Uncertainty In Estimates Research Articles

Likelihood-Ratio-Based Confidence Intervals for Multiple Threshold Parameters

Abstract This paper proposes the inversion of likelihood ratio tests for the construction of confidence intervals for multiple threshold parameters. Using Monte Carlo simulations, conservative likelihood-ratio-based confidence intervals are shown to exhibit empirical coverage rates at least as high as nominal levels for all threshold parameters, while still being informative in the sense of only including relatively few observations in each confidence interval. These findings are robust to the magnitude of the threshold effect, the sample size and the presence of serial correlation. Applications to existing models with multiple thresholds for U.S. real GDP growth and for the wage Phillips curve demonstrate how the proposed approach is empirically relevant to make inferences about the uncertainty of threshold estimates.

Treating gaps and biases in biodiversity data as a missing data problem.

Big biodiversity data sets have great potential for monitoring and research because of their large taxonomic, geographic and temporal scope. Such data sets have become especially important for assessing temporal changes in species' populations and distributions. Gaps in the available data, especially spatial and temporal gaps, often mean that the data are not representative of the target population. This hinders drawing large-scale inferences, such as about species' trends, and may lead to misplaced conservation action. Here, we conceptualise gaps in biodiversity monitoring data as a missing data problem, which provides a unifying framework for the challenges and potential solutions across different types of biodiversity data sets. We characterise the typical types of data gaps as different classes of missing data and then use missing data theory to explore the implications for questions about species' trends and factors affecting occurrences/abundances. By using this framework, we show that bias due to data gaps can arise when the factors affecting sampling and/or data availability overlap with those affecting species. But a data set per se is not biased. The outcome depends on the ecological question and statistical approach, which determine choices around which sources of variation are taken into account. We argue that typical approaches to long-term species trend modelling using monitoring data are especially susceptible to data gaps since such models do not tend to account for the factors driving missingness. To identify general solutions to this problem, we review empirical studies and use simulation studies to compare some of the most frequently employed approaches to deal with data gaps, including subsampling, weighting and imputation. All these methods have the potential to reduce bias but may come at the cost of increased uncertainty of parameter estimates. Weighting techniques are arguably the least used so far in ecology and have the potential to reduce both the bias and variance of parameter estimates. Regardless of the method, the ability to reduce bias critically depends on knowledge of, and the availability of data on, the factors creating data gaps. We use this review to outline the necessary considerations when dealing with data gaps at different stages of the data collection and analysis workflow.

Cost and budget impact of mass drug administration compared to expanded school-based targeted preventive chemotherapy for soil-transmitted helminth control in Zamboanga Peninsula, the Philippines

School-based targeted preventive chemotherapy (PC), the primary strategy for soil-transmitted helminth (STH) control, typically focusing on primary schoolchildren, was expanded to secondary school students in the Philippines in 2016. This program still excludes adults, who may also suffer from considerable morbidity and can be a significant reservoir of infection. Mass drug administration (MDA), where the entire population is treated, would bring additional health benefits but will also increase implementation costs. The incremental cost of implementing MDA for STH control compared to expanded school-based targeted PC, however, is unknown. A cost survey was conducted in Zamboanga Peninsula region in 2021 to estimate the economic and financial cost of implementing MDA compared to the expanded school-based targeted PC from a government payer perspective. A budget impact analysis was conducted to estimate the financial cost to the government of implementing MDA over a five-year timeframe. Monte Carlo simulation accounted for uncertainty in cost estimates. Costs were reported in 2021 United States Dollars ($). The economic cost of MDA was $809,000 per year (95% CI: $679,000-$950,000) or $0.22 per person targeted (95% CI: $0.19-$0.26), while the expanded school-based targeted PC would cost $625,000 (95% CI: $549,000-$706,000) or $0.57 per person targeted (95% CI: $0.50-$0.64). Over five years, the financial cost to the government for MDA would be $3,113,000 (95% CI: $2,475,000-$3,810,000); $740,000 (95% CI: $486,000-$1,019,000) higher than expanded school-based targeted PC. Implementing MDA in the region will increase the economic and financial costs by 29% and 31%, respectively, when compared to expanded school-based targeted PC. Implementing MDA would require the Department of Health to increase their total expenditure for STH control by 0.2% and could be key in addressing the ongoing STH burden. The project was funded by the Australian Centre for the Control and Elimination of Neglected Tropical Diseases (NHMRC GA19028), and JPCDT was supported by a UNSW Scientia PhD Scholarship. SVN is funded by an NHMRC Investigator Grant (APP 2018220).

Emergent constraints on historical and future global gross primary productivity.

Terrestrial gross primary productivity (GPP) is the largest carbon flux in the global carbon cycle and plays a crucial role in terrestrial carbon sequestration. However, historical and future global GPP estimates still vary markedly. In this study, we reduced uncertainties in global GPP estimates by employing an innovative emergent constraint method on remote sensing-based GPP datasets (RS-GPP), using ground-based estimates of GPP from flux towers as the observational constraint. Using this approach, the global GPP in 2001-2014 was estimated to be 126.8 ± 6.4 PgC year-1, compared to the original RS-GPP ensemble mean of 120.9 ± 10.6 PgC year-1, which reduced the uncertainty range by 39.6%. Independent space- and time-based (different latitudinal zones, different vegetation types, and individual year) constraints further confirmed the robustness of the global GPP estimate. Building on these insights, we extended our constraints to project global GPP estimates in 2081-2100 under various Shared Socioeconomic Pathway (SSP) scenarios: SSP126 (140.6 ± 9.3 PgC year-1), SSP245 (153.5 ± 13.4 PgC year-1), SSP370 (170.7 ± 16.9 PgC year-1), and SSP585 (194.1 ± 23.2 PgC year-1). These findings have important implications for understanding and projecting climate change, helping to develop more effective climate policies and carbon reduction strategies.

Laser-excited surface acoustic wave method for detecting subsurface damage of processed silicon nitride ceramics

The non-destructive testing for subsurface damage of processed silicon nitride ceramics is significant to the improvement of processing technology and evaluation of product performance. A novel method for the measurement of subsurface damage based on dispersion of laser-excited surface acoustics wave is proposed in this paper. The subsurface damage distribution is quantified as the damage density and depth of the damage layer. The forward model of surface acoustics wave propagation in damage layer is derived by the stiffness matrix method. Bayesian inversion is applied to estimate model parameters and uncertainties of subsurface damage from the experimental dispersion data. Laser ultrasonic experiments are carried out on four samples with different substrate material parameters and surface qualities to demonstrate the feasibility of the method for detecting varying degrees of subsurface damage. The results prove that with reasonable use of prior information about damage depth predicted by surface roughness, subsurface damage with a depth ∼10 times smaller than the minimum wavelength of surface acoustics wave is accurately characterized. The reliability of the results is further verified by Vickers microhardness tester.

Project management in healthcare: An examination of organizational competence

This research aims to identify the factors that influence the organizational competence (OC) of hospitals in effectively managing projects. It follows a quantitative approach, analyzing data collected from three Portuguese hospitals (51 respondents) using R software. An organizational competence score (OCS) was calculated based on standardized values across five different competence areas.The results indicate significant associations between the competence areas and project categories, organizational alignment, governance, management, people's competence, and resources. Furthermore, the study explores the relationships between the competence areas and quantitative variables such as the number of project team members and former experience in projects. The OCS is also examined regarding project categories, region, experience in projects, and number of project members.The findings provide insights into the features that impact the OC of hospitals in PM, highlight the importance of organizational alignment, effective governance, competent management, skilled personnel, and adequate resources in enhancing PM capabilities. However, the wide confidence interval for the Odds Ratio indicates a high degree of uncertainty in the effect size estimates, suggesting the need for larger sample sizes and more robust models in future research to obtain more precise estimates.

Assessment of the effectiveness of coarse resolution fire products in monitoring long-term changes in fire regime within protected areas in South Africa

Although high-resolution satellite and in situ airborne observations are becoming a common data source for fire monitoring, reconstruction of the historical fire record is based on less suitable data. In many regions, managers and policy-makers rely solely on coarse resolution global fire datasets. This study assessed the suitability of readily available products to accurately depict several fire metrics by comparing them to the manually derived fire scars within three natural reserves in South Africa (2003–2020). Contrary to previous findings, we showed that MCD64A1 and FireCCI51 products detected the majority of the burned area (70–97 %) within protected areas and showed high temporal consistency. Additionally, active and burned area products accurately detected the length and the peak of the fire season. Nonetheless, we found neither medium nor coarse resolution data suitable for evaluating long-term changes in fire size distribution. Coarse resolution burned area products demonstrated a very limited capacity to detect fires smaller than 100 ha, while a lack of sufficient non-cloudy Landsat images led to substantial gaps in fire reconstruction, bringing an unknown level of uncertainties in fire metrics estimates. Similarly, the only available medium-resolution burned area product (GABAM) was unsuitable for fire monitoring since unmapped areas were classified as unburned, making no distinction between regions with an absence of data and an absence of fires. Meanwhile, the performance of FireCCILT11 was ranked the lowest among all the available fire products. Given the lack of resources in many protected areas in South Africa and the pressing need to evaluate current and historical fire management practices and policies, we conclude that, in the absence of a more accurate higher-resolution alternative, MCD64A1 was the most suitable product for capturing year-to-year fluctuations in fire activity and a combination of MODIS and VIIRS active fire products for monitoring changes in fire seasonality.

Heterogeneity in effect size estimates

A typical empirical study involves choosing a sample, a research design, and an analysis path. Variation in such choices across studies leads to heterogeneity in results that introduce an additional layer of uncertainty, limiting the generalizability of published scientific findings. We provide a framework for studying heterogeneity in the social sciences and divide heterogeneity into population, design, and analytical heterogeneity. Our framework suggests that after accounting for heterogeneity, the probability that the tested hypothesis is true for the average population, design, and analysis path can be much lower than implied by nominal error rates of statistically significant individual studies. We estimate each type's heterogeneity from 70 multilab replication studies, 11 prospective meta-analyses of studies employing different experimental designs, and 5 multianalyst studies. In our data, population heterogeneity tends to be relatively small, whereas design and analytical heterogeneity are large. Our results should, however, be interpreted cautiously due to the limited number of studies and the large uncertainty in the heterogeneity estimates. We discuss several ways to parse and account for heterogeneity in the context of different methodologies.

Per Capita Cropland Estimations for Traditional Agricultural Areas of China over Past Millennium

Studying changes in land use per capita is critical for understanding the interactions between humans and ecosystems, and for modeling the impacts of land use changes on climate systems. However, many uncertainties in historical estimates significantly hinder climate modeling. This study estimated the per capita cropland area in traditional agricultural regions of China over the past millennium using historical-document-based and modern statistical cropland and population data. The findings showed that changes in the per capita cropland area in the provinces of the middle and lower reaches of the Yellow River could be characterized into three stages: slow decrease, rapid increase, and fluctuating decrease, whereas, in the provinces of the middle and lower reaches of the Yangtze River, there was a continuous decrease. Spatially, the per capita cropland area was higher in the middle and lower reaches of the Yellow River and lower in the middle and lower reaches of the Yangtze River during the study period. The per capita cropland areas showed clear differences in the HYDE dataset and our study; the corresponding values of our study were 2.1–8.0, 1.7–8.2, and 1.6–8.8 times higher than those from the HYDE dataset for the early Song, Yuan, and Ming dynasties, respectively.

Interannual variability and seasonality of litterfall in three temperate and boreal forest ecosystems of eastern Canada: A synthesis of long-term monitoring

Litterfall is a major pathway for transferring aboveground biomass to the forest floor and thus plays an important role in building forest soil carbon stocks. However, inter- and intra-annual variability of litterfall remains poorly documented, especially in North American temperate and boreal forests, due to the lack of recent long-term studies at high sampling frequencies. This potentially creates uncertainties in estimates of forest carbon budget models. The objectives of the present study were to 1) quantify the mean annual flux, interannual variability, and seasonality of litterfall in three sites (dominated respectively by sugar maple (Acer saccharum Marsh.), balsam fir (Abies balsamea (L.) Mill. 1768), and black spruce (Picea mariana (Mill.) B.S.P.)) in eastern Canada over a period of 22–32 years, 2) relate the litterfall amounts and temporal variations to the changes in the size of major organic matter pools in these ecosystems, and 3) compare our litterfall estimates with reference values used in national greenhouse gas inventories. Litterfall production decreased from the sugar maple to the balsam fir and black spruce sites, preponderantly due to species composition. Litterfall evolution was related to the aboveground biomass of live trees in both conifer sites; in contrast, in the broadleaf site, changes in forest composition and structure were apparently the main drivers. The litterfall seasonality varied between broadleaf and conifer sites and could be explained by a sigmoidal model. Substantial departures from the seasonality for some given years were likely due to important climatic anomalies. Forest floor biomass remained stable over time at all three sites despite the increase in litterfall at the balsam fir and sugar maple sites and rapid forest floor turnover at the latter site. Our analyses of litterfall suggest that reference values from the literature used for national greenhouse gas inventories underestimate annual litterfall and forest floor carbon stocks for temperate and boreal forests.

Projecting Future Tropical Cyclone Frequencies by Combining Uncertain Empirical Estimates of Baseline Frequencies with Climate Model Estimates of Change

Various studies have given projections for how frequencies of tropical cyclones (TCs) might change under climate change. In this study, we combine a set of such projections with uncertain estimates of frequencies of tropical cyclones in a baseline climate to produce probabilistic projections of tropical cyclone frequencies for the next 50 years. The novel aspect of our projections is the inclusion of baseline uncertainty. We consider frequencies of Saffir-Simpson Hurricane Wind Scale category 0-5 and category 4-5 storms for the six major tropical cyclone basins. We find that in several cases the means and medians of the frequency of category 0-5 storms are projected to decrease, but that increasing uncertainty nevertheless leads to increases in the likelihood of high rates of TC activity in the future. We then show how the variance of the distributions of uncertainty can be decomposed into terms due to baseline uncertainty and climate change uncertainty. We use this decomposition to determine the year in which climate change uncertainty overtakes baseline uncertainty. Over the next 20 years we find that in some basins baseline uncertainty dominates and in other basins climate change uncertainty dominates. We are able to relate these variations between basins to the coefficients of variation of the baseline and climate change inputs. Finally, we quantify how climate change affects estimates of near-term TC frequency, including the extent to which it increases the uncertainty. These results help us understand two of the major sources of uncertainty in estimates of tropical cyclone behaviour now and in the future, and the role climate change is playing in changing that behaviour. They also demonstrate how estimates of TC change can be combined with observations to create projections of future TC climate.

Spatiotemporal heterogeneity of lake carbon dioxide flux leads to substantial uncertainties in regional upscaling estimates

Limited field samplings result in significant uncertainties in regional and global estimates of lake carbon dioxide (CO2) emissions. However, quantitative analysis of uncertainty in regional lake CO2 emission estimates remains unclear. In this study, we utilized satellite data to estimate carbon dioxide flux from 113 eastern China lakes, revealing substantial spatial and temporal variations in flux, averaging 18.07 ± 81.83 mg m−2 d−1. Additionally, satellite-estimated total CO2 effluxes indicated previous upscaling studies had overestimated total CO2 effluxes from these studied lakes by approximately 3–11 times, primarily due to substantial variations in lake CO2 fluxes. Insufficient sampling resolution resulted in considerable uncertainty in upscaling estimations. Temporal variations in carbon dioxide contributed greater upscaling uncertainties than spatial variations in carbon dioxide. To capture the dynamics of lake CO2, increasing the number of sampling points and events is necessary as lake size decreases and trophic state increases. Finally, we propose a prediction for the optimal sampling resolution based on lake area and trophic state, recommending an average of 5 points per lake and bi-monthly sampling as the ideal resolution for similar shallow eutrophic lakes. This approach has been validated as effective in lakes across North America and Europe. We believe that future global-scale lake carbon budget estimates would benefit from field observations conducted at more reasonable sampling points and frequency.

A Bayesian deep learning approach for video-based estimation and uncertainty quantification of urban rainfall intensity

Accurate, high-resolution spatiotemporal estimates of rainfall intensity (RI) are essential for effective prevention and control of urban flooding. Traditional methods are often costly and provide inadequate coverage. Meanwhile, the associated uncertainty of RI estimates is often overlooked, potentially leading to poor decisions in management of urban floods. Here we examine the potential of video imagery recorded by surveillance cameras in urban areas, for providing real-time estimates of RI. Specifically, we propose the use of Bayesian deep learning (DL) to estimate RI and its related uncertainty in a real-time manner. Our DL approach combines the strengths of convolutional neural network (CNN) and long short-term memory (LSTM) to construct a suitable model for this task, and uses variational inference to quantify the uncertainty of the CNN-LSTM model. The proposed approach is tested using video imagery captured under various light-intensity conditions, including daytime, nighttime, early morning, and early evening, and evaluated via experiments using both random samples and independent rainfall events. Further, we show that the temporal processing provided by the LSTM network is important to achieve good performance of RI estimation. The results indicate the strong potential for leveraging urban camera networks to obtain high-precision spatiotemporal RI estimates at low cost, which can be extremely valuable for implementing effective flood control measures and planning emergency responses in the urban areas.

Rapid inference of larval fish recruitment potential from size spectrum models

The ratio of mortality to growth of larval fish provides a metric of a cohort’s “recruitment potential”. Estimating recruitment potential is arduous, requiring growth and mortality to be estimated independently. Here, we propose using the exponent of size spectrum models to indicate recruitment potential from body measurement data alone. This approach has several advantages including (1) reducing data collection times, (2) removing uncertainty in estimates generated from age estimation, and (3) allowing re-analysis of larval fish databases or archived collections. To test the validity of this approach, we conduct simulations comparing estimates of recruitment potential from an abundance spectrum model with other common methods. By varying larval flux rates, growth, mortality, and measurement error, we show the abundance spectrum model is more accurate and precise at smaller sample sizes, and more robust to variance in individual rates and measurement error of ages, but more susceptible to measurement error of size. We highlight that a size-based approach to estimating recruitment potential provides another useful tool for understanding larval survival, reducing resource demands on research compared to traditional methods.

Machine learning-based estimation of land surface temperature variability over a large region: a temporally consistent approach using single-day satellite imagery.

Accurate retrieval of LST is crucial for understanding and mitigating the effects of urban heat islands, and ultimately addressing the broader challenge of global warming. This study emphasizes the importance of a single day satellite imageries for large-scale LST retrieval. It explores the impact of Spectral indices of the surface parameters, using machine learning algorithms to enhance accuracy. The research proposes a novel approach of capturing satellite data on a single day to reduce uncertainties in LST estimations. A case study over Chandigarh city using Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine, and Random Forest (RF) reveals RF's superior performance in LST estimations during both summer and winter seasons. All the ML models gave an R-square of above 0.8 and RF with slightly higher R-square during both summer (0.93) and winter (0.85). Building on these findings, the study extends its focus to Ranchi, demonstrating RF's robustness with impressive accuracy in capturing LST variations. The research contributes to bridging existing gaps in large-scale LST estimation methodologies, offering valuable insights for its diverse applications in understanding Earth's dynamic systems.

Shift in house price estimates during COVID-19 reveals effect of crisis on collective speculation

We exploit a city-level panel comprised of individual house price estimates to estimate the impact of COVID-19 on both small and big real-estate markets in California USA. Descriptive analysis of spot house price estimates, including contemporaneous price uncertainty and 30-day price change for individual properties listed on the online real-estate platform Zillow.com, together facilitate quantifying both the excess valuation and valuation confidence attributable to this global socio-economic shock. Our quasi-experimental pre-/post-COVID-19 design spans several years around 2020 and leverages contemporaneous price estimates of rental properties – i.e., off-market real estate entering the habitation market, just not for purchase and hence free of speculation – as an appropriate counterfactual to properties listed for sale, which are subject to on-market speculation. Combining unit-level matching and multivariate difference-in-difference regression approaches, we obtain consistent estimates regarding the sign and magnitude of excess price growth observed after the pandemic onset. Specifically, our results indicate that properties listed for sale appreciated an additional 1% per month above what would be expected in the absence of the pandemic. This corresponds to an excess annual price growth of roughly 12.7 percentage points, which accounts for more than half of the actual annual price growth in 2021 observed across the studied regions. Simultaneously, uncertainty in price estimates decreased, signaling the irrational confidence characteristic of prior asset bubbles. We explore how these two trends are related to market size, local market supply and borrowing costs, which altogether lend support for the counterintuitive roles of uncertainty and interruptions in decision-making.

Improving the Estimation of Lake Ice Thickness with High-Resolution Radar Altimetry Data

Lake ice thickness (LIT) is a sensitive indicator of climate change, identified as a thematic variable of Lakes as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). Here, we present a novel and efficient analytically based retracking approach for estimating LIT from high-resolution Ku-band (13.6 GHz) synthetic-aperture radar (SAR) altimetry data. The retracker method is based on the analytical modeling of the SAR radar echoes over ice-covered lakes that show a characteristic double-peak feature attributed to the reflection of the Ku-band radar waves at the snow–ice and ice–water interfaces. The method is applied to Sentinel-6 Unfocused SAR (UFSAR) and Fully Focused SAR (FFSAR) data, with their corresponding tailored waveform model, referred to as the SAR_LIT and FFSAR_LIT retracker, respectively. We found that LIT retrievals from Sentinel-6 high-resolution SAR data at different posting rates are fully consistent with the LIT estimations obtained from thermodynamic lake ice model simulations and from low-resolution mode (LRM) Sentinel-6 and Jason-3 data over two ice seasons during the tandem phase of the two satellites, demonstrating the continuity between LRM and SAR LIT retrievals. By comparing the Sentinel-6 SAR LIT estimates to optical/radar images, we found that the Sentinel-6 LIT measurements are fully consistent with the evolution of the lake surface conditions, accurately capturing the seasonal transitions of ice formation and melt. The uncertainty in the LIT estimates obtained with Sentinel-6 UFSAR data at 20 Hz is in the order of 5 cm, meeting the GCOS requirements for LIT measurements. This uncertainty is significantly smaller, by a factor of 2 to 3 times, than the uncertainty obtained with LRM data. The FFSAR processing at 140 Hz provides even better LIT estimates, with 20% smaller uncertainties. The LIT retracker analysis performed on data at the higher posting rate (140 Hz) shows increased performance in comparison to the 20 Hz data, especially during the melt transition period, due to the increased statistics. The LIT analysis has been performed over two representative lakes, Great Slave Lake and Baker Lake (Canada), demonstrating that the results are robust and hold for lake targets that differ in terms of size, bathymetry, snow/ice properties, and seasonal evolution of LIT. The SAR LIT retrackers presented are promising tools for monitoring the inter-annual variability and trends in LIT from current and future SAR altimetry missions.

Different Vegetation Covers Leading to the Uncertainty and Consistency of ET Estimation: A Case Study Assessment with Extended Triple Collocation

Accurate and reliable estimation of actual evapotranspiration (AET) is essential for various hydrological studies, including drought prediction, water resource management, and the analysis of atmospheric–terrestrial carbon exchanges. Gridded AET products offer potential for application in ungauged areas, but their uncertainties may be significant, making it difficult to identify the best products for specific regions. While in situ data directly estimate gridded ET products, their applicability is limited in ungauged areas that require FLUXNET data. This paper employs an Extended Triple Collocation (ETC) method to estimate the uncertainty of Global Land Evaporation Amsterdam Model (GLEAM), Famine Early Warning Systems Network (FLDAS), and Maximum Entropy Production (MEP) AET product without requiring prior information. Subsequently, a merged ET product is generated by combining ET estimates from three original products. Furthermore, the study quantifies the uncertainty of each individual product across different vegetation covers and then compares three original products and the Merged ET with data from 645 in situ sites. The results indicate that GLEAM covers the largest area, accounting for 39.1% based on the correlation coefficient criterion and 39.9% based on the error variation criterion. Meanwhile, FLDAS and MEP exhibit similar performance characteristics. The merged ET derived from the ETC method demonstrates the ability to mitigate uncertainty in ET estimates in North American (NA) and European (EU) regions, as well as tundra, forest, grassland, and shrubland areas. This merged ET could be effectively utilized to reduce uncertainty in AET estimates from multiple products for ungauged areas.

Adaptive fixed-time neural consensus control for a class of uncertain nonlinear multi-agent systems with full state constraints

This paper is concerned with the fixed-time consensus control problem for non-strict feedback multi-agent systems with asymmetric output constraints and full state constraints. Considering the feasibility of controlling execution, a novel practical virtual control signal is developed utilizing both saturation function and hyperbolic tangent function to ensure that this signal can remain within the same restricted range as the corresponding state variable throughout entire operation process. In backstepping steps, the design of ideal virtual control signal also adopts a different form of piecewise function than before, introducing high-order polynomial functions to avoid singularity problems in the derivation process. In addition, function approximation ability of radial basis function neural networks technique is applied to estimate uncertainties derived from the system functions and controller design procedure. Moreover, universal barrier Lyapunov function approach is improved for constructing an adaptive constrained synchronization control scheme. By fixed-time stability theory, it is shown that the tracking errors of the MAS converge to an adjustable region around the origin in a fixed time and the state variables always obey their constraints. And the upper bound of the settling time is merely dependent on design parameters, which is not affected by the initial states of MAS. The effectiveness of the proposed control strategy is shown by a numerical simulation example at last. Two scenarios are provided to demonstrate the advantages of the control protocol proposed in this paper.

On the robustness of methods to account for background bias in data assimilation to uncertainties in the bias estimates

On the robustness of methods to account for background bias in data assimilation to uncertainties in the bias estimates