To effectively reduce the contagion risks in the "last-mile" of emergency logistics in epidemic-stricken areas, a truck-drone collaborative delivery mode was first designed. A "basic reproduction number" function was constructed based on epidemic transmission dynamics to quantify the number of infections at various demand points. Then, a routing optimization model for truck-drone collaborative emergency supply delivery was established, aiming to minimize both the total number of infections and the total delivery time. In view of the multi-objective and non-linear characteristics of the model, the IMOABCA was developed. Finally, experiments were carried out through multiple types of instances. The results show that the IMOABCA could scientifically optimize delivery routes by integrating epidemic data, demand point distribution, and population size. Compared with the basic multi-objective artificial bee colony algorithm (MOABC)and Non-dominated Sorting Genetic Algorithm-II(NSGA-II), the total number of infections is reduced by 922 and 746, respectively. Additionally, the total delivery time is saved by 3.71% and 1.41%, and the task completion time can be shortened by 14.06% and 3.6%, respectively.

To address the challenges of multi-modal vehicle coordination and dynamic scheduling in emergency supply delivery for mountain forest fires, this study proposes an optimized route planning method for emergency supply distribution. Considering factors such as mountainous road network structures, demand urgency, motorcycle and drone participation, and air-ground collaborative delivery, a dynamic collaborative truck-drone-motorcycle route optimization model for mountain forest fire emergency supply delivery is constructed, aiming to minimize delivery time. An improved adaptive large neighborhood search (IALNS) algorithm with a repair operator is designed to solve the model, and it is compared with traditional ALNS and tabu search algorithms(TSA). Research findings indicate: The proposed model and algorithm are applicable to emergency supply delivery in mountainous forest fire scenarios. The IALNS outperforms both ALNS and TSA. Dynamic coordination strategies yield superior results compared to static coordination, reducing the total delivery time by 2.49% to 25.75%. Leveraging the advantages of motorcycles and drones is essential for establishing an optimized multi-vehicle collaborative delivery model.

To maintain the operational capability of airports during wartime, an analysis was conducted on the vulnerability of the airport runway to deliberate attacks with conventional weapons. By analyzing the target and functional characteristics of airport runways, a vulnerability assessment framework for airport runways under deliberate attacks by multiple types of conventional weapons is constructed based on the Monte Carlo method. A airport runway vulnerability assessment model is established, and a runway at an airport on the coast of China is selected as a case study. The results show that the binary attack strategy is significantly superior to the random attack strategy. When attacking airport runway targets, the impact of the weapon's circular error probable (CEP) decreases as the weapon's lethal radius increases, while the impact of the weapon's lethal radius decreases as the weapon's CEP increases. Both the CEP and lethal radius impacts decrease with an increase in the number of strikes. Optimization of attack strategies can to some extent compensate for the shortcomings of weapon performance. Under the binary attack strategy, the strike effectiveness of different weapon systems can be ranked in descending order as follows: conventional foreign cruise missiles, large unmanned aerial vehicles, long-range rocket launchers, and howitzers. In terms of protective measures, measures such as multi-band smoke screen interference, Global Positioning System (GPS) jamming, and enhancing the strength grade of concrete can be adopted to reduce the accuracy of weapon strikes and the radius of weapon damage, thereby improving the take-off and landing efficiency of the airport runway.

To solve the limitations of traditional fault trees in accurately capturing the complex correlations among components of communication base station systems, this study proposes a method for seismic fragility and importance analysis based on T-S fault tree. First, a three-subsystem architecture consisting of power supply, machine room, and transmission is constructed, and a T-S fault tree model for post-earthquake functional loss of communication base station systems is established. The functional correlations among components and subsystems are quantified using gate rule tables. Second, the seismic fragility models of communication base station system and the machine room subsystem are comparatively analyzed. Then, the seismic fragility of communication base station system is calculated using traditional fault trees and Monte Carlo simulation, and compared with the results from T-S fault tree. Finally, key impact factors are identified by combining the T-S critical importance analysis, and the weak links of the system are located through the sensitivity analysis of component fragility parameters. The results show that analyzing only the seismic fragility of the machine room subsystem underestimates the risk of system functional loss, and it is necessary to comprehensively analyze the seismic fragility of the communication base station system by integrating the three subsystems. The T-S fault tree method has advantages in describing the fuzzy logic relationships among components, and its results are more reliable than those from traditional fault tree. Cables under the slight damage state and machine room buildings under the severe damage state are the key impact factors. Substations, transmission lines, and machine room buildings have the most significant impact on the system function.

In order to address the challenges of quantitatively evaluating public behavioral responses to rainstorm disasters and clarifying the degree of their alignment with disaster risks, this study took the Shenzhen "9·7" rainstorm as a case study. Employing the PSR model and public LBS data, public response behaviors during the disaster were comprehensively evaluated from three dimensions: pressure, state, and response. A response adaptation index was established to measure the alignment between public behavior and rainstorm disaster risks. The findings indicate that, compared to normal conditions, the public's travel patterns during rainstorms exhibit similar spatial characteristics but with reduced intensity. Across all time phases, the response effectiveness is highest during non-peak daytime hours. Among different administrative districts, residents in Futian and Longhua District demonstrate the highest level of responsiveness, while those in Yantian District exhibit a relatively weaker response. Among various functional area types, schools and recreational areas show the most significant reduction in travel intensity, indicating the most positive public response, whereas residential and office areas showed a comparatively weaker response.

To achieve scientific prevention and control of avalanche disasters in the southeastern Tibetan Plateau, a dynamic deduction and assessment model for avalanche hazard chains was developed, focusing on a high-frequency avalanche site along National Highway G219. A foundational geospatial dataset was first constructed using high-resolution digital elevation models, multi-source remote sensing imagery, and field survey data to support numerical modeling. Building upon snowpack instability mechanisms and physical kinematic theory, a multi-factor-driven model was established to quantify key physical parameters of avalanche processes, including flow trajectories, dynamic characteristics, and deposit morphologies. The SEEP/W model was then employed to evaluate the internal seepage stability of avalanche-induced natural dams under different water-level gradients. Finally, the Hydrologic Engineering Center's River Analysis System (HEC-RAS) hydrodynamic model was used to simulate the post-breach flood propagation, calculating critical variables such as downstream water levels and flow velocities. Results reveal that the dynamic modeling method proposed in this study can quantitatively characterize the evolution of the hazard chain: avalanches in this region are characterized by dense snowpack, high flow velocities, and strong impact forces, with deposited material prone to obstructing adjacent rivers and forming temporary dammed lakes. These provisional dams exhibit poor structural stability and are highly susceptible to rapid breaching, generating destructive floods that pose severe inundation threats to downstream infrastructure and nearby settlements.

In order to promote the implementation of the responsibilities of participating entities in the prevention and handling of mine safety accidents, improve the efficiency of safety governance and reduce the risk of mine safety accidents, the assumption of an enterprises' illegal liability was analyzed. The authority and responsibility system of regulatory authorities was sort out. The guarantee mechanisms for the professionalism and objectivity of work safety social service institutions were explored. Drawing on the provisions and practical experience of foreign jurisdictions regarding the primary responsibilities for work safety, and putting forward legal suggestions for improving the prevention and handling of mine safety accidents in China. The findings show that the addition of punitive damages and differentiated penalties for repeated illegal acts are important factors for enterprises to consider the costs and liabilities of illegal acts, which can raise enterprises' awareness of the importance of wore safety and firmly establish their sense of responsibility for work safety. Allocating the responsibilities of regulatory authorities by stages and constructing a regulatory model that attaches equal importance to inspections and unannounced investigations can straighten out the predicament of unclear division of powers and responsibilities of regulatory authorities and improve regulatory efficiency. Refining the access threshold for work safety social service institutions, establishing an information disclosure platform, and clarifying the provisions on accountability and the practice withdrawal system can ensure the professionalism, transparency and objectivity of the services provided by work safety social service institutions.

To prevent the occurrence of the butterfly effect in risk management, and in response to the scientific need to enhance the effectiveness of resilience governance in the face of risk amplification, a binary explanation framework for the butterfly effect was constructed based on the duality of risk. This framework encompassed social and cascading amplification of risk, clarifying the social amplification mechanism and cascading transmission pattern of the butterfly effect. On this basis, human-centered resilience construction mechanism in response to the butterfly effect was further explored. The results show that the duality of risk emphasizes the dialectical unity between the objective reality of risk its social constructiveness. Meanwhile, based on this duality, the uncertainty arising from the butterfly effect can be deconstructed, rooted in the complexity of modern socio-technical systems and the ambiguity resulting from the social construction of risk. On the one hand, modern socio-technical risks are interlinked and interdependent, forming an intricate risk network that exhibits endogenous complexity and provides impetus for risk cascade amplification. On the other hand, constrained by risk knowledge, the public's risk perception and behavior are influenced by social, institutional, and cultural factors, among others, manifesting as social ambiguity that creates conditions for the social amplification of risk. The duality of risk necessitates the establishment of a human-centered resilience governance system, that focus on improving risk monitoring and early warning system, refining risk communication mechanism, and promoting multi-stakeholder participation in governance, thereby enhancing the effectiveness of resilience management against the butterfly effect.

In order to effectively identify the unsafe behaviors of construction workers in high-altitude environments, a recognition model based on MCT-GCN was proposed. Firstly, a data preprocessing module was designed to convert video surveillance data into three-dimensional skeleton data. Secondly, a tri-component dynamic adjacency graph convolution was constructed. It integrated learnable structural prior topology, channel correlation adaptive topology, and relative position encoding topology to dynamically build adjacency matrices adapted to different actions. Furthermore, a multi-scale separable temporal convolution was proposed to decompose standard convolutions into deep temporal convolution and point-by-point convolution, thereby independently modeling the temporal characteristics and spatial distribution characteristics of construction workers' actions. Finally, experimental verification and comparative analysis were conducted on both public datasets and a self-built dataset of workers' unsafe behaviors. The results demonstrate that the proposed model outperforms existing methods in terms of recognition accuracy and cross-scene generalization. On the self-built dataset, the model achieved a peak recognition accuracy of 95.8%, significantly enhancing the detection of workers' unsafe behaviors in complex and dynamic construction environments, and making a significant contribution to the development of intelligent monitoring and control in the construction industry.

With the continuous expansion of electric vehicle charging stations, the power grid faces increasing risks such as power overload, load fluctuations, and uneven demand distribution. To address these issues, this paper proposes an Hybrid Deep Fusion(HDF)-Long Short-Term Memory(LSTM)-based method for load forecasting and graded early warning. The method integrates LSTM, Gated Recurrent Unit(GRU), and Transformer architectures with multi-source data, including historical load, meteorological conditions, and traffic flow. Pearson correlation analysis and a dynamic weight allocation mechanism are employed to improve nonlinear feature representation. Based on the transformer capacity and simultaneity factor specified in the Code for Design of Electric Vehicle Charging Stations, a three-level early warning mechanism is developed for rapid alerting near critical thresholds. Results show that the proposed model outperforms eXtreme Gradient Boosting(XGBoost), GRU, LSTM, and Transformer models, with an Mean Squared Error(MSE) of 0.185 2, an Mean Absolute Error(MAE) of 0.2682, and an R² of 0.985 7. The model also shows good computational efficiency and application potential in charging station load forecasting and operational risk warning.