Interval Estimation Research Articles

Predicting Microbiome Growth Dynamics under Environmental Perturbations

MicroGrowthPredictor is a model that leverages Long Short-Term Memory (LSTM) networks to predict dynamic changes in microbiome growth in response to varying environmental perturbations. In this article, we present the innovative capabilities of MicroGrowthPredictor, which include the integration of LSTM modeling with a novel confidence interval estimation technique. The LSTM network captures the complex temporal dynamics of microbiome systems, while the novel confidence intervals provide a robust measure of prediction uncertainty. We include two examples—one illustrating the human gut microbiota composition and diversity due to recurrent antibiotic treatment and the other demonstrating the application of MicroGrowthPredictor on an artificial gut dataset. The results demonstrate the enhanced accuracy and reliability of the LSTM-based predictions facilitated by MicroGrowthPredictor. The inclusion of specific metrics, such as the mean square error, validates the model’s predictive performance. Our model holds immense potential for applications in environmental sciences, healthcare, and biotechnology, fostering advancements in microbiome research and analysis. Moreover, it is noteworthy that MicroGrowthPredictor is applicable to real data with small sample sizes and temporal observations under environmental perturbations, thus ensuring its practical utility across various domains.

Bayesian estimation and prediction under progressive-stress accelerated life test for a log-logistic model

Accelerated life tests play a very critical role in reliability analysis because highly reliable products are being produced with recent advanced technologies to sustain the market demand and competition. A progressive-stress accelerated life test is one of the kinds of accelerated life tests that allows applied stress to change continuously. Considering the importance of progressive-stress accelerated life tests, this paper deals with progressive-stress accelerated life tests when the lifetimes of units follow the log-logistic distribution and are progressively type-II censored where the associated scale parameter conforms to the inverse power law. Different estimators of model parameters are derived using maximum likelihood and Bayesian methods. Interval estimation is also considered. In the sequel, approximate confidence, bootstrap, and Bayes credible intervals are constructed. Bayes estimators are obtained under squared error, LINEX, and entropy loss functions using proper and improper prior distributions. A simulation study is conducted based on various censoring schemes. The coverage percentages and interval widths are computed via Monte Carlo simulations. Bayes predictive estimates and intervals are also obtained. Finally, two different accelerated life test data are analyzed for illustration purposes.

Reliability estimation of s-out-of-k system with Kumaraswamy distribution based on partially constant stress accelerated life tests

In reliability theory, the reliability inference for s-out-of-k systems holds significant importance. In this paper, we explore the estimation of reliability for s-out-of-k systems based on partially constant stress accelerated life tests. Assume that the latent failure times of the components follow the Kumaraswamy distribution. Maximum likelihood estimates for the unknown parameters are established, and their uniqueness is demonstrated. In addition, confidence intervals for the unknown parameters are constructed using the covariance matrix. Confidence intervals for the reliability functions are determined by the Delta method, while Bootstrap intervals are provided for comparison purposes. Subsequently, Bayesian point and interval estimates based on MCMC techniques considering different loss functions are discussed. Lastly, we conduct an extensive simulation study and analyse one real data set, which reveals that the Bayesian approach yields the best results.

Zonotopic state estimation and sensor fault detection for a wastewater treatment bioprocess

AbstractIn this paper, a fault diagnosis method for the basic process of microbial growth in wastewater treatment based on interval estimation technology is proposed. First, a nonlinear microbial growth model is converted into a linear time‐varying system. A time‐varying observer is then designed for the generated system, which takes into account an performance. Furthermore, a zonotope‐based set‐membership estimation algorithm is analyzed to synchronize interval estimation operations. In addition, interval residuals are computed from the generated interval state estimation and fault diagnosis is performed for a wastewater treatment bioprocess with sensor fault signals. Finally, the feasibility of the proposed fault diagnosis strategy for the wastewater treatment bioprocess is illustrated through numerical simulations.

A membership function-dependent L∞ performance based output interval estimation method for discrete-time Takagi–Sugeno fuzzy systems applied to fault detection

In the design process of traditional L∞ methods, a common performance index is often considered in different fuzzy rules. This may introduce a certain conservatism when most of the whole fuzzy system operates under a certain fuzzy rule or a few fuzzy rules. Therefore, this paper focuses on robust interval estimation problems for discrete-time Takagi–Sugeno fuzzy systems, and a new membership function-dependent L∞ performance index is proposed. The proposed L∞ performance can ensure better results in practical engineering applications by using the priori knowledge that the model works most of the time on some specific fuzzy systems to design performance index. By means of fuzzy basis-dependent Lyapunov functions, a membership function-dependent L∞ performance based interval estimation strategy applied to fault detection is presented. Through simulation analysis, the L∞ index in this paper is better than the conventional one and a tighter interval estimation is obtained leading to more accurate and faster fault detection effects. Meanwhile, the simulation results of this method also reflect the membership function-dependent property well.

Analysis of a new extension version of the exponential model using improved adaptive progressive censored data and its applications

This article covers the issue of evaluating the two shape parameters and reliability metrics of a novel Kumaraswamy-exponential lifetime distribution, whose density exhibits a left-skewed, right-skewed, or symmetric shape, through a type-II improved adaptive progressively censored sample. Both conventional and Bayesian viewpoints are used to evaluate the various parameters, which include point and interval estimations. While the estimation of one of the shape parameters requires a numerical solution, the other shape parameter estimation can be carried out in closed form by the classical method. Besides, the likelihood method’s asymptotic traits are employed to provide interval estimations for all parameters. Leveraging the Markov chain Monte Carlo process, the symmetric squared loss function and independent gamma priors are taken into account for calculating Bayes points and the highest posterior density interval estimations. To illustrate the accuracy, compare estimation methods, and show the applicability of the various suggested methods, a simulation examination and a pair of applications are looked at. In the end, four accuracy indicators are taken into consideration to figure out the best progressive censoring pattern. The numerical results indicate that when collecting samples using the suggested censored procedure, it is advisable to use the Bayesian estimation approach for evaluating the Kumaraswamy-exponential distribution.

Identification of a single plasma parcel during a radial alignment of the Parker Solar Probe and Solar Orbiter

Configurations in which two spacecraft, such as the Parker Solar Probe (PSP) and Solar Orbiter, are radially aligned provide opportunities for studying the evolution of a single solar wind parcel during so-called plasma line-ups. The most critical part of these studies arguably is the identification of what can be considered the same plasma crossing both spacecraft. We present here a method that allowed us to determine what we think to be the same plasma parcel that passed through PSP (∼0.075 au) and Solar Orbiter (∼0.9 au) after their radial alignment on April 29, 2021. We started by modeling the plasma propagation in order to obtain a first estimation of the plasma line-up intervals. The identification of the same density structure (with a crossing duration ∼1.5 h) that passed through the two spacecraft allowed us to specify and confirm this estimate. Our main finding is that the density structure was very stable and remained well recognizable from PSP to Solar Orbiter despite its journey of ∼137 hours in the inner heliosphere. We found, moreover, that the slow solar wind plasma parcel was significantly accelerated (from ∼200 to ∼300 km s−1) during its propagation.

No evidence in favor of the existence of "intentional" binding.

Intentional binding refers to the subjective temporal compression between a voluntary action and its subsequent sensory outcome. Despite some studies challenging the link between temporal compression and intentional action, intentional binding is still widely used as an implicit measure for the sense of agency. The debate remains unsettled primarily because the experimental conditions used in previous studies were confounded with various alternative causes for temporal compression, and action intention has not yet been tested comprehensively against all potential alternative causes in a single study. Here, we solve this puzzle by jointly comparing participants' estimates of the interval between three types of triggering events with comparable predictability-voluntary movement, passive movement, and external sensory event-and an external sensory outcome (auditory or visual across experiments). The results failed to show intentional binding, that is, no shorter interval estimation for the voluntary than the passive movement conditions. Instead, we observed temporal (but not intentional) binding when comparing both movement conditions with the external sensory condition. Thus, temporal binding appears to originate from sensory integration and temporal prediction, not from action intention. As such, these findings underscore the need to reconsider the use of "intentional binding" as a reliable proxy of the sense of agency. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

On Modi-Exponential Distribution under Conventional Type-I and Type-II Censoring

In this article, a new Modi-Exponential distribution is considered under Type-I and Type-II censoring. Some statistical and reliability characteristics of the distribution are specified. Statistical inferences of the Modi-Exponential parameters based on Type-I and Type-II censored data are discussed. The maximum likelihood estimation method is used for estimation of unknown parameters along with reliability characteristics. Asymptotic confidence intervals are also obtained for interval estimation. Monte-Carlo Simulation study is performed to study the performances of estimators. Further, two real data sets are provided to illustrate the results.. KEYWORDS :Censoring, Maximum likelihood estimation, Confidence bounds, Simulation.

An iterative interval estimation approach to nonlinear discrete-time systems

This paper is devoted to the iterative interval estimation for nonlinear discrete-time systems. To reconstruct the system state, a sequence of iterative observers is established based on the iterative disturbance estimation and measured output. By means of the Lipschitz condition and [Formula: see text] technique, sufficient conditions are built by the Lyapunov function method to make observation errors convergent. Resorting to the zonotope-based reachability analysis, the reachable set of nonlinear terms and observation errors are analyzed such that the state interval can be supplied. The presented approach is validated by a simulation comparison.

Association between gender-affirming hormone therapy and measures of glucose metabolism: A longitudinal study.

The long-term effect of gender-affirming hormone therapy (GAHT) on glucose metabolism is an area of priority in transgender health research. To evaluate the relation between GAHT and changes in fasting blood glucose (FG) and glycosylated hemoglobin (HbA1c) in transmasculine (TM) and transfeminine (TF) persons relative to the corresponding temporal changes in presumably cisgender persons (i.e. without any evidence of TGD status). Retrospective cohort study. Three large integrated health systems. 2,425 TF and 2,127 TM persons compared with 33,995 cisgender males (CM) and 38,913 cisgender females (CF) enrolled in the same health plans. Temporal changes in FG and HbA1c levels examined using linear mixed models with main results expressed as ratios-of-ratios. The pre- versus post-GAHT ratios-of-ratio (95% confidence interval) estimates adjusted for age, race/ethnicity, study site, and body mass index in the model comparing TF and CM groups were 1.05 (1.01, 1.09) for FG and 1.03 (0.99, 1.06) for HbA1c. By contrast, the corresponding results in the models contrasting TM and cisgender cohort members were in the 0.99-1.00 range. The ratio-of-ratios comparing post-GAHT changes among transgender and cisgender persons were close to the null and without a discernable pattern. Though the within-transgender cohort data suggest an increase in the levels of FG and HbA1c following feminizing GAHT initiation, these changes were no longer evident when compared with the corresponding changes in cisgender referents. Based on these results, clinically important effects of GAHT on routine laboratory markers of glucose metabolism appear unlikely.

A fundamental study on postmortem submersion interval estimation by metabolomics analyzing of gastrocnemius muscle from submersed rat models in freshwater.

In forensic practice, determining the postmortem submersion interval (PMSI) and cause-of-death of cadavers in aquatic ecosystems has always been challenging task. Traditional approaches are not yet able to address these issues effectively and adequately. Our previous study proposed novel models to predict the PMSI and cause-of-death based on metabolites of blood from rats immersed in freshwater. However, with the advance of putrefaction, it is hardly to obtain blood samples beyond 3 days postmortem. To further assess the feasibility of PMSI estimation and drowning diagnosis in the later postmortem phase, gastrocnemius, the more degradation-resistant tissue, was collected from drowned rats and postmortem submersion model in freshwater immediately after death, and at 1day, 3 days, 5 days, 7 days, and 10 days postmortem respectively. Then the samples were analyzed with liquid chromatography-tandem mass spectrometry (LC-MS/MS) to investigate the dynamic changes of the metabolites. A total of 924 metabolites were identified. Similar chronological changes of gastrocnemius metabolites were observed in the drowning and postmortem submersion groups. The difference in metabolic profiles between drowning and postmortem submersion groups was only evident in the initial 1day postmortem, which was faded as the PMSI extension. Nineteen metabolites representing temporally-dynamic patterns were selected as biomarkers for PMSI estimation. A regression model was built based on these biomarkers with random forest algorithm, which yielded a mean absolute error (± SE) of 5.856 (± 1.296) h on validation samples from an independent experiment. These findings added to our knowledge of chronological changes in muscle metabolites from submerged vertebrate remains during decomposition, which provided a new perspective for PMSI estimation.

Estimating postmortem interval based on oral microbial community succession in rat cadavers

The accurate estimation of the postmortem interval has been one of the crucial issues to be solved in forensic research, and it is influenced by various factors in the process of decay. With the development of high-throughput sequencing technology, forensic microbiology has become the major hot topic in forensic science, which provides new research options for postmortem interval estimation. The oral microbial community is one of the most diverse of microbiomes, ranking as the second most abundant microbiota following the gastrointestinal tract. It is remarkable that oral microorganisms have a significant function in the decay process of cadavers. Therefore, we collected outdoor soil to simulate the death environment and focused on the relationship between oral microbial community succession and PMI in rats above the soil. In addition, linear regression models and random forest regression models were developed for the relationship between the relative abundance of oral microbes and PMI. We also identified a number of microorganisms that may be important to estimate PMI, including: Ignatzschineria, Morganella, Proteus, Lysinibacillus, Pseudomonas, Globicatella, Corynebacterium, Streptococcus, Rothia, Aerococcus, Staphylococcus, and so on.

A Study On The Effectiveness Of Workplace Health And Safety Practices Upon Employee Wellbeing At Sri Vigneshwara Cold Forge

This research paper explores effectiveness of employee workplace health and safety upon employee wellbeing which includes physical health, mental health, engagement, productivity and job satisfaction. Key variables including training programs, ergonomics measures, safety protocols and organizational support will be examined in relation to employee physical and mental wellbeing indicators such as injury rates, absenteeism, job satisfaction and stress levels. The total population is 200 and sample size is 132. The tools used for non-parametric U test, H test and Spearman Rank Correlation, Chi-square, Weighted average and Interval Estimation. Through a combination of qualitative and quantitative research methods, including surveys, interviews and data analysis, this research aims to suggest suitable health and safety measures for employee wellbeing for Sri Vigneshwara Cold Forge. Key words: Physical Health Impact, Safety Perception, Hazard Identification, Policy Compliance, Training Programs.

Binomial Confidence Intervals for Rare Events: Importance of Defining Margin of Error Relative to Magnitude of Proportion

Confidence interval performance is typically assessed in terms of two criteria: coverage probability and interval width (or margin of error). In this article, we assess the performance of four common proportion interval estimators: the Wald, Clopper-Pearson (exact), Wilson and Agresti-Coull, in the context of rare-event probabilities. We define the interval precision in terms of a relative margin of error which ensures consistency with the magnitude of the proportion. Thus, confidence interval estimators are assessed in terms of achieving a desired coverage probability whilst simultaneously satisfying the specified relative margin of error. We illustrate the importance of considering both coverage probability and relative margin of error when estimating rare-event proportions, and show that within this framework, all four interval estimators perform somewhat similarly for a given sample size and confidence level. We identify relative margin of error values that result in satisfactory coverage while being conservative in terms of sample size requirements, and hence suggest a range of values that can be adopted in practice. The proposed relative margin of error scheme is evaluated analytically, by simulation, and by application to a number of recent studies from the literature.

Atmospheric motion vector (AMV) error characterization and bias correction by leveraging independent lidar data: a simulation using an observing system simulation experiment (OSSE) and optical flow AMVs

Abstract. Accurate estimation of global winds is crucial for various scientific and practical applications, such as global chemical transport modeling and numerical weather prediction. One valuable source of wind measurements is atmospheric motion vectors (AMVs), which play a vital role in the global observing system and numerical weather prediction models. However, errors in AMV retrievals need to be addressed before their assimilation into data assimilation systems, as they can affect the accuracy of outputs. An assessment of the bias and uncertainty in passive-sensor AMVs can be done by comparing them with information from independent sources such as active-sensor winds. In this paper, we examine the benefit and performance of a colocation scheme using independent and sparse lidar wind observations as a dependent variable in a supervised machine learning model. We demonstrate the feasibility and performance of this approach in an observing system simulation experiment (OSSE) framework, with reference geophysical state data obtained from high-resolution Weather Research and Forecasting (WRF) model simulations of three different weather events. Lidar wind data are typically available in only one direction, and our study demonstrates that this single component of wind in high-precision active-sensor data can be leveraged (via a machine learning algorithm to model the conditional mean) to reduce the bias in the passive-sensor winds. Further, this active-sensor wind information can be leveraged through an algorithm that models the conditional quantiles to produce stable estimates of the prediction intervals, which are helpful in the design and application of error analysis, such as quality filters.

Expert insights into future trajectories: assessing cost reductions and scalability of carbon dioxide removal technologies

IntroductionTo achieve net-zero targets, it is essential to evaluate and model the costs and scalability of emerging carbon dioxide removal technologies like direct air capture with CO2 storage (DACCS) and bioenergy with carbon capture and storage (BECCS). Yet such efforts are often impeded by varying assessments of the climate impact and potential contributions of these technologies. This study explores the future costs and scalability of DACCS and BECCS to advance net-zero goals.MethodsWe analyze expert opinions on these technologies’ potential costs and deployment scales for 2030, 2040, and 2050. Data was collected from 34 experts, comprising 21 DACCS and 13 BECCS specialists. They provided 90% confidence interval estimates and ‘best estimates’ for future costs and deployment under two International Energy Agency (IEA) policy scenarios—Stated Policies (STEPS) and Net Zero Emissions by 2050 (NZE).ResultsWe find that BECCS costs start at a lower level but decrease more slowly, whereas DACCS costs decline more steeply from a higher initial cost. However, DACCS estimates varied significantly among experts, showing no convergence over time. Regarding potential scalability, both technologies are associated with substantially higher deployment under the NZE scenario. Yet the combined estimated capacity of DACCS and BECCS by 2050 is only about a quarter of the CO2 removals projected by the IEA for its NZE scenario (1.9 GtCO2).DiscussionThis study provides valuable insights into the future of DACCS and BECCS technologies in Europe, especially since our experts expect that DACCS and BECCS costs will be even higher (and deployment scales lower) than those predicted by recent IEA tracking, opening future research directions.

Confidence intervals for the difference, relative risk and odds ratio based on inverse sampling

. The problems of finding confidence intervals (CIs) for the difference, ratio, and ratio of odds of proportions based on samples obtained using inverse sampling. Interval estimates based on large sample methods and fiducial approach are proposed. Simple, closed-form, approximate fiducial CIs are also proposed. The new CIs are evaluated for accuracy and compared with those available in the literature. Comparison study indicates that our confidence intervals are not only simple but also better than the available ones in terms of coverage probability and precision. The methods are illustrated using two examples with clinical data.

Volatility forecasts by clustering: Applications for VaR estimation

It is well known that volatility has time-varying and clustering characteristics. The information content of volatility clustering is particularly important in turbulent periods, such as the stage of financial crisis. How to fully mine the implicit information within clusters to predict the volatility in the future is a rarely discussed issue. In this paper, we put forward a partition model to segment volatility into non-overlapping clusters by Fisher’s optimal dissection methodology. Using this model, we can quickly identify the points of structural changes in volatility. By utilizing the information of the nearest cluster, we can perform point estimation and interval estimation on future volatility. In the end, we conduct some empirical examples based on the returns of S&P 500, DAX 30 and FTSE 100 index. We find that our method can improve the volatility forecast and VaR estimations.

The relationship between young college students' recognition of national COVID-19 crisis governance capabilities and the improvement of national identity: the mediating role of online participation in public health critical events.

Improving the young college students' national identity is crucial for ensuring social stability and fostering development during public health critical events such as COVID-19. Young college students' recognition of national COVID-19 crisis governance capabilities can influence their national identity, and online participation in public health criticalevents may serve as a crucial role in shaping this intricate relationship. To investigate this possibility, the present study established an intermediary model to examine the impact of online participation in public health critical events on young college students' recognition of national COVID-19 crisis governance capabilities and improvement of national identity. This cross-sectional survey study employed a convenience sampling method to investigate a total of 3041 young college students in China. The correlations between study variables were analyzed using Spearman's rank correlation. The mediation model was established using PROCESS Model 4 with 5000 bootstrap samples in SPSS. The bias-corrected bootstrap method provided statistical efficacy and identification interval estimation. Young college students' recognition of national COVID-19 crisis governance capabilities (r=0.729, P<0.001) and online participation in public health critical events (r=0.609, P<0.001) were positively correlated with improvement of their national identity. The relationship between these two factors was partially mediated by online participation in public health critical events (Indirect effect estimate=0.196, P<0.001). Online participation in public health critical events played a mediating role in the association between college students' recognition of national COVID-19 crisis governance capabilities and the improvement of national identity. Our findings provide a novel intervention strategy for improving college students' national identity, which is to encourage their online participation in public health critical events.