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Employee silence research stipulates that silence requires input to share, which we posit stems from encountering workplace events. We argue that the validity of relationships between employee silence and outcomes is limited without taking preceding events into account: Employees might not encounter relevant events and therefore cannot show silence. Further, workplace events can have independent effects on the outcomes attributed to silence, potentially confounding the effects of silence and preceding events. Drawing on an existing cross‐cultural dataset (N = 8222 in 35 samples), we show that some samples include up to 60% of participants who did not encounter relevant events. Using data from a German utility company (N = 572 in 129 teams), we demonstrate that the associations between employee silence and outcomes like burnout are overestimated if effects of events are left unaccounted. Lastly, a simulation study shows that biased silence–outcome relations are generalizable whenever events have independent effects on the outcome of interest. [ABSTRACT FROM AUTHOR]

The primary purpose of this article is to examine the issue of estimating the finite population distribution function from auxiliary information, such as population mean and rank of the auxiliary variables, that are already known. In order to better estimate the distribution function (DF) of a finite population, two improved estimators are developed. The bias and mean squared error of the suggested and existing estimators are derived up to the first order of approximation. To improve the efficiency of an estimators, we compare the suggested estimators with existing counterpart. Based on the numerical outcomes, it is to be noted that the suggested classes of estimators perform well using six actual data sets. The strength and generalization of the suggested estimators are also verified using a simulation analysis. Based on the result of actual data sets and a simulation study, we observe that the suggested estimator outperforms as compared to all existing estimators which is compared in this study. [ABSTRACT FROM AUTHOR]

Background and objectives: Comprehending the research dataset is crucial for obtaining reliable and valid outcomes. Health analysts must have a deep comprehension of the data being analyzed. This comprehension allows them to suggest practical solutions for handling missing data, in a clinical data source. Accurate handling of missing values is critical for producing precise estimates and making informed decisions, especially in crucial areas like clinical research. With data's increasing diversity and complexity, numerous scholars have developed a range of imputation techniques. To address this, we conducted a systematic review to introduce various imputation techniques based on tabular dataset characteristics, including the mechanism, pattern, and ratio of missingness, to identify the most appropriate imputation methods in the healthcare field. Materials and methods: We searched four information databases namely PubMed, Web of Science, Scopus, and IEEE Xplore, for articles published up to September 20, 2023, that discussed imputation methods for addressing missing values in a clinically structured dataset. Our investigation of selected articles focused on four key aspects: the mechanism, pattern, ratio of missingness, and various imputation strategies. By synthesizing insights from these perspectives, we constructed an evidence map to recommend suitable imputation methods for handling missing values in a tabular dataset. Results: Out of 2955 articles, 58 were included in the analysis. The findings from the development of the evidence map, based on the structure of the missing values and the types of imputation methods used in the extracted items from these studies, revealed that 45% of the studies employed conventional statistical methods, 31% utilized machine learning and deep learning methods, and 24% applied hybrid imputation techniques for handling missing values. Conclusion: Considering the structure and characteristics of missing values in a clinical dataset is essential for choosing the most appropriate data imputation technique, especially within conventional statistical methods. Accurately estimating missing values to reflect reality enhances the likelihood of obtaining high-quality and reusable data, contributing significantly to precise medical decision-making processes. Performing this review study creates a guideline for choosing the most appropriate imputation methods in data preprocessing stages to perform analytical processes on structured clinical datasets. Highlights: • The evidence map emphasized the importance of considering missing data characteristics when choosing an imputation method, providing insights for researchers in method selection. • The distinction between statistical and learning-based approaches highlighted the strengths and considerations of each method based on data structure. • Understanding missing data structures can enhance the quality and reliability of data imputation techniques, and improve medical decision-making accuracy. • Simulation studies are crucial for validating imputation techniques and enhancing their robustness in practical applications. • Considering missing data mechanisms, patterns, and ratios can aid researchers in making informed decisions on selecting appropriate imputation methods, leading to high-quality, and reusable data for precise medical decision-making. [ABSTRACT FROM AUTHOR]

Ranked set sampling (RSS) is known to increase the efficiency of the estimators while comparing it with simple random sampling. The problem of missingness creates a gap in the information that needs to be addressed before proceeding for estimation. Negligible amount of work has been carried out to deal with missingness utilizing RSS. This paper proposes some logarithmic type methods of imputation for the estimation of population mean under RSS using auxiliary information. The properties of the suggested imputation procedures are examined. A simulation study is accomplished to show that the proposed imputation procedures exhibit better results in comparison to some of the existing imputation procedures. Few real applications of the proposed imputation procedures is also provided to generalize the simulation study. [ABSTRACT FROM AUTHOR]

Defining a probability model suitable for analyzing intricate and skewed real datasets holds significant importance in statistics. Here In this study, a novel class of models using the maximum of independent identical generalized Rayleigh random variables with the number of variables that have a power series class of distributions is proposed. This novel family of distributions contain several sub-models, including Poisson-generalized Rayleigh, geometric-generalized Rayleigh, binomial-generalized Rayleigh, and logarithmic-generalized Rayleigh models. The so obtained model can be unimodal and positively skewed, and it can be applied in modeling asymmetric data. We provide numerous distributional and statistical properties of this new family of distribution, notably, probability density, cumulative function, hazard rate function, k -moment, mean, variance, and quantile function. Different techniques consider the estimation of model parameters, likely maximum likelihood estimators, Expectation–Maximization (EM) and its Modified techniques, and extensive Monte Carlo (MC) simulation studies have been employed to see the efficiency of the suggested estimation techniques. At the end, three real data sets are analyzed for illustrative purposes. The results underscore the superior performance of the proposed Geom-GR model compared to its competitors, showcasing its effectiveness in accurately representing the analyzed datasets. [ABSTRACT FROM AUTHOR]

The most common detector material in the PC CT system, cannot achieve the best performance at a relatively higher photon flux rate. In the reconstruction view, the most commonly used filtered back projection, is not able to provide sufficient reconstructed image quality in spectral computed tomography (CT). Developing a triple-source saddle-curve cone-beam photon counting CT image reconstruction method can improve the temporal resolution. Triple-source saddle-curve cone-beam trajectory was rearranged into four trajectory sets for simulation and reconstruction. Projection images in different energy bins were simulated by forward projection and photon counting CT respond model simulation. After simulation, the object was reconstructed using Katsevich's theory after photon counts correction using the pseudo inverse of photon counting CT response matrix. The material decomposition can be performed based on images in different energy bins. Root mean square error (RMSE) and structural similarity index (SSIM) are calculated to quantify the image quality of reconstruction images. Compared with FDK images, the RMSE for the triple-source image was improved by 27%, 21%, 14%, 8%, and 6% for the reconstrued image of 20–33, 33–47, 47–58, 58–69, 69–80 keV energy bin. The SSIM was improved by 1.031%, 0.665%, 0.396%, 0.235%, 0.174% for corresponding energy bin. The decomposition image based on corrected images shows improved RMSE and SSIM, each by 33.861% and 0.345%. SSIM of corrected decomposition image of iodine reaches 99.415% of the original image. A new Triple-source saddle-curve cone-beam PC CT image reconstruction method was developed in this work. The exact reconstruction of the triple-source saddle-curve improved both the image quality and temporal resolution. [ABSTRACT FROM AUTHOR]

In this article, we present a comprehensive class of estimators designed for population proportion estimation by leveraging auxiliary attributes within the framework of simple random sampling. The proposed class encompasses a diverse range of estimators, each of which undergoes a thorough examination. We provide numerical expressions for both bias and mean squared error, employing a first-order approximation. The significance of the introduced class of estimators is underscored through a detailed analysis of numerical results. These findings demonstrate the marked superiority of the suggested estimators over their existing counterparts in terms of mean squared error and percentage relative efficiency, as observed in both actual and simulated data scenarios. Consequently, we advocate for the adoption of the proposed class of estimators, asserting its potential to yield improved outcomes when estimating population proportions through the utilization of simple random sampling techniques. [ABSTRACT FROM AUTHOR]

Abstract The primary purpose of this article is to examine the issue of estimating the finite population distribution function from auxiliary information, such as population mean and rank of the auxiliary variables, that are already known. In order to better estimate the distribution function (DF) of a finite population, two improved estimators are developed. The bias and mean squared error of the suggested and existing estimators are derived up to the first order of approximation. To improve the efficiency of an estimators, we compare the suggested estimators with existing counterpart. Based on the numerical outcomes, it is to be noted that the suggested classes of estimators perform well using six actual data sets. The strength and generalization of the suggested estimators are also verified using a simulation analysis. Based on the result of actual data sets and a simulation study, we observe that the suggested estimator outperforms as compared to all existing estimators which is compared in this study.

Purpose: The most common detector material in the PC CT system, cannot achieve the best performance at a relatively higher photon flux rate. In the reconstruction view, the most commonly used filtered back projection, is not able to provide sufficient reconstructed image quality in spectral computed tomography (CT). Developing a triple-source saddle-curve cone-beam photon counting CT image reconstruction method can improve the temporal resolution. Methods: Triple-source saddle-curve cone-beam trajectory was rearranged into four trajectory sets for simulation and reconstruction. Projection images in different energy bins were simulated by forward projection and photon counting CT respond model simulation. After simulation, the object was reconstructed using Katsevich’s theory after photon counts correction using the pseudo inverse of photon counting CT response matrix. The material decomposition can be performed based on images in different energy bins. Results: Root mean square error (RMSE) and structural similarity index (SSIM) are calculated to quantify the image quality of reconstruction images. Compared with FDK images, the RMSE for the triple-source image was improved by 27%, 21%, 14%, 8%, and 6% for the reconstrued image of 20–33, 33–47, 47–58, 58–69, 69–80 keV energy bin. The SSIM was improved by 1.031%, 0.665%, 0.396%, 0.235%, 0.174% for corresponding energy bin. The decomposition image based on corrected images shows improved RMSE and SSIM, each by 33.861% and 0.345%. SSIM of corrected decomposition image of iodine reaches 99.415% of the original image. Conclusions: A new Triple-source saddle-curve cone-beam PC CT image reconstruction method was developed in this work. The exact reconstruction of the triple-source saddle-curve improved both the image quality and temporal resolution.

Abstract Ranked set sampling (RSS) is known to increase the efficiency of the estimators while comparing it with simple random sampling. The problem of missingness creates a gap in the information that needs to be addressed before proceeding for estimation. Negligible amount of work has been carried out to deal with missingness utilizing RSS. This paper proposes some logarithmic type methods of imputation for the estimation of population mean under RSS using auxiliary information. The properties of the suggested imputation procedures are examined. A simulation study is accomplished to show that the proposed imputation procedures exhibit better results in comparison to some of the existing imputation procedures. Few real applications of the proposed imputation procedures is also provided to generalize the simulation study.

Defining a probability model suitable for analyzing intricate and skewed real datasets holds significant importance in statistics. Here In this study, a novel class of models using the maximum of independent identical generalized Rayleigh random variables with the number of variables that have a power series class of distributions is proposed. This novel family of distributions contain several sub-models, including Poisson-generalized Rayleigh, geometric-generalized Rayleigh, binomial-generalized Rayleigh, and logarithmic-generalized Rayleigh models. The so obtained model can be unimodal and positively skewed, and it can be applied in modeling asymmetric data. We provide numerous distributional and statistical properties of this new family of distribution, notably, probability density, cumulative function, hazard rate function, k-moment, mean, variance, and quantile function. Different techniques consider the estimation of model parameters, likely maximum likelihood estimators, Expectation–Maximization (EM) and its Modified techniques, and extensive Monte Carlo (MC) simulation studies have been employed to see the efficiency of the suggested estimation techniques. At the end, three real data sets are analyzed for illustrative purposes. The results underscore the superior performance of the proposed Geom-GR model compared to its competitors, showcasing its effectiveness in accurately representing the analyzed datasets.

In this article, we present a comprehensive class of estimators designed for population proportion estimation by leveraging auxiliary attributes within the framework of simple random sampling. The proposed class encompasses a diverse range of estimators, each of which undergoes a thorough examination. We provide numerical expressions for both bias and mean squared error, employing a first-order approximation. The significance of the introduced class of estimators is underscored through a detailed analysis of numerical results. These findings demonstrate the marked superiority of the suggested estimators over their existing counterparts in terms of mean squared error and percentage relative efficiency, as observed in both actual and simulated data scenarios. Consequently, we advocate for the adoption of the proposed class of estimators, asserting its potential to yield improved outcomes when estimating population proportions through the utilization of simple random sampling techniques.

Sampling is crucial in basic statistics due to the large size of certain populations. Sampling theory focuses on selecting samples based on appropriate population characteristics and approaches to estimating population characteristics from the sample. Firstly, we use the ratio-type estimator proposed by Prasad (1989). We adapt this unbiased ratio-type estimator to two new different estimators using the Hartley-Ross and non-response approaches. Finally, we combine these approaches and propose a new estimator. The theoretical results were supported by COVID-19 data and a simulation study with various scenarios. Based on the COVID-19 data result, it was concluded that the new tN3 estimator is the most effective estimator among those proposed and compared, with the minimum MSE and maximum PRE. Upon examining the changes in the COVID-19 data set in detail, it is evident that the PRE value increases with an increase in sample size, non-response rate, and k values. As in the COVID-19 dataset, the tN3 estimator was the most effective in the simulation study results. It is also important to note that these results are not applicable to only one scenario in simulation study. The tN2 estimator was the most effective in the scenario with rho=0.90, T=1000, n=400, non-response rate=0.30 and k equal to 2, 4 and 5.

Abstract Linkage maps are essential for genetic mapping of phenotypic traits, gene map-based cloning, and marker-assisted selection in breeding applications. Construction of a high-quality saturated map requires high-quality genotypic data on a large number of molecular markers. Errors in genotyping cannot be completely avoided, no matter what platform is used. When genotyping error reaches a threshold level, it will seriously affect the accuracy of the constructed map and the reliability of consequent genetic studies. In this study, repeated genotyping of two recombinant inbred line (RIL) populations derived from crosses Yangxiaomai × Zhongyou 9507 and Jingshuang 16 × Bainong 64 was used to investigate the effect of genotyping errors on linkage map construction. Inconsistent data points between the two replications were regarded as genotyping errors, which were classified into three types. Genotyping errors were treated as missing values, and therefore the non-erroneous data set was generated. Firstly, linkage maps were constructed using the two replicates as well as the non-erroneous data set. Secondly, error correction methods implemented in software packages QTL IciMapping (EC) and Genotype-Corrector (GC) were applied to the two replicates. Linkage maps were therefore constructed based on the corrected genotypes and then compared with those from the non-erroneous data set. Simulation study was performed by considering different levels of genotyping errors to investigate the impact of errors and the accuracy of error correction methods. Results indicated that map length and marker order differed among the two replicates and the non-erroneous data sets in both RIL populations. For both actual and simulated populations, map length was expanded as the increase in error rate, and the correlation coefficient between linkage and physical maps became lower. Map quality can be improved by repeated genotyping and error correction algorithm. When it is impossible to genotype the whole mapping population repeatedly, 30% would be recommended in repeated genotyping. The EC method had a much lower false positive rate than did the GC method under different error rates. This study systematically expounded the impact of genotyping errors on linkage analysis, providing potential guidelines for improving the accuracy of linkage maps in the presence of genotyping errors.

In the present article, we derive a new Modified-X (NM-X) family of probability distributions by using a popular approach known as the T-X method. For the NM-X family, some of the main distributional (or statistical) properties such as quantile function, identifiability property, order statistics, residual life and reverse residual life, moments, and moments generating function are determined. A well-known method of estimation such as the Maximum Likelihood estimation method is used for estimating the model parameters of the introduced family of distributions. A Monte Carlo simulation method is incorporated to judge the performance of the estimators with different selected values of parameters. To check the tails behaviors of the proposed model, the risk measurements such as value-at-risk (VaR), tailed-value-at-risk (TVaR), tailed-variance (TV), and tailed-variance-premium (TVP) are investigated. For illustrative purposes, a special sub-model of the proposed class is derived and named a New Modified Weibull distribution (abbreviated as the NM-Weib). Furthermore, four applications from the field of medicine are analyzed to demonstrate the application, and effectiveness of the NM-Weib distribution. By adopting different diagnostic criteria, the NM-Weib distribution is compared with Weibull (Weib) distribution, Alpha Power Transformed-Weib (APTra-Weib) distribution, new Exponent-Weib (NExpo-Weib) distribution, Flexible Reduced Logarithmic-Weib (FRLog-Weib) distribution, Marshal Olkin-Weib (MO-Weib) distribution, and Kumaraswamy-Weib (K-Weib) distribution. Finally, based on the diagnostic criteria, it is observed that the NM-Weib distribution provides a suitable and best fit for each considered biomedical data set.

In recent years, the variability in the composition of cement raw materials has increasingly impacted the quality of cement products. However, there has been relatively little research on the homogenization effects of equipment in the cement production process. Existing studies mainly focus on the primary functions of equipment, such as the grinding efficiency of ball mills, the thermal decomposition in cyclone preheaters, and the thermal decomposition in rotary kilns. This study selected four typical pieces of equipment with significant homogenization functions for an in-depth investigation: ball mills, pneumatic homogenizing silos, cyclone preheaters, and rotary kilns. To assess the homogenization efficacy of each apparatus, scaled-down models of these devices were constructed and subjected to simulated experiments. To improve experimental efficiency and realistically simulate actual production conditions in a laboratory setting, this study used the uniformity of the electrical capacitance of mixed powders instead of compositional uniformity to analyze homogenization effects. The test material in the experiment consisted of a mixture of raw meal from a cement factory with a high dielectric constant and Fe3O4 powder. The parallel plate capacitance method was employed to ascertain the capacitance value of the mixed powder prior to and subsequent to treatment by each equipment model. The fluctuation of the input and output curves was analyzed, and the standard deviation (S), coefficient of variation (R), and homogenization multiplier (H) were calculated in order to evaluate the homogenization effect of each equipment model on the raw meal. The findings of the study indicated that the pneumatic homogenizer exhibited an exemplary homogenization effect, followed by the ball mill. For the ball mill, a higher proportion of small balls in the gradation can significantly enhance the homogenization effect without considering the grinding efficiency. The five-stage cyclone preheater also has a better homogenization effect, while the rotary kiln has a less significant homogenization effect on raw meal. Finally, the raw meal processed by each equipment model was used for clinker calcination and the preparation of cement mortar samples. After curing for three days, the compressive and flexural strengths of the samples were tested, thereby indirectly verifying the homogenization effect of each equipment model on the raw meal. This study helps to understand the homogenization process of raw materials by equipment in cement production and provides certain reference and data support for equipment selection, operation optimization, and quality control in the cement production process. [ABSTRACT FROM AUTHOR]

Cross-sectional data allow the investigation of how genetics influence health at a single time point, but to understand how the genome impacts phenotype development, one must use repeated measures data. Ignoring the dependency inherent in repeated measures can exacerbate false positives and requires the utilization of methods other than general or generalized linear models. Many methods can accommodate longitudinal data, including the commonly used linear mixed model and generalized estimating equation, as well as the less popular fixed-effects model, cluster-robust standard error adjustment, and aggregate regression. We simulated longitudinal data and applied these five methods alongside naïve linear regression, which ignored the dependency and served as a baseline, to compare their power, false positive rate, estimation accuracy, and precision. The results showed that the naïve linear regression and fixed-effects models incurred high false positive rates when analyzing a predictor that is fixed over time, making them unviable for studying time-invariant genetic effects. The linear mixed models maintained low false positive rates and unbiased estimation. The generalized estimating equation was similar to the former in terms of power and estimation, but it had increased false positives when the sample size was low, as did cluster-robust standard error adjustment. Aggregate regression produced biased estimates when predictor effects varied over time. To show how the method choice affects downstream results, we performed longitudinal analyses in an adolescent cohort of African and European ancestry. We examined how developing post-traumatic stress symptoms were predicted by polygenic risk, traumatic events, exposure to sexual abuse, and income using four approaches--linear mixed models, generalized estimating equations, cluster-robust standard error adjustment, and aggregate regression. While the directions of effect were generally consistent, coefficient magnitudes and statistical significance differed across methods. Our in-depth comparison of longitudinal methods showed that linear mixed models and generalized estimating equations were applicable in most scenarios requiring longitudinal modeling, but no approach produced identical results even if fit to the same data. Since result discrepancies can result from methodological choices, it is crucial that researchers determine their model a priori, refrain from testing multiple approaches to obtain favorable results, and utilize as similar as possible methods when seeking to replicate results. [ABSTRACT FROM AUTHOR]

In the social sciences, accurately identifying the dimensionality of measurement scales is crucial for understanding latent constructs such as anxiety, happiness, and self-efficacy. This study presents a rigorous comparison between Parallel Analysis (PA) and Exploratory Graph Analysis (EGA) for assessing the dimensionality of scales, particularly focusing on ordinal data. Through an extensive simulation study, we evaluated the effectiveness of these methods under various conditions, including varying sample size, number of factors and their association, patterns of loading magnitudes, and symmetrical or skewed item distributions with assumed underlying normality or non-normality. Results show that the performance of each method varies across different scenarios, depending on the context. EGA consistently outperforms PA in correctly identifying the number of factors, particularly in complex scenarios characterized by more than a single factor, high inter-factor correlations and low to medium primary loadings. However, for datasets with simpler and stronger factor structures, specifically those with a single factor, high primary loadings, low cross-loadings, and low to moderate interfactor correlations, PA is suggested as the method of choice. Skewed item distributions with assumed underlying normality or non-normality were found to noticeably impact the performance of both methods, particularly in complex scenarios. The results provide valuable insights for researchers utilizing these methods in scale development and validation, ensuring that measurement instruments accurately reflect theoretical constructs. [ABSTRACT FROM AUTHOR]

Stratification is an important design feature of many studies using complex sampling designs and it is often used in large-scale assessment (LSA) studies, such as the Programme for International Student Assessment (PISA), for two main reasons. First, stratification variables that achieve a high between and low within strata variance can improve the efficiency of a survey design. Second, stratification allows one to, explicitly or implicitly, control for sample sizes across subpopulations. It ensures that some parts of a population are in the sample in predetermined proportions. In this study, we determine through simulation which stratification scheme is best for PISA in Germany. For this, we consider the constraints imposed by the international sampling design, the available information about schools, and specific national characteristics of the German educational system. We examine seven different stratification designs selected based on scenarios used in past LSAs in Germany and theoretical considerations for future implementations. The chosen scenarios were compared with two reference scenarios: (1) an unstratified design and (2) a synthetic optimal stratification design. The simulation study reveals that the stratification design currently applied in PISA produces satisfactory results regarding sampling precision. The present stratification design is based on Germany's federal states and school types. However, this approach leads to small strata, which has been problematic for estimating sampling variance in previous cycles. Therefore, alternative stratification scenarios were considered and, in addition to overcoming the small-strata problem, also led to smaller standard errors for estimates of student mean performance in mathematics, science, and reading. As a result of this study, we recommend considering three different stratification designs for Germany in future cycles of PISA. These recommendations aim to: (1) improve the sampling efficiency while keeping the sample size constant, (2) follow a sound methodological approach, and (3) make conservative and cautious changes while maintaining a reflection of the structure of the German federal school system with different school types. These suggestions include a reinvented stratification of grouped German federal states and designs with school types as explicit stratifiers and federal states as implicit stratifiers. [ABSTRACT FROM AUTHOR]

The traditional stratification weight is widely used in survey sampling for estimation under stratified random sampling (StRS). A neutrosophic calibration approach is proposed under neutrosophic statistics for the first time with the aim of improving conventional stratification weight. This addresses the challenge of estimating the empirical cumulative distribution function (CDF) of a finite population using the neutrosophic technique. The neutrosophic technique extends traditional statistics, dealing with indeterminate, vague, and uncertain values. Thus, using additional information, we are able to obtain an effective estimate of the neutrosophic CDF. The suggested estimator yields an interval range in which the population empirical CDF is likely to exist rather than a single numerical value. The proposed family of neutrosophic estimators will be defined under suitable calibration constraints. A simulation study is also computed in order to assess the effectiveness of the suggested and adapted neutrosophic estimators using real-life symmetric and asymmetric datasets. [ABSTRACT FROM AUTHOR]

Symmetrical as well as asymmetrical statistical models play a prominent role in describing and predicting the real-world phenomena of nature. Among other fields, these models are very useful for modeling data in the sector of civil engineering. Due to the applicability of the statistical models in civil engineering and other related sectors, this paper offers a statistical methodology to improve the distributional flexibility of traditional models. The suggested method/approach is called the extended-X family of distributions. The proposed method has the ability to generate symmetrical and asymmetrical probability distributions. Based on the extended-X family approach, an updated version of the Weibull model, namely, the extended Weibull model, is studied. The proposed model is very flexible and has the ability to capture the symmetrical and asymmetrical shapes of its density function. For the extended-X method, the estimation of parameters, a simulation study, and some mathematical properties are derived. Finally, the practical illustration/usefulness of the suggested model is shown by analyzing two data sets taken from the field of engineering. Both data sets represent the fracture toughness of alumina (Al2O3). [ABSTRACT FROM AUTHOR]

Hydrogen is one of the key components in renewable energy systems. Its storage and transport, however, are challenging. The Liquid Organic Hydrogen Carrier (LOHC) technology is a possible solution for this issue. With suitable organic components, hydrogen can be stored in a chemically bound form which is safer and has a higher energy density than other solutions. Furthermore, the storage and transport of the LOHC component with high hydrogen content can be provided under normal environmental conditions. The LOHC process cycle involves a catalytic hydrogenation and dehydrogenation step where the LOHC component can be recovered and recycled. The paper reviews a few relevant studies regarding the possible LOHC compound pairs, the potential catalyst systems, and the necessary equipment. Then, the paper discusses a simulation study of three LOHC systems, determining suitable operating conditions and estimating costs. The toluene-methylcyclohexane system was found to be the most promising for LOHC application. Two energy integration opportunities were also examined. The first case study was based on separate hydrogenation and dehydrogenation sites, while the second was based on one site utilization. The results show a lower energy demand in the case of a toluene system, with a reduction of 70% in heating and 45% in cooling. [ABSTRACT FROM AUTHOR]

Linkage maps are essential for genetic mapping of phenotypic traits, gene map-based cloning, and marker-assisted selection in breeding applications. Construction of a high-quality saturated map requires high-quality genotypic data on a large number of molecular markers. Errors in genotyping cannot be completely avoided, no matter what platform is used. When genotyping error reaches a threshold level, it will seriously affect the accuracy of the constructed map and the reliability of consequent genetic studies. In this study, repeated genotyping of two recombinant inbred line (RIL) populations derived from crosses Yangxiaomai × Zhongyou 9507 and Jingshuang 16 × Bainong 64 was used to investigate the effect of genotyping errors on linkage map construction. Inconsistent data points between the two replications were regarded as genotyping errors, which were classified into three types. Genotyping errors were treated as missing values, and therefore the non-erroneous data set was generated. Firstly, linkage maps were constructed using the two replicates as well as the non-erroneous data set. Secondly, error correction methods implemented in software packages QTL IciMapping (EC) and Genotype-Corrector (GC) were applied to the two replicates. Linkage maps were therefore constructed based on the corrected genotypes and then compared with those from the non-erroneous data set. Simulation study was performed by considering different levels of genotyping errors to investigate the impact of errors and the accuracy of error correction methods. Results indicated that map length and marker order differed among the two replicates and the non-erroneous data sets in both RIL populations. For both actual and simulated populations, map length was expanded as the increase in error rate, and the correlation coefficient between linkage and physical maps became lower. Map quality can be improved by repeated genotyping and error correction algorithm. When it is impossible to genotype the whole mapping population repeatedly, 30% would be recommended in repeated genotyping. The EC method had a much lower false positive rate than did the GC method under different error rates. This study systematically expounded the impact of genotyping errors on linkage analysis, providing potential guidelines for improving the accuracy of linkage maps in the presence of genotyping errors. [ABSTRACT FROM AUTHOR]

Different formulations of foaming polyurethane grout offer controlled expansion rates. This is crucial for precision in filling voids without exerting excessive pressure on surrounding structures, which could potentially cause damage. This study focuses on the impact of composition on the expansion performance of tailor-made polyurethane grouting materials. Initially, multiple unknown chemical reaction kinetic parameters were identified by combining free expansion tests, which involved measuring density and temperature changes, with the particle swarm optimization algorithm. A numerical simulation, integrating chemical kinetic models and fluid flow equations, was established to replicate the free expansion process of polyurethane grout in a cup, aligning with our experimental results. Subsequently, we analyzed the polymerization process of polyurethane grout with varying compositions to determine the effect of composition ratios on grout expansion. Our findings reveal that the expansion ratio of foaming polyurethane is predominantly influenced by the concentrations of physical and chemical foaming agents, followed by isocyanate concentration. Polyol, in contrast, exerts a relatively minor influence. Furthermore, the solubility of the physical foaming agent in the grout determines both its maximum allowable concentration and its maximum contribution to volume increase. This study provides valuable insights for the design and selection of polyurethane grout components tailored to diverse applications. [ABSTRACT FROM AUTHOR]

In the present article, we derive a new Modified-X (NM-X) family of probability distributions by using a popular approach known as the T-X method. For the NM-X family, some of the main distributional (or statistical) properties such as quantile function, identifiability property, order statistics, residual life and reverse residual life, moments, and moments generating function are determined. A well-known method of estimation such as the Maximum Likelihood estimation method is used for estimating the model parameters of the introduced family of distributions. A Monte Carlo simulation method is incorporated to judge the performance of the estimators with different selected values of parameters. To check the tails behaviors of the proposed model, the risk measurements such as value-at-risk (VaR), tailed-value-at-risk (TVaR), tailed-variance (TV), and tailed-variance-premium (TVP) are investigated. For illustrative purposes, a special sub-model of the proposed class is derived and named a New Modified Weibull distribution (abbreviated as the NM-Weib). Furthermore, four applications from the field of medicine are analyzed to demonstrate the application, and effectiveness of the NM-Weib distribution. By adopting different diagnostic criteria, the NM-Weib distribution is compared with Weibull (Weib) distribution, Alpha Power Transformed-Weib (APTra-Weib) distribution, new Exponent-Weib (NExpo-Weib) distribution, Flexible Reduced Logarithmic-Weib (FRLog-Weib) distribution, Marshal Olkin-Weib (MO-Weib) distribution, and Kumaraswamy-Weib (K-Weib) distribution. Finally, based on the diagnostic criteria, it is observed that the NM-Weib distribution provides a suitable and best fit for each considered biomedical data set. [ABSTRACT FROM AUTHOR]

Desired speed or free speed distributions are important input parameters for microscopic traffic flow simulations. Whereas driven speeds can be measured, desired speeds are not detectable for all vehicles due to vehicles constraining each other. An established approach to estimating desired speed distributions is the modified Kaplan-Meier approach, which estimates desired speed distributions based on single vehicle data from stationary detectors. Due to a lack of a ground truth, it is not possible to validate the estimation results based on empirical data. Therefore, we employ a microscopic traffic simulation model as a virtual testbed to generate a synthetic ground truth. Our results highlight that when applying the modified Kaplan-Meier approach, it is crucial to ensure that the underlying empirical data are adequately filtered to exclude periods where the traffic density on the left lane exceeds 10 veh/km. Otherwise, the estimation may not yield valid results. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the impact of transformed auxiliary variables on the performance of variance estimators of finite population under adaptive cluster sampling scheme. Further, the formulation of an efficient variance estimator of a finite population is also under consideration in this article. Specifically, we explore the gain in efficiency obtained through various transformations and define dominance space for each transformation. These dominance regions provide valuable insights into the circumstances under which one transformation prevails over another regarding precision and accuracy. The theoretical properties of the suggested estimators have been discussed along with the dominance region under each transformation. The bias and Mean Square Error (MSE) have been derived up to the first order of approximation. To evaluate and empirically validate our methodology, we conduct a numerical analysis using real-life ecological data of blue-winged teal. The finding reflects the superior performance of the suggested variance estimators over the competing estimators, thereby substantiating its importance in making informed decisions in real-world applications.

PURPOSE: Capture-recapture methods estimate the size of hidden populations by leveraging the proportion of overlap of the population on independent lists. Log-linear modeling relaxes the assumption of list independence, but best model selection criteria remain uncertain. Incorrect model selection can deliver incorrect and even implausible size estimates. METHODS: We used simulations to model when capture-recapture methods deliver biased or unbiased estimates and compare model selection criteria. Simulations included five scenarios for list dependence among three incomplete lists of population of interest. We compared metrics of log-linear model selection, accuracy, and precision. We also compared log-linear model performance to the decomposable graph approach (a Bayesian model average), the sparse multiple systems estimation (SparseMSE) approach that accounts for zero or low cell counts, and the Sample Coverage approach. RESULTS: Log-linear models selected by Akaikes information criterion (AIC) calculated accurate population size estimates in all scenarios except for those with sparse or zero cell counts. In these scenarios, the decomposable graph and the Sample Coverage models produced more accurate size estimates. CONCLUSIONS: Conventional capture-recapture model selection fails with sparse cell counts. This naïve approach to model selection should be replaced with the implementation of multiple different models in order triangulate the truth in real-world applications.

Abstract A neutrosophic statistic is a random variable and it has a neutrosophic probability distribution. So, in this paper, we introduce the new neutrosophic Birnbaum–Saunders distribution. Some statistical properties are derived, using Mathematica 13.1.1 and R-Studio Software. Two different estimation methods for parameters estimation are introduced for new distribution: maximum likelihood estimation method and Bayesian estimation method. A Monte-Carlo simulation study is used to investigate the behavior of parameters estimates of new distribution, compare the performance of different estimates, and compare between our distribution and the classical version of Birnbaum–Saunders. Finally, study the validity of our new distribution in real life.

Aiming at the problem of energy supplement for ocean buoys, an energy supplement system for ocean buoys is designed. The buoy shakes under the action of waves. The energy supplement system located inside the buoy collects the kinetic energy of shaking and converts it into stable electric energy for the buoy to recharge or store electricity; the working principle, design scheme and structural composition of the energy supplement system of the marine buoy are determined; according to the motion principle of the sloshing pendulum, the torque equation of the sloshing pendulum is established; AQWA is used to simulate the motion characteristics of the float under the action of first-order regular and irregular waves, and the roll angular displacement of the float is taken as the input of the simulation model. The electromechanical coupling simulation model of the system is built by using the joint simulation of Adams and AMESim, which verifies the rationality of the design scheme. The results show that under the action of first-order regular wave and irregular wave, the energy supplement system can generate 3.3 V and 1 A DC power to realize stable power output and recharge the buoy or battery.

In response to the current situation in which an imbalance in educational redistribution has led to an inefficient and slow educational environment in the long-term, difficulty in satisfying the interests of subjects, and a lack of effective targeting of school policy measures, an SD evolutionary game model targeting educational reform, with schools, administrative institutions, and professors as the three parties, was established. Based on expected utility theory and Lyapunov’s first law, a feedback regulation simulation study was conducted on subjects’ decision making behavior. The research results show that: the evolutionary trajectory of the subjects in the game is affected by the redistribution of education funding benefits, but the evolution of the final system is in a state of stable equilibrium (push, reform, reform); the decision-making behavior of the pushing party plays a decisive role in the stability of the system, and if the education subsidies of university professors and the income from the value of research results are greater than the education subsidies before the education reform, evolution over time is conducive to prompting the professor group to prefer the education reform strategy, while the effect of school push on the stability of the educational reform system is significant in schools with lower returns on the value of research funding.

Abstract Estimation of mortality rates and mortality rate ratios (MRR) of diseased and non-diseased individuals is a core metric of disease impact used in chronic disease epidemiology. Estimation of mortality rates is often conducted through retrospective linkage of information from nationwide surveys such as the National Health Interview Survey (NHIS) and death registries. These surveys usually collect information on disease status during only one study visit. This infrequency leads to missing disease information (with right censored survival times) for deceased individuals who were disease-free at study participation, and a possibly biased estimation of the MRR because of possible undetected disease onset after study participation. This occurrence is called “misclassification of disease status at death (MicDaD)” and it is a potentially common source of bias in epidemiologic studies. In this study, we conducted a simulation analysis with a high and a low incidence setting to assess the extent of MicDaD-bias in the estimated mortality. For the simulated populations, MRR for diseased and non-diseased individuals with and without MicDaD were calculated and compared. Magnitude of MicDaD-bias depends on and is driven by the incidence of the chronic disease under consideration; our analysis revealed a noticeable shift towards underestimation for high incidences when MicDaD is present. Impact of MicDaD was smaller for lower incidence (but associated with greater uncertainty in the estimation of MRR in general). Further research can consider the amount of missing information and potential influencers such as duration and risk factors of the disease.

Abstract Background In the last decades, medical research fields studying rare conditions such as spinal cord injury (SCI) have made extensive efforts to collect large-scale data. However, most analysis methods rely on complete data. This is particularly troublesome when studying clinical data as they are prone to missingness. Often, researchers mitigate this problem by removing patients with missing data from the analyses. Less commonly, imputation methods to infer likely values are applied. Objective Our objective was to study how handling missing data influences the results reported, taking the example of SCI registries. We aimed to raise awareness on the effects of missing data and provide guidelines to be applied for future research projects, in SCI research and beyond. Methods Using the Sygen clinical trial data (n = 797), we analyzed the impact of the type of variable in which data is missing, the pattern according to which data is missing, and the imputation strategy (e.g. mean imputation, last observation carried forward, multiple imputation). Results Our simulations show that mean imputation may lead to results strongly deviating from the underlying expected results. For repeated measures missing at late stages (> = 6 months after injury in this simulation study), carrying the last observation forward seems the preferable option for the imputation. This simulation study could show that a one-size-fit-all imputation strategy falls short in SCI data sets. Conclusions Data-tailored imputation strategies are required (e.g., characterisation of the missingness pattern, last observation carried forward for repeated measures evolving to a plateau over time). Therefore, systematically reporting the extent, kind and decisions made regarding missing data will be essential to improve the interpretation, transparency, and reproducibility of the research presented.

Background: A more sustainable diet with fewer animal-based products has a lower ecological impact but might lead to a lower protein quantity and quality. The extent to which shifting to more plant-based diets impacts the adequacy of protein intake in older adults needs to be studied. Objectives: We simulated how a transition towards a more plant-based diet (flexitarian, pescetarian, vegetarian, or vegan) affects protein availability in the diets of older adults. Setting: Community. Participants: Data from the Dutch National Food Consumption Survey 2019–2021 of community-dwelling older adults (n = 607) was used Measurements: Food consumption data was collected via two 24 -h dietary recalls per participant. Protein availability was expressed as total protein, digestible protein, and utilizable protein (based on digestibility corrected amino acid score) intake. The percentage below estimated average requirements (EAR) for utilizable protein was assessed using an adjusted EAR. Results: Compared to the original diet (∼62% animal-based), utilizable protein intake decreased by about 5% in the flexitarian, pescetarian and vegetarian scenarios. In the vegan scenario, both total protein intake and utilizable protein were lower, leading to nearly 50% less utilizable protein compared to the original diet. In the original diet, the protein intake of 7.5% of men and 11.1% of women did not meet the EAR. This slightly increased in the flexitarian, pescetarian, and vegetarian scenarios. In the vegan scenario, 83.3% (both genders) had a protein intake below EAR. Conclusions: Replacing animal-based protein sources with plant-based food products in older adults reduces both protein quantity and quality, albeit minimally in non-vegan plant-rich diets. In a vegan scenario, the risk of an inadequate protein intake is imminent.

Simple Summary: The impact of breast cancer on individuals and in populations can be reduced if the cancer is detected in an early stage of disease as treatments are generally less harsh and more successful. Knowing which factors to modify and by what amount that could shift breast cancer diagnoses to early stages of disease were studied in this project. Factors related to stage at diagnosis in a previous analysis of women diagnosed with breast cancer were assessed using data that mimicked the real data. The proportions of modifiable lifestyle factors in a population were increased or decreased, such as those ever having a mammogram, to see the effect on detecting more cancers in the earliest stages. Similarly, the average amounts of protein eaten per day or calories consumed were also increased and decreased. Increasing the total amount of protein eaten per day by even just 5 grams was the most important factor found in this study to improve early breast cancer diagnoses. The findings from this study could be helpful in breast cancer prevention programs that could target key modifiable factors and by other researchers studying other types of cancer. Background: Breast cancer is the most common cancer in Canadian women; nearly 25% of women diagnosed with cancer have breast cancer. The early detection of breast cancer is a major challenge because tumours often grow without causing symptom. The diagnosis of breast cancer at an early stage (stages I and II) improves survival outcomes because treatments are more effective and better tolerated. To better inform the prevention of and screening for breast cancer, simulations using modifiable rather than non-modifiable risk factors may be helpful in shifting the stage at diagnosis downward. Methods: Breast cancer stages were simulated using the data distributions from Alberta's Tomorrow Project participants who developed breast cancer. Using multivariable partial proportional odds regression models, modifiable lifestyle factors associated with the stage of cancer at diagnosis were evaluated. The proportions or mean levels of these lifestyle factors in the simulated population were systematically changed, then multiplied by their corresponding estimated odds ratios from the real data example. The effects of these changes were evaluated singly as well as cumulatively. Results: Increasing total dietary protein (g/day) intake was the single most important lifestyle factor in shifting the breast cancer stage downwards followed by decreasing total dietary energy intake (kcal/day). Increasing the proportion of women who spend time in the sun between 11 am and 4 pm in the summer months, who have had a mammogram, who have been pregnant or reducing the proportion who are in stressful situations had much smaller effects. The percentage of Stage I diagnoses could be increased by approximately 12% with small modifications of these lifestyle factors. Conclusion: Shifting the breast cancer stage at diagnosis of a population may be achieved through changes to lifestyle factors. This proof of principle study that evaluated multiple factors associated with the stage at diagnosis in a population can be expanded to other cancers as well, providing opportunities for cancer prevention programs to target specific factors and identify populations at higher risk. [ABSTRACT FROM AUTHOR]

Although the change in the electoral system used in elections to the Chamber of Deputies has attracted the attention of lawyers, political scientists, and sociologists, we still lack a comprehensive comparative analysis of the new system with the original one or other alternatives. The main reason for this is the lack of empirical data. This article overcomes this problem using a simulation of electoral results that correspond to the real Czech election environment. On the basis of this simulated dataset it is possible to generate generalisable conclusions about the proportionality, integration effect, and legitimacy of three electoral formulas: the original D'Hondt divisor, the newly adopted Imperiali quota, and the Hare quota. Our results show that the Hare quota is clearly the best choice. Its advantages in proportionality significantly outweigh its disadvantages in integrative effect. Moreover, this formula sees the least disruption to the logical sequence of results, i.e. where a party with fewer votes gets more seats, a phenomenon that is undesirable and undermines the legitimacy of elections. We are convinced that among the three formulas compared the Hare quota is the one that best fits the constitutional requirements of the electoral system as interpreted by the Constitutional Court, and that - unless the legislature is planning to change other parameters of the electoral system - it is the one that should be implemented. [ABSTRACT FROM AUTHOR]

Background: Several approaches are commonly used to estimate the effect of diet on changes of various intermediate disease markers in prospective studies, including "change-score analysis", "concurrent change-change analysis" and "lagged change-change analysis". Although empirical evidence suggests that concurrent change-change analysis is most robust, consistent, and biologically plausible, in-depth dissection and comparison of these approaches from a causal inference perspective is lacking. We intend to explicitly elucidate and compare the underlying causal model, causal estimand and interpretation of these approaches, intuitively illustrate it with directed acyclic graph (DAG), and further clarify strengths and limitations of the recommended concurrent change-change analysis through simulations. Methods: Causal model and DAG are deployed to clarify the causal estimand and interpretation of each approach theoretically. Monte Carlo simulation is used to explore the performance of distinct approaches under different extents of time-invariant heterogeneity and the performance of concurrent change-change analysis when its causal identification assumptions are violated. Results: Concurrent change-change analysis targets the contemporaneous effect of exposure on outcome (measured at the same survey wave), which is more relevant and plausible in studying the associations of diet and intermediate biomarkers in prospective studies, while change-score analysis and lagged change-change analysis target the effect of exposure on outcome after one-period timespan (typically several years). Concurrent change-change analysis always yields unbiased estimates even with severe unobserved time-invariant confounding, while the other two approaches are always biased even without time-invariant heterogeneity. However, concurrent change-change analysis produces almost linearly increasing estimation bias with violation of its causal identification assumptions becoming more serious. Conclusions: Concurrent change-change analysis might be the most superior method in studying the diet and intermediate biomarkers in prospective studies, which targets the most plausible estimand and circumvents the bias from unobserved individual heterogeneity. Importantly, careful examination of the vital identification assumptions behind it should be underscored before applying this promising method. [ABSTRACT FROM AUTHOR]

The presence of missing values consistently emerges as a critical issue in most machine learning tasks, as they can alter the distribution of the training data and consequently lead to overfitting. The theoretical framework for missing value imputation has reached a considerable level of maturity, with numerous imputation models having been proposed. However, there has been limited research conducted on the underlying causes of missing values and scenarios where imbalanced data is significantly correlated with target variables due to business logic. In this study, we conducted simulation studies to evaluate the imputation performance of six imputation models on six datasets under three missing mechanisms, including random dropout, imbalance dropout based on features, and imbalance dropout based on labels, to identify an appropriate approach to deal with imbalanced missing data with certain patterns. By recognizing the missing pattern and imputing the data with a suitable imputation method, the overfitting issue caused by missingness has been significantly mitigated in a real-world application. [ABSTRACT FROM AUTHOR]

Drying is considered one of the stages of the greatest energy expenditure in grain production. And from a financial standpoint, this practice remains costly for small farmers. This study aims to evaluate the efficiency of a spiral polyethylene tube solar collector prototype (which has been tested under three different conditions) destined to heating air for agro-industrial activity for small rural properties, using a new regression model based on the extension of the normal distribution. The results indicate that the solar collectors in the three conditions present significant differences, and the solar collector with glass cover has the highest average outlet temperatures. Also, significant differences occur for the average temperature readings performed at distances of 5, 10, 20 and 25 m along the polyethylene tube. The best thermal performance of the polyethylene tube solar collector is achieved for a 20-m-long tube. The new modeling can be considered as an alternative tool for sustainable energy data analysis. [ABSTRACT FROM AUTHOR]

Background: In the last decades, medical research fields studying rare conditions such as spinal cord injury (SCI) have made extensive efforts to collect large-scale data. However, most analysis methods rely on complete data. This is particularly troublesome when studying clinical data as they are prone to missingness. Often, researchers mitigate this problem by removing patients with missing data from the analyses. Less commonly, imputation methods to infer likely values are applied. Objective: Our objective was to study how handling missing data influences the results reported, taking the example of SCI registries. We aimed to raise awareness on the effects of missing data and provide guidelines to be applied for future research projects, in SCI research and beyond. Methods: Using the Sygen clinical trial data (n = 797), we analyzed the impact of the type of variable in which data is missing, the pattern according to which data is missing, and the imputation strategy (e.g. mean imputation, last observation carried forward, multiple imputation). Results: Our simulations show that mean imputation may lead to results strongly deviating from the underlying expected results. For repeated measures missing at late stages (> = 6 months after injury in this simulation study), carrying the last observation forward seems the preferable option for the imputation. This simulation study could show that a one-size-fit-all imputation strategy falls short in SCI data sets. Conclusions: Data-tailored imputation strategies are required (e.g., characterisation of the missingness pattern, last observation carried forward for repeated measures evolving to a plateau over time). Therefore, systematically reporting the extent, kind and decisions made regarding missing data will be essential to improve the interpretation, transparency, and reproducibility of the research presented. [ABSTRACT FROM AUTHOR]

Estimation of mortality rates and mortality rate ratios (MRR) of diseased and non-diseased individuals is a core metric of disease impact used in chronic disease epidemiology. Estimation of mortality rates is often conducted through retrospective linkage of information from nationwide surveys such as the National Health Interview Survey (NHIS) and death registries. These surveys usually collect information on disease status during only one study visit. This infrequency leads to missing disease information (with right censored survival times) for deceased individuals who were disease-free at study participation, and a possibly biased estimation of the MRR because of possible undetected disease onset after study participation. This occurrence is called "misclassification of disease status at death (MicDaD)" and it is a potentially common source of bias in epidemiologic studies. In this study, we conducted a simulation analysis with a high and a low incidence setting to assess the extent of MicDaD-bias in the estimated mortality. For the simulated populations, MRR for diseased and non-diseased individuals with and without MicDaD were calculated and compared. Magnitude of MicDaD-bias depends on and is driven by the incidence of the chronic disease under consideration; our analysis revealed a noticeable shift towards underestimation for high incidences when MicDaD is present. Impact of MicDaD was smaller for lower incidence (but associated with greater uncertainty in the estimation of MRR in general). Further research can consider the amount of missing information and potential influencers such as duration and risk factors of the disease. [ABSTRACT FROM AUTHOR]

Probability distributions are widely utilized in applied sciences, especially in the field of biomedical science. Biomedical data typically exhibit positive skewness, necessitating the use of flexible, skewed distributions to effectively model such phenomena. In this study, we introduce a novel approach to characterize new lifetime distributions, known as the New Flexible Exponent Power (NFEP) Family of distributions. This involves the addition of a new parameter to existing distributions. A specific sub-model within the proposed class, known as the New Flexible Exponent Power Weibull (NFEP-Wei), is derived to illustrate the concept of flexibility. We employ the well-established Maximum Likelihood Estimation (MLE) method to estimate the unknown parameters in this family of distributions. A simulation study is conducted to assess the behavior of the estimators in various scenarios. To gauge the flexibility and effectiveness of the NFEP-Wei distribution, we compare it with the AP-Wei (alpha power Weibull), MO-Wei (Marshal Olkin Weibull), classical Wei (Weibull), NEP-Wei (new exponent power Weibull), FRLog-Wei (flexible reduced logarithmic Weibull), and Kum-Wei (Kumaraswamy Weibull) distributions by analyzing four distinct biomedical datasets. The results demonstrate that the NFEP-Wei distribution outperforms the compared distributions.

This article proposes and discusses a novel approach for generating trigonometric G-families using hybrid generalizers of distributions. The proposed generalizer is constructed by utilizing the tangent trigonometric function and distribution function of base model G(x). The newly proposed family of uni-variate continuous distributions is named the “Lomax Tangent Generalized Family of Distributions (LT-G)” and structural-mathematical-statistical properties are derived. Some special and sub-models of the proposed family are also presented.A Weibull-based model, ‘The Lomax Tangent Weibull (LT-W) Distribution,” is discussed and the plots of density (pdf) and hazard (hrf) functions are also explained. Model parameter estimates are estimated by employing the maximum likelihood estimation (MLE) procedure. The accuracy of the MLEs is evaluated through Monte Carlo simulation. Last but not least, to demonstrate the flexibility and potential of the proposed distribution, two actual hydrological and strength data sets are analyzed. The obtained results are compared with well-known, competitive, and related existing distributions.

The concept of connected autonomous shuttles is gaining recognition for its potential to revolutionize traditional public transport by offering a safer and more consistent travel experience. Despite technological advancements facilitating their integration into current traffic systems, research in this area is still exploratory, with public acceptance and trust in autonomous technology posing significant challenges. This paper contributes to the field by validating the benefits of deploying connected autonomous shuttles and examining their impact on ridership via an immersive experience. We introduce a VR-enabled co-simulation strategy to analyze the effects of replacing a traditional bus service line with connected shuttles in Melbourne, Australia. Our case study results reveal that while autonomous shuttles can reduce travel time because of optimized vehicle motion, their traffic efficiency is affected by fleet size, with the optimal fleet size identified as four in the study area. Furthermore, we observed a mismatch between participants’ stated intentions and actual boarding behavior, indicating that the attractive appearance of this novel mobility mode may not necessarily enhance ridership. Our work offers an alternative approach to simulation studies in futuristic public transportation and complement existing literature in the field.

Cross-sectional data allow the investigation of how genetics influence health at a single time point, but to understand how the genome impacts phenotype development, one must use repeated measures data. Ignoring the dependency inherent in repeated measures can exacerbate false positives and requires the utilization of methods other than general or generalized linear models. Many methods can accommodate longitudinal data, including the commonly used linear mixed model and generalized estimating equation, as well as the less popular fixed-effects model, cluster-robust standard error adjustment, and aggregate regression. We simulated longitudinal data and applied these five methods alongside naïve linear regression, which ignored the dependency and served as a baseline, to compare their power, false positive rate, estimation accuracy, and precision. The results showed that the naïve linear regression and fixed-effects models incurred high false positive rates when analyzing a predictor that is fixed over time, making them unviable for studying time-invariant genetic effects. The linear mixed models maintained low false positive rates and unbiased estimation. The generalized estimating equation was similar to the former in terms of power and estimation, but it had increased false positives when the sample size was low, as did cluster-robust standard error adjustment. Aggregate regression produced biased estimates when predictor effects varied over time. To show how the method choice affects downstream results, we performed longitudinal analyses in an adolescent cohort of African and European ancestry. We examined how developing post-traumatic stress symptoms were predicted by polygenic risk, traumatic events, exposure to sexual abuse, and income using four approaches—linear mixed models, generalized estimating equations, cluster-robust standard error adjustment, and aggregate regression. While the directions of effect were generally consistent, coefficient magnitudes and statistical significance differed across methods. Our in-depth comparison of longitudinal methods showed that linear mixed models and generalized estimating equations were applicable in most scenarios requiring longitudinal modeling, but no approach produced identical results even if fit to the same data. Since result discrepancies can result from methodological choices, it is crucial that researchers determine their model a priori, refrain from testing multiple approaches to obtain favorable results, and utilize as similar as possible methods when seeking to replicate results.

In the social sciences, accurately identifying the dimensionality of measurement scales is crucial for understanding latent constructs such as anxiety, happiness, and self-efficacy. This study presents a rigorous comparison between Parallel Analysis (PA) and Exploratory Graph Analysis (EGA) for assessing the dimensionality of scales, particularly focusing on ordinal data. Through an extensive simulation study, we evaluated the effectiveness of these methods under various conditions, including varying sample size, number of factors and their association, patterns of loading magnitudes, and symmetrical or skewed item distributions with assumed underlying normality or non-normality. Results show that the performance of each method varies across different scenarios, depending on the context. EGA consistently outperforms PA in correctly identifying the number of factors, particularly in complex scenarios characterized by more than a single factor, high inter-factor correlations and low to medium primary loadings. However, for datasets with simpler and stronger factor structures, specifically those with a single factor, high primary loadings, low cross-loadings, and low to moderate interfactor correlations, PA is suggested as the method of choice. Skewed item distributions with assumed underlying normality or non-normality were found to noticeably impact the performance of both methods, particularly in complex scenarios. The results provide valuable insights for researchers utilizing these methods in scale development and validation, ensuring that measurement instruments accurately reflect theoretical constructs.

Aim: This simulation study is to assess the utility of physician’s prescribing preference (PPP) as an instrumental variable for moderate and smaller sample sizes. Materials & methods: We designed a simulation study to imitate a comparative effectiveness research under different sample sizes.We compare the performance of instrumental variable (IV) and non-IV approaches using two-stage least squares (2SLS) and ordinary least squares (OLS) methods, respectively. Further, we test the performance of different forms of proxies for PPP as an IV. Results: The percent bias of 2SLS is around approximately 20%, while the percent bias of OLS is close to 60%. The sample size is not associated with the level of bias for the PPP IV approach. Conclusion: Irrespective of sample size, the PPP IV approach leads to less biased estimates of treatment effectiveness than OLS adjusting for known confounding only. Particularly for smaller sample sizes, we recommend constructing PPP from long prescribing histories to improve statistical power.

Assessment practices are, among other things, concerned with issues of fairness and appropriate score interpretation, in particular when making claims about subgroup differences in performance are of interest. In order to make such claims, a psychometric concept of measurement invariance or differential item functioning (DIF) ought to be considered and met. Over the last decades, researchers have proposed and developed a plethora of methods aimed at detecting DIF. However, DIF detection methods that allow multilevel data structures to be modeled are limited and understudied. In the current study, we evaluated the performance of four methods, including the model-based multilevel Wald and the score-based multilevel Mantel–Haenszel (MH), and two well-established single-level methods, the model-based single-level Lord and the score-based single-level MH. We conducted a simulation study that mimics real-world scenarios. Our results suggested that when data were generated as multilevel, mixed results regarding performances were observed, and not one method consistently outperformed the others. Single-level Lord and multilevel Wald yielded best control of the Type I error rates, in particular in conditions when latent means were generated as equal for the two groups. Power rates were low across all four methods in conditions with small number of between- and within-level units and when small DIF was modeled. However, in those conditions, single-level MH and multilevel MH yielded higher power rates than either single-level Lord or multilevel Wald. This suggests that current practices in detecting DIF should strongly consider adopting one of the more recent methods only in certain contexts as the tradeoff between power and complexity of the method may not warrant a blanket recommendation in favor of a single method. Limitations and future research directions are also discussed.

AbstractThe present study suggests an enhanced class of estimators for the population mean estimation utilizing simple random sampling (SRS). The bias, mean square errors (MSE), and minimum MSE of the suggested estimators are computed to the approximation of order one. The efficiency conditions are obtained by comparing the MSE of the proposed and existing estimators. An empirical investigation is carried out using some real and artificially generated symmetric and asymmetric populations. The empirical findings are observed to be positive, clearly demonstrating the preponderance of the suggested estimators over the existing estimators.

The main objective of the current study is to suggest an enhanced family of log ratio-exponential type estimators for population distribution function (DF) using auxiliary information under stratified random sampling. Putting different choices in our suggested generalized class of estimators, we found some Specific estimators. The bias and MSE expressions of the estimators have been approximated up to the first order. By using the actual and simulated data sets, we measured the performance of estimators. Based on the results, the suggested estimators for DF show better performance as compared to the preliminary estimators considered here. The suggested estimators have a advanced efficiency than the other estimators examined with the estimators F‾ˆlogPR(st)2, and F‾ˆlogPR(st)4 for both the actual and simulated data sets. The magnitude of the improvement in efficiency is noteworthy, indicating the superiority of the proposed estimators in terms of MSE.