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.

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 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.

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.

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 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.

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.

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.

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.

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.