High Probability Events Research Articles

Safety considerations for large constellations of satellites

The deployment of constellations of satellites within low Earth orbit (LEO) has implications for space operations and for the broader space environment. A large active satellite population will experience high numbers of conjunctions with other resident space objects (RSOs). Even if only a small proportion are high-probability events, the substantial number of conjunctions will still lead to many potentially high-risk encounters with other RSOs and a correspondingly high burden for their operators to mitigate them via maneuvers. This burden is exacerbated if the operator adopts an approach whereby risk mitigation maneuvers are conducted at collision probability levels below the widely accepted 1E-4 (1-in-10,000). Despite these significant efforts the remaining aggregate risk may still be relatively high because of the large number of conjunctions experienced by some constellations, leading to ongoing concern over the safety of these space systems. Through an analysis of conjunction assessment data, simulations using the DAMAGE computational model, and a new mapping approach, the risks from conjunctions between large constellations and other RSOs have been investigated. The results show that some existing constellations currently face more than a 10 % annual collision probability even after accounting for their robust risk mitigation approaches, with implications for the safety and long-term sustainability of large constellations and the broader LEO environment. Overall, the work emphasizes the need for new research and guidance on this aspect of space operations.

Systematic bias in representation of reaction time distribution.

A correct perception of one's own abilities is essential for making appropriate decisions. A well-known bias in probability perception is that rare events are overestimated. Here, we examined whether such a bias also exists for action outcomes using a simple reaction task. In Experiment 1, after completing a set of 30 trials of the simple reaction task, participants were required to judge the probability that they would be able to respond before a given reference time when performing the task next. We assessed the difference between the actual reaction times and the probability judgement and found that the represented probability distribution was more widely distributed than the actual one, suggesting that low-probability events were overestimated and high-probability events were underestimated. Experiment 2 confirmed the presence of such a bias in the representation of both one's own and another's reaction times. In addition, Experiment 3 showed the presence of such a bias regardless of the difference between the representation of another's reaction times and the mere numerical representation. Furthermore, Experiment 4 found the presence of such a bias even when the information regarding actual reaction times was visually shown before the representation. The present results reveal the existence of a highly robust bias in the representation of motor performance, which reflects the ubiquitous bias in probability perception and is difficult to eliminate.

Modeling Parking Choice Behavior Using Cumulative Prospect Theory

In order to capture the driver’s attitude and preference towards risk during the decision-making process, this paper establishes a parking choice model considering driver heterogeneity based on the cumulative prospect theory. This research innovatively considers the influence of the unreliability of cruise time on drivers’ parking choice behavior and adds the psychological cost of drivers’ anxiety caused by lateness into the model. At the same time, according to the driver’s parking preference for parking, the latent category model is used to divide the driver into time-sensitive and money-sensitive categories. This paper analyzes the influence of unreliable cruising time on drivers’ parking choice behavior and finds that drivers have the characteristics of overestimating high-probability events and underestimating low-probability events in the decision-making process. By comparing the parking choice results of rational and irrational drivers, it is found that the model considering the risk attitude of drivers in the decision-making process is more in line with reality.

A resilience-oriented multi-stage operation strategy for distribution networks considering multi-type resources

With the increasing severity of global warming and the rapid evolution of cyber-attack techniques, the enhancement of distribution network (DN) resilience that can effectively cope with natural disasters and man-made attacks has received extensive attention. Hence, in the context of a high impact low probability (HILP) event where attackers launch malicious attacks after a natural disaster occurs, a multi-stage DN operation strategy for resilience enhancement is proposed considering multi-type resources, such as gas turbines, stationary energy storage systems, mobile energy storage systems (MESSs), repair crews (RCs) and network reconfiguration. Firstly, the operation process of DN is divided into five stages based on the time lag between the natural disaster and the man-made attacks: the normal operation stage, the disaster disruption stage, the post-disaster degradation stage, the man-made attack stage and the defense and recovery stage. Secondly, under the background of a coupled power-transportation system, the optimal scheduling model for each stage of DN is established considering the effects of dynamic changes in traffic flow on the movement process of MESSs and RCs. Finally, the model is converted into a 5-layer mixed integer linear programming (MILP) problem that can be solved by sophisticated optimization software according to the time series relationship, and a case study is carried out with a modified IEEE 33-node system and the corresponding transportation network. The results demonstrate that under extreme scenarios, the proposed model can guarantee the load-side power supply as much as possible through flexible cooperation between multi-type resources, effectively enhancing the resilience of the DN.

Punishment credibility and cooperation in public good games

This paper investigates the efficacy of a punishment mechanism in promoting cooperative behaviour in a public goods game when punishment enforcement is risky. Experimental studies have found that a sanctioning system can induce individuals to adopt behaviour deemed socially acceptable in such games. Our experiment qualifies this result by showing that a sanctioning system can only promote cooperative behaviour if subjects perceive punishment enforcement as a high-probability event. This result supports the view that sanctioning systems can only induce people to comply with social norms that enhance efficiency if such systems are sufficiently credible. We also find that the more punishment points towards a player were not being enforced in the history of the game, the more punishment from others was directed to them. This suggests that bygones are not bygones and that punishment behaviour attempts to compensate for the history of free-riding that goes unpunished.

Modeling Optimal Energy Exchange Operation of Microgrids Considering Renewable Energy Resources, Risk-based Strategies, and Reliability Aspect Using Multi-objective Adolescent Identity Search Algorithm

The inherent volatility and unpredictable nature of renewable resources and load demand have posed significant challenges to the optimal energy exchange of microgrids (MGs). Therefore, this study presents a novel risk-based multi-objective energy exchange optimization for the networked MGs from economic and reliability standpoints under load consumption and renewable power generation uncertainties. For this purpose, three different risk-based strategies are distinguished using the conditional value at risk approach. The proposed model is specified as an objective function in two distinct ways. The first function minimizes operation and maintenance costs, the cost of exchanging power between the upstream grid and MGs, and the cost of power loss, whereas the second function minimizes the amount of energy that is not supplied. Moreover, a stochastic scenario-based approach is incorporated into this approach to control uncertainties. Also, the scenario reduction method is implemented to reduce the computational burden. Finally, a developed multi-objective adolescent identity search algorithm (AISA) is proposed. Pareto theory and non-dominated sorting are employed to develop the proposed model. In the proposed data development model, it has been tried to improve the local and global search in a better way in order to solve local points and early convergence. The fuzzy model is used to select the optimal solution from the set of solutions. The modified IEEE 33-bus distribution system is used to implement the suggested model and assess its efficacy. The obtained results show that the proposed risk-based model can control the risk of the EMS problem due to the stochastic behavior of the input parameters. In this paper, for risk assessment high consequences and low probability events are considered extreme events. In the risk aversion strategy, the cost of the event is significantly reduced compared to the risk strategy, while the additional cost is much less than this is.

Managing Geotechnical Uncertainty With Simulation Models: An Introduction

In a standard deterministic analysis discrete scenarios are considered, and a moderately conservative “characteristic” value is used as a design basis. However, fixed or exact values in a real-world geotechnical site seldom occurs. Deterministic approaches may not explicitly consider the ground uncertainty. Simulations using various probabilities provides for this uncertainty as each parameter input is treated as a random variable within certain measured ranges or ability to evaluate. Monte Carlo (MC) sampling is a traditional technique for generating random numbers to sample from a probability distribution. When low probability events occur, a small number of MC iterations might not sample sufficient quantities of these outcomes for inclusion in the simulation model. Latin Hypercube (LH) sampling uses stratification of the input probability distributions, to overcome the limitations of Monte Carlo sampling. The simulation results show low probability outcomes are included in the sampling for the simulation model. At a high number of simulation iterations both provide similar outputs, but at low simulation iterations the LH is more reliable. However, both the MC and LH sampling suffer from impractical values at low or high probability events when the normal probability density function (PDF) is adopted. The normal PDF is commonly used in statistical modelling. Non-normal PDFs often represent the best fit PDF when a goodness of fit test is carried out. The errors associated with using the common normal PDF are shown with the above-mentioned simulation models. This best fit PDF applies whether simulation models as described above is used or even with simple “what if” sensitivity models in traditional analysis.

A broadly applicable, stress-mediated bacterial death pathway regulated by the phosphotransferase system (PTS) and the cAMP-Crp cascade

Recent work indicates that killing of bacteria by diverse antimicrobial classes can involve reactive oxygen species (ROS), as if a common, self-destructive response to antibiotics occurs. However, the ROS-bacterial death theory has been challenged. To better understand stress-mediated bacterial death, we enriched spontaneous antideath mutants of Escherichia coli that survive treatment by diverse bactericidal agents that include antibiotics, disinfectants, and environmental stressors, without a priori consideration of ROS. The mutants retained bacteriostatic susceptibility, thereby ruling out resistance. Surprisingly, pan-tolerance arose from carbohydrate metabolism deficiencies in ptsI (phosphotransferase) and cyaA (adenyl cyclase); these genes displayed the activity of upstream regulators of a widely shared, stress-mediated death pathway. The antideath effect was reversed by genetic complementation, exogenous cAMP, or a Crp variant that bypasses cAMP binding for activation. Downstream events comprised a metabolic shift from the TCA cycle to glycolysis and to the pentose phosphate pathway, suppression of stress-mediated ATP surges, and reduced accumulation of ROS. These observations reveal how upstream signals from diverse stress-mediated lesions stimulate shared, late-stage, ROS-mediated events. Cultures of these stable, pan-tolerant mutants grew normally and were therefore distinct from tolerance derived from growth defects described previously. Pan-tolerance raises the potential for unrestricted disinfectant use to contribute to antibiotic tolerance and resistance. It also weakens host defenses, because three agents (hypochlorite, hydrogen peroxide, and low pH) affected by pan-tolerance are used by the immune system to fight infections. Understanding and manipulating the PtsI-CyaA-Crp–mediated death process can help better control pathogens and maintain beneficial microbiota during antimicrobial treatment.

Safety Performance Functions to predict Separation Minima Infringements in en-route airspace

The objective of this methodology is to characterise the safety of air routes under a systematic framework, in terms of Separation Minimum Infringements (SMI) between en-route aircraft based on models known as Safety Performance Functions (SPF). Bayesian Networks have been selected as techniques with high predictive capability and low probability event estimation. Moreover, they allow the integration of knowledge modelling with data inference. It is complex to develop a Bayesian Network model for SMI prediction. It is necessary to integrate the available knowledge about the precursors of SMIs together with the accumulated knowledge from the analysis of the collected data into a conceptual framework. This framework underpinning the Bayesian Network model focuses on the Closest Point of Approach (CPA) analysis and considers the general scenario of aircraft evolution in an air traffic sector. In order to translate this conceptual framework into a set of causal sub-networks, the ATM barrier and event tree models are used.

Coordinating multi-energy to improve urban integrated energy system resilience against extreme weather events

The urban integrated energy system (UIES)’s ability to deliver power, heat and gas to users uninterruptedly even under a high impact and low probability event is critical to the safety and living quality of city residents and also the economy of the urban area. This paper proposes a load restoration strategy based on multi-energy coordination to improve the resilience of UIES, with the goal of restoring important loads as much as possible. The proposed restoration strategy introduces the minimum diameter spanning tree (MDST) method to UIES, and changes the topology of the system accordingly to reorganize the various “islands” after the extreme event, so as to ensure that the operating radius of the area is the smallest, and to alleviate the drops of voltage, air pressure and temperature. On this basis, the proposed restoration strategy also includes a method to better utilize the complementarity of energy sources by coordinating all power, gas and heat sources in the area to ensure the uninterrupted energy supply for important loads in the integrated electricity/gas/heating system. Finally, this paper verifies the feasibility and effectiveness of the proposed method through UIES E33-G14-H6 test system and UIES E123-G48-H32 test system.

Need for discriminating between diagnostic and screening efficacy to estimate a biomarker based on case control and cohort studies

This study proposes the comprehensive index of biomarker (CIB), based on the consistency of a biomarker in case control (Youden index, J) and cohort studies (Crc), to evaluate biomarker efficacy. CIB was calculated as the mean of J and Crc. Analysis of the effect of sensitivity and specificity on CIB and ROC analysis of CIB were performed in simulated and actual datasets. J and CIB had similar values for high-probability events (say probability was 0.50), but there was a significant difference between J and CIB for low-probability events (say probability was 0.05). Therefore, as the subjects considered for diagnosis are usually symptomatic, the occurrence of a disease can be assumed to be a high-probability event. In contrast, as the subjects considered in screening for a disease are usually healthy and asymptomatic, the occurrence of a disease is assumed to be a low-probability event. Although J is the common index used to evaluate the diagnostic effectiveness, unfortunately, the J value is significantly larger than CIB value in a low-probability event, showing overestimation for screening purpose. CIB could have more potential than J for determining the screening efficacy of a biomarker. The efficacy of a biomarker could differ for diagnostic, screening, predictive, and prognostic purposes, and it would be better to evaluate the efficacy of biomarkers for specific systems or contexts.

Establishment, simulation and verification of firepower safety control model

On a narrow warship platform, the coordinated use of shipborne weapon systems may cause firepower conflicts, which seriously endangers the ship safety. Meanwhile, with directed-energy weapons mounted on ships, firepower conflicts between weapons become a “high probability event”. Aiming at the problem of firepower safety control, based on the research about the collision probability model of air crafts and space targets and according to the cone of fire model of conventional weapons and directed-energy weapons, this paper solved the firepower conflict probabilities between conventional weapons as well as between conventional weapons and directed-energy weapons respectively using the methods of probability theory, and established the firepower safety control model. Then the calculation of firepower conflict probability was carried out using the dimensionality reduction method based on the equivalent conversion of polar coordinates and the power series method based on Laplace transform. The simulation results revealed that the proposed model and calculation methods are effective and reliable, which can provide theoretical basis and technical support for resolution of firepower conflicts between weapons.

Individual differences in the perception of probability.

In recent studies of humans estimating non-stationary probabilities, estimates appear to be unbiased on average, across the full range of probability values to be estimated. This finding is surprising given that experiments measuring probability estimation in other contexts have often identified conservatism: individuals tend to overestimate low probability events and underestimate high probability events. In other contexts, repulsive biases have also been documented, with individuals producing judgments that tend toward extreme values instead. Using extensive data from a probability estimation task that produces unbiased performance on average, we find substantial biases at the individual level; we document the coexistence of both conservative and repulsive biases in the same experimental context. Individual biases persist despite extensive experience with the task, and are also correlated with other behavioral differences, such as individual variation in response speed and adjustment rates. We conclude that the rich computational demands of our task give rise to a variety of behavioral patterns, and that the apparent unbiasedness of the pooled data is an artifact of the aggregation of heterogeneous biases.

A Long Short-Term Memory Network-Based Intrusion Detection Method for Power Grid Middle Platform

The power grid middle platform data has the feature of time-correlation, high dimensionality, and susceptibility to external factors. Based on the inherent characteristics of the power data and the theory of Long Short-Term Memory networks (LSTM), this paper proposed a stacked-LSTM based intrusion detection algorithm for power grid middle platform data. Technically, the strategy of constructing a dataset with replacement is adopted firstly, thus K sets of data is constructed. Then, a three-layer LSTM network is employed to conduct the feature extraction of high-dimensional data. Meanwhile, a two-layer fully connected hidden layer is used to generate a neural network and achieved user feature data matching. The high probability events are used to classify the node that appears the most in the results of the K data sets. Finally, experiments on real dataset proved that compared with traditional algorithms, the proposed algorithm has improved detection accuracy while reduced false alarm rates.

Water Supply Delivery Failures—A Scenario-Based Approach to Assess Economic Losses and Risk Reduction Options

Access to a reliable water supply is central for a well-functioning society. However, water supply systems are subject to a wide range of threats which may affect their ability to provide water to society. This paper presents a novel risk assessment approach that enables thorough analyses of economic losses and associated uncertainties under a range of water supply disruption scenarios. The purpose is to avoid sub-optimization when prioritizing between risk reduction measures, by integrating the full range of possible outcomes from low to high probability events. By combining risk analysis with cost-benefit analysis, additional information is provided on measures for leveraging investments in managing and reducing the risks. This enables the identification of the most economically profitable risk reduction alternatives and enables decision makers to build strategic capacity for operating in difficult and uncertain futures. The presented approach is exemplified on the island of Gotland, one of the most water scarce areas of Sweden.

Insurance Purchase for Low-Probability Losses

It is widely accepted that individuals tend to underinsure against low-probability, high-loss events relative to high-probability, low-loss events. This conventional wisdom is based largely on field studies, as there is very little experimental evidence. We reexamine this issue with an experiment that accounts for possible confounds in prior insurance experiments. Our results are counter to the prior experimental evidence, as we observe subjects buying more insurance for low-probability events than the higher-probability events, given a constant expected loss and load factor. Our results suggest that, to the extent underinsurance for catastrophic risk is observed in the field, it can be attributed to factors other than the relative probability of the loss events.

A Defect Detection Method for the Image With Intersecting Feature

Since the intersecting feature between the defect and the background of the image, the defect detection often results in under-segmentation or over-segmentation. To solve this problem, we propose a new defect extraction method by calculating the maximum mutual information of intersecting features. Firstly, we construct a new two-dimensional histogram according to the defect features. The new histogram is called Gray Level and Local Spatial Difference histogram (GLSD), which is constructed by grayscale and the improved local gray difference with the spatial relationship. Secondly, considering the geometric distribution of high-probability background events, we improve the segmentation shape of the background event distribution and divide the GLSD histogram preliminary. Finally, we calculate the maximum mutual information of the intersecting feature between the defect and the background. At this point, the boundary of the intersecting feature interval of the GLSD histogram is determined. To verify the effectiveness of the proposed method, we used two sets of databases for performance evaluation. The experimental results show that the proposed method is suitable for non-obvious defect detection under the local uniform background. Meanwhile, it can improve the sensitivity, specificity, and accuracy of defect detection compared with the classical threshold segmentation methods.

Generalized Belief Operator and Robustness in Binary‐Action Supermodular Games

This paper studies the robustness of an equilibrium to incomplete information in binary‐action supermodular games. Using a generalized version of belief operator, we explore the restrictions that prior beliefs impose on higher order beliefs. In particular, we obtain a nontrivial lower bound on the probability of a common belief event, uniform over type spaces, when the underlying game has a monotone potential. Conversely, when the game has no monotone potential, we construct a type space with an arbitrarily high probability event in which players never have common belief about that event. As an implication of these results, we show for generic binary‐action supermodular games that an action profile is robust to incomplete information if and only if it is a monotone potential maximizer. Our study offers new methodology and insight to the analysis of global game equilibrium selection.

Identifying Optimal Portfolios of Resilient Network Investments Against Natural Hazards, With Applications to Earthquakes

Although extreme natural disasters have occurred all over the world throughout history, power systems planners do not usually recognize them within network investment methodologies. Moreover, planners had historically focused on reliability approaches based on average (rather than risk) performance indicators, undermining the effects of high impact and low probability events on investment decisions. To move towards a resilience centred approach, we propose a practical framework that can be used to identify network investments that offer the highest level of hedge against risks caused by natural hazards. In a first level, our framework proposes network enhancements and, in a second level, uses a simulation to evaluate the resilience level improvements associated with the network investment propositions. The simulator includes 4 phases: threat characterization, vulnerability of systems components, system response, and system restoration, which are simulated in a sequential Monte Carlo fashion. We use this modeling framework to find optimal portfolio solutions for resilient network enhancements. Through several case studies with applications to earthquakes, we distinguish the fundamental differences between reliability- and resilience-driven enhancements, and demonstrate the advantages of combining transmission investments with installation of backup distributed generation.

Individual Differences in the Perception of Probability

In recent studies of humans estimating non-stationary probabilities, estimates appear to be unbiased on average, across the full range of probability values to be estimated. This finding is surprising given that experiments measuring probability estimation in other contexts have often identified conservatism: individuals tend to overestimate low probability events and underestimate high probability events. In other contexts, repulsive biases have also been documented, with individuals producing judgments that tend toward extreme values instead. Using extensive data from a probability estimation task that produces unbiased performance on average, we find substantial biases at the individual level, and we document the coexistence of both conservative and repulsive biases in the same context. Individual biases persist despite extensive experience with the task, and are also correlated with other behavioral differences, such as individual variation in response speed and adjustment rates. We conclude that the rich computational demands of our task give rise to a variety of behavioral patterns, and that the apparent unbiasedness of the pooled data is an artifact of aggregation of heterogeneous biases.