emission and green RTP emission (a lifetime of 343 ms and phosphorescence quantum of 15.3%) under air condition. This study presents an organic fluorescence for biological imaging and RTP for anti‐counterfeiting and encryption based on amino acids, maleic anhydride and 4‐vinylbenzenesulfonic acid sodium salt hydrate. This study provides a strategy for nonconventional luminophores in designing and synthesizing pure organic RTP materials. [ABSTRACT FROM AUTHOR]

Converting spent lithium‐ion batteries (LIBs) cathode materials into environmental catalysts has drawn more and more attention. Herein, we fabricated a Co3O4‐based catalyst from spent LiCoO2 LIBs (Co3O4‐LIBs) and found that the role of Al and Cu from current collectors on its performance is nonnegligible. The density functional theory calculations confirmed that the doping of Al and/or Cu upshifts the d‐band center of Co. A Fenton‐like reaction based on peroxymonosulfate (PMS) activation was adopted to evaluate its activity. Interestingly, Al doping strengthened chemisorption for PMS (from −2.615 eV to −2.623 eV) and shortened Co−O bond length (from 2.540 Å to 2.344 Å) between them, whereas Cu doping reduced interfacial charge‐transfer resistance (from 28.347 kΩ to 6.689 kΩ) excepting for the enhancement of the above characteristics. As expected, the degradation activity toward bisphenol A of Co3O4‐LIBs (0.523 min−1) was superior to that of Co3O4 prepared from commercial CoC2O4 (0.287 min−1). Simultaneously, the reasons for improved activity were further verified by comparing activity with catalysts doped Al and/or Cu into Co3O4. This work reveals the role of elements from current collectors on the performance of functional materials from spent LIBs, which is beneficial to the sustainable utilization of spent LIBs. [ABSTRACT FROM AUTHOR]

Converting spent lithium‐ion batteries (LIBs) cathode materials into environmental catalysts has drawn more and more attention. Herein, we fabricated a Co3O4‐based catalyst from spent LiCoO2 LIBs (Co3O4‐LIBs) and found that the role of Al and Cu from current collectors on its performance is nonnegligible. The density functional theory calculations confirmed that the doping of Al and/or Cu upshifts the d‐band center of Co. A Fenton‐like reaction based on peroxymonosulfate (PMS) activation was adopted to evaluate its activity. Interestingly, Al doping strengthened chemisorption for PMS (from −2.615 eV to −2.623 eV) and shortened Co−O bond length (from 2.540 Å to 2.344 Å) between them, whereas Cu doping reduced interfacial charge‐transfer resistance (from 28.347 kΩ to 6.689 kΩ) excepting for the enhancement of the above characteristics. As expected, the degradation activity toward bisphenol A of Co3O4‐LIBs (0.523 min−1) was superior to that of Co3O4 prepared from commercial CoC2O4 (0.287 min−1). Simultaneously, the reasons for improved activity were further verified by comparing activity with catalysts doped Al and/or Cu into Co3O4. This work reveals the role of elements from current collectors on the performance of functional materials from spent LIBs, which is beneficial to the sustainable utilization of spent LIBs. [ABSTRACT FROM AUTHOR]

Stable combustion is desirable for efficient operation of scramjet engines at high flight Mach numbers, and being able to reconstruct the flow-field wave patterns in stable combustion facilitates proactive evaluation of engine operating conditions. Proposed here is a multi-level connected shifted-window transformer (MCSwinT) model for reconstructing the flow-field wave patterns of stable combustion in a supersonic combustor. A combustion feature conversion block is used to convert high-dimensional and low-dimensional combustion features; a deep pressure feature extraction block is used to extract the flow-field wave patterns, and MCSwinT blocks enable multi-level fusion, thereby extracting the high-dimensional combustion features of the flow-field wave system. A dynamic loss function unifies spatial content loss and feature space loss, leading to enhanced reconstruction results. Separately, data on the stable combustion process of a hydrogen-fueled scramjet engine were collected in a direct-connect supersonic pulse combustion wind tunnel, and these data are used to validate the robustness and generalization capability of MCSwinT. The experimental results show that the flow-field wave patterns of stable combustion are reconstructed successfully using MCSwinT of different scales. Compared to other models, MCSwinT exhibits lower model complexity while achieving performance improvements of 7% and 17% in peak signal-to-noise ratio and structural similarity index, respectively. Additionally, the high generalization ability of the proposed model is validated in a sparsity experiment. This model effectively reconstructs the flow-field wave patterns of stable combustion, providing a crucial foundation for further research on scramjet engines. [ABSTRACT FROM AUTHOR]

To alleviate the problem of high-fidelity data dependence and inexplicability in pure data-driven neural network models, physical informed neural networks (PINNs) provide a new learning paradigm. This study constructs an efficient, accurate, and robust PINN framework for predicting unsteady combustion flow fields based on Navier–Stokes (NS) equation constraints. To achieve fast prediction of a multi-physical field in a scramjet combustion chamber, we propose a U-shaped residual neural network model based on feature information fusion. The model uses a residual neural network module as the backbone, uses jump connection to improve model generalization, and uses the U-shaped structure to fuse the receptive field features with different scales to enhance the feature expression ability of the model. To prevent improper assumptions from leading to wrong method constraints, we consider the flow characteristic mechanism of each physical field to constrain the neural network and verify its accuracy through numerical simulation of the unsteady flow field in the scramjet combustor with Mach number (Ma) 2.0. This method can accurately predict the multi-physical field of unsteady turbulent combustion based on the time, space, Ma and turbulent eddy viscosity coefficients of a small number of samples. Specially, the proposed physical driven and data driven fusion proxy model can predict the unsteady combustion flow field in milliseconds. It has important reference value to solve the problem of low calculation efficiency of a traditional numerical simulation method of a combustion process. [ABSTRACT FROM AUTHOR]

Clanis bilineata Walker (Lepidoptera: Sphingidae), a burgeoning edible insect, is experiencing rising demand in China and other regions. Despite this interest, larval production is currently constrained by the limitations of artificial production technologies, particularly the selection of optimal host plants. This study rigorously evaluated the performance of C. bilineatha larvae on four main host plants: round-leaf soybean, pointed-leaf soybean, black locust, and kudzu. Preference tests demonstrated that the larvae were most attracted to black locust (34.76 ± 4.65%), with subsequent preferences for kudzu (25.00 ± 6.12%), round-leaf soybean (23.17 ± 2.79%), and pointed-leaf soybean (14.02 ± 4.74%). No significant preference differences were noted between round-leaf soybean and either black locust or kudzu. In feeding assays, the larvae exhibited a marked preference for round-leaf soybean (37.36 ± 0.81 g, total feeding amount for larvae), followed by kudzu (37.26 ± 0.82 g), pointed-leaf soybean (35.38 ± 1.31 g), and black locust (28.53 ± 0.81 g). When the larvae were fed on round-leaf soybean, they exhibited significantly higher survival rate (39.33 ± 0.90%), body weight (9.75 ± 0.07 g), total biomass (383.43 ± 7.35 g), pupation rate (87.78 ± 1.73%), and egg production (189.80 ± 1.06 eggs/female) compared to other hosts. These findings uncovered that round-leaf soybean significantly enhances larval performance, suggesting its potential for improving C. bilineata larval production and sustainability in cultivation systems. [ABSTRACT FROM AUTHOR]

Objective: To compare the clinical efficacy of anterior column reconstruction using single or double titanium mesh cage (TMC) after total en bloc spondylectomy (TES) of thoracic and lumbar spinal tumors. Methods: A retrospective cohort study was performed involving 39 patients with thoracic or lumbar spinal tumors. All patients underwent TES, followed by anterior reconstruction and screw-rod instrumentation via a posterior-only procedure. Twenty-two patients in group A were treated with a single TMC to reconstruct the anterior column, whereas 17 patients in group B were reconstructed with double TMCs. Results: The overall follow-up is 20.5 ± 4.6 months. There is no significant difference between the 2 groups regarding age, sex, body mass index, tumor location, operative time, and intraoperative blood loss. The time for TMC placement was significantly shortened in the double TMCs group (5.2 ± 1.3 minutes vs. 15.6 ± 3.3 minutes, p = 0.004). Additionally, postoperative neural complications were significantly reduced with double TMCs (5/22 vs. 0/17, p = 0.046). The kyphotic Cobb angle and mean intervertebral height were significantly corrected in both groups (p ≤ 0.001), without obvious loss of correction at the last follow-up in either group. The bone fusion rates for single TMC and double TMCs were 77.3% and 76.5%, respectively. Conclusion: Using 2 smaller TMCs instead of a single large one eases the placement of TMC by shortening the time and avoiding nerve impingement. Anterior column reconstruction with double TMC is a clinically feasible, and safe alternative following TES for thoracic and lumbar tumors. [ABSTRACT FROM AUTHOR]

Precise and efficient regulation of microglia is vital for ischemic stroke therapy and prognosis. The infiltration of neutrophils into the brain provides opportunities for regulatory drugs across the blood–brain barrier, while hindered by neutrophil extracellular traps (NETs) and targeted delivery of intracerebral drugs to microglia. This study reports an efficient neutrophil hijacking nanoplatform (referred to as APTS) for targeted A151 (a telomerase repeat sequence) delivery to microglia without the generation of NETs. In the middle cerebral artery occlusion (MCAO) mouse model, the delivery efficiency to ischemic stroke tissues increases by fourfold. APTS dramatically reduces the formation of NETs by 2.2‐fold via reprogramming NETosis to apoptosis in neutrophils via a reactive oxygen species scavenging‐mediated citrullinated histone 3 inhibition pathway. Noteworthy, A151 within neutrophils is repackaged into apoptotic bodies following the death pattern reprogramming, which, when engulfed by microglia, polarizes microglia to an anti‐inflammatory M2 phenotype. After four times treatment, the cerebral infarction area in the APTS group decreases by 5.1‐fold. Thus, APTS provides a feasible, efficient, and practical drug delivery approach for reshaping the immune microenvironment and treating brain disorders in the central nervous system. [ABSTRACT FROM AUTHOR]

Hydroponic cultivation of lettuce is an increasingly popular sustainable agricultural technique. However, Escherichia coli, a prevalent bacterium, poses significant concerns for the quality and safety of hydroponically grown lettuce. This study aimed to develop a growth model for E. coli and background microflora in hydroponically grown lettuce. The experiment involved inoculating hydroponically grown lettuce with E. coli and incubated at 4, 10, 15, 25, 30, 36 °C. Growth models for E. coli and background microflora were then developed using Origin 2022 (9.9) and IPMP 2013 software and validated at 5 °C and 20 °C by calculating root mean square errors (RMSEs). The result showed that E. coli was unable to grow at 4 °C and the SGompertz model was determined as the most appropriate primary model. From this primary model, the Ratkowsky square root model and polynomial model were derived as secondary models for E. coli-R168 and background microflora, respectively. These secondary models determined that the minimum temperature (Tmin) required for the growth of E. coli and background microflora in hydroponically grown lettuce was 6.1 °C and 8.7 °C, respectively. Moreover, the RMSE values ranged from 0.11 to 0.24 CFU/g, indicating that the models and their associated kinetic parameters accurately represented the proliferation of E. coli and background microflora in hydroponically grown lettuce. [ABSTRACT FROM AUTHOR]

Afidopyropen, a novel biorational insecticide, was registered in China in 2023 for the control of tea leafhoppers in tea plantation. In this study, an analytical method was developed for the determination of afidopyropen and its metabolite M440I007 in tea tissues by a modified QuEChERS method combined with ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Roots, stems and leaves of tea plants were extracted by water and acetonitrile, and purified with primary secondary amine (PSA), hydroxylated multi-walled carbon nanotubes (MWCNT-OH) and graphitized carbon black (GCB). The target compounds were determined by UPLC-MS/MS in multiple reaction monitoring (MRM) mode, and quantified by external standard method with matrix-matched standard curve. Both targets display excellent linearity (R²>0.999 5) in the range of 0.002-5.000 mg·L-1. The recoveries of both compounds at the spiked levels of 0.005-2.000 mg·kg-1 ranged from 78.3% to 106.0% with the relative standard deviations (RSD) ≤ 12.85%. The limits of quantification (LOQs) and detection (LODs) of the method were 0.005 mg·kg-1 and 0.002 mg·kg-1, respectively. The developed method was applied to detect afidopyrofen and M440I007 in tea seedling tissues cultured in soil treated with afidopyropen in a pot experiment for 7 days. The results show that the distribution of afidopyropen in the tissues was roots (0.102 mg·kg-1) > stems (0.078 mg·kg-1) > leaves (0.007 mg·kg-1). The translocation factors, TFroot-stem and TFstem-leaf, were both less than 1, and the residue levels of the metabolite M440I007 were below the LOQ. The method established in this study is low-cost, accurate and sensitive, which can serve as a technical basis for further research on the uptake and transport behaviors of afidopyropen and M440I007 in tea plants. [ABSTRACT FROM AUTHOR]

Traditional turbulence models suffer from low accuracy and weak applicability when predicting complex separated flows, such as those that occur in shock boundary layers. To overcome this problem, the present paper considers a cavity-ramp structure and calibrates the turbulence model parameters using a deep neural network (DNN) surrogate model and a genetic algorithm (GA). The non-intrusive polynomial chaos expansion method is used to quantify the uncertainty of the shear stress transport (SST) turbulence model parameters and determine the effects of these parameters on the wall pressure, allowing suitable feature identification parameters to be selected for the DNN turbulence surrogate model. The DNN is compared with the traditional polynomial chaos expansion method, and the results highlight the advantages of using the DNN method to construct the surrogate model. Finally, a GA is used to optimize and calibrate the SST turbulence model parameters based on the surrogate model and experimental data. Experimental results show that the DNN turbulence surrogate model is highly accurate, with the predicted wall pressure, achieving a coefficient of determination above 0.998. The DNN has higher precision, stronger feature extraction ability, and faster prediction times than the traditional polynomial chaos expansion method. The calibrated SST turbulence model produces wall pressures that are close to the experimental data, verifying the feasibility of the proposed method. It is expected that the approach proposed in this paper will improve the calculation accuracy of the SST turbulence model. [ABSTRACT FROM AUTHOR]

Clarifying the effects of various land‐use and management practices on soil erosion and nutrient loss of sloping land can help optimize land use patterns and management strategies. This study assessed the effects of five long‐term land‐use and management practices on runoff, soil erosion, and nutrient losses. Field data from runoff plots established in 2006 in bare land (BL), natural vegetation restoration (NVR), no tillage (NT), reduced tillage (RT), and conventional tillage (CT) were used in two seasons of natural rainfall (from July to September in 2021 and 2022). The results indicated that the runoff depth (RD) was 84.1% and 43.2%–81.7% lower, the sediment yield (SY) was 99.9% and 96.9%–99.8% lower, the total nitrogen (TN) loss was 99.1% and 93.1%–98.6% lower, and the total phosphorous (TP) loss was 99.8% and 96.7%–99.7% lower in the NVR and three tillage practices, respectively than in the BL. The RD, SY, and nutrient losses were significantly lower in NT than in RT and CT. The TN loss in runoff contributed to 88.3% and 87.6% of the TN loss in the NVR and NT, respectively, whereas the TN loss in the sediment (TN‐SL) for BL accounted for 94.7% of the TN loss in BL. Meanwhile, most of the TP loss (56.1%–99.7%) was attributed to soil erosion rather than runoff in the five treatments. Furthermore, the effect of runoff and SY on nutrient losses differed significantly for the five land‐use and management practices. The effects of runoff and SY on the TP loss were negligible in the NVR and three tillage treatments. However, the effects on the TN loss were larger. Rainfall erosivity had the largest effect on runoff, SY, and nutrient loss in the five treatments. Our findings can help land managers to select sustainable measures to control soil and nutrient losses in sloped farmland in Northeast China. [ABSTRACT FROM AUTHOR]

This study aimed to compare the properties and safety of self-designed plates in type II traumatic spondylolisthesis of the axis with those of traditional devices via finite element (FE) analysis. We constructed a hangman's fracture FE model from the occipital bone (C0) level to the C3 level. Then, FE models were constructed for the following four fixation systems: an anterior cervical L-shaped plate with four vertebral screws (4-ACLP), or six screws (6-ACLP), an anterior cervical orion plate (ACOP), and a posterior fixation system. A preloaded compressive force of 50 N and a moment of 1.5 N·m were applied to each model under six working conditions. The mobility of the C2/3 segment decreased significantly in four fixation models. In the Mises stress cloud diagram, 4-ACLP showed a better stress distribution in both the bone graft and fixation system than 6-ACLP and ACOP. The resultant force of 4-ACLP was lower but higher than ACOP in axial force. Additionally, the cage in the 4-ACLP configuration experienced the highest stress in the six working conditions. Hence, this novel self-designed plate has the potential to mitigate the operational difficulties, provide sufficient stability, reduce the risk of plate or screw fractures, and improve bone fusion. [ABSTRACT FROM AUTHOR]

The Reynolds‐Averaged Navier–Stokes (RANS) model is the main model in engineering applications today. However, the normal value of the closure coefficient of the RANS turbulence model is determined based on some simple basic flows and may no longer be applicable for complex flows. In this paper, the closure coefficient of shear stress transport (SST) turbulence model is recalibrated by combining Bayesian method and particle swarm optimization algorithm, so as to improve the numerical simulation accuracy of wall pressure in supersonic flow. First, the obtained prior samples were numerically calculated, and the Sobol index of the closure coefficient was calculated by sensitivity analysis method to characterize the sensitivity of the wall pressure to the model parameters. Second, combined with the uncertainty of propagation parameters by non‐intrusive polynomial chaos (NIPC). Finally, Bayesian optimization is used to quantify the uncertainty and obtain the maximum likelihood function estimation and optimal parameters. The results show that the maximum relative error of wall pressure predicted by the SST turbulence model decreases from 29.71% to 9.00%, and the average relative error decreases from 9.86% to 3.67% through the parameter calibration of Bayesian optimization method. In addition, the system evaluated the calibration effect of three criteria, and the calibration results parameters under the three criteria were all better than the calculated results of the nominal values. Meanwhile, the velocity profile and density profile of the flow field were also analyzed. Finally, the same calibration method was applied to the supersonic hollow cylinder and BSL (Baseline) turbulence model, and the same calibration results were obtained, which verified the universality of the calibration method. [ABSTRACT FROM AUTHOR]

This study delves into the parametric design of the scramjet nozzles, utilizing the Catmull–Rom curve, to meet the high-performance design requirements. It establishes a high-dimensional, multi-objective optimization design method based on a surrogate model for the nozzle. In addition, this research proposes a surrogate model for nozzle performance to enhance the accuracy of the traditional surrogate model and prevent the multi-objective optimization design method from optimizing in an incorrect direction. This model incorporates the grey wolf optimization (GWO) algorithm and kernel extreme learning machine (KELM). Various machine learning algorithms are compared and analyzed, demonstrating that the performance parameters predicted by the GWO-KELM model are the most accurate, and the generalization of GWO-KELM is verified. Utilizing the particle swarm optimization algorithm assisted by GWO-KELM, the multi-objective optimization of the nozzle is further investigated. This study obtains the optimal Pareto front, analyzes the distribution of design variables in the Pareto solution set, and reveals the impact of geometric parameters on nozzle performance. Comparing the representative nozzle from the Pareto front with the truncated maximum thrust nozzle, it is found that the thrust, lift, and outlet Mach number increase by 3.3%, 12.2%, and 0.5%, respectively, while the outlet height decreases by 5.3%. This research contributes to overcoming the limitations of traditional design methods, which are typically time-consuming. The proposed GWO-KELM surrogate model effectively tackles the issue of low prediction accuracy. [ABSTRACT FROM AUTHOR]

Understanding the spatial distribution of soil organic carbon (SOC) and soil nutrients is crucial for optimizing land management in watersheds. This study explored the spatial distribution of SOC, total nitrogen (TN) and total phosphorus (TP) in topsoil (0-20 cm) at a hilly small watershed site in Northeast China, and identified the controlling factors. The Gaussian mixture model optimally described SOC and TN with the ratio of nugget to sill variance (C0/[C + C0]) indicating moderate spatial dependence for SOC (63.7%) and TN (59.3%). TP was best modelled by the spherical model and demonstrated weak spatial autocorrelation and strong human influence, with a nugget to sill variance of 0.841. Land use significantly affected SOC, TN and TP contents, with the highest values recorded in woodland, followed by farmland and shrubland. Soil properties including bulk density (BD), gravel content (GC), moisture content (MC), soil porosity (SP) and the mean weight diameter (MWD) of water-stable aggregates significantly correlated with SOC and TN content, but not with TP. Redundancy analysis revealed that soil properties (BD, GC and SP), topography (slope and aspect) and gully erosion contributed 55.7%, 24.7% and 4.3% of the spatial variation in SOC, TN and TP, respectively. Hence, these are the dominant factors shaping the spatial variation of SOC and soil nutrients at the site. These results can aid the further development of optimized land management strategies. [ABSTRACT FROM AUTHOR]

To prevent scramjet inlet unstart, stringent control over the shock train's leading edge in the isolator is necessary. Therefore, this study proposes a fuel flow active control scheme combining an adaptive particle swarm optimization algorithm (APSO) and active disturbance rejection control (ADRC) to enhance the traditional control (such as proportional-integral-derivative, PID) system's stability and anti-interference capabilities in complex and uncertain environments. Within this context, a simple, semi-empirical mathematical model of a dual-mode scramjet is constructed. An ADRC controller is designed to counteract the nonlinearity and disturbance of the dual-mode scramjet. ADRC, not requiring an accurate mathematical model, utilizes its extended state observer to estimate all uncertain factors affecting the controlled object based on the relationship between the shock train's leading edge and the controller's output, treating them as unknown disturbances for compensation, thereby exhibiting strong anti-interference capabilities. In order to optimize the performance, the APSO is introduced to fine-tune the ADRC controller parameters. The APSO reduces the rise time of ADRC from 22.28 to 11.39 ms, and the adjustment time from 40.05 to 13.64 ms. The APSO effectively tunes the parameters of the ADRC controller, which ensures rapidity comparable to the PID and all-coefficient adaptive control (ACAC) in managing the shock train's leading edge. At the same time, ADRC has stronger anti-interference ability and adaptive ability than ACAC and PID. This research preliminarily verifies the feasibility and advancement of the ADRC for shock train leading-edge control in scramjet isolators. [ABSTRACT FROM AUTHOR]

Climate change significantly impacts forest ecosystems in arid and semi-arid regions. However, spatiotemporal patterns of climate-sensitive changes in individual tree growth under increased climate warming and precipitation in north–west China is unclear. The dendrochronological method was used to study climate response sensitivity of radial growth of Picea schrenkiana from 158 trees at six sites during 1990–2020. The results show that climate warming and increased precipitation significantly promoted the growth of trees. The response to temperature first increased, then decreased. However, the response to increased precipitation and the self-calibrating Palmer Drought Severity Index (scPDSI) increased significantly. In most areas of the Tianshan Mountains, the proportion of trees under increased precipitation and scPDSI positive response was relatively high. Over time, small-diameter trees were strongly affected by drought stress. It is predicted that under continuous warming and increased precipitation, trees in most areas of the Tianshan Mountains, especially those with small diameters, will be more affected by precipitation. [ABSTRACT FROM AUTHOR]

The rapid and accurate reconstruction of the supersonic combustor flow field is of great significance for sensing and predicting the combustion state. Existing deep learning methods pay less attention to the convergence speed of flow field reconstruction, which results in longer training and prediction times for the models. This study proposes a method for reconstructing the flow field in supersonic combustor by combining a reduced-order model based on proper orthogonal decomposition (POD) with a multi-branch convolutional neural network. This method first analyzes the effectiveness of POD reconstruction. Then, based on the wall pressure data of the supersonic engine combustor, it performs flow field image reconstruction. Finally, through error calculation and gradient updating with low-resolution principal component flow field shadow images obtained from the POD algorithm, the high-precision and efficient prediction of flow field images is achieved. Different equivalence ratio hydrogen fuel combustion experiments were conducted in a pulsed combustion wind tunnel with an incoming flow Mach number of 2.5. The learning model was trained and tested using the dataset obtained from these experiments. Numerous experiments demonstrated that the model can effectively reconstruct the wave structures of complex flow fields. Multiple evaluation indicators indicated that the reconstructed flow field of the combustor shows good agreement with that obtained from ground wind tunnel testing. Furthermore, after introducing the POD dimensionality reduction model, the training time was reduced by 32.03%, effectively improving the training time complexity of the model. [ABSTRACT FROM AUTHOR]

This paper proposed the physical information residual spatial pyramid pooling (PIResSpp) convolutional neural network that is highly robust and introduces a residual neural network architecture that can satisfactorily fit high-dimensional functions by using jumping connections to reduce the risk of overfitting. Key features of the flow field were extracted by using pooling kernels of different sizes and were then stitched together to fuse its local and global features. The axisymmetric inlet of the scramjet generated by the Bezier curve was established through highly precise numerical simulations, and datasets of flow fields under different geometric configurations were constructed according to the parametric design. The PIResSpp model was trained on a sample dataset, and mapping relationships were established between the parameters of incoming flow/those of the geometry of the inlet, and the velocity, pressure, and density fields in it. Finally, the results of reconstruction of the flow field at the inlet with different design parameters were tested and compared with the outcomes of various deep learning models. The results show that the average peak signal-to-noise ratio of the flow field reconstructed by the proposed model was 36.427, with a correlation coefficient higher than 97%. [ABSTRACT FROM AUTHOR]

Freeze and thaw cycles (FTCs) influence the process of nutrient transformation and migration in soils; however, it is still not clear how soil available phosphorus (AP) distribution changes after FTCs at the watershed scale. In this study, 750 soil samples were collected at 0–50 mm, 50–100 mm, and 100–200 mm soil depths before and after FTCs in a Mollisol watershed in Northeast China. Factorial analysis, structural equation modeling, geostatistical analysis and random forest model were used to detect the changes in the AP and the main driving mechanisms after FTCs. The results showed that, after FTCs in the watershed, (1) the AP in 51.2–55.2% of areas decreased in the 0–200 mm soil depth, and the mean value decreased by 2.3–8.5%, while the spatial autocorrelation (Moran's I) of AP typically increased at all soil depths. (2) The AP decrease was higher in forestland than in farmland at all soil depths and was higher on the south-facing slope than on the north-facing slope. (3) Slope steepness, as the key factor interacting with slope position, slope aspect, land use type, crop type, and tillage method, influenced the AP change. (4) AP, pH and the ratios among carbon, nitrogen and phosphorus were the key factors influencing the AP change in the watershed after FTCs, and this process was also influenced by the original distribution of soil erosion and deposition. Generally, the AP change in the 0–200 mm soil layer was severely influenced by landscape pattern and soil properties, which was deeply driven by snowmelt erosion in the watershed. [ABSTRACT FROM AUTHOR]

BACKGROUND: Afidopyropen is a novel biorational insecticide for controlling piercing pests with great potential for application in tea gardens that can form the metabolite M440I007 when utilized for crops. However, because of a lack of analytical method for afidopyropen and M440I007 in tea, there is no means of monitoring the residues. Therefore, method development, validation and simultaneous determination of afidopyropen and M440I007 in fresh tea leaves, dried tea and tea infusion is of prime significance. RESULTS: A TPT cartridge‐based method was developed for the solid phase extraction of afidopyropen and M440I007 from tea matrices. Extraction and clean‐up conditions, including the composition, volume and temperature of elutions, were optimized to achieve the best results. Both targets were extracted using water and acetonitrile, with a water:acetonitrile (v/v) ratio of 4:10 for fresh leaves and 8:10 for dried tea, which were then cleaned and analyzed using ultraperformance liquid chromatography‐tandem mass spectrometry. Both analytes demonstrated excellent linearity with a correlation coefficient above 0.998. The optimized analytical method offered limits of quantifications of 0.005, 0.005 and 0.002 mg kg−1 (converted to dried tea) in fresh tea shoots, dried tea and tea infusion for both targets, respectively. Average recoveries of afidopyropen and M440I007 ranged from 79.0% to 101.5%, with relative standard deviations ≤ 14.7%. CONCLUSION: The results showed that the method of determination for these insecticides in tea matrices was practical and efficient. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

The generic inlet is depicted based on a smooth Bézier curve, and the results and insights from high-dimensional dynamic multi-objective optimization of small-sample high Mach number axisymmetric scramjet inlets are discussed in detail. The optimization is performed by integrating a Kriging surrogate model-assisted improved congestion distance multi-objective particle swarm optimization algorithm and computational fluid dynamics simulation. The steady-state flow field is derived by solving the Euler equation using self-developed hypersonic internal and external flow coupling numerical simulation software, which is designed to minimize inlet surface area and drag while improving the total pressure recovery factor. The results revealed that the generic inlet can achieve a total pressure recovery capability exceeding 95%, with minimal surface area and drag. The prediction error, mean absolute percentage error, of the performance dynamic surrogate model based on Kriging is less than 1%, and the performance parameter optimization shows an improvement greater than 8% compared to static multi-objective optimization results. Ultimately, the obtained Pareto solution set is grouped by K-means feature recognition, contributing to a comprehensive understanding of the flow physics knowledge related to optimal geometric local shape control. Finally, an inward-turning inlet is designed by streamline tracking technology based on the optimized axisymmetric scramjet inlet primary flow field. [ABSTRACT FROM AUTHOR]

This paper considers optimizing the performance of high-temperature combustion chamber of an aero-engine based on a concentric and hierarchical model. First, sample data for the design variables are obtained based on Latin hypercube sampling method, and a one-dimensional program is used to obtain the true values of combustion efficiency and total pressure loss corresponding to each group of variables. The obtained data are then pre-processed to establish a dataset. Second, a multi-layer artificial neural network (ANN) architecture is designed and a surrogate model of the combustion-related performance of the combustor is established using a data-driven method. The results of global sensitivity analysis based on variance show that ratio of fuel flow to air flow (fuel–air ratio) and the total inlet pressure are the most important factors influencing the two objective functions. Finally, we optimize the multi-objective combustion-related performance of the surrogate model by applying the particle swarm optimization algorithm to it. The results of experiments show that the ANN-based model could accurately predict the efficiency of combustion and total pressure loss of the chamber, yielding root mean-squared errors of 0.0107 and 0.3032%, respectively. It also had better generalization ability than the cubic polynomial surrogate model. Compared with the cubic polynomial model, it generated an optimal Pareto solution set as prediction that had higher values in both objective functions. The proposed model might require better data that can be obtained using intelligent sampling methods so that deeper neural networks can be designed to reduce error and improve its optimization design. [ABSTRACT FROM AUTHOR]

The Greater and Lesser Khingan Mountains (GKM and LKM), together form one of the main resources of the terrestrial natural ecosystem in northeast Asia and play a crucial role in climate regulation and soil and water conservation due to their distinctive geographical features and abundant vegetation cover. Nonetheless, the morphology and distribution of gullies in the two study areas remain unclear. This study focused on an investigation area of approximately 100 km2 within the forest areas of the GKM and LKM, where field measurements were conducted to record and analyze the morphological characteristics of the gullies. The study also explored the impact of slope and the aspects of gully development and established a gully volume estimation model in the study area. The findings indicate the following. Firstly, that the proportions of gullies with a length of 200–1000 m, a width of 2–6 m and a depth of 1–2 is 59.4%, 51.3% and 45.9%, respectively in the GKM, and 42.5%, 75.7% and 56%, respectively in the LKM. The measured gully density in the GKM was 0.3 gullies per km2, with an average length, width, and depth of 524.4 m, 2.4 m, and 1.0 m, respectively. In contrast, the measured gully density in the LKM was 0.45 gullies per km2, with an average length, width, and depth of 560.1 m, 3.9 m, and 1.8 m, respectively. Secondly, as the slope increased, the density of gullies and the degree of surface fragmentation gradually decreased. In the measured area of the GKM, gullies developed faster on the semi-sunny slope. However, in the measured area of the LKM, gullies were more evenly distributed across different slopes. A significant power function relationship between the volume and area (V-A) of gullies in the measured areas of the GKM (V = 0.37 A1.11, R2 = 0.94) and LKM (V = 0.32 A1.17, R2 = 0.94) was observed. These findings have important implications for soil conversation in forested areas of the black soil region in Northeast China. [ABSTRACT FROM AUTHOR]

High thermal conductivity and electrical insulation materials are necessary for packaging materials of miniaturized electronic equipment at present. Graphene nanosheets (GNs) have great potential in polymer composites due to its excellent thermal conductivity. However, the ultra‐high conductivity of GNs will reduce the insulation of polymer composites, so that the GN composites have good thermal conductivity and electrical insulation has become the focus of research. Herein, the closed cell polystyrene nanocomposites are polymerized by using a Pickering emulsion route. Based on the exfoliating effect of pristine graphite at the oil–water interface and the amphiphilic properties of GNs during the preparation of emulsion, the resulting graphite can achieve good dispersion in the polystyrene resin. Furthermore, the high heat transfer rate of the resulting graphite makes the temperature of polystyrene resin increase by 60.2°C at room temperature within 1 min. Meanwhile, the high volume electrical resistivity (3.63 × 109 Ω·m) and breakdown strength (33.8 kV·mm−1) confirms the PNGL0.610%/PS composite keep excellent electrical insulation at 10.0 wt% filler loading. [ABSTRACT FROM AUTHOR]

In view of the long calculation cycle, high processing test and cost of the traditional aero-engine combustion chamber design process, which restricts the engine optimization design cycle, this paper innovatively proposes a surrogate model for the performance of aero-engine combustion chambers based on the POD-Hierarchical-Kriging method. Through experiments, the predicted results of the POD-Hierarchical-Kriging model are compared and analyzed with the calculated results of the one-dimensional program, and the root mean square error of the predicted values of combustion efficiency and total pressure loss is 0.0064% and 0.1995%, respectively. The accuracy of the POD-Hierarchical-Kriging model is compared with the cubic polynomial model, the basic Kriging model and the Hierarchical-Kriging model. It verifies the feasibility and accuracy of the POD-Hierarchical-Kriging model for the prediction of performance of aero-engine combustion chambers. The global sensitivity analysis method is applied to obtain the influence effect of design variables on the performance. Then, a multi-objective optimization method based on the NSGA-II algorithm is studied, and finally the optimal set of Pareto solutions is obtained and analyzed, which can be used to guide the optimal design of aero-engine combustion chambers and accelerate the progress of aero-engine development. [ABSTRACT FROM AUTHOR]

Plant byproducts and waste present enormous environmental challenges and an opportunity for valorization and industrial application. Due to consumer demands for natural compounds, the evident paucity of novel antimicrobial agents against foodborne pathogens, and the urgent need to improve the arsenal against infectious diseases and antimicrobial resistance (AMR), plant byproduct compounds have attracted significant research interest. Emerging research highlighted their promising antimicrobial activity, yet the inhibitory mechanisms remain largely unexplored. Therefore, this review summarizes the overall research on the antimicrobial activity and inhibitory mechanisms of plant byproduct compounds. A total of 315 natural antimicrobials from plant byproducts, totaling 1338 minimum inhibitory concentrations (MIC) (in μg/mL) against a broad spectrum of bacteria, were identified, and a particular emphasis was given to compounds with high or good antimicrobial activity (typically <100 μg/mL MIC). Moreover, the antimicrobial mechanisms, particularly against bacterial pathogens, were discussed in‐depth, summarizing the latest research on using natural compounds to combat pathogenic microorganisms and AMR. Furthermore, safety concerns, relevant legislation, consumer perspective, and current gaps in the valorization of plant byproducts–derived compounds were comprehensively discussed. This comprehensive review covering up‐to‐date information on antimicrobial activity and mechanisms represents a powerful tool for screening and selecting the most promising plant byproduct compounds and sources for developing novel antimicrobial agents. [ABSTRACT FROM AUTHOR]

Two models based on the deep learning-based convolutional neural network (CNN) and the re-parameterized convolutional neural network (RepCNN) were designed to reconstruct the flame in the combustor. Experiments were performed on a ground-pulse combustion wind tunnel at a fixed inlet Mach number of 2.5 and different pressures to inject hydrogen to obtain the relevant datasets. The results showed that both models could reconstruct the image of the flame in the combustor based on pressures of the upper and lower walls as well as the pressure at which hydrogen was injected. The average structural similarity index between the reconstructed image of the flame and its actual/original image was 0.9553, the average peak signal-to-noise ratio was 34.201, and the average correlation coefficient was 0.9819. The speed of reconstruction of the image using the RepCNN model improved by 40.7% at the cost of a slightly lower accuracy compared with the CNN model, and it took only 2.85 ms to reconstruct the image of a single flame. The lightweight feature of the RepCNN provides an important foundation for monitoring the model to reconstruct the image of the flame in real time. The work here simplifies requirements on the hardware for ground wind tunnel tests and provides a new idea for examining the characteristics of the flame in small combustors. [ABSTRACT FROM AUTHOR]

This paper innovatively designs a surrogate model for the performance of aeroengine high-temperature rising combustor based on cubic polynomials. According to the comparison of the one-dimensional calculation method, the three-dimensional computational fluid dynamics (CFD) numerical simulation, and the experiment, the feasibility of the one-dimensional (1D) calculation method and the reliability of the sample data calculated by this method are proved. The predictions of the metamodel were compared with those calculated by using the 1D program, and the root-mean square errors of the two target parameters were 0.000077% and 0.001725%. Based on this, sets of test data were used to verify the accuracy of using the surrogate model. The test results were compared with those of an artificial neural network (ANN) model and the 1D program. The root-mean square error (RMSE) of the cubic polynomial model and the ANN model in terms of combustion efficiency and total pressure loss were 0.0150% and 0.0107%, and 0.1628% and 0.3032%, respectively. A global sensitivity analysis identified the oil-to-gas ratio and the total inlet pressure as the most important factors affecting the combustion efficiency and total pressure loss. Particle swarm optimization (PSO) was used to obtain the optimal nondominant Pareto solution set. It provides physical insights for the optimization design of aeroengine combustor. [ABSTRACT FROM AUTHOR]

Purpose: On-wall flow on gully headwalls plays a critical role in gully headcut erosion, and the erosion morphology of gully headwalls caused by on-wall flow scouring varies under different land uses/covers due to variations in soil resistance. However, it is unclear how vegetation roots affect the soil resistance of gully headwalls to on-wall flow scouring. Materials and methods: Taking bare land as the control, this study analysed the vertical distribution of vegetation roots and its influence on the soil properties and antiscourability (ANS) of gully headwalls under three land uses (forestland, grassland, and farmland). Results and discussion: The results showed that root mass density (RMD), root length density (RLD), root surface area density (RAD), and root volume density (RVD) decreased overall with increasing soil vertical depth at the gully headwall under the three land use types. The soil ANS ranked highest to lowest in forestland, grassland, farmland, and bare land. Compared with that of bare land, the ANS of each soil layer (0–20, 20–40, 40–60, 60–80, and 80–100 cm) under the three land use types increased by 3.0–9.1, 6.7–8.6, 2.6–10.5, 3.9–5.6, and 0.2–1.9 times, respectively. The ANS of the gully headwalls had a logarithmic relationship with RLD, RAD, and RVD (R2 = 0.45–0.56, P < 0.01). In particular, the most significant correlation was found between the ANS and RVD of fine roots (diameters of 0–0.5 mm). The ANS decreased with the decrease in root density with vertical depth. Conclusions: Our results reveal that vertically distributed roots determine the vertical variations in soil ANS on gully headwalls in the gullied Loess Plateau. [ABSTRACT FROM AUTHOR]

The rapid dissemination of antibiotic resistance accelerates the desire for new antibacterial agents. Here, a class of antimicrobial peptides (AMPs) is designed by modifying the structural parameters of a natural chickpea‐derived AMP–Leg2, termed "functionalized chickpea‐derived Leg2 antimicrobial peptides" (FCLAPs). Among the FCLAPs, KTA and KTR show superior antibacterial efficacy against the foodborne pathogen Escherichia coli (E. coli) O157:H7 (with MICs in the range of 2.5–4.7 µmol L−1) and demonstrate satisfactory feasibility in alleviating E. coli O157:H7‐induced intestinal infection. Additionally, the low cytotoxicity along with insusceptibility to antimicrobial resistance increases the potential of FCLAPs as appealing antimicrobials. Combining the multi‐omics profiling andpeptide‐membrane interaction assays, a unique dual‐targeting mode of action is characterized. To specify the antibacterial mechanism, microscopical observations, membrane‐related physicochemical properties studies, and mass spectrometry assays are further performed. Data indicate that KTA and KTR induce membrane damage by initially targeting the lipopolysaccharide (LPS), thus promoting the peptides to traverse the outer membrane. Subsequently, the peptides intercalate into the peptidoglycan (PGN) layer, blocking its synthesis, and causing a collapse of membrane structure. These findings altogether imply the great potential of KTA and KTR as promising antibacterial candidates in combating the growing threat of E. coli O157:H7. [ABSTRACT FROM AUTHOR]

Luminogens without recognizable luminophores are attracting increasing attention owing to their particular photophysical properties and outstanding biocompatibility features. Despite rapid advances, it is still a formidable challenge to synthesize nonconjugated fluorescent materials with efficient near-infrared emission. Unlike traditional organic luminescent materials with long-range conjugation, the limited degree of electron delocalization and large energy gap are not favorable for long-wavelength emission. Herein, we observed universal intrinsic fluorescence formed from the mixtures of itaconic anhydride (ITA) and electron-rich solvents/monomers. Further, triethylamine (TEA) could promote the ring opening of ITA and subsequently generated the zwitterionic compound (I-TEA). The photophysical properties of I-TEA showed that I-TEA had the typical characteristics of concentration enhanced emission (CEE), excitation-dependent emission (EDF), temperature-sensitive emission (TSE), and efficient red emissions (λex = 585 nm, QY = 12.63%). The cytotoxicity assay indicated that I-TEA had low toxicity for MC3T3-E1 cells and was suitable for cell imaging. In addition, the PL properties and emission source of the mixtures of ITA and electron-rich solvents (DMSO and DMF) were preliminary investigated by NMR, fluorescence, and UV-vis spectroscopy techniques and theoretical calculations. This study reveals the general intrinsic fluorescence from mixtures of ITA and electron-rich solvents or monomers and provides a strategy for preparing nonconventional luminophores with efficient long-wavelength emission. [ABSTRACT FROM AUTHOR]

Soil structure and its change are good indicators for soil quality and environmental stability, and land-use change is known to be an important factor that affects soil structure. We investigated the seasonal and temporal variability in the soil structure and evaluated soil erodibility under different land-use and land-management practices in the Mollisols region of Northeast China. We considered five land-use and land-management modes in the sloping land (5°), i.e., bare land (BL), natural vegetation restoration (NVR), artificial forest (AF), no-tillage (NT), and conventional tillage (CT). The bulk density (BD), aggregate size distribution, and aggregate stability (AS) were determined for each mode. Meanwhile, we estimated the runoff depth (RD) and sediment yield (SY) to assess the soil erodibility. The lowest BD in CT occurred in May, but no seasonal change in BD was observed in other treatments. After 15 years, the NVR and CT had increased BD values, while the BD of NT declined significantly over time. The >2 mm size fractions and the AS of NVR, NT, and CT were the largest in August, while those of BL decreased during the growing season. Furthermore, the >2 mm size fractions and the AS of BL, NT, and CT decreased over the long-term study period, but NVR and AF exhibited no significant inter-annual changes in the AS. The BD and AS in NVR and AF were greater than those in BL. After 15 years, the BD of NT and CT were not significantly different, but NT had a greater AS. The AS was greater in the subsurface soil than in the topsoil of NT and CT. Moreover, the NVR and NT had significantly reduced RD and SY compared with BL and CT. These results provide a scientific basis for choosing sustainable agriculture and land development modes to control soil erosion in Northeast China. [ABSTRACT FROM AUTHOR]

The reconstruction of the self-luminous image of a flame through deep learning can inform research on the characteristics of combustion of a scramjet. In this study, the authors propose a residual network model based on the channel and spatial attention mechanisms to reconstruct the self-luminous image of a flame from schlieren images of the flow field of a scramjet. We compare the reconstruction-related performance of single-path and dual-path models under different conditions. The channel and spatial attention mechanisms enable the model to focus on important feature-related information, and the residual connection prevents gradient disappearance to improve the capability of the model for generalization. The proposed method was tested through a supersonic combustion experiment in a ground wind tunnel under different equivalence ratios, and data on the flow field of the combustion chamber and the evolution of the flame were recorded as a dataset. A number of experiments as well as subjective and objective analyses were subsequently carried out on this dataset. The results show that the effect of reconstruction is consistent with the original image of the flame, and the geometric characteristics of the flame are accurately reconstructed. [ABSTRACT FROM AUTHOR]

The flame development prediction of a scramjet combustor forecasts the combustion state and provides valuable information for active flow control. Experiments were performed on a hydrogen-fueled scramjet at different equivalence ratios in a ground pulse combustion wind tunnel with a Mach-2.5 incoming flow. Five image datasets of the flame evolution process were constructed at different predicted periods. The memory fusion cascade network (MFCN) was developed to predict flame images after a certain span using flame image sequences of the previous periods. A complete evaluation system was constructed to compare and analyze the performances of MFCN, Kongs, and ResNet16 models in multi- and long-span conditions. Experimental results show that MFCN achieves a maximum increase of 46.16% of the peak signal-to-noise ratio index, 69.14% of the structural correlation coefficient index, and 5.72% of the correlation coefficient index in the independent test set. Moreover, the volume of the model only reaches the KB level, which has the characteristics of being lightweight. MFCN outperforms other methods in terms of the prediction accuracy and maintains stable prediction results during multi- and long-span tasks. [ABSTRACT FROM AUTHOR]

Although abundant studies have confirmed the positive effect of vegetation on runoff and soil erosion reduction, few studies have clarified the effects of planting density and its components (aboveground parts and roots) on slope runoff and soil erosion mechanisms. A series of field experimental plots were constructed on two slopes (3°, 6°), including a bare land plot as the control, three grassed plots with three grass (Agropyron cristatum) planting densities of 20 cm × 20 cm, 15 cm × 15 cm, and 10 cm × 10 cm, and a plot without aboveground parts, to examine runoff and soil erosion process under simulated overland flow of 4, 8, 12, and 16 L min−1. The runoff regime was transformed from supercritical‐turbulent flow to subcritical‐laminar flow with increasing planting density. Grass significantly reduced the runoff rate (RR), velocity (V), shear stress (τ), stream power (ω), and soil loss rate (SLR), with average reductions of 38.6%, 56.6%, 76.5%, 87.6%, and 61.9%, respectively. The aboveground parts contributed 74.1% and 75.1% of the total reduction in RR and V, respectively, while the grass roots contributed to 84.9%, 89.7%, 84.9%, and 69.7% of the reductions in Reynolds number, τ, ω, and SLR, respectively. RR, V, τ, ω, and SLR were significantly affected by slope, inflow discharge, root density, and the ratio of grass stem area to plot area. The runoff–sediment relationship changed from positively exponential function to linear and power functions with the planting density increased. Grass significantly weakened the sensitivity of soil erosion by reducing soil erodibility and improving critical hydrodynamic triggering soil erosion. This study improved the understanding of grass controlling soil and water loss, and the plants with well‐developed roots and planting space of 10–15 cm are preferred to provide a scientific reference for revegetation strategy. [ABSTRACT FROM AUTHOR]

Aiming to resolve azimuth-dense targets in interference environments, the radar needs to have the ability of single snap echo angular super-resolution with anti-interference. To solve the problem, the angular super-resolution algorithm based on single snap echo while anti-interference with blocking matrix method is studied for active phased array radar (APAR) in this paper. Since the super-resolution ability of the conventional MUSIC algorithm and iterative adaptive algorithm (IAA) algorithm are limited in single snap echo, the iterative re-weighted least squares (IRLS) algorithm under p-norm constraint is proposed. Further, the near main lobe interference suppression ability is enhanced by the adaptive diagonal loading method. The performance differences of IAA and IRLS algorithms for single-target detection and double-target angular super-resolution are analyzed in detail by numerical simulation on three scenes of no interference, side-lobe interference, and near-main-lobe interference. The simulation results show that the proposed algorithm can effectively solve the problem of target angle estimation and super-resolution based on single sample echo in an interference environment, including near main-lobe interference. [ABSTRACT FROM AUTHOR]

Shallow landslides restrict local sustainable socioeconomic development and threaten human lives and property in loess tableland. Therefore, the appropriate creation of risk maps is critical for mitigating shallow landslide disasters. The first task to be done was to evaluate the vulnerability of shallow landslides based on a machine learning model (random forest (RF), a support vector machine (SVM) and logistic regression (Log)), and a physical model (SINMAP) in the loess tableland area. By comparing the differences, the best method for evaluating the vulnerability of shallow landslide was selected. The nonlinear response relationship between shallow landslides and environmental factors was quantified based on the frequency ratio. Multicollinearity analysis was used to identify 10 factors that were applied on ML to construct the spatial distribution model. The SINMAP model used a DEM and soil physical parameters to determine the stability coefficient of the study area. The results showed that (1) shallow landslides in Dongzhiyuan mainly occurred on shady slopes with an elevation of 1068–1249 m, a slope gradient of 36°–60° and a concave shape. The stream power and stream transport indexes increased with increasing rainfall erosion, making shallow landslides likely. The susceptibility of shallow landslides changed parabolically with the change in the NDVI and mainly occurred in grassland and shrubland. (2) The four methods performed similarly in predicting the sensitivity of shallow landslides. The high-incidence areas were on both sides of eroded gully slopes. The tableland and gully bottom areas were not prone to shallow landslides. (3) The highest area under the curve (AUC) values were generated from the RF training and validation datasets of 0.92 and 0.93, respectively, followed by SVM AUC values of 0.91 and 0.92, respectively; Log AUC values of 0.91 and 0.89, respectively, and the SINMAP model AUC values of 0.69 and 0.74, respectively. In conclusion, the RF model best predicted the susceptibility of shallow landslides in the study area. The results provide a scientific basis for disaster mitigation on the Loess Plateau. [ABSTRACT FROM AUTHOR]

Our study was conducted to investigate the potential correlation between cardiac oxidative stress and inflammatory cytokine response following myocardial infarction. A total of 120 patients harboring acute myocardial infarction who underwent percutaneous coronary intervention (PCI) at our hospital were included. Their general clinical data were analyzed, and comparisons were made regarding the levels of inflammatory factors, oxidative stress markers, heart pump function, and cardiac function. The correlation between cardiac oxidative stress and inflammatory cytokine response was assessed using Pearson's linear correlation. Following treatment, significant reductions were seen in the serum levels of cortisol, thyroid-stimulating hormone (TSH), B-type natriuretic peptide (BNP), C-reactive protein (CRP), signal transducer and activator of transcription 3 (STAT3), interleukin-6 (IL-6), interleukin-8 (IL-8), tumor necrosis factor (TNF), and catalase (CAT) compared to pre-treatment levels. Conversely, the levels of growth hormone (GH), superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), total antioxidant capacity (T-AOC), cardiac output (CO), and cardiac index (CI) were significantly elevated. Serum cortisol (r=0.481, P=.001), BNP (r =0.437, P =.001), CRP (r=0.542, P =.001), STAT3 (r=0.835, P =.001), TSH (P =.001), IL-8 (r =0.867, P =.001), TNF-α (r =0.439, P =.001), and cardiac oxidative stress demonstrated significantly positive correlations (P < .05). Additionally, a signifi- cant negative correlation was found between GH (r =-0.654, P =.001) and immune balance (P < .05). This study evaluated the severity of myocardial infarction using indicators such as CO and CI. This study found a significant correlation between cardiac oxidative stress and inflammatory cytokines after myocardial infarction, suggesting their potential as predictors of myocardial infarction severity. [ABSTRACT FROM AUTHOR]

Vegetation has been proven to be an effective measure to mitigate soil erosion in most regions and climates. However, it is not clear how some herbaceous plants affect the ability of soil to resist slope flow erosion in the Mollisol region of Northeast China. In this study, four herbaceous plant plots of 50 m × 4.5 m, including Zea mays L., Sorghum bicolor × Sudanense, Avena sativa L. and Lolium multiflorum Lam., were established in a sloping land with an abandoned land as the control to detect the effect of herbaceous plants on soil anti-scourability (ANS). A hydraulic flume experiment was carried out to determine the soil ANS, and the root traits and soil properties were also measured at different slope positions. The results showed that the mean soil ANS ranged from 17.55 to 94.77 L g−1 among different herbaceous plants, of which the Lolium multiflorum Lam. showed the strongest controlling effect on soil ANS (259.87%), followed by Sorghum bicolor × Sudanense (66.87%) and Avena sativa L. (18.12%), while the soil ANS of Zea mays L. decreased by 33.37% compared with the control. Soil ANS varied with slope position, and the mean soil ANS at the upslope was 116.50–134.21% higher than that of the middle slope and downslope. Additionally, soil ANS was positively related to root mass density (RMD), root length density (RLD), root surface area density (RSAD), soil total porosity and field capacity but was negatively related to soil bulk density (p < 0.05). Furthermore, the Lolium multiflorum Lam. exhibited better root distribution (i.e., high RSAD, RLD, RMD, and low root diameter) and soil physical structure (i.e., high soil porosity structure, water-holding capacity and low bulk density) than other plant species. Thus, the Lolium multiflorum Lam. is beneficial for enhancing soil erosion resistance to overland flow, especially at the up and middle slopes, and it could be preferred to control sloped soil erosion in Northeast China. [ABSTRACT FROM AUTHOR]

Series of α, β, γ, δ type MnO2 supported on LaMnO3 perovskite was developed by a one-pot synthesis route. Compared with α-MnO2, β-MnO2, γ-MnO2, δ-MnO2 and LaMnO3 oxides, all MnO2/LaMnO3 showed promotional catalytic performance for toluene degradation. Among them, α-MnO2/LaMnO3 holds the best active and mineralization efficiency. By the analysis of N2 adsorption-desorption, XPS and H2-TPR, it can be inferred that the improved activity should be ascribed to the higher proportion of lattice oxygen, better low-temperature reducibility and larger specific surface area. Besides, the byproducts from the low-temperature reaction of toluene oxidation were detected by a TD/GC-MS, confirming the presence of the intermediates. Combined with the in-situ DRIFTS, the catalytic degradation path of toluene oxidation has also been discussed in depth. [ABSTRACT FROM AUTHOR]

Extraction of the wave structure of the flow field in the combustor of the scramjet is important for main flow control and performance evaluation of the scramjet. In this study, a deep learning-based method based on the fully convolutional network with 8-pixel stride is proposed to segment the schlieren image to extract the wave structure. First, use a residual neural network with 34 layers as the backbone network to extract features, which ensures highly efficient learning through residual blocks to extract multi-dimension semantic information. Second, dilated convolution is utilized to expand the receptive fields of deepened layers to obtain high-dimensional features and increase the degree of aggregation of contextual information contained in the high-dimensional features. Finally, the channel and spatial attention module are introduced to the decoding stage to enable the model to focus on key information to improve the segmentation accuracy. A large number of experiments are carried out on a dataset of schlieren images of the flow field in the combustor of scramjet that were compiled by the authors. The proposed method recorded higher values of the pixel accuracy, recall, intersection over union, and F1 score than compared methods, with values of 78.47%, 83.81%, 67.51%, and 80.32%, respectively. This method can effectively complete the wave structure extraction and provide important basic support for related research work of scramjet. [ABSTRACT FROM AUTHOR]

A flow field reconstruction algorithm based on deep learning is an effective method to detect the evolution of wave structure in a scramjet combustor, which is of great significance for accurately predicting the operating performance of the scramjet. This paper proposes a dual-branch fusion model based on a multi-head attention mechanism to reconstruct the flow field schlieren image in a supersonic combustor. The proposed model adopts a dual-path fusion mode. One branch is composed of transposed convolution and conventional convolution, forming a symmetrical structure for dimension enhancement and feature extraction. The other is formed by a multi-head attention mechanism and a full connection layer in series. It utilizes the same attention mechanism to obtain different sensitive features and enhance the global model perception. The proposed model was trained and tested on a dataset constructed from hydrogen-fueled scramjet experiments in a direct-connected supersonic pulse combustion wind tunnel at Mach 2.5. Numerous experiments prove that the model can effectively reconstruct the basic wave system structure of a complex flow field, and it is in good agreement with the original flow field. The average peak signal-to-noise ratio, structural similarity, and average linear correlation coefficient of the proposed model are reached 20.92, 0.602, and 0.943, respectively, which verify the effectiveness of the proposed model in reconstructing the supersonic flow field. [ABSTRACT FROM AUTHOR]