Reliable Method For Estimation Research Articles

Sustainability assessment of the micellar optimized HPLC method for concomitant quantification of a common triple regimen for cardiovascular disorders: application to marketed formulations and in vitro dissolution testing.

Hypertension and dyslipidemia are two of the most frequently co-occurring cardiovascular risk factors. The combined regimen of hydrochlorothiazide (HCTZ), rosuvastatin (ROS), and losartan (LOS) helped in the successful management of both conditions. This work's objective is to develop an eco-friendly, sensitive, simple, and reliable chromatographic method for the simultaneous estimation of HCTZ, ROS, and the LOS ternary mixture in their pure form, and pharmaceutical formulations. Utilizing sodium dodecyl sulphate (SDS) in combination with polyoxyethylene-23-lauryl ether (Brij-35) could be a greener option with minimal environmental impact in liquid chromatographic separation techniques. Therefore, a micellar HPLC protocol was optimized and validated employing a mobile phase comprising a mixture of micelles of SDS (0.15 mol L-1) and Brij-35 (0.03 mol L-1) with 0.01 M monobasic potassium phosphate buffer, pH 3.5. The cited drugs were detected at 230 nm within five minutes, using a symmetry RP-C18 column, an isocratic elution mode, and a flow speed of 1 mL min-1. Method validation was successfully performed as stated by ICH. The suggested HPLC approach was effectively applied to determine the mentioned drugs in three pharmaceutical preparations. It was also employed for monitoring their in vitro release pattern using two innovator tablet formulations, applying the USFDA guidelines. The applied procedure was evaluated and compared with previously published HPLC approaches using various metrics, including the Eco-Scale Scoring method (AES), the Green Analytical Procedure Index (GAPI), and the Analytical Greenness Calculator (AGREE) for greenness, the most current Blue Applicability Grade Index (BAGI) for blueness, and the more inclusive RGB 12 algorism for whiteness assessment.

A Novel Lie Group-based Reliable IMM Estimation Method for SINS/GNSS/OD/NHC Integrated Navigation in Complex Environments

A Novel Lie Group-based Reliable IMM Estimation Method for SINS/GNSS/OD/NHC Integrated Navigation in Complex Environments

Reliable Disparity Estimation Using Multiocular Vision with Adjustable Baseline.

Accurate estimation of three-dimensional (3D) information from captured images is essential in numerous computer vision applications. Although binocular stereo vision has been extensively investigated for this task, its reliability is conditioned by the baseline between cameras. A larger baseline improves the resolution of disparity estimation but increases the probability of matching errors. This research presents a reliable method for disparity estimation through progressive baseline increases in multiocular vision. First, a robust rectification method for multiocular images is introduced, satisfying epipolar constraints and minimizing induced distortion. This method can improve rectification error by 25% for binocular images and 80% for multiocular images compared to well-known existing methods. Next, a dense disparity map is estimated by stereo matching from the rectified images with the shortest baseline. Afterwards, the disparity map for the subsequent images with an extended baseline is estimated within a short optimized interval, minimizing the probability of matching errors and further error propagation. This process is iterated until the disparity map for the images with the longest baseline is obtained. The proposed method increases disparity estimation accuracy by 20% for multiocular images compared to a similar existing method. The proposed approach enables accurate scene characterization and spatial point computation from disparity maps with improved resolution. The effectiveness of the proposed method is verified through exhaustive evaluations using well-known multiocular image datasets and physical scenes, achieving superior performance over similar existing methods in terms of objective measures.

Examination of Scales for Test Anxiety with Thematic Content Analysis

This study aims to examine the descriptive and psychometric properties of test anxiety scales in Türkiye through thematic content analysis. A total of 12 scales were obtained by means of the review of studies conducted since 2013. Two studies were not evaluated because they failed to meet the requisite criteria. Document analysis was used as the data collection method. Many of the scales examined consisted of articles. The majority of scale development and adaptation studies were conducted between 2016 and 2018. Most of the scales were of the Likert-type and the number of items ranged from 11 to 30. The multidimensional structure of test anxiety was not considered in some scales, while a number of scales were developed to measure the unidimensional structure of test anxiety. In reliability estimates, the Cronbach alpha coefficient was calculated for all scales, but different reliability estimation methods were also used, including Composite reliability, Spearman-Brown Split half reliability, and test-retest. Cronbach's alpha coefficient was calculated as a minimum of 0.70 and a maximum of 0.94. When the validity data of the scales were reviewed, it was observed that construct validity was the most frequently examined construct, followed by criterion validity. However, content validity was not investigated in any of the studies. Findings regarding EFA and CFA values in the studies on construct validity and the scales used in the studies on criterion validity are presented. At the same time, the analysis of the psychometric properties revealed certain deficiencies, and suggestions were made accordingly.

Quantitative relationships between Munsell colour attributes and organic carbon in highly weathered tropical soils

Soil organic carbon (SOC) is a very important parameter for assessing the quality of agricultural soils. However, the availability and use of such data by resource-poor farmers, especially in Sub-Saharan Africa, remains a major challenge due to the financial and time constrains involved. Thus, there is need to assess and adopt reliable methods for the rapid estimation of soil organic carbon content by indigenous farmers and field users. The objective of this study was to evaluate the quantitative relationships between soil organic carbon and Munsell colour attributes (value and chroma) in highly weathered tropical soils of the Northwestern Highlands of Cameroon. Forty-six soil profiles (28 Acrisols and 18 Ferralsols), including 46 surface (A) horizons and 181 subsurface horizons (Bo, Bt) were used. Soil organic carbon data and Munsell colour attributes, obtained through standard procedures, were subjected to descriptive statistical, correlation, regression and principal components analyses, in order to evaluate the relationships existing between SOC and Munsell colour attributes. In general, there were negative and significant (p < 0.001) correlations between SOC and all Munsell colour attributes (chroma, value, value + chroma, and value +0.5 chroma). The best models relating SOC and Munsell colour attributes were logarithmic models, with soil colour explaining >70 % of the variance. The results indicate that SOC in highly weathered tropical soils can be conveniently estimated using Munsell soil colour attributes (value + chroma). Better estimates were obtained using logarithmic models for surface (A horizon) soil samples having a sand content >50 %.

Assessing lead fraction derived from passive microwave images and improving estimates at pixel-wise level

Passive microwave remote sensing provides unique pan-Arctic light- and cloud-independent daily coverage of lead fraction (LF) for Arctic winter and spring. In this study, we conducted a quantitative assessment of various sea ice concentration (SIC) data products and LF retrieval algorithms to evaluate their accuracy in deriving lead fractions at both overall and pixel-wise levels. Our results indicate that SIC data products are not sensitive to refrozen leads in winter but tend to display clear lead structures in spring. However, the absolute SIC values differ significantly from LF and cannot be directly used as a proxy. As for the LF retrieval algorithms, we proved that the overall accuracy can be largely improved by adjusting upper tie-points. To further minimize errors, we developed an Artificial Neural Network model that outperformed conventional approaches at the pixel-wise level, offering a more reliable estimation method for absolute fraction values.

Construction and Demolition Waste Generation Prediction by Using Artificial Neural Networks and Metaheuristic Algorithms

In the actual estimation of construction and demolition waste (C&amp;DW), it is significantly relevant to effective management, design, and planning at project stages, but the lack of reliable estimation methods and historical data prevents the estimation of C&amp;DW quantities for both short- and long-term planning. To address this gap, this study aims to predict C&amp;DW quantities in construction projects more accurately by integrating the gray wolf optimization algorithm (GWO) and the Archimedes optimization algorithm (AOA) into an artificial neural network (ANN). This study uses data concerning the actual quantities of work in 200 real-life construction and demolition projects performed in the Gaza Strip. Different performance parameters, such as mean absolute error (MAE), mean square error (MSE), root mean squared error (RMSE), and the coefficient of determination (R2), are used to evaluate the effectiveness of the models developed. The results of this study have shown that the AOA-ANN model outperforms the other models in terms of accuracy (R2 = 0.023728, MSE = 0.00056304, RMSE = 0.023728, MAE = 0.0086648). Moreover, this new hybrid model yields more accurate estimations of C&amp;DW quantities with minimal input parameters, making the process of estimation more feasible.

Distilling Knowledge in Machine Translation of Agglutinative Languages with Backward and Morphological Decoders

Agglutinative languages often have morphologically complex words(MCWs) composed of multiple morphemes arranged in a hierarchical structure, posing significant challenges in translation tasks. We present a novel Knowledge Distillation approach tailored for improving the translation of such languages. Our method involves an encoder, a forward decoder, and two auxiliary decoders: a backward decoder and a morphological decoder. The forward decoder generates target morphemes autoregressively and is augmented by distilling knowledge from the auxiliary decoders. The backward decoder incorporates future context, while the morphological decoder integrates target-side morphological information. We have also designed a reliability estimation method to selectively distill only the reliable knowledge from these auxiliary decoders. Our approach relies on morphological word segmentation. We show that the word segmentation method based on unsupervised morphology learning outperforms the commonly used Byte Pair Encoding method on highly agglutinative languages in translation tasks. Our experiments conducted on English-Tamil, English-Manipuri, and English-Marathi datasets show that our proposed approach achieves significant improvements over strong Transformer-based NMT baselines.

Inverse Open Circuit Voltage Curve Model for LiCoO2 Battery at Different Temperatures

Lithium-ion batteries are widely used in a variety of applications. For effective battery management, accurate estimation of the state of charge (SOC) is essential. One of the most commonly employed methods for SOC estimation relies on the open circuit voltage (OCV) curve with respect to SOC. However, inverting the OCV-SOC function is not always straightforward. This paper proposes a novel analytical function that directly models the inverse OCV-SOC function, providing a more efficient and reliable method for SOC estimation. Moreover, the dependency of the proposed function on battery temperature is also being investigated, allowing for a wider application of the method under different OCV measuring conditions.

Model Optimization and Application of Straw Mulch Quantity Using Remote Sensing

Straw mulch quantity is an important indicator in the detection of straw returned to the field in conservation tillage, but there is a lack of large-scale automated measurement methods. In this study, we estimated global straw mulch quantity and completed the detection of straw returned to the field. We used an unmanned aerial vehicle (UAV) carrying a multispectral camera to acquire remote sensing images of straw in the field. First, the spectral index was selected using the Elastic-net (ENET) algorithm. Then, we used the Genetic Algorithm Hybrid Particle Swarm Optimization (GA-HPSO) algorithm, which embeds crossover and mutation operators from the Genetic Algorithm (GA) into the improved Particle Swarm Optimization (PSO) algorithm to solve the problem of machine learning model prediction performance being greatly affected by parameters. Finally, we used the Monte Carlo method to achieve a global estimation of straw mulch quantity and complete the rapid detection of field plots. The results indicate that the inversion model optimized using the GA-HPSO algorithm performed the best, with the coefficient of determination (R2) reaching 0.75 and the root mean square error (RMSE) only being 0.044. At the same time, the Monte Carlo estimation method achieved an average accuracy of 88.69% for the estimation of global straw mulch quantity, which was effective and applicable in the detection of global mulch quantity. This study provides a scientific reference for the detection of straw mulch quantity in conservation tillage and also provides a reliable model inversion estimation method for the estimation of straw mulch quantity in other crops.

Sustainable Stabilizer Derived from Calcium- and Phosphorus-Rich Biowaste for Remediation of Heavy Metal-Contaminated Soil: A Critical Review

Soil pollution by heavy metals (HMs) is a major environmental problem around the world. The addition of biowaste-based stabilizers for HM remediation has recently gained attention due to its relatively low cost and eco-risk, abundance, ease of operation, and quick remediation results. Among these stabilizers, shell (crustacean shell, bivalve shell, and eggshell), starfish, and bone-based stabilizers are particularly attractive because of their high Ca and P contents, allowing for highly efficient HM immobilization and simultaneous supplement of nutrients to the soil. However, a comprehensive review focusing on these stabilizers is currently missing. Therefore, this review attempts to summarize the HM immobilization efficiency of these stabilizers and the mechanisms associated with HM stabilization, and perform an operation cost estimation and cost comparison. Cost comparisons among different stabilizers are widely ignored in reviews due to the lack of reliable cost estimation tools or methods. However, for practical application in soil remediation, cost is one of the most important factors to consider. Thus, a simple but reasonable cost estimation method is developed and discussed in this review. Bivalve shell-based stabilizers demonstrated the most promising results for the immobilization of soil HMs in terms of higher performance and lower cost. Current research limitations, challenges, and recommendations regarding possible future research directions are also provided.

Current Progress of EUV Spectral Responsivity Calibration for EUV Lithography at CMS/ITRI

Abstract To support the development of advanced EUV lithography for the semiconductor industry in Taiwan, a calibration system for photodiodes’ spectral responsivity was established. The current system utilizes the synchrotron radiation light source and the method is traceable to PTB, Germany. The wavelength range is from 10 nm to 15 nm, including the most often used 13.5 nm. Several techniques were studied to compensate for light source fluctuation and to reduce the measurement uncertainty. The relative expanded uncertainty of the spectral responsivity calibration at 13.5 nm is 4.6 % (k=2). A wafer-type EUV radiant power meter designed to be used in EUV lithography chambers is being developed. Our goal is to develop simple and reliable methods for on-site EUV optical power measurement and dose estimation.

Age Estimation through Hounsfield Unit Analysis of Pelvic Bone in the Romanian Population.

Background: Bone density is affected by age- and sex-related changes in the os coxae, often known as the pelvic bone. Recent developments in computed tomography (CT) imaging have created new opportunities for quantitative analysis, notably regarding Hounsfield Units (HU). Objectives: The study aims to investigate the possibility of using HU obtained from os coxae CT scans to estimate age in the Romanian population. Methods: A statistical analysis was conducted on a sample of 80 pelvic CT scans in order to find any significant correlation between age, sex, and variation in density among the different pelvic bone locations of interest. According to the research, pelvic radiodensity measurements varied significantly between male and female participants, with men having greater levels. This technique may be valuable for determining an individual's sex precisely, as evidenced by the substantial association found between HU levels and changes in bone density associated with sex. Results: The analysis of variance underscores that HU values exhibit a significant negative relationship with radiodensity, with a general trend of decreasing HU with increasing age. The equation derived from the ordinary least squares OLS regression analysis can be used to estimate the age of individuals in the Romanian population based on their HU values at specific pelvic sites. Conclusions: In conclusion, the application of HU analysis in CT imaging of the coxae represents a non-invasive and potentially reliable method for age and sex estimation, and a promising avenue in the field of human identification.

Calibration and Evaluation of Alternative Methods for Reliable Estimation of Reference Evapotranspiration in South Korea

Calibration and Evaluation of Alternative Methods for Reliable Estimation of Reference Evapotranspiration in South Korea

Establishing legal threshold of 18-years based on the assessment of mandibular molars using three different methods - An observational study.

The study evaluates the feasibility of employing the radiographic visibility of the root pulp and periodontal ligament in mandibular molars for age estimation, particularly focusing on the 18 years of age threshold. This study additionally investigates the potential of root canal width reduction in mandibular molars, as a reliable method for forensic age estimation in living individuals. A cross-sectional study was conducted to assess the radiographic visibility of the root pulp (RPV) and the root canal width (RCW) of mandibular first, second, and third molars along with the radiographic visibility of the periodontal ligament (PLV) of mandibular third molars, in a sample of 403 individuals aged 16-25 years (220 males and 183 females). Data regarding age for different stages of RPV and PLV and various types of RCW were recorded and observed for sex-based differences. Results obtained were tabulated and descriptive statistics were applied to summarise the findings. Individuals over 18 years old were classified with higher accuracy using stage 3 of the RPV scoring system in all mandibular molars (first, second, and third) compared to stage 2, which was also effective for the second and third molars. This result held regardless of sex and side examined. Additionally, root canal width (RCW) assessment demonstrated that individuals with RCW types A, B, and C were more likely to be under 18 years old in both sexes. Conversely, individuals with RCW type U on the right side for males and the left side for females exhibited a higher likelihood of being above 18 years old. The study suggests that the assessment of mandibular molars could potentially serve as an auxiliary tool in age estimation methods, particularly for approximating individuals around the 18 years of age threshold. Further investigation is warranted to explore the potential application of root canal width measurements in forensic age estimation.

STATISTICAL INFERENCE ON SINE-EXPONENTIAL DISTRIBUTION PARAMETER

The Sine-Exponential (Sine-E) distribution is a probability distribution that combines the periodic behavior of the sine function with the decay characteristic of the exponential function. This study addresses the problem of identifying the most accurate and reliable estimation method for the parameter of the Sine-E distribution. The objective is to evaluate various parameter estimation techniques, including Maximum Likelihood Estimation (MLE), Least Squares Estimation (LSE), Weighted Least Squares Estimation (WLSE), Maximum Product of Spacing Estimation (MPSE), Cramer-von-Mises Estimation (CVME), and Anderson-Darling Estimation (ADE), using Mean Square Error (MSE) as the criterion for determining the technique with the minimum error. The study’s findings reveal that as sample size increases, the parameter estimates for all techniques converge to the true parameter value, with decreases in bias, MSE, and mean relative estimates. Among the techniques evaluated, the MPSE method consistently provides estimates closest to the true parameter value and exhibits the least bias and lowest MSE across small, moderate, and large sample sizes, making it the best estimator for the Sine-E distribution.

Contrastive fine-grained domain adaptation network for EEG-based vigilance estimation

Vigilance state is crucial for the effective performance of users in brain-computer interface (BCI) systems. Most vigilance estimation methods rely on a large amount of labeled data to train a satisfactory model for the specific subject, which limits the practical application of the methods. This study aimed to build a reliable vigilance estimation method using a small amount of unlabeled calibration data. We conducted a vigilance experiment in the designed BCI-based cursor-control task. Electroencephalogram (EEG) signals of eighteen participants were recorded in two sessions on two different days. And, we proposed a contrastive fine-grained domain adaptation network (CFGDAN) for vigilance estimation. Here, an adaptive graph convolution network (GCN) was built to project the EEG data of different domains into a common space. The fine-grained feature alignment mechanism was designed to weight and align the feature distributions across domains at the EEG channel level, and the contrastive information preservation module was developed to preserve the useful target-specific information during the feature alignment. The experimental results show that the proposed CFGDAN outperforms the compared methods in our BCI vigilance dataset and SEED-VIG dataset. Moreover, the visualization results demonstrate the efficacy of the designed feature alignment mechanisms. These results indicate the effectiveness of our method for vigilance estimation. Our study is helpful for reducing calibration efforts and promoting the practical application potential of vigilance estimation methods.

Incorporating Honey Badger Algorithm in Estimating Gamma Distribution With Application to Stock Price Modelling

This study evaluates the performance of various estimation methods in stock price analysis across diverse parameters, focusing on the Honey Badger Algorithm (HBA). The purpose is to determine the most accurate and reliable method for parameter estimation. Methodologically, we analyze data spanning eight years from publicly traded Malaysian property companies, employing financial metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Our findings highlight HBA’s consistent precision in parameter estimation, with values closely aligning with initial parameters across different stock sizes. For example, HBA-Gamma model achieves an MAE of 0.0592 and an RMSE of 0.8458 for 13 stocks, demonstrating its proficiency in capturing stock price distributions in dynamic markets. In contrast, the Artificial Immune System (AIS) provides reasonable estimates but with higher variability. The Regression Method exhibits mixed outcomes, displaying accuracy in some cases but notable variability and reduced precision, especially with larger datasets. The Moment Method, while adequate, shows slightly higher variance compared to both HBA and AIS. Further analysis using Log Likelihood values confirms HBA’s superior fit to the data, consistently surpassing AIS, Regression Method, and Moment Method in likelihood maximization across various stock numbers. Specifically, HBA exhibits lower MAE and RMSE values of 0.1034 and 0.06723, respectively, for 26 stocks, further validating its effectiveness in parameter estimation and stock price prediction. These findings underscore the importance of integrated approaches that account for market nuances rather than relying solely on individual model forecasts. The results affirm HBA’s potential for informed investment decision-making, emphasizing its robust performance and enhanced predictive capabilities compared to alternative methodologies. However, further research is needed to assess the generalizability of these findings to other markets and contexts.

Intervention and adaptation options for measuring brand trust in the context of its dimensionality with an emphasis on the generational context

Objective: This article aims to analyse the model of perception of trust in the selectedbrand (Subway) with the help of the modified tool “Perceived Brand Trust”, and to investigatethe difference in generational perception of trust in the investigated brand.Methodology: In this article, we use statistical methods such as elements of descriptive statisticsand inductive statistics with an emphasis on confirmatory factor analysis, reliabilityestimation methods, and one-way ANOVA.Results: The result is a modification of the three-dimensional brand trust perception modeland a demonstration of its use in practice.Limitations: Limitations can be observed in self-collection and the use of respondents’personal reflection in the context of brand trust.Practical implications: The article has academic, theoretical and practical benefits. Thepractical benefit can be seen in the creation and verification of a model for measuringbrand trust perception, which can be applied to any brand.

Sampling and active learning methods for network reliability estimation using K-terminal spanning tree

Network reliability analysis remains a challenge due to the increasing size and complexity of networks. This paper presents a novel sampling method and an active learning method for efficient and accurate network reliability estimation under node failure and edge failure scenarios. The proposed sampling method adopts Monte Carlo technique to sample component lifetimes and the K-terminal spanning tree algorithm to accelerate structure function computation. Unlike existing methods that compute only one structure function value per sample, our method generates multiple component state vectors and corresponding structure function values from each sample. Network reliability is estimated based on survival signatures derived from these values. A transformation technique extends this method to handle both node failure and edge failure. To enhance efficiency of proposed sampling method and achieve adaptability to network topology changes, we introduce an active learning method utilizing a random forest (RF) classifier. This classifier directly predicts structure function values, integrates network behaviors across diverse topologies, and undergoes iterative refinement to enhance predictive accuracy. Importantly, the trained RF classifier can directly predict reliability for variant networks, a capability beyond the sampling method alone. Through investigating several network examples and two practical applications, the effectiveness of both proposed methods is demonstrated.