Hydrogen energy is a promising secondary low-carbon energy source in the 21st century of global energy transition. However, HE hydrogen embrittlement in metallic materials refers to the diffusion of hydrogen into the metal in different forms, either in solid solution or as hydrides, causing severe lattice distortion, reducing ductility and toughness, and leading to embrittlement and fracture. It has raised a number of safety issues and limited the service life of hydrogen storage systems due to its insidious nature, abruptness, and diverse failures. In recent years, scholars have been conducting extensive research on hydrogen embrittlement, benefiting from improved experimental tests and numerical simulation methods. A comprehensive review of the latest advancements in hydrogen embrittlement research was provided: elucidating the concept of hydrogen embrittlement and the prevailing mechanism, analyzing the characteristics and factors influencing hydrogen embrittlement in storage vessels, and summarizing the characteristics and primary application scopes of the existing macro to micro, static and dynamic experimental methods in the evaluation of material hydrogen embrittlement. Special attention is given to the research progress in combining these methods with numerical simulation analyses, including their applicability and limitations in practical engineering. Insights and references might be offered to the ongoing development of evaluation methodologies for the hydrogen embrittlement resistance of metal materials.

In recent years, rapid development has been continuously observed in China’s manufacturing industry, where AGVs automated guided vehicles have been increasingly adopted by enterprises as core equipment in intelligent logistics systems. To ensure the efficiency of warehouse operations, addressing the issue of transportation path conflicts among AGVs has garnered growing attention from researchers. A literature review on the issue of multi-AGV path conflicts in warehouses was conducted from two perspectives. First, from the perspective of conflict types, the research problems were categorized into collision problems and deadlock problems, and the current state of research on multi-AGV collision avoidance strategies under different conflict types was analyzed. Second, from the perspective of model-solving algorithms, the study divides the approaches into heuristic algorithms and reinforcement learning algorithms, analyzing their application in multi-AGV path conflict issues in warehouses in recent years. Finally, the existing literature was summarized, and future directions for addressing multi-AGV path conflicts in warehouses were proposed.

CLT(cross-laminated timber) shear wall structure has emerged as one of the rapidly advancing mid-to-high-rise timber structural systems in recent years. Extensive research on the lateral resistance performance of CLT shear walls has been conducted by both domestic and international scholars. A comprehensive synthesis of findings concerning lateral resistance capabilities was conducted for CLT shear walls, including single-panel, multi-panel, and CLT shear walls with openings. Failure modes, load-bearing capacities, and stiffness characteristics were systematically examined across these structural variations. Comparative evaluations of multiple calculation methods for lateral bearing capacity and stiffness determination were performed, alongside a compilation of standardized methodologies from domestic and international specifications for timber shear wall analysis. Specialized recommendations were formulated specifically for CLT structural applications. Current research advancements were consolidated, and strategic directions were proposed to guide subsequent investigations into CLT shear wall performance under lateral loading conditions, establishing critical references for ongoing research development in this specialized engineering field.

Buildings are important carriers of human production activities. Accurate and fast extraction of building areas can play an important role in the field of natural resource management. Although significant progress has been made in building extraction from remote sensing images based on CNN(convolutional neural network), the constructed network model still needs to be optimized in feature extraction and feature fusion. Therefore, a coordinate attention and CCFNet(convolutional enhanced full-scale fusion building extraction network) was proposed. The constructed model consists of a residual encoder enhanced by coordinate attention and convolution and a full-scale fusion decoder. Coordinate attention was used in the encoder to build inter-channel dependencies and capture global information. The asymmetric convolution was used to enhance the edge feature extraction of ground objects, and it is more robust to rotation, flip distortion and uneven aspect ratio of ground objects. The full-scale fusion method used in the decoder helps to reconstruct the buildings. The experimental results on the dataset of typical Chinese city buildings show that compared with other building extraction networks, The CCFNet model constructed in this paper achieves the best experimental Accuracy of 93.84%, 84.08%, 72.53% and 82.59% in the four segmentation evaluation indicators of accuracy, F₁, IOU and MIOU, respectively. Experimental results show that the model can effectively extract building regions.

Accurately and in real-time understanding the changes in the scope and species communities of intertidal wetlands is an important foundation for achieving sustainable development and management of wetland intertidal zones. In recent years, global warming, rising sea levels, and human activities such as coastal development, reclamation, and aquaculture have caused serious damage to the intertidal zone. At present, there is a lack of systematic research on the classification of mangrove tidal flats in the intertidal zone of Guangxi. In order to achieve large-scale and high-precision extraction of intertidal resources in Guangxi, this article was based on GEE (Google Earth Engine) platform, using Landsat series image data of Guangxi coastal zone from 2012 to 2022, and threshold segmentation processing of the images. The various remote sensing features under the influence of tidal dynamic inundation were analyzed, and the intertidal zone range of coastal wetlands in Guangxi was extracted. Achieved the classification of tidal flats and water bodies, mangrove vegetation, and non-mangrove vegetation in the study area, with areas of 5 641.67 hm², 1 625.29 hm², and 2 156.04 hm² respectively. The overall classification accuracy reached 93.3%, with a Kappa coefficient of 0.9.

Further analysis is needed to comprehend how the trend of land subsidence in the Beijing plain area evolves following the implementation of a series of prevention and control measures. Based on the Sentinel-1A image data from 2017 to 2022, the PS-InSAR technique was employed to assess the current situation of land subsidence in the plain area of Beijing, and the geographical detector was utilized to analyze the main influencing factors of land subsidence and their interaction effects. The findings reveal the following: The main conclusions were as follows. The distribution of land subsidence in Beijing plain is uneven, and the maximum subsidence rate reaches 90 mm/a. The subsidence rate of non-funnel area shows a certain degree of slowing trend from 2020 to 2021, while the slowing trend of subsidence rate in funnel area is not obvious. Groundwater as the primary influencing factor of land subsidence, with the thickness of the compressible layer closely following. The interaction among all influencing factors demonstrates a factor enhancement relationship, with the interaction between groundwater and subway infrastructure exerting the most significant impact on land subsidence. This highlights that groundwater and urban construction jointly propel land subsidence in the Beijing plain area. These research findings provide a scientific foundation for the comprehensive assessment, precise prediction, and integrated prevention and control of land subsidence in the Beijing plain.

In order to reveal the spatiotemporal distribution characteristics and causative factors of the ground subsidence in Jiangdong New District, SBAS-InSAR(small baseline subsets interferometric synthetic aperture radar) technology was adopted to process and analyze 175 scenes of Sentinel-1A imagery data from January 2018 to February 2024, extracting the deformation parameters. The study found that the subsidence rate in Jiangdong New District has undergone a variation from very slow to fast and then back to slow, forming several severe subsidence areas mainly distributed along major traffic arteries and reclamation areas. The main causative factors of the subsidence were qualitatively and quantitatively analyzed, which include poor engineering geological conditions, consolidation in the reclamation areas, and land use change, etc. The acceleration of subsidence rate is closely related to the rapid development and construction of the region, among which the excessive load on the ground surface is becoming the main influencing factor of subsidence.

With the improvement of intelligence, the drilling industry ’s demand for real-time identification of lithology while drilling was becoming more and more urgent. An intelligent inversion method of lithology while drilling is proposed based on the acoustic signal and vibration signal ( acoustic vibration signal ) of broken rock during drilling. Firstly, the original signal samples were obtained by drilling seven different types of rocks through indoor micro-drilling experiments. During the acquisition process, the drilling parameters ( drilling speed, rotation speed, bit size ) were changed and the corresponding signal data were obtained. According to the characteristics of the collected acoustic vibration signal, the time-frequency image with signal characteristics was obtained by short-time Fourier transform. On this basis, an improved VGG16 convolutional neural network model was constructed to realize the intelligent identification of lithology, and the training, evaluation and tuning of the model are realized by hyperparameter optimization. Then, the transfer learning training strategy is introduced, and different drilling parameters were used as data labels. According to the parameter values, the source domain and the target domain were divided to realize the rapid identification of the small sample target domain. The experimental results show that the transfer learning results of the model are different with the change of drilling parameters. The lithology inversion model based on acoustic-vibration signal training has high prediction accuracy and strong generalization ability. The accuracy of the acoustic signal test set is up to 99%, and the accuracy of the vibration signal test set is up to 100%. Under the change of penetration rate, the acoustic and vibration signals are least affected, which can achieve more excellent results when used as data labels for lithology inversion, and the accuracy of lithology inversion is the highest when the penetration rate is small as the target domain. In the process of lithology inversion, different signal types are suitable for different rocks. Among them, the sound signal has the highest applicability to coarse yellow sandstone, and the vibration signal is more suitable for granite. The research results have certain reference value for improving the intelligent degree of working face drilling.

To explore the application regularity and mechanism of formulas containing Huang Qin (Scutellariae radix)-Bai Zhu (Atractylodis macrocephalae Rhizoma) (HQ-BZ) herb pair in the treatment of ICP (intrahepatic cholestasis of pregnancy). All literature on prescriptions containing HQ-BZ herb pairs against ICP was screened from the VIP, Wanfang, and CNKI databases. Subsequently, the R language was employed to analyze and summarize its medication rules and core prescriptions. Network pharmacology was used to predict the mechanism of core prescriptions against ICP, followed by molecular docking and experimental verification to confirm the potential mechanism. A total of 68 prescriptions were included, involving 67 herbs characterized mainly by cold, bitter, and spleen meridian. The core prescription “Artemisiae scopariae Herba-Rhei radix et Rhizoma-Gardeniae fructus-Scutellariae radix-Atractylodis macrocephalae Rhizoma-Poriacocos” was obtained based on the comprehensive analysis of traditional Chinese medicine data, among which quercetin, apigenin, and other key active components may act on core targets such as AKT1 (serine/threonine kinase B1), BAX (BCL2-associated X protein), and participate in PI3K-AKT (phosphatidylinositol 3-kinase/protein kinase B), apoptosis, and other multiple targets and pathways to play the role of ICP therapy. The molecular docking results showed that apigenin demonstrated superior binding affinity with the top 11 core targets compared to quercetin and beta-sitosterol. HTR-8/SVneo cell experiments proved that apigenin significantly reduced the apoptosis rate induced by TCA (taurocholic acid) and elevated the protein expression levels of Bax/Bcl-2 (P<0.01), as well as p-PI3K/PI3K, and p-AKT/AKT (P<0.01). Pre-treatment with LY294002 could reverse the anti-apoptosis effects and the expression levels of the aforementioned proteins induced by apigenin. In summary, the core prescription that includes the HQ-BZ can provide references for the clinical prescription of ICP. Apigenin, a key component of core prescription, can inhibit the apoptosis in HTR-8/SVneo cells induced by TCA and has the potential to treat ICP, and its mechanism may be related to the regulation of the PI3K-AKT signaling pathway.

The degradation of mechanical properties of PE pipe materials caused by hot oxygen aging will inevitably reduce the service life of the pipe, which is a serious hidden danger to social and economic development and people’s life and property safety. A nonlinear ultrasonic evaluation method for thermo-oxygen aging pipes was proposed. The nonlinear ultrasonic detection signals of PE pipes were extracted by building a nonlinear ultrasonic detection system for water immersion, and a correlation model between the characteristic parameters of nonlinear ultrasonic detection and elongation at break and fracture stress was established. The nonlinear ultrasonic detection mechanism was revealed by observing the micro-structure changes of PE pipes after aging. The experimental results show that the elongation at break and profit at break of PE pipe materials decrease with the increase of the nonlinear coefficient. The surface micro-cracks, holes and folds formed after the aging of the pipe are the main reasons for the mechanical degradation of the pipe and the increase of the nonlinear ultrasonic detection coefficient. The nonlinear coefficient can be used to evaluate the mechanical degradation of the pipe caused by thermal oxygen aging.

To address the reduction in support bearing capacity caused by concrete shrinkage and to improve the damping performance of supports under mining-induced seismic conditions, this study focuses on the energy-absorbing and shock-resisting performance of single arch sand-filled steel tubular frames and the damping performance of multi-arch combined support systems. Finite element software was employed to establish models of surrounding rock and sand-filled steel tubular frames, as well as multi-arch support systems connected with flexible cables and dampers. The performance of sand-filled steel tubular frames under static and dynamic loading, as well as their seismic resistance under mining-induced tremors, was investigated. The results indicate that the deformation of the tunnel under static loading remains stable, while the support effectiveness is satisfactory under impact loading except for relatively large deformations at the crown. Under static and dynamic loading, the equivalent plastic strain at the crown of the sand-filled steel tubular frames shows a significant increase, while changes in other areas remain minimal, demonstrating good load-bearing capacity. In the three-arch support system, the third arch experiences reduced vibration amplitude due to the dual energy dissipation effects of flexible cables and dampers. Calculations of the safety factor at the maximum shear stress of the tunnel reveal a significant improvement in the seismic performance of adjacent supports, providing insights for further studies on support damping mechanisms.

In view of the problem of limited pressure relief and anti-impact ability of large diameter boreholes in high stress and strong disturbance coal seams. The methods of theoretical analysis, numerical simulation and field test were adopted to study the mechanism of pressure relief and anti-impact of large diameter boreholes, and the control measures of pressure relief and anti-impact of large diameter boreholes were proposed to improve the pressure relief and anti-impact performance of high stress and strong disturbance coal seams. The results show that the pressure relief range of large diameter boreholes is directly proportional to the drilling radius, surrounding rock stress, and disturbance stress increment, and inversely proportional to the constraint force of the fracture zone and plastic zone. Increasing the pressure relief range of large diameter boreholes can increase the attenuation of dynamic stress wave energy, transfer static load energy to the deep surrounding rock, and reduce the risk of coal seam rockburst. As the disturbance coefficient increases, the range of pressure relief for large diameter boreholes and the gathering elastic energy around the boreholes show an increasing trend. The vibration acceleration of borehole increases exponentially with the increase of disturbance coefficient and shock energy. When the disturbance coefficient is greater than 2, the impact dynamic load is superimposed, and the large diameter borehole is blocked to achieve the ultimate anti-impact ability. The use of dense drilling method and repeated drilling method can increase the pressure relief range of large diameter drilling, reduce the risk of coal seam rockburst. After field test, the deformation of surrounding rock of roadway is reduced by 31.5%~67.0%, the stress value of coal body is lower than the critical warning value, and the stability of surrounding rock of roadway is significantly improved.

The variation law of C₄AF and C₃A corrosion products and the formation rate coefficient of CaCO₃ of cement single ore were quantitatively analyzed by SEM, XRD and TG analysis and test methods. The experimental results showed that both C₄AF and C₃A produced a large number of flocculent phases after CO₂ corrosion, but C₃A produced more lumpy and flocculent phases after corrosion than C₄AF corrosion. The relative crystallinity of C₃AH₆ decreases and the relative crystallinity of aragonite increases in the later stage of corrosion reaction, and the quantitative analysis results show that the content of CaCO₃ in C₄AF is higher than that of C₃A, and the molar formation rate of CaCO₃ in C₄AF is 28.36 mol/d and that of C₃A sample is only 4.23 mol/d after 1 day of corrosion reaction. With the extension of the corrosion reaction time to 28 days, the molar formation rate of corrosion products of C₄AF and C₃A continued to decrease, which was 1.83 mol/d and 1.48 mol/d, respectively. The coefficient of corrosion product formation α rate of C₄AF was 32.62 after fitting, which was much higher than that of C₃A single ore (2.74). The corrosion resistance of C₃A ore in CCUS environment is stronger than that of C₄AF, which not only provides theoretical guidance for the development of high performance cement materials resistant to CO₂ corrosion, but also provides a basis for the application of cement in CCUS environment.

Due to the complex reservoir conditions and multi-scale pore structure of shale gas, the production shows significant nonlinear characteristics over time. Traditional production prediction methods, which rely on statistical analysis of geological and engineering data, find it difficult to adapt to the complexity of geological conditions and thus cannot achieve high accuracy. A method that combines the hyperbolic decline model with a composite function having time attributes was proposed. The improved A-PSO (adaptive particle swarm optimization algorithm) was used to find the optimal model parameters, establishing a composite time hyperbolic decline model. The research results show as follows. The A-PSO optimization algorithm can automatically adjust parameters and model structure according to the complexity of production data and data changes, finding the optimal parameter combination more quickly and accurately, thereby improving prediction accuracy. The production fluctuates greatly over time, making it difficult for conventional decline models to reflect its characteristics. The composite time decline model, with its strong flexibility, can consider the complexity and variability of oil and gas reservoirs, more accurately describe the production changes of shale gas wells at different stages, and provide higher fitting accuracy, making the production prediction closer to the actual value.

In order to analyze the design parameters and technology adaptability of multi-stage temporary plugging and fracturing in the Triassic Chang-6 reservoir of a block in Suijing Oilfield. Based on the geological and engineering design data of the block, the influencing factors of multi-stage temporary plugging and fracturing were quantitatively analyzed in terms of reservoir characteristics, technology parameters, and construction effect. The combined weighting method was used to determine the weights of each parameter, and the ridge-type membership function was used to determine the membership degree of each factor. Based on the principles of fuzzy transformation and maximum membership degree, a fuzzy comprehensive evaluation model for multi-stage temporary plugging and fracturing was established. This model transforms the traditional single-index qualitative development effect evaluation into multi-factor quantitative evaluation. The model was applied to evaluate and analyze the Triassic Chang-6 reservoir in a block of Suijing Oilfield. The results show that this evaluation model results have an adaptation rate of 89.13% when compared to the actual effect data in the field, which can effectively evaluate the effectiveness of the implementation of temporary plugging and fracturing measures in the oil wells of the study area. The evaluation system can provide valuable references for the next implementation of temporary plugging and fracturing measures in the study area, and even optimize parameters for multi-stage temporary plugging and fracturing transformation in similar reservoirs.

The length of the impactor is generally about one meter according to the current application of all kinds of impactors.As the existing impactor increases the distance between the stabilizer and the drill bit, it will cause the theoretical build slope and the lateral force of the drill bit to decrease when it is used in connection with the drill bit. This will in turn affects the drilling deviation section.In this regard, the multi-dimensional impactor with built-in drill bit can effectively solve this problem.Firstly, in order to maximize the performance of the multi-dimensional impactor with built-in drill bit and reduce the pressure loss, the PB (Plackett-Burman) screening test design was adopted to conduct screening tests on the internal parameters of the impactor. The effect of each parameter on the tool performance was as follows: Jet channel width > inlet area > Outlet area > length of oscillating cavity > width of double feedback channel > curvature radius of wall attached surface > wedge Angle. Then, BBD (Box-Behnken design) response surface method was used to provide an in-depth analysis of the top three significant impact parameters.The optimized combination of the internal structure of the multi-dimensional impacter with built-in drill bit was obtained as follows: the inlet area is 1 203.416 mm², the jet channel width is 14 mm, and the outlet area is 455 mm². Finally, the effectiveness of the optimization method was verified by the simulation of Fluent software, which met the design requirements.

In traditional blind deconvolution algorithms, recalculating the gradient or redesigning the optimization approach for filter coefficients becomes necessary when altering the characterization index. This requirement can render the development process of new blind deconvolution algorithms inflexible. To address these issues, a blind deconvolution algorithm that employs NRO(Newton-Raphson optimizer) to seek out the optimal filter coefficients was proposed. Initially, generalized spherical coordinate transformation was used to define the search range for the filter coefficients. Subsequently, the generalized l_p/l_q norm of the envelope spectrum was adopted as the characterization index. The proposed blind deconvolution algorithm is then utilized for the early detection of minor faults in rolling bearings. Both simulation and experimental results confirm the efficacy of the proposed algorithm, demonstrating its faster convergence rate compared to classical PSO(particle swarm optimization).

The transportation via high-pressure long-tube trailers serves as a crucial method for medium-short distance transfer of flammable and explosive gases such as hydrogen and natural gas. As the core equipment in this system, the trailer filling compressor operates under continuously varying discharge pressures across wide ranges during gas loading processes. Current research and development phases face challenges in fully replicating real-world operating conditions for thermal performance testing. Addressing this requirement, this study proposes a closed-loop experimental system with gas staged recovery and continuous release functions, featuring continuous backpressure regulation capability for trailer filling compressors. The system enables the simulation of actual filling processes by creating both stable and dynamic operating conditions with wide-ranging discharge pressure variations for comprehensive compressor testing. A mathematical model of the testing system was established using zero-dimensional simulation methodology. Systematic investigations were conducted on parameter variations and operational characteristics throughout complete testing procedures, including initial pressurization, compressor startup/shutdown, and wide-range operational testing. Through optimized improvements in system configuration and component matching, critical operational constraints were achieved: gas reservoir temperatures were maintained below 85 °C during testing, and post-recovery system pressures were reduced below 1.5 MPa. These optimizations resulted in the development of a refined and rational testing system and methodology for trailer filling compressors, effectively addressing the technical challenges in simulating actual working conditions during compressor development phases.

Under the background of the energy transition, energy storage, as an important technical means to support a high proportion of renewable energy access and consumption, has gradually become an indispensable part of virtual power plants. Among them, energy storage selection is a key issue to ensure the safe and stable operation of the power grid and improve the scheduling efficiency of virtual power plants. Therefore, an energy storage selection method based on game combination weighting and GRA-MARCOS was proposed for virtual power plants. Firstly, the evaluation indicator system of energy storage adaptability was constructed on the basis of considering the technical, economic, security, and environmental protection indicators of energy storage. Secondly, based on game theory, the comprehensive subjective and objective weights of the indicators were obtained by combining the subjective weights from FAHP(fuzzy analytic hierarchy process) method with the objective weights derived from the CRITIC and MEREC methods. Finally, the utility function of each alternative energy storage technology was calculated using the MARCOS method improved by GRA(grey relational analysis), and this was used to rank and make selection decisions for energy storage. The validity and robustness of the proposed energy storage selection method were verified through examples and sensitivity analysis. This method provides a reasonable and effective decision-making scheme for energy storage selection in different scenarios of virtual power plants.

Power plants utilizing substandard coal for electricity generation often face reduced denitrification efficiency due to high fly ash concentrations in the flue gas, which complicates achieving ultra-low emissions. The flue gas duct from the economizer outlet to the riser duct entrance in the W-shaped flame boiler at Guizhou Chayuan Power Plant was investigated. Numerical simulations were conducted to analyze flue gas flow and fly ash particle trajectories. The results show that the economizer ash hopper is minimally effective, with a collection rate of only 9.99%. Simulation results reveale fly ash deposition on the wall at the SCR riser flue bend at 1.12 kg/s, representing 11.33% of total fly ash. Based on these findings, two design schemes for positioning a denitrification ash hopper below the riser flue are proposed to enhance fly ash collection efficiency. Optimal results are obtained with a 45° angle between the inclined surface and horizontal wall, increasing fly ash collection by 8.38% with a 2.25 Pa pressure drop increase.

The technological demand for distributed wind energy and wind-solar complementary energy utilization on building roofs was addressed. A new type of spatial support frame for wind-solar complementary systems was proposed. The power generation efficiency of small vertical axis wind turbines was enhanced by using flow deflectors with combined wind collection and flow stabilization functions. The wind collection effect and power generation efficiency of the framework were analyzed through theoretical methods. Numerical simulations were conducted to study the impact of flow deflector distancing and width on internal airflow velocity and turbulent kinetic energy at different wind attack angles. Optimization parameters were identified. Wind tunnel experiments were performed to investigate the power generation performance of small wind turbines with the spatial support framework. The results show that the framework significantly increases the airflow velocity entering the wind collection device and reduces turbulent kinetic energy in the internal space. When the flow deflectors have a distance of 0.53 m and a width of 0.12 m, the wind speed increases by 1.21 times and the generator power increases by about 1.77 times.

In recent years, the scale of wind turbine grid connection has been increasing, for the deep learning of wind speed prediction requires a large amount of data, as well as stochastic differential equations for wind power system modeling fail to portray the impact of wind speed correlation on the output power and grid-connection point voltage, a Markov switching stochastic differential equation modeling method considering stochastic factors and wind speed correlation was proposed for power systems containing wind power. The Nataf and LSTM were introduced to construct the wind speed spatio-temporal correlation model, the Markov switching stochastic differential equation was used to segment and linearize the wind power system into various linear segments. Then the effects of wind speed correlation and stochastic excitation strength on the voltage at the grid-connection point were studied, and the critical stable excitation strength of the wind power system was analyzed. Finally, the stochastic simulation of the constructed system model was carried out by numerical analysis methods, and the results show that the system state variable fluctuates in the stable region within the critical value of the random excitation intensity, and the comparison with the stable waveform of voltage in the Simulink simulation circuit verifies the validity of the modeling method in this paper, and provides a theoretical basis for the stability analysis of the new wind farm access to the power system.

The hybrid DC transmission system has problems such as inconsistent boundary components, inconsistent fault response characteristics, difficult resolution of high resistance fault effects, and low accuracy in identifying near end faults, which reduces the reliability of protection schemes. Therefore, the phase characteristics of the regional refractive index of the hybrid DC transmission system were analyzed for the first time, and a single ended protection scheme suitable for hybrid boundaries was proposed based on this. Firstly, establish a hybrid DC transmission system model and analyze the traveling wave transmission characteristics of different fault types. Subsequently, the fault areas of the hybrid DC transmission system were divided, and the refractive index expressions and phase frequency characteristics of the areas were derived separately. Finally, a single ended protection scheme based on a specific frequency refractive index is proposed and its performance is tested. The test results show that the proposed protection scheme not only has the speed of traditional protection schemes, but also has better resistance to high impedance faults, noise interference, and other abilities.

With the rapid development of the energy industry and technological innovation, a large number of professional terms and expressions are constantly updated, and new words continue to emerge. However, traditional neologism discovery methods often rely on dictionaries or rules, and it is difficult to efficiently process and update a large number of specialized terms, especially in the rapidly changing energy field. Therefore, combined with the characteristics of text data in the energy field, a new word discovery method in ENFM(energy field combining N-Gram and multiple attention mechanism) was proposed. Firstly, the N-Gram model was used to process the text data in the field of energy, and the candidate list of new words was generated by statistics and analysis of word frequency. Subsequently, the ERNIE-BiLSTM-CRF model integrating multiple attention mechanism was introduced to further improve the accuracy and efficiency of neologism discovery. Compared with the traditional neologism discovery technology, the accurate identification and overall efficiency of neologism have been significantly improved. The accuracy rate, recall rate and F₁ value of neologism in the data set of policy text in the energy field are 95.71%, 95.56% and 95.63%, respectively. The experimental results show that this method can accurately identify new words in a large number of text data in the field of energy, effectively identify the specific words and expressions in the field of energy, and significantly improve the recognition ability of professional terms in the field of energy in Chinese word segmentation tasks.

To address the limitations of traditional protocol recognition methods caused by the presence of numerous non-standard protocols in IC (industrial control) sector, a method based on edge-distributed deep learning was studied to enhance IC protocol recognition technology. A recognition method based on CNN (convolutional neural networks) was proposed: real IC protocol data from the network was collected and preprocessed, and an appropriate CNN model was selected according to protocol characteristics to implicitly extract the essential features of the protocols. This achieved classification and recognition of seven types of IC protocols with an accuracy of up to 99.92%. Furthermore, the IC protocol recognition model was deployed at the network edge, leveraging a data-parallel distributed strategy for collaborative training within an edge server computing cluster. This improved the training efficiency of the model by 1.87~2.81 times while maintaining high accuracy. The results show that this method significantly improves the accuracy of IC protocol recognition, greatly enhances model training efficiency, and is well-suited for deployment in edge computing environments. It is evident that this method has significant value in optimizing IC protocol recognition performance.

sEMG (surface electromyography) signals are physiological signal closely related to human movement, and the analysis of sEMG signals play an important role in the field of human-machine interaction. Aiming at the difficulty of both efficiency and accuracy of electromyographic signal classification, an upper limb sEMG classification method was innovatively proposed, which combined feature screening with classifier hyperparameter optimization. BPSO (binary particle swarm optimization) algorithm was adopted to screen the features. PSO (particle swarm optimization) algorithm was further utilized to adjust the hyperparameters of the LSSVM (least-squares support vector machine). By collecting sEMG signals from four parts of the human upper body and extracting 48-dimensional features from them, classification experiments were conducted on four common movements of upper limb. The results show that the BPSO-PSO-LSSVM algorithm retains only the 21-dimensional features of the EMG data, and the average classification accuracy obtained reaches 97.54%. It is proved that this method can effectively screen out the optimal combination of features for upper limb motion classification and improve the accuracy of movement classification.

In order to solve the problem of insufficient feature extraction of human dynamic skeleton features in abnormal behavior recognition, an unsupervised abnormal behavior recognition method based on enhanced spatiotemporal graph normalization flow was proposed. Transformer and convolution block attention module were employed to enhance the feature expression capability of the model and the performance of the abnormal behavior recognition algorithm in the global and spatiotemporal domains. Firstly, the Transformer module was incorporated into the affine layer of the normalized flow to augment the efficacy of dynamic skeleton feature information at the global level. Subsequently, the convolution attention was introduced into the convolution module of space and time graphs respectively to effectively enhance the spatial and temporal representation of dynamic skeleton features. Finally, simulation verification was conducted on the ShanghaiTech and UBnormal datasets, and the recognition accuracy attains 86.4% and 70.2% respectively, thereby demonstrating the effectiveness of the method.

Traditional whole-brain dynamical modeling techniques are typically constrained by static single features, neglecting dynamic fluctuations in brain networks and lacking qualitative analysis of corresponding indicators, which limits modeling accuracy and comprehensibility. In order to address this issue, a multi-objective expectation maximization algorithm based on bifurcation analysis was proposed. This approach integrates a dynamic mean-field model with brain structural-functional features extracted from multi-mode imaging data for modeling purposes. Bifurcation theory was employed to qualitatively analyze multiple constraint indicators of the model, including functional connectivity, dynamic functional connectivity, and metastability for model inversion. Initial parameter values were determined through bifurcation analysis, and parameter combinations were iteratively refined using an expectation maximization algorithm. Quantitative analysis validates the accuracy and stability of this method.

Over the past half-century, global warming and humidification have led to an accelerated rate of glacier melting in China, highlighting the increasing importance of monitoring glacier distribution. However, current automated glacier extraction methods have significant limitations, such as boundary fragmentation, omission of glaciers in shaded mountain areas, and misclassification in cloud-covered regions. To address these issues, this study selected Menyuan County in Qinghai Province as the experimental area. Sentinel-2 imagery and DEM data were utilized, applying object-oriented automatic classification technology in combination with the C5.0 decision tree model to develop a multi-feature glacier extraction rule set and a neighborhood feature rule set. An improved two-stage object-oriented glacier extraction method was subsequently proposed. The findings revealed that glaciers exhibited distinct response patterns across various features, including spectral mean, spectral standard deviation, NDSI (normalized difference snow index), DEM (digital elevation model), adjacency, and slope orientation. A two-stage glacier extraction method effectively enabled automatic glacier extraction. It also significantly enhanced the recognition accuracy in cloud-covered and shaded mountain regions, achieving an overall glacier recognition accuracy of 98.50%.

Crack detection is crucial to maintaining the structural safety of buildings. In recent years, convolutional neural networks based on deep learning have provided new solutions for crack detection. However, this comes at the cost of huge computing resources, so there are problems of poor real-time performance and low detection efficiency in practical applications. To address this problem, a lightweight MSFC (multi-scale dynamic fusion convolution module) based on the U-Net architecture was proposed to improve the efficiency of crack segmentation. To verify the effectiveness of the proposed method, a dataset Crack2045 containing 2 045 crack images was constructed and experiments were conducted on this dataset. The experimental results show that compared with the original U-Net model, the model using the MSFC module reduces 78.51% of the parameters and 63.75% of the computational complexity while maintaining the same accuracy. At the same time, the MSFC module has a certain degree of generalization and can be seamlessly integrated into different semantic segmentation models. This study not only provides an efficient deep learning method for crack detection, but also provides new possibilities for model deployment in resource-constrained environments.

To analyze the crack development characteristics of different reinforcement concrete beams during bending failure, and to explore the acoustic emission characteristics and bending performance degradation of the beams, a four-point bending test was conducted in combination with acoustic emission technology to establish the relationship between the bending failure process and acoustic emission signals of three different concrete beams. The experimental results show that the RA(rise time/maximum amplitude)-AF(average frequency) signals during the damage evolution of concrete beams with different reinforcements have obvious differences, the proportion of shear crack signal RA of reinforced beams, less-reinforced beams, and super-reinforced beams is much higher than the proportion of diagonal crack signal AF. The common characteristics of the amplitude distribution of bending damage at various stages of differently reinforced beams are mainly reflected in the amplitude peaks in the frequency bands 4~6 kHz, 13.5~16 kHz, and 53~57 kHz. When the beam reaches a certain load, the amplitude peaks in the high-frequency band will suddenly rise, indicating the yielding of the internal reinforcement of the beam and the entry of the structure into the failure stage. This can be used as an important basis for monitoring the extent of internal damage to the structure through time-frequency transformation of acoustic emission signals.

To evaluate the micro-structural degradation characteristics and strength attenuation law of multi source solid waste solidified lake sediment under freeze-thaw cycle conditions, the macroscopic engineering characteristics of the solidified sediment material under freeze-thaw cycle conditions, such as unconfined compressive strength, volume deformation and permeability coefficient were observed through multiple repeated freeze-thaw cycle tests on samples of granulated blast furnace slag, desulfurization gypsum, and construction waste co solidified sediment. By combining micro testing methods such as X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, energy spectrum analysis, then the mineral composition, functional groups, surface morphology and elemental composition of solidified sediment materials during freeze-thaw cycles were systematically analyzed, revealing the micro mechanism of structural degradation of solidified sediment under freeze-thaw cycles. The results show that with the increase of freeze-thaw cycles, fibrous ettringite and columnar gypsum and other cementitious products fracture and overlapped with each other to form a network, and the internal pores increased. This may be the reason for the decrease in strength and increase in permeability coefficient of the solidified sediment. The obtained mechanical characteristics and degradation patterns of solidified sediment during freeze-thaw cycles can provide basic data for the application and promotion of this material in regions with significant freeze-thaw cycle characteristics such as northwest and northeast of China.

In order to investigate the water-holding mechanism and infiltration law of modified glutinous rice-based reconstructed soil layer under rainfall, soil column infiltration tests were firstly conducted to analyze the influence of modified glutinous rice-based material dosage variations on macroscopic vertical infiltration patterns of reconstructed soil. NMR (nuclear magnetic resonance) and scanning electron microscopy technologies were employed to investigate microporous structure and water-holding characteristics under different material dosages. Based on the findings, reconstructed soil with optimal material dosage (12.5%) was selected for rainfall slope modeling tests, through which moisture transport patterns and post-precipitation water redistribution characteristics in reconstructed soil layers were investigated.The results show as follows. With the increase of the dosage of modified glutinous rice-based materials, the number of effective pores (mesopores) increases and then decreases, the number of small pores gradually increases and the number of large pores gradually decreases, and the soil water-holding capacity is optimal when the dosage is 12.5%. increases and the number of large pores gradually decreases, and the soil water-holding capacity is optimal when the dosage is 12.5%. Modified glutinous rice-based materials wrap around, adsorb to soil particles, and combine with gravel to form agglomerates, thereby changing the pore structure of the soil, enhancing the soil water retention capacity, and improving the effectiveness of soil water. Under the condition of 25 mm/h rainfall intensity, an increase in slope gradient led to the decrease of infiltration depth of each cross-section, and the infiltration site shifted significantly (from the top to the foot of the slope). The depth of slope infiltration during the entire rainfall period decreased significantly with the increase of slope gradient, and water in the slope was redistributed at the end of rainfall. The average infiltration depth of the slope at 35°, 55°, and 75° was 10 cm, 8 cm, and 5 cm, respectively. This study is significant for improving the technical system of ecological slope restoration and guiding conservation and management efforts.

In order to study the development law of pile top cumulative displacement of ring wing single pile foundation under horizontal cyclic load, a three-dimensional numerical model of the interaction between ring wing single pile and saturated clay was established through secondary development using ABAQUS, and also simulated the process of soil stiffness attenuation. Numerical results indicate that installing gravity type ring wings at the mud surface position of traditional single pile foundations can enhance the overall horizontal resistance of the ring wing single pile foundation, thereby reducing the cumulative displacement of the ring wing single pile under cyclic loads. The displacement of the pile top will decrease with the increase of the height and diameter of the gravity type ring wing, but increasing the diameter of the gravity type ring wing has a more significant effect on reducing the horizontal displacement of the pile top. Increasing the depth of the pile into the soil can significantly reduce the cumulative displacement at the top of a single circular wing pile under cyclic loading.

Strong pulsation is one of the most important causes of damage to hydraulic structures such as stilling basins, so it is crucial to clarify the characteristics of pulsation in hydraulic structures to ensure the safe operation of the project. In this study, a fine numerical model of the hydraulic model of a stilling basin was established based on DES (detached eddy simulation) model and VOF (volume of fluid) method, and the simulation results were in good agreement with the experimental results. Based on the numerical simulation results, the distribution of pressure fluctuation, pulsating velocity, vorticity, and turbulent kinetic energy in the stilling basin were analyzed. The results show that the pressure fluctuation in the stilling basin shows a bimodal distribution along the flow direction, and the pulsation accounts for more than 10% in the flow impact area. The spanwise and streamwise velocity pulsation dominate in the stilling basin, and the vertical pulsation is weaker, while the spanwise pulsation at the centerline of the bottom floor in the collision area decreases rapidly. The streamwise and vertical vorticity on the centerline of bottom floor and surface outlet are characterized by “small time-average value and large pulsation value”, and the transverse-axis vortex caused by the drop sill has a large influence on the distribution of the spanwise vortices. The results of turbulent kinetic energy spectrum analysis show that the pulsations in the stilling basin mainly consist of a large number of low-frequency pulsations below 1 Hz, indicating that the turbulent fluctuations in the stilling basin are mainly controlled by large-scale and low-frequency vortices. The analysis helps to deepen the knowledge of the flow characteristics of flood discharge and energy dissipation structures such as stilling basins, and provides a certain reference for design and safety assessment.

In order to solve the contradiction between ecological maintenance and agricultural production water demand, the minimum water demand for ecological restoration in arid areas was determined. The lower reaches of the Tarim River were selected as the typical study area. Suitable areas for vegetation growth were determined through the PNV (potential natural vegetation) simulation method, while the suitable growth ranges and distribution areas of trees, shrubs, and grasslands were analyzed. The minimum ecological water demand for the lower reaches of the Tarim River was calculated by the multi-year evapotranspiration data from the AET dataset. The results show as follows. The PNV results obtaine in the lower reaches of the Tarim River are dominated by shrubs, forests and grasslands are highly dependent on water resources, mainly distributed around river channels. The forest and grassland in the study area show high growth potential, while the shrub distribution areas far from the river show a degradation trend. According to PNV simulation results, the ecological water demand in the lower reaches of Tarim River is about 11 279.23×10⁴ m³, of which shrubs account for the largest proportion, while woodland and grassland account for 7.4% and 5.36% respectively due to their small areas. The research results provide a new method for ecological restoration and determination of water transport capacity in arid areas, which can clarify the scope of ecological restoration and vegetation types in the basin, and contribute to the management and optimal allocation of regional water resources.

The internal damage of the steel rail is serious, but the non-destructive testing B-display detection image has a lot of noise and noise, and the spatiotemporal distribution characteristics of different damages are not obvious, making it difficult to effectively identify. In response to this situation, a rail screw hole crack B-image recognition algorithm based on improved YOLOv8 was studied to improve the accuracy of intelligent identification of rail damage. Firstly, to reduce the missed detection of small damage targets, RepHGNetv2 network was used to optimize the YOLOv8 backbone network and improve the detection recall rate. Then, in order to improve the adaptability of the model to different types of damage detection, the detection head of YOLOv8 was replaced with Effientnet to improve the detection accuracy of the model. Finally, the LSKA attention mechanism was introduced into the SPPF module to enhance the model’s anti-interference ability against noise signals and improve its accuracy. The actual line detection results have verified that the detection accuracy of the above model reaches 95.1%, the recall rate reaches 93.8%, and the average accuracy reaches 97.6%, which is improved compared to other commonly used algorithms.

Aiming at the problems of poor real-time detection, low accuracy, and false detection and omission of pavement disease detection including hole and crack, an improved algorithm based on YOLOv9 was proposed to resolve the problem. Firstly, AKConv (alterable kernel convolution) was introduced into the backbone network to replace the convolution module in RepNCSPELAN4, which improves the feature extraction ability of the network for different diseases and effectively solve the problem that road disease is difficult to distinguish from background environment features. Secondly, selective image attention mechanism (SimAM) and DySample sampling modules were introduced to focus on the key information in the detection head, and the capability to extract information features was enhanced more efficiently. Finally, the inner-IOU function was used to optimize the weight parameters of the model to improve the learning ability of mixed samples. The experimental comparison between YOLOv9-c and our model showed that the accuracy, recall rate and MAP of the improved model are increased by 40.17%, 15.99% and 20.95% respectively. The performance has been significantly improved, and the detection effect is more accurately and efficiently, and the accuracy and generalization ability of pavement disease detection algorithm are improved.

When tunnels with extra-large cross-sections pass through layered rock formations, given the unique structural characteristics of these layers, suitable control strategies must be implemented to mitigate any adverse impacts. Based on the extra-large section tunnel project of Chongqing Guobo Center Station passing through layered rock, numerical simulations, and field monitoring are adopted in this study to compare the mechanical differences between balanced and unbalanced anchor cable supports during tunneling. The results show that compared with the balanced anchor cable support, the influence of the unbalanced anchor cable support on the control difference of displacement and plastic zone of surrounding rock is not significant. However, the unbalanced anchor cable supportcould significantly affect the bending moment distribution range of the vault of lining structure, while having little influence on the peak bending moment and the distribution and magnitude of axial force, with the differences between them being 8% and 11.2% respectively. The reduction in the safety redundancy of the lining structure under the unbalanced anchor cable support is less, and the stability of the tunnel in layered rock could still be maintained. From the perspectives of economy and construction convenience, the amount of anchor cable could be saved by 51%, and the economic advantages are more prominent, which is more advantageous in the construction of tunnels with large sections in layered rock. The effectiveness of the unbalanced support in maintaining tunnel stability is confirmed by field monitoring. The discrepancy between the numerical simulation and field monitoring results is merely 2 mm, demonstrating high consistency and validating the accuracy of the calculations. Critical insights for the design and construction of future similar tunnels in layered rock are provided.

The cornering stiffness of automobile tires is closely linked to the vehicle’s handling characteristics, and accurately estimating the tire cornering stiffness in real time is of significant importance for enhancing the stability of vehicle handling. Addressing the challenge of direct measurement of cornering stiffness, a real-time identification method based on the estimation of tire lateral force and slip angle was proposed. Firstly, considering the influence of longitudinal force on lateral force, a tire lateral force estimator was designed based on the yaw dynamics model and sliding mode observer algorithm, followed by the design of a slip angle feedback estimator based on the estimation error of lateral force. Secondly, a nonlinear tire force model that describes the relationship among tire lateral force, slip angle, and cornering stiffness was established. Taking the real-time estimated lateral force and slip angle as inputs, a recursive least squares online identification algorithm with limited memory was designed to address the issue of estimation error due to “data saturation” and improve identification accuracy. Finally, joint simulation experiments using Simulink and CarSim were conducted. The experimental results indicate that the estimation error of tire lateral force is approximately 4.153 9% on average, while the estimation error of tire slip angle is 3.285 2% on average. The identification model based on the recursive least squares method is robust to changes in road conditions, demonstrating good tracking accuracy and stability under both high and low adhesion conditions, with an average estimation accuracy of tire cornering stiffness of approximately 98.379 3%.

Aiming at the U-turn scenario of autonomous vehicles in two-way single lanes, a safety decision-making method was proposed by fuzzy reasoning, and a U-turn mathematical model was established based on the spatial distribution relationship of vehicles, seven key control points were determined, the search strategy of particle swarm optimization was improved, and an efficient and comfortable U-turn trajectory planning method was proposed. The safety decision-making method firstly establishes a membership relationship between the relative distance between the vehicle and the vehicle on the target lane and the minimum safety distance during steering when making a U-turn, and selects the time with higher safety to make a U-turn. The trajectory planning method combines the spatial distribution characteristics of vehicles, improves the constraints of particle swarm optimization, and proposes a new search strategy, which can quickly converge to the optimal extreme value and plan the optimal path of U-turn. The results show that the proposed decision-making and trajectory planning methods can complete the U-turn safely and efficiently.

To explore the impact of the community built environment on the walking time of elderly people, and considering gender differences among the elderly group, a CatBoost model was constructed and the SHAP (Shapley additive explanations) explanation method was integrated. The relative importance and nonlinear relationships of the community built environment features with the walking time of elderly people of different genders were comparatively analyzed. The study findings indicate that the community built environment variables have a more significant influence on the walking time of the elderly compared to personal socioeconomic attributes. However, the impact varies between genders. Compared to elderly males, elderly females pay more attention to built environment variables closely related to social needs, such as NDVI and population density. In contrast, the walking time of elderly males is more closely associated with personal socioeconomic attributes, often linked to transportation convenience and travel efficiency.

The response of an aircraft engine to bird strikes has the fan blade as its primary component, and the flight safety of the aircraft is directly impacted by the dynamic damage caused by stress changes. A three-dimensional model of a near-real bird body was established in this paper based on the structural features of the “bar-headed goose”. The dynamic damage of the blade was studied in consideration of the take-off-climb and approach-landing stages where bird strike accidents are most likely to occur for aircraft, with the effects of different impact speeds, fan blade speeds, and bird impact attitudes being taken into account. It is indicated by the results that the axial damage and deformation of fan blades tend to be increased monotonically with the increasing of aero-engine speed and relative velocity of bird strike blades. Additionally, as the fan blade speed is increased, the stress peak value after a bird strike shows a V-shaped trend, with the smallest stress peak value being occurred at 2 005 r/min. Furthermore, as the contact area between the bird body and fan blade at the initial collision moment is increased, both the stress and damage degree of the blade are gradually increased across different postures. When impacted at a 90° posture, the axial damage deformation of the blade is reached to 60.887 mm. Valuable reference for anti-bird strike design considerations for aero-engine fan blades is provided by these research findings.

The perforated walls of transonic wind tunnels with different parameters have a considerable influence on the flow field quality of the test section, therefore, the characterization of the perforated wall parameters is extremely essential for the design of the test section of transonic wind tunnels. The relationship between the characteristic parameters near the perforated wall of three-dimensional and two-dimensional perforated wall models was studied using the single straight perforated hole of the FL-3 wind tunnel. The mass and velocity distributions of the two-dimensional and three-dimensional perforated wall show obvious linear characteristics under different pressure difference coefficients. It is proposed that the two-dimensional perforated wall can be equivalent to the flow characteristic parameters of the three-dimensional perforated wall by the corresponding coefficient transformation under the same incoming flow Mach number when the wall pressure difference coefficient and the boundary layer displacement thickness are satisfied. A two-dimensional calculation model of the transonic wind tunnel was established, and the effects of perforated wall parameters and free stream Mach number on the flow field and flow characteristic parameters near the wall in the test section were analyzed by numerical method. When l / d = 1, the increase in perforated wall size makes the wall pressure difference coefficient increase, otherwise, the relative area of flow in the perforated wall decreases. As d = 2 mm, the flow field was proposed. When l / d > 2, ΔC_p tends to be stable. When l / d = 3, m' and S / d are the maximum values, in the Ma = 0.8 ~ 0.9 range, m' is positively correlated with the incoming Mach number, but ΔC_p changes little. The pressure difference coefficient and velocity component obtained under different perforated wall parameters have certain guiding significance for understanding the perforated wall flow and adjusting the perforated wall of the test section.

The extremely short-term prediction of seaplane motion can provide the rocking motion posture over the next few seconds, which is considered essential for ensuring safety during take-off and landing phases under adverse wind and wave conditions. Although some research has been conducted on extremely short-term prediction methods for seaplane motion, limited attention has been given to analyzing differences in the applicability of various methods. In this context, the NACA TN 2929 aircraft was taken as an example, and the three degree of freedom motion simulation data under typical working conditions were calculated based on potential flow theory. To compare the forecasting performance under different forecasting conditions, three typical extremely short-term prediction of seaplane motion models, namely AR (auto-regressive), LSTM (long short term memory), and TCN (temporal convolutional network), were constructed. The results show that compared to the AR model, the LSTM and TCN neural network models exhibit superior forecasting accuracy for longer prediction durations, effectively enabling accurate predictions of the heave, roll, and pitch motions of the seaplane at the ten-second level, providing a valuable theoretical reference for the selection of seaplane motion prediction algorithms.

To determine the presence of sulfhydryl groups on natural aquatic biofilms and their adsorption characteristics for typical heavy metals, a method for sulfhydryl masking in the biofilms based on a specific masking agent was established in this study. Based on this method, surface concentration of sulfhydryl group in natural biofilms and their adsorption characteristics for typical heavy metals, including Cu, Pb, and Cd, at different pH were investigated. The results indicate that the established masking method can effectively mask sulfhydryl groups and has little effect on microorganisms in biofilms. There are relatively low concentrations of sulfhydryl groups on the surface of natural biofilms, with a concentration of (5.8 ± 0.6) μmol/g, accounting for 5.7% of the total site concentration on the biofilms. Despite the low concentration of sulfhydryl groups, their stronger metal binding capacity makes them significantly contribute to metal adsorption when the metal concentration is low (the theoretical loading of heavy metals by the biofilm is less than 1.0 μmol/g). This pattern is essentially unaffected by the pH of the adsorption system and the type of heavy metal. This proves that sulfhydryl groups in biofilms also have an important impact on the behavior and risk of heavy metals in natural aquatic environments with low metal content, further highlighting the environmental significance of natural biofilms.

The Chishui River Basin has been recognized as an important ecological security barrier in the upstream of the Yangtze River Basin. Research on the ecosystem service value of the Chishui River Basin under different future development scenarios is of great significance to carry out the environmental protection policies and the environmental protection measures. Therefore, Chishui River Basin has been selected as the research area and the FLUS (future land use simulation) model and InVEST (integrated valuation of ecosystem services and trade off) model were functioned to predict the ecosystem services, including water yield, soil conservation, and water purification, under three scenarios, such as natural development, environmental protection, and economic development in 2040. The high ecosystem services functions in all three scenarios are identified as the key protected areas. The results show as follows. There are significant changes in agriculture area and urban area under different scenarios, especially, the agriculture area under the economic development scenario has increased 510.55 km² and 1 475.76 km² compared to the natural development and environmental protection scenarios, respectively. In the environmental protection scenario, the areas with high water yield (>700 mm) and high soil conservation function (>2 000 t/hm²) account for approximately 36.81% and 47.15% of the Chishui River basin area, respectively. The proportion of key protected areas for ecosystem services functions in the Zunyi City account for approximately 62.65%. The results of this study aim to provide certain support for identifying key protected areas in the Chishui River Basin and promoting the implementation of spatial refinement protection and management.