Estimation Method Research Articles

Defining Overweight and Obesity by Percent Body Fat instead of Body Mass Index.

Thresholds for overweight and obesity are currently defined by body mass index (BMI), a poor surrogate marker of actual adiposity (percent body fat, %BF). Practical modern technologies provide estimates of %BF but medical providers need outcome-based %BF thresholds to guide patients. This analysis determines %BF thresholds based on key obesity-related comorbidities, exhibited as metabolic syndrome (MetSyn). These limits were compared to existing BMI thresholds of overweight and obesity. Correlational analysis of data from cross sectional sampling of 16,918 adults (8,734 men and 8,184 women) from the US population, accessed by the National Health and Nutrition Examination Survey (NHANES) public use datasets. Individuals measured by BMI as overweight (BMI>25 kg/m2) and with obesity (BMI>30 kg/m2) included 5% and 35% of individuals with MetSyn, respectively. For men, there were no cases of MetSyn below 18%BF, %BF equivalence to "overweight" (i.e., 5% of MetSyn individuals) occurred at 25%BF, and "obesity" (i.e., 35% of MetSyn individuals) corresponded to 30%BF. For women, there were no cases of MetSyn below 30%BF, "overweight" occurred at 36%BF, and "obesity" corresponded to 42%BF. Comparison of BMI to %BF illustrates the wide range of variability in BMI prediction of %BF, highlighting the potential importance of using more direct measures of adiposity to manage obesity-related disease. Practical methods of body composition estimation can now replace the indirect BMI assessment for obesity management, using threshold values provided from this study. Clinically relevant "overweight" can be defined as 25 and 36% BF for men and women, respectively, and "obesity" is defined as 30 and 42% BF for men and women.

Estimation of fetal weight at term by clinical and sonographic assessment and its correlation with the birth weight – A prospective cohort study in a tertiary care hospital

Estimation of fetal weight is necessary to decide on the time and route of delivery. Different clinical and ultrasound methods for estimating fetal weight are followed by different institutions. Antenatal assessment of fetal weight is necessary to achieve better feto-maternal outcomes. The objectives of this study were: (a) fetal weight estimation by ultrasound (Hadlock’s formula) and clinical methods (Johnson’s formula and Dare’s formula) and (b) to compare them with the actual weight of the baby after birth. A prospective cohort study was conducted in a tertiary care centre at Puducherry, India including all singleton, term pregnant women with vertex presentation, normal AFI and BMI <35 kg/m who delivered within the next 48 hours of assessment. The agreement between the different methods of fetal weight estimation and actual birth weight were calculated using intraclass correlation coefficient. Bland and Altman plot was done to identify the intervals of agreements. A total of 400 term antenatal women with age range of 17 to 44 years participated in the study. Intraclass correlation coefficient for Johnson’s formula was 0.816, Dare’s formula was 0.672 and Hadlock’s formula was 0.912. All the three methods had statistically significant correlation with the actual birth weight. Ultrasound estimation of fetal weight is the best out of the three methods and it correlated well with the actual birth weight. Johnson’s formula also gave results close to actual birth weight. With a correlation coefficient comparable to that of ultrasound estimation, Johnson’s formula can be used in low resource settings where ultrasound facilities are not available.

THE EFFECT OF FOREIGN DIRECT INVESTMENT ON THE ECONOMIC DEVELOPMENT OF NIGERIA

The study examined the effect of Foreign Direct Investment (FDI) on development in Nigeria. The ultimate goal of developing countries is to attain development. Most studies on the effect of Foreign Direct Investment on the development of the Nigerian economy used growth proxied by GPP as a measure of development. This study however uses the Human Development Index (HDI) which is a complementary measure of development as it considers the three aspect of development (health, knowledge and standard of living). Current data from secondary data source. were used Secondary data were collected and OLS estimation method was used for the analysis of data. The result shows that the stock of FDI inflow has a positive and statistically significant effect on Economic development in Nigeria. Therefore to experience sustainable development, government should aim at providing infrastructure and conducive trade and investment environment for both foreign and local investors.

Comparative Analysis of Four Methods for Accurate Estimation of Soil Phosphorus Storage Capacity: a Case Study in a Typical Red Soil

Comparative Analysis of Four Methods for Accurate Estimation of Soil Phosphorus Storage Capacity: a Case Study in a Typical Red Soil

Sometimes simple is good enough: An analysis of methods for residential building population estimation

AbstractResidential building population data can be useful in a breadth of urban planning, health, transportation, and business applications. Unfortunately, complete datasets of residential building populations are not widely available for use in Canada, and therefore either larger census geographies are used or residential building populations must be estimated. This research explores four different methods of estimating residential building populations, including: an equal allocation method, two measures based on building volume, and a novel method that integrates census data at the dissemination area level to calibrate a population estimation model. This work comprises three parts: 1) a description of these approaches, 2) an evaluation of their validity in a case study in Hamilton, Ontario, and 3) an application of these methods in measuring spatial accessibility to schools. Our results show that most methods yield very similar results, and most provide reasonable estimates of building populations that could be useful for some analytical tasks. However, all methods resulted in instances of error, particularly for the largest population buildings. We conclude that while more complex methods do not significantly outperform simpler methods based on building volume alone, the blend of these methods could yield more accurate population predictions.

The Alternating Direction Method of Multipliers for Sufficient Dimension Reduction

The minimum average variance estimation (MAVE) method has proven to be an effective approach to sufficient dimension reduction. In this study, we apply the computationally efficient optimization algorithm named alternating direction method of multipliers (ADMM) to a particular approach (MAVE or minimum average variance estimation) to the problem of sufficient dimension reduction (SDR). Under some assumptions, we prove that the iterative sequence generated by ADMM converges to some point of the associated augmented Lagrangian function. Moreover, that point is stationary. It also presents some numerical simulations on synthetic data to demonstrate the computational efficiency of the algorithm.

Tomographic cross correlations between galaxy surveys and the CMB gravitational lensing potential. Effect of the redshift bin mismatch

Upcoming surveys of the large-scale structure of our Universe will employ a large coverage area of about half of the sky and will significantly increase the observational depth. With these surveys, we will be able to cross-correlate cosmic microwave background (CMB) gravitational lensing and galaxy surveys divided into narrow redshift bins to map the evolution of the cosmological parameters with redshift. We study the effect of the redshift bin mismatch of objects that is due to photometric redshift errors in tomographic cross-correlation measurements. We used the code FLASK to create Monte Carlo simulations of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) and Planck CMB lensing convergence. We simulated log-normal fields and divided galaxies into nine redshift bins with the Gaussian and modified Lorentzian photometric redshift errors. To estimate the parameters, we used angular power spectra of CMB lensing and galaxy density contrast fields and the maximum likelihood estimation method. We show that even with simple Gaussian errors with a standard deviation of $ the galaxy auto-power spectra in tomographic bins are offset by $2-15<!PCT!>$. The estimated cross-power spectra between galaxy clustering and CMB lensing are also biased, with smaller deviations $<5<!PCT!>$. As a result, the $ $ parameter deviates between $0.2-1.2\ due to the redshift bin mismatch of the objects. We propose a computationally fast and robust method based on the scattering matrix approach to correct for the redshift bin mismatch of the objects. The estimates of the parameters in tomographic studies such as the linear galaxy bias, the cross-correlation amplitude, and $ $ are biased due to the redshift bin mismatch of the objects. The biases in these parameters are alleviated with our scattering matrix approach.

Comparative evaluation of two different dental age estimation methods using tooth pulp: A CBCT study.

To estimate the age of an individual based on 3D radiographic evaluation of pulp width of maxillary central incisor. This study included 185 CBCT images of individuals within age range of 14-64 years. Images were evaluated for maxillary central incisors and pulp width measurements were taken from cementoenamel junction and middle third of root. Obtained data was subjected to correlation and regression analysis from which the age of an individual was predicted. Results of the present study were compared with another study by the same authors. A negative linear relationship was obtained between age and pulp width. The standard error of estimate (SEE) in sagittal section was 11.36 years and that in coronal section was 11.23 years. The coefficient of determination for sagittal section was 0.107 and for coronal section was 0.127. An obtained regression formula was highly significant. Division of samples into various age groups reduced SEE drastically. It can be concluded that pulp width of maxillary central incisor is a reliable indicator of age estimation.

VIS-SLAM: A Real-Time Dynamic SLAM Algorithm Based on the Fusion of Visual, Inertial, and Semantic Information

A deep learning-based Visual Inertial SLAM technique is proposed in this paper to ensure accurate autonomous localization of mobile robots in environments with dynamic objects. Addressing the limitations of real-time performance in deep learning algorithms and the poor robustness of pure visual geometry algorithms, this paper presents a deep learning-based Visual Inertial SLAM technique. Firstly, a non-blocking model is designed to extract semantic information from images. Then, a motion probability hierarchy model is proposed to obtain prior motion probabilities of feature points. For image frames without semantic information, a motion probability propagation model is designed to determine the prior motion probabilities of feature points. Furthermore, considering that the output of inertial measurements is unaffected by dynamic objects, this paper integrates inertial measurement information to improve the estimation accuracy of feature point motion probabilities. An adaptive threshold-based motion probability estimation method is proposed, and finally, the positioning accuracy is enhanced by eliminating feature points with excessively high motion probabilities. Experimental results demonstrate that the proposed algorithm achieves accurate localization in dynamic environments while maintaining real-time performance.

Angular acceleration estimation and aerodynamic parameter identification based on angular velocity equivalent model

Conventional angular acceleration estimation methods generate non-negligible errors when the control surface rapidly deflects; hence, the estimated angular acceleration cannot be used for aerodynamic parameter identification directly. This paper proposes an angular acceleration estimation method based on angular velocity equivalent model. The angular acceleration of the angular velocity equivalent model, whose angular velocity response is consistent with the flight test data, is used as the estimated angular acceleration. Firstly, the equivalent angular velocity model is established by combining the generic aerodynamic model with the rotational dynamic equations of aircraft. Secondly, the angular velocity data and the control surface deflection data are synchronized. Finally, the angular velocity response of the equivalent model is used as the predictor, and the estimated angular acceleration is obtained by extended Kalman filter. The aerodynamic parameter identification and validation are carried out using the estimated angular acceleration of a large civil aircraft flight test data. The results show that the angular acceleration obtained through the proposed angular acceleration estimation method meets the accuracy requirement of aerodynamic parameter identification.

Crystallization-Inspired Design and Modeling of Self-Assembly Lattice-Formation Swarm Robotics

Self-assembly formation is a key research topic for realizing practical applications in swarm robotics. Due to its inherent complexity, designing high-performance self-assembly formation strategies and proposing corresponding macroscopic models remain formidable challenges and present an open research frontier. Taking inspiration from crystallization, this paper introduces a distributed self-assembly formation strategy by defining free, moving, growing, and solid states for robots. Robots in these states can spontaneously organize into user-specified two-dimensional shape formations with lattice structures through local interactions and communications. To address the challenges posed by complex spatial structures in modeling a macroscopic model, this work introduces the structural features estimation method. Subsequently, a corresponding non-spatial macroscopic model is developed to predict and analyze the self-assembly behavior, employing the proposed estimation method and a stock and flow diagram. Real-robot experiments and simulations validate the flexibility, scalability, and high efficiency of the proposed self-assembly formation strategy. Moreover, extensive experimental and simulation results demonstrate the model’s accuracy in predicting the self-assembly process under different conditions. Model-based analysis indicates that the proposed self-assembly formation strategy can fully utilize the performance of individual robots and exhibits strong self-stability.

A novel method for rapid estimation of active bone marrow dose for radiotherapy patients in epidemiological studies.

In a dedicated effort to improve the assessment of clonal hematopoiesis (CH) and study leukemia risk following radiotherapy, we are developing a large-scale cohort study among cancer patients who received radiation. To that end, it will be critical to analyze dosimetric parameters of red bone marrow (ABM) exposure in relation to CH and its progression to myeloid neoplasms, requiring reconstruction method for ABM doses of a large-scale patients rapidly and accurately. To support a large-scale cohort study on the assessment of clonal hematopoiesis and leukemia risk following radiotherapy, we present a new method for the rapid reconstruction of ABM doses of radiotherapy among cancer patients. The key idea of the presented method is to segment patient bones rapidly and automatically by matching a whole-body computational human phantom, in which the skeletal system is divided into 34 bone sites, to patient CT images via 3D skeletal registration. The automatic approach was used to segment site-specific bones for 40 radiotherapy patients. Also, we segmented the bones manually. The bones segmented both manually and automatically were then combined with the patient dose matrix calculated by the treatment planning system (TPS) to derive patient ABM dose. We evaluated the performance of the automatic method in geometric and dosimetric accuracy by comparison with the manual approach. The pelvis showed the best geometric performance [volume overlap fraction (VOF): 52% (mean) with 23% (σ) and average distance (AD): 0.8cm (mean) with 0.5cm (σ)]. The pelvis also showed the best dosimetry performance [absorbed dose difference (ADD): 0.7Gy (mean) with 1.0Gy (σ)]. Some bones showed unsatisfactory performances such as the cervical vertebrae [ADD: 5.2Gy (mean) with 10.8Gy (σ)]. This impact on the total ABM dose, however, was not significant. An excellent agreement for the total ABM dose was indeed observed [ADD: 0.4Gy (mean) with 0.4Gy (σ)]. The computation time required for dose calculation using our method was robust (about one minute per patient). We confirmed that our method estimates ABM doses across treatment sites accurately, while providing high computational efficiency. The method will be used to reconstruct patient-specific ABM doses for dose-response assessment in a large cohort study. The method can also be applied to prospective dose calculation within a clinical TPS to support clinical decision making at the point of care.

Non‐linear nexus of mineral rents, coal rents, foreign direct investment, and environmental sustainability: Importance of institutional quality in E‐7 nations

AbstractThis study investigates the impact of natural resources (NRs), economic growth (GDP), institutional quality, mining and coal rents, and foreign direct investment (FDI) on the environmental quality in the E‐7 countries (Brazil, China, India, Indonesia, Russia, Turkey, and Mexico) from 1990 to 2021. To do this, such advanced estimation methods are used as the method of moments quantile regression (MMQR), with the artificial neural network utilized for robustness checks. The initial diagnostic test results unveiled the presence of cross‐sectional dependence, slope heterogeneity, and long‐run cointegration. The MMQR analysis results indicate that coal and mineral rents escalate environmental pollution by reducing the load capacity factor (LCF) across all quantiles. The results further indicate that FDI and economic growth negatively affect while institutional quality (INS) significantly promotes the LCF in E‐7 countries. Moreover, INS significantly moderates the relationship between mineral resources and LCF, as well as coal resources and LCF. The robustness test also validates these findings. Based on these results, policy measures aimed at strengthening institutional quality, particularly within the NR sector, are crucial for mitigating the adverse impacts of coal and mineral extraction on LCF and promoting sustainable development in the E‐7 countries.

Effects of Energy Delivery Guided by Indirect Calorimetry in Critically Ill Patients: A Systematic Review and Meta-Analysis

Background: The utility of using indirect calorimetry (IC) to estimate energy needs and methods for its application to this purpose remain unclear. This systematic review investigated whether using IC to estimate energy expenditure in critically ill patients is more meaningful for improving survival than other estimation methods. Methods: Comprehensive searches were conducted in MEDLINE using PubMed, Cochrane Central Register of Controlled Trials, and Igaku-Chuo-Zasshi up to March 2023. Results: Nine RCTs involving 1178 patients were included in the meta-analysis. The evidence obtained suggested that energy delivery by IC improved short-term mortality (risk ratio, 0.86; 95% confidence interval [CI], 0.70 to 1.06). However, the use of IC did not appear to affect the length of ICU stay (mean difference [MD], 0.86; 95% CI, −0.98 to 2.70) or the duration of mechanical ventilation (MD, 0.66; 95% CI, −0.39 to 1.72). Post hoc analyses using short-term mortality as the outcome found no significant difference by target calories in resting energy expenditure, whereas more frequent IC estimates were associated with lower short-term mortality and were more effective in mechanically ventilated patients. Conclusions: This updated meta-analysis revealed that the use of IC may improve short-term mortality in patients with critical illness and did not increase adverse events.

A STOCHASTIC MODEL FOR LENGTH BIASED LOAI DISTRIBUTION WITH PROPERTIES AND ITS APPLICATIONS

One of the most important applications of statistical analysis is in health research and application. Cancer studies are mostly required special statistical considerations in order to find the appropriate model for fitting the survival data. In this paper, we examine some general models leading to a weighted distribution. The developed distribution, also known as the length biased model, is referred to as the length biased Loai distribution. This distribution is a specific type of basic distribution, the Loai distribution. The length biased distribution has been compared with the original distribution. Some statistical properties of this distribution are derived, such as moments, the moment generating function, the reliability analysis, and the included functions. Also, the distribution of order statistics, quantile function, and likelihood ratio test are presented. The Bonferroni and Lorenz carvers, as well as the Rnyi and Tsallis entropies, are derived. The method of maximum likelihood estimation is used to estimate the distribution parameters. A simulation study is performed to investigate the performance of the estimation. The real data applications demonstrate that the proposed distribution can provide better results than several well-known distributions.

Special feature: Stein estimation and statistical shrinkage methods

Special feature: Stein estimation and statistical shrinkage methods

Comparison of some Bayesian estimation methods for type-I generalized extreme value distribution with simulation

The Weibull distribution is considered one of the Type-I Generalized Extreme Value (GEV) distribution, and it plays a crucial role in modeling extreme events in various fields, such as hydrology, finance, and environmental sciences. Bayesian methods play a strong, decisive role in estimating the parameters of the GEV distribution due to their ability to incorporate prior knowledge and handle small sample sizes effectively. In this research, we compare several shrinkage Bayesian estimation methods based on the squared error and the linear exponential loss functions. They were adopted and compared by the Monte Carlo simulation method. The performance of these methods is assessed based on their accuracy and computational efficiency in estimating the scale parameter of the Weibull distribution. To evaluate their performance, we generate simulated datasets with different sample sizes and varying parameter values. A technique for pre-estimation shrinkage is suggested to enhance the precision of estimation. Simulation experiments proved that the Bayesian shrinkage estimator and shrinkage pre-estimation under the squared loss function method are better than the other methods because they give the least mean square error. Overall, our findings highlight the advantages of shrinkage Bayesian estimation methods for the proposed distribution. Researchers and practitioners in fields reliant on extreme value analysis can benefit from these findings when selecting appropriate Bayesian estimation techniques for modeling extreme events accurately and efficiently.

Artificial intelligence and socioeconomic forces: transforming the landscape of religion

This research examines the influences of artificial intelligence and socioeconomic factors on religious freedom across 20 countries from 2000 to 2022. Employing a detailed model that integrates both specific effects related to individual countries and annual changes, our study offers an extensive empirical analysis of how artificial intelligence relates to religious freedom. The results indicate a notable negative impact of artificial intelligence on religious freedom. Furthermore, the study sheds light on key factors that affect religious freedom, uncovering a positive correlation with elements such as economic growth, political stability, and education levels. However, it was also observed that increased digitalization correlates negatively with religious freedom. These conclusions are reinforced by findings from the system-generalized method of moment estimation, which strongly support our initial results. Consequently, this study establishes that the relationship between artificial intelligence and religious freedom is intricate and shaped by a variety of socioeconomic factors. Our findings emphasize the necessity for thoughtful consideration of the broader societal impacts of artificial intelligence, especially regarding essential human rights like religious freedom.

Effects of female CEO and female CFO on internationalization and firm performance

Purpose The study aims to examine the influence of female chief executive officer (CEO) and female chief financial officer (CFO) on the linkage between internationalization and firm performance. Design/methodology/approach This study used 2926 firm-year observations of nonfinancial firms listed on the Pakistan Stock Exchange over the period 2012–2021. This study used ordinary least squares regression method to test the hypotheses, and additionally, generalized method of moments estimation and fixed effect analysis were used to check for the robustness of the results. Findings Using the framework of upper echelons theory and resource dependence theory, this study reports that internationalization has a positive impact on firm performance. Moreover, the results show that the presence of female CEO and female CFO strengthens the positive relationship between internationalization and firm performance. The results add to the gender diversity literature by highlighting the positive role of female CEOs and female CFOs on the internationalization and performance of firms in a male-dominated society. Originality/value This study adds to the limited literature on the internationalization of businesses in an emerging market and provides empirical support to upper echelons theory and resource dependence theory by highlighting the benefits brought to the firm through female CEOs and female CFOs.

Quality by design‐engineered reversed‐phase high‐performance liquid chromatography method development and validation for simultaneous estimation of neomycin sulfate and beclomethasone dipropionate in bulk and pharmaceutical dosage form

AbstractThe aim of this study was to develop and validate a robust reversed‐phase high‐performance liquid chromatography (RP‐HPLC) method for the simultaneous estimation of neomycin sulfate (NEO) and beclomethasone dipropionate (BECLO) in both bulk drug and pharmaceutical dosage forms. The analysis was conducted using the Box‐Behnken design. The separation of NEO and BECLO was conducted on a Phenomenex Luna C‐18 column (4.6 × 150 mm, 5 µm), employing a mobile phase comprising a mixture of methanol and trifluoroacetic acid in a ratio of 88:12% v/v. The separation was performed at a flow rate of 0.6 mL/min. NEO and BECLO were analyzed at a wavelength of 240 nm employing a photodiode array detector. The validation of the methodology followed the guidelines outlined in the International Council for Harmonization Q2 R (1). The validation process involved assessing critical parameters such as linearity, accuracy, system suitability, precision, and robustness. The results for each parameter were found to be within the acceptable range. The results indicate that the established RP‐HPLC method can effectively be employed for the routine analysis of NEO and BECLO in bulk drug and pharmaceutical dosage forms.