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This study proposes a computational fluid dynamics and computational structure dynamics (CFD/CSD) coupled method for calculating the buffet response of a variant tail wing. The large-scale separated flow in the buffet is simulated by the detached vortex approach, vibration deformation of the tail wing is solved by the dynamic mesh generation technique, and structural modeling is based on the mode method. The aerodynamic elastic coupling is calculated through the cyclic iteration of aerodynamics and the structural solution in the time domain. We verify the correctness of the proposed method through a typical delta wing calculation case, further simulate the buffet response of a variant tail wing in maneuver state, and finally realize buffet mitigation using an active excitation method. Overall, this study can provide an important reference for the design of variant-tailed aircraft.

This article studies a data-driven prescribed performance platooning control method for nonlinear connected automated vehicle systems (CAVs) under aperiodic denial-of-service (DoS) attacks. Firstly, the dynamic linearization technique is employed to transform the nonlinear CAV system into an equivalent linearized data model. Secondly, to improve the system’s transient performance, a prescribed performance transformation (PPT) scheme is proposed to transform the constrained output into the unconstrained one. In addition, an attack compensation mechanism is designed to reduce the adverse impact. Combining the PPT scheme and the attack compensation mechanism, the data-driven adaptive platooning control scheme is proposed to achieve the vehicular tracking control task. Lastly, the merits of the developed control method are illustrated by an actual simulation.

The development of a water-conducting fracture zone is a critical factor in understanding and predicting water inrush from coal mine roofs, as these zones facilitate the inflow of water from overlying aquifers. However, traditional methods such as empirical formulas and drilling, as well as single physical investigations (such as transient electromagnetic), can only estimate the height of these fault zones and often cannot accurately obtain precursor information of roof water inrush during the mining process. In this study, a comprehensive evaluation of the 13,101 working face of the Shengfu Coal Mine in Shaanxi, China, is being carried out using microseismic monitoring and parallel electrical methods. On the basis of the microseismic results, the height of the water-conducting fracture zone is obtained to be 57 m. The average variation of the natural potential of the coal seam roof was obtained by the parallel electric method, which was all less than 250 mV, with no obvious anomalous seepage electric field and no large centralized water-conducting channels found. However, the decrease in resistivity and natural potential indicates the precursor information of water seepage when the fracture zone develops to 40–50 m. This approach aims to monitor the development of water-conducting fracture zones by combining microseismic monitoring with parallel electrical methods while simultaneously capturing changes in roof seepage. This will enhance safety and facilitate more informed decision-making in complex hydrogeological environments.

Construction of the genome of Hericium coralloides, a species of edible mushroom, and identification of the genes involved in terpenoid biosynthesis can determine the biology and genetics of terpenoids. The present study describes the assembly of a high-quality chromosome-scale genome of H. coralloides using Pacbio HiFi sequencing and Hi-C technology. This genome consisted of 13 chromosomes, a total size of 43.6 Mb, contigs of N50 3.6 Mb, GC content at 54%, and BUSCOs integrity of 96.9%. Genes associated with terpenoid biosynthesis were predicted by KEGG enrichment analysis and homologous alignment. The Her011461 and Her008335 genes, encoding proteins in the terpenoid backbone synthesis pathway, were found to encode geranylgeranyl pyrophosphate and farnesyl diphosphate synthases, key enzymes in the biosynthesis of geranylgeranyl diphosphate, a precursor of several diterpenoids. Her011463 was found to be involved in regulating diterpene cyclase. The Her005433, Her006724, Her010605, and Her010608 genes were found to encode sesquiterpene synthesis. Most of these genes were more highly expressed in dikaryotic mycelia than in the primordium and fruiting bodies, indicating that terpenoids may be more abundant in dikaryotic mycelia. To our knowledge, this study is the first to assemble the H. coralloides genome at the chromosome scale and to identify the genes involved in terpenoid biosynthesis.

The spatial–temporal sampling errors arising from the differences in geographical locations and measurement times between co-located Global Navigation Satellite System (GNSS) radio occultation (RO) and radiosonde (RS) data represent systematic errors in the three-cornered hat (3CH) method. In this study, we propose a novel spatial–temporal sampling correction method to mitigate the sampling errors associated with both RO–RS and RS–model pairs. We analyze the 3CH processing chain with this new correction method in comparison to traditional approaches, utilizing Fengyun-3E (FY-3E) GNSS Occultation Sounder II (GNOS II) RO data, atmospheric models, and RS datasets from the Hailar and Xisha stations. Overall, the results demonstrate that the improved 3CH method performs better in terms of spatial–temporal sampling errors and the variances of atmospheric parameters, including refractivity, temperature, and specific humidity. Subsequently, we assess the error variances of the FY-3E GNOS II RO, RS and model atmospheric parameters in China, in particular the northern China and southern China regions, based on large ensemble datasets using the improved 3CH data processing chain. The results indicate that the FY-3E GNOS II BeiDou navigation satellite system (BDS) RO and Global Positioning System (GPS) RO show good consistency, with the average error variances of refractivity, temperature, and specific humidity being less than 1.12%2, 0.13%2, and 700%2, respectively. A comparison of the datasets from northern and southern China reveals that the error variances for refractivity are smaller in northern China, while temperature and specific humidity exhibit smaller error variances in southern China, which is attributable to the differing climatic conditions.

During mining, the brittle fracture structure of coal makes it highly susceptible to disturbance, leading to changes in the permeability of the coal seam from non-conductive to water-conductive, which poses a significant threat to the stability and safety of coal pillars in goafs. Therefore, understanding the damage mechanisms of coal during water–rock interactions is crucial for ensuring mine safety. In this paper, based on laboratory seepage tests, the impact of hydrodynamic forces on the microstructure of fissured coal and its subsequent effect on permeability is examined. The study found that increasing confining pressure causes the “closure” of coal fissures, leading to a reduction in permeability. Additionally, during the initial stage of seepage, fine particles within the coal samples are mobilized due to seepage damage, leading to channel blockages and further reductions in permeability. However, as seepage continues, the hydraulic channels eventually open fully, resulting in a sharp increase in permeability. Furthermore, using a two-dimensional fracture seepage model, the study investigated how the scale of fractures in the water-conducting channels influences seepage behavior. A critical fracture width method was proposed to predict permeability surges, offering a new approach for analyzing the stability of coal pillars in mining areas.

We have demonstrated a multiple-access transfer of time and frequency signal over a fiber and free-space link based on an optical frequency comb (OFC). With this transfer technique, two time–frequency signals were disseminated separately from a master site to two slave sites over a 3.9 km fiber and 100 m free-space link for 10,000 s. The timing fluctuations and instabilities of the time and frequency transfer were measured, estimated, and discussed. The experimental results show that the total root-mean-square (RMS) timing fluctuation of the transfer from site A to B is about 119 ps, with a fractional frequency instability on the order of 3.3 × 10−11 at 1 s and 2.8 × 10−14 at 2000 s. The RMS timing fluctuation of the transfer from site A to C is about 59.5 ps, with a fractional frequency instability on the order of 3.0 × 10−11 at 1 s and 2.6 × 10−14 at 2000 s. These results indicate that the multiple-access transfer technique proposed in this paper can provide important support for the application of a large-scale time–frequency synchronization network.

This study aimed to create Pickering emulsions with varying oil fractions and assess the impact of ultrasonic treatment on the properties of Whey Protein Isolates (WPIs). At 640 W for 30 min, ultrasound reduced WPI aggregate size, raised zeta potential, and improved foaming, emulsifying, and water-holding capacities. FTIR analysis showed structural changes, while fluorescence and hydrophobicity increased, indicating tertiary structure alterations. This suggests that sonication efficiently modifies WPI functionality. Under ideal conditions, φ = 80 emulsions were most stable, with no foaming or phase separation. Laser scanning revealed well-organized emulsions at φ = 80. This study provides a reference for modifying and utilizing WPI.

Sarcomyxa edulis is a characteristic low-temperature, edible mushroom in Northeast China. It has a delicious taste and rich nutritional and medicinal value. The artificial cultivation of S. edulis has been increasing in recent years. However, the number of S. edulis varieties is scarce, and strain degradation is a serious issue, affecting the yield and quality of S. edulis. Therefore, we collected 21 wild strains of S. edulis (Y1–21) in this study, aiming to develop strains of S. edulis. Five strains without antagonistic reaction were eliminated via the antagonism test, and the remaining sixteen strains were identified as S. edulis using internal transcribed spacer (ITS) marker identification. The mycelial growth rate, mildew resistance, fruiting body yield, agronomic traits, and nutrient content of the 16 strains were determined. The results demonstrate that Y12, Y13, Y14, and Y15 exhibited a rapid mycelial growth rate (6.43–6.8 mm·day−1). Their colony density was moderate; their edges were neat; and their colonies were leathery and had obvious pigmentation. Moreover, they exhibited strong mildew resistance and a low Trichoderma contamination rate (S. edulis strains exhibited high yield, excellent traits, and good quality for commercial production and food production with high nutrient contents. This study provides a foundation for the further cross-breeding and matrix improvement of S. edulis.

As a crucial spatial decision support tool, Geographic Information Systems (GISystems) are widely used in fields such as digital watersheds, resource management, environmental assessment, and regional governance, with their core strength lying in the integration of geographic simulation models from various disciplines, enabling the analysis of complex geographical phenomena and the resolution of comprehensive spatial problems. With the rapid advancement of artificial intelligence, deep neural network-based geographic simulation models (DNN-GSMs) have increasingly replaced traditional models, offering significant advantages in simulation accuracy and inference speed, and have become indispensable components in GISystems. However, existing integration methods do not adequately account for the specific characteristics of DNN-GSMs, such as their formats and input/output data types. To address this gap, we propose a novel tight integration framework for DNN-GSMs, comprising four key interfaces: the data representation interface, the model representation interface, the data conversion interface, and the model application interface. These interfaces are designed to describe spatial data, the simulation model, the adaptation between spatial data and the model, and the model’s application process within the GISystem, respectively. To validate the proposed method, we construct a spatial morphology simulation model based on CNN-LSTM, integrate it into a GISystem using the proposed interfaces, and conduct a series of predictive experiments on island morphology evolution. The results demonstrate the effectiveness of the proposed integration framework for DNN-GSMs.

Due to the high detection efficiency of the airborne time-domain electromagnetic method, it can quickly collect electromagnetic response data for large area-wide regions, but it also brings great challenges to the inversion interpretation of the data because there are numerous survey data that need to be inverted. Conventional optimal inversion and fast imaging methods still take a long time to obtain conductivity and depth information, which affect the efficiency of real-time data interpretation. In this paper, we present a deep learning inversion method that can be used to solve the fast inversion problem of airborne time-domain electromagnetic data; the method uses a one-dimensional convolutional neural network. The network structure consists of two parts containing different numbers of convolutional and pooling layers. The training sample dataset was generated via two ways of constructing geoelectric models through forward modelling. To check the effectiveness of our deep learning inversion strategy, we tested it on synthetic data and two types of survey data. The experimental results show that this inversion method is effective and that it can be applied to airborne time-domain electromagnetic data collected using different observation systems. The proposed inversion method can obtain better inversion results for both simple and complex stratigraphic structures and requires significantly less computation time compared to conventional optimal inversion methods.

This article explores the emerging field of nanomedicine as a drug delivery system, aimed at enhancing the therapeutic efficacy of active pharmaceutical ingredients in medicinal plants. The traditional methods of applying medicinal plants present several limitations, such as low bioavailability, poor solubility, challenges in accurately controlling drug dosage, and inadequate targeting. Nanoformulations represent an innovative approach in drug preparation that employs nanotechnology to produce nanoscale particles or carriers, which are designed to overcome these limitations. Nanoformulations offer distinct advantages, significantly enhancing the solubility and bioavailability of drugs, particularly for the poorly soluble components of medicinal plants. These formulations effectively enhance solubility, thereby facilitating better absorption and utilization by the human body, which in turn improves drug efficacy. Furthermore, nanomedicine enables targeted drug delivery, ensuring precise administration to the lesion site and minimizing side effects on healthy tissues. Additionally, nanoformulations can regulate drug release rates, extend the duration of therapeutic action, and enhance the stability of treatment effects. However, nanoformulations present certain limitations and potential risks; their stability and safety require further investigation, particularly regarding the potential toxicity with long-term use. Nevertheless, nanomaterials demonstrate substantial potential in augmenting the efficacy of active pharmaceutical ingredients in medicinal plants, offering novel approaches and methodologies for their development and application.

The carrying-soil effect is an important factor affecting the success or failure of rectangular pipe jacking construction. Because it is difficult to explore the dynamic evolution process of the carrying-soil effect in the field test, this paper relies on the pipe jacking project of a comprehensive pipe gallery in Suzhou City, and uses ABAQUS 2021 finite element software to simulate the jacking process of rectangular pipe jacking. Based on the theory of plastic mechanics of rock and soil and the related research and application status, the equivalent plastic strain index is introduced as the basis for judging the failure boundary of soil in the numerical simulation results, and the boundary between the carrying-soil area and the surrounding soil is determined, that is, the shear fracture surface. According to the principle of plastic potential energy, the development direction of the fracture surface is consistent with the direction of plastic flow, so as to predict the development direction of the boundary of the carrying-soil area. According to the change in the equivalent plastic strain cloud map with the jacking mileage, the evolution process of the carrying-soil effect is clarified, and the dynamic evolution law of the carrying-soil area is revealed, which is of great significance for predicting the disturbance of the surrounding soil during the construction of rectangular pipe jacking and accurately evaluating the environmental effect of the construction.

In this study, as a vital part of the production of Mn-increased 5083 Al alloy, i.e., homogenization annealing before hot rolling, the target states of key Al6Mn precipitation, including the dispersed, initial coarsening and intensive coarsening states, were designed, and the corresponding precipitates formed via the control of the temperature and holding time in the annealing process. By means of metallographic corrosion and nitric acid mass loss tests (NAMLT) for assessing the intergranular corrosion (IGC) resistance, temperatures ranging from 175 °C to 225 °C were determined to induce a transition from sensitization to stabilization for this innovative 5083. At a temperature of 175 °C for a duration of up to 24 h (2 h, 4 h, 8 h, 16 h, 24 h), the results show that when the soak time is 24 h, the sample with initially coarsened Al6Mn phases has a lower degree of sensitization (DOS) compared to the samples with Al6Mn phases in both the dispersed and intensive coarsening states, and its NAMLT is reduced by 11% and 15%, respectively. Subsequently, transmission electron microscopy (TEM) analysis has investigated that for the sample with the best IGC resistance, i.e., that with initially coarsened Al6Mn phases, plate-like Al6Mn particles (200~500 nm) can act as heterogenous nucleation sites for β phases, driving their preferential precipitation on Al6Mn particles and resisting their precipitation along grain boundaries, ultimately improving the IGC resistance of 5083 Al alloy after homogenization annealing.

In this work, epoxy was added into commercial Sn-3.0Ag-0.5Cu (SAC305) solder paste to enhance the thermal cycling reliability of the joint. The microstructure and fracture surface were observed using a scanning electron microscope/energy dispersive spectrometer (SEM/EDS), and a shear test was performed on the thermally cycled joint samples. The results indicated that during the thermal cycling test, the epoxy protective layer on the surface of the epoxy-enhanced SAC305 solder joint could significantly alleviate the thermal stress caused by coefficients of thermal expansion (CTE) mismatch, resulting in fewer structural defects. The interfacial compound of the original SAC305 solder joints gradually coarsened due to the accelerated atomic diffusion, but epoxy-enhanced SAC305 solder joints demonstrated a thinner interfacial layer and a smaller IMC grain size. Due to the reduced stress concentration and the additional mechanical support provided by the cured epoxy layer, epoxy-enhanced SAC305 solder joints displayed superior shear performance compared to the original joint during the thermal cycling test. After 1000 thermal cycles, Cu-Sn IMC regions were observed on the fracture surfaces of the original SAC305 solder joint, exhibiting brittle fracture characteristics. However, the fracture of the SAC305 solder joint with 8 wt.% epoxy remained within the solder bulk and exhibited a ductile fracture mode. This work indicates that epoxy-enhanced SAC305 solder pastes display high thermal cycling reliability and could meet the design needs of advanced packaging technology for high-performance electronic packaging materials.

The requirement for high-speed and high-precision contouring with free-form surfaces in the CNC machining process is increasing. The contour error is an essential criterion for the quality of CNC machining. For the problem of contour error control, the present research is mainly based on position tracking, and the control method directly aiming at contour control has not been well studied. This paper proposed a new type of coordinate transformation-based direct contour control method (CT-DCC) for free-form surface machining trajectories. By real-time monitoring of the contour error and the foot point of the free-form parametric trajectory, the contouring task is decomposed into the contour error control task in the normal direction and the velocity control problem in the feed direction. Using coordinate transformation, the output commands of the two tasks are converted to the axial motion command. CT-DCC completely eliminates position tracking in the traditional control to achieve direct control of the contour error, which provides a new solution for the contour control problem of free-form surface machining. Both the axial velocity control and the axial torque control-based CT-DCC scheme were studied, and the two-axis and three-axis direct contour controllers were tested. The simulation and experiment results show the feasibility of the proposed method.

In thermal management applications using two-phase flow boiling, rectangular microchannels hold significant promise due to their ease of manufacturing and effective heat transfer characteristics. In this work, we combined experimental and theoretical analyses to propose a theoretical model based on thin liquid film evaporation for predicting heat transfer performance in rectangular cross-sectional microchannels. The heat transfer model is segmented into five zones based on two-phase flow patterns and transient liquid film thickness. These zones represent different flow boiling heat transfer mechanisms over time in microchannels: the liquid slug zone, elongated bubble zone, long-side wall dryout zone, corner liquid evaporation zone, and full dryout zone. The new model comprehensively explains experimental phenomena observed, including long-side wall dryout and thinning of the liquid film on the short-side wall. To validate our model, numerical solutions were computed to study the spatial and temporal variations in heat transfer coefficients. The results exhibited a consistent trend with experimental data regarding average heat transfer coefficients. We also analyzed factors influencing flow boiling characteristics, such as microchannel aspect ratio, hydraulic diameter, measurement location, fluid mass flux, and wall heat flux.

In order to solve the problems of high impurity rate and large loss rate in the whole fresh peanut harvesting and production process in hilly areas of China, the method of computational fluid dynamics (CFD) and discrete element (DEM) coupling is used to examine the gas–solid two-phase simulation of the cleaning device of the crawler fresh-peanut-picking combine harvester. In addition, the Box–Behnken test method is used to analyze the influence of different parameters on the impurity content and loss rate of materials in the cleaning process by taking the fan speed, feed amount, and air inlet angle as the test factors, and the optimal combination of working parameters is sought. The simulation results show that when the fan speed is 2905.07 r/min, the feed rate is 0.80 kg/s, the air inlet angle is 43.14°, the impurity content is 7.32%, and the loss rate is 4.78%. Compared with the simulation test results, the impurity content is increased by 0.68%, and the loss rate is increased by 1.24%, which verifies the reliability of the simulation model, and the research results provide some technical support for the improvement of the cleaning device in the later stages.

Joint deflection during curved pipe jacking in power tunnels poses a significant risk of structural failure due to the resulting eccentric and diagonal loading on the pipes. This study investigated the axial stress and strain characteristics of reinforced-concrete pipes under varying joint deflection angles and jacking forces, using a combined approach of experimental model testing and finite element method (FEM) numerical simulations. The experimental setup replicated curved pipe jacking conditions, allowing for the measurement of strains and deformation under controlled loading. Numerical simulations, validated against experimental data, provided detailed insights into the stress distribution patterns. The results revealed distinct stress states in different pipe sections. The pipe closest to the jacking force (3# pipe) experienced eccentric loading, leading to localized stress concentrations and inelastic strain on the inner wall at the point of eccentricity, indicating vulnerability to compressive failure. The middle pipe section (2# pipe) underwent complex diagonal loading, resulting in the development of inelastic strain on both the inner and outer walls at specific orientations, highlighting a risk of both compressive and shear failure modes. The study also demonstrated that the magnitude of the axial jacking force and the degree of joint deflection significantly influence the stress distribution and the extent of inelastic strain. These findings provide important information for optimizing the design and construction of curved pipe jacking projects in power tunnels. The identified failure mechanisms and the influence of key parameters on pipe behavior can inform strategies to mitigate the risk of structural failure, improve the resilience of pipe systems, and enhance the overall safety and reliability of underground power tunnel infrastructure.

The effective allocation of labor and cultivated land resources to ensure food security is a global concern. Understanding the relationship between rural labor off-farm employment and the eco-efficiency of cultivated land use (ECLU) is critical, yet current research in this area remains insufficient. This study explores the dynamics between off-farm employment and ECLU using the North China Plain as a case study, analyzing panel data from 2001 to 2020 through spatial econometric models. The findings reveal significant temporal expansion and spatial differentiation in off-farm employment, with growth rates gradually slowing and spatial disparities diminishing. The average ECLU initially declined from 2001 to 2003, followed by fluctuating increases, with a notable acceleration in growth after 2017. A “U-shaped” relationship between off-farm employment and ECLU was identified, with a turning point at an off-farm employment ratio of 40.73%, occurring around 2003–2004 based on regional averages. Before this threshold, off-farm employment negatively impacted ECLU, while beyond this point, the impact became positive. The study also observed significant spatial spillover effects of off-farm employment on ECLU in the North China Plain. These findings underscore the complex interplay between rural labor migration and agricultural productivity. To maximize the benefits of off-farm employment, policies should encourage the reinvestment of income into sustainable agricultural practices. Furthermore, the significant spatial spillover effects call for enhanced regional coordination and tailored policy interventions to optimize labor allocation and improve ECLU.

Some problems exist in classical optimization algorithms to solve multi-modal optimization problems and other complex systems. A Dynamic Niches-based Improved Hybrid Breeding Optimization (DNIHBO) algorithm is proposed to address the multi-modal optimization problem in the paper. By dynamically adjusting the niche scale, it effectively addresses the issue of niche parameter sensitivity. The structure of the algorithm includes three distinct groups: maintainer, restorer, and sterile lines for updating operations. However, the maintainer individuals often stagnate, leading to the risk of the local optima. To overcome this, neighborhood search and elite mutation strategies are incorporated, enhancing the balance between exploration and exploitation. To further improve individual utilization within niches, a niche restart strategy is introduced, ensuring sustained population diversity. The efficacy of DNIHBO is validated through simulations on 16 multi-modal test functions, followed by comparative analyses with various multi-modal optimization algorithms. The results convincingly demonstrate that DNIHBO not only effectively locates multiple global optima but also consistently outperforms other algorithms on test functions. These findings underscore the superiority of DNIHBO as a high-performing solution for multi-modal optimization.

When a synthetic aperture sonar (SAS) system operates under low-frequency broadband conditions, the azimuth range coupling of the point target reference spectrum (PTRS) is severe, and the high-resolution imaging range is limited. To solve the above issue, we first convert multi-receivers’ signal into the equivalent monostatic signal and then divide the equivalent monostatic signal into range subblocks and the range frequency subbands within each range subblock in order. The azimuth range coupling terms are converted into linear terms based on piece-wise linear approximation (PLA), and the phase error of the PTRS within each subband is less than π/4. Then, we use the chirp-z transform (CZT) to correct range cell migration (RCM) to obtain low-resolution results for different subbands. After RCM correction, the subbands’ signals are coherently summed in the range frequency domain to obtain a high-resolution image. Finally, different subblocks are concatenated in the range time domain to obtain the final result of the whole swath. The processing of different subblocks and different subbands can be implemented in parallel. Computer simulation experiments and field data have verified the superiority of the proposed method over existing methods.

High-voltage cables are the main arteries of urban power supply. Cable accessories are connecting components between different sections of cables or between cables and other electrical equipment. The stress in the cold shrink tube of cable accessories is a key parameter to ensure the stable operation of the power system. This paper attempts to explore a method for measuring the stress in the cold shrink tube of high-voltage cable accessories based on ultrasonic longitudinal wave attenuation. Firstly, a pulse ultrasonic longitudinal wave testing system based on FPGA is designed, where the ultrasonic sensor operates in a single-transmit, single-receive mode with a frequency of 3 MHz, a repetition frequency of 50 Hz, and a data acquisition and transmission frequency of 40 MHz. Then, through experiments and theoretical calculations, the transmission and attenuation characteristics of ultrasonic longitudinal waves in multi-layer elastic media are studied, revealing an exponential relationship between ultrasonic wave attenuation and the thickness of the cold shrink tube. Finally, by establishing a theoretical model of the radial stress of the cold shrink tube, using the thickness of the cold shrink tube as an intermediate variable, an effective measurement of the stress of the cold shrink tube was achieved.

Accurate classification of ground-based auroral images is essential for studying variations in auroral morphology and uncovering magnetospheric mechanisms. However, distinguishing subtle morphological differences among different categories of auroral images presents a significant challenge. To excavate more discriminative information from ground-based auroral images, a novel method named learning representative channel attention information from second-order statistics (LRCAISS) is proposed. The LRCAISS is highlighted with two innovative techniques: a second-order convolutional network and a novel second-order channel attention block. The LRCAISS extends from Resnet50 architecture by incorporating a second-order convolutional network to capture more detailed statistical representation. Meanwhile, the novel second-order channel attention block effectively recalibrates these features. LACAISS is evaluated on two public ground-based auroral image datasets, and the experimental results demonstrate that LRCAISS achieves competitive performance compared to existing methods.

Ensuring safety in autonomous driving is crucial for effective motion planning and navigation. However, most end-to-end planning methodologies lack sufficient safety measures. This study tackles this issue by formulating the control optimization problem in autonomous driving as Constrained Markov Decision Processes (CMDPs). We introduce an innovative, model-based approach for policy optimization, employing a conditional Value-at-Risk (VaR)-based soft actor-critic (SAC) to handle constraints in complex, high-dimensional state spaces. Our method features a worst-case actor to ensure strict compliance with safety requirements, even in unpredictable scenarios. The policy optimization leverages the augmented Lagrangian method and leverages latent diffusion models to forecast and simulate future trajectories. This dual strategy ensures safe navigation through environments and enhances policy performance by incorporating distribution modeling to address environmental uncertainties. Empirical evaluations conducted in both simulated and real environments demonstrate that our approach surpasses existing methods in terms of safety, efficiency, and decision-making capabilities.

Timely discovery and disposal of road risk sources constitute the cornerstone of road operation safety. Presently, the detection of road risk sources frequently relies on manual inspections via inspection vehicles, a process that is both inefficient and time-consuming. To tackle this challenge, this paper introduces a novel automated approach for detecting road risk sources, termed the multi-scale lightweight network (MSLN). This method primarily focuses on identifying road surfaces, potholes, and scattered objects. To mitigate the influence of real-world factors such as noise and uneven brightness on test results, pavement images were carefully collected. Initially, the collected images underwent grayscale processing. Subsequently, the median filtering algorithm was employed to filter out noise interference. Furthermore, adaptive histogram equalization techniques were utilized to enhance the visibility of cracks and the road background. Following these preprocessing steps, the MSLN model was deployed for the detection of road risk sources. Addressing the challenges associated with two-stage network models, such as prolonged training and testing times, as well as deployment difficulties, this study adopted the lightweight feature extraction network MobileNetV2. Additionally, transfer learning was incorporated to elevate the model’s training efficiency. Moreover, this paper established a mapping relationship model that transitions from the world coordinate system to the pixel coordinate system. This model enables the calculation of risk source dimensions based on detection outcomes. Experimental results reveal that the MSLN model exhibits a notably faster convergence rate. This enhanced convergence not only boosts training speed but also elevates the precision of risk source detection. Furthermore, the proposed mapping relationship coordinate transformation model proves highly effective in determining the scale of risk sources.

In view of the problem that peanut harvesting in hilly areas mainly involves fresh food, and that the peanut-picking purity rate is low and there is high breakage in the peanut-harvesting process, key components such as the picking system of a fresh-peanut-picking crawler combine harvester, the picking tooth, and the concave screen were designed, and ANSYS Workbench 2020 software was used to check the reliability of the picking roller under working conditions in hilly areas. In the process of equipment operation, the picking purity rate and breakage rate were the main evaluation indexes, and the Box–Behnken test method was used to study the speed of the peanut-picking roller, the feeding amount, and the picking gap as the test factors. The results showed that the picking purity rate is 98.95%, with an error margin of 0.98%, compared to the predicted value under the conditions of 342 r/min speed, 0.75 kg/s feeding amount, and 32 mm picking gap. The breakage rate is 4.23% and the error is 0.4% compared with the predicted value, indicating that the optimized model is reliable and predictive. This study provides a theoretical basis for the optimal design of the peanut-picking system of peanut-picking combine harvesters in hilly areas.

Hard rock tunnel boring machines (TBMs) are increasingly widely used in tunnel construction today; however, TBMs are deeply buried underground and have a low perception of the underground surrounding rock conditions and excavation parameters. In order to ensure the safety of TBM digging, this paper describes the research carried out relating to the accurate prediction of TBM digging parameters and the precise prediction of tunnel surrounding rock grades. Based on the on-site excavation parameters and geological data of a certain water diversion project in Xinjiang, the thrust, torque, rotational speed, net excavation speed, construction speed, and excavation specific energy of the stable section of TBM excavation are selected as the input parameters for the model. A convolutional neural network optimized–long short-term time series prediction model (CNN-LSTM model) is established to predict the excavation parameters under various levels of surrounding rock conditions. The research results indicate that the CNN-LSTM model has a high prediction accuracy, with most data having a relative prediction error rate (Er) within 10%, root mean square error (RMSE) within 5%, mean absolute percentage error (MAPE) within 10%, and goodness of fit (R2) above 0.9. The model can assist in parameter setting, engineering planning and disposal of high-risk holes in the TBM digging process, and improve the safety level of TBM digging.

Due to their compact structure and low laying cost, three-core power cables are widely used for power distribution networks. The three-phases of such cables are distributed symmetrically with a 120° shift to each other. Phase current is an important parameter to reflect the operation state of the power system and three-core cable. Three-core symmetrical power cables use a common shield, leading to magnetic field cancelation outside the cable during steady operation. Thus, traditional magnetic-based current transformers cannot measure the phase current on three-core cable non-invasively. In order to measure the phase current more conveniently, a phase current measurement method for three-core cables based on a magnetic sensor is proposed in this paper. Nonlinear equations of a phase current and the magnetic field of a measuring point are constructed. The calculated magnetic field distribution of the three-core cable is verified using a finite element simulation. The effectiveness of the measurement method is further validated through experiments. This proposed method is able to conveniently detect the phase current of three-core power cables, which can help cable maintenance.

A factorial design consisting of four N treatments (no N fertilization, 0.70, 0.14, and 0.28 mol N·plant−1) combined with two water conditions, drought (D = 25 ± 5% soil moisture content) and well-watered (W = 65 ± 5% soil moisture content), was used. Overall, the gas exchange parameters, chlorophyll, and growth of T. sinensis seedlings were significantly inhibited under drought conditions, while all of them showed improvement with N fertilizer, particularly at 0.14~0.28 mol N·plant−1. Under drought conditions, the root length and root surface area of T. sinensis increased; N application positively influenced the above root morphological changes. The activities of antioxidant enzymes such as superoxide dismutase (SOD; EC1.15.1.1) and peroxidase (POD; EC1.11.1.7) and the contents of osmotic adjustment substances such as soluble sugar and proline increased upon drought stress, but decreased under N application conditions. Overall, T. sinensis responds to drought stress through the synergistic action of drought resistance and drought tolerance mechanisms. N application enhances photosynthesis and improves root morphology, compensating for the need for osmotic regulation and reactive oxygen species scavenging.

Taitema Lake, situated at the terminus of the Tarim River Basin in Northwest China, represents a crucial ecological resource impacted by climate variability and anthropogenic interventions. In this study, we investigate the dynamic changes in Taitema Lake’s area and water quality resulting from the implementation of an ecological water transfer project in 2000. Leveraging Landsat remote sensing data and comprehensive water quality monitoring, we analyzed the relationship between lake area variations and shifts in water quality parameters. Notably, our findings reveal a significant increase in Taitema Lake’s area from 9.4 km2 in 2000 to 320 km2 in 2013. Concurrently, water quality indicators exhibited marked fluctuations, with total salt content ranging from 45,323.6 mg/L in 2000 to 970.4 mg/L in 2010 before increasing to 14,586.3 mg/L by 2014. Furthermore, a linear regression analysis highlights the moderate positive correlation between lake area and mineralization (R2 = 0.506) and sodium levels (R2 = 0.4907). Additionally, chloride (R2 = 0.5681) and sulfate (R2 = 0.6213) concentrations demonstrated a strong negative correlation with the lake area, indicative of a dilution effect. Furthermore, a comparison of water quality indicators between the years of minimum (2008) and maximum (2013) lake area underscores improvements in pH, chemical oxygen demand, and anionic surfactant concentrations as the lake area increased. Our study provides valuable insights into the effectiveness of ecological water management strategies in restoring and maintaining the ecological health of Taitema Lake, thereby informing evidence-based decision-making for the sustainable management of freshwater resources in arid environments.

Rainfall erosivity (RE), a pivotal external force driving soil erosion, is impacted by El Niño/Southern Oscillation (ENSO). Studying the spatiotemporal variations in RE and their response to ENSO is essential for regional ecological security. In this study, a daily RE model was identified as a calculation model through an evaluation of model suitability. Daily precipitation data from 1971 to 2020 from 38 meteorological stations on Hainan Island, China, were utilized to calculate the RE. The multivariate ENSO index (MEI), Southern Oscillation Index (SOI), and Oceanic Niño Index (ONI) were used as the ENSO characterization indices, and the effects of ENSO on RE were investigated via cross-wavelet analysis and binary and multivariate wavelet coherence analysis. During the whole study period, the average RE of Hainan Island was 15,671.28 MJ·mm·ha−1·h−1, with a fluctuating overall upward trend. There were spatial and temporal distribution differences in RE, with temporal concentrations in summer (June–August) and a spatial pattern of decreasing from east to west. During ENSO events, the RE was greater during the El Niño period than during the La Niña period. For the ENSO characterization indices, the MEI, SOI, and ONI showed significant correlations and resonance effects with RE, but there were differences in the time of occurrence, direction of action, and intensity. In addition, the MEI and MEI–ONI affected RE individually or jointly at different time scales. This study contributes to a deeper understanding of the influence of ENSO on RE and can provide important insights for the prediction of soil erosion and the development of related coping strategies.

In the context of high-quality development, scientifically and objectively assessing regional ecosystem health (EH) is important for ecological civilization. However, the commonly used EH assessment framework typically neglects intrinsic connections, mutual adaptability, and coordination among interrelated indicators. The coupling coordination model was utilized to improve the classic pressure–state–response assessment (PSR) model. The carbon footprint, water footprint, landscape pattern, and response status of the Manas River Basin were used to construct a medium-scale regional EH assessment framework linking natural ecosystems with human socioeconomic elements. A quantitative assessment was conducted on the EH conditions of the Manas River Basin from 2000 to 2020. Over the past 21 years, the EH conditions of the Manas River Basin have fluctuated upward. The ecosystem health index (EHI) increased from 0.18 to 0.37. Compared with the conventional PSR model, the coupling coordination pressure–state–response model (CC–PSR) better reflected the fluctuations in EH conditions caused by “pressure”, “state”, and “response” level changes. In the early stage (2000–2006), increasing human activity strongly pressured the regional ecosystem, limiting EH improvements. The increase in “pressure” was reflected in the increasing trends of the water footprint, carbon footprint, and ecological footprint. During the middle to late period (2009–2020), as the “response” level improved, the regional EH condition continued to increase, and the EHI stabilized between 0.29 and 0.38. Ecosystem resilience improvements and human afforestation projects enhanced the “response” level, but their impacts were noticeably delayed. Over the past 21 years, regional landscape diversity, landscape connectance, and landscape contagion have remained high. The well-maintained landscape pattern has laid the foundation for consolidating and improving the regional EH. The EHI is increasing; its fluctuations stem from periodic fluctuations in the regional water yield and carbon sequestration capacity, which are constrained by the basin climate and vegetation coverage. This study provides a scientific model for basin EH assessment.

Engineered circular RNAs (circRNAs) are a class of single-stranded RNAs with head-to-tail covalently linked structures that integrate open reading frames (ORFs) and internal ribosome entry sites (IRESs) with the function of coding and expressing proteins. Compared to mRNA vaccines, circRNA vaccines offer a more improved method that is safe, stable, and simple to manufacture. With the rapid revelation of the biological functions of circRNA and the success of Severe Acute Respiratory Coronavirus Type II (SARS-CoV-2) mRNA vaccines, biopharmaceutical companies and researchers around the globe are attempting to develop more stable circRNA vaccines for illness prevention and treatment. Nevertheless, research on circRNA vaccines is still in its infancy, and more work and assessment are needed for their synthesis, delivery, and use. In this review, based on the current understanding of the molecular biological properties and immunotherapeutic mechanisms of circRNA, we summarize the current preparation methods of circRNA vaccines, including design, synthesis, purification, and identification. We discuss their delivery strategies and summarize the challenges facing the clinical application of circRNAs to provide references for circRNA vaccine-related research.

With the rapid development of sensors and other devices, precise control for the generation of new energy, especially in the context of highly stochastic wind power generation, has been strongly supported. However, large-scale wind farm grid connection can cause the power system to enter a low inertia state, leading to frequency instability. Battery energy storage systems (BESSs) have the advantages of a fast response speed and high flexibility, and can be applied to wind farm systems to improve the frequency fluctuation problem in the process of grid connection. To address the frequency fluctuation problem caused by the parameter error of the fuzzy membership function in the fuzzy control of a doubly fed induction generator (DFIG) and a BESS, this paper proposes an improved Artificial Bee Colony (ABC) algorithm based on multi-source sensor data for optimizing the fuzzy controller to improve the frequency control ability of BESSs and DFIGs. A Gaussian wandering mechanism was introduced to improve the ABC algorithm and enhance the convergence speed of the algorithm, and the improved ABC algorithm was optimized for the selection of fuzzy control affiliation function parameters to improve the frequency response performance. The effectiveness of the proposed control strategy was verified on the MATLAB/Simulink simulation platform. After optimization using the proposed control strategy, the oscillation amplitude was reduced by 0.15 Hz, the precision was increased by 40%, and the steady-state frequency deviation was reduced by 26%. The results show that the method proposed in this paper provides a great improvement in the frequency stability of coordinated systems of wind farms and BESSs.

Representing and rendering dynamic underwater scenes present significant challenges due to the medium’s inherent properties, which result in image blurring and information ambiguity. To overcome these challenges and accomplish real-time rendering of dynamic underwater environments while maintaining efficient training and storage, we propose Underwater Dynamic Scene Reconstruction Gaussian Splatting (UDR-GS), a method based on Gaussian Splatting. By leveraging prior information from a pre-trained depth estimation model and smoothness constraints between adjacent images, our approach uses the estimated depth as a geometric prior to aid in color-based optimization, significantly reducing artifacts and improving geometric accuracy. By integrating depth guidance into the Gaussian Splatting (GS) optimization process, we achieve more precise geometric estimations. To ensure higher stability, smoothness constraints are applied between adjacent images, maintaining consistent depth for neighboring 3D points in the absence of boundary conditions. The symmetry concept is inherently applied in our method by maintaining uniform depth and color information across multiple viewpoints, which enhances the reconstruction quality and visual coherence. Using 4D Gaussian Splatting (4DGS) as a baseline, our strategy demonstrates superior performance in both RGB novel view synthesis and 3D geometric reconstruction. On average, across multiple datasets, our method shows an improvement of approximately 1.41% in PSNR and a 0.75% increase in SSIM compared with the baseline 4DGS method, significantly enhancing the visual quality and geometric fidelity of dynamic underwater scenes.

Iron (Fe) is a crucial trace element in marine ecosystems, playing a vital role in regulating marine primary productivity and driving marine biogeochemical cycling processes. However, understanding seasonal iron variations and the response of chlorophyll-a (Chl-a) to coastal waters remains limited. The aim of this study was to find out about the spatial and seasonal variations in iron concentrations and their impact on chlorophyll-a levels in Zhanjiang Bay. We conducted seasonal monitoring of surface seawater for iron in 2019, alongside assessments of terrestrial iron inputs during three precipitation seasons. The monitoring results showed that the iron content in Zhanjiang Bay ranged from 0.83 to 339.2 μg·L−1 with an average of 54.34 ± 75.91 μg·L−1. The annual average iron content in the central bay is higher than that in the bay mouth and inner bay. The iron content in autumn is much higher than that in other seasons, which may be due to the influence of river dredging. Correlation analysis revealed that temperature and pH are the main factors affecting the iron content in Zhanjiang Bay and the spatial distribution of iron is influenced by rainfall, river inputs, and human activities, particularly channel dredging. Iron content and chlorophyll-a were negatively correlated between different seasons, which was more significant with the increase in iron content. This may mean that the increase in iron concentration may inhibit the synthesis of chlorophyll-a, thus affecting primary productivity. We need to carry out more research experiments to verify this hypothesis. This study reveals the spatial and temporal changes in iron in urban coastal waters and its relationship with environmental factors, which is of great significance for understanding the marine biogeochemical cycle of iron in coastal eutrophic waters and specifying effective environmental management strategies.

The traditional centralized optimization method encounters challenges in representing the interaction among multi-agents and cannot consider the interests of each agent. In traditional low-carbon scheduling, the fixed carbon quota trading price can easily cause arbitrage behavior of the trading subject, and the carbon reduction effect is poor. This paper proposes a two-layer dynamic community integrated energy system (CIES) low-carbon collaborative optimization operation method. Firstly, a multi-agent stage feedback carbon trading model is proposed, which calculates carbon trading costs in stages and introduces feedback factors to reduce carbon emissions indirectly. Secondly, a two-layer CIES low-carbon optimal scheduling model is constructed. The upper energy seller (ES) sets energy prices. The lower layer is the combined cooling, heating, and power (CCHP) system and load aggregator (LA), which is responsible for energy output and consumption. The energy supply and consumption are determined according to the ES energy price strategy, which reversely affects the energy quotation. Then, the non-dominated sorting genetic algorithm embedded with quadratic programming is utilized to solve the established scheduling model, which reduces the difficulty and improves the solving efficiency. Finally, the simulation results under the actual CIES example show that compared with the traditional centralized scheduling method, the total carbon emission of the proposed method is reduced by 16.34%, which can improve the income of each subject and make the energy supply lower carbon economy.

Urban green spaces (UGSs) provide important support for the health of urban residents and the realization of sustainable urban development. However, the spatiotemporal pattern of urban resident exposure to UGSs in cities is unclear, especially at the national scale in China. Based on the annual 30 m resolution Normalized Difference Vegetation Index (NDVI) data of the Landsat satellite, we quantitatively analyzed the change in UGS coverage from 2000 to 2020 for 320 cities in China and combined it with population data to understand the changing patterns of urban population exposure to different UGS coverage. The results indicated that the average UGS coverage decreased from 63% to 44% from 2000 to 2020 in China, which could be divided into two stages: a rapid decline phase (2000–2014) and a progressive decline phase (2015–2020). Geographically, UGS coverage declined faster in southwestern and eastern cities than in other regions, particularly in medium-sized cities. We also found that urban pixel-based areas in cities with the highest UGS coverage (80–100%) decreased rapidly, and the proportion of the urban population exposed to the highest UGS coverage also declined significantly from 2000 to 2020. Urban pixel-based areas with low UGS coverage (20–40%) continued to expand, and there was a rapid increase in the proportion of the urban population exposed to low UGS coverage, with an increase of 146 million people from 2000 to 2020. The expansion of impervious surfaces had the most significant effect on the change in UGS coverage during different periods (2000–2020, 2000–2014, and 2015–2020). Natural factors such as precipitation, surface maximum temperature, and soil moisture also affected UGS coverage change. These findings provide insights into the impact of urbanization on the natural environment of cities, availability of UGS for residents, and sustainable urban development under rapid urbanization.

This study aimed to investigate the effects of applying arbuscular mycorrhizal fungi (AMF) on maize root growth and yield formation under different soil conditions. This study was conducted under sandy soil (S) and saline–alkali soil (Y), with treatments of AMF application (AM) and no AMF application (CK). The root characteristics, yield, and quality of maize were measured. High-throughput sequencing technology was employed to assess the impact of AMF on the soil microbial community structure, and the correlation between soil microbes and soil physicochemical properties was elucidated. The results show that under both sandy and saline–alkali soil conditions, AMF application significantly enhanced maize root growth, yield, grain quality, and soil available nitrogen (AN), available phosphorus (AP), and available potassium (AK) contents compared to the CK treatment. Soil microbial Alpha diversity analysis indicated that AMF application effectively increased soil microbial diversity and richness. Principal coordinate analysis (PCoA) and microbial community structure analysis revealed significant differences in bacterial communities between AM treatment in sandy soil (SAM) and CK in sandy soil (SCK), and significant differences in both bacterial and fungal communities between AM treatment in saline–alkali soil (YAM) and CK in saline–alkali soil (YCK). Furthermore, significant correlations between microbial communities and soil physicochemical properties were found, such as AN, AP, AK, soil salinity (SS), and organic matter (OM) content. AMF application had a greater impact on bacterial communities than on fungal communities. This study demonstrated that the use of AMF as a bio-fungal fertilizer was effective in improving spring maize yields, especially in terms of yield increase and quality stability in sandy and saline soils, thereby contributing to safe and sustainable cropping practices.

Considering that the existing path planning algorithms for mobile robots under rugged terrain do not consider the ground flatness and the lack of optimality, which leads to the instability of the center of mass of the mobile robot, this paper proposes an improved A* algorithm for mobile robots under rugged terrain based on the ground accessibility model and the ground ruggedness model. Firstly, the ground accessibility and ruggedness models are established based on the elevation map, expressing the ground flatness. Secondly, the elevation cost function that can obtain the optimal path is designed based on the two types of models combined with the characteristics of the A* algorithm, and the continuous cost function is established by connecting with the original distance cost function, which avoids the center-of-mass instability caused by the non-optimal path. Finally, the effectiveness of the improved algorithm is verified by simulation and experiment. The results show that compared with the existing commonly used path planning algorithms under rugged terrain, the enhanced algorithm improves the smoothness of paths and the optimization degree of paths in the path planning process under rough terrain.

In order to improve the basic pavement performance of high-elastic asphalt concrete filled in the expansion longitudinal joints of seamless bridges, rubber particles and polyester fibers were added to optimize the mix proportion of elastic asphalt concrete, and the optimal asphalt–aggregate ratio was determined. The influence of rubber particles and polyester fibers on the basic pavement performance of high-elastic asphalt concrete was studied. The results show that when the dosage of polyester fiber is not more than 0.6%, the optimal asphalt–aggregate ratio is 1:5, and when it exceeds 0.6%, the optimal asphalt–aggregate ratio is 1:4. The incorporation of rubber particles reduces the compressive strength of high-elastic asphalt concrete but enhances its high-temperature stability, fracture performance, and deformation recovery ability. The incorporation of polyester fibers improves its compressive strength, high-temperature stability, fracture performance, and deformation recovery ability. In addition, the incorporation of rubber granules and polyester fibers promotes the use of green building materials and provides strong support for sustainable building practices.

For hydraulic engineering, accurate geometric modeling is crucial for design, analysis, and maintenance. In recent years, point cloud technology, with its high precision and detailed data capture capabilities, has gradually become the preferred method for geometric modeling in hydraulic engineering. However, traditional point cloud processing methods exhibit slow data processing speeds and poor mesh quality when reconstructing 3D models. Therefore, we have studied the meshing method for the 3D reconstruction of large-scale discrete point cloud data and proposed a meshing method based on the Delaunay criterion and the growth method to achieve rapid meshing of discrete point cloud data. To demonstrate the efficiency and convenience of the proposed method, point cloud reconstruction experiments are conducted on various buildings. We compare our approach with the Poisson, BPA, and ONet methods. Additionally, we apply it to the Jiangsu Gaogang Water Conservancy Hub in China. The results show that our proposed method is superior in both reconstruction quality and efficiency, providing a promising solution for the reconstruction of 3D building models from point clouds.

The Paleocene ore deposits related to the India–Asia continental collision are widely distributed in the Gangdese metallogenic belt. Among these, Sinongduo is the first discovered epithermal Ag-Pb-Zn deposit in the Lhasa terrane. However, there is still controversy over the ore-forming magma in this deposit. This study mainly reports new zircon U-Pb isotopic ages, whole-rock geochemistry, and Sr-Nd isotopic data for the granite porphyry from the Sinongduo deposit, aiming to discuss the petrogenesis and tectonic setting of the granite porphyry and its genetic link between the Ag-Pb-Zn mineralization. The results show that zircon U-Pb analyses yield ages of 62.9 ± 0.5 Ma and 59.0 ± 0.7 Ma for the granite porphyry, indicating that it formed during the Paleocene period. The timing of the granite porphyry intrusion is contemporaneous with the mineralization, suggesting that it is most likely the ore-forming magma in the Sinongduo deposit. The granite porphyry has high SiO2 and K2O, moderate Al2O3, and low Na2O, CaO, and FeOT contents, and it displays significant enrichments in LREEs and LILEs and depletions in HREEs and HFSEs, with negative Eu anomaly. The granite porphyry is a peraluminous series and can be classified as S-type granite. Moreover, the granite porphyry shows relatively high ratios of (87Sr/86Sr)i and low values of εNd(t). The geochemical and isotopic compositions of the granite porphyry from the Sinongduo area are similar to those of the upper continental crust, which suggests that the granite porphyry was most likely derived from the melting of the upper continental crust in the Lhasa terrane during the India–Asia collisional tectonic setting.

Wildfires significantly affect climate and environmental changes, closely tied to extreme weather responses. Vegetation combustion emits greenhouse gases (CO2, CH4, CO), warming the climate. Climate shifts, in turn, impact vegetation growth, altering combustible material types and quantities, thus affecting wildfire intensity, duration, and frequency. Wildfires profoundly affect ecosystems, influenced by factors like atmospheric oxygen and climate. Their combustion gases impact climate and vegetation growth. Recent advancements in studying ancient wildfires include analyzing nanoparticles as key indicators. This study discovered six types of nanoparticles in ancient wildfire remains, with sizes ranging from 50 nm to 500 nm and diverse compositions including elements such as C, O, Mg, Al, Ti, Fe, S, Ca, and P. These findings indicate that wildfires generate a variety of nanoparticles, offering new insights into ancient fire events. Elemental analysis revealed low magnesium but high calcium and aluminum levels, suggesting a warm, humid paleoclimate during these fires. The presence of high Ti-O ratios and carbon-rich nanoparticles points to ground fires with incomplete combustion. This research underscores the significance of nanoparticles in understanding the history and characteristics of ancient wildfires.

Molybdenum (Mo) is a rare and important element extensively utilised in aerospace, radar communications, optoelectronic devices, and the military. This study proposes an environmentally friendly physical method based on photon–phonon resonance absorption for the separation of Mo from sodium molybdate (Na2MoO4). We examined the vibrational spectrum of Na2MoO4 using the CASTEP code, employing first-principles density functional theory. Through dynamic process analysis, we analysed the vibrational modes and assigned peaks corresponding to experimental infrared (IR) and Raman data. We focused on the vibrational modes associated with Mo and identified that the highest-intensity IR-active peak at 858 cm−1 corresponded to Mo–O bond asymmetric stretching. Therefore, we propose the use of a high-power terahertz laser at ~25 THz to facilitate the separation of Mo from Na2MoO4. Experimental investigations are expected in the future.

In a study aimed at developing a rapid and nondestructive method for testing the quality of strawberries, spectral data from four strawberry varieties at different ripening stages were collected using a geophysical spectrometer, primarily focusing on the 350–1800 nm band. The spectra were preprocessed using Savitzky–Golay (SG) filtering, and characteristic bands were extracted using Pearson correlation coefficient (PCC) analysis. Models for predicting strawberry quality were built using random forest (RF), support vector machine (SVM), partial least squares (PLS), and Gaussian regression (GPR). The results indicated that the SVM model exhibited relatively high accuracy in predicting anthocyanin, hardness, and soluble solids content in strawberries. For the test set, the SVM model achieved R2 and RMSE values of 0.81, 0.87, and 0.89, and 0.04 mg/g, 0.33 kg/cm2, and 0.72%, respectively. Additionally, the PLS model demonstrated relatively high accuracy in predicting the titratable acid content of strawberries, achieving R2 and RMSE values of 0.85 and 0.03%, respectively, for the test set. These findings provided a solid foundation for strawberry quality modeling and a veritable guide for non-destructive assessment of strawberry quality.

To explore a method of improving the reproductive performance of the striped bamboo shark, three groups (D0, D1, and D2) of mature individuals were fed squid with (D1 and D2) or without (D0) a nutritional fortifier during the breeding seasons of 2022 and 2023. Compared with the D0 group, the D1 and D2 groups had an increase of 20.90% and 31.34% in total eggs, increases of 32.73% and 41.82% in the proportion of lecithal eggs, and a total 119.07% increase in hatching rate, respectively, in 2022. In 2023, the corresponding increase was 17.12% and 9.91% in total eggs, 19.63% and 12.15% in the proportion of lecithal eggs, 43.37% and 43.94% in fertilization rate, 23.94% and 22.22% in hatchability rate, and 66.70% and 8.70% in the survival rate of fry. Moreover, the levels of serum estradiol, testosterone, progesterone, albumin, and total antioxidant capacity and the levels of ARA, EPA, DHA, n-3 PUFA, and n-6 PUFA in both serum and lecithal eggs significantly increased, while the levels of serum triglyceride and total cholesterol were the opposite (p < 0.05). The results demonstrate that feeding the sharks with a nutritional fortifier can increase spawn production and the quality of eggs, regulate the production of sex steroids, and improve the nutrition of eggs and the health of broodstocks.

Fast and accurate ship target detection technology plays an important role in improving driving safety, rescue at sea, marine environmental protection, and sea traffic control. It is also one of the key technologies for the development of ship informatization and intelligence. However, the current ship target detection models used at different scales in multiple scenarios exhibit high complexity and slow inference speed. The trade-off between model detection speed and accuracy limits the deployment of ship target detection models on edge devices. This study proposes a lightweight multi-scale ship target detection model based on the Yolov5s model. In the proposed model, the lightweight EfficientnetV2 and C3Ghost networks are integrated into the backbone and neck networks of the Yolov5s model to compress the computational and parametric quantities of the model and improve the detection speed. The Shuffle Attention mechanism is embedded in the neck network component of the model to enhance the representation of important feature information, suppress irrelevant feature information, and improve the model’s detection performance. The improved method is trained and verified on the dataset collected and labeled by the authors. Compared with the baseline model, the inference speed of the proposed model increased by 29.58%, mAP0.5 improved by 0.1%, and the parameters and floating-point operations decreased by 42.82% and 68.35%, respectively. The file size of the model is 8.02MB, which is 41.46% lower than the baseline model. Compared with other lightweight models, the method proposed in this study is more favored in edge computing.

The gait rehabilitation knee exoskeleton is an advanced rehabilitative assistive device designed to help patients with knee joint dysfunction regain normal gait through training and activity support. This paper introduces a design framework based on the process knowledge representation method to optimize the design and control efficiency of the knee exoskeleton. This framework integrates knowledge of design objects and processes, specifically including requirements, functions, principle work areas, and the representation and multi-dimensional dynamic mapping of the Behavior–Structure (RFPBS) matrix, achieving multi-dimensional dynamic mapping of the knee exoskeleton. This method incorporates biomechanical and physiological knowledge from the rehabilitation process to more effectively simulate and support gait movements during rehabilitation. Research results indicate that the knee rehabilitation exoskeleton design, based on the RFPBS process knowledge representation model, accomplishes multi-dimensional dynamic mapping, providing a scientific basis and effective support for the rehabilitation of patients with knee joint dysfunction.