Estimates Of Structural Parameters Research Articles

L-Band Synthetic Aperture Radar and Its Application for Forest Parameter Estimation, 1972 to 2024: A Review.

Optical remote sensing can effectively capture 2-dimensional (2D) forest information, such as woodland area and percentage forest cover. However, accurately estimating forest vertical-structure relevant parameters such as height using optical images remains challenging, which leads to low accuracy of estimating forest stocks like biomass and carbon stocks. Thus, accurately obtaining vertical structure information of forests has become a significant bottleneck in the application of optical remote sensing to forestry. Microwave remote sensing such as synthetic aperture radar (SAR) and polarimetric SAR provides the capability to penetrate forest canopies with the L-band signal, and is particularly adept at capturing the vertical structure information of forests, which is an alternative ideal remote-sensing data source to overcome the aforementioned limitation. This paper utilizes the Citexs data analysis platform, along with the CNKI and PubMed databases, to investigate the advancements of applying L-band SAR technology to forest canopy penetration and structure-parameter estimation, and provides a comprehensive review based on 58 relevant articles from 1978 to 2024 in the PubMed database. The metrics, including annual publication numbers, countries/regions from which the publications come, institutions, and first authors, with the visualization of results, were utilized to identify development trends. The paper summarizes the state of the art and effectiveness of L-band SAR in addressing the estimation of forest height, moisture, and forest stocks, and also examines the penetration depth of the L-band in forests and highlights key influencing factors. This review identifies existing limitations and suggests research directions in the future and the potential of using L-band SAR technology for forest parameter estimation.

Health professionals’ acceptance of mobile-based clinical guideline application in a resource-limited setting: using a modified UTAUT model

IntroductionClinical guidelines are crucial for assisting health professionals to make correct clinical decisions. However, manual clinical guidelines are not accessible, and this increases the workload. So, a mobile-based clinical guideline application is needed to provide real-time information access. Hence, this study aimed to assess health professionals’ intention to accept mobile-based clinical guideline applications and verify the unified theory of acceptance and technology utilization model.MethodsInstitutional-based cross-sectional study design was used among 803 study participants. The sample size was determined based on structural equation model parameter estimation criteria with stratified random sampling. Amos version 23 software was used for analysis. Internal consistency of latent variable items, and convergent and divergent validity, were evaluated using composite reliability, AVE, and a cross-loading matrix. Model fitness of the data was assessed based on a set of criteria, and it was achieved. P-value < 0.05 was considered for assessing the formulated hypothesis.ResultsEffort expectancy and social influence had a significant effect on health professionals’ attitudes, with path coefficients of (β = 0.61, P-value < 0.01), and (β = 0.510, P-value < 0.01) respectively. Performance expectancy, facilitating condition, and attitude had significant effects on health professionals’ acceptance of mobile-based clinical guideline applications with path coefficients of (β = 0.37, P-value < 0.001), (β = 0.44, P-value < 0.001) and (β = 0.57, P-value < 0.05) respectively. Effort expectancy and social influence were mediated by attitude and had a significant partial relationship with health professionals’ acceptance of mobile-based clinical guideline application with standardized estimation coefficients of (β = 0.22, P-value = 0.027), and (β = 0.19, P-value = 0.031) respectively. All the latent variables accounted for 57% of health professionals’ attitudes, and latent variables with attitudes accounted for 63% of individuals’ acceptance of mobile-based clinical guideline applications.ConclusionsThe unified theory of acceptance and use of the technology model was a good model for assessing individuals’ acceptance of mobile-based clinical guidelines applications. So, enhancing health professionals’ attitudes, and computer literacy through training are needed. Mobile application development based on user requirements is critical for technology adoption, and people’s support is also important for health professionals to accept and use the application.

Improving the Estimation of Structural Parameters of a Mixed Conifer–Broadleaf Forest Using Structural, Textural, and Spectral Metrics Derived from Unmanned Aerial Vehicle Red Green Blue (RGB) Imagery

Forest structural parameters are crucial for assessing ecological functions and forest quality. To improve the accuracy of estimating these parameters, various approaches based on remote sensing platforms have been employed. Although remote sensing yields high prediction accuracy in uniform, even-aged, simply structured forests, it struggles in complex structures, where accurately predicting forest structural parameters remains a significant challenge. Recent advancements in unmanned aerial vehicle (UAV) photogrammetry have opened new avenues for the accurate estimation of forest structural parameters. However, many studies have relied on a limited set of remote sensing metrics, despite the fact that selecting appropriate metrics as powerful explanatory variables and applying diverse models are essential for achieving high estimation accuracy. In this study, high-resolution RGB imagery from DJI Matrice 300 real-time kinematics was utilized to estimate forest structural parameters in a mixed conifer–broadleaf forest at the University of Tokyo Hokkaido Forest (Hokkaido, Japan). Structural and textual metrics were extracted from canopy height models, and spectral metrics were extracted from orthomosaics. Using random forest and multiple linear regression models, we achieved relatively high estimation accuracy for dominant tree height, mean tree diameter at breast height, basal area, mean stand volume, stem density, and broadleaf ratio. Including a large number of explanatory variables proved advantageous in this complex forest, as its structure is influenced by numerous factors. Our results will aid foresters in predicting forest structural parameters using UAV photogrammetry, thereby contributing to sustainable forest management.

Neural networks taking probability distributions as input: A framework for analyzing exchangeable networks

In recent years, exchangeable network structures that take datasets as input have been widely used to obtain representations of various datasets. Although they perform well, analyzing exchangeable network with a dataset as input is challenging. Given that this type of network can be viewed as a function acting on probability measures since datasets are drawn from various distributions, this paper theoretically analyzes exchangeable network structures from a probabilistic perspective. This paper proposes a probabilistic analytical framework that neural networks acting on probability measures, which is an extension of Multi-Layer Perceptrons (MLP). When only samples from each distribution are available, in this new analytical framework, neural networks acting on probability measures correspond to the traditional exchangeable structure defined on datasets. Using this new analytical framework, we can demonstrate the ability of exchangeable network structures to capture complex patterns, as it provides the universal approximation property of exchangeable network structures. Furthermore, we derive a consistency result that shows the parameter estimation of exchangeable network structures is consistent statistically under certain conditions.

A robust probabilistic approach to stochastic subspace identification

Modal parameter estimation of operational structures is often a challenging task when confronted with unwanted distortions (outliers) in field measurements. Atypical observations present a problem to operational modal analysis (OMA) algorithms, such as stochastic subspace identification (SSI), severely biasing parameter estimates and resulting in misidentification of the system. Despite this predicament, no simple mechanism currently exists capable of dealing with such anomalies in SSI. Addressing this problem, this paper first introduces a novel probabilistic formulation of stochastic subspace identification (Prob-SSI), realised using probabilistic projections. Mathematically, the equivalence between this model and the classic algorithm is demonstrated. This fresh perspective, viewing SSI as a problem in probabilistic inference, lays the necessary mathematical foundation to enable a collection of new OMA approaches. To this end, a statistically robust SSI algorithm (robust Prob-SSI) is developed, capable of providing a principled and automatic way of handling outlying or anomalous data in the measured timeseries, such as may occur in field recordings, e.g. intermittent sensor dropout. Robust Prob-SSI is shown to outperform conventional SSI when confronted with ‘corrupted’ data, exhibiting improved identification performance and higher levels of confidence in the found poles when viewing stabilisation diagrams. Similar benefits are also demonstrated on the Z24 Bridge benchmark dataset, highlighting enhanced performance on measured systems.

Automated structural parameter estimation of semi-rigid complex joints in a benchmark laboratory steel grid by experimental modal analysis

The mechanical properties of joints may impact a system’s static and dynamic behavior. Since connections are usually complex, modeling their geometric details could take time and effort. Thus, joints of structures are often overlooked in model creation and calibration. Therefore, reliable analytical modeling of in-service structures requires accurate and efficient parameter estimation of the connections in their simplified models. However, joints are physically small parts of a system, and parameter estimation techniques may not be sufficiently sensitive to the variations of connections’ mechanical properties. This paper examines the finite element model updating of a laboratory steel grid focusing on the structural parameter estimation of its complex connections using modal data. The mechanical properties of the joints are parametrized by added mass and reduced rigidity. Therefore, several modified models with different combinations of heavier semi-rigid joints are developed. Each model is updated using two modal-based error functions, and the most representative updated model is selected. The results demonstrate how the grid modal outputs are influenced by updating the mass and stiffness of its connections. Moreover, mass and stiffness interactions of the grid joints in the parameter estimation procedure are illustrated. The updated models can efficiently simulate the structural behavior of the grid with increased confidence and reliability.

New Generalized Nadarajah Haghighi Distribution: Characterization and Applications

A new Generalized Nadarajah Haghighi (NGNH) distribution following the Exponential T-X (ETX) family was studied. Structural statistical properties and parameter estimation discussed. Simulation study was executed with impressive results. The applicability and usefulness of NGNH was demonstrated using lifetime data sets. The NGNH distribution was compared with other competing distributions.

A Group MCP Approach for Structure Identification in Non-Parametric Accelerated Failure Time Additive Regression Model

In biomedical research, identifying genes associated with diseases is of paramount importance. However, only a small fraction of genes are related to specific diseases among the multitude of genes. Therefore, gene selection and estimation are necessary, and the accelerated failure time model is often used to address such issues. Hence, this article presents a method for structural identification and parameter estimation based on a non-parametric additive accelerated failure time model for censored data. Regularized estimation and variable selection are achieved using the Group MCP penalty method. The non-parametric component of the model is approximated using B-spline basis functions, and a group coordinate descent algorithm is employed for model solving. This approach effectively identifies both linear and nonlinear factors in the model. The Group MCP penalty estimation exhibits consistency and oracle properties under regularization conditions, meaning that the selected variable set tends to have a probability of approaching 1 and asymptotically includes the actual predictive factors. Numerical simulations and a lung cancer data analysis demonstrate that the Group MCP method outperforms the Group Lasso method in terms of predictive performance, with the proposed algorithm showing faster convergence rates.

Nonlinear grey Bernoulli model with physics-preserving Cusum operator

The Cusum (cumulative sum) operator is a fundamental prerequisite for the nonlinear grey Bernoulli model. Traditionally, it is believed to visually identify the underlying dynamic pattern of the original time series. This paper presents the misconceptions concerning the Cusum operation and the over-optimization of the initial condition in the classical nonlinear grey Bernoulli model, both of which inspire the proposal of a physics-preserving Cusum operator. Under a state space framework, separable nonlinear least squares and nonlinear least squares are formulated to generate simultaneous estimates of structural parameters and initial condition. By combining the Cusum operators and parameter estimation methods, four modeling paradigms are generated and comprehensively compared. The simulation results show that (i) the physics-preserving Cusum outperforms the traditional Cusum and (ii) nonlinear least squares outperforms separable nonlinear least squares, especially in irregular sampling settings with large time intervals and high noise levels. Finally, the proposed approach is used to identify the underlying dynamics from short-term traffic flow data, and the results validate its effectiveness.

Physics-guided generative adversarial network for probabilistic structural system identification

Structural system identification (ID) is an important tool for many structural and infrastructure applications, such as structural health monitoring and structural model updating. A novel physics-guided generative adversarial network (PG-GAN) is proposed in this study for probabilistic structural parameter ID. The PG-GAN leverages the powerful distribution learning ability of GANs while also enabling physical awareness by incorporating additional generators for physical parameters and developing a physics-based loss function that links the data generator with the parameter generators through existing physics knowledge to guide the training of generative models. Two experiments are performed to validate the effectiveness of the PG-GAN for structural parameter ID and demonstrate its application in structural damage detection. The PG-GAN is shown to successfully capture the uncertainty inherent in structural parameter estimation and accurately locate and quantify structural damages in terms of probabilistic distributions of the damage extent. The main contributions of this study are threefold: (1) compared with traditional methods for structural parameter ID, the proposed PG-GAN provides an intelligent framework for probabilistic identification of structural parameters without the need for any assumption on the data distribution and does not require an initial guess of parameters; (2) unlike traditional data-driven GANs, the proposed PG-GAN provides a linkage between GAN and physics knowledge to enables physical awareness in generative model training; and (3) the proposed PG-GAN can serve as an intelligent diagnostic system to better inform the decision-making on structural health management as it can quantify the uncertainty in the structural damage extent.

Bayesian Vehicle Load Estimation, Vehicle Position Tracking, and Structural Identification for Bridges with Strain Measurement

Vehicle load estimation and health monitoring of bridges are of great importance for the health monitoring of bridge structure under vehicle loads. Traditional methods for the estimation of vehicle load require the positions of the vehicles. The vehicle position tracking is generally conducted in offline manner and requires the installation of additional sensors. To resolve these problems, we developed a Bayesian probabilistic approach for the online estimation of vehicle loads, vehicle positions, and structural parameters for bridges. The crux is to model the vehicle load vector as a modulated filtered Gaussian white noise due to the fact that the vehicle-bridge interaction forces are in essence the responses of the vehicle-bridge coupled system under the excitation of the road roughness described by Gaussian random field and the constant vehicle weights. Furthermore, the vehicle speed vector is introduced to track the unknown positions of vehicles. There are three appealing features in this approach. First, it allows the simultaneous estimation of vehicle loads, vehicle positions, and structural parameters in an online manner. Second, this method allows for time-varying vehicle speed tracking. Third, the proposed method is applicable to the case with multiple vehicles. Examples for the case where single/multiple vehicles pass across bridges with uniform speeds/variable speeds are presented to demonstrate the feasibility of the proposed method for vehicle load estimation, vehicle position tracking, and bridge structural identification using only strain measurements.

Synergy of random balance design method and intelligent optimization technique for model updating of the 4 MW offshore wind turbine benchmark

The technical challenges for model updating of real marine engineering structures include the extraction of modal parameters associated with incomplete measurement information and the correction of a large number of structural parameters. As it is difficult to verify the effectiveness of model updating methods for engineering applications, the representative models have to be adopted in finite element (FE) analysis or lab-scale tests, leading to the incomplete reflection of actual structural performances. Based on these factors, a practical model updating method is developed in this study to make full use of field measurement data from a 4 MW offshore wind turbine for the accurate estimation of structural parameters using the random balance designs-Fourier amplitude sensitivity test (RBD-FAST) strategy and an improved particle swarm optimization (IPSO). Leveraging the measured acceleration signals from offshore wind turbines under the conditions including operation, shutdown, collision and typhoon scenarios, the complex exponential decomposition method is applied to accurately extract the time-varying acceleration components (TVAC) for the construction of the frequency response function (FRF). Following that, RBD-FAST is implemented into IPSO with adaptive inertia weights and asynchronously varying learning factors to enable efficient selection of numerous updated physical parameters, thus improving time cost and computational accuracy. The correctness of the proposed method is verified by a numerical jacket platform model. Furthermore, the 4 MW offshore wind turbine benchmark is developed to assess the feasibility of the proposed model updating method using field measured data from different scenarios. Results show that the synergy of RBD-FAST and IPSO in the proposed method can accurately update parameters and minimize a maximum discrepancy of 0.8970% for the first-three orders of natural frequencies between the benchmark and the updated models in the collision scenario. Summarily, the present work provides a potential technique and practical engineering references for model updating of offshore wind turbines subject to harsh marine environments.

Identifying and approaching for obscured stairs

We present an autonomous approaching scheme for mobile robot traversing obstacle stairwells, which overcomes the restricted field of vision caused by obstacles. The scheme consists of three parts: stair localization, structural parameter estimation, and nearing the stair. We use LIDAR with full-angle and long-distance measurement capabilities to initially determine the location of the stairs, and estimate staircase parameters using a depth camera with intensive data. Then, we propose an algorithm to optimize the location of stairs for real-time correction of target point locations during approaching. Finally, we conducted tests to verify the accuracy of the estimated stair positions and structural parameters and performed a comprehensive experiment to demonstrate the effectiveness of the scheme, in which the robot approaches autonomously to the front of the stairs around multiple obstacles.

ELECTROTRANSPOT CHARACTERISTICS OF POLYANILINE-MODIFIED CATION EXCHANGE MEMBRANES IN SOLUTIONS OF NICKEL AND CHROMIUM SULFATES AND SULFURIC ACID

The electrotranspot and structural characteristics of the MK-40 and MF-4SK sulfocationic membranes modified with polyaniline in an electrodialysis apparatus were studied in solutions of nickel and chromium sulfates and sulfuric acid. The decrease in the conductivity of membranes and the increase in their diffusion permeability in solutions of all electrolytes after modification with polyaniline were estimated. The key effect of the charge of the counterion on the electrical conductivity of the initial and modified membranes was confirmed, and an unusual effect of reducing the electrical conductivity of the MF-4SK/PANI membrane with an increase in the concentration of a solution containing multi-charged counter-ion was found. The information on the effect of multi-charged ions on the structure of a homogeneous and heterogeneous membranes, obtained by the method of standard contact porometry, is supplemented by the estimation of transport and structural parameters of microheterogeneous model. Based on the analysis of the parameters of current-voltage curves in solutions of nickel and chromium sulfates and sulfuric acid, the prospects of using the polyaniline-modified membranes in the processes of electrodialysis treatment of acid solutions containing multi-charged ions are evaluated.

Implementation Aspects in Regularized Structural Equation Models

This article reviews several implementation aspects in estimating regularized single-group and multiple-group structural equation models (SEM). It is demonstrated that approximate estimation approaches that rely on a differentiable approximation of non-differentiable penalty functions perform similarly to the coordinate descent optimization approach of regularized SEMs. Furthermore, using a fixed regularization parameter can sometimes be superior to an optimal regularization parameter selected by the Bayesian information criterion when it comes to the estimation of structural parameters. Moreover, the widespread penalty functions of regularized SEM implemented in several R packages were compared with the estimation based on a recently proposed penalty function in the Mplus software. Finally, we also investigate the performance of a clever replacement of the optimization function in regularized SEM with a smoothed differentiable approximation of the Bayesian information criterion proposed by O’Neill and Burke in 2023. The findings were derived through two simulation studies and are intended to guide the practical implementation of regularized SEM in future software pieces.

The Effect of Market Orientation, HR Competence, Knowledge Management on Competitive Advantage, Mediated by Company Performance

Purpose: The condition of competition between coal mining contractors causes the company leaders to manage the company and try as optimally as possible to get a competitive advantage compared to other similar companies. This study discusses the relationship of Competitive Advantage built from Market Orientation variables, HR Competence, and Knowledge Management to the performance of coal mining contractors in a comprehensive manner. It explains the transformation process of Company Performance to Competitive Advantage. Research implications: A research population is a number of 37 Coal mining contractors in Indonesia, including employees who are directly involved in strategic decision making in the company, namely the Project Head, Head Office Manager, and Director as respondents. Based on these data, Smart PLS 4 software processes structural parameter estimates according to the relational model described in the study's conceptual framework. Results and conclusion: The results showed that Market Orientation, Knowledge Management and Company Performance have a significant effect on Competitive Advantage, Quality of Human Resources, Market Orientation and Knowledge Management also has a direct effect on Company Performance, but Quality of Human Resources has no significant effect on Competitive Advantage. While the Company's Performance is a Full Mediation of the Quality of Human Resources on Competitive Advantage. Originality/value: This research contributes to the development of RBV theory, especially the study of company performance on competitive advantage in a resource-based view perspective through market orientation, HR competence, knowledge management, and the practice of applying SECI knowledge to this research object, which is proven to be able to increase competitive advantage. Theoretical reference: The originality of the theoretical model in this study contributes to the development of the RBV theory, the implementation of the concept of Competitive Advantage regarding the integration of internal key resources between intangible and capability aspects of coal mining contractors.

Improving Estimation of Tree Parameters by Fusing ALS and TLS Point Cloud Data Based on Canopy Gap Shape Feature Points

Airborne laser scanning (ALS) and terrestrial laser scanning (TLS) are two ways to obtain forest three-dimensional (3D) spatial information. Due to canopy occlusion and the features of different scanning methods, some of the forest point clouds acquired by a single scanning platform may be missing, resulting in an inaccurate estimation of forest structure parameters. Hence, the registration of ALS and TLS point clouds is an alternative for improving the estimation accuracy of forest structure parameters. Currently, forest point cloud registration is mainly conducted based on individual tree attributes (e.g., location, diameter at breast height, and tree height), but the registration is affected by individual tree segmentation and is inefficient. In this study, we proposed a method to automatically fuse ALS and TLS point clouds by using feature points of canopy gap shapes. First, the ALS and TLS canopy gap boundary vectors were extracted by the canopy point cloud density model, and the turning or feature points were obtained from the canopy gap vectors using the weighted effective area (WEA) algorithm. The feature points were then aligned, the transformation parameters were solved using the coherent point drift (CPD) algorithm, and the TLS point clouds were further aligned using the recovery transformation matrix and refined by utilizing the iterative closest point (ICP) algorithm. Finally, individual tree segmentations were performed to estimate tree parameters using the TLS and fusion point clouds, respectively. The results show that the proposed method achieved more accurate registration of ALS and TLS point clouds in four plots, with the average distance residuals of coarse and fine registration of 194.83 cm and 2.14 cm being much smaller compared with those from the widely used crown feature point-based method. Using the fused point cloud data led to more accurate estimates of tree height than using the TLS point cloud data alone. Thus, the proposed method has the potential to improve the registration of ALS and TLS point cloud data and the accuracy of tree height estimation.

Increasing marginal costs, firm heterogeneity, and the gains from “deep” international trade agreements

Two parameters are central to modern quantitative models of trade flows: the elasticity of substitution in consumption (σ) and the inverse index of heterogeneity of firms' productivities (θ). However, structural parameter estimation using the seminal Feenstra econometric methodology focuses on estimates of only σ and a bilateral export-supply elasticity (labeled γ). Separately, modern trade agreements are increasingly “deep,” meaning they reduce fixed trade costs alongside variable trade costs. First, in the spirit of Arkolakis (2010), we extend the Melitz model of trade to allow for increasing marginal market-penetration costs in an empirically tractable manner to help understand the relative impacts on trade, extensive margins, intensive margins, and welfare of reducing fixed trade costs and variable trade costs. Second, we provide a microeconomic foundation for estimating all three parameters using the Feenstra methodology alongside a gravity equation. Third, we demonstrate the importance of increasing marginal costs for shallow and deep trade-agreement liberalizations using two counterfactual exercises.

Recent Applications of System Identification Techniques at IPEV

This paper presents an overview of the system identification techniques developed and applied at the Flight Test and Research Institute during the last decade. System identification techniques have been used extensively to obtain aerodynamic models for a flight-test simulator, which was developed in house and specifically tailored for flight-test instruction and preparation for actual test flights. The methods applied were mostly the output error for data compatibility check and equation error in the time domain for model structure selection and parameter estimation. The most relevant applications are summarized in this paper, including identification of a high angle of attack model with compressibility effects for a jet trainer; estimation of drag polars at high subsonic regime for a jet fighter; identification of aerodynamic model in real-time for a propeller-driven fighter; application of flight-path reconstruction techniques to spin tests, in order to correct errors in the air data sensors; and identification of power effects on the longitudinal stability of a propeller-driven trainer. The examples show that system identification has become crucial to 1) obtain accurate aerodynamic models for training in the flight-test simulator and 2) develop more efficient flight-test techniques.

Economic aspects of urban greenness along a dryland rainfall gradient: A time-series analysis

Studies at the global scale show that urban greenness is not equally distributed across and within cities. Yet, quantification of urban greening trends in drylands is still lacking. We have modeled urban greenness dynamics and its determinants, using Landsat-based time-series analyses of NDVI and census data, of 23 localities along a dryland climatic gradient in southern Israel, between 1997 and 2019. NDVI trends and their associated temporal changes in local average wages, age of locality, and average annual rainfall were analyzed by exploiting a panel structure and model parameters estimation with fixed effects to control for unobserved differences between localities, and to strengthen causal interpretation. Results show positive NDVI trends in all localities—indicating that urban construction increases greenness in drylands. However, the localities varied greatly in the slopes of their respective linear NDVI trends (0.05 <α < 0.161). Specifically, we found that, ceteris paribus, the differences in average wages between localities is the most important factor in explaining spatio-temporal differences between the respective NDVI of localities. Given the well-known high correlation between individuals’ wages and other characteristic such as education and income (all important determinants of the Socio-Economic classification of local authorities), this leads us to conclude that residents’ economic characteristics are an important predictor of the level of greenness. Harlin’s Granger causality test for wages and NDVI panel data indicate that wage is a Granger cause of urban greenness (p < 0.001)—however, greenness was not found to be the driving force of wage.