Estimation Process Research Articles

Analysis of Measurement Uncertainty of Natural Source Zone Depletion Rate of Hydrocarbons as a Function of the Uncertainty of Subsurface Temperature

Environmental monitoring and remediation of hydrocarbon contamination in soil, particularly through passive methods, has become a critical area of focus for oil companies adapting to current environmental standards. Natural source zone depletion (NSZD) is a passive remediation process in which the degradation of petroleum hydrocarbons by microorganisms produces measurable thermal effects on the subsurface. Accurate estimation of the NSZD rate is heavily dependent on the precision of temperature measurements, as small uncertainties in temperature can cause significant variations in the estimated rates. Despite growing interest in using subsurface temperature data for NSZD monitoring, there is a lack of studies addressing the impact of temperature measurement uncertainty on the reliability of depletion rate estimates. This paper proposes a Monte Carlo method approach to assess the propagation of temperature measurement uncertainties through the NSZD rate estimation process. By simulating different uncertainty scenarios, this work defines acceptable limits for temperature measurement errors to ensure accurate and representative NSZD rate calculations. For the analyzed case study, it was determined that the relationship between uncertainties was nearly linear, with a slope of 52.5 L m−2 year−1 in the estimated NSZD rate for each degree Celsius of uncertainty in the temperature measurements.

Dataset of noise signals generated by smart attackers for disrupting state of health and state of charge estimations of battery energy storage systems.

This dataset is generated from real-time simulations conducted in MATLAB/Simscape, focusing on the impact of smart noise signals on battery energy storage systems (BESS). Using Deep Reinforcement Learning (DRL) agent known as Proximal Policy Optimization (PPO), noise signals in the form of subtle millivolt and milliampere variations are strategically created to represent realistic cases of False Data Injection Attacks (FDIA). These signals are designed to disrupt the State of Charge (SoC) and State of Health (SoH) estimation blocks within Unscented Kalman Filters (UKF). The low-magnitude noise signals are specifically crafted to be stealthy, evading easy detection while still effectively causing malfunctions in the estimation processes. Additionally, we introduce a verification case using a different battery model and estimation algorithm to enhance generalization. This case involves high-noise signals with defined thresholds for current and voltage noise levels, which cause significant disruptions to Kalman Filters. These signals serve as a complementary example of adversarial attacks, demonstrating how such noise can destabilize estimation algorithms and lead to critical control errors. This dataset is valuable for researchers and engineers aiming to understand and mitigate the effects of smart cyber-physical attacks on BESS. These attacks can disrupt real-time BESS controllers by injecting false input data related to SoC and SoH, leading to physical control manipulations, increased energy costs, inefficiencies in demand-side management, and incorrect day-ahead scheduling, thereby destabilizing grid operations. The data is reusable in studies focused on enhancing the resilience of SoC and SoH estimation methods, as well as in developing robust defensive strategies against smart DRL-based adversarial attacks.

Global burden of antimicrobial resistance in lower respiratory infections in 2021: A systematic analysis.

The research aimed to provide a worldwide evaluation of antimicrobial resistance (AMR), focusing specifically on AMR related to lower respiratory infections (LRI). The data were derived from the Global Antimicrobial Resistance Burden 2021 (GARB 2021). Two counterfactuals were utilized to estimate the deaths attributable to AMR and the deaths associated with AMR. The primary estimation process involved various statistical methodologies, including polynomial estimation and ensemble spatiotemporal Gaussian regression models. Using the DisMod-MR 2.1 modeling framework, the incidence and prevalence of LRI were estimated, the mortality rates were subsequently calculated, and stratified by pathogens, regions, and age groups. In addition, these indexes were identified and visualized to present global burden of AMR. In 2021, there were 20.89 (95% uncertain interval: 18.27-23.50) deaths per 100 000 individuals associated with AMR in LRI, and 5.05 (95% UI: 4.29-5.51) deaths per 100 000 individuals attributable to AMR in LRI. Trimethoprim-sulfamethoxazole-resistant S. pneumoniae exhibited the highest mortality rate of 5.15 (95% UI: 3.96- 6.34) deaths per 100 000 individuals associated with AMR, while Carbapenem -resistant S. pneumoniae exhibited the highest mortality rate of 0.66 (95% UI: 0.45-0.86) deaths per 100 000 individuals attributable to AMR. S. pneumoniae exhibited the greatest burden of AMR, followed by S. aureus. Central Sub-Saharan Africa had the highest AMR burden, with mortality rates of 73.75 (95% UI: 56.61-90.89) deaths per 100 000 individuals associated with AMR and 17.73 (95% UI: 12.71-2.74) deaths per 100 000 individuals attributable to AMR, followed by Eastern Sub-Saharan Africa and Western Sub-Saharan Africa. The individuals aged under 5 and over 65 years exhibited high prevalence of antibiotic resistance especially to Carbapenems, Methicillin, and Fluoroquinolones. AMR in the LRI is still a pressing global health issue, particularly in developing countries and neonatal age groups. Global interventions need to be taken to reduce the prevalence of AMR.

Stepwise Parameter Estimation Approach for Enhanced Single Particle Models in Lithium-Ion Batteries Using Genetic Algorithm

Abstract With the growing demand for electric vehicles, there is an increasing need to accurately assess the internal states of lithium-ion batteries to enhance both performance and safety. This study introduces a novel parameter estimation approach based on an enhanced single particle model, which ensures high accuracy compared to experimental data. The parameter estimation process is carried out in four stages: determination of stoichiometric values (Stage 1), model parameter estimation using genetic algorithm (GA) (Stage 2), evaluation of entropy coefficients with SOC (Stage 3), and re-estimation of model parameters (Stage 4). The optimization algorithm aims to minimize the root mean square error between the experimental data and model results, targeting voltage and temperature errors within 30 mV and 0.5°C, respectively. The proposed approach is validated using three commercial cylindrical LIBs with different chemistries (NCA, NMC, and LFP as cathode materials). The comparison results under constant current discharge and US06 driving test power cycle show good accuracy for both electrochemical and thermal characteristics.

AI-enhanced channel estimation and signal processing for MIMO systems in 5g/6g radio frequency networks

AI-enhanced channel estimation and signal processing for MIMO systems in 5g/6g radio frequency networks

RRphylogeography: A new method to find the area of origin of species and the history of past contacts between species

Abstract One of the main goals of historical biogeography is understanding where species originated, and how climate change and ecological interactions shaped their distribution. The task is complicated by both active and passive mechanisms, including habitat tracking, the separation of species into metapopulations of variably interconnected demes, and long‐distance dispersal, which may all obscure the geographic signature of species origin. Current historical biogeography tools use phylogenies to infer the area of origin (AOO). They work by discretizing the geographic range occupied by the species into distinct areas and then applying ancestral character estimation to identify the area occupied at speciation. These methods are powerful and can account for different modes of speciation. Yet, they are bound to assume that the discrete areas currently occupied by the species are faithful representation of their climatic and historic affiliation and ignore metapopulation structures. Still, most methods cannot take advantage of fossil information or work with phylogenies including extinct species. Although explicit bioclimatic modelling is now possible under some implementations, these limitations are partly unresolved, which burdens the accuracy of the AOO estimation process. We present a new tool written in R, named RRphylogeography, meant to find the AOO of species, and to locate feasible zones of contact between species throughout their history. RRphylogeography starts from the bioclimatic modelling of the species, identifies potential habitat patches occupied during speciation and finds the habitat patches most likely to represent the AOO or contact. By using virtual species simulations, we compared RRphylogeography to common historical biogeography tools. We found RRphylogeography statistically outcompetes these alternatives under all study conditions, reaching especially accurate predictions. We additionally used RRphylogeography to investigate the complex phylogeographic history of the polar bear Ursus maritimus. The method placed the origin of the species in Northern Beringia. Intriguingly, it further shows possible contact zones between polar and brown bear in northwestern Europe during the late Pleistocene and in Beringia during the Pleistocene to Holocene transition, which is in perfect agreement with the known hybridization history between the two species.

Potential Benefits of Seismic Monitoring for Ground-Shaking Estimation and Loss Assessment

ABSTRACT Seismic networks are fundamental to compute ground shaking in regions affected by destructive events, which can then be used for damage and loss assessment. Ground-motion data recordings can be used to estimate the event bias (or interevent residual) for one or multiple ground-motion models, as well as to condition the ground-shaking estimation process to reduce the intraevent residuals at sites near seismic stations. We evaluate how the error in the estimation of the event bias and ground shaking for a set of target sites can be reduced by incorporating an increasing number of seismic stations, using recordings from the 1999 M 7.7 Chi-Chi, Taiwan, earthquake and a large set of ground-motion fields for Taiwan using stochastic simulations. Then, we evaluate how the incorporation of data from seismic stations in the estimation of several impact metrics (i.e., economic losses, collapsed buildings, and fatalities) can reduce both the bias and the uncertainty, using the district of Lisbon (Portugal) as a case study. The results from this study indicate that the error in the estimation of the impact metrics can be reduced by one order of magnitude if at least 10 stations are considered, especially if the configuration of the seismic network considers the location of vulnerable buildings or the spatial distribution of earthquake risk.

Experimental Study on Newly Construction of Rigid Pavement and Material Estimation

The construction of internal Cement Concrete (CC) roads plays a pivotal role in enhancing connectivity, durability, and sustainability within residential, commercial, and industrial areas. The construction involves systematic stages, including site preparation, sub grade preparation, laying sub-base, placement of formwork, and pouring concrete. The choice of materials, such as Portland cement, coarse and fine aggregates, and water, is critical to achieving strength and longevity. The estimation process considers the total length, width, and thickness of the road, along with labor, machinery, and material costs. Advanced estimation tools and techniques, including software-based analysis, are employed to ensure accurate budgeting and resource allocation. This study emphasizes adopting efficient construction practices, quality control measures, and sustainable materials to minimize environmental impact while maximizing economic value

Uncertainty Analysis of Remote Sensing Estimation of Chinese Fir (Cunninghamia lanceolata) Aboveground Biomass in Southern China

Forest aboveground biomass (AGB) is not only the basis for forest carbon stock research, but also an important parameter for assessing the forest carbon cycle and ecological functions of forests. However, there are various uncertainties in the estimation process, limiting the accuracy of AGB estimation. Therefore, we extracted the spectral features, vegetation indices and texture factors from remote sensing images based on the field data and Landsat 8 OLI remote sensing images in Southern China to quantify the uncertainties. Then, we established three AGB estimation models, including K Nearest Neighbor Regression (KNN), Gradient Boosted Regression Tree (GBRT) and Random Forest (RF). Uncertainties at the plot scale and models were measured by using error equations to analyze the influences of uncertainties at different scales on AGB estimation. Results were as follows: (1) The R2 of the per-tree biomass model for Cunninghamia lanceolata was 0.970, while the uncertainty of the residual and parameters for per-tree biomass model was 4.62% and 4.81%, respectively; and the uncertainty transferred to the plot scale was 3.23%. (2) The estimation methods had the most significant effects on the remote sensing models. RF was more accurate than other two methods, and had the highest accuracy (R2 = 0.867, RMSE = 19.325 t/ha) and lowest uncertainty (5.93%), which outperformed both the KNN and GBRT models (KNN: R2 = 0.368, RMSE = 42.314 t/ha, uncertainty = 14.88%; GBRT: R2 = 0.636, RMSE = 32.056 t/ha, uncertainty = 6.3%). Compared to KNN and GBRT, the R2 of RF was enhanced by 0.499 and 0.231, while the uncertainty was decreased by 8.95% and 0.37%, respectively. The uncertainty associated with the scale of remote sensing models remains the primary source of uncertainty when compared to the plot scale. On the remote sensing scale, RF is the model with the best estimation effect. This study examines the impact of both plot-scale and remote sensing model-scale methodologies on the estimation of AGB for Cunninghamia lanceolata. The findings aim to offer valuable insights and considerations for enhancing the accuracy of AGB estimations.

Indoor Walking Trajectory Estimation Using Mobile Device Sensors for Hand-Held and Hand-Swinging Modes

We propose an indoor location estimation method using sensors of mobile devices. First, we perform attitude estimation using each sensor. This estimation is used to estimate the attitude of the mobile device with respect to the earth. Based on the acceleration and other information obtained from the attitude estimation, we then estimate the step detection, step length, and direction of the step. Finally, the location is calculated using all the estimation results. To eliminate the need to hold the mobile device in place during the estimation process, the method is configured so that estimates may be performed while walking, while looking at the screen, and while walking and holding the device in one hand. As the proposed method does not use indoor location fingerprinting or machine learning, real-time estimation can be performed. Although the accuracy could be higher, our experimental results show that the proposed method is able to effectively estimate the location and walking trajectory.

P0192 Activities of Sorghum bicolor diet on microbial gut-brain axis during experimental Crohn’s Colitis in adult male Wistar rats

Abstract Background Crohn’s colitis (CC) is a form of inflammatory bowel disease with increasing resistance to synthetic medications hence the search for easily-accessible medicinal plants with anti-inflammatory activities. During CC, there are reports of dysbiosis, neuro-inflammation accompanied with defective brain structure, irregular neural signaling, and associated depressive-like behavior. Sorghum bicolor leaf (SBLD) and seed (SBSD) are accessible medicinal plant with anti-inflammatory bioactive compounds. However, there is limited information regarding its activities on microbial-gut-brain axis during CC which this study evaluates. Methods Ethical approval was obtained from the University of Ibadan (UI-ACUREC/071-1222/31) and S.bicolor plant was identified by the institutional herbarium (UIH-23294). Eighty male Wistar rats (100-150g, n=10) were divided thus: Groups 1-normal rats, 2-colitis untreated (CCU), 3-CC+Azathioprine, 4-CC+Resveratrol, 5-CC+10% SBLD, 6-CC+20% SBLD, 7-CC+10% SBSD, 8-CC+20% SBLD. Groups 2-8 were induced with CC (1ml/kg of 1.4% NaOH single intra-rectal) by day 14 of treatment which continued for 7 days. Daily body weights, neuro-behavioural studies, and stool score were monitored. Animals were euthanized, and colon was excised for ulcer score. The excised colon and brain were submitted for biochemical estimation and histological processing and evaluation by days 3 and 7 post CC induction. Fecal samples were processed for gut microflora using 16S rDNA gene sequencing. Results were subjected to one-way ANOVA, values expressed as MEAN±SEM and statistically significant at p≤0.05. Results Body weight, colon weight/length ratio, stool and ulcer scores in SBLD and SBSD-treated groups significantly reduced compared with CCU, and CC+Resveratrol on days 3 and 7. Colon healing zone, and neuro-behaviour traits (rearing, grooming, and line crossing time) in SBLD and SBSD-treated groups significantly increased compared with CCU. Colon and brain malondialdehyde, carbonyl, nitric oxide, significantly reduced but increased in hydrogen peroxide, mucin, sulfhydryl, glutathione-reductase, and calcium ATPase pump activities in the SBLD and SBSD-treated groups compared with CCU on both days. Reactive cortico-hippocampal cells in SBLD and SBSD-treated groups significantly reduced compared with CCU, CC+Azathioprine and CC+Resveratrol. Lactobacillus species in the SBLD and SBSD-treated groups significantly increased compared with CCU on both days. Conclusion Sorghum bicolor leaf and seed diet facilitated healing of Crohn’s colitis and attenuated adverse neurobehavioural traits through reduced oxidative stress, and increased gut Lactobaccillus sp. with gut-brain signaling.

Digital Twins and AI Decision Models: Advancing Cost Modelling in Off-Site Construction

The rising demand for housing continues to outpace traditional construction processes, highlighting the need for innovative, efficient, and sustainable delivery models. Off-site construction (OSC) has emerged as a promising alternative, offering faster project timelines and enhanced cost management. However, current research on cost models for OSC, particularly in automating material take-offs and optimising cost performance, remains limited. This study addresses this gap by proposing a new cost model integrating Digital Twin (DT) technology and AI-driven decision models for modular housing in the UK. The research explores the role of DTs in enhancing cost estimation and decision-making processes. By leveraging DTs and AI, the proposed model evaluates the impact of emergent technologies on cost performance, material efficiency, and sustainability across social, environmental, and economic dimensions. As proposed, this integrated approach enables a cost model tailored for OSC systems, providing a data-driven foundation for cost optimisation and material take-offs. The study’s findings highlight the potential of combining DTs and AI decision models to enhance cost modelling in modular construction, offering new capabilities to support sustainable and performance-driven housing delivery. The paper introduces a dynamic, data-driven cost model integrating real-time data acquisition through DTs and AI-powered predictive analytics. This dynamic approach enhances cost accuracy, reduces lifecycle cost variability, and supports adaptive decision-making throughout the OSC project lifecycle.

Accelerated Degradation Data Analysis Based on Gamma Process With Random Effects

ABSTRACTThe Gamma constant‐stress accelerated degradation model is a natural model for monotonous degradation processes. However, unit heterogeneity often exists in practice, necessitating a more realistic model. This study develops a Gamma process with random effects to accurately capture accelerated degradation data for reliability analysis, encompassing both point and interval estimation. First, the Expectation‐Maximization (EM) algorithm is developed to obtain point estimates of the proposed model. Since these estimates are sensitive to initial values, potentially impacting the outcomes, an improved EM algorithm is proposed, which iteratively refines the estimation quality by executing two different M‐steps, thereby enhancing overall estimation accuracy. Secondly, given the complexity of the model and the constraint of small sample sizes and limited stress levels, a three‐step interval estimation method is devised. This method segregates the parameters into three distinct parts and addresses them individually using the generalized pivotal quantity method, which simplifies the parameter interval estimation process and enhances the estimation accuracy. Finally, simulation studies and a real example of O‐rings are presented to demonstrate the effectiveness of the proposed method.

Instance Segmentation and 3D Pose Estimation of Tea Bud Leaves for Autonomous Harvesting Robots

In unstructured tea garden environments, accurate recognition and pose estimation of tea bud leaves are critical for autonomous harvesting robots. Due to variations in imaging distance, tea bud leaves exhibit diverse scale and pose characteristics in camera views, which significantly complicates the recognition and pose estimation process. This study proposes a method using an RGB-D camera for precise recognition and pose estimation of tea bud leaves. The approach first constructs an for tea bud leaves, followed by a dynamic weight estimation strategy to achieve adaptive pose estimation. Quantitative experiments demonstrate that the instance segmentation model achieves an mAP@50 of 92.0% for box detection and 91.9% for mask detection, improving by 3.2% and 3.4%, respectively, compared to the YOLOv8s-seg instance segmentation model. The pose estimation results indicate a maximum angular error of 7.76°, a mean angular error of 3.41°, a median angular error of 3.69°, and a median absolute deviation of 1.42°. The corresponding distance errors are 8.60 mm, 2.83 mm, 2.57 mm, and 0.81 mm, further confirming the accuracy and robustness of the proposed method. These results indicate that the proposed method can be applied in unstructured tea garden environments for non-destructive and precise harvesting with autonomous tea bud-leave harvesting robots.

Multivariate GARCH models with spherical parameterizations: an oil price application

In popular Baba-Engle-Kraft-Kroner (BEKK) and dynamic conditional correlation (DCC) multivariate generalized autoregressive conditional heteroskedasticity models, the large number of parameters and the requirement of positive definiteness of the covariance and correlation matrices pose some difficulties during the estimation process. To avoid these issues, we propose two modifications to the BEKK and DCC models that employ two spherical parameterizations applied to the Cholesky decompositions of the covariance and correlation matrices. In their full specifications, the introduced Cholesky-BEKK and Cholesky-DCC models allow for a reduction in the number of parameters compared with their traditional counterparts. Moreover, the application of spherical transformation does not require the imposition of inequality constraints on the parameters during the estimation. An application to two crude oils, WTI and Brent, and the main exchange rate prices demonstrates that the Cholesky-BEKK and Cholesky-DCC models can capture the dynamics of covariances and correlations. In addition, the Kupiec test on different portfolio compositions confirms the satisfactory performance of the proposed models.

Bad Data Processing in Fast-Decoupled State Estimation via Geometric Approach

Bad Data Processing in Fast-Decoupled State Estimation via Geometric Approach

A Bernstein-type Inequality for High Dimensional Linear Processes with Applications to Robust Estimation of Time Series Regressions

Time series regression models are commonly used in time series analysis. However, in modern real-world applications, serially correlated data with an ultra-high dimension and fat tails are prevalent. This presents a challenge in developing new statistical tools for time series analysis. In this paper, we propose a novel Bernstein-type inequality for high-dimensional linear processes and apply it to investigate two high-dimensional robust estimation problems: (1) time series regression with fat-tailed and correlated covariates and errors, and (2) fat-tailed vector autoregression. Our proposed approach allows for exponential increases in dimension with sample size under mild moment and dependence conditions, while ensuring consistency in the estimation process.

Leveraging Digital Mobility Data to Estimate Visitation in National Wildlife Refuges

The US Fish and Wildlife Service (USFWS) manages over 560 National Wildlife Refuges and dozens of National Fish Hatcheries across the United States. Accurately estimating visitor numbers to these areas is essential for understanding current recreation demand, planning for future use, and ensuring the ongoing protection of the ecosystems that refuges safeguard. However, accurately estimating visitation across the entire refuge system presents significant challenges. Building on previous research conducted on other federal lands, this study evaluates methods to overcome constraints in estimating visitation levels using statistical models and digital mobility data. We develop and test a visitation modeling approach using multiple linear regression, incorporating predictors from eight mobility data sources, including four social media platforms, one community science platform, and three mobile device location datasets from two commercial vendors. We find that the total number of observed visitors to refuges correlates with the volume of data from each mobility data source. However, neither social media nor mobile device location data alone provide reliable proxies for visitation due to inconsistent relationships with observed visitation; these relationships vary by data source, refuge, and time. Our results demonstrate that a visitation model integrating multiple mobility datasets accounts for this variability and outperforms models based on individual mobility datasets. We find that a refuge-level effect is the single most important predictor, suggesting that including site characteristics in future models will make them more generalizable. We conclude that statistical models which incorporate digital mobility data have the potential to improve the accuracy of visitor estimates, standardize data collection methods, and simplify the estimation process for agency staff.

The Marshall-Olkin Type II exponentiated half logistic family of distributions with properties and applications

A new Marshall-Olkin Type II Exponentiated Half Logistic-G (MO-TIIEHL-G) distribution is developed. Some properties and special models together with estimates of the parameters are presented. The effectiveness of the estimation process is evaluated using Monte Carlo simulation. Lastly, by applying a special case to actual data sets, the flexibility and significance of the new family of models are shown.

Centralized Fusion Estimation in Networked Systems: Addressing Deception Attacks and Packet Dropouts with a Zero-Order Hold Approach

Article Centralized Fusion Estimation in Networked Systems: Addressing Deception Attacks and Packet Dropouts with a Zero-Order Hold Approach Caballero-Águila Raquel 1,*, and Linares-Pérez Josefa 2 1 Departamento de Estadística e I.O., Universidad de Jaén, Campus Las Lagunillas, 23071 Jaén, Spain 2 Departamento de Estadística e I.O., Universidad de Granada, Campus Fuentenueva, 18071 Granada, Spain * Correspondence: raguila@ujaen.es Received: 26 August 2024 Accepted: 11 October 2024 Published: 24 December 2024 Abstract: This paper addresses the centralized fusion estimation problem in networked systems with stochastic uncertainties characterized by random parameter matrices together with multiplicative and additive noises. To reflect real-world engineering situations, it is further assumed that the network transmissions are simultaneously subject to random packet dropouts and deception attacks which randomly alter real measurements by replacing them with noises. A novel approach is proposed that avoids the need for a specific state equation, relying instead only on the mean and covariance functions of the processes involved. The additive noises in the sensor measurements are considered to be time-correlated and packet dropouts are managed through a zero-order hold compensation strategy that attenuates the effect of data loss on the estimation process. On the basis of the available measurement information, recursive fusion filtering and smoothing algorithms are developed using an innovation-based methodology. The proposed approach is validated by numerical simulations, demonstrating its feasibility and correctness. Comparative results show the superior performance of the proposed fusion estimation scheme over existing filters in the literature, highlighting its effectiveness in mitigating the impact of deception attacks and packet dropouts in networked systems.