Importance Metric Research Articles

System-of-Systems Resilience Analysis and Design Using Bayesian and Dynamic Bayesian Networks

A System-of-Systems (SoS) is characterized both by independence and by inter-dependency. This inter-dependency, while allowing an SoS to achieve its objectives, also means that failures can cascade throughout the SoS. An SoS needs to be resilient to deal with the impact of complex internal and external environments. In this paper, we propose a resilience analysis method of an SoS based on a hierarchy structure. Firstly, we establish a hierarchy structure, which is ranked from high to low as capability level, activity level and system level. Then, Bayesian Networks (BNs) and Dynamic Bayesian Networks (DBNs) are used to analyze the resilience of the SoS. A resilience-based system importance metric is introduced, which is used in the budget allocation optimization problem during the development domain of an SoS. This paper proposes a mathematical programming model aimed at optimizing SoS resilience by optimally using budget to the subsystem. The application of the proposed approach is demonstrated using a case study: a Next Generation Air Transportation setting. The study results provide evidence that the proposed inter-dependency analysis based on Bayesian theory and the SoS resilience design approach can assist SoS system engineers in increasing expected SoS resilience during the development domain.

A roulette wheel-based pruning method to simplify cumbersome deep neural networks

Deep neural networks (DNNs) have been applied in many pattern recognition or object detection applications. DNNs generally consist of millions or even billions of parameters. These demanding computational storage and requirements impede deployments of DNNs in resource-limited devices, such as mobile devices, micro-controllers. Simplification techniques such as pruning have commonly been used to slim DNN sizes. Pruning approaches generally quantify the importance of each component such as network weight. Weight values or weight gradients in training are commonly used as the importance metric. Small weights are pruned and large weights are kept. However, small weights are possible to be connected with significant weights which have impact to DNN outputs. DNN accuracy can be degraded significantly after the pruning process. This paper proposes a roulette wheel-like pruning algorithm, in order to simplify a trained DNN while keeping the DNN accuracy. The proposed algorithm generates a branch of pruned DNNs which are generated by a roulette wheel operator. Similar to the roulette wheel selection in genetic algorithms, small weights are more likely to be pruned but they can be kept; large weights are more likely to be kept but they can be pruned. The slimmest DNN with the best accuracy is selected from the branch. The performance of the proposed pruning algorithm is evaluated by two deterministic datasets and four non-deterministic datasets. Experimental results show that the proposed pruning algorithm generates simpler DNNs while DNN accuracy can be kept, compared to several existing pruning approaches.

IgG Antibody Responses to Epstein-Barr Virus in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Their Effective Potential for Disease Diagnosis and Pathological Antigenic Mimicry.

Background and Objectives: The diagnosis and pathology of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) remain under debate. However, there is a growing body of evidence for an autoimmune component in ME/CFS caused by the Epstein-Barr virus (EBV) and other viral infections. Materials and Methods: In this work, we analyzed a large public dataset on the IgG antibodies to 3054 EBV peptides to understand whether these immune responses could help diagnose patients and trigger pathological autoimmunity; we used healthy controls (HCs) as a comparator cohort. Subsequently, we aimed at predicting the disease status of the study participants using a super learner algorithm targeting an accuracy of 85% when splitting data into train and test datasets. Results: When we compared the data of all ME/CFS patients or the data of a subgroup of those patients with non-infectious or unknown disease triggers to the data of the HC, we could not find an antibody-based classifier that would meet the desired accuracy in the test dataset. However, we could identify a 26-antibody classifier that could distinguish ME/CFS patients with an infectious disease trigger from the HCs with 100% and 90% accuracies in the train and test sets, respectively. We finally performed a bioinformatic analysis of the EBV peptides associated with these 26 antibodies. We found no correlation between the importance metric of the selected antibodies in the classifier and the maximal sequence homology between human proteins and each EBV peptide recognized by these antibodies. Conclusions: In conclusion, these 26 antibodies against EBV have an effective potential for disease diagnosis in a subset of patients. However, the peptides associated with these antibodies are less likely to induce autoimmune B-cell responses that could explain the pathogenesis of ME/CFS.

Deformation prediction based on denoising techniques and ensemble learning algorithms for concrete dams

Constructing a deformation prediction model for dams that can accurately capture deformation trends is crucial to ensure their operational safety. The accurate monitoring data are critical to the presentation of the variation patterns of the monitored quantities. However, due to various disturbances, there can be a lot of noise in the raw deformation monitoring data acquired by the sensors. Therefore, the reduction of the noise level in the data sequence is necessary for the improvement of the accuracy and reliability of the prediction model. This paper presents data denoising techniques for the removal of outliers from data sequences, including complete ensemble empirical mode decomposition with adaptive noise, garrote threshold functions, and sample entropy. The ensemble learning algorithm was then employed to complete the deformation prediction of concrete dams. Through example analysis and comparison with a benchmark model, the proposed model's performance was validated, highlighting its effectiveness and superiority. Additionally, this paper enhances the model's interpretability by visualizing the optimization process. The degree of influence of the input factors on the deformation prediction results is also analyzed using the feature importance metric. The proposed model is expected to be applied to deformation monitoring of other civil and hydraulic structures, as it can effectively reduce the impact of noise on prediction.

Sensitivity Analysis of Effectiveness of FLEX Strategies Using a CDF-Based Importance Measure Under Accident Conditions

One of the lessons learned from the Fukushima Daiichi nuclear power plant (NPP) accident is strengthening the station blackout (SBO) mitigation capabilities by enhancing defense in depth for all existing and new NPPs. One of the possible remedies is diverse and flexible coping strategies (FLEX). The objective of this study is to address the benefits of FLEX in various accident scenarios in terms of both risk and cost. FLEX was originally devised against SBO accidents. In this research, we investigate the fundamental plant responses against accidents considering two fundamentally different cases: accidents that lead to high pressure on the primary side and accidents that lead to low pressure on the primary side. Several uncertainties are associated with the characteristics of the FLEX portable equipment. Specifically, the time for FLEX deployment may depend on several factors such as type of accident, point of injection, availability of safety systems, battery backup timings, and human actions. This study utilizes a dynamic risk assessment framework to analyze accident scenarios and suggests a novel importance measure, which is a cumulative distribution function–based importance metric that characterizes the influence of input distribution on complete output distribution. The importance of the existing and newly developed FLEX strategy based on risk significance is illustrated with examples. The suggested measure provides clear insight into how FLEX affects risk of the whole system and additional risk margins thanks to new safety systems.

Enhancing the robustness of influential seeds towards structural failures on competitive networks via a Memetic algorithm

Networked systems present the correlative relation between entities, and the related knowledge excavation problem has been greatly emphasized in the last decade. The determination of influential nodes is modeled as the influence maximization problem, and the robustness issue has also been studied. Meanwhile, a specific network may maintain multiple diffusive groups, and the robustness of seeds under this environment is important to solve propagation tasks. Rational diffusion models have been developed in related studies, and valid algorithms are devised to find effective seeds. However, existing studies only consider the node-based attacks, and omit the impact of link removals on the robustness of seeds under competitive environments. As an indispensable component of networks, links show significance in sustaining the functionality, the analysis on link-based attacks is thus necessary. Aiming at addressing this deficiency, an importance metric is designed to evaluate the centrality of links in the diffusion process on competitive networks. Equipped with this metric, a numerical measure is developed to evaluate the performance of a given seed set. Further, the structural information and genetic information is considered to guide the search process. Several problem-directed operators are given to combine valuable knowledge from both myopic and holistic solution spaces. A Memetic algorithm is constructed to tackle the robust influence maximization problem on competitive networks under link-based attacks, termed MA-RCIM-L. The algorithm is tested on both synthetic and real-world networks, whose competitiveness against existing approaches is demonstrated.

The critical node detection problem in hypergraphs using weighted node degree centrality.

Network analysis is an indispensable part of today's academic field. Among the different types of networks, the more complex hypergraphs can provide an excellent challenge and new angles for analysis. This study proposes a variant of the critical node detection problem for hypergraphs using weighted node degree centrality as a form of importance metric. An analysis is done on both generated synthetic networks and real-world derived data on the topic of United States House and Senate committees, using a newly designed algorithm. The numerical results show that the combination of the critical node detection on hypergraphs with the weighted node degree centrality provides promising results and the topic is worth exploring further.

Endpoint Device Risk-Scoring Algorithm Proposal for Zero Trust

The rapid expansion of remote work following the COVID-19 pandemic has necessitated the development of more robust and secure endpoint device security solutions. Companies have begun to adopt the zero trust security concept as an alternative to traditional network boundary security measures, which requires that every device and user be considered untrustworthy until proven otherwise. Despite the potential benefits of implementing zero trust, the stringent security measures can inadvertently lead to low availability by denying access to legitimate users or limiting their ability to access necessary resources. To address this challenge, we propose a risk-scoring algorithm that balances confidentiality and availability by evaluating the user’s impact on resources. Our contributions include (1) summarizing the limitations of existing risk scoring systems in companies that implement zero trust, (2) proposing a dynamic importance metric that measures the importance of resources accessible to users within zero trust systems, and (3) introducing a risk-scoring algorithm that employs the dynamic importance metric to enhance both security and availability in zero trust environments. By incorporating the dynamic importance metric, our proposed algorithm provides a more accurate representation of risk, leading to better security decisions and improved resource availability for legitimate users. This proposal aims to help organizations achieve a more balanced approach to endpoint device security, addressing the unique challenges posed by the increasing prevalence of remote work.

On the ramifications of airspace bans in aero-political conflicts: Towards a country importance ranking

The aviation industry is facing highly volatile developments in the recent years: Following a steady growth phase with prosperous projections, the COVID-19 pandemic has hit the aviation system rather hard. While having gradually entered a recovery process in the years 2021 and 2022, with load factors close to those in the year 2019, the airspace bans between Russia, Europe, and other regions in the world, as part of the ongoing conflict centered around Russia and Ukraine, threaten the orderly operation of flights. This study explores the byproducts and potential impact of airspace bans on the aviation system and its stakeholders, by deriving a ranking of country importance and how they have the potential to influence our aviation system. While being rooted in the analysis of the Russian–Ukrainian conflict, our framework is built up in a generic way and computes an overall country importance metric. We believe that our study contributes to the better understanding of the consequences airspace bans have on our society and the severe needs for effective policies and regulations of such emerging forms of conflict resolution.

ClustEHR: a tool for generating synthetic EHR data for unsupervised learning experiments.

ObjectivesClustering algorithms are commonly used to identify disease clusters. New Clustering algorithms are benchmarked on synthetic data to assess their accuracy. These datasets lack the complexities of real electronic health record (EHR) producing a partial assessment of the algorithm. We developed a synthetic EHR cluster generator for benchmarking clustering algorithms. ApproachWe have created a synthetic EHR cluster generator, clustEHR, based on Synthea, a synthetic EHR generator that produces datasets (with parameterized noise and cluster separation) of known clusters and with clinically relevant patient outcomes. We evaluated clustEHR by generating multiple datasets of variable cluster separation and percentage of noise variables to reflect easy and hard clustering problems. We used a linear model to assess the relationship between these parameters and cluster problem difficulty. K-means accuracy was used as a proxy to measure cluster problem difficulty. ResultsWe have developed a tool for generating synthetic EHR cluster data with clinically relevant outcomes based on the rate of decline of medical observations (e.g. blood pressure). The following parameters are supported: a) number of clusters, b) number of patients in each cluster, c) number and data type of features, d) separation through defining clusters as either diseases such as COPD or dementia (high separability) or inter-disease conditions such as emphysema and chronic bronchitis within COPD (low separability), and e) noise variables through identifying variables not predictive of true cluster outcomes random forest feature importance metric. We show that high cluster separation significantly increases k-means accuracy (coefficient of 0.33). Smaller percent of noise variables increase accuracy though not significantly (coefficient 0.42). ConclusionClustEHR offers realistic mixed data types as well as outcomes which are frequently used to evaluate clusters when subtyping diseases. The evaluating results suggest that the difficulty of the cluster data can be user determined. The tool can be used to create realistic datasets for evaluating clustering approaches.

A flexible approach for predictive biomarker discovery.

An endeavor central to precision medicine is predictive biomarker discovery; they define patient subpopulations which stand to benefit most, or least, from a given treatment. The identification of these biomarkers is often the byproduct of the related but fundamentally different task of treatment rule estimation. Using treatment rule estimation methods to identify predictive biomarkers in clinical trials where the number of covariates exceeds the number of participants often results in high false discovery rates. The higher than expected number of false positives translates to wasted resources when conducting follow-up experiments for drug target identification and diagnostic assay development. Patient outcomes are in turn negatively affected. We propose a variable importance parameter for directly assessing the importance of potentially predictive biomarkers and develop a flexible nonparametric inference procedure for this estimand. We prove that our estimator is double robust and asymptotically linear under loose conditions in the data-generating process, permitting valid inference about the importance metric. The statistical guarantees of the method are verified in a thorough simulation study representative of randomized control trials with moderate and high-dimensional covariate vectors. Our procedure is then used to discover predictive biomarkers from among the tumor gene expression data of metastatic renal cell carcinoma patients enrolled in recently completed clinical trials. We find that our approach more readily discerns predictive from nonpredictive biomarkers than procedures whose primary purpose is treatment rule estimation. An open-source software implementation of the methodology, the uniCATE R package, is briefly introduced.

Health status assessment of radar systems at aerospace launch sites by fuzzy analytic hierarchy process

AbstractThe radar system is a fundamental part of the launch site ground equipment, and its health status is critical to the success of testing tasks and launch missions at aerospace launch sites. The health status assessment of radar systems is, however, challenging due to multiple health indicators of its components and epistemic uncertainty associated with the elicited data. In this article, a fuzzy analytic hierarchy process method, which combines the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation methods, is put forth to assess the health status of radar systems at aerospace launch sites. In the first place, the four‐layer hierarchical structure, including the system, subsystem, performance indicator, and parameter index layers, of a real‐world radar system at an aerospace launch site is constructed. The AHP is, then, leveraged to identify the weights of each layer of the hierarchical structure. By eliciting the data of parameter indices as fuzzy numbers and using the fuzzy comprehensive evaluation method to calculate the layer‐by‐layer health status of the hierarchical structure, the health grade membership of the radar system is calculated. The health status of the radar system is, then, assessed in accordance to the maximum membership principle. A new importance metric is proposed to assess the influence of parameter indices on the current system's health status. The results of the importance metric can provide valuable insight for reliability enhancement and health management of the radar system.

An Intelligent Deep Learning Model for CO2 Adsorption Prediction

In this paper, we propose a supervised deep learning neural network (D-CNN) approach to predict CO2 adsorption form the textural and compositional features of biomass porous carbon waste and adsorption features. Both the textural and compositional features of biomass porous carbon waste are utilized as inputs for the D-CNN architecture. A deep learning neural network (D-CNN) is proposed to predict the adsorption rate of [Formula: see text] on zeolites. The adsorbed amount will be classified and predicted by the D-CNN. Three tree machine learning models, namely, gradient decision model (GDM), scalable boosting tree model (SBT), and gradient variant decision tree model (GVD), were fused. A feature importance metric was proposed using feature permutation, and the effect of each feature on the target output variable was investigated. The important extracted features from the three employed model were fused and used as the fusion feature set in our proposed model: fusion matrix deep learning model (FMDL). A dataset of 1400 data items, on adsorbent type and various adsorption pressure, is used as inputs for the D-CNN model. Comparison of the proposed model is done against the three tree models, which utilizes a single training layer. The error measure of the D-CNN and the tree model architectures utilize the mean square error confirming the efficiency of 0.00003 for our model, 0.00062 for the SBT, 0.00091 for the GDM, and 0.00098 for the GVD, after 150 epochs. The produced weight matrix was able to predict the [Formula: see text] adsorption under diverse process settings with high accuracy of 96.4%.

Localized Defect Detection from Spatially Mapped, In-Situ Process Data With Machine Learning

In powder bed fusion additive manufacturing, machines are often equipped with in-situ sensors to monitor the build environment as well as machine actuators and subsystems. The data from these sensors offer rich information about the consistency of the fabrication process within a build and across builds. This information may be used for process monitoring and defect detection; however, little has been done to leverage this data from the machines for more than just coarse-grained process monitoring. In this work we demonstrate how these inherently temporal data may be mapped spatially by leveraging scan path information. We then train a XGBoost machine learning model to predict localized defects—specifically soot–using only the mapped process data of builds from a laser powder bed fusion process as input features. The XGBoost model offers a feature importance metric that will help to elucidate possible relationships between the process data and observed defects. Finally, we analyze the model performance spatially and rationalize areas of greater and lesser performance.

Random survival forests for dynamic predictions of a time-to-event outcome using a longitudinal biomarker

BackgroundRisk prediction models for time-to-event outcomes play a vital role in personalized decision-making. A patient’s biomarker values, such as medical lab results, are often measured over time but traditional prediction models ignore their longitudinal nature, using only baseline information. Dynamic prediction incorporates longitudinal information to produce updated survival predictions during follow-up. Existing methods for dynamic prediction include joint modeling, which often suffers from computational complexity and poor performance under misspecification, and landmarking, which has a straightforward implementation but typically relies on a proportional hazards model. Random survival forests (RSF), a machine learning algorithm for time-to-event outcomes, can capture complex relationships between the predictors and survival without requiring prior specification and has been shown to have superior predictive performance.MethodsWe propose an alternative approach for dynamic prediction using random survival forests in a landmarking framework. With a simulation study, we compared the predictive performance of our proposed method with Cox landmarking and joint modeling in situations where the proportional hazards assumption does not hold and the longitudinal marker(s) have a complex relationship with the survival outcome. We illustrated the use of the RSF landmark approach in two clinical applications to assess the performance of various RSF model building decisions and to demonstrate its use in obtaining dynamic predictions.ResultsIn simulation studies, RSF landmarking outperformed joint modeling and Cox landmarking when a complex relationship between the survival and longitudinal marker processes was present. It was also useful in application when there were several predictors for which the clinical relevance was unknown and multiple longitudinal biomarkers were present. Individualized dynamic predictions can be obtained from this method and the variable importance metric is useful for examining the changing predictive power of variables over time. In addition, RSF landmarking is easily implementable in standard software and using suggested specifications requires less computation time than joint modeling.ConclusionsRSF landmarking is a nonparametric, machine learning alternative to current methods for obtaining dynamic predictions when there are complex or unknown relationships present. It requires little upfront decision-making and has comparable predictive performance and has preferable computational speed.

Determination of Effective Weather Parameters on Rainfed Wheat Yield Using Backward Multiple Linear Regressions Based on Relative Importance Metrics

Wheat (Triticum aestivum L.) is the most imperative crop for man feeding and is planted in numerous countries under rainfed conditions in semiarid zones. It is necessary for decision-makers and governments to predict the yield of rainfed wheat before harvest and to determine the effect of the major factors on it. Different methods have been suggested for forecasting yield with various levels of accuracy. One of these approaches is the statistical regression model, which is simple and applicable for regions with scarce data available. Since the weather is the most important factor affecting the production of wheat, particularly in rainfed cultivation, regression models using weather parameters are very common. However, the coefficients of these models are location based and should be determined locally. Therefore, in this research, backward multiple linear regression (BMLR) technique based on relative importance metrics was used to determine the most important effective weather parameters (11 parameters) on rainfed wheat productions in Fars Province, south of Iran, during 2006–2013. The influence of each parameter in the final model was analyzed using the values of LMG relative importance metric. The result indicated that sunshine hours had the biggest LMG (34.73%) and, therefore, was the most effective parameter. Also, among the other considered parameters, rainy days, minimum relative humidity, and average relative humidity with LMG values of 21.97%, 21.69%, and 21.62%, respectively, had the most effects on rainfed wheat yield in the studied area. All parameters except for the sunshine hours positively affected rainfed wheat yield. The most important reason for the significance of these parameters can be the prevailing dry and semidry climate in the southern areas of Iran. The proposed model for determination of weather parameters effects on rainfed wheat could be a great guidance and aid for different stakeholders such as farmers, decision-makers, and governments.

Optimizing infrastructure resilience under budgetary constraint

Communities located in regions prone to natural and man-made disasters endure hardship and financial loss in the face of these events. Investment to enhance infrastructure resilience is vital to reduce the consequences of these low probability high-impact events. Budget and resources are limited, and they must be allocated wisely to infrastructure components to build a resilient community. The complexity of infrastructure makes it difficult to show the effects of component enhancements on system resilience. This paper proposes a mathematical programming model aimed at optimizing infrastructure resilience against a set of adverse events by optimally allocating budget to the infrastructure components. Investment, component enhancement, and corresponding functionality are combined with the resilience-based component importance to tackle the system complexity. Three utility functions are presented to determine the possible component enhancement alternatives for an allocated budget and to choose the optimal alternative. A resilience-based component importance metric is introduced, which is used in the budget allocation optimization problem. This approach establishes a relationship between amount allocated to a component and changes in its absorption and recovery, and the aggregate of all such changes on the components on the system functionality. The results show that the utility function of a component impacts the resilience enhancement of the system.

Estimating treatment importance in multidrug-resistant tuberculosis using Targeted Learning: An observational individual patient data network meta-analysis.

Persons with multidrug-resistant tuberculosis (MDR-TB) have a disease resulting from a strain of tuberculosis (TB) that does not respond to at least isoniazid and rifampicin, the two most effective anti-TB drugs. MDR-TB is always treated with multiple antimicrobial agents. Our data consist of individual patient data from 31 international observational studies with varying prescription practices, access to medications, and distributions of antibiotic resistance. In this study, we develop identifiability criteria for the estimation of a global treatment importance metric in the context where not all medications are observed in all studies. With stronger causal assumptions, this treatment importance metric can be interpreted as the effect of adding a medication to the existing treatments. We then use this metric to rank 15 observed antimicrobial agents in terms of their estimated add-on value. Using the concept of transportability, we propose an implementation of targeted maximum likelihood estimation, a doubly robust and locally efficient plug-in estimator, to estimate the treatment importance metric. A clustered sandwich estimator is adopted to compute variance estimates and produce confidence intervals. Simulation studies are conducted to assess the performance of our estimator, verify the double robustness property, and assess the appropriateness of the variance estimationapproach.

Modeling Intersecting Processes of Wetland Shrinkage and Urban Expansion by a Time-Varying Methodology

Continuous urban expansion worldwide has resulted in significant wetland degradation and loss. A limited number of studies have addressed the coupling of wetland and urban dynamics, but this relationship remains unclear. In this study, a time-varying methodology of predicting wetland distribution was developed to support decision-making. The novelty of the methodology is its ability to dynamically simulate wetland shrinkage together with urban expansion and reveal conflicts and potential tradeoffs under different scenarios. The developed methodology consists of three modules: a historical change detection of wetland and urban areas module, a spatial urban sprawl simulation and forecasting module that can accommodate different development priorities, and a wetland distribution module with time-varying logistic regression. The methodology was applied and tested in the Tonghu Wetland as a case study. The wetland and urban extents presented a spatially intersecting shift, where wetlands lost more than 40% of their area from 1977 to 2017, while urban areas expanded by 10-fold, threatening wetlands. The increase in the relative importance metric of the time-varying regression model indicated an enhanced influence of urban expansion on the wetland. An accuracy assessment validated a robust statistical result and a good visual fit between spatially distributed wetland occurrence probabilities and the actual distribution of wetland. Incorporating the new variable of urban expansion improved modeling performance and, particularly, realized a greater ability to predict potential wetland loss than provided by the traditional method. Future wetland loss probabilities were visualized under different scenarios. The historical trend scenario predicted continuously expanding urban growth and wetland shrinkage to 2030. However, a specific urban development strategy scenario was designed interactively to control the potential wetland loss. Consideration of such scenarios can facilitate identifying tradeoffs to support wetland conservation.

Leveraging Social Network Analysis for Characterizing Cohesion of Human-Managed Animals

Social network analysis (SNA) is a technique to study behavioral dynamics within a social group. In SNA, it is an open question whether it is possible to characterize animal-level behaviors by using group-level information. Also, it was believed that the combined use of SNA would provide a more comprehensive understanding of social dynamics. In light of these two factors, here we explain an approach to evaluate animal importance to a group by considering the variability in group-level structural information, which is computed by joining the animal- and group-level SNA measures node centrality and network entropy, respectively. Moreover, two other metrics, animal social interaction range and nearest-neighbor frequency matrix, which represent a social affiliation of each animal within the group, are computed to help address the general challenges in graph-based SNA and, thereby, improve the precision of animal importance measures. Finally, we derive the joint distribution of animal importance of the group in detecting atypical social behaviors. The approach is tested using tracking data of dairy cows. The reliability of the derived animal importance was superior to the already existing animal importance measures. To illustrate the usability of the animal importance metric, a simulation study was conducted to identify sick and estrus animals in a group. The social affiliation of sick cows was less when compared to healthy cows. Also, their individual distributions of animal importance were shifted toward the left of the mean of the animal importance distributions of healthy cows. Consequently, the joint distribution of animal importance of the group exhibited a bimodal distribution with a left tailored shape. The behavior of cows in estrus was opposite to that of sick cows. Moreover, with the increasing number of sick and estrus cows in the group, respectively, the group entropy decreased with larger variance and slightly increased with less variance. Therefore, the entropy-based animal importance metric has superior performances when evaluating animal importance to the group compared to the existing metrics. It can be used for generating alerts for the early detection of atypical social behaviors associated with, for instance, animal health, veterinary, and welfare.