Groundwater plays a pivotal role in the production and sustenance of life. However, the potential geologic risks associated with its exploitation must be acknowledged. Changes in groundwater levels have been shown to precipitate geologic disasters such as landslides, mudslides, and ground subsidence. Therefore, the mastery of groundwater information is of great scientific significance for disaster prevention, mitigation, and the rational use of water resources. The temperature tracing method is recognized as a promising technique with significant applications in preventing and providing early warning of geological hazards, such as landslides and mudslides. Among the many methods available, this technique was noted for its great potential. The recent groundwater exploration methods, theoretical research, and new indoor experimental research methods was focused on. The latest research progress related to the groundwater method of geothermal inversion in the seepage of rivers and dams, landslides, and groundwater exploration was reviewed. Through comparative analysis with the traditional electric method of exploration, current theoretical models and new problems faced by the practice of engineering exploration were analyzed. Future research should focus on multi-field coupling, multi-parameter integration, analysis of groundwater patterns in special soil sites, dynamic monitoring of groundwater for major projects, and early warning and prediction of geological disasters will be focused on. These research directions will provide essential scientific and technical support for the prevention of geological disasters and water resources management.

The velocity measurement of trunk canals and rivers is regarded as an important basis for water resources management and flood prediction. The techniques and methods for trunk canals velocity measurement based on machine vision were analyzed and synthesized. Particular focus was placed on reviewing the principles, technologies, and recent developments of particle image velocimetry, particle tracking velocimetry, space-time image velocimetry, optical flow methods, and deep learning-based flow measurement methods in recent years. Finally, the existing challenges and issues were addressed, and potential future development directions were proposed.

Squeeze film damper (SFD) is a commonly used vibration reduction device in rotating machinery such as aero-engine. With the development of aviation science and technology, many new structures of SFD have been derived and developed. The categories of new structural squeeze film dampers from the aspects of structural characteristics, vibration reduction characteristics, vibration reduction effects, and application situations were summarized. Besides, the current research status of new structural squeeze film dampers in China in recent years were also summarized. The shortcomings of current research on new structural squeeze film dampers were pointed out, and an outlook of proposes directions and prospects for future research on new structural squeeze film dampers was made. Besides,the application prospects of new structural squeeze film dampers were pointed out. The results provides a reference for the application and selection of new squeeze film dampers in the vibration reduction design of rotor systems in rotating machinery such as aero engines.

In order to evaluate the development potential of coalbed methane in Yangjiapo block on the eastern margin of Ordos Basin and optimize the division method of its development geological units, the geological, resource, reservoir, hydrological and commingled production geological conditions of No.4+5 and No.8+9 coal seams in the block were described by reservoir fine description technology. By using the analogy method and multi-level fuzzy comprehensive evaluation method, the geological conditions of the Baode block in the north were compared, the difficulties of CBM development in the Yangjiapo block were analyzed and summarized, and the evaluation of CBM development potential and division method of development geological units were optimized. The results show that the resource conditions of a single coal seam in the Yangjiapo block are poor, and the cumulative resource abundance of the two coal seams is 1.08×10⁸ m³/km². The reservoir permeability is good, the difference in physical properties between the layers is small, and it has the potential for combined layer development. Faults have an important influence on the gas content and permeability of coal reservoirs within 300 m, and increase the risk of communicating with roof aquifers. The geological unit division method of coalbed methane development was applied to evaluate the development potential and commingled production compatibility of coalbed methane in the Yangjiapo Block. The six geological development units were divided into three levels of potential areas. It is pointed out that the middle part of Yangjiapo block is suitable for commingled development, some areas in the northwest are suitable for commingled development, and the northeast is suitable for replacement development.

The study area is located in the Dongxiang Volcanic Basin in the middle part of the Qin-Hang mineralization belt, where several gold-lead-zinc (Au-Pb-Zn)-based mineralization and mineralization sites have been detected. Based on the principle of “from the known to the unknown”, combineing the results of physical and chemical exploration, such as the excitation middle gradient measurement and soil geochemistry, the characteristics of the combined parameters of the gamma spectra of Qiaoxi (HS-1) and Oujia (HS-2) was compared and analyzed, it was concluded that the anomaly centers of the characteristic parameters of the ground γ-ray were more concentrated than those of the anomalies obtained from soil geochemistry measurements. It was believed that the anomaly centers of the ground γ-ray energy spectral parameters were more concentrated than those of the soil geochemical measurements, and the F parameters were positively correlated with the anomalies in the K alteration zone, and the N parameters were positively correlated with the anomalies in the Na chemotaxis zone; and it was hypothesized that the HS-2 area has a relatively good prospect of gold polymetallic prospecting. The results provides a new theoretical basis for unit and super-unit delineation of volcanic areas, and proves that in practical work, the geological situation and characteristics of the area was comprehensively analyzed, and the appropriate combination of parameters was determine to indirectly explain the nature or cause of mineralized alteration in the area, which is more instructive than the traditional single-element spectral study.

The Shetianqiao Formation of the Devonian in central Hunan is an important shale gas reservoir in the Taihetang- Guanjiazui area. However, limited understanding of its geophysical and geochemical signatures has hindered the gas reservoir's large-scale exploration and exploitation. A research and development methodology was presented here based on sample analysis and geophysical characterization. Specifically, the method utilized quantification of shale gas occurrence characteristics and distribution patterns in the Shetianqiao Formation using sample analysis and the magnetotelluric (MT) geophysical data inversion technique. The results show that the overall resistivity of favorable layers in the Shetianqiao Formation is less than 200 Ω·m, with a thickness range of 600 to 900 meters and an average burial depth of 0~1 000 meters. Furthermore, the MT method reveals 15 faults and their associated multiplexing segments. It is also confirms that the organic matter in the Shetianqiao Formation is primarily II₂, with an organic carbon content ranging from 0.32% to 3.46% and an average at 0.80%. The average maturity R_o of the formation is 1.78%, indicating a high maturity stage.

In order to enhance the understanding of the characteristics and potential of geothermal resources in the Dunhuang Basin to support exploration and development initiatives, through the application of geothermal exploration techniques and hydrogeochemical analysis, the methods including hydrogen and oxygen isotope testing, Piper diagram analysis, the K-Mg geothermometer, and thermal storage estimation was used to investigate the chemical properties of geothermal water, the recharge source and age, as well as to calculate the thermal storage temperature and geothermal water circulation depth. Furthermore, the potential of geothermal resources in the basin was comprehensively evaluated. The results indicate that the thermal reservoir is primarily composed of argillaceous sandstone, pebbly fine sandstone, and glutenite located in the lower member of the Neogene Shulehe Formation, with the caprock consisting of Quaternary loose rock as well as mudstone and sandy mudstone in the upper member of the same formation. The primary heat source is heat conduction from the upper mantle and deep crust. The geothermal waters are dominated by Cl·SO₄-Na hydrochemical components, with temperatures ranging between 28.7 ℃ and 38.0 ℃ and total dissolved solids (TDS) values varying from 1 146 mg/L to 3 250 mg/L. Isotopic analysis, including δD, δ¹⁸O,³H, and¹⁴C, reveals that the geothermal water represents a mixture of deep groundwater and modern precipitation. The estimated reservoir temperatures range from 39.49 ℃ to 42.75 ℃, and the calculated circulation depths are between 1 020.65 m and 1 268.34 m. These findings suggest that the geothermal resources in the Dunhuang Basin are derived from deep groundwater circulation, replenished by atmospheric precipitation from the southern mountainous regions. The calculated thermal potential modulus of the geothermal fluid is 1.78 × 10⁹ kJ/(km²·a), indicating significant resource potential for future utilization.

A large number of towns in the western mountainous areas are within the hazard range of disaster chains such as landslides and debris flows, making the safety and disaster risk of mountain towns a key focus. The landslide-debris flow disaster chain in Lijie Gully, Lijie Town, Zhouqu County, Gansu Province was taken as a case study. Based on field investigations and remote sensing analysis, surface deformation was analyzed using interferometric synthetic aperture radar (InSAR) technology to determine the source of the disaster chain. The RAMMS dynamic model was then employed for the process and risk analysis of debris flows, followed by vulnerability and risk assessment. The research results indicate these as follows. The main subsidence areas within the basin are located at the edge and top of the ancient landslide area above Lijie Gully on the North Mountain, with the main cause of deformation being the seasonal freezing and thawing of soil. Based on the deformation results and remote sensing image analysis, the RAMMS dynamic model is used to conduct risk analysis of the Lijie Gully debris flow under three rainfall frequencies of 1%, 2%, and 5%. The comprehensive evaluation results show that the high-risk area accounts for 70.51%, largely distributed in the loose accumulation surface of the North Mountain landslide, the gully, both banks of the gully, and the accumulation fan at the gully mouth. Based on the risk and vulnerability assessment results, the high-risk area of the Lijie Gully debris flow accounts for 13.10%, mainly distributed in the gully mouth and along both sides of the gully where buildings are located. The large volume of loose accumulation above Lijie Gully is still in a continuous process of creeping-deformation-sliding, providing a large amount of material source for debris flows. The risk zoning results under different rainfall frequencies provide a reference for the disaster reduction of urban debris flows.

Suction anchors are widely used as a foundation type in deepwater environments. Unlike homogeneous soil, the mechanical penetration characteristics of suction anchors in layered soils are extremely complex, influenced by the variation in soil properties and interfaces between layers. Therefore, it is necessary to study the mechanical behavior of suction anchors during penetration in layered soils. An Euler-Lagrange coupling method was used to simulate the penetration process of suction anchors, and numerical simulations were validated through model tests. The mechanical penetration characteristics of suction anchors in layered soils were analyzed. The results show that, influenced by the mechanical properties of layered soils, vertical stress gradually increases during penetration, forming a vertical stress arch at the anchor tip. The stronger the soil strength, the more concentrated the vertical stress. Additionally, when a suction anchor penetrates from silty clay into silty clay, the mechanical properties of the soil change, causing the side friction and end resistance to increase rapidly. This study, which combines experimental and numerical methods, investigates the mechanical behavior of suction anchors during penetration in layered soils and provides valuable references for deep-water suction anchor penetration in layered soils, offering significant engineering practical value.

Schizophrenia is a persistent mental disorder manifested by significant abnormalities in perception, emotion, and behavior. Nevertheless, the neural mechanisms underlying this disorder are still not fully understood. In order to explore the differences in whole-brain causal connectivity between patients with schizophrenia and healthy controls in the resting state, a hierarchical degree (HD) index was proposed based on eigenmode method to overcome the inadequacy of node degree measured at a single level in traditional graph theory. It was found that the node degree of the whole-brain causal network of schizophrenia patients reduced. In addition, the most significant changes in in-degree were found in the motor system, whereas the most significant changes in out-degree were found in the default mode system. Higher-order node degree was further extracted and found to be superior to traditional graph theory degree in distinguishing schizophrenia patients from healthy controls based on a machine learning approach, and more accurately predicted positive and negative symptoms of schizophrenia, suggesting that higher-order network features can be used as biological indicators of schizophrenia. The findings of this paper reveal abnormal higher-order network features of schizophrenia and contribute to the advancement of objective diagnostic technologies for schizophrenia.

In order to explore the deformation and failure mechanisms, as well as degradation characteristics of silt slopes under rainfall conditions, a large-scale slope model test apparatus was designed with silt slopes as the research subject. Multi-sensor internal monitoring and 3D laser scanning technology were applied. During slope instability under a rainfall intensity of 30 mm/h, data on moisture content, pore water pressure, soil pressure, and deformation and failure characteristics at various slope locations were collected. Results indicate that sensors at the slope toe have the fastest response, showing the highest rate of change. Rainwater accumulates at the slope toe, causing horizontal seepage, which leads to rapid increases in moisture content at the toe and lower middle sections of the slope, along with a reduction in soil shear strength. Under rainfall intensity conditions, the deformation and failure of silty slopes initiate at the slope toe, where small-scale collapses first occur. These progressively develop into transverse through-cracks, accompanied by minor-scale failures that extend upslope. Ultimately, these processes lead to overall slope failure. The findings offer theoretical insights to support engineering construction and protection in silty slope regions.

To study the surface deformation law of insufficient mining goaf with thick loose layers after grouting, and evaluate the effectiveness of grouting and filling method in treating strip goaf areas, taking a thick loose layer strip goaf of Daizhuang Coal Mine in Jining as an example, theoretical analysis, numerical calculation, surface deformation observation, and deep rock optical fiber monitoring methods were used to compare and analyze the surface deformation laws of the goaf before and after grouting treatment. The results show these as follows. Under the influence of superposition of strip type insufficient mining in the goaf of thick loose layer, the subsidence of goaf decreases during the active period of surface movement, while the residual deformation increases in the later period, the maximum total subsidence of goaf is 611.8 mm and the residual subsidence is 157.8~288.1 mm by using probability integral method and numerical calculation, the surface residual deformation of goaf has great influence on the proposed high-rise buildings. The cumulative subsidence of the surface in the goaf treated by grouting filling method during observation period after grouting is -5.6~-1.5 mm. The comprehensive analysis of deep distributed optical fiber monitoring and surface leveling observation shows that the subsidence of the overlying rock in the goaf after grouting is less than 1 mm/a, and the analysis shows that the surface deformation after grouting is mainly caused by the settlement of the upper thick loose layer. The adoption of grouting filling method can significantly shorten the movement time of overlying strata in the goaf and effectively reduce the deformation of deep strata in the goaf, calculate and analyze the surface deformation value after grouting tends to be stable, the goaf site is in a stable state, and the grouting treatment effect is good, meeting the needs of high-strength engineering construction. The research results can provide guidance for surface deformation prediction, site stability evaluation and treatment effect detection after grouting in goaf under similar conditions.

In order to reveal the influence of strain rates on the macroscopic failure characteristics and microscopic crack propagation laws of rock, sandstone was taken as the research object, and the uniaxial compression tests and real-time monitoring of acoustic emission information were carried out under different loading rates. The influence of loading rates on the macroscopic mechanical response and microscopic fracture morphology of sandstone specimens, such as strength and deformation characteristics, failure mode and fracture characteristics, was analyzed. Based on the evolution of acoustic emission b value with the loading process, the internal crack propagation laws of sandstone specimens under different loading rates were explored. The research results show that within the loading rate range of 1×10^-5~1×10^-2 s^-1, the uniaxial compressive strength and elastic modulus of sandstone samples were positively correlated with loading rates. For every 10 times increase in loading rate, the uniaxial compressive strength and elastic modulus increased by 2.66 MPa and 0.087 GPa, respectively, while the peak strain decreases by 0.213‰. As the loading rate increased, the failure characteristics showed a trend of gradually transitioning from a single inclined through fracture surface to a cross distribution of multiple fracture surfaces, and the average size of the fragments decreased, indicating an increase in the failure of the sandstone sample. At low loading rates, the microstructure of the fracture surface was mainly characterized by intergranular cracks, while as the loading rate increased, transgranular cracks and intergranular cracks alternated, and the fracture characteristics at the intersection of the cracks were obvious, resulting in large-scale grain peeling. As the loading rate increased, the ratio of stress to peak stress corresponding to the turning point where the acoustic emission b value changes from increasing to decreasing decreased. This indicated that the higher the loading rate, the more likely cracks were to propagate inside the specimen and form more obvious large cracks, leading to more severe damage characteristics and complex crack propagation patterns in the specimen. The research results have important guiding significance for understanding the failure characteristics of engineering surrounding rock under complex stress conditions, as well as predicting the damage deterioration law of the internal structure of surrounding rock based on acoustic emission monitoring information.

The traditional geological method relies too much on the resolution of seismic reflection and the quality of well data for the determination of geological profiles. In view of the fact that the number of well data that can be used for calibration in the early stage of development is very small, and the traditional geological modeling method generates geological profiles. The efficiency is low and it is difficult to support model establishment and frequent updating. A geological profile generation method based on improved Pix2Pix network was proposed. Firstly, the initial three-dimensional data was sliced. Based on the comprehensive analysis of deep learning network, a Pix2Pix network model based on residual and multi-scale discriminator was constructed. The residual mechanism was introduced in the generator part to improve the learning ability of the network to geological features, and a multi-scale discriminator was set for the model to enhance the discriminant performance of the network. The real seismic reflection data and geological profile data of the oilfield were used to train the model. The experimental results show that the performance of the network model is significantly improved after the introduction of residual mechanism and multi-scale discriminator. The SSIM (structural similarity) score of the generated results and the real geological profile can reach 91.89 %, and the geological features in the generated results are highly fitted with the actual situation.

In order to explore the problem of fracturing fluid filtration in the process of open hole fracturing, based on the classical filtration model, combined with the two-dimensional model of hydraulic fracturing, ABAQUS finite element software was used for simulation calculation. It is found that in the initial stage of fracture propagation, the pore pressure increases rapidly and increased linearly, and the filtration loss of fracturing fluid also increase rapidly, which is the initial stage of fracture propagation. With the continuous injection of fracturing fluid, the increasing trend of pore pressure becomes slower, and the filtration loss of fracturing fluid also increases slowly. As the crack width gradually widens, it is the crack propagation stage. In the later stage of fracture propagation, when the fracture length reaches a certain length, the change of pore pressure is gradually stable, and the filtration loss of fracturing fluid is slightly reduced compared with the pressure holding state and the fracture propagation stage. In the whole process, the calculation of filtration loss takes into account the dynamic expansion of cracks, which is of certain significance for the actual engineering filtration situation.

To address the issue of composite scale in dense sandstone formations, an oil-in-acid microemulsion unblocking agent comprising acid, white oil, composite emulsifiers, and additives was developed. The internal structure and morphology of the microemulsion unblocking agent were characterized using transmission electron microscopy (TEM) and dynamic light scattering (DLS). Its performance was assessed in terms of thermal stability, dissolution efficiency for inorganic and organic scale plugs, and impact on reservoir permeability. The mechanism of unblocking was investigated using X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS). The microemulsion unblocking agent exhibited a spherical dispersion in solution with an average particle size of 6 nm, and maintained stability at 90 ℃ for over 48 h. The dissolution rates for scale formed at different locations were 87.61% and 77.41%. The microemulsion unblocking agent the ability to transition from oil wettingto water wetting, and proved more efficient and faster in resolving complex scale plugging compared to traditional acid solvents. The microemulsion unblocking agent enhanced core permeability recovery rates to over 80%. Results highlight the advantages of the microemulsion unblocking agent, including excellent thermal stability, high scale dissolution capacity, and effective removal of both organic and inorganic composite scales, They can efficiently eliminate obstructions in the wellbore and near-wellbore regions, thereby clearing the wellbore and enhancing the flow paths in the formation and increasing oil and gas production capacity.

Complex equipment such as wind turbine blades faces both performance degradation and random shocks. There are two interdependent relationships between these two failure modes: the interdependence between internal factors in the degradation process and the interdependence between degradation and shock processes. These characteristics pose challenges to reliability analysis. To solve this problem, a new mutually dependent competing failure processes (MDCFPs) model was proposed based on two MDCFPs models. This new model integrated two interdependencies. Taking the wind turbine blade stiffness degradation model based on Gamma process and the extreme shock model based on homogeneous Poisson process as examples, the accuracy and differences of three models were analyzed using the control variate method, and the influence of key parameters was studied. The results show that, under the same conditions, the new model's reliability is closest to the observed empirical reliability, with an absolute error of no more than 0.12. At the same time, the new model's reliability is lower than that of the two base models, with maximum absolute errors of 0.26 and 0.40, respectively. After adjusting the parameters of the new model, the absolute errors in reliability compared to the base models are limited to 0.03 and 0.02. These findings suggest that the new model effectively accounts for interdependencies among factors, prevents overestimation of reliability, and can replace base models, demonstrating broader applicability.

Bearing local fault occurs in the turboprop engine reduction gearbox, which affects engine running safely. Due to the complex structure and high speed of the reducer gearbox, the vibration signal used to monitor the operation state of the mechanical components is complicated. In order to detect the bearing local fault signal in time and extract fault feature accurately from the vibration signal, a method which was based on vibration signal combining FFT, fast kurtogram and envelope spectrum was proposed. Firstly, during the engine operation, the FFT(fast Fourier transform) spectrum was used to detect whether there was bearing fault component in the vibration signal; and then the fast kurtogram was applied to determine the frequency band distribution of the fault component; finally, the bearing fault characteristic frequency was acquired through envelope spectrum analysis. In the course of certain type of turboprop engine ground bench test, this method was used to accurately detect and diagnose the local spalling fault of the inner raceway. Therefore, this method can provide a basis for the engineering application on local fault detection and diagnosis of bearings in aero-engine reduction gearbox.

The slewing and luffing coupling motion of tower cranes can easily induce structural vibrations in the crane mast and swing angles of the payload, potentially leading to operational faults. To investigate these vibration patterns, an elastic crane model was developed under combined slewing and luffing dynamic motions, incorporating Lagrange dynamics, air resistance, and beam deflection. The model was analyzed across phases from acceleration to constant speed and then to deceleration. The effectiveness of this nonlinear coupled motion model was validated using a designed experimental platform. The study examined the effects of varying accelerations and initial swing angles. The results indicate that luffing acceleration influences structural vibration, while slewing acceleration has a significant impact on it. Additionally, initial angles greater than or equal to 0.2 rad greatly affect structural vibration. When the slewing acceleration exceeds 0.04 m/s², the frequency of mast vibration increases. Understanding the structural vibration law during coupled motion is crucial for enhancing the design of dynamic systems.

Considering the complex loading environment of the floating offshore wind turbine(FOWT), especially the fatigue damage risks brought by continuous and periodic wave actions, the fatigue performance of FOWT under wave coupling excitation was studied and a long-term fatigue damage assessment method for FOWT was proposed. Taking the Spar FOWT as an example, a nonlinear model under the wave coupling excitation of 8-DOF (dgree of freedom) was established based on the Lagrange equation, and the accuracy of the model was verified. Subsequently, on the basis of the established nonlinear model, the fatigue performance of the example FOWT was discussed according to the proposed method. The results indicate that the fatigue damage of FOWT is closely related to the wave load characteristics, and different damage performance is exhibited under different working conditions. Due to the randomness of sea conditions, only short-term fatigue estimation of the wind turbine is not enough to accurately understand its fatigue performance, and long-term fatigue analysis is needed. Moreover, the peak of short-term fatigue damage at the root of the wind turbine tower occurs near the tower's natural vibration period, while the peak of long-term fatigue damage occurs within the range of high probability sea state periods in the sea. Therefore, efforts should be made to avoid the natural vibration period of the FOWT coinciding with the peak period to prevent high-level damage accumulation and reduce fatigue damage. The analysis also demonstrates the effectiveness of the proposed Monte Carlo based long-term fatigue calculation method for FOWT, which not only has high accuracy but also less time consumption. The proposed improvement method can reduce output fluctuations, enhance stability, and provide more precise results.

Hydrogen-injected into natural gas for pipeline transportation is an effective way to solve the high cost of hydrogen transportation. The rapid and uniform mixing of hydrogen and natural gas is an unavoidable problem to ensure the safety of hydrogen-blended natural gas pipeline transportation. Because the mixed gas flows back to the mixing pipe through the reflux pipe, it can continue to mix with the hydrogen-blended natural gas after entering, and improve the mixing uniformity. Therefore, a reflux mixer was used to mix hydrogen and natural gas. The effects of the inclination angle, diameter ratio and number of reflux pipes on the mixing properties of reflux mixers were analyzed by CFD. The results show that the reflux mixer has low pressure loss, high mixing uniformity and high reflux rate. When the inclination angle of reflux pipe, the diameter ratio of reflux pipe and the number of reflux pipe are 10°, 0.5 and 3, the reflux mixer achieves the best effect, and its pressure loss, mixing uniformity and the reflux rate are 76 Pa, 99.7% and 44.5%, respectively. The research results can provide reference for the parameters design and mixing performance of hydrogen-blended natural gas mixer.

In order to solve the problems of wind power fluctuations and intermittency during grid integration, which affect the stable operation of the power grid, a capacity optimization configuration scheme for a flywheel-lithium battery hybrid energy storage system was proposed. This scheme combined empirical mode decomposition (EMD) and variational mode decomposition (VMD). Firstly, typical daily data was obtained using the K-means algorithm, and EMD was applied to decompose the output power signal of these typical wind power daily data into grid-connected power that meets fluctuation limits and power that needs to be smoothed by the hybrid energy storage system. Then, the sparrow search algorithm was used to optimize the number of decomposition modes K and the quadratic penalty factor α in the VMD algorithm. By decomposing the power that needs smoothing using VMD, a reasonable allocation between lithium batteries and flywheel energy storage was achieved. Finally, considering the constraints of energy storage charging and discharging power and state of charge, an economic model was constructed with energy storage cost as the objective function. The actual power generation data of Qiejidunqu wind farm in Gonghe County, Hainan Prefecture, Qinghai Province were simulated and calculated by MATLAB platform. The results show that the proposed strategy not only effectively mitigates wind power fluctuations but also improves the overall economy of the system.

It is of great significance for accurate forecasting of multi-load to be carried out to improve the consumption of new energy, realize energy saving and emission reduction, and ensure the safe and reliable operation of the power grid. To enhance the accuracy of simultaneous multi-load forecasting,a model which singular spectrum analysis and bi-directional long short-term memory networks SSA-BiLSTM (singular spectrum analysis-bidirectional long short-term memory) was proposed. First, A approach Pearson correlation coefficients for coupled feature extraction was proposed to identify correlations and dependencies within multivariate load data. Then, SSA was employed for feature extraction to capture dynamic characteristics and reduced forecasting complexity. Finally, a multi-ask learning framework was introduced to leverage shared information among multiple forecasting tasks, improving prediction accuracy. Experimental using datasets from multi-area electricity, heat, cold multivariate loads, flexible and wind-solar power generation, the effectiveness of the model. The results show that the proposed model average improves in mean absolute percentage error (MAPE) for the prediction of electrical, heating, and cooling loads in multiple regions is 0.41%, with an average root mean square error (RMSE) increase of 0.02 MW.

In order to make rational and efficient use of biomass resources, considering the operating cost and environmental cost of each microsource unit, an economic dispatching model of combined cooling heating and power microgrid based on improved biomass gasification was designed. In order to solve the problem that the sparrow search algorithm is easy to fall into the local optimum, an improved sparrow search algorithm(ISSA) was proposed to solve the proposed model. First, a sine chaos map was used to generate spatially evenly distributed early sparrow populations. Secondly, a mutually beneficial learning mechanism was added and a mutation strategy was introduced to enhance the information sharing and global search ability among individuals in this field. Finally, by comparing the iterative results of ISSA, SSA, gray wolf algorithm, whale algorithm and marine predator algorithm, it is proved that ISSA has good optimization effect and stability. Through the analysis of typical simulation cases, the effectiveness of the ISSA algorithm in solving the economic dispatching problem of combined cooling, heating and power microgrid is verified.

In order to explore the connection between brain and vision and improve the clarity and accuracy of brain activity reconstruction video, a new method called high quality electroencephalogram video reconstruction (HQEEGVR) was proposed to reconstruct video from EEG (electroencephalogram) signals. Firstly, the masking spatio-temporal frequency fusion network (MSTFFNet), a three-branch EEG feature extraction network, was proposed to extract brain activity information from EEG signals and dig deeper into the semantics behind brain activity changes, spatio-temporal frequency information was extracted at the same time. Secondly, cross-modal contrast learning was introduced to align EEG, text and image features for use in the generation stage. Then, a cascade video diffusion model was proposed, specifically, the stable diffusion model was used to generate reference video frames based on EEG features, and then the video frames were used as references, motion vectors were integrated, and the video diffusion model was introduced to capture the video time features. High quality videos were ultimately generated. The results show that the model performs well in the reconstruction of the subject, motion, color and semantics of the video. It can be seen that the EEG signal can be used to capture the visual and semantic information of the brain activity, so as to reconstruct the video with high fidelity and visual authenticity.

To address the issues of low path planning efficiency, poor obstacle avoidance capability, and low path quality of the RRT-Connect algorithm in complex environments, an improved RRT-Connect algorithm was proposed. Firstly, a bidirectional goal bias strategy was introduced to enhance the goal-directedness and path planning efficiency of the algorithm. Secondly, an obstacle avoidance optimization strategy was proposed to increase the algorithm's active obstacle avoidance capability and passage ability in complex environments. Finally, a path recombination strategy and a smoothing strategy were added to optimize the generated initial path, reducing path length and the number of turns, and improving path quality. The improved algorithm was compared with other algorithms in three complex environments using MATLAB. Simulation results show that the improved algorithm has less planning time, shorter path length, fewer sampling times, and a higher success rate of path planning, demonstrating the effectiveness of the improved algorithm in complex environments.

Aiming at the nonlinearity and multi-disturbance problems of the electric regulating valve control system in the actual production process, a control method based on the improved ant colony algorithm to optimize the single neuron PID (proportional integral derivative) was proposed and applied to the valve opening control. The self-learning and self-adaptive ability of the single-neuron network was used to achieve the online tuning of PID control parameters. The improved ant colony optimization algorithm was adopted to optimize the learning rate and neuron ratio coefficients in the single-neuron PID, which effectively overcomed the shortcomings of the single-neuron PID where the learning rate and neuron ratio coefficients could not achieve the expected control effect due to the empirical setting. The simulation comparison results show that, compared with the traditional PID, single neuron PID, and single neuron PID based on ant colony optimization algorithm optimization of the three control methods, the control method proposed overshoots the amount of reduction of 10.2%, 6.1%, and 1.8%, respectively. At the same time, the regulation time is correspondingly shortened by 0.22s, 0.07s, and 0.03s. It shows a stronger adaptive and anti-interference ability, which can make the valve opening control more stable and reliable.

Network intrusion detection systems (NIDS) are critical for maintaining cybersecurity. However, due to the complexity of network traffic data and the issue of class imbalance, existing detection models often exhibit high false alarm rates and insufficient detection accuracy for different types of attacks. To address these challenges, an imbalanced learning method for network intrusion detection, based on topological data analysis (TDA) and named TopoSMOTE, was proposed. This method aims to balance the training dataset by generating new minority class samples. The core of TopoSMOTE lied in constructing topological graphs to synthesize new samples. Firstly, the method used TDA to map the spatial relationships and connection patterns in network traffic data, forming a topological graph. Then, based on the topological graph, a minority class sample selection strategy was designed, which synthesized new data by selecting the nearest neighbor samples with topological relationships in a low-dimensional mapped space. Experiments were conducted on two imbalanced datasets. The experimental results show that the TopoSMOTE method achieves higher detection accuracy and lower false alarm rates compared to advanced oversampling methods and intrusion detection models.

In order to solve the problems of inaccurate dense target recognition and difficult detection of small targets in bird recognition, a bird recognition algorithm based on improved YOLOv8 was proposed. Firstly, in order to solve the problem of difficult dense object recognition, the multi-scale linear attention mechanism EfficientViT was used to replace the backbone network to realize the global receptive field and multi-scale learning, improve the performance and efficiency of the model, and improve the dense object recognition effect. Then, in order to solve the problem that it is difficult to detect small target birds and is prone to missed detection, an efficient multi-scale attention EMA (efficient multi-scale attention) mechanism was introduced to realize cross-dimensional aggregation features through channel recombination, so as to better capture global information, realize multi-scale feature fusion, and reduce the probability of missed detection. The experimental results show that the mAP50 of the improved model on the benchmark dataset CUB-200-2011 and birds28 reaches 77.1% and 88.4%, respectively, which is 4.5 and 5.4 percentage points higher than the original YOLOv8 model, respectively, which verifies the effectiveness of the improved model.

Aiming at the limitations of current intelligent traceability and authenticity identification systems in extracting multiple surface texture features (such as continuous, non-continuous, etc.) of automotive components, a micro-visual and neural network-based automotive parts anti-counterfeiting feature extraction and automatic matching algorithm was proposed. This algorithm integrated artificial intelligence-based automatic matching technology with micro-visual image processing and a neural network hybrid algorithm for anti-counterfeiting feature extraction and identification of automotive parts. Initially, the micro-visual feature images of the automotive component surfaces were processed with frequency-domain transformation, filtering, and noise reduction. Subsequently, the texture types (including continuous, non-continuous, and contour types) were determined based on the two-dimensional frequency-domain features. For each texture type, an appropriate algorithm was selected from the algorithm library to extract and analyze key attribute feature points. Finally, a deep learning framework was constructed, and a micro-visual feature recognition model for automotive parts was built, which was then matched with a priori feature libraries to complete classification and authenticity determination. Experimental results demonstrate that the proposed algorithm effectively extracts and identifies anti-counterfeiting features on the surface of automotive components, achieving a significant improvement in accuracy compared to traditional methods. Through matching with the a priori feature library, the algorithm accurately distinguishes between genuine and counterfeit components, providing reliable anti-counterfeiting verification results. This method effectively addresses the complexity of extracting various surface texture features of automotive parts, enhancing the accuracy of anti-counterfeiting and traceability systems. The micro-visual and neural network-based automatic matching technology significantly improves the precision of authenticity identification, offering an innovative and efficient solution for automotive parts anti-counterfeiting.

In order to explore the strengthening effect of reinforced concrete beams strengthened by carbon fiber reinforced polymer reinforced geopolymer matrix (FRGM), the mechanical properties of reinforced concrete beams reinforced with flexural reinforcement reinforced concrete in FRGM system were studied by numerical simulation. Three-dimensional finite element models were established to simulate the failure mode, characteristic load and load-mid-span deflection curve of reinforced concrete beams reinforced by FRGM system, and the influence of the thickness and length of the FRGM layer and the pre-damage degree of the original specimen on the reinforcement effect was discussed. The results show that the thickness of the FRGM layer has no obvious effect on the ultimate bearing capacity of reinforced concrete beam, and the increase range is 72.29%~79.38%, but the stiffness of the FRGM layer is improved to a certain extent, up to 38%. The ultimate bearing capacity of reinforced concrete beams can be increased by increasing the length of the FRGM layer, but with the increase of the length of the FRGM layer, the increase of the ultimate bearing capacity gradually weakens, indicating that there is no linear increase relationship between the length of the FRGM layer and the ultimate bearing capacity. Compared with the original components, the bearing capacity of reinforced concrete beam members is improved to a certain extent after pre-damage reinforcement(36.5%~73.66%), which shows the effectiveness of FRGM reinforcement method.

The slope geological structure characteristics is detected with the high density resistivity method by the inversion of the soil resistivity, and it could provide a geological model for slope stability analysis. However, the indirect evaluation of the resistivity for the soil shear strength is still limited. Taking the laterite on the slope as an example, the resistivity and shear strength of laterite samples with different dry density and water contents were tested to discuss the relationship between the resistivity and undrained shear strength of the laterite, and finally the corresponding quantitative model was established. The results show that the resistivity of laterite decreases with the increasing water content and increases with the increasing porosity. The undrained shear strength of laterite increases first and then decreases with the increasing water content (the peak of shear strength near the optimal water content ) and decreases with the increasing porosity. The evaluation model of undrained shear strength resistivity of unsaturated laterite considering critical saturation is derived, which is based on the three-phase conductivity theory of unsaturated soil and the shear strength theory of soil. The accuracy of the model is verified to be high, and there is a corresponding critical resistivity value for the peak change of undrained shear strength of laterite. As a physical parameter of soil, resistivity can be quickly detected and obtained. This model can provide new ideas for shear strength calibration, slope stability analysis and monitoring and early warning of laterite slope.

The stiffly-expanded composite pile is a new type of composite pile formed by sinking rigid pile in cement soil mixing pile. In order to study its bearing characteristics in complex soft soil-sandy soil foundation, a numerical analysis model of rigid composite pile was established based on finite element software ABAQUS, with the effect of core pile length and diameter, soil-cement pile length, diameter and elastic modulus, as well as soil-cement and pre-stressed high-strength concrete(PHC) pile interface and soil-soil interface shear strength on the bearing performance was analyzed. The results show that the bearing capacity and economic advantages of the stiffened composite pile are obvious. The bearing capacity of composite pile increases with the increase of research parameters, but the improvement is limited by changing the research parameters beyond a certain range. With the increase of load, the slope of axial force distribution curve of composite pile increases, and the load proportion of PHC core pile is about 93.32%~95.40%. When the load exceeds 4 000 kN, the axial force of soil-cement pile increases sharply within the depth range of 10~16 m, and the axial stress ratio of core pile/soil-cement pile changes significantly near the depth of about 10 m. The research outcomes could provide references for the engineering design and application of the stiffly-expanded composite piles.

To ensure the safety of formwork engineering, and to accurately calculate the lateral pressure of fresh concrete on formwork, the problems of the formulas for lateral pressure of fresh concrete on formwork, provided by the national current standards, were analyzed firstly. Subsequently, based on the basic physical quantities of the international system of units, five key factors affecting the lateral pressure of fresh concrete on formwork were identified, the range of concrete slump was emphatically analyzed. By taking concrete slump as an important factor, it was directly introduced into the derivation. According to the relation between the depth of the concrete from the top of the placement to the point of consideration in the formwork, and the product of the rate of placing concrete in forms and initial setting time, formula for calculating the lateral pressure of fresh concrete on formwork was derived. Finally, the accuracy of the proposed formula was verified by using the experimental data in the literature. The results show that the proposed formula accords with the results of dimensional analysis, and the formula is more accurate in the application range of the formula provided by the national current standards. Moreover, beyond the applicable range of the formula provided by the national current standards, the proposed formula is of high accuracy and is generally safe.

The accurate prediction of soil compaction parameters has practical significance for improving soil bearing capacity and reducing compressibility in geotechnical engineering. The existing models have certain limitations in prediction progress and engineering applicability, and ignore the quantification of model prediction uncertainty. Genetic programming (GP) was used to model and predict two important soil compaction parameters (optimal water content and maximum dry density) for 226 groups of soil compaction test data with extensive and representativeness. The optimal display models of optimal water content and maximum dry density were obtained respectively, and the prediction results were compared with the results of existing prediction models. The GP model was quantified by combining quantile regression method and uncertainty statistics. The results show that the compaction parameters are most affected by fine grain content and plastic limit, while the gravel content and liquid limit have the least influence on them. Therefore, in practical engineering, the optimal compaction effect can be achieved by preferentially adjusting the fine grain content and plastic limit, while the gravel content (CG) and the liquid limit have the least influence on them. Therefore, in practical engineering, the optimal compaction effect can be achieved by preferentially adjusting the fine grain content (CF) and the plastic limit in the soil. In addition, the quantile regression (QR) method provides 90 % confidence and the mean prediction interval (MPI) is less than 0.3.At the same time, most of the data fall within the range of uncertain bands, indicating that the GP algorithm has strong prediction ability and high prediction accuracy. This interpretable display model is more convenient for engineering applications.

The applicability of prefabricated subway station structures in inclined liquefiable sites was investigated. Based on the actual project of Shuangfeng subway station in Changchun City, the finite difference software FLAC3D was used to carry out the seismic response analysis of prefabricated subway station structures in liquefiable soil, for example, the pore water pressure of the foundation, the lateral motion of the liquefied soil, the dynamic response and uplift characteristics of the subway station structure, and the deformation characteristics of the prefabricated subway station structures were analyzed. The results show that the negative pore pressure phenomenon of pore water pressure on both sides of prefabricated subway station under inclined liquefiable site conditions is present, and the phenomenon becomes more obvious the closer the location of the station structure is to the station, and at the same time, the negative pore pressure of the soil on the left side of the structure (uphill) is significantly greater than that on the right side (downhill). The further away the foundation soil is from the station structure, the more pronounced the liquefaction is. The phenomenon of lateral slippage of the surrounding soil is significantly suppressed by prefabricated subway station structures. The principal stresses in the upslope sidewall (member C1) are greater than those in the downslope sidewall (member C2), and the principal stresses at the bottom of the upslope sidewall of the structure are greatest, so the upslope sidewall of the structure should be given priority in the seismic design.

In order to prevent and control the snow drifting disaster along the Karamay-Tacheng Railway in northwest of Xinjiang, numerical simulation method was adopted to study the influence of different height and angle parameters of wind deflector on the snow flow field around a given railway embankment in 50-year recurrence period in this paper. The results show that increasing the angle between the wind deflector and the prevailing wind direction will weaken its effect on snow dispersion in the 50-year recurrence period. When the angle is set to 60°, the snow dispersion effect is the best. Increasing the height of the wind deflector will increase the coverage of the acceleration zone, which is more conducive to disperse snow flow and reduce the probability of snow particle accumulation. When the height is set to 2 m, it's a cost-effective solution.

With respect to the surrounding rock collapse and water gushing in the water-rich fault, a high-speed railway tunnel in Yunnan was taken as the engineering background. The fluid-solid coupling numerical calculation of tunnel construction with the three-step method was carried out, and the deformation mechanism and groundwater seepage law of surrounding rock through water-rich fault were researched combined with the deformation field monitoring results. The results show that when the tunnel face is excavated to the water-rich fault, the rock and soil in the upper wall of the reverse fault will collapse downward, and the settlement of the arch roof will increase sharply. At the fault, the rock and soil mass of the middle and lower excavation parts cannot provide stable support for the surrounding rock, so the tunnel clearance increases first and then decreases. The groundwater mainly percolates along the step surface and the palm surface, and there is still a large pore pressure above the tunnel, so the drainage pipe can be added to lead the water into the side ditch.

To analyze the main influencing factors and pathways of low altitude travel intention, a structural equation model was constructed based on travel intention model and value risk analysis. The interaction mechanism between travel preference, travel characteristics, perceived value, and perceived risk on low altitude aircraft travel intention was quantified. The path coefficients were solved using unweighted least squares method, and the mediating effects of perceived value, perceived risk, and other factors on travel preference were analyzed. Additionally, a multi group model invariance analysis of individual information such as gender and age on travelers was conducted.Finally, the fuzzy set qualitative comparative analysis (fsQCA) method was used to analyze the configuration of the antecedent variables of travel intention. The results showed that the chi square degree of freedom ratio, RMSEA (root mean square error of approximation)value, and CFI(comparative fit index) value of the structural model were 3.803, 0.063, and 0.938, respectively, which passed the model validation. Perceived value (0.38) is the most important factor affecting travel intention. Travel characteristics (0.08) have a positive direct impact on travel intention, while perceived risk (-0.22) has a negative direct impact. However, travel preferences have no significant impact on travel intention; Travel preferences have a negative effect on travel willingness, but travel characteristics and perceived value have a masking effect on travel preferences, while perceived risk has a mediating effect on them; The pre tax annual income in the individual information of travelers has a moderating effect on the model. As the travel distance increases, the high-income group is more willing to use low altitude aircraft than the low-income group. At the same time, the high-income group is more sensitive to the perceived risks of low altitude aircraft in terms of technological maturity and accident severity. fsQCA analysis shows that there are three configurations that can form travel intention, among which configuration 3 ( type of travel characteristics&perceived value) has the highest sample coverage, explaining 48.9% of the sample cases. When travelers are necessary to travel during peak hours and have a positive understanding of low altitude travel comfort, privacy, etc., they will develop a low altitude travel tendency. The research findings can provide data support for the promotion and policy formulation of low altitude aircraft.

The structure and materials used in bridge deck pavement layers significantly impact their road performance. To design a pavement layer more suitable for cold regions, the layered modification of pavement materials was optimized based on the principle of layer function design. Firstly, materials for the surface functional layer and overall functional layer were selected using the layer function division, with lignocellulose and polyester fibers as modifiers, respectively. Secondly, the appropriate content of polyester fibers was determined through road performance tests. Finally, the choice of layered materials and appropriate modification methods were established. The results indicate that both the surface functional layer and the overall functional layer should use the same high-viscosity, high-elasticity modified asphalt, with lignocellulose and polyester fibers as modifiers, respectively. This approach avoids differences in thermal contraction coefficients of the layered materials, ensuring coordinated thermal deformation between the pavement layer and the bridge structure, thereby effectively improving the road performance of the asphalt bridge deck pavement. Furthermore, under engineering economic requirements, when the polyester fiber content is 1% of the total mixture mass, the prepared asphalt mixture's high-temperature performance increases by 7%, low-temperature performance by 23%, and water stability by 5%.

In order to further analyze the visual characteristics and risk conditions of drivers at the confluence sections of entrance ramps at adjacent urban underground roadways, data collected from real-vehicle experiments were used. A linear fitting model was constructed and the pupil area growth rate was calculated to investigate the patterns of pupil area changes. The K-medoids clustering method was employed to classify the regions of interest in drivers' gaze patterns, and the characteristics of drivers' gaze behavior were analyzed. A game theory-extension cloud evaluation model was constructed to evaluate the driving risks at the confluence sections of entrance ramps at adjacent urban underground roadways. The results show that the pupil area increases linearly in the entrance section and decreases linearly in the exit section. The overall load follows the order of adjacent entrance confluence section > adjacent entrance split section > adjacent exit split section > adjacent exit confluence section. Additionally, drivers face certain driving risks due to various factors at the confluence sections of entrance ramps at adjacent urban underground roadways. Based on the evaluation and actual survey, the risk factors for each section were analyzed, and optimization and improvement suggestions were proposed.

In order to explore the impact of altitude on the emergency evacuation of aircraft passengers at airports, a simulation model for emergency evacuation of aircraft accidents at airports was built. By introducing panic factor, age factor, gender factor and altitude correction factor, passenger walking speed at different altitudes was quantitatively characterized, and passenger evacuation effects were analyzed under conditions such as different altitudes, with or without evacuation guidance, and cabin aisle spacing, dual aircraft evacuation. The results show that the impact of emergency evacuation time in a high plateau environment is mainly reflected in the middle and late stages of cabin evacuation and ground evacuation. When the altitude is 4 280 m, the cabin evacuation time is increased by 19.6 s and the overall evacuation time is increased by 44.1 s compared with the plain area. By setting cabin crew guidance, cabin evacuation time is reduced by 18.1 s and evacuation efficiency is increased by 16.5%. When the width of aisle spacing is increased to 60 cm, the evacuation time in the cabin is reduced by 20.8 s, and the evacuation efficiency is increased by 19%. Compared with single-aircraft evacuation, the evacuation time of dual-aircraft evacuation is significantly increased, so it is necessary to optimize the evacuation strategy. The research results can provide theoretical support for the optimization of aircraft emergency evacuation schemes at high plateau airport.

In order to more accurately predict flight delays at different times of the year,flight delay prediction trends was investigated using operational and meteorological data from Atlanta Airport in the United States for the year 2023. A CA-PCA-Informer flight delay prediction model,incorporating correlation analysis (CA),principal component analysis (PCA),and the Informer model,was proposed. Mean absolute error (MAE) and root mean square error (RMSE) were utilized as evaluation metrics to assess the prediction error. The findings reveal that the CA-PCA-Informer model outperforms simpler combined models,demonstrating the lowest error compared to the CA-PCA-LSTM and CA-PCA-GRU models,with MAE and RMSE reductions of 20.2%~20.7% and 12.7%~14.1%,respectively. The CA-PCA-Informer model is particularly effective for one-hour ahead predictions,providing decision-makers with more accurate flight delay trends to enhance efficient flight operations.

The operational efficiency of aircraft and maximization of aviation transportation benefits have consistantly been pursued by CAAC. It has been indicated by research that the Established on RNP AR (EoR) approach plays an irreplaceable role in improving efficiency, especially for short-distance parallel runway operations, thus, it has received widespread attention in the industry. The integration of EoR approach with sorting strategies was undertaken, with the aim of minimizing flight delay time serving as the objective function, leading to the establishment of a sorting model based on EoR. The impact of EoR-based independent operation versus correlated operation on flight delay time had been compared and analyzed. Addressing the large solution space and the time-sensitive nature of large-scale flight sequencing calculations, The S-shaped function based adaptive particle swarm optimization (SA-PSO) algorithm was proposed to solve the model. Taking the Kunming Changshui International Airport terminal area as an example for case verification, the RECAT-CN operational standard was adopted for wake turbulence safety separation. The results show that, compared to correlated operations, independent EoR operations result in an approximately 38% reduction in total delays. Additionally, the algorithm proposed in the study, when operated independently under the EoR mode results in a reduction of total delays by approximately 15.3%, compared to the first come first served (FCFS) algorithm.

To investigate the contents and pollution of heavy metals in calcium-containing biological minerals, 16 samples were collected from three primary categories: eggshells, shells, and animal bones. Inductively coupled plasma optical emission spectrometry (ICP-OES) was utilized to analyze the contents of seven metalloids and heavy metals (As, Cd, Pb, Cr, Cu, Zn, and Mn). The pollution degree and risk of heavy metals were evaluated using single-factor, Nemerow comprehensive pollution index method, Hakanson potential ecological risk index method, and health risk comprehensive assessment. The results show that As and Cd are not detected in all calcium-containing biological minerals, meanwhile Pb is not detected in both eggshells and shells. Five heavy metals (Pb, Cr, Cu, Zn, and Mn) are detected in the remaining samples, with their contents remaining below standard. Differences in heavy metal contents are observed among different categories and species. Cu, Mn, and Zn have the highest contents of 14.85, 21.47, and 201.99 mg/kg, respectively accumulated in eggshells, shells, and animal bones. The risk assessment results show that the single-factor pollution index of four heavy metals (Pb, Cr, Cu, Zn) in calcium-containing biological minerals is less than 1.0 and the comprehensive pollution index is less than 0.7. All samples are indicated as unpolluted. The potential ecological risk among three types of calcium-containing biological minerals is in descending order: animal bones > eggshells > shells. The non-carcinogenic total risk index of two exposure pathways for minors and adults is less than the safety threshold of 1.0. This finding indicates that five heavy metals in calcium-containing biological minerals are unlikely to threaten human health. Overall, calcium-containing biological minerals can be used as potential sources of fertilizers and soil conditioners in agricultural production. However, their usage should be controlled to prevent heavy metal accumulation and pollution.

In order to improve the fire extinguishing efficiency of forest fires and reduce the damage to forest resources and ecological environment, a three-phase class A foam extinguishing agent consisting of class A foam extinguishing agent with nano-SiO₂ particles was prepared. The effects of nano-SiO₂ mass fraction on foam stability and fire extinguishing efficacy were investigated by using the double injector method and the wood stack fire extinguishing test platform. The results show that the nano-SiO₂ significantly enhanced the foam stability and delaye the foam precipitation and foam coarsening; the three-phase class A foam extinguishing agent extinguishe the flame more quickly and efficiently under the same driving pressure without rekindling and at a lower dosage than the traditional class A foam extinguishing agent; and the three-phase class A foam extinguishing agent with 1.5% SiO₂ show the best extinguishing efficacy. The three-phase A-type foam extinguishing agent significantly improves the fire extinguishing efficiency, reduces the consumption of extinguishing agent, and has low cost, which makes it suitable for large-scale forest fires. The study provides a new solution for the efficient extinguishing of forest fires and has a wide range of practical applications.