Defining the migration and diffusion behavior of radioactive contaminants in porous media constitutes the theoretical foundation for the effective prevention and control of pollution in deep geological disposal systems. In this study, a fractal capillary seepage model is developed by simplifying the pore structure of porous media using a capillary bundle model, incorporating fractal theory to characterize the heterogeneous and irregular pore geometry, and considering three primary transport mechanisms: molecular diffusion, advection, and mechanical dispersion. Through theoretical derivation and numerical analysis, the influence of porosity on seepage velocity is systematically investigated. The results show that seepage velocity initially increases and then decreases with porosity, reaching a maximum at a porosity of 0.26. When the porosity is below 0.26, the increase in seepage velocity is primarily attributed to the enlargement of the average and maximum pore radii. When porosity exceeds 0.26, although the total flux of radioactive contaminants continues to rise, the total pore surface area increases at a higher rate, leading to a decrease in seepage velocity per unit area. This study provides theoretical insights into the transport mechanisms of radioactive contaminants and supports the optimization of subsurface barrier design.

Risks originating from technology, management, and the external environment are a concern during the operation of nuclear power systems. The spread of these risks can impact the safe operation of nuclear power, especially when a component failure accelerates the risk impact, potentially leading to accidents and disasters. This paper develops a risk contagion dynamics model for nuclear power systems using epidemic dynamics mechanisms. The risk contagion coefficient due to reliability failure is utilized to adjust the reliability degradation equation. The SEIQRS model and Hamiltonian equation for nuclear power reliability risk contagion dynamics are reconstructed by introducing maintenance strategies like preventive maintenance, isolation maintenance, and defect maintenance. Optimal control methods are applied to mitigate the impact of reliability risk. Using a nuclear power plant operation system as an example shows how reliability failures amplify risks. Implementing maintenance strategies enhances reliability while reducing failures. Comparative analysis results underscore the effectiveness of addressing risk contagion impact through reliability degradation failure, thereby improving the reliability risk dynamic mechanism of nuclear power. The optimal maintenance strategy significantly boosts the reliability of nuclear power systems while mitigating the risk of contagion, providing valuable insights for optimal decision-making in nuclear power safety management.

The permeability of porous media is jointly controlled by the geometric characteristics of pore structures and the surface morphology of pores. Particularly in low-permeability porous media, the multiscale nature of micro- and nanoscale pore structures, the three-dimensional tortuosity of flow paths, and the effects of pore surface roughness exert a significant influence on seepage behavior. To address the limitations of existing fractal permeability models, which commonly adopt two-dimensional structural assumptions and inadequately characterize pore surface roughness effects, this study develops a three-dimensional analytical fractal permeability model that incorporates pore surface roughness, starting from the microscopic geometric structure of porous media. First, based on a three-dimensional pore structure model composed of spherical particle clusters, the complex and irregular pore space is equivalently transformed into a capillary bundle structure under idealized geometric assumptions, enabling a quantitative description of pore size distribution characteristics and flow-path tortuosity. On this basis, the Hagen-Poiseuille equation is modified to derive an analytical expression for permeability that accounts for pore surface roughness effects. The results demonstrate that, over a wide range of permeability values, the model predictions are in good agreement with experimental measurements, indicating that the proposed model provides an effective reference for theoretical analysis of seepage characteristics and permeability prediction in complex porous media.

The decommissioning of uranium mill tailings pond generally employs covering layer to diminish radon exhalation, with soil permeability, thickness, and moisture content being essential considerations in assessing the efficacy of radon reduction. This research uses a proprietary multi-field coupled seepage testing apparatus and a simulated covering layer radon exhalation device to evaluate samples’ permeability and radon control effectiveness across different covering layer thicknesses, pressure gradients, and moisture content levels. The findings demonstrate that gas permeability diminishes with increasing covering layer thickness, exhibiting a more pronounced decline at pressures below 0.35 MPa. Moreover, both moisture content and covering layer thickness significantly influence the radon exhalation rate, which decreases nonlinearly with the rise of both variables. A positive link exists between the radon exhalation rate and permeability, aligning with a natural exponential function model.

To address traditional SIR models' failure to capture memory effects, medical resource saturation, and infectious latency, this study proposes a fractional-order time-delayed SIR model with Hattaf-Yousfi functional responses and medical observation control. Using Caputo derivatives, Lyapunov theory, and Pontryagin's maximum principle, we analyzed stability, bifurcation, and optimal control, and validated them via Adams-BashforthMoulton simulations. Key results: i) R-0 < min{1, R-a} ensures global stability of the disease-free equilibrium. ii) When k >= min{k(1), k(3)}, an increase in the time lag leads to a Hopf bifurcation, and the critical latency tau(0) decreases as theta. iii) Each 0.02 decrease in theta resulted in a prolongation of eradication time by approximately 16.7%. and iv) Optimal control was effective in reducing the proportion of infections, reducing the mean eradication time by 32.8% at different orders, and preventing bifurcation. The model provides precise support for cost-effective epidemic control.

Infectious diseases pose a significant threat to human life, health, and safety. Therefore, it is crucial to develop effective control strategies. In this paper, we aim to address this concern through the construction of a SEIQRS model on complex networks. This model focuses on viruses that have an incubation period and are infectious during this period. In order to minimize the costs, optimal control theory is used to solve the time-varying control problem of vaccination, quarantine and treatment. Subsequently, numerical simulations are performed to analyze the pros and cons of different control combinations, as well as the impact of parameters on the effectiveness of control. By doing so, better control strategies can be developed and the relationship between parameters, contagion, and control can be revealed.

This paper presents a fractional-order modeling framework for risk propagation in power systems. We develop a FO-SEIQRS model by combining network contagion dynamics with fractional calculus, and propose a distributed control strategy featuring power flow optimization, switching power supply isolation, and frequency regulation. Tested on the IEEE 1354 system, the model shows significant improvements over integer-order approaches: a 64.2 % reduction in risk control costs and 42.9 % faster convergence. The control strategy also achieves a 94.8 % suppression rate of risk propagation. These results demonstrate the effectiveness of fractional-order methods in power grid risk analysis and control, offering new theoretical and practical tools for power system security.

Modern risk management philosophy emphasizes the invulnerability of human beings to cope with all kinds of emergencies. The Nuclear Accidents Emergency Response Organization (NAERO) of Nuclear Power Plant (NPP) is the primary body responsible for nuclear accidents emergency response. The invulnerability of the organization to disturbance or attack from internal and external sources is crucial in the completion of its response missions, reduction of severity of accidents, and assurance of public and environmental safety. This paper focused on the NAERO of a certain NPP in China, and applied the complex network theory to construct the network model of the organization. The topological characteristics of the network were analyzed. Four importance evaluation indexes of network nodes including Degree Centrality (DC), Betweeness Centrality (BC), Closeness Centrality (CC) and Eigenvector Centrality (EC), along with Pearson coefficient correlation among the indexes were calculated and analyzed. Size of the Largest Connected Component (LCC) and Network Efficiency were used as measures regarding the invulnerability of the network. Simulation experiments were conducted to assess the invulnerability of network against various attack strategies. These experiments were conducted both in the absence of node protection measures and under protection measures with different node protection rates. This study evaluated the invulnerability of the NAERO network, and provided significant decision-making basis for the enhancement of the network's invulnerability.

The use of covering material is an important measure to control the radon migration of uranium tailings. Radon diffusion and migration are affected by cover layer parameters, such as diffusion coefficient, overburden thickness, particle size, and ore body width. The radon reduction effect of single-layer mulching is often less than that of double-layer, and the material parameters of the cover layer are uncertain; however, they can be explained by a fuzzy dynamic equation. Firstly, the radon exhalation model is constructed with the radon percolation diffusion and migration method in a double-layer covering. Secondly, a fuzzy target of radon exhalation and a fuzzy constraint model are constructed subject to the total cost and thickness of covering material by a triangular membership function. Lastly, the models are aimed at solving the corresponding extreme value interval of the fuzzy target of radon exhalation by immune genetic algorithm, to reconstruct the fuzzy target, fuzzy constraint, and fuzzy aggregation function, where, ultimately, the optimal radon control decision can be obtained by swarm intelligence algorithm subject to different levels between possibility and importance. An example demonstrates a database of optimal decision-making schemes for double-layer coverage, and flexible management of radioactive pollutants is realized.

Radon and radon progeny being natural radioactive pollutants, seriously affect the health of uranium miners. Radon reduction by ventilation is an essential means to improve the working environment. Firstly, the relational model is built between the radon exhalation rate of the loose body and the ventilation parameters in the stope with radon percolation-diffusion migration dynamics. Secondly, the model parameters of radon exhalation dynamics are uncertain and described by triangular membership functions. The objective functions of the left and right equations of the radon exhalation model are constructed according to different possibility levels, and their extreme value intervals are obtained by the immune particle swarm optimization algorithm (IPSO). The fuzzy target and fuzzy constraint models of radon exhalation are constructed, respectively. Lastly, the fuzzy aggregation function is reconstructed according to the importance of the fuzzy target and fuzzy constraint models. The optimal control decision with different possibility levels and importance can be obtained using the swarm intelligence algorithm. The case study indicates that the fuzzy aggregation function of radon exhalation has an upward trend with the increase of the cut set, and fuzzy optimization provides the optimal decision-making database of radon treatment and prevention under different decision-making criteria.

保障矿山通风网络可靠是实现矿山采场作业点需风量要求的首要策略之一.首先分析铀矿山通风网络在不同通风参数下巷道内氡浓度分布,估算通风巷道所需总通风量.然后利用路径熵理论,将风流传输路径、网络流量、氡浓度变化结合,推导出表征铀矿通风风力性能的风路熵定义.最后结合最大熵理论,提出考虑网络拓扑的介数中心性对风路熵进行加权的网络可靠性模型,并对模型进行求解分析.结果表明:距离入风口路径长度的增加,使各路径运移氡的概率不均,网络末端节点可靠性降低,增加入风口及回风的巷道有利于提高网络可靠性.

Radon exhalation from uranium tailings has seriously affected environmental safety and human health. Many uncertain parameters, such as diffusion coefficient, porosity, percolation rate, material particle size, etc., are related to the diffusion and migration of radon. Moreover, cover materials, cover layers, and cover thickness are the main instruments to control radon exhalation, and the radon reduction effect of single-layer mulching is often inferior to that of the multilayer. Hence, achieving radon control with multilayer coverage under uncertain environment is an urgent problem that must be solved in the area of nuclear safety and radiation environment. In an attempt to address the issue, a dynamic model of radon exhalation with multilayer coverage is constructed using radon percolation-diffusion migration equation, and triangular membership functions inscribe the model parameters; the objective functions of the left and right equations of the model are constructed, and their extreme value intervals are obtained using immunogenetic algorithm. Then, subject to the total cost and thickness of multilayer covering materials, the fuzzy objective and constraint models of radon exhalation are constructed, and the fuzzy aggregation function is reconstructed according to the importance of the fuzzy objective and constraint models, where ultimately, the optimal radon control decision by swarm intelligence algorithm under different possibility levels and importance conditions can be obtained. An example is then used to validate the effectiveness of the radon exhalation model, and to demonstrate that fuzzy optimization provides a database of decision-making schemes regarding multilayer coverage, and guidance for optimal control and flexible construction management.

根据农产品供应链属性,风险沿链条在供应商、收购商、零售商等节点间传播,为探究其中风险扩散机理,借鉴传播动力学模型,引入预期惩罚力度和外界助力程度两个因子,构建了农产品供应链网络风险传染SIR(susceptible-infected-recovered)模型,并将该模型应用于湖北省某粮油公司.研究结果表明:企业预期惩罚力度作为风险阻碍因子有效抑制风险在供应链中蔓延,但该值增大也一定程度上延长了企业脱离风险的时间;来自网络联盟等组织的助力可以大大缩短系统到达平衡态的用时,降低风险传染规模,在提高感染企业恢复概率方面可发挥积极作用,这为农产品供应链网络风险防范与控制开拓了新思路.

建设一流本科教育、创建一流本科专业,是新时期地方高校强化内涵发展、提升自身竞争力、服务区域经济的必然选择.南华大学在60余年办学历程中,高度重视一流本科专业建设与发展,逐步明晰和确立了学校本科专业建设的发展目标与优势,并积极推进一流本科专业建设的理论与实践探索,在增强学校核心竞争力、优化学科专业体系、提高教师整体素质以及全面提升人才培养质量等方面取得了显著的成效.

本文利用复杂网络理论构建了江西电网拓扑模型,对其网络特性进行分析,通过TOPSIS(technique for order preference by similarity to ideal solution)算法评估电网中的重要节点,并利用SIR(susceptible infected recovered model)模型对重要节点的传播影响力进行仿真分析.结果表明江西电网节点度分布服从幂律分布,网络呈现出无标度和小世界特性.网络中的重要度排名前10的节点分别为罗坊、抚州、梦山、鹰潭、乐平、永修、南昌、文山、赣州和红都.SIR仿真结果说明重要节点对网络的传播影响力极大,其中TOP SIS方法下的最终节点感染规模相比单一指标至少有5％的增幅.

为了分析尾矿库溃坝事故中的关键隐患,降低溃坝风险,提出了基于网络云模型的关键节点分析方法.首先确定尾矿库溃坝安全风险指标,构建尾矿库溃坝安全风险评价指标体系,应用云模型理论判断各指标的风险等级;然后结合指标体系关联关系构建复杂网络模型,运用TOPSIS算法,将融合后的中心性作为网络节点的重要度评估依据,得到节点重要度排序结果;最后分析得到较高风险等级下的关键风险指标,包括地震烈度、浸润线高度、日常管理和平均粒径.

为有效抑制病毒在网络中扩散,提出一种基于节点度、介数和聚类系数的复合免疫策略,该算法综合计算三类特征量得出节点重要性并免疫重要性高的节点,在人工生成网络和真实农产品供应链网络上分别进行仿真实验,结果表明:实施该复合免疫后网络中感染节点密度值低于同等免疫比例下的度免疫、节点介数免疫和聚类系数免疫策略,具有更高的免疫效率.此外,该复合免疫算法中节点度占比大小会影响免疫效果,存在一个最优的度权值使免疫效果最优.

研究高速公路交通事故风险重要度并进行排序,对提高风险控制效率,减少交通事故的发生的重复性具有重要意义.为获得风险全面且客观的排序,首先应用人因分析及分类模型确定道路交通安全风险及分类,构建道路交通风险网络模型;然后以网络中心性为指标,建立改进的TOPSIS方法(PPR-TOPSIS)对网络中的风险综合排序.运用此法分析京港澳高速公路衡阳段"6·29"重大道路交通事故,并与熵权TOPSIS法对比,结果表明:疲劳驾驶对事故影响最大,贴近度超过80％,企业及运管局的监督管理因素对事故影响较大,贴近度超过50％,PPR-TOPSIS比熵权TOPSIS法更具有综合性与准确度,更适用于重大交通事故.

The stable supply of iron ore resources is not only related to energy security, but also to a country's sustainable development. The accurate forecast of iron ore demand is of great significance to the industrialization development of a country and even the world. Researchers have not yet reached a consensus about the methods of forecasting iron ore demand. Combining different algorithms and making full use of the advantages of each algorithm is an effective way to develop a prediction model with high accuracy, reliability and generalization performance. The traditional statistical and econometric techniques of the Holt-Winters (HW) non-seasonal exponential smoothing model and autoregressive integrated moving average (ARIMA) model can capture linear processes in data time series. The machine learning methods of support vector machine (SVM) and extreme learning machine (ELM) have the ability to obtain nonlinear features from data of iron ore demand. The advantages of the HW, ARIMA, SVM, and ELM methods are combined in various degrees by intelligent optimization algorithms, including the genetic algorithm (GA), particle swarm optimization (PSO) algorithm and simulated annealing (SA) algorithm. Then the combined forecast models are constructed. The contrastive results clearly show that how a high forecasting accuracy and an excellent robustness could be achieved by the particle swarm optimization algorithm combined model, it is more suitable for predicting data pertaining to the iron ore demand.