During the manufacturing and application of fiber-reinforced composites (FRP), issues such as impact damage and fatigue accumulation cause irreversible subtle damage to the internal structure. Acoustic emission (AE) technology, with its high precision and real-time property, has become an important means to monitor the damage evolution and failure mechanisms of FRP. The applications of acoustic emission technology in the damage characterization of FRP in recent years was reviewed. By conducting research on AE technical means such as parameter analysis, waveform analysis, pattern analysis, and deep-learning analysis, the results showed that parameter analysis and waveform analysis could complement each other in terms of signal characteristics during the detection process, achieving a qualitative description of damage behaviors such as the deformation and fracture of composite structures. Methods such as deep-learning analysis provided important theoretical support for the health monitoring and life prediction of fiber-reinforced composites. Overall, acoustic emission technology can monitor and evaluate the composite structures in operation in real-time. It has great development potential for maintaining the health of FRP materials and preventing sudden failures. In the future, it can be further combined with artificial intelligence technology to improve the accuracy and efficiency of damage identification.

To effectively address the contradiction between the uncertainty of the internal and external environment in the construction industry and the complexity and vulnerability of the construction supply chain, as well as to promote the overall security and stability of the construction supply chain network, and to prevent and mitigate the risk of disruption among node enterprises, a construction supply chain network invulnerability analysis method was proposed based on the complex network theory and cascade failure model. Firstly, from the perspectives of business, resources and information flow, the TOPSIS(technique for order preference by similarity to an ideal solution ) method was used to assess the importance of node enterprises based on multiple complex network centrality indicators. Secondly, combined with the operational characteristics of the construction supply chain, an improved load-capacity-elasticity cascade failure model was established to measure the impact of enterprise disruption from the perspective of network loss under intentional attack, and to analyze and explore the network invulnerability improvement strategy from the perspectives of node capacity, load, and resilience. After numerical simulation and analysis, the results show these as follows. When the upstream node enterprises of the construction supply chain network suffer from the impact of disruption risk, the supply chain network can show strong network invulnerability, but it should focus on the downstream supplier enterprises, so as to avoid the network as a whole suffering from more losses due to the shortage of the supply of construction materials or basic services. To keep the small difference in the business capacity among node enterprises, the large difference in the business load and an appropriate high level of risk remediation cost investment can effectively reduce the loss of the supply chain network when the node enterprises are interrupted, thus improving the level of network invulnerability. Among the multiple types of strategies, the node capacity strategy is better than the node resilience strategy and the node load strategy in order to improve the network invulnerability. The results of the study can provide scientific references for improving the security level of construction supply chain and proposing disruption risk management strategies.

To design and prepare high-quality one-dimensional hexagonal quasicrystal nano-composites, the interface and interface phase models were applied to study the infinite one-dimensional hexagonal quasicrystal anti-plane fracture problem with cylindrical inclusions containing nano coatings by using the complex function method and Gurtin-Murdoch's surface/interface elasticity theory. Under two different models, the series form expressions of phonon and phason field stress fields in matrix, coating and inclusion were obtained, respectively. Numerical examples were used to analyze the effects of interface elastic constants and size effects on the stress field around inclusions. The results showed that the positive or negative values of interface elastic constants would affect the stress field around nano-inclusions. As the size of nano-inclusions increased, the stress field exhibited significant size dependence, and surface effects had significant differences in their effects on the stress fields of dimensionless phonon and phason fields. The relevant results provide a certain theoretical reference for studying the mechanical behavior of quasicrystalline nano-inclusions.

Corn is one of the important grain reserve crops in China, and its yield directly impacts national food security. The chlorophyll content of corn is closely related to its photosynthetic capacity and significantly affects the photosynthetic rate of the leaves and vegetation productivity. It is an important crop parameter for monitoring crop growth, pest and disease surveillance, and maturity prediction. Real-time and accurate monitoring is of great significance for corn parameters and yield prediction. This study was conducted in the typical black soil area of Lishu County, Siping City, Jilin Province. To solve the problem of missing effective images that may occur during the revisit period of Sentinel-2 satellites, a method for retrieving corn leaf chlorophyll based on the fusion data of Sentinel-2 and MODIS images was proposed. Using fused imagery, three machine learning algorithms were employed: random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBOOST) to construct a model for estimating corn leaf chlorophyll content, and the accuracy of the model was verified. The conclusions obtained were as follows. The data simulated using the ESTARFM data fusion algorithm maintained a high correlation with the real imagery. Among the leaf chlorophyll inversion models for missing image dates, where input variables included fused image band reflectance and vegetation index, the XGBOOST model showed good fitting accuracy The research demonstrates that accurate estimation of leaf chlorophyll content can be achieved even on days with missing imagery, when fusion image feature bands are integrated with machine learning algorithms. This notably improves the temporal precision of corn chlorophyll content measurement, presenting a novel method for daily or large-scale inversion studies of leaf chlorophyll content, particularly in scenarios involving image gaps. Furthermore, it illuminates the potential for refined monitoring of physiological and biochemical parameters across a wider range of crops, with shortened time intervals.

Geothermal tail water reinjection is the main bottleneck that restricts the development and utilization of geothermal resources in Lanzhou Basin. In order to break through the technological gap of geothermal tail water reinjection in Lanzhou Basin sandstone-type thermal storage, relying on the geothermal heating demonstration project in Pengjiaping, Lanzhou City, for the first time, the natural reinjection experiment with graded flow rates of 15, 20, 25, 28 m³/h and graded pressure pressurised reinjection experiment of 0.1, 0.2, 0.3, 0.4, 0.5, 0.6 MPa were designed and carried out, the maximum stable natural reinjection volume of 27.96 m³/h and the stable reinjection volume of 51.68 m³/h for 0.667 MPa pressure were firstly obtained from the sandstone-type thermal storage of Pengjiaping, Lanzhou Basin, and the impact of geothermal tail water reinjection on the water level, water temperature, and temperature field of the extraction wells was also investigated. After a heating season of productive reinjection experiment verification, a set of suitable and feasible sandstone-type thermal storage geothermal tail water reinjection technology process has been successfully explored in Lanzhou Basin, which is of great reference and significance for the large-scale and high-quality development and utilization of geothermal resources in Lanzhou Basin.

In order to find out the susceptibility of debris flow after fire at different time points, the burned land where a serious fire occurred in Lushan Mountain, Changshou Township in March 2020 was selected as a demonstration research area. Based on the idea of “space for time”, the whole study was carried out. Through laboratory experiments, the root soil mechanical parameters of the study area at different time after fire were obtained. By using the experimental parameters obtained, the slope instability coefficient of different years after fire was obtained through the slope instability model. According to the slope stability division standard, the slope instability area of different years after fire was obtained. Finally, the source strength indexes of different years after fire were extracted. The dynamic evaluation index system of post-fire debris flow susceptibility at small watershed scale was established by taking source strength as static evaluation index and topographic and geomorphic index as static evaluation index. Using the entropy weight method to calculate the weight of index factors combined with the comprehensive index method, the dynamic susceptibility assessment of debris flow was carried out on the burned land of Lushan Mountain in Changshou Township. Based on the results, targeted remediation of watersheds that remain highly susceptible for many years after a fire and those that are highly susceptible within a short period of time can effectively prevent and reduce the probability of mud slides, while also saving economic costs and achieving truly effective disaster prevention and mitigation.

The oscillating water column buoy utilizes wave energy by channelling waves into an air chamber, inducing oscillations within the water column. However, wave impact also causes buoy oscillation and rocking, which reduces the relative water column displacement. To address this, a double-layer concave damping plate with a weight-enhancing ring was implemented to stabilize buoy movement. A double-concave damping plate with a weight-enhancing ring was used to inhibit the movement of the floating buoy and increase the pressure of the air chamber. Based on the small amplitude wave theory and Newton's second law, a theoretical model was developed to analyze wave energy capture by the damping plate-enhanced oscillating water column buoy, calculating buoy oscillations and air chamber pressure characteristics. The finite element simulations, conducted using AQWA software, replicated the wave-induced hydrodynamic effects on the buoy. Air chamber pressure was simulated via Fluent software's fluid volume method and open channel wave-making method, and the model's accuracy was validated against theoretical calculations. The simulation results show how effective the damping plate is in limiting buoy motion, raising mass and buoy inertia, and improving water column stability in the air chamber. The theoretical calculation of the air chamber air pressure parameters provides a basis for the design of the damping plate oscillating water column buoy wave energy harvesting system and the green low-carbon energy conversion structure.

In order to investigate the role of rheumatoid arthritis (RA)-related pathways in lung squamous cell carcinoma (LSCC). By obtaining gene expression data for RA and LSCC from the GEO and TCGA database, differentially expressed genes were screened using GEO2R tool and Rstudio software. GO/KEGG functional enrichment analysis identified key genes in the RA signaling pathway. Combining SNP data from the IEUopenGWAS database, Mendelian randomization analysis was used to assess the causal relationship between the RA signaling pathway and LSCC. The constructed gene-drug and ceRNA networks, along with immune cell infiltration analysis, revealed 188 co-expressed differential genes, mainly enriched in the RA signaling pathway. Mendelian randomization analysis showed that increased activity of the RA signaling pathway is associated with a reduced risk of LSCC. This study provides new insights into the pathogenesis and potential research directions for the treatment of LSCC.

At present, complex interstitial lung diseases have the problems of low classification accuracy and lack of auxiliary diagnostic information. To address these problems, an image retrieval framework based on multi-feature fusion and supervised contrastive learning methods was proposed. Interstitial lung disease features were extracted using Res-Net50 and radiomics feature extraction modules. In order to fuse two features of different modalities and scales, a feature fusion module was designed that can jointly represent the spatial calculation feature correlation of two features. The feature discrimination between interstitial lung disease categories was improved through supervised contrastive learning methods, and a typical interstitial lung disease database was retrieved. The highest precision, recall rate and F₁ score were obtained in the retrieval task of interstitial lung disease data, and a silhouette coefficient of 0.482 was obtained in the feature vector discrimination index for image retrieval. The experimental results show that compared with the traditional deep learning single feature modality method, the proposed method can effectively improve the classification retrieval accuracy of interstitial lung disease images and improve the interpretability of interstitial lung disease diagnosis.

Forest fires have the characteristics of strong suddenness, great destructiveness, many uncertain factors and high risk of fighting. In order to study effective strategies of firefighting, firstly, based on the theory of cellular automata, the forest fire system was analyzed, and a forest fire model considering external factors such as wind and flame retardant was established. Then, on this basis, the fire-fighting agent was modeled and correlated with the forest fire model, so as to build the fire-fighting model. Finally, the simulation algorithm based on cellular automata was designed to simulate the effect of fire-fighting strategies under the influence of different environmental factors, and the effect of different fire force allocation strategies. The results show that the method can combine fire-fighting simulation with actual decision making, and provide visualized and quantified strategy scheme for relevant departments to make fire-fighting decision, which is helpful to reduce forest fire loss and rescue cost.

To investigate the mechanisms of floor heave and support methods for large-section inclined shafts in highly expansive and weak rock, a case study was conducted on the main inclined shaft of a mine in the Yaojie Coal and Electricity Group. Through field surveys and laboratory tests, characteristics such as low rock mass strength, poor integrity, high clay content, and softening and swelling upon water exposure were identified. A Flac3D numerical model was established to simulate the deformation patterns of the tunnel under actual engineering conditions, elucidating the mechanisms of floor heave deformation in large-section soft rock inclined shafts. Three optimized prevention strategies were summarized, and an optimized support scheme of “inverse floor arch and anchor cable” was proposed based on the original support design. Field measurement data indicated that the optimized support scheme significantly improved the floor heave control in soft rock tunnels, enhanced the bearing capacity of the surrounding rock, reduced floor heave deformation, and achieved stability control of the tunnel surrounding rock. This provides an important reference for the control of floor heave in large-section shallow soft rock inclined shafts.

The Yingxiongling shale oil reservoirs in the Qaidam Basin are notably characterized by the development of the laminated shale and thin-layered shale. In order to identify the superior sweet spots, an experimental study on the physical and mechanical properties of laminated and thin-layered shales was conducted, and the sweet spots of the Yingxiongling shale oil reservoirs were systematically evaluated in conjunction with the analysis of oilfield production data. The results show that laminated shale exhibits higher total organic carbon (TOC) content, stronger hydrocarbon generation capacity, and higher initial oil saturation compared to thin-layered shale. Although laminated shale has lower porosity, it exhibits stronger anisotropy, higher stress sensitivity coefficient, and more developed initial natural microcracks. Additionally, the laminated shale demonstrates high horizontal permeability, strong fluid absorption capacity, and effective imbibition oil displacement ability. Its lower compressive strength facilitates the formation of complex fracture networks. The experimental research results are consistent with the in site fluid production analysis. The fracture morphology generated by the laminated shale is more complex with strong oil displacement ability and high oil displacement efficiency, indicating that the laminated shales produce fluids first; therefore, it is concluded that the laminated shales are the preferred sweet spots. The findings of this study have important theoretical significance for the exploration and development of shale oil in Yingxiongling.

Micro-nano pores are developed in unconventional oil and gas reservoirs such as shale and tight sandstone, and the study of oil-water two-phase seepage law and relative permeability in micro-nano pores is the theoretical basis for the effective development of such reservoirs. Addressing the two-phase flow of oil and water within nanopores, a mathematical model was established for nanopore-scale two-phase oil-water flow, grounded on the Hagen-Poiseuille (HP) equation and taking into account microscale seepage mechanisms such as oil-water distribution, viscous coupling, multi-layer adsorption, and slippage. Based on this model, a calculation method for relative permeability was developed. The validity of the model was verified by fitting the results from Lattice Boltzmann method (LBM) simulations. Furthermore, through a parametric sensitivity analysis, the characteristics of two-phase oil-water flow and the influence patterns of relative permeability within nanopores were investigated. The results showed that the slip length significantly impacted the velocity distribution of oil and water. An increase in the wetting contact angle led to varying degrees of augmentation in both oil and water relative permeabilities. As the viscosity ratio rose, the oil-phase relative permeability experienced a notable increase, while the water-phase relative permeability remained relatively unchanged. The presence of positive slip could result in relative permeabilities exceeding unity. As the pore radius enlarged, the pore area available for fluid flow expanded, thereby enhancing both oil and water relative permeabilities. This study holds guiding significance for elucidating the fluid flow mechanisms in micro-porous media and facilitating the exploitation of shale oil.

The small pore throat radius, high starting pressure gradient, poor formation physical property and large gas production difference of horizontal wells after fracturing in tight sandstone reservoirs seriously affect the increase of tight gas reserves and production. In view of the above problems, the production dynamics of gas wells under different levels of sweet spots in the eastern Ordos Basin were analyzed, and a new tight gas fracturing method based on sweet spot distribution was proposed. Based on the evaluation of rock mechanics parameters, energy storage coefficient and argillaceous content parameters, the evaluation model of geological sweet spot and engineering sweet spot was established, and the reservoir quality of tight sandstone gas reservoir was divided by fuzzy comprehensive evaluation method. According to different sweet spot types, the production characteristics and sweet spot distribution characteristics of Class I, II and III horizontal wells were summarized, and three fracturing modes based on geological sweet spot and engineering sweet spot were formed. The results show that when the horizontal well is drilled in Class I geological sweet spot and Class I engineering sweet spot, the cumulative gas production is higher than expected (Class I well), and a larger fracturing scale and displacement can be adopted to maximize the economic benefit (Model 1). When the horizontal well is drilled in Class I geological sweet spot and Class II engineering sweet spot combination, the cumulative gas production is lower than Class I well (Class II well), and the fracturing scale should be reduced appropriately to pursue a certain production capacity and economic benefit (Model 2). When horizontal wells are drilled in geological sweet spots below Class II and combinations of Class I/II engineering sweet spots, the production effect is the worst (Class III wells), and the fracturing scale should be controlled by reducing displacement to avoid the fracture from entering the risk layer and obtain a certain production rate (Model 3). The above new fracturing method was applied to a newly drilled horizontal well in a tight gas reservoir, and the open flow and gas production of the horizontal well after fracturing reached the expectation, which has certain guiding significance for the development of tight sandstone gas reservoir.

After fracturing, the solid particles carried by the high speed liquid will cause serious erosion to the oil nozzle, and it is difficult to ensure the stable operation of the oil nozzle. To address the serious erosion problem of the nozzle, numerical simulation was employed to study the erosion wear of the nozzle, and the influence patterns of sand content, sand grain diameter, sand grain density, pump displacement, and liquid viscosity on the erosion wear of the nozzle were analyzed. The research indicates that: when the sand content and liquid viscosity increase, the maximum erosion rate exhibits linear growth; when the sand grain density and pump displacement increase, the maximum erosion rate exhibits exponential growth; and when the sand grain diameter increases, the maximum erosion rate shows exponential decrease. The orthogonal test method is used to judge the significance of each factor. The factors affecting the erosion wear of the nozzle are as follows: sand content ratio > pump displacement > sand density > sand diameter > liquid viscosity.Based on the results of numerical simulation, the machine learning method is used to compare and analyze SVR(support vector regression), CNN(convolutional neural network), BP(back propagation) neural network and RFR(random forest regression) algorithm to predict the erosion wear results of oil nozzle respectively. By preferring the SVR algorithm and adopting the particle swarm optimization algorithm to optimize the prediction model, a better nozzle erosion prediction model is obtained.

To address the issues of incomplete feature extraction, poor stability, and limited generalization in traditional fault diagnosis models, a model based on a multi-scale convolutional neural networks (MCNN), bidirectional gated recurrent units (BiGRU), and multi-head self-attention mechanism (MSA) was proposed. The model was designed to achieve comprehensive feature extraction from both spatial and temporal perspectives. It took raw vibration signals as input, and multi-scale features were extracted through convolution kernels of different sizes. A multi-head self-attention mechanism was used to dynamically adjust output weights, disregarding redundant information and weighting the extracted features for fusion. Then the fused features were input into a BiGRU network, which utilized a bidirectional information fusion mechanism to explore information from both past and future directions, capturing dependencies between different parts of the input sequence. Finally, Softmax was employed for classification. Experimental validation was conducted using three bearing fault datasets, and the results show that the proposed model has excellent performance metrics on different datasets and showcases good generalization and feasibility.

Conventional diagnostic methods that require a large amount of data support in practical engineering are difficult to effectively perform centrifugal pump fault diagnosis under small sample conditions. Therefore, the residual network (ResNet) in deep learning was combined with dilated convolution and extended into a siamese network to construct a dilated residual siamese network (DRSN). The dilated residual network was used as the feature extraction module of the siamese network, which enhanced the feature extraction ability of the model. Positive and negative sample pairs were constructed to extract more information from each sample, and make more effective use of limited data.The two sub-networks share parameters, the number of free parameters and lowering the risk of overfitting was reduced when the sample was insufficient. The proposed network model alleviated the problem of insufficient training samples, improved the efficiency of data utilization, and realized the fault classification of centrifugal pump under the condition of small samples. The research results show that even in the most sample-scarce situation, the accuracy of the model on the centrifugal pump test dataset can still reach 82.20%, which is at least 8.8 percentage points higher than other models.

Aiming at the problem that the vibration signals in train operation are complex and nonlinear, and the information of single channel signal is incomplete, a fault diagnosis method of yaw damper based on multi-channel signal fusion on car body and bogie was proposed. Firstly, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was performed on the signals of multiple train channels, and the intrinsic mode function (IMF) was extracted to compose the feature set of refined composite multiscale dispersion entropy. Secondly, kernel principal component analysis (KPCA) was used to reduce the dimensionality of the extracted feature set. Finally, the optimal feature subset was inputted into the snake optimized kernel extreme learning machine (SO-KELM) to diagnose the yaw damper fault types. The experimental results show that the multi-channel fusion feature set optimized by kernel principal component analysis can accurately reflect the signal characteristics of different fault types of yaw damper, and realize the fault diagnosis of yaw damper. The superiority of this method is verified by comparing with other models.

Compressed air energy storage, as a new energy storage technology, plays an important role in peak shaving and valley filling. Based on the compressed air energy storage with abandoned oil wellbores, a pipeline-wellbore gas storage chamber, that is, the storage space was composed of above ground pipelines and underground wellbores, was proposed. Its inflation process was simulated, with a focus on analyzing the thermodynamics and flow characteristics of the internal gas. The results showed that with compressed gas flowed into the pipeline wellbore gas storage chamber, the gas temperature rapidly increased under the heating effect of the high-temperature wellbore wall. Subsequently, the temperature of the gas became slightly higher than that of the wall, at this point, a heat release of the gas to the wall. The gas temperature and heat dissipation tended to remain stable until the gas storage pressure rose to about 3 MPa. Due to the presence of geothermal gradient, there were significant differences in gas characteristics in different areas of the underground wellbore during the inflation process. As the depth of the wellbore increased, gas flow rate, density, and frictional resistance decreased. With the increase of the gas storage pressure, the differences in the gas flow rate and frictional resistance in different areas diminished. The results of this study provide significant theoretical insights that can effectively inform the practical application of compressed air energy storage systems, particularly those that employ underground wellbores as the repository for gas storage.

To address the issue that traditional fault diagnosis methods struggle to accurately diagnose faults in the nuclear reactor coolant system (RCS) of nuclear power plants under uncertain conditions, a dynamic fuzzy radial basis function neural network (DFRBFNN) model was established for RCS fault diagnosis following these steps. First, based on the fault types and sample data of the RCS, the initial structure of the DFRBFNN model was determined. Then, using the radial basis function neural network method, the initial DFRBFNN model for RCS fault diagnosis was constructed, and a random initialization method was applied to initialize the connection weights from the defuzzification layer to the output layer of the initial DFRBFNN model. Finally, the error reduction rate method was used to adjust the structure and parameters of the initial DFRBFNN model, resulting in the final DFRBFNN model for RCS fault diagnosis. The established model was applied to diagnose loss of coolant, flow loss, and steam generator tube rupture accidents, and its performance was compared with traditional fault diagnosis models to verify its effectiveness. The research shows that the constructed DFRBFNN model can accurately diagnose RCS faults under uncertain conditions.

In order to meet the requirements of high-voltage DC circuit breakers for rapidity actuation of their actuating mechanisms, a new electromagnetic repulsion mechanism was designed for 40.5 kV vacuum circuit breaker, which had high breaking and closing speeds and was suitable for quick disconnection. It consisted of a coil-plate and a double-coil mechanism connected in series.Firstly, the electromagnetic field simulation was carried out by the finite element method, the feasibility of the mechanism was initially verified by analyzing the electromagnetic repulsion,displacement/time characteristics and velocity/time characteristics. Then, the single-variable method was used to simulate and analyze its motion characteristics. the law of the influence of each parameter on the motion characteristics was obtained, and the optimization parameters were determined. Finally, in order to reduce the tripping bounce of the long-stroke fast electromagnetic repulsion mechanism, an electromagnetic buffer was designed. the effects of buffer current input time and buffer driving circuit parameters on buffer characteristics were analyzed. The results show that the new electromagnetic repulsion mechanism combines the advantages of the fast response of the coil-plate mechanism and the high drive efficiency of the double-coil mechanism,with its short response time, large motion speed and short full stroke time, and meet the need of quick disconnection. Under the parameter conditions of 3 500 μF capacitance and 1 200 V for the driving circuit and buffer circuit capacitance 3 500 μF capacitance and 1 300 V for the buffer circuit, the full travel time of the designedelectromagnetic repulsion mechanism is only 2.18 ms.

Aiming at obvious load fluctuation trend, strong randomness and low accuracy caused by unreasonable parameter values of the prediction model involved into the power load forecasting process, a combined prediction model composing of ALIF (adaptive local iterative filtering), VMD (variational mode decomposition), NGO (northern goshawk optimization) and CNN-LSTM (convolutional neural networks - long short-term memory) was established. Firstly, CCM (convergent cross-mapping) method was used to identify the key factors affecting the power load. Secondly, an innovative combination of ALIF, NGO-based VMD and FE (fuzzy entropy) was employed for combinatorial decomposition and necessary recombination of original load sequence. Next, based on the modal components generated after decomposition and recombination, combined with optimal hyperparameter combination of CNN-LSTM determined by NGO method, an NGO-CNN-LSTM day-ahead power load combination prediction model with the high prediction accuracy, short training time and fast convergence speed was formulated. Compared with other benchmark models, the obtained results demonstrated that the proposed model has the better adaptability and prediction accuracy, and can provide important technical support for the safe, reliable and economical operation of power system.

The estimation of the state of health (SOH) for lithium-ion batteries is considered crucial for ensuring the safe and stable operation of battery management system. However, the accurate estimation of SOH has been a challenge due to the capacity regeneration phenomenon during the discharge process of lithium-ion batteries. To improve estimation accuracy, a hybrid model based on variational mode decomposition (VMD) and bidirectional long short-term memory network with attention mechanism (BiLSTM-ATT) was proposed. First, the battery capacity was decomposed using the VMD algorithm, producing a set of stable sub-sequences. Then, permutation entropy was introduced to reconstruct the sub-sequences to reduce computational complexity. The reconstructed sequences were input into the BiLSTM-ATT model, and feature weights were assigned by the attention mechanism. The SOH values were trained and estimated by the BiLSTM model. Finally, the complete SOH estimation result was obtained by summing all estimated values. Validation was performed using the CS2_36, CS2_38, and CX2_35 datasets from the CALCE lithium battery dataset. The results show that the proposed algorithm maintains a root mean square error within 0.6% and a mean absolute error within 0.4%, which demonstrates higher accuracy and performance compared to other estimation models.

In order to solve the problems of the traditional interactive multiple model (IMM) algorithm in vehicle target tracking, such as the model probability change is not obvious and the tracking accuracy is insufficient, an improved adaptive IMM-UKF(unscented Kalman filter) algorithm was proposed. Firstly, the vehicle motion model was established by using uniform speed straight line, uniform acceleration straight line and uniform turning, and the vehicle target was tracked by unscented Kalman filter. Then, the probability change rate of sub model was used as the correction parameter of IMM algorithm, and different correction strategies were adopted for the main diagonal and non main diagonal elements of Markov matrix. Finally, the decision window was set to modify the main diagonal element of the normalized Markov matrix to expand the probability of matching model. The results show that the probability of the improved algorithm model changes more obviously, and the root mean square errors of position and velocity are less than the original algorithm, which effectively improves the tracking accuracy.

In view of the high cost of controlling all the nodes in the traffic network, a pinning control framework for urban traffic network analysis and control network in the case of limited resources were constructed by this paper, and a new pinning control algorithm for urban traffic network was proposed.By using the mutual coupling and containment relationship between nodes and controlling some key nodes in the road network, the expected behavior of the whole network was guaranteed, and the limitation that the system consumes too much computing and control resources were effectively solved.The control input was set as the variation of the duration of the green light, a new pinning controller was designed, and the conditions for effectively ensuring the stability of the urban road traffic network are proposed.Through the simulation analysis, the signal control method proposed can make the urban road traffic network achieve the desired state, and effectively improve the utilization of road resources in the case of limited infrastructure and control costs.

The high-precision fault diagnosis of cross modal high-dimensional fault data under unsupervised conditions is a challenging problem. To address this issue, a rotating machinery fault diagnosis method based on unsupervised cross-modal Euler discriminant space (UCEDS) was proposed. In this method, cross-modal fault data samples were mapped to Euler representations through cosine metrics to enhance the differences and separability between different types of fault samples. Then, an unsupervised cross modal Euler discriminant space learning model was constructed in this space, and the analytical solution of the model was theoretically derived. This model not only considered the local neighborhood structure of fault samples, but also effectively discovered the local structural information of complex and nonlinear fault feature samples. At the same time, on the basis of cross modal consistent discriminative fusion, it further improved the complementarity between low dimensional discriminative feature subsets. Targeted experiments on the Paderborn fault bearing dataseht showed that the proposed UCEDS method had superior fault diagnosis and classification performance.

To tackle the computational cost and registration time challenges in traditional point cloud registration methods like ICP (iterative closest point) such as LIO-SAM (tightly-coupled lidar inertial odometry via smoothing and mapping) and newer models utilizing deep neural networks such as HRegNet(hierarchical registration network), a lightweight and real-time HKRNet (hierarchical kcpstack registration network) network model was proposed. The model was developed by thoroughly studying the HRegNet neural network point cloud registration framework. Initially, a combined filtering approach involving point cloud voxelization and Gaussian threshold downsampling was used to remove redundant points from ground radar scans, reducing the point count from around 130 000 to about 70 000. Subsequently, the computationally intense KNN (K-nearest neighbors) point cloud clustering algorithm within the HRegNet model was enhanced by optimizing it to a KD-Tree (K-dimensional tree) algorithm, resulting in a 25% improvement in processing speed while upholding accuracy. Lastly, to address high memory usage and low computational efficiency of the convolutional modules in the model, a lightweight convolutional module leveraging tensor decomposition and a hierarchical singular value decomposition algorithm was introduced. This leaded to a compressed model size of 86.1% of the original and a decrease of 61.2% in computational cost. The outcomes indicate that the HKRNet network, in comparison to the HRegNet network, can reduce registration time by 40% with minimal loss of accuracy, achieving a single registration time not exceeding 84ms, thus meeting real-time registration requirements.

Existing methods for audio-visual cross-modal association learning often adopt a dual-stream network structure, but they still face challenges in reducing computational complexity, model light weighting, and efficient feature fusion. To improve model performance and enhance the efficiency of cross-modal learning, a single-stream network-based approach for audio-visual cross-modal learning was proposed. Firstly, preprocessed data from both modalities were fed into a single-stream feature extraction network, where a class-information-based loss function was employed to learn and extract feature vectors from both modalities. Subsequently, attention-based feature fusion was performed on the extracted feature vectors from both modalities. Finally, a combination of cosine similarity algorithm and cross-entropy loss was used to learn the association between the two modalities, thus completing the cross-modal association learning task. Experimental results demonstrate that the proposed method achieves promising performance in audio-visual cross-modal verification, matching, and retrieval tasks, ensuring excellent performance while considering the lightness and flexibility of the network structure.

An open world object detection method based on shape perception and class balance optimization was proposed to address the issue of poor prediction performance of unknown class objects in open world object detection. Unknown classes referred to classes that were not labeled during the training phase. Due to the lack of guidance from labels, detecting unknown class objects was a challenging task. An unknown class enhanced detector has been constructed as an unknown class detection branch. During training, this detector was supervised using only known class labels, allowing it to learn the similarities in features of known class objects and generalize to unknown class objects. To improve the detector's sensitivity to unknown classes, the region proposal network (RPN) module's ability to distinguish between foreground and background was utilized. A specific filtering method was employed to select results with “unknown class potential” from the RPN output, which were then used as pseudo labels in the training process. Due to the absence of confidence scores, traditional non-maximum suppression (NMS) methods were difficult to apply for post-processing unknown objects. Therefore, a redundant unknown object suppression mechanism was designed, consisting of a center point-based grouping strategy and a redundancy score matrix based on shape perception. The center point-based grouping strategy included three methods based on the unknown class center points to determine the suppression range. Subsequently, a redundancy score matrix was constructed based on the redundancy scores of each prediction box within the group to suppress highly redundant predictions. Experimental results on open world object detection datasets demonstrated that the open world object detection based on shape perception and class balance optimization maintained high recall rates for unknown classes while achieving high prediction accuracy. This method effectively addressed the challenges of open world scenarios and avoided generating a large number of useless predictions.

Addressing the limitation of existing sequence labeling approaches in effectively recognizing nested entities within Chinese electronic health records (EHRs), a novel named entity recognition model that integrates MacBERT and a global pointer network was proposed. Initially, the MacBERT-large pre-trained model transformed the text into context-sensitive dynamic vectors. Subsequently, the fast gradient method (FGM) was employed to generate adversarial samples, which were incorporated into the original vectors and fed into a BiLSTM (bi-directional long short-term memory) network to capture contextual features. To enhance the capture of long-distance semantic features, an attention mechanism was introduced. Finally, a global pointer network model was leveraged to decode simultaneously considering both head and tail feature information, thereby achieving superior prediction performance for medical nested entities. Experimental results demonstrate that compared to the state-of-the-art global pointer model, the proposed model achieves an improvement of 1.8%, 1.37%, and 1.72% in F₁-score on the CCKS2019 dataset and two versions of the CMeEE Chinese EHR dataset, respectively, validating the effectiveness of the proposed approach.

Defect detection is regarded as an indispensable step in the industrial production process. At present, manual detection is faced with the problems of low efficiency and high cost. A ceramic small target defect detection algorithm based on deep learning was proposed. For small target defects, a slice pre-training layer was first added to reduce the loss of graphics memory resources by large-size images. Secondly, a small target detection layer was added for the detection of small target defects, and a large target detection layer was removed to reduce the number of parameters. In addition, a feature selection fusion module based on MLCA (mixed local channel attention) was proposed to improve the perception of small target defects. Finally, a detection head with shared parameters was designed to further reduce the number of learnable parameters of the algorithm. By comparing with the baseline model, taking the ceramic cup as an example, the detection accuracy of this algorithm has been improved by 20.9%. Combined with the developed detection software and experimental platform, the detection efficiency of the ceramic cup has been enhanced by about 46.9%.

In order to address the issues of incomplete collection, vulnerability to attacks, and limitations in specific recognition scenarios in single modal biometric information, a multi-level fusion recognition model for faces and iris was proposed, a multi-modal biometric recognition system was designed and implemented to integrate the proposed model in a modular manner. The lightweight convolutional neural networks was used as feature extractors, intra class correlations between different modal features was utilized on the feature level, normalizing and concatenating the features of different modalities. The minimum strategy was used to fuse left and right iris scores on the score layer, the average strategy was used to fuse iris scores and face scores. Homologous multi-modal datasets was extracted from the CASIA-IrisV4-Distance dataset for experiment verification, feature layer fusion algorithm and score layer fusion algorithm both achieves an accuracy of 99.8%. It is observed in the experiment that this system has robustness and generalization.

The tension at the suspension point of transmission towers is an important parameter for line renovation and collapse accident warning. Although airborne LiDAR can provide high-precision spatial information for transmission line scenes-LiDAR point cloud data, lacks timely feedback on the operational status information of elements (such as tension of suspension points). A tension extraction method for tower suspension points based on LiDAR point clouds was proposed, and a new approach for studying micro physical parameters of transmission line spatial information was designed. Firstly, a three-dimensional spatial model of the transmission line was reconstructed from point cloud. Then, a mapping model between the spatial curve model and the horizontal stress of the transmission line was constructed. Finally, based on the tension vector relationship of adjacent transmission lines, the tension at the suspension point of the tower was extracted. This method reconstruct the three-dimensional model of transmission lines and extract the tension of tower suspension points in real-time operation based on LiDAR point cloud. Compared with finite element analysis method, the maximum relative error is 3.26%. The research can be extended and applied to the safety inspection and state parameter detection of large-scale transmission lines.

Seasonal segmentation of building electricity consumption time series (BECTS) is of great significance for accurate load forecasting and pattern mining. Aiming at the problem that accurate BECTS seasonal segmentation is difficult to be realized by traditional timing segmentation, fixed-temperature segmentation and adaptive five-days temperature segmentation methods, a new adaptive seasonal segmentation method for BECTS based on Toeplitz inversed covariance-based clustering (TICC) was proposed. The method was based on the binary time series of building hourly electricity load and outdoor dry bulb temperature, and the TICC algorithm was used for real-time segmentation and clustering. A large public building electricity load case in a hot summer and warm winter area was analyzed, and the result showed that the similarity between samples of the same type and the difference between samples of different types were enhanced by the method. Compared with the timing segmentation, fixed-temperature segmentation and adaptive five-days temperature segmentation methods, the average dynamic time warping (DTW) distance of each category after TICC segmentation was improved respectively by 46.54%, 35.73% and 7.59%. This method can be used as data preprocessing to provide data support for single building data mining analysis, such as building electricity consumption pattern mining and load forecasting.

To investigate the flexural bearing capacity of timber-concrete composite beams, Yunnan pine was selected as the base material, and high-strength self-tapping screws were used as shear connectors. Self-tapping screws were drilled into the timber to connect with cast-in-place concrete slabs, with partial slotting of the connection surface on the timber beam as a variable, to study the flexural performance of timber-concrete composite beams. Two groups of four specimens were designed in total, and a stepwise loading method was adopted to conduct four-point bending tests to analyze the mechanical properties of the composite beams. The results indicated that the overall performance of the composite beams was good, with the partially slotted design exhibiting better flexural stiffness and overall performance compared to the unslotted beams. Under the same load conditions, the deflection of the partially slotted beams was reduced by 57% compared to the unslotted beams, and the interface slip was reduced by 50%. Theoretical analysis results were in good agreement with the experimental findings, showing that the effective flexural stiffness and composite effect coefficient of the partially slotted beams were higher than those of the unslotted beams. The partially slotted design of the composite beams demonstrated superior overall performance, as well as improved stiffness and strength.

The stress-strain characteristics of gas hydrate-bearing sediments (GHBS) are essential for achieving safe and secure extraction of marine natural gas hydrate. To quantitatively investigate the influence of hydrate formation on the mechanical properties of GHBS, laboratory tests were conducted under different confining pressures and various hydrate saturations, and it was found that hydrate primarily enhanced the cohesive strength of GHBS, while the internal friction angle remained relatively unchanged. To comprehensively describe the intricate mechanical properties of GHBS, the previously established elastic-plastic constitutive model was employed for predictive purposes. A comparative analysis revealed that the constitutive model exhibited a certain level of applicability. Subsequently, a computer program was developed for the constitutive model by leveraging the UMAT user material subroutine interface provided by ABAQUS. The program was written in FORTRAN language and integrated into ABAQUS, facilitating the implementation of the constitutive model. To validate the effectiveness and stability of the developed model, finite element simulations were conducted on single elements as well as triaxial specimens of GHBS. The results of these simulations confirm the efficacy and reliability of the developed model.

Because Hainan Province is located in the tropics, it is often hit by extreme weather such as typhoons and rains, so it is very prone to geological disasters such as slope collapse and landslides, which eventually cause irreparable losses. In order to improve the stability of tropical soil slope in a green and environmentally friendly way, the slope was strengthened by microbial induced calcium carbonate precipitation (MICP) technology and carpet grass root slope consolidation. Suitable microbial strains were first screened out, and the preparation process of related microbial agents was optimized. Subsequently, carpet grass root system was implanted in the slope soil indoors, and MICP treatment was carried out after the formation of the root-soil complex. Subsequently, a series of laboratory tests and numerical simulation analyses were carried out to evaluate the reinforcement effect of this technology. The results showed that MICP technology and plant root treatment complemented each other in terms of mechanical brittleness and integrity of slope, and significantly improved the unconfined compressive strength and shear strength performance of soil, effectively enhanced the stability of slope, and reduced the risk of slope erosion. Finally, the numerical simulation verification was carried out by using Abaqus finite element software, which enhanced the reliability of the research results. It can be seen that the MICP combined with carpet grass root reinforcement method provides an effective reinforcement method for tropical soil slopes, which not only improves the mechanical properties of slopes, but also promotes ecological restoration and environmental sustainability. This result provides a new technical approach for the ecological reinforcement of slopes in tropical areas, and has certain theoretical and practical significance for protecting the ecological environment and reducing the risk of geological disasters.

Dynamic compaction is one of the effective methods to eliminate collapsibility of loess foundation. In order to study the mechanism of eliminating loess collapsibility by dynamic compaction, particle size test, collapsibility test, scanning electron microscope test and soil-water characteristic curve (SWCC) test were conducted on undistributed loess and compacted loess in Tongchuan City. The above experimental data was used for analyzing the effect of dynamic compaction on the particle size distribution, microstructural and unsaturated characteristic of loess, as well as the relation between these factors and collapsibility. It is shown that the clay content of the loess in Tongchuan area increases significantly under dynamic compaction. It is found that when the clay content is greater than 36.2%, the loess is no longer collapsible. It is observed that dynamic compaction leads to the formation of small and micro pores from collapsed macropores and mesopores. As a consequence, the density of particles increases and the collapsibility decreases. The slope of the SWCC curve desaturation section of compacted loess is greater than that for undistributed loess. This is because clay particles formed under dynamic compaction act as cementation materials, which is considered to increase the strength of cementation on the one hand, and to be filled in the pores on the other hand, increasing the density of loess. Finally, the results show that the SWCC curve fitting parameter a is related to the proportion of micro-pore area and the collapsibility coefficient. The collapsibility of loess can be judged indirectly from the characteristics of the SWCC curve.

To study the damage characteristics of rock masses in cold regions under the coupling effect of freeze-thaw cycles and fatigue loads, freeze-thaw cycle tests, nuclear magnetic resonance microscopic tests, and triaxial compression fatigue macroscopic tests under different confining pressures were carried out on single crack marble. The pore structure expansion characteristics of fissured marble after freeze-thaw cycles, as well as the mechanical properties and deformation failure characteristics of fissured marble under freeze-thaw cycles and fatigue loads were analyzed. A single crack marble fatigue damage constitutive model based on Nishihara creep model was established by introducing damage variables under freeze-thaw and fatigue loads. The results showed that with the increase of freeze-thaw cycles, the mass loss rate and longitudinal wave velocity loss rate of fissured marble gradually increased. The nuclear magnetic resonance T₂ curve showed a three peak characteristic, with the whole curve shifting to the right. The second peak gradually connected with the third peak, and the total peak area continued to increase. There was a positive correlation between confining pressure and the fatigue strength of fissured marble. With the increase of confining pressure, the fatigue resistance and ductility of fissured marble increased, and the failure mode of the rock sample gradually changed from local shear failure to overall shear failure. The theoretical curve of the established fatigue damage constitutive model was basically consistent with the experimental data. The research results could provide reference for the prevention and control of rock engineering disasters in cold regions.

Due to ignoring the influence of foundation pit unloading effect on the bearing properties of uprooted piles, the accuracy of in-situ test results needs to be studied when the excavation unloading area is large, and the current research on uplift piles mostly reflects the bearing properties of piles based on pile side friction resistance, and there are few studies on the normal pressure of pile-soil interface on pile sides. Compared with the frictional resistance of the pile side, the normal pressure of the pile-soil interface on the pile side can intuitively reflect the influence of foundation pit excavation and unloading on the bearing properties of the uprooted pile. In view of this, the combination of model test and numerical simulation was used to explore the stress response and deformation law of uplift piles under excavation and unloading conditions by changing the variables such as foundation pit excavation range and pile roughness, and then the bearing mechanism of uplift piles was revealed. The results show that the unloading effect of foundation pit excavation is related to the depth and width of the foundation pit, the effective pile length and the pile side roughness, according to the degree of influence: the effective pile length> the depth of the foundation pit> the roughness of the pile side > the width of the foundation pit. When the width of the foundation pit is greater than the critical value, the increase of the width of the foundation pit has little effect on the bearing characteristics of the uplift pile. Under the condition of the same effective pile length, the normal stress of the excavated pile side is proportional to the depth of the foundation pit and inversely proportional to the width of the foundation pit, and the relationship with the pile roughness is small. Under the condition of the same effective pile length, the frictional resistance of the pile after excavation is directly proportional to the depth of the foundation pit and the roughness of the pile, and inversely proportional to the width of the foundation pit.

Grouting pavements are susceptible to cracking, which can significantly reduce their service life. For this reason, water-borne epoxy resin (WER) was added to the grouting materials to improve the performance of grouting asphalt mixtures. Grouting materials with different levels of WER were prepared and characterised for their flow properties, setting time, mechanical strength and micromorphology. The road performance of the grouting asphalt mixtures was evaluated through wheel tracking test, low-temperature bending test and water immersion Marshall test. The results show that, WER can form a membrane structure on the surface of hydration products, improving the flexibility of grouting materials. However, it also delays the setting time of grouting materials. The addition of WER slightly diminishes the high-temperature performance of grouting asphalt mixtures, but improves the low-temperature cracking resistance and water stability of grouting asphalt mixtures. In particular, 7.5% WER increases the low-temperature destructive strain of grouting asphalt mixtures by 29.2%.

To meet the increasingly refined individual-level traffic management and travel service needs in the new era, a vehicle travel destination prediction method that comprehensively considers temporal and spatial correlation was proposed based on the traditional prediction method based on historical trajectories. Using data from video AI recognition and vehicle satellite positioning, the vehicle stopping points were identified to segment the vehicle's full-day travel trajectories and establish a historical vehicle travel trajectory database. By studying the temporal and spatial characteristics of vehicle travel, a calculation method for the temporal and spatial correlation of vehicle travel trajectories was proposed, and a vehicle travel destination prediction model was constructed using temporal and spatial correlation as weights. Taking the vehicle travel in Futian Central District of Shenzhen as an example, four typical vehicle travel trajectories including private cars and taxis were selected to establish a model prediction accuracy evaluation function. The prediction accuracy of travel destinations for different types of travel and different degrees of trajectory completion was analyzed and compared with the historical trajectory-based prediction method. The results show that the prediction accuracy of travel destinations for different types of vehicles is basically positively correlated with the degree of trajectory completion. When the trajectory completion rate reaches 80%, the accuracy of travel prediction basically reaches over 80%. Compared with the traditional prediction method based on historical trajectories, the prediction method considering temporal and spatial correlation has higher prediction accuracy, especially for taxis services with no fixed commuting travel characteristics. The prediction accuracy of travel destinations has been improved by more than 16%. The research results can better meet the needs of global traffic management.

To investigate driving workload on mountainous expressways, a naturalistic driving study was conducted utilizing an eye tracker to capture drivers' eye movement data in a realistic driving environment. Employing the change rate of pupil area, average saccade time, blink frequency, and fixation time ratio as primary indicators, a quantitative driving workload model was formulated through a combined weighting approach. This model aimed to reveal the driving workload evolution mechanism in typical scenarios of mountain expressways such as bridge and tunnel clusters, tunnel clusters, and short distances between tunnels and intersections. A clustering algorithm was applied to determine the classification thresholds for driving workload, thereby identifying high-risk scenarios characterized by heightened workload. The results show that the types of bridges within bridge-tunnel groups, the length of connection sections between tunnel groups, and the proximity of tunnels to interchanges significantly influence driving workload. A positive correlation was observed between driving workload and bridge size, whereas driving workload exhibited a negative correlation with the length of connection sections between tunnel groups and the distance from tunnels to interchanges. The thresholds of high, medium and low intensity levels of driving workload on mountain expressway are 0.54 and 0.26. Scenarios with bridge-tunnel groups composed of large or super-large bridges, tunnel groups with distances less than 300 m, and tunnel-to-interchange sections with distances less than 400 m were classified as high-risk driving workload scenarios. It is advisable to implement an intelligent lighting system within expressway tunnels, establish light-reducing structures at tunnel entrances, and in scenarios where tunnels are located in close proximity to interchanges, consider installing designated lane-changing zones within suitable tunnel sections to facilitate smooth lane transitions.

Under the context of the rapid rise of smart airports, the widespread deployment of autonomous vehicles requires an efficient safety operation system. In order to develop a collision warning method based on collision probability for airport unmanned driving vehicles, using ADS-B data as a foundation, considering the interaction between aircraft and vehicles at taxiway segments and intersections. The collision probability analysis was conducted for these two types of interactive environments. Through the analysis of single-vehicle warning simulation diagrams, different levels of warning thresholds were set. When the collision probability was 0.3≤p(c)≤0.5, the following vehicle entered the secondary warning state, and the vehicle braking acceleration took a value range of 0.5~1.5 m/s². When p(c)＞0.5, the following vehicle entered the primary warning state, and the vehicle braking acceleration took the maximum value of 2 m/s², and carrying out simulation analysis for the same taxiway and intersection according to the set warning threshold, the simulation test showed that the collision warning method based on collision probability could calculate the probability of collisions occurring during vehicle movement on the taxiway, and perform deceleration braking according to the corresponding warning threshold, effectively reducing the possibility of collision accidents. Through Monte Carlo random simulation experiments, the collision probability change diagram under different driving modes at crossroads was obtained, and the effectiveness of the warning algorithm was verified by using hierarchical warnings for simulation analysis. The simulation experiment proved that regardless of the driving mode, the warning algorithm could effectively avoid collision conflicts, further proving that the proposed method had high adaptability. A collision probability-based collision warning method was constructs for airport unmanned driving vehicles, which can effectively avoid the occurrence of airport field collision conflicts. Meanwhile, it can significantly improve the safety of unmanned driving vehicles in the airport environment.

Transonic fan rotor blades will suffer from morphological decay problems such as leading edge erosion in actual operation, and the flow field structure in the tip zone will change and then induce aerodynamic performance degradation. The effects of leading edge erosion on the tip leakage flow of the fan rotor were investigated from constant numerical computation in this paper. The results show that at the stall point of the eroded blade, its isentropic efficiency, total pressure ratio and mass flow rate decrease by 4.3%, 0.43% and 5.63%, respectively, and the leading-edge erosion also causes a decrease in the stabilized operating margin by 0.69%. For the flow in the tip zone, the leading-edge erosion causes an increase in the entropy increase area and intensity in the flow surface of S1 at the tip zone and the flow surface of S3 in the exit region, and the formation of a low Mach Number flow region in different blade heights; the leakage flow structure under the leading edge erosion undergoes malignant changes, the demarcation point of the main leakage flow and the secondary leakage flow is advanced, carrying more fluid for secondary leakage, and the leakage vortex deflection angle of the eroded blade reaches 30° from 15°, hitting the leading edge of the blade directly and causing flow blockage.

To address the issue of land and capital waste caused by suboptimal site selection and construction models for urban drone landing and takeoff sites, the maximum coverage model is initially used for site selection. However, due to the uneven distribution of demand points and overly simplistic coverage determination criteria, the results show low coverage rates and overly concentrated site selection. To solve this problem, a method based on spatially continuous demand for the maximum coverage model of drone landing and takeoff site selection was proposed, considering factors such as no-fly zones and application scenarios. Demand objects were determined using a regular grid, and candidate sites were identified using the PIPS(polygon intersection point set) method. The feasibility of the improved model was validated through a case study of site selection for urban drone landing and takeoff sites in Binhai New Area, Tianjin. When the number of landing and takeoff sites was fixed at 14, the improved model increased the actual service area coverage rate from 62.03% to 88.61%. The results indicate that this method better meets the practical requirements for drone landing and takeoff site selection, resulting in more evenly distributed and rational site layouts, and significantly enhancing the service coverage rate of the drone landing and takeoff sites.

The transformation of sludge into biochar adsorbents for the removal of tetracycline contaminants in water bodies represents one of the effective approaches for the resource utilization of sludge and enables the realization of the circular economy concept of “treating waste with waste”. Municipal sludge was employed as the raw material, and sludge biochar was fabricated through pyrolysis for the adsorption and removal of tetracycline. The adsorption and removal efficacy of tetracycline was investigated, and the preparation conditions of sludge biochar and adsorption environmental conditions were optimized. Additionally, by combining methods such as scanning electron microscopy, infrared spectroscopy, and BET(Brunauer,Emmett,Teller) specific surface area testing, the structural characteristics of sludge biochar and the underlying mechanism of its adsorption behavior towards tetracycline were explored. The results indicate that the sludge biochar prepared under a pyrolysis temperature of 800 ℃ and a pyrolysis duration of 4 hours exhibits the optimal adsorption performance for tetracycline. The pH value exerts a significant influence on the adsorption effect. In a weakly acidic environment, the adsorption effect of sludge biochar on tetracycline is the most favorable, with a maximum adsorption capacity reaching 45.33 mg/g. Thermodynamic and kinetic analyses demonstrate that the pseudo-second-order kinetic model and the Langmuir adsorption isotherm model can appropriately fit the adsorption process of tetracycline by sludge biochar. The adsorption process is primarily monolayer adsorption, dominated by surface chemical adsorption. In conjunction with the analysis of characterization test results, the chemical adsorption mainly involves processes such as electrostatic attraction, cation exchange, complex precipitation, π-π conjugation, and hydrogen bonding. Simultaneously, the pore structure characteristics of sludge biochar result in the adsorption process of tetracycline also encompassing pore filling and Van der Waals force.

To enhance market acceptance of construction waste recycling products, the impact of awe on consumers’ purchasing willingness investigated was investigated. Using an emotion assessment scale, purchase intention scores, and fNIRS, along with a virtual purchase experimental setup, the effects of awe induced by nature videos on subjects’willingness to purchase construction waste recycling products were examined. Measurements were conducted to separately assess the emotions induced by nature videos, the willingness to purchase construction waste recycling products, and the changes in brain activity during the viewing of nature videos. The results from the emotion assessment scale revealed that nature videos significantly induced awe emotions. The fNIRS data demonstrated deactivation in the brain's default mode network (DMN), associated with self-processing. This suggests that the experience of awe may be linked to reduced self-consciousness. The scoring data indicated that the awe experienced significantly enhanced the subjects' willingness to purchase construction waste recycling household products, however, the subjects' willingness to purchase construction waste recycling materials were not being significantly influenced by awe. Therefore, in construction waste recycling household product marketing, leveraging awe through natural videos can increase the willingness to purchase construction waste recycling household products, subsequently improving its market acceptance.

The explosive popularity of the new generation of artificial intelligence technologies will profoundly impact the risk experience of perceptual subjects within risk societies. Factor analysis and multiple indicators and multiple causes (MIMIC) model were used to study 12 risk scenarios of generative AI, besides four indicators reflecting the public's risk perception and five dimensions affecting the public's risk perception were explored. The results show that the public's perception of the risks of generative AI can be reflected by expectations of safety, technology, user and corporate regulatory. The public's risk perception is affected by its subjective evaluation of technology risks, macro risks, equity risks, subject risks and application risks, among which both equity risks and macro risks have the most significant impact. It shows that the public's risk perception of generative artificial intelligence is mainly characterized by “self-oriented” and “precautionary”. On this basis, the public's risk perception of generative artificial intelligence from the perspectives of history and culture, risk communication and technology governance was analyzed further, and corresponding countermeasures was put forward.