Uncertainty In Model Parameters Research Articles

Active and passive heave compensation system based on feedback linearization sliding mode variable structure control

This study presents a novel electric-hydraulic servo system with an active–passive lift compensation feature designed for a lifting crane used in offshore operations. Additionally, the study explores the control methodology of this system. Firstly, the principle of the combined active–passive lift-compensated system is explained, and the mathematical model of the response is established. To eliminate the strong nonlinearities inherent and mismatched term, the method of exact input-state linearization is employed. For observing the uncertain disturbance terms in the model, a new type of expanded state sliding mode observer(ESSMO) is designed. Subsequently, a control strategy of sliding mode with variable structure is designed using the method of hyperbolic convergence law. The stability of the controller is proved based on Lyapunov’s stability theorem. Finally, the control method is simulated and analyzed under different working conditions and compared with traditional PID control and sliding mode control strategies directly designed by the nonlinear model. The simulation results demonstrate that the feedback linearized hyperbolic convergence law sliding mode control strategy not only reduces dependence on exact parameters of the original system but also exhibits strong robustness and suppression of model nonlinearities and parameter uncertainty.

Local DER Control with Reduced Loop Interactions in Active Distribution Networks

Active Distribution Networks are Multi-Input Multi-Output (MIMO) systems with coupled dynamics, which cause interactions among the control loops of Distributed Energy Resources (DERs). This undesired effect leads to performance degradation of voltage control. To mitigate the effects of this unavoidable coupling, the present paper proposes a systematic design procedure based on the analysis of the interaction’s sources. In detail, each DER is equipped with a double-loop PI to control the active and reactive power output of the voltage source converter, which connects the DER to the network’s node. Furthermore, to guarantee ancillary services, the two loops are coupled by a simple mechanism of cooperation of the active power to voltage regulation realized by a filtered droop law. To achieve voltage regulation with reduced loop interactions, the PI parameters and the filter’s pulse are designed according to a procedure with two sequential steps based on the Internal Model Control (IMC) technique. Simulation studies are finally presented to demonstrate that the proposed design method achieves both reduction of the loop interaction and robust voltage control in the presence of model parameter uncertainty in the MIMO plant, modeling various operating conditions of the ADN, including a step connection of large loads, renewable energy source variations, and changes in the substation transformer ratio.

Degradation Modelling of and Optimising the Timing of Replacements for Batch Vacuum Pans

AbstractReplacing a pan involves the expenditure of significant capital for a sugar mill. Replacing a pan too late may result in excessive downtime, maintenance costs, and risk of catastrophic failure. On the other hand, replacing a pan too early will lead to wasting residual life and an unnecessary allocation of capital funds that may have been spent better elsewhere in the mill. This paper reports on the development of a replacement policy for batch vacuum pan components based on a stochastic model of degradation. Degradation data, principally wall-thickness measurements, were collected from the vacuum pans of an Australian sugar factory and used to develop component degradation models. Unlike the conventional approach of using a line of best fit to identify the end of life of the pan, the methods adopted account for the uncertainties due to seasonal operating conditions and inherent uncertainty in the degradation model parameters. The quantification of the uncertainty in identifying the end of life of a vacuum pan has shown that there is significant risk of a pan failing earlier than the straight-line prediction. Employing this quantification of the risk, a component replacement plan was developed by optimising the replacement of each component individually and subsequently optimising the replacement plan for the entire pan. This strategy is demonstrated using a case study with and without parametric uncertainty to evaluate its impact on maintenance optimisation. Including parametric uncertainty leads to the determination of greater risk earlier, proposing the replacement of components earlier than when parameters are considered as ‘known’. It is, therefore, important to consider parametric uncertainty in the planning of pan component replacements to better manage risk.

Modeling the Ecosystem Water Use Efficiency of Croplands Using Machine Learning and MODIS Data

The ecosystem water use efficiency (WUE), which is the ratio of carbon gain (gross primary productivity, GPP) to water consumption (evapotranspiration, ET), is widely regarded as a crucial link in coupling the carbon and water cycles of terrestrial ecosystems on a global scale. The estimation of cropland WUE is considerably important for improving agricultural management, simulating the carbon–water cycle processes of croplands, and enhancing crop yields. Currently, the estimate of WUE primary relies on the modeled GPP and ET data. However, differences in model structures and parameter uncertainties cause remarkable differences in the accuracy of the estimated WUE among different models. The eddy covariance technology enables the continuous observation and research of carbon and water fluxes at a site scale, ensuring simulation accuracy. However, the technology is constrained by its limited spatial coverage. In this study, we integrated data from 20 global cropland sites and built 7 machine learning models based on remote sensing to directly estimate cropland WUE, thereby expanding the spatial scope of WUE simulations, omitting the simulation process for obtaining GPP and ET, and ensuring a certain degree of precision. Results show that, compared with other machine learning algorithms, the ensemble learning model has the strongest ability to reproduce cropland WUE, with a determination coefficient of 0.90 and a root mean square error of 1.54–1.75 . These results indicate that a combination of machine learning methods and remote sensing can reasonably simulate the WUE of agricultural ecosystems.

Probabilistic risk assessment for reinforced concrete frame structures subject to mainshock‐aftershock sequences

AbstractRecent earthquakes have highlighted that aftershocks can considerably increase the structural demand and seismic risk of engineering structures. This study presents a probabilistic approach to assess the seismic risk of reinforced concrete (RC) frame structures subjected to mainshock‐aftershock sequences. In this approach, a predictive fragility method is used to evaluate the probabilities of structural damage under sequential excitations. The Bayes theorem is employed to generate posterior distributions of unknown model parameters. Then, a practical seismic hazard assessment method is used to conduct mainshock‐aftershock hazard analysis. The Copula technique is employed to develop a joint distribution model of the mainshock and aftershock intensity measures. Finally, the seismic risk is evaluated using the classical risk integration equation with the mainshock‐aftershock fragilities and hazard surfaces. Confidence bounds for fragilities and seismic risks are also obtained to account for the uncertainties of model parameters caused by aftershocks. The proposed approach is demonstrated by considering a seismic‐designed RC frame building. It can be concluded that aftershocks can significantly increase the seismic risk throughout the entire structural service life. The additional uncertainties caused by aftershocks result in wider confidence bounds for seismic risk.

Dynamic-Linearization-Based Predictive Control of a Voltage-Source Inverter

In pursuit of accurate and fast trajectory tracking of power converters, an explicit model is commonly used in the finite control-set model predictive control (FCS-MPC) framework to predict precise behaviors of controlled variables. In reality, however, the model mismatch is inevitable, which causes the inherent challenges of parameter sensitivity and model uncertainties of the FCS-MPC method. This article proposes a dynamic-linearization-based predictive control architecture to circumvent such model dependence while keeping the attractive features of the conventional FCS-MPC method. By integrating the data-driven feature of the dynamic-linearization approach, the detailed model used in the FCS-MPC controller is replaced by a virtual equivalent data model, creating a data-driven predictive control architecture. The suggested method selects optimal control action solely based on the input-output data, exhibiting strong rejection against parameter variations whilst inheriting the distinctive property of the conventional FCS-MPC method. Finally, the proposed design is validated through comparative simulation and experimental results on a three-level neutral-point-clamped inverter.

Robust fixed-time flight controller for a dual-system convertible UAV in the cruise mode

This paper investigates the attitude tracking control problem for the cruise mode of a dual-system convertible unmanned aerial vehicle (UAV) in the presence of parameter uncertainties, unmodeled uncertainties and wind disturbances. First, a fixed-time disturbance observer (FXDO) based on the bi-limit homogeneity theory is designed to estimate the lumped disturbance of the convertible UAV model. Then, a fixed-time integral sliding mode control (FXISMC) is combined with the FXDO to achieve strong robustness and chattering reduction. Bi-limit homogeneity theory and Lyapunov theory are applied to provide detailed proof of the fixed-time stability. Finally, numerical simulation experimental results verify the robustness of the proposed algorithm to model parameter uncertainties and wind disturbances. In addition, the proposed algorithm is deployed in a open-source UAV autopilot and its effectiveness is further demonstrated by hardware-in-the-loop experimental results.

Oil and Gas Production Forecasting Using Decision Trees, Random Forst, and XGBoost

Oil and gas production forecasting has always been a hot topic in the petroleum industry. Production forecasting in this sector aims to estimate future production rates, facilitating operational planning, production optimization, and resource allocation for companies. Scientists have traditionally attempted to forecast oil and gas production using methods such as Numerical Reservoir Simulation (NRS) and Decline Curve Analysis (DCA). However, these methods present challenges including time-consuming processes lasting hours or even days, uncertain accuracy, reliance on accurate static models, and uncertainty in dynamic model parameters. In this research, aim to address these limitations by leveraging machine learning models for production forecasting. These models enable faster and more precise decision-making by accurately predicting future outcomes based on historical data. Our study employs three models: Decision Trees (DTR), Random Forest (RFR), and XGBoost. In this reserch utilize the Python programming language and a dataset from wells in New York State, USA. Experimental results demonstrate that the RFR model achieves the highest accuracy (99%) for oil and gas production compared to the XGBoost and DTR models.

The intermediate neutron capture process

Context. The observed surface abundance distributions of carbon-enhanced metal-poor (CEMP) r/s stars suggest that these stars could have been polluted by an intermediate neutron capture process (the so-called i-process) occurring at intermediate neutron densities between the r- and s-processes. Triggered by the ingestion of protons inside a convective He-burning zone, the i-process could be hosted in several sites, a promising one being the early AGB phase of low-mass, low-metallicity stars. The i-process remains affected however by many uncertainties, including those of nuclear origin, since it involves hundreds of nuclei for which reaction rates have not yet been determined experimentally. Aims. We investigate both the systematic and statistical uncertainties associated with theoretical nuclear reaction rates of relevance during the i-process and explore their impact on the i-process elemental production, and subsequently on the surface enrichment, of a low-mass, low-metallicity star during the early AGB phase. Methods. We used the TALYS reaction code to estimate both the model and parameter uncertainties affecting the photon strength function and the nuclear level densities, and hence the radiative neutron capture rates. The impact of correlated systematic uncertainties was estimated by considering different nuclear models, as was detailed in Paper II. In contrast, the uncorrelated uncertainties associated with local variation in model parameters were estimated using a variant of the backward-forward Monte Carlo method to constrain the parameter changes to experimentally known cross sections before propagating them consistently to the neutron capture rates. The STAREVOL code (Siess 2006, A&A, 448, 717) was used to determine the impact of nuclear uncertainties on the i-process nucleosynthesis in a 1 M⊙ [Fe/H] = –2.5 model star during the proton ingestion event in the early AGB phase. A large nuclear network of 1160 species coherently coupled to the transport processes was solved to follow the i-process nucleosynthesis. Results. We find that the uncorrelated parameter uncertainties lead the surface abundance uncertainties of elements with Z ≥ 40 to range between 0.5 and 1.0 dex, with odd-Z elements displaying higher uncertainties. The correlated model uncertainties are of the same order of magnitude, and both model and parameter uncertainties have an important impact on potential observable tracers such as Eu and La. We find around 125 important (n, γ) reactions impacting the surface abundances, including 28 reactions that have a medium to high impact on the surface abundance of elements that are taken as observable tracers of i-process nucleosynthesis in CEMP stars. Conclusions. Both the correlated model and uncorrelated parameter uncertainties need to be estimated coherently before being propagated to astrophysical observables through multi-zone stellar evolution models. Many reactions are found to affect the i-process predictions and will require improved nuclear models guided by experimental constraints. Priority should be given to the reactions influencing the observable tracers.

Eliminating invisible deaths: the woeful state of global rabies data and its impact on progress towards 2030 sustainable development goals for neglected tropical diseases

Like other neglected diseases, surveillance data for rabies is insufficient and incompatible with the need to accurately describe the burden of disease. Multiple modeling studies central to estimating global human rabies deaths have been conducted in the last two decades, with results ranging from 14,000 to 74,000 deaths annually. Yet, uncertainty in model parameters, inconsistency in modeling approaches, and discrepancies in data quality per country included in global burden studies have led to recent skepticism about the magnitude of rabies mortality. Lack of data not only limits the efficiency and monitoring of rabies elimination strategies but also severely diminishes abilities to advocate for support from international funding agencies. Meanwhile, the most vulnerable communities continue to suffer from deaths that could have been prevented through more robust reporting. The Zero by 30 global strategy to eliminate dog-mediated human rabies by 2030 recommends endemic countries adopt the intersectoral approach, Integrated Bite Case Management (IBCM), as a cost-effective method to enhance surveillance. However, effective implementation of IBCM is impeded by challenges such as limited capacity, resources, knowledge, skills, and attitudes toward compliance. To address this, the World Health Organization and United Against Rabies Forum have developed several open-access tools to guide national control programs in strong data collection practices, and online data repositories to pragmatically streamline reporting and encourage data sharing. Here, we discuss how current and future initiatives can be best employed to improve the implementation of existing surveillance tools and prioritization of effective data reporting/sharing to optimize progress toward 2030 elimination.

Overview of State Estimation Methods and Research Progress of Power Lithium Batteries for Electric Vehicles

This paper presents a comprehensive research framework for state estimation of power lithium batteries by summarizing both model-based and non-model-based approaches. It provides an in-depth analysis of various methods, supported by experimental results. In the first part, the paper surveys model-based state estimation techniques, including the ECM (Equivalent Circuit Model), electrochemical models, and data-driven models. It systematically explains the principles behind these models and their respective applications. Additionally, the study delves into model-independent state estimation, emphasizing the utilization of machine learning algorithms in battery state estimation and the advancements in artificial intelligence technology. The potential of hybrid methods and intelligent algorithms in state estimation is also explored, highlighting possible future directions. The findings indicate that while model-based methods can attain high estimation accuracy in specific scenarios, they are constrained by model complexity and parameter uncertainty. The application of fusion method and intelligent algorithm further improves the performance of state estimation and provides strong support for real-time monitoring and prediction of power lithium batteries.

Improving parameterization of an evapotranspiration estimation model with eddy covariance measurements for a regional irrigation scheduling program

Actual evapotranspiration (ETa) is an essential variable in linking energy cycles, carbon, and water, yet challenging to measure. Inputs uncertainty and deficiencies in the key elements of hydrologic models are fundamental challenges for optimizing model performance. Furthermore, the performance of land surface model-based ETa, reanalysis, and remote sensing products varies with spatiotemporal scales. Here, we evaluate sources of bias in the regional Wisconsin Irrigation and Scheduling Program (WISP) model and develop a correction using eddy covariance (EC) observations. ETa, observations were made for five years (2018–2022) using EC systems in agricultural fields in Wisconsin. WISP model ETa bias was linked to underestimation of net longwave radiation (LWnet) that was traced to incorrect specification of effective clear sky atmospheric emissivity (εa,clr). Applying a correction to the εa,clr led to reduced WISP model percent bias (pbias) and error for both LWnet and ETa. The calibrated model more accurately represented observed ETa. The results indicate that explicit treatment of the LWnet balance decreases the uncertainty of model parameters and improves the WISP model performance at independent sites. Applying this improved model parameterization reduced the bias of LWnet radiation from 62.8% to -6.2%, which improved the Nash-Sutcliffe Efficiency (NSE) from -0.08 to 0.52 for ETa on training sites. Additionally, overall pbias was significantly reduced (p = 0.035) for validation sites after WISP correction. Hence, WISP performance improved for different crop types when optimal regional parameters were used, confirming the physical parameters' reliability. Our results highlight that model development should focus on energy balance parameterizations to improve ET simulation and the accuracy of hydrologic and climatic simulations for understanding critical processes underlying hydrologic and climatic variability and change over land.

Using gravity data uncertainties in forward modeling to estimate uncertainties in model parameters: A case history in estimating the dip and the dip uncertainty of the Porcupine Destor Fault

When using forward modeling to estimate model parameters, such as the dip, it also is important to estimate the corresponding uncertainty in the model parameters. For gravity data, these uncertainties are dependent on the uncertainty in the Bouguer-corrected data. The uncertainty in the gravity meter reading and the height used in the free-air and Bouguer corrections are among the most important factors influencing the uncertainty in the Bouguer-corrected data. We use two methods for estimating the uncertainty in the Bouguer-corrected data, which give similar answers (0.121 mGal and 0.109 mGal). The uncertainty in the model parameters can be estimated by perturbing the corrected data multiple times by amounts consistent with the estimated uncertainty in the corrected gravity. The standard deviation of the model parameters derived from each perturbed data set gives an estimate of their uncertainty. Using this procedure for Bouguer gravity profiles that cross the Porcupine Destor Fault (a fault that is prospective for gold in the Timmins camp of Ontario, Canada), we find that the uncertainty in the dip is 1° or 2°, assuming a planar or linear fault. If the uncertainty in the corrected data had been 1 mGal (a value typical of regional surveys, instead of 0.1 mGal for a local survey), then the uncertainty in the dip is 41° for the same model. Thus, knowing the uncertainties in the corrected data are very important for estimating the uncertainty in model parameters. Conversely, if a model parameter is known to be required to a specific precision, the survey can be planned so that the corrected gravity has an uncertainty appropriate to achieve that precision.

Monitoring Ethanol Fermentation in Real Time by a Robust State Observer for Uncertainties

AbstractIn this article, the problem of real‐time estimation of the fermentative ethanol process is tackled. The considered observer is a model‐based technique that is robust regarding the model parameter uncertainties and inline noisy measurements. An unstructured kinetic model was used to describe the production of ethanol in a batch bioreactor for Saccharomyces cerevisiae. The biomass concentration was selected as the measured bioreactor´s output via an inline device, where the estimate variables were the substrate and ethanol concentrations. An experimental prototype was constructed to demonstrate the observer's real‐time performance. The experimental results show that the robust, smooth sliding mode observer performs better than the standard proportional sliding mode observer.

Automatic berthing of unmanned surface vessels with predetermined performance

To solve the problem of ship automatic berthing control due to unknown time-varying disturbance and dynamic uncertainty of model parameters, an automatic berthing control law based on predefined performance time function is proposed. First, a predefined performance time function is designed and coupled with tracking error to achieve the predetermined performance of tracking error. Secondly, radial basis function neural network is used to approach the dynamic uncertainty of ship model parameters, and the complex uncertainty of model parameters and unknown time-varying disturbance is represented by linearized parameter form with single virtual parameter, which makes the calculation simple and easy to implement in engineering. On this basis, the reverse step control law is designed. Thirdly, the stability of the system is proved based on Lyapunov stability theory. Finally, the simulation results show that the control law can make the ship reach the desired position and heading angle, and realize the automatic berthing of the ship. The control law and berthing controller designed in this paper have good applicability and robustness, which provides a theoretical basis for the subsequent control research of surface intelligent ships.

Bayesian analysis of optimal burn‐in policy for heterogeneous components with minimal repair

AbstractThis study develops a Bayesian method to analyze optimal burn‐in policy for heterogeneous components with minimal repair. We use the non‐homogeneous Poisson processes with different power law intensity functions to model the minimal repair processes for heterogeneous components. Since the component from the weak subpopulation may fail more frequently, the total number of minimal repairs can be used for establishing a screening rule. By screening, we can decide whether the component after burn‐in should be eliminated or not. Considering the uncertainty of model parameters, we generate a minimally repaired data set of components by simulation. Then, we obtain the model parameters estimators by using Bayesian method combined with the existing data. We further analyze and get the optimal burn‐in settings under cost and performance optimization model. A numerical example is used to verify the effectiveness of the Bayesian method.

Systematic quantification of modeling uncertainties in tank–foundation coupled systems

Traditionally, uncertainty quantification in the context of liquid storage tanks has primarily focused on ground motion record-to-record variability, along with partial consideration of material randomness. This paper presents a comprehensive framework for addressing “modeling uncertainty” in the seismic analysis of rectangular tanks, including the influence of foundation interactions. The framework encompasses uncertainties inherent in both general finite element and mathematical modeling techniques, as well as uncertainty in modeling parameters (e.g., hydrodynamic mass and concrete constitutive model). This is achieved through the implementation of an efficient design of experiments. By employing an innovative intensifying artificial acceleration, over 26,000 transient simulations are conducted, spanning the full spectrum of structural behavior from linear to nonlinear regimes. Through a series of uncertainty quantification and sensitivity analyses, this study assesses bias and dispersion arising from diverse model assumptions. Furthermore, fragility analysis and machine learning post-processing techniques are employed to extract latent insights from the generated data. The outcomes underscore that numerical and mathematical modeling uncertainties can bear comparable significance to ground motion record-to-record variability. The adoption of distinct modeling strategies introduces biases in fragility curves, particularly within higher damage states. Consequently, it is advised to account for modeling randomness directly (via simulation) or approximately (leveraging the insights from this study) in any uncertainty quantification related to storage tanks.

The Adaptive Bilateral Control of Underwater Manipulator Teleoperation System with Uncertain Parameters and External Disturbance

A novel self-adaptive bilateral control strategy is introduced to manage uncertainties inherent in the teleoperation of an underwater manipulator system effectively. In response to uncertainties stemming from both the mathematical model and external disturbances, our approach offers innovative solutions. Firstly, to address uncertainties in the master model parameters, we propose a reference adaptive impedance control based on a nominal model. This control strategy dynamically adjusts the reference position of the desired model, leveraging adaptive control laws to compensate for model uncertainties. Additionally, to tackle uncertainties specific to the slave manipulator, we employ adaptive compensation using radial basis function (RBF) networks. Our unique combination of sliding mode variable structure controllers and robust adaptive controllers aims to mitigate approximation errors, ensuring precise tracking of the master manipulator’s position by the slave manipulator. By employing Lyapunov function analysis, we demonstrate the system’s superior tracking performance and global stability, with assured asymptotic convergence for force–position tracking. Through comprehensive experimentation, our results showcase the exceptional force–position tracking capabilities of the overall control system, even under challenging conditions of model uncertainties and external disturbances. Moreover, our system exhibits remarkable stability, reliability, and robustness, underscoring the effectiveness of our proposed adaptive control approach.

Trajectory Tracking Control of Remotely Operated Vehicles via a Fast-Sliding Mode Controller with a Fixed-Time Disturbance Observer

Time-varying nonlinear external disturbances, as well as uncertainties in model and hydrodynamic parameters, make remotely operated vehicles (ROVs) trajectory tracking control complex and difficult. To solve this problem, this paper proposes a fast sliding mode controller with a fixed-time disturbance observer (FSMC-FDO), which consists of a sliding mode controller based on a fast reaching law and a novel fixed-time disturbance observer. The FSMC can solve the contradiction between system response time and chatter amplitude in sliding mode control. The FDO can compensate for time-varying external disturbances. The Lyapunov theory is used to prove the stability of the entire control scheme. Simulation results show that FSMC-FDO exhibits a good trajectory tracking performance with a better robustness than the conventional sliding mode control (CSMC) on the basis of exponential reaching law (ERL), while significantly reducing chatter.

Assessing the dynamics and impact of COVID-19 vaccination on disease spread: A data-driven approach

The COVID-19 pandemic has significantly impacted global health, social, and economic situations since its emergence in December 2019. The primary focus of this study is to propose a distinct vaccination policy and assess its impact on controlling COVID-19 transmission in Malaysia using a Bayesian data-driven approach, concentrating on the year 2021. We employ a compartmental Susceptible-Exposed-Infected-Recovered-Vaccinated (SEIRV) model, incorporating a time-varying transmission rate and a data-driven method for its estimation through an Exploratory Data Analysis (EDA) approach. While no vaccine guarantees total immunity against the disease, and vaccine immunity wanes over time, it is critical to include and accurately estimate vaccine efficacy, as well as a constant vaccine immunity decay or wane factor, to better simulate the dynamics of vaccine-induced protection over time. Based on the distribution and effectiveness of vaccines, we integrated a data-driven estimation of vaccine efficacy, calculated at 75% for Malaysia, underscoring the model's realism and relevance to the specific context of the country. The Bayesian inference framework is used to assimilate various data sources and account for underlying uncertainties in model parameters. The model is fitted to real-world data from Malaysia to analyze disease spread trends and evaluate the effectiveness of our proposed vaccination policy. Our findings reveal that this distinct vaccination policy, which emphasizes an accelerated vaccination rate during the initial stages of the program, is highly effective in mitigating the spread of COVID-19 and substantially reducing the pandemic peak and new infections. The study found that vaccinating 57–66% of the population (as opposed to 76% in the real data) with a better vaccination policy such as proposed here is able to significantly reduce the number of new infections and ultimately reduce the costs associated with new infections. The study contributes to the development of a robust and informative representation of COVID-19 transmission and vaccination, offering valuable insights for policymakers on the potential benefits and limitations of different vaccination policies, particularly highlighting the importance of a well-planned and efficient vaccination rollout strategy. While the methodology used in this study is specifically applied to national data from Malaysia, its successful application to local regions within Malaysia, such as Selangor and Johor, indicates its adaptability and potential for broader application. This demonstrates the model's adaptability for policy assessment and improvement across various demographic and epidemiological landscapes, implying its usefulness for similar datasets from various geographical regions.