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In the process of deepening the comprehensive risk management of financial companies, the construction of financial approval risk control is a complex and long-term system project that requires the establishment of the risk control concept and overall improvement of the risk prevention awareness in all employees. Guided by the financial approval risk control, the system construction mechanism of the company is improved to raise the operation and management level, enhance the self-improvement capacity, and provide a strong guarantee for achieving business objectives.

This article discusses the concept and applications of cognitive dynamic systems (CDS), which are a type of intelligent system inspired by the brain. There are two branches of CDS, one for linear and Gaussian environments (LGEs), such as cognitive radio and cognitive radar, and another one for non-Gaussian and nonlinear environments (NGNLEs), such as cyber processing in smart systems. Both branches use the same principle, called the perception action cycle (PAC), to make decisions. The focus of this review is on the applications of CDS, including cognitive radios, cognitive radar, cognitive control, cyber security, self-driving cars, and smart grids for LGEs. For NGNLEs, the article reviews the use of CDS in smart e-healthcare applications and software-defined optical communication systems (SDOCS), such as smart fiber optic links. The results of implementing CDS in these systems are very promising, with improved accuracy, performance, and lower computational costs. For example, CDS implementation in cognitive radars achieved a range estimation error that is as good as 0.47 (m) and a velocity estimation error of 3.30 (m/s), outperforming traditional active radars. Similarly, CDS implementation in smart fiber optic links improved the quality factor by 7 dB and the maximum achievable data rate by 43% compared to those of other mitigation techniques.

Effective control of plant disease remains a key challenge. Eradication attempts often involve removal of host plants within a certain radius of detection, targeting asymptomatic infection. Here we develop and test potentially more effective, epidemiologically motivated, control strategies, using a mathematical model previously fitted to the spread of citrus canker in Florida. We test risk-based control, which preferentially removes hosts expected to cause a high number of infections in the remaining host population. Removals then depend on past patterns of pathogen spread and host removal, which might be nontransparent to affected stake-holders. This motivates a variable radius strategy, which approximates risk-based control via removal radii that vary by location, but which are fixed in advance of any epidemic. Risk-based control outperforms variable radius control, which in turn outperforms constant radius removal. This result is robust to changes in disease spread parameters and initial patterns of susceptible host plants. However, efficiency degrades if epidemiological parameters are incorrectly characterised. Risk-based control including additional epidemiology can be used to improve disease management, but it requires good prior knowledge for optimal performance. This focuses attention on gaining maximal information from past epidemics, on understanding model transferability between locations and on adaptive management strategies that change over time.

This study presents an innovative approach to risk‐aware decision‐making in water resource management. We focus on a case study in the Netherlands, where risk awareness is key to water system design and policy‐making. Recognizing the limitations of deterministic methods in the face of weather, energy system, and market uncertainties, we propose a scalable stochastic Model Predictive Control (MPC) framework that integrates probabilistic forecasting, scenario generation, and stochastic optimal control. We utilize Combined Quantile Regression Deep Neural Networks and Non‐parametric Bayesian Networks to generate probabilistic scenarios that capture realistic temporal dependencies. The energy distance metric is applied to optimize scenario selection and generate scenario trees, ensuring computational feasibility without compromising decision quality. A key feature of our approach is the introduction of Exceedance Risk (ER) constraints, inspired by Conditional‐Value‐at‐Risk (CVaR), to enable more nuanced and risk‐aware decision‐making while maintaining computational efficiency. In this work, we enable the Noordzeekanaal–Amsterdam‐Rijnkanaal (NZK‐ARK) system to participate in Demand Response (DR) services by dynamically scheduling pumps to align with low hourly electricity prices on the Day Ahead and Intraday markets. Through historical simulations using real water system and electricity price data, we demonstrate that incorporating uncertainty can significantly reduce operational costs—by up to 44 percentage points compared to a deterministic approach—while maintaining safe water levels. The modular nature of the framework also makes it adaptable to a wide range of applications, including hydropower and battery storage systems. Plain Language Summary This research introduces a new method for managing water resources in the Netherlands that considers uncertainties in weather, sea‐levels, and energy markets. Traditional methods often ignore these uncertainties, which can lead to inefficiencies and higher costs. Our approach combines advanced forecasting techniques and decision‐making strategies to better account for these unpredictable factors. Using cutting‐edge computer models, we predict future conditions as multiple scenarios with assigned probabilities. These predictions guide decisions about when to operate water pumps, aiming to save energy costs while keeping water levels safe. By enabling the Noordzeekanaal–Amsterdam‐Rijnkanaal (NZK‐ARK) system to adjust pumping schedules in response to electricity price fluctuations, we show that this approach can achieve significant cost savings—up to 44 percent compared to traditional methods. Our method is flexible and can be applied to other systems like hydropower plants or battery storage facilities, demonstrating its potential for broader use. This study shows how combining risk‐aware forecasting and operational strategies can improve the efficiency and safety of water management systems while supporting the energy transition. Key Points A framework is proposed to estimate operational uncertainty, generate scenarios, and the application of risk‐aware decision‐making Taking operational uncertainty into account in control can lead to up to 44 p.p. lower energy cost compared to a deterministic forecast The use of Exceedence Risk constraints on water levels allows for a nuanced approach, leading to safe water levels and lower energy cost

The lack of the internal control conditions cause the bank accounting’s internal control risk. The cost of the internal control is large. The internal control mode is imperfect and the execution is not in place. The internal control supervision system, supervision and restraint are lack. How to perfect the internal control system and resolve the bank accounting internal control risk effectively Based on the analysis of the causes of the risk, we think that establish risk management responsibility system and strengthen the assessment of employees are the targeted suggestions.

【Objective】Water pollution incidents pose a serious threat to public drinking water. Assessing watershed water environment risk is necessary to prevent and reduce impact of such incidents. Taking Yiluo River Basin in Luoyang City of Henan Province as an example, this paper proposes a method to assess its environmental risks.【Method】The analysis was based on analytic hierarchy process (AHP). We constructed an environmental risk assessment framework that encompasses three key factors: ① intensity of environmental risk sources, ② vulnerability of environmental receptors, and ③ capacity for environmental risk prevention, control, and emergency response. From this framework, a water environmental risk assessment was conducted for fixed-source pollution emergencies in the study region.【Result】The highest-risk areas in the basin are in proximity to the river. With the exception of certain counties in the eastern part of the basin, high-risk sectors are chemicals, petroleum, and hazardous material industries. Overall, the basin is in a medium-to-high environmental risk level. Specifically, most counties and districts in the basin are at high risk, except for parts of Luoning County in the west and Xin’an, Mengjin, and Chanhe Huiyuan District in the north, which show comparatively low risk levels.【Conclusion】The dominant environmental risk factors in the basin include the efficiency of emergency command mechanisms, availability of emergency facilities and materials, and proximity to the nearest river. Our findings provide a scientific basis for emergency preparedness and risk management of pollution incidents in the Yiluo River Basin.

In order to find out the main causes of coal mine safety accidents and improve the pertinence of coal mine safety risk management and control, the identification and analysis of coal mine safety risks and hidden dangers are carried out based on the analysis of coal mine accident reports. Combing the complex network theory, a complex network model for the evolution of coal mine safety risks is constructed. The key elements that affect coal mine safety risk accidents are obtained through quantitative research on the characteristic indicators of the complex network model of coal mine safety risks. And the key nodes of coal mine safety risk spread network are obtained through network interference to the overall efficiency. The research results show that the complex network of coal mine safety risks illustrate the characteristics of a small-world network, and the spread of a certain risk is likely to cause coal mine safety accidents. Strengthening the risk management and control of hidden dangers with higher intermediate centrality can isolate the spread of coal mine safety risks and reduce the possibility of coal mine accidents.

As the most severe damage form of tailings ponds, dam failure causes a serious threat and damage to the surrounding lives and environment. Therefore, based on the systematic collection and consultation of relevant data at home and abroad, the literature source analysis on tailings dam failure disasters is conducted using the CiteSpace scientometric tool. The research on tailings dam failure disasters can be classified into two stages: the preliminary germination stage and rapid development stage. Based on the scientometric knowledge map, the research hotspots of tailings dam failure disasters are analyzed and summarized as three main research directions: environmental impact, risk assessment, and mechanical behavior. With the maturity of the research on ecological problems caused by tailings leakage, ecological restoration has also gradually become a hot research topic. Through the analysis of keyword bursts and co-cited bursts, the research frontier of tailings dam break disaster is explored. “Risk management,” “real-time monitoring,” and “tailings characteristic” represent the current research frontier. Among them, risk management is burst for the longest time and is expected to be a very important research direction in the future. Finally, a tailings pond risk management and control suggestion is proposed with risk management as the core, emphasizing risk monitor, and combined with dynamic risk control, which provides a foundation for the construction of tailings dam safety management and dynamic monitoring systems.

Prefabricated buildings have advantages when it comes to environmental protection. However, the dynamics and complexity of building hoisting operations bring significant safety risks. Existing research on hoisting safety risk lacks a real-time information interaction mechanism and lacks scientific control decision-making tools based on considering the correlation between safety risks. Digital twin (DT) has the advantage of real-time interaction. This paper presents a safety risk control framework for controlling prefabricated building hoisting operations based on DT. In the case of considering the correlation of the safety risk index of hoisting, the safety risk hierarchy model of hoisting is defined in the process of building the DT model. The authors have established a Bayesian network model into the process of the integrated analysis of the digital twin mechanism model and monitoring data to realize the visualization of the decision analysis process of hoisting safety risk control. The key degree of the indirect inducement variable to direct inducement variable was calculated according to probability. The key factor leading to the occurrence of risk was found. The effectiveness of the hoisting safety risk control method is verified by a large, prefabricated building project. This method provides decision tools for hoisting safety risk control, assists in formulating effective control schemes, and improves the efficiency of information integration and sharing.

This paper proposes a novel method to transform the results of qualitative risk analysis into a numeric optimal control problem for autonomous ship navigation. Today, making autonomous high-level decisions replacing a crew onboard is considered difficult, in some part due to the complexity of managing the operational risks involved. Although human supervisors, e.g., located in remote operating control centers are still needed for safety and liability reasons, there is a growing demand for complex decisions to be made by the onboard control system itself, both during normal operations and in emergencies. This paper suggests general principles for how the results from systems-theoretic process analysis (STPA) can be transformed into a quantitative and computationally tractable optimization problem, solved by a MPC-based decision-making algorithm for autonomous navigation. The proposed method is demonstrated and evaluated by simulating an autonomous ship navigating in a coastal environment. It is concluded that the proposed method may serve as a reasonable and valuable bridge between the realms of qualitative risk analysis and numerical optimal control for risk-aware autonomous control and decision-making.