Modeling Estimation Research Articles

Conway-Maxwell-Poisson profile monitoring with rk-Shewhart control chart: a comparative study

A control chart is an essential tool in Statistical Quality Control for monitoring the production process. It provides a visual means of identifying process irregularities. In this study, we focus on the Shewhart control chart based on the rk-deviance residuals, namely rk-Shewhart control charts to examine the Conway–Maxwell–Poisson (COM-Poisson) profile, which is used to model the count data with varying degrees of dispersion. The primary goal of this study is to identify the biasing parameter that produces the best result among newly presented biasing parameters developed based on existing ones. It provides a short overview of the COM-Poisson distribution, its modeling, and rk parameter estimation in the case of multicollinearity, as well as the construction of the deviance-residual-based Shewhart chart. To evaluate the performance of the rk-Shewhart, we conduct an analysis using a real-life data set, considering various shift sizes. By employing different biasing parameters, we examine the effectiveness of the rk-Shewhart control chart. The performance evaluation outcomes of the rk-Shewhart charts are compared to the ML-deviance-based Shewhart chart and within themselves based on the biasing parameters. The results demonstrate the advantage of the rk-Shewhart charts over the ML-deviance-based control chart in detecting out-of-control signals. Among the considered biasing parameters, the rk-Shewhart chart utilizing the adjusted biasing parameter k_4 shows the best performance based on the ARL metric.

Preliminary Experiences in Determining the Soil–Water Characteristic Curve of a Sandy Soil Using Physical Slope Modeling

Relating soil moisture content to soil suction, the soil–water characteristic curve (SWCC) represents an essential feature in unsaturated soil mechanics that enables estimation of different unsaturated soil property functions and modeling of the macro-scale soil behavior. However, depending on the soil and processes under consideration, proper hydraulic characterization of a soil through direct laboratory measurements can be difficult, time-consuming, and involve many uncertainties. In the case of uniformly graded sands, there is a highly nonlinear and steep shape of the SWCC, with only a few kPa of soil suction separating saturated and residual soil moisture conditions, which makes measurements for determinations of SWCC especially challenging. This study encompasses an investigation of the sandy type of soil’s behavior and presents some preliminary results and experiences on the determination of SWCC through the use of physical slope model tests. The 30 cm deep slope, inclined at 35 degrees and instrumented with soil moisture and pore water pressure sensors, was exposed to series of rainfall intensities, ranging from 37 up to 300 mm/h. The results indicated that the data on hydraulic response in monitored points are not only useful for the determination of SWCC, but that the approach is useful for investigation of hydraulic hysteresis phenomena, as well as its effects on soil moisture and pore water pressure conditions, which also affects the stability conditions of a slope. In particular, the best-fit parameters of the van Genuchten model suggested air entry values of 1.6 and 1.1 kPa for the drying and the wetting curves of the SWCC, respectively, with the two branches shifted by about 1 kPa of soil suction.

Relating state-of-charge to impedance and equivalent circuit parameters in lithium-ion cells: An experimental study

The wide-band impedance of Lithium-ion (Li-ion) batteries has become a focal point for researchers interested in State-of-Charge (SOC) estimation and battery modeling. However, the interplay between SOC, wide-band impedance, and model parameters has not been extensively explored. This paper addresses this gap by investigating how SOC impacts the impedance and Equivalent Circuit Model (ECM) parameters of a Li-ion battery cell through empirical measurements. The study utilizes discharge tests to vary the SOC and employs the Coulomb Counting Method (CCM) to estimate the approximate SOC from the response signals. A novel measurement technique is introduced and applied to determine a cell’s wide-band impedance at different SOC levels. This innovative approach uses a Pseudo-random Impulse Sequence (PRIS) perturbation signal to measure wide-band cell impedance. The collected impedance data is then used to estimate the ECM parameters. Experimental findings reveal an inverse relationship between impedance and SOC. Additionally, the ECM parameters exhibit significant correlation with SOC, particularly in mid- to low-frequency regions. This research highlights that SOC profoundly influences ECM parameters, offering valuable insights into the performance of Li-ion batteries under various conditions.

Grade estimation and 3D modelling of epithermal gold deposits using an improved integrated SVM-metaheuristic method in Zailik, NW of Iran

Grade estimation and 3D modelling of epithermal gold deposits using an improved integrated SVM-metaheuristic method in Zailik, NW of Iran

Risk assessment of soil compaction due to machinery traffic used in infield transportation of sugarcane during mechanized harvesting

Soil stress imposed by tire-soil contact from transshipment vehicles used in mechanized sugarcane harvesting results in increase in the degree of compactness. In this study we aimed to: (i) estimate the magnitude and propagation of vertical stress into soil caused by sugarcane transshipment wheeling; and (ii) assess the risk and occurrence of soil compaction in an Oxisol of southeastern Brazil. Three tractor-trailers with a carrying capacity of 10, 21, and 30 Mg, and an autonomous truck-trailer with a carrying capacity of 20 Mg were evaluated as field traffic treatments. For each treatment, magnitude and distribution of vertical soil stress (σz) and soil strength (precompression stress, σp) were predicted by modeling estimation and risk of compaction was assessed by σp/σz relationship. The occurrence of soil compaction was evaluated based on measured changes in the bulk density and macroporosity (equivalent pore diameter > 50 μm) up to 0.70 m deep after wheeling. Estimates indicated that the autonomous truck applied stresses that reached 595 kPa at the soil surface beneath the centerline of the tire, imposing the greatest risk of compaction up to a depth of 0.35 m depth. The stresses at the soil surface beneath the centerline from the tractor-towed trailer tires ranged from 275 to 279 kPa, imposing a risk of compaction that did not exceed 0.29 m depth. All assessed transshipment configurations promoted soil compaction, detected by increases in the soil bulk density and decreases in the macroporosity, even at greater depths where risk prediction had not alerted to the soil compaction. The most significant impact on soil compaction was observed for the autonomous truck-trailer and the tractor-towed trailer with a carrying capacity of 30 Mg, also affecting subsoil, as evidenced by a relative increase in soil bulk density of 5 % and 7 % in the 0.30−0.50 m and 0.50−0.70 m layers, respectively, and a relative reduction in macroporosity of 40 % and 50 % in these same soil layers.

Attaining Equity of Access to Research: Perspective on Research in Pregnancy and Breastfeeding Following Dolores Shockley Lecture at ASCPT2024.

Everybody deserves access to evidence-based information to make decisions about their health. However, in many situations, clinical trial eligibility criteria mean that specific data do not exist for certain groups of individuals. These include pregnant and breastfeeding women, children, older people, those with hepatic and renal dysfunction, those with acute severe illness, and those with multiple co-morbidities and interacting medications. Resultantly, there may not be specific drug-dosing information for many patients who are treated in a clinical setting. The ASCPT2024 Dolores Shockley Lecture focused on the equitable access to research with a specific focus on clinical pharmacology studies in pregnancy and breastfeeding. To ensure the safe, effective use of medication in pregnancy and breastfeeding, women should be included in clinical trials and pharmacokinetic studies when a medication is anticipated to be used in women of childbearing potential. Community groups should be involved at all stages of research to maintain transparency and trust. This ensures that local priorities are investigated, that communities understand the findings and are empowered to make evidence-based decisions about their own medication use. Principles informing the design of such studies in pregnancy and lactation are in existence. Mathematical techniques such as physiologically-based pharmacokinetic modeling and stochastic simulation and estimation can enhance study design, and population pharmacokinetic modeling be used to understand variability within and between individuals. Data should be made findable, accessible, interoperable, and reusable (FAIR). Information (and where necessary, training) regarding the use of these approaches should be provided to decision-making stakeholders such as ethics committees and regulatory bodies.

Applications of machine‐learning interatomic potentials for modeling ceramics, glass, and electrolytes: A review

AbstractThe emergence of artificial intelligence has provided efficient methodologies to pursue innovative findings in material science. Over the past two decades, machine‐learning potential (MLP) has emerged as an alternative technology to density functional theory (DFT) and classical molecular dynamics (CMD) simulations for computational modeling of materials and estimation of their properties. The MLP offers more efficient computation compared to DFT, while providing higher accuracy compared to CMD. This enables us to conduct more realistic simulations using models with more atoms and for longer simulation times. Indeed, the number of research studies utilizing MLPs has significantly increased since 2015, covering a broad range of materials and their structures, ranging from simple to complex, as well as various chemical and physical phenomena. As a result, there are high expectations for further applications of MLPs in the field of material science and industrial development. This review aims to summarize the applications, particularly in ceramics and glass science, and fundamental theories of MLPs to facilitate future progress and utilization. Finally, we provide a summary and discuss perspectives on the next challenges in the development and application of MLPs.

Parameter Estimation of Cellular Communication Systems Models in Computational MATLAB Environment: A Systematic Solver-based Numerical Optimization Approaches

Model-based parameter estimation, identification, and optimisation play a dominant role in many aspects of physical and operational processes in applied sciences, engineering, and other related disciplines. The intricate task involves engaging and fitting the most appropriate parametric model with nonlinear or linear features to experimental field datasets priori to selecting the best optimisation algorithm with the best configuration. Thus, the task is usually geared towards solving a clear optimsation problem. In this paper, a systematic-stepwise approach has been employed to review and benchmark six numerical-based optimization algorithms in MATLAB computational Environment. The algorithms include the Gradient Descent (GRA), Levenberg-Marguardt (LEM), Quasi-Newton (QAN), Gauss-Newton (GUN), Nelda-Meald (NEM), and Trust-Region-Dogleg (TRD). This has been accomplished by engaging them to solve an intricate radio frequency propagation modelling and parametric estimation in connection with practical spatial signal data. The spatial signal data were obtained via real-time field drive test conducted around six eNodeBs transmitters, with case studies taken from different terrains where 4G LTE transmitters are operational. Accordingly, three criteria in connection with rate of convergence Results show that the approximate hessian-based QAN algorithm, followed by the LEM algorithm yielded the best results in optimizing and estimating the RF propagation models parameters. The resultant approach and output of this paper will be of countless assets in assisting the end-users to select the most preferable optimization algorithm to handle their respective intricate problems.

The effects of non-local observations on the adjoint estimation of local model parameters: An example of Manning’s n coefficient in a tidal model over the Bohai, Yellow, and East China Seas

Accurate parameter estimation is a key issue for enhancing the performance of hydrological models. With the rapid development of satellite technology, integrating satellite data and numerical modelling for parameter estimation has gained more prominence. The limited spatial coverage of altimeters necessitates areas without direct local observations to rely only on information from nearby non-local observations. However, the relation is far from clear between non-local observations and the estimation of local model parameters. In this study, a two-dimensional adjoint tidal model over the Bohai, Yellow, and East China Seas (BYECS) is developed, assimilating observations from TOPEX/Poseidon, Jason-1, and Jason-2 altimeters in conjunction with tide-gauge stations. In twin experiments, the feasibility and reliability of utilizing non-local observations are emphasized by varying synthetic observations. The experiments also reveal how the number and distance of non-local observations affect the ill-posedness of local parameter estimation. The estimation errors can be reduced by increasing observations within a ‘critical distance’, beyond which the accuracy does not improve further. Subsequently, the robustness of ‘critical distance’ is discussed under different prescribed parameter distributions, sizes of predefined optimized area, and random observational errors, indicating that the ‘critical distance’ is approximately 1.0°-2.0°. These results provide a positive reference for practical experiment. In BYECS, the order of Manning’s n coefficient ranges from 10-4 to 10-1, with an average value of 0.0246 in shallow waters. The spatial distribution reveals 9 high-value areas, primarily modulated by a concurrence of sandy sediment and weak tidal currents. This study offers valuable insights into refining the adjoint model for physical parameter estimation, particularly in anticipation of the expanded spatial coverage of SWOT. Compared to only assimilating T/P series altimeters, incorporating SWOT reduces discrepancies by over 38%. Additionally, the spatial Manning’s n coefficient provides guidance for enhancing tidal simulation in other models.

A hierarchical mission planning system for multi-uncrewed ground vehicles using fast cost evaluation and ant colony optimisation

Mission Planning for Multi-Uncrewed Ground Vehicle (multi-UGV) missions is a key functional module for achieving effective autonomy and coordination within a fleet of vehicles. However, the complexity of mission planning is compounded by the interconnected sub-problems and the challenging environments encountered by UGVs. Aiming to devise efficient and effective techniques to tackle the intricacies of mission planning in complex and cluttered environments, this paper presents an algorithmic architecture tailored for hierarchical multi-UGV mission planning systems. Specifically, this paper designs a Modified Cost Approximation Method integrated with two-layer environmental modelling for fast estimation of the travelling cost graphs of target points. A Hybrid Clustering Method that merges k-means clustering with a marginal cost-based assignment is proposed to streamline task decomposition and task assignment. Furthermore, a three-layer path planner is developed by integrating A*, post-processing steps, and Multi-operator Continuous Ant Colony optimisation, aiming to find paths with reduced cost for UGVs in challenging terrains. To evaluate the proposed techniques, a benchmark set for multi-UGV mission planning problems is designed using the robotic simulation platform CoppeliaSim. Simulation results demonstrate the superior performance of the proposed planning techniques.

Three-dimensional stochastic dynamical modeling for wind farm flow estimation

Modifying turbine blade pitch, generator torque, and nacelle direction (yaw) are conventional approaches for enhancing energy output and alleviating structural loads. However, the efficacy of such methods is challenged by the lag in adjusting such settings after atmospheric variations are detected. Without reliable short-term wind forecasting tools, current practice, which mostly relies on data collected at or just behind turbines, can result in sub-optimal performance. Data-assimilation strategies can achieve real-time wind forecasting capabilities by correcting model-based predictions of the incoming wind using various field measurements. In this paper, we revisit the development of a class of prior models for real-time estimation via Kalman filtering algorithms that track atmospheric variations using ground-level pressure sensors. This class of models is given by the stochastically forced linearized Navier-Stokes equations around the three-dimensional waked velocity profile defined by a curled wake model. The stochastic input to these models is devised using convex optimization to achieve statistical consistency with high-fidelity large-eddy simulations. We demonstrate the ability of such models in reproducing the second-order statistical signatures of the turbulent velocity field. In support of assimilating ground-level pressure measurements with the predictions of said models, we also highlight the significance of the wall-normal dimension in enhancing two-point correlations of the pressure field between the ground and the computational domain.

Wind energy relevant characteristics of turbulence over boreal forests

Turbulence statistics from three tall meteorological masts and LES in forested landscapes are compared to standard turbulence models used for wind turbine design. The comparison is split into different atmospheric conditions to highlight the impact of stratification on the character of turbulence. The aim of the work is to clarify to which extent standard turbulence models are accurate over forested regions. To this end, different spectral measures such as power spectra and coherence are examined as well as vertical profiles of turbulence characteristics relevant to the design and siting of wind turbines. The measurements are used to investigate vertically separated 2-point statistics and the LES to investigate laterally separated statistics. The results show that in neutral stratification and for smaller separation distances, in the order of half a radius, the standard turbulence models apply, but in non-neutral stratification, particularly in stable conditions and for larger separations the disparity between observations and standard turbulence grow. This effect is mainly attributed to the effect of stratification, while features in the turbulence statistics specifically related to the forest cover is absent at heights relevant to wind energy. The results of the study are expected to be of interest for turbine design purposes as well as wind resource estimation and wind modelling in forested areas.

Servant leadership’s impact on trust, commitment, and performance in higher education

<span lang="EN-US">This research examined the relationship between servant leadership (SL), organizational trust (OT), organizational commitment (OC), and task performance (TP). It employed a quantitative research design with partial least squares-structural equation modeling (PLS-SEM) and bootstrap estimation. Data were collected through a survey of 111 lecturers from 12 universities in Eastern Indonesia. It found that SL does not affect TP and OC. Meanwhile, OT affects OC but not TP. Further, OC does not affect TP. Therefore, OT does not affect TP through OC. Likewise, SL does not affect TP through OC. It practically implies that universities should be concerned the selection and placement of professional leaders. Successful professional leaders support and enhance the OC and TP of lecturers with SL. This study enriches leadership literature empirically examining SL, TP, OT, and OC in religious higher education. Besides, it provides evidence that SL in tertiary institutions does not exhibit a positive impact on lecturer performance. Institutional limitations lead to difficult situations and allow lecturers to accept reality. Furthermore, it uncovers challenges and demands the need for in-depth investigation of SL studies, which are paradoxical and contradictory to the previous SL theories and findings.</span>

Motion Modeling and State Estimation With Bistatic Range and Doppler Measurements

Motion Modeling and State Estimation With Bistatic Range and Doppler Measurements

Reliability Modeling and Parameter Estimation for High-Speed Train Wheels Subject to Multi-Dimensional Degradation Processes Considering Mutual Dependency

Reliability Modeling and Parameter Estimation for High-Speed Train Wheels Subject to Multi-Dimensional Degradation Processes Considering Mutual Dependency

Exploring large language model for generic and robust state-of-charge estimation of Li-ion batteries: A mixed prompt learning method

State-of-charge (SOC) estimation is important to ensure safe functioning of Li-ion batteries (LIBs). However, distinct measurements, harsh temperatures, and dynamic operations pose challenges in estimating intricate SOC changes of multiple LIBs with one estimator. Hence, this paper proposes a novel mixed prompt learning method to explore a large-scale pretrained language model (PLM) for SOC estimation. A new hard prompt generator is proposed to translate LIB data into instruction and answer text, eliciting the inherent representational capability of PLM to jointly learn the sequential patterns and contextual semantics of measurements through language modeling for accurate SOC estimation. A new soft prompt adapter is proposed to encode task-specific information of different LIBs into a small set of independent vectors, ensuring PLM adaptation with minimal parameters while maintaining good generalization. By integrating soft prompt vectors along the forward propagation, the hidden states of PLM are dynamically regulated to characterize volatile and variable SOC for robust estimation. Extensive experiments show that by leveraging mixed hard-soft prompts, PLM can make accurate, generic, and robust estimates for multiple LIBs simultaneously in subzero temperature, charge-pause-discharge, and low-capacity discharge scenarios, with the average MAE, RMSE, and MAX of all tasks as low as 1.20 %, 1.51 %, and 5.29 %, respectively.

Comparative Analysis of Traditional Methods and Building Information Modeling (BIM) in Construction Project Cost Estimation

Abstract: Building Information Modeling (BIM) has revolutionized the construction industry by providing digital images of the physical and functional aspects of the site. This article explores the use of BIM in project management and compares it to traditional methods. The study focused on parameters such as efficiency, cost, accuracy, and coordination. Research shows that BIM increases efficiency, accuracy, and coordination while reducing costs and risks. However, challenges remain, such as developing regulations and reaching consensus on requirements among stakeholders. Despite these challenges, the article concludes that BIM has the potential to transform project management.

Spatial Durbin Model on the Utilization of Delivery at Health Facilities: A 2017 Indonesian Demographic and Health Survey Analysis Authors

The utilization of delivery at health facilities is a major intervention in reducing 16 to 33% of deaths. This study aimed to determine the model of utilization of delivery at health facilities in Indonesia in 2017 and its influential factors. This study used secondary data from the 2017 Indonesian Demographic and Health Survey using a Spatial Durbin Model (SDM) approach. The population was mothers aged 15 – 49 years, spread across 34 provinces of Indonesia, and had 15,321 samples. The results showed that the Moran’s I value was positive (0.146) and significant at p-value = 0.007, indicating clustered regions with similar characteristics. The SDM modeling estimation results (R2 = 91.61%) presented those dependent and independent variables that influenced the utilization of delivery at health facilities and its influential factors. The significant and most dominant direct factor that influenced the utilization of delivery at health facilities was pregnancy visits, while the most dominant indirect factor was socioeconomic status. Therefore, further policy planning is expected to be based on regional specificities, and effective intervention programs should be designed based on these factors.

Comparison of dose selection based on target engagement versus inhibition of receptor-ligand interaction for checkpoint inhibitors.

Immune checkpoint inhibitors block the interaction between a receptor on one cell and its ligand on another cell, thus preventing the transduction of an immunosuppressive signal. While inhibition of the receptor-ligand interaction is key to the pharmacological activity of these drugs, it can be technically challenging to measure these intercellular interactions directly. Instead, target engagement (or receptor occupancy) is commonly measured, but may not always be an accurate predictor of receptor-ligand inhibition, and can be misleading when used to inform clinical dose projections for this class of drugs. In this study, a mathematical model explicitly representing the intercellular receptor-ligand interaction is used to compare dose prediction based on target engagement or receptor-ligand inhibition for two checkpoint inhibitors, atezolizumab and magrolimab. For atezolizumab, there is little difference between target engagement and receptor-ligand inhibition, but for magrolimab, the model predicts that receptor-ligand inhibition is significantly less than target engagement. The key variables explaining the difference between these two drugs are the relative concentrations of the target receptors and their ligands. Drug-target affinity and receptor-ligand affinity can also have divergent effects on target engagement and inhibition. These results suggest that it is important to consider ligand-receptor inhibition in addition to target engagement and demonstrate the impact of using modeling for efficacious dose estimation.

Determinants of land management technology adoptions by rural households in the Goyrie watershed of southern Ethiopia: Multivariate probit modeling estimation

Land management technology (LMT) adoption is one of Ethiopia's crucial strategies to combat soil depletion and promote agricultural production. However, there is scant information concerning the intensity, interdependent nature, and households' decision to adopt multiple LMTs. Thus, the purpose of this study is to identify factors influencing households' decisions to adopt multiple LMTs and the intensity and interdependency of the technologies in the Goyrie watershed of southern Ethiopia. The data was collected from 291 randomly selected household heads, focus group discussion participants, and key informant interview respondents. The quantitative data was analyzed using descriptive statistics and econometric methods like multivariate probit and ordered probit modeling, while the qualitative data was presented through content analysis. The result indicated that more than half of respondents (67 %) applied one or two LMTs. The highest complementary effects were observed in mixed soil bunds with desho grasses and manure applications. However, soil bunds and fanya-juu, manure application and agroforestry showed interchangeability with one another. Sex, education, family size, landholding size, access to development agents and credit institutions, training, and village membership increased the probability of adopting multiple LMTs, whereas age, land rent, and crop sharing discouraged the likelihood of households' decisions to adopt LMT. The results of the ordered probit model revealed that village membership and contact with extension agents highly encouraged the intensity of LMT adoptions. Thus, policymakers and planners should consider social, institutional, human asset, and technological related factors to increase adoption rates and intensity of land management technologies.