Estimation Method Research Articles

AteMeVs: An R package for the estimation of the average treatment effect with measurement error and variable selection for confounders.

In causal inference, the estimation of the average treatment effect is often of interest. For example, in cancer research, an interesting question is to assess the effects of the chemotherapy treatment on cancer, with the information of gene expressions taken into account. Two crucial challenges in this analysis involve addressing measurement error in gene expressions and handling noninformative gene expressions. While analytical methods have been developed to address those challenges, no user-friendly computational software packages seem to be available to implement those methods. To close this gap, we develop an R package, called AteMeVs, to estimate the average treatment effect using the inverse-probability-weighting estimation method to handle data with both measurement error and spurious variables. This developed package accommodates the method proposed by Yi and Chen (2023) as a special case, and further extends its application to a broader scope. The usage of the developed R package is illustrated by applying it to analyze a cancer dataset with information of gene expressions.

A sustainable RP-HPLC method for concurrent estimation of capecitabine and celecoxib in liposomal formulation: Greenness and whiteness appraisal.

Liposomes have been reported for combination therapy due to their ability to carry both hydrophilic and lipophilic drugs together. The current investigation aims to develop a novel, eco-friendly, and sustainable reverse-phase high-performance liquid chromatography (RP-HPLC) method for the simultaneous quantification of capecitabine and celecoxib co-encapsulated in liposomes. The method reported herein uses a C18 column (4.6 × 250 mm2, 5 μm) and a mobile phase consisting of water, and acetonitrile/methanol in a ratio of 60:40, containing 0.1% formic acid in both the phases. The flow rate is maintained at 1 mL/min, with an injection volume of 10 μL in the gradient mode. Detection is set at λmax = 240 nm for capecitabine and 252 nm for celecoxib. The developed liposomes are mono-disperse with a surface potential of -6.93 mV. The average size of the liposomes is 142 nm. The percentage entrapment efficiency for capecitabine is 52.39 ± 0.94%, and for celecoxib, it is 77.13 ± 0.74%. The Analytical Greenness Metric of 0.61 and Analytical Eco-Scale Score of 75 signify the greenness of the developed method. Also, the Red-Green-Blue model shows excellent whiteness, with a score of 83.2. Thus, the developed method offers a reliable, accurate, precise, buffer-free, and environment-friendly RP-HPLC approach for the simultaneous analysis of capecitabine and celecoxib co-encapsulated in liposomes.

Fractal Characterization on Three-Dimensional Tortuosity of Fault Tectonic

Faults, as a kind of fracture tectonics, play a role in reservoir closure or provide oil and gas transportation channels. The accurate understanding of the distribution characteristics of faults is significant for oil and gas exploration. The traditional fractal dimension for fault number (Df3) cannot comprehensively characterize the complexity and heterogeneity of fault network distribution. In this paper, a fractal characterization method on three-dimensional (3D) tortuosity of fault tectonics is proposed based on 3D seismic exploration. The methodology is described in detail to establish the model on the fractal dimension for the 3D tortuosity of fault tectonics. The results show the proposed method of estimation of the DT3 displaying high accuracy and rationality. Compared with the traditional fractal dimension Df3, the proposed DT3 can comprehensively characterize the fractal characteristics of faults network systems in the 3D space. This study achieves a breakthrough in the fractal characterization of the 3D tortuosity of fault tectonics. It is worth further study for establishing an analytical fractal equation based on the DT3 and oil or gas transfer, which can provide the theoretical foundation and technical support for oil and gas exploration.

Comparison of Empirical Methods to Estimated Reference Evapotranspiration

Evapotranspiration plays an important role in agricultural water management and crop modelling. Estimating reference Evapotranspiration (ETo) using meteorological variables, both theoretical and empirical methods, is highly recommended considering the availability of weather data in several locations. The estimation method recommended as the standard method is FAO Penman Monteith (FAOPM), but due to the limited meteorological data in a region and the difficulty and complexity of FAOPM, it is recommended to use the empirical method which is easier and only requires a few simple meteorological variables. The aim of this research is to compare and evaluated empirical methods for estimating ETo against the FAOPM. The statistical analysis using in this research are RSME, MAE, coefficient Correlation, NSE, Average bias, index of agreement, and confidence index (c). Evaluation for the best models based on statistic analyzed shows that several empirical methods show terrible performance in estimating the monthly average ETo (mm/day), which are Thornthwaite-Mather, Hargraves-Samani, Makkink, Hamon, Romaneko, and Kharauffa. Modified Blaney-Criddle method showed a good performance method, while PMAWS showed very good performance The Turc and Hansen method showed excellent performance with RMSE, MAE, NSE, and C values ​​for the Turc method, are 0.12, 0.11, 0.78, 0.92 respectively, and for the Hansen method are 0.12, 0.1, 0.8, and 0.89 respectively.

Drive force allocation control based on pump displacement optimization for hydraulic hybrid motor-assisted system

To achieve rational coordination of the front and rear axle torque of commercial vehicles equipped with hydraulic hybrid motor-assisted systems (HHMAS), and further enhance the vehicle’s fuel efficiency and traction capability. A hydraulic pump displacement control strategy based on energy-efficiency-orientation (EEO) is proposed for driving force distribution. Firstly, the efficiency is analyzed from the perspective of system energy flow. Then, considering the time-varying efficiency characteristics of the hydraulic variable pump and hydraulic motor, the stochastic dynamic optimization problem is modeled as a markov decision process, and a value function estimation method is introduced to approximate the optimal energy efficiency of the entire HHMAS. Finally, the results indicate that the EEO strategy can enhance the fuel economy of vehicles, with a fuel-saving rate of 6.3% compared to the wheel-speed-following-control (WSFC) strategy. In addition, the proposed strategy exhibits superior traction capacity and climbing ability compared to the traditional WSFC strategy.

Enhancing Sex Estimation Accuracy with Cranial Angle Measurements and Machine Learning

The development of current sexing methods largely depends on the use of adequate sources of data and adjustable classification techniques. Most sex estimation methods have been based on linear measurements, while the angles have been largely ignored, potentially leading to the loss of valuable information for sex discrimination. This study aims to evaluate the usefulness of cranial angles for sex estimation and to differentiate the most dimorphic ones by training machine learning algorithms. Computed tomography images of 154 males and 180 females were used to derive data of 36 cranial angles. The classification models were created by support vector machines, naïve Bayes, logistic regression, and the rule-induction algorithm CN2. A series of cranial angle subsets was arranged by an attribute selection scheme. The algorithms achieved the highest accuracy on subsets of cranial angles, most of which correspond to well-known features for sex discrimination. Angles characterizing the lower forehead and upper midface were included in the best-performing models of all algorithms. The accuracy results showed the considerable classification potential of the cranial angles. The study demonstrates the value of the cranial angles as sex indicators and the possibility to enhance the sex estimation accuracy by using them.

Estimating Rainfall Intensity Using an Image-Based Convolutional Neural Network Inversion Technique for Potential Crowdsourcing Applications in Urban Areas

High-quality rainfall data are essential in many water management problems, including stormwater management, water resources management, and more. Due to the high spatial–temporal variations, rainfall measurement could be challenging and costly, especially in urban areas. This could be even more challenging in tropical regions with their typical short-duration and high-intensity rainfall events, as some of the undeveloped or developing countries in those regions lack a dense rain gauge network and have limited resources to use radar and satellite readings. Thus, exploring alternative rainfall estimation methods could be helpful to back up some shortcomings. Recently, a few studies have examined the utilisation of citizen science methods to collect rainfall data as a complement to the existing rain gauge networks. However, these attempts are in the early stages, and limited works have been published on improving the quality of such data. Therefore, this study focuses on image-based rainfall estimation with potential usage in citizen science. For this, a novel convolutional neural network (CNN) model is developed to predict rainfall intensity by processing the images captured by citizens (e.g., by smartphones or security cameras) in an urban area. The developed model is merely a complementary sensing tool (e.g., better spatial coverage) to the existing rain gauge network in an urban area and is not meant to replace it. This study also presents one of the most extensive datasets of rain image data ever published in the literature. The estimated rainfall data by the proposed CNN model of this study using images captured by surveillance cameras and smartphone cameras are compared with observed rainfall by a weather station and exhibit strong R2 values of 0.955 and 0.840, respectively.

Simulation of Gas‐Particle Partitioning of Semi‐Volatile n‐Alkanes and PAHs in Nanjing, China, and Denver, United States: Effects of Vapor Pressure and Surface Adsorption Estimation

AbstractThe concentration data of semi‐volatile n‐alkanes and PAHs in the gas phase and in PM2.5 were obtained from Nanjing and Denver. First, the gas‐particle partitioning coefficients of the target compounds were calculated and compared with the predictions based on the equilibrium absorptive partitioning theory. Although the vapor pressure (poL) estimation method was selected to improve the agreement between measured and predicted partitioning coefficients, n‐alkanes in Denver and PAHs in both cities exhibited stronger sorption to PM2.5 than predicted. By including the adsorption mechanism, the average logarithms and temporal variations of the partitioning coefficients were well simulated in both Nanjing and Denver. The partitioning of n‐alkanes in Nanjing can be primarily explained by the absorption mechanism, while the adsorption mechanism dominates that for n‐alkanes in Denver and PAHs in both cities. To obtain an optimal simulation, the poL of n‐alkanes and PAHs was estimated using the SPARC and SIMPOL—two group contribution methods, respectively, and the difference between desorption and vaporization enthalpies was set between 2 and 3 kcal mol−1 for n‐alkanes in Denver and PAHs in both cities. Therefore, the estimation of poL and surface adsorption should be carefully considered when parameterizing the gas‐particle partitioning of non‐polar organic compounds in future field and modeling studies.

Analytical Quality by Design (AQbD) Principles in Development and Validation of Stability‐Indicating RP‐HPLC Method for Simultaneous Estimation of Diltiazem HCl and Eugenol in Nanoethosomes

Analytical Quality by Design (AQbD) Principles in Development and Validation of Stability‐Indicating RP‐HPLC Method for Simultaneous Estimation of Diltiazem HCl and Eugenol in Nanoethosomes

Linear measurements of the mandible on panoramic radiograph for sex estimation in populations in Yogyakarta, Indonesia

BackgroundIdentification is one of the main aspects of forensics. Sex estimation is an essential part of identification because it can simplify the whole process. Mandible is the largest, strongest, and sexually dimorphic bone and is part of the orofacial structure, which has the second highest level of dimorphism after the pelvic bone. Sex estimation using the mandible can be performed by conducting various linear measurements on a panoramic radiograph, including right minimum ramus width (RMiRW), left minimum ramus width (LMiRW), right projective height of ramus (RPHR), left projective height of ramus (LPHR), right maximum ramus width (RMxRW), left maximum ramus width (LMxRW), right coronoid height (RCH), left coronoid height (LCH), symphysis height (SH), right mandibular corpus height (RMCH), and left mandibular corpus height (LMCH). This study aimed to analyze how linear measurements in panoramic radiographs differ between men and women and to estimate sex by utilizing these parameters.ResultsIn this study, 195 panoramic radiographs from 95 men and 100 women aged 20–40 years obtained from our dental hospital were used as a training data. Meanwhile, 61 panoramic radiographs from 29 men and 32 women outside the training data with same characteristics were used as the testing data. The linear measurements of the mandible using panoramic radiographs were taken with EzDent-i Vatech software. Independent t-test showed significant difference (p < 0.05) in some linear measurements of the mandible between adult men and women. These significantly different linear measurements were then subjected to discriminant function analysis to produce sex estimation equations. The equation accuracy percentage ranged between 63.6 and 94.4% for the training sample and 59.5% and 85% for the testing sample.ConclusionThe linear measurements of the mandible using diagnostically acceptable panoramic radiographs taken from a patient with standardized head positioning can serve as an alternative method for sex estimation. The accuracy of discriminant analysis for sex estimation varies depending on the parameter used in the estimation.

Comparison of the Wind Speed Estimation Algorithms of Wind Turbines Using a Drive Train Model and Extended Kalman Filter

To compare and validate wind speed estimation algorithms applied to wind turbines, wind speed estimators were designed in this study, based on two methods presented in the literature, and their performance was validated using the NREL 5MW model. The first method for wind speed estimation involves a three-dimensional (3D) look-up table-based approach, constructed using drive train differential equations. The second method involves applying a continuous–discrete extended Kalman filter. To verify and compare the performance of the algorithms designed using these different methods, feed-forward control algorithms, available power estimation algorithms, and a linear quadratic regulator, based on fuzzy logic (LQRF) control algorithms, were selected and applied as verification means, using the estimated wind speed as the input. Based on the simulation results, the performance of the two methods was compared. The method using drive train differential equations demonstrated superior performance in terms of reductions in the standard deviations of rotor speed and electrical power, as well as in its prediction accuracy for the available power.

Quantitative evaluation of Scout Accelerated Motion Estimation and Reduction (SAMER) MPRAGE for morphometric analysis of brain tissue in patients undergoing evaluation for memory loss

BackgroundThree-dimensional (3D) T1-weighted MRI sequences such as the magnetization prepared rapid gradient echo (MPRAGE) sequence are important for assessing regional cortical atrophy in the clinical evaluation of dementia but have long acquisition times and are prone to motion artifact. The recently developed Scout Accelerated Motion Estimation and Reduction (SAMER) retrospective motion correction method addresses motion artifact within clinically-acceptable computation times and has been validated through qualitative evaluation in inpatient and emergency settings. MethodsWe evaluated the quantitative accuracy of morphometric analysis of SAMER motion-corrected compared to non-motion-corrected MPRAGE images by estimating cortical volume and thickness across neuroanatomical regions in two subject groups: (1) healthy volunteers and (2) patients undergoing evaluation for dementia. In part (1), we used a set of 108 MPRAGE reconstructed images derived from 12 healthy volunteers to systematically assess the effectiveness of SAMER in correcting varying degrees of motion corruption, ranging from mild to severe. In part (2), 29 patients who were scheduled for brain MRI with memory loss protocol and had motion corruption on their clinical MPRAGE scans were prospectively enrolled. ResultsIn part (1), SAMER resulted in effective correction of motion-induced cortical volume and thickness reductions. We observed systematic increases in the estimated cortical volume and thickness across all neuroanatomical regions and a relative reduction in percent error values compared to reference standard scans of up to 66 % for the cerebral white matter volume. In part (2), SAMER resulted in statistically significant volume increases across anatomical regions, with the most pronounced increases seen in the parietal and temporal lobes, and general reductions in percent error relative to reference standard clinical scans. ConclusionSAMER improves the accuracy of morphometry through systematic increases and recovery of the estimated cortical volume and cortical thickness following motion correction, which may affect the evaluation of regional cortical atrophy in patients undergoing evaluation for dementia.

Efficient approximation of global population dynamic models through statistical inference using local data

Biological growth curves are pivotal in predicting natural growth across disciplines, typically analyzed using nonlinear least squares or maximum likelihood methods. Bhowmick et al. (2014) introduced the interval-specific rate of parameters (ISRP) for growth equations, improving the estimation of relative growth rate (RGR) and model selection accuracy. Despite its effectiveness, computing these model-specific RGR estimates involves complex calculations and lacks explicit expressions for many nonlinear models. Also, for highly nonlinear models and non-monotonic data where the parameters are non-linearly related, the computation of interval estimates is almost impossible and may suffer from significant approximation errors. So, the need for a more efficient computation method for ISRP remains a significant challenge in growth studies. In this article, we propose a computational approach to obtain interval estimates of parameters based on the maximum likelihood estimation method. The likelihood function is maximized using the data on smaller intervals. Our study underscores the importance of an efficient ISRP computation technique, providing a more stable, unbiased, and normally distributed estimator. The most important advantage is that it can be implemented using existing optimizers in software packages efficiently, therefore, giving more accessibility to the practitioners. Both simulation studies and real data analysis have been carried out to validate the proposed estimation process. Additionally, its applicability to non-monotonic growth profiles and its robustness in handling highly non-linear growth equations highlight its versatility. We also developed a web application GpEM-R which is freely available for researchers and practitioners to analyze growth data.

Full-Body Pose Estimation of Humanoid Robots Using Head-Worn Cameras for Digital Human-Augmented Robotic Telepresence

We envision a telepresence system that enhances remote work by facilitating both physical and immersive visual interactions between individuals. However, during robot teleoperation, communication often lacks realism, as users see the robot’s body rather than the remote individual. To address this, we propose a method for overlaying a digital human model onto a humanoid robot using XR visualization, enabling an immersive 3D telepresence experience. Our approach employs a learning-based method to estimate the 2D poses of the humanoid robot from head-worn stereo views, leveraging a newly collected dataset of full-body poses for humanoid robots. The stereo 2D poses and sparse inertial measurements from the remote operator are optimized to compute 3D poses over time. The digital human is localized from the perspective of a continuously moving observer, utilizing the estimated 3D pose of the humanoid robot. Our moving camera-based pose estimation method does not rely on any markers or external knowledge of the robot’s status, effectively overcoming challenges such as marker occlusion, calibration issues, and dependencies on headset tracking errors. We demonstrate the system in a remote physical training scenario, achieving real-time performance at 40 fps, which enables simultaneous immersive and physical interactions. Experimental results show that our learning-based 3D pose estimation method, which operates without prior knowledge of the robot, significantly outperforms alternative approaches requiring the robot’s global pose, particularly during rapid headset movements, achieving markerless digital human augmentation from head-worn views.

Multi-Stress Accelerated Degradation Testing Reliability Assessment of LED Lamp Beads Considering Generalized Coupling

Due to the complex operating environment of LED lamp beads (hereinafter referred to as LED), the test method that only considers the action of multiple stresses alone ignores the mutual influence between stresses, making it difficult to accurately obtain the life indicators of LEDs, resulting in a large deviation in the theoretical life results. In this regard, this paper proposes a multi-stress accelerated degradation evaluation method considering generalized coupling and conducts an accelerated degradation test (ADT) to evaluate the life of LEDs. We identified three stress sources and designed five new high-gradient ADTs. Through experimental data, we found that the three stress sources are strongly coupled on this LED. Then, a generalized coupling maximum likelihood estimation method (MLE) for the entire sample was constructed, and the particle swarm algorithm was used to solve the parameters. Finally, the life of this LED was evaluated based on the experimental data. The results show that the life of the LED considering multiple-stress coupling is within 6.5% of the historical life scatter point, which is more in line with the actual working environment.

Spectrum distribution‐based blind calibration method of mismatches for time‐interleaved ADCs

AbstractThis brief proposes a calibration method for offset, gain, and timing‐skew mismatches in time‐interleaved analog‐to‐digital converters. The calibration method includes the estimation method and the correction method. The estimation method is based on the difference between the spectral distribution of the error harmonics and the input signal. The correction method adopts sequential correction. Simulation results demonstrate that the proposed method calibrated all three mismatches simultaneously and eased the limit for input signals compared with the traditional method.

Clinical Significance of Pose Estimation Methods Compared with Radiographic Parameters in Adolescent Patients with Idiopathic Scoliosis

Clinical Significance of Pose Estimation Methods Compared with Radiographic Parameters in Adolescent Patients with Idiopathic Scoliosis

Inversion-based fuzzy adaptive control with prespecifiable tracking accuracy for uncertain hysteretic systems

Inversion-based control strategies have the outstanding effectiveness in the compensation for hysteresis nonlinearities. However, when the plant is driven by smart material-based actuator with the information absence of hysteresis output, there still exist technical gaps in the construction of hysteresis inverse controller. Therefore, this study aims to address this gap by proposing a novel control scheme. Technically, a novel hysteresis inverse algorithm, significantly reducing computational burden, has been proposed based on a novel adaptive estimation method for the unknown parameter (density function) in the Preisach-typed hysteresis nonlinearity. Furthermore, the inverse compensation error, the nonlinearities and uncertainties appeared in the controlled plant are accommodated by the innovative adaptive fuzzy feedback controller. With our scheme, it can ensure that the closed-loop stability, predefined steady-stated tracking performance and the convergence of algorithms including inverse algorithm and adaptive updating laws. In addition to theoretical analysis, we have validated the effectiveness of the proposed control scheme through simulation comparisons and experimental results conducted by the real-life piezoelectric actuator.

Timeliness-aware rumor sources identification in community-structured dynamic online social networks

Identifying rumor sources in online social networks (OSNs) plays a crucial role in controlling the spread of rumors and mitigating their damage. However, the community structure of OSNs and timeliness of rumors increase the complexity of accurately characterizing rumor-spreading behavior, making it extremely challenging to identify the source of rumor in OSNs. Conventional studies on rumor source identification often overlook the community structure of OSNs and timeliness of rumors. This paper proposes a rumor sources identification framework that take these two aspects into consideration. First, we transfer the snapshots of a dynamic OSN into a community-structured dynamic OSN through 4 meticulously designed phases. Second, instead of considering a constant influence of rumors in traditional techniques, we introduce a time-varying dynamic propagation parameter to quantify the timeliness of rumor, and apply the propagation parameter to establish a microscopic rumor spreading model. This process addresses the issue of modeling the timeliness of rumor. Third, we adopt sensor-based observation techniques to collect the propagation information of rumor, and a hybrid sensor deployment strategy is designed to improve the efficiency of information collection. Fourth, we propose an algorithm for identifying single and multiple rumor sources in community-structured dynamic OSN, this algorithm initiates with the utilization of some criteria to analyze the gathered propagation information and estimate the suspicious communities that harbor the source of rumors. Subsequently, it applies maximum likelihood estimation method within each community to determine the ultimate source of the rumor. Experimental results on real-world and synthetic networks indicate that our method can accurately identify the real rumor sources. To the best of our knowledge, the proposed method is the first that can be used to identify rumor sources in community-structured dynamic OSNs.

Ionic surfactants critical micelle concentration prediction in water/organic solvent mixtures by artificial neural network

Abstract Critical micellar concentration (CMC) is a key physicochemical property of surfactants used to study their behaviour. This property is affected by factors such as temperature, pressure, pH, the type of organic solvent/water mixture, the chemical structure of the surfactants and the presence of electrolytes. Most of the existing studies in the literature have predicted the CMC under fixed conditions based on the chemical parameters of the surfactant. In this study, a machine learning approach using artificial neural network (ANN) models was used to estimate the CMC of some ionic surfactants. These models considered variables defining both the organic solvent-water mixture (T, molecular weight, molar fraction and log P) and the chemical structure of the surfactant (number of atoms of each element). A database consisting of a total of 258 CMC values for 10 ionic surfactants was collected from the literature. The ANN architecture consisting of an input layer with 12 neurons, an intermediate layer with 25 neurons and one neuron in the output layer is proposed. According to the results, the normalized ANN models provided the best statistical adjustments for the CMC prediction. These ANN models could be a promising method for CMC estimation.