Analysis of Supply Chain Super-Network Coordination Considering Risk Function in Emergencies

Economic globalization continues to expand the scope of the supply chain network structure, while increasing its own complexity, as well as the uncertainty of the network operating environment and the fragility of the operating system. An emergency on a single node or line in the supply chain network usually affects other nodes in the supply chain and brings significant risks to the enterprise. The impact of other nodes can cause the entire supply chain network to collapse, especially if the production and operation of a single-node enterprise in the supply chain may be interrupted or malfunctioned, especially in the event of an emergency. It also threatens development greatly, affecting the production and livelihoods of enterprises in the supply chain and people's lives, and has a major negative impact on social and economic development. These emergencies continue to affect the supply chain network, and the originally fragile companies face greater risks. This paper establishes a supply chain hyper-network model considering the risk function under emergencies. When an emergency occurs, the demand in the consumer market decreases or increases due to different emergencies. Therefore, revenue sharing contracts are used to coordinate, build a supply chain network model under emergencies, and solve them to obtain a model equilibrium Solution, that is, the new equilibrium state after the occurrence of an emergency.

Modeling of Risk Measure Bonds Using the Beta Model

Duration and convexity are important measures in fixed-income portfolio management. In this paper, we analyze this measure of the bonds by applying the beta model. The general usefulness of the beta probability distribution enhances its applicability in a wide range of reliability analyses, especially in the theory and practice of reliability management. We estimate the beta density function of the duration/convexity. This estimate is based on two important and simple models of short rates, namely, Vasicek and CIR (Cox, Ingersoll, and Ross CIR). The models are described and then their sensitivity of the models with respect to changes in the parameters is studied. We generate the stochastic interest rate on the duration and convexity model. The main results show that the beta probability distribution can be applied to model each phase of the risk function. This distribution approved its effectiveness, simplicity and flexibility. In this paper, we are interested in providing a decision-making tool for the manager in order to minimize the portfolio risk. It is helpful to have a model that is reasonably simple and suitable to different maturity of bonds. Also, it is widely used by investors for choosing bond portfolio immunization through the investment strategy. The finding also shows that the probability of risk measured by the reliability function is to highlight the relationship between duration/convexity and different risk levels. With these new results, this paper offers several implications for investors and risk management purposes.

Mathematical Problems of Managing the Risks of Complex Systems under Targeted Attacks with Known Structures

This paper deals with the problem of managing the risks of complex systems under targeted attacks. It is usually solved by using Defender–Attacker models or similar ones. However, such models do not consider the influence of the defending system structure on the expected attack outcome. Our goal was to study how the structure of an abstract system affects its integral risk. To achieve this, we considered a situation where the Defender knows the structure of the expected attack and can arrange the elements to achieve a minimum of integral risk. In this paper, we consider a particular case of a simple chain attack structure. We generalized the concept of a local risk function to account for structural effects and found an ordering criterion that ensures the optimal placement of the defending system’s elements inside a given simple chain structure. The obtained result is the first step to formulate the principles of optimally placing system elements within an arbitrarily complex network. Knowledge of these principles, in turn, will allow solving the problems of optimal allocation of resources to minimize the risks of a complex system, considering its structure.

613Monitoring chronic kidney disease evolution using frailty models

Background Frailty models improves traditional survival models such that a latent multiplicative effect is introduced on the risk function, representing non-observed characteristics such as genetic and behavioral. The aim of this study is to explore this kind of model on chronic kidney disease (CKD) monitoring for intermediary outcomes. Methods Using a retrospective cohort comprising 778 patients with diagnosed CKD, parametric survival models were adjusted for the months until decay of renal function ≥5mL as outcome. Models included diabetes, hypertension and CKDEPI as covariates. Latent effect were incorporated, with Gamma distribution, to the best model. Results Just diabetes presented relevant effect on outcome. Best model were Weibull. Without frailty component, estimated diabetes parameter was 0.70 (CI95% 0.54; 0.89), indicating diabetic patients present outcome 30% earlier than non-diabetic. When incorporating the Gamma fragility to the models, the effect was 0.75, (CI95% 0.61, 0.92), or 25% faster on diabetes. The 5 percent points difference between parameters on both models represent, on average, a 20-day difference, having the survival median time of 13 months as reference. It’s possible to address individual specific frailty, an important feature for clinical follow-up. Conclusion Including frailty on modeling made possible to know that in average diabetic patients would experience a fast decay in renal function earlier than what a traditional survival model could evidence. This may be crucial for clinical decision making. All models were adjusted using commercial software. Key message Modern clinical epidemiology must foster use of modern statistics

An IMM-based POMDP decision algorithm using collision-risk function in mandatory lane change

In order to make safe and reasonable decisions in some high-risk environments such as the mandatory lane change, we propose an IMM-based partially observable Markov decision process (POMDP) decision algorithm using the collision-risk function which combines the time-to-collision (TTC), the intervehicular time (IT), and the collision function for mandatory lane change. The newly proposed collision-risk function contains two parts: the vehicle impact factor and the collision function, which is used to assess the risk and determines whether the autonomous vehicle collides with surrounding vehicles. The IMM-base POMDP is used for decision-making and we apply the Monte Carlo Tree Search (MCTS) to solve the problem. In the decision-making process, the belief state is obtained by the Interacting Multiple Model (IMM) algorithm. With the collision-risk function and the probability distribution of the states of surrounding vehicles in the future, the proposed POMDP decision algorithm can determine whether the autonomous vehicle accelerates lane changing or decelerates lane changing, and obtain the acceleration corresponding to each path point. Finally, in order to verify the effectiveness of the algorithm, we perform a driver-in-the-loop simulation through Prescan. We use aggressive driver and conservative driver to control the rear vehicle of the target lane, respectively. Simulation results show that the proposed algorithm can accurately predict the accelerations of surrounding vehicles and make safe and reasonable decisions under two scenarios, which is superior to the general POMDP.

Influence of Strain Post-Processing on Brain Injury Prediction

Finite element head models are a tool to better understand brain injury mechanisms. Many of the models use strain as output but with different percentile values such as 100th, 95th, 90th, and 50th percentiles. Some use the element value, whereas other use the nodal average value for the element. Little is known how strain post-processing is affecting the injury predictions and evaluation of different prevention systems. The objective of this study was to evaluate the influence of strain output on injury prediction and ranking. Two models with different mesh densities were evaluated (KTH Royal Institute of Technology head model and the Total Human Models for Safety (THUMS)). Pulses from reconstructions of American football impacts with and without a diagnosis of mild traumatic brain injury were applied to the models. The value for 100th, 99th, 95th, 90th, and 50th percentile for element and nodal averaged element strain was evaluated based on peak values, injury risk functions, injury predictability, correlation in ranking, and linear correlation. The injury risk functions were affected by the post-processing of the strain, especially the 100th percentile element value stood out. Meanwhile, the area under the curve (AUC) value was less affected, as well as the correlation in ranking (Kendall's tau 0.71-1.00) and the linear correlation (Pearson's r2 0.72-1.00). With the results presented in this study, it is important to stress that the same post-processed strain should be used for injury predictions as the one used to develop the risk function.

ON THE ISSUE OF THE RISK FUNCTION OF FINANCIAL MANAGEMENT IN MODERN CONDITIONS

The article considers types of financial risks and their impact on the performance of organizations, justifies the need to introduce a risk function among the functions of financial management, reveals its content and role in financial management of commercial organizations forced to lack financial and credit resources in the conditions of recovery from the economic crisis.

Power System Transient Stability Assessment Based on Snapshot Ensemble LSTM Network

Data-driven methods using synchrophasor measurements have a broad application prospect in Transient Stability Assessment (TSA). Most previous studies only focused on predicting whether the power system is stable or not after disturbance, which lacked a quantitative analysis of the risk of transient stability. Therefore, this paper proposes a two-stage power system TSA method based on snapshot ensemble long short-term memory (LSTM) network. This method can efficiently build an ensemble model through a single training process, and employ the disturbed trajectory measurements as the inputs, which can realize rapid end-to-end TSA. In the first stage, dynamic hierarchical assessment is carried out through the classifier, so as to screen out credible samples step by step. In the second stage, the regressor is used to predict the transient stability margin of the credible stable samples and the undetermined samples, and combined with the built risk function to realize the risk quantification of transient angle stability. Furthermore, by modifying the loss function of the model, it effectively overcomes sample imbalance and overlapping. The simulation results show that the proposed method can not only accurately predict binary information representing transient stability status of samples, but also reasonably reflect the transient safety risk level of power systems, providing reliable reference for the subsequent control.

A Portfolio Selection Model Based on the Interval Number

Traditional portfolio theory uses probability theory to analyze the uncertainty of financial market. The assets’ return in a portfolio is regarded as a random variable which follows a certain probability distribution. However, it is difficult to estimate the assets return in the real financial market, so the interval distribution of asset return can be estimated according to the relevant suggestions of experts and decision makers, that is, the interval number is used to describe the distribution of asset return. Therefore, this paper establishes a portfolio selection model based on the interval number. In this model, the semiabsolute deviation risk function is used to measure the portfolio’s risk, and the solution of the model is obtained by using the order relation of the interval number. At the same time, a satisfactory solution of the model is obtained by using the concept of acceptability of the interval number. Finally, an example is given to illustrate the practicability of the model.