Project description:ObjectivesWhen examining the relationship between smoking intensity and toxicant exposure biomarkers in an effort to understand the potential risk for smoking-related disease, individual biomarkers may not be strongly associated with smoking intensity because of the inherent variability in biomarkers. Structural equation modeling (SEM) offers a powerful solution by modeling the relationship between smoking intensity and multiple biomarkers through a latent variable.MethodsBaseline data from a randomized trial (N = 1250) were used to estimate the relationship between smoking intensity and a latent toxicant exposure variable summarizing five volatile organic compound biomarkers. Two variables of smoking intensity were analyzed: the self-report cigarettes smoked per day and total nicotine equivalents in urine. SEM was compared with linear regression with each biomarker analyzed individually or with the sum score of the five biomarkers.ResultsSEM models showed strong relationships between smoking intensity and the latent toxicant exposure variable, and the relationship was stronger than its counterparts in linear regression with each biomarker analyzed separately or with the sum score.ConclusionsSEM is a powerful multivariate statistical method for studying multiple biomarkers assessing the same class of harmful constituents. This method could be used to evaluate exposure from different combusted tobacco products.
Project description:At least 282 Food Policy Councils (FPCs) are currently working to improve access to healthy foods in their communities by connecting food system sectors, gathering community input, and advising food policy. Empirical research on FPCs is limited. This study empirically evaluates FPCs to better understand the relationships between Organizational Capacity, Social Capital, and Council Effectiveness by testing a FPC Framework adapted from Allen and colleagues (2012). Members of all FPCs in the U.S., Canada, and Native American Tribes and First Nations were invited to complete the Food Policy Council Self-Assessment Tool (FPC-SAT). Structural equation modeling was used to test the FPC Framework. Three hundred and fifty-four FPC members from 95 councils completed the FPC-SAT. After slight modification, a revised FPC Framework was a good fit with the data (?2 = 40.085, df = 24, p-value = .021, comparative fit index = 0.988, Tucker Lewis index = 0.982, root mean squared error of approximation = 0.044, p-close = .650). A moderation analysis revealed that community context influences the relationship between Social Capital and Council Effectiveness within the FPC Framework. The FPC Framework can guide capacity building interventions and FPC evaluations. The empirically tested framework can help FPCs efficiently work toward achieving their missions and improving their local food system.
Project description:In a context where pandemic crises and chronic conditions are a constant and increasing threat, the success of public health projects is absolutely critical. However, little is known about the factors that influence the success of projects that aim to provide conditions for people to be healthy and prolong the life of the population as a whole. A mixed-method study was carried out to fill the literature gap, resulting in a new model of success factors for public health projects. The research work theorizes the success factors that impact public health project success, providing relevant knowledge for project managers and contributing to the successful management of public health projects.
Project description:BackgroundThe structural abnormality of the heart and its blood vessels at the time of birth is known as congenital heart disease. Every year in Pakistan, sixty thousand children are born with CHD, and 44 in 1000 die before they are a month old. Various studies used different techniques to estimate the risk factors of congenital heart disease, but these techniques suffer from a deficiency of capacity to present human understanding and a deficiency of adequate data. The current study provided an innovative approach by defining the latent variables to handle this issue and building a reasonable model.MethodData used in this study has been collected from mothers and hospital records of the children. The dataset contains information on 3900 children who visited the OPD of the Chaudry Pervaiz Elahi Institute of Cardiology (CPEIC) Multan, Pakistan from October 2021 to September 2022. The latent variables were defined from the data and structural equation modeling was used to model them.ResultThe results show that there are 53.6% of males have acyanotic CHD and 54.5% have cyanotic CHD. There are 46.4% of females have acyanotic CHD and 45.5% have cyanotic CHD. The children who have no diabetes in the family are 64.0% and children who have diabetes in the family are 36.0% in acyanotic CHD, the children who have no diabetes in the family are 59.7% and children have diabetes in the family are 40.3% in cyanotic CHD. The value of standardized root mean residual is 0.087 is less than 0.089 which shows that the model is a good fit. The value of root mean square error of approximation is 0.113 is less than 0.20 which also shows the good fit of the model.ConclusionIt was concluded that the model is a good fit. Also, the latent variables, socioeconomic factors, and environmental factors of mothers during pregnancy have a significant effect in causing cyanotic while poor general health factor increases the risk of Acyanotic congenital heart disease.
Project description:This paper examines the construct validity of the spiritual leadership model proposed by (Fry et al. 2005). The analysis focused on examining the relationships proposed by the model through CFA and structural equation modeling (SEM). A confirmatory factor analysis indicated the SL scale provides acceptable reliability and convergent validity indexes; however, it did not achieve discriminant validity. Model convergence was obtained using MLR (Robust Maximum Likelihood) methods. However, when the robustness indices were analyzed, it was found that some obtained acceptable results and others were deficient, so that an acceptable model fit was not achieved. Regarding the relationship between the hypotheses, it was found that they were significant in all cases except for the reciprocal relationship between vision and altruistic love. In light of this finding, alternative models were developed that also failed to yield significant results. The theoretical and methodological discussion focuses on the relationships of Fry's model and addresses the need to review its causal nature, considering recursive and non-recursive aspects.
Project description:In the field of structural equation modeling (SEM), all commonly used case influence measures are model-based measures whose performance are affected by target-model-misspecification-error. This problem casts light on the need to come up with a model-free measure which avoids the misspecification problem. the main purpose of this study is to introduce a model-free case influence measure, the Deleted- One-Covariance-Residual (DOCR), and then evaluating its performance compared to that of Mahalanobis distance (MD) and generalized Cook's distance (gCD). The data of this study were simulated under three systematically manipulated conditions: the sample size, the proportion of target cases to non-target cases, and the type of model used to generate the data. The findings suggest that the DOCR measure generally performed better than MD and gCD in identifying the target cases across all simulated conditions. However, the performance of the DOCR measure under a small sample size was not satisfactory, and it raised a red flag about the sensitivity of this measure to small sample size. Therefore, researchers and practitioners should only use the DOCR measure with a sufficiently large sample size, but not larger than 600.
Project description:In our social lives, movement's attractiveness greatly affects interpersonal cognition, and gait kinematics mediates walkers' attractiveness. However, no model using gait kinematics has so far predicted gait attractiveness. Thus, this study constructed models of female gait attractiveness with gait kinematics and physique factors as explanatory variables for both barefoot and high-heel walking. First, using motion capture data from 17 women walking, including seven professional runway models, we created gait animations. We also calculated the following gait kinematics as candidate variables to explain walking's attractiveness: four body-silhouette-related variables and six health-related variables. Then, 60 observers evaluated each gait animation's attractiveness and femininity. We performed correlation analysis between these variables and evaluation scores to obtain explanatory variables. Structural equation modeling suggested two models for gait attractiveness, one composed of trunk and head silhouette factors and the other of physique, trunk silhouette, and health-related gait factors. The study's results deepened our understanding of mechanisms behind nonverbal interpersonal cognition through physical movement and brought us closer to realization of artificial generation of attractive gait motions.
Project description:Within structural equation modeling, the most prevalent model to investigate measurement bias is the multigroup model. Equal factor loadings and intercepts across groups in a multigroup model represent strong factorial invariance (absence of measurement bias) across groups. Although this approach is possible in principle, it is hardly practical when the number of groups is large or when the group size is relatively small. Jak et al. (2013) showed how strong factorial invariance across large numbers of groups can be tested in a multilevel structural equation modeling framework, by treating group as a random instead of a fixed variable. In the present study, this model is extended for use with three-level data. The proposed method is illustrated with an investigation of strong factorial invariance across 156 school classes and 50 schools in a Dutch dyscalculia test, using three-level structural equation modeling.
Project description:In human sperm motility analysis, sperm segmentation plays an important role to determine the location of multiple sperms. To ensure an improved segmentation result, the Laplacian of Gaussian filter is implemented as a kernel in a pre-processing step before applying the image segmentation process to automatically segment and detect human spermatozoa. This study proposes an intersecting cortical model (ICM), which was derived from several visual cortex models, to segment the sperm head region. However, the proposed method suffered from parameter selection; thus, the ICM network is optimised using particle swarm optimization where feature mutual information is introduced as the new fitness function. The final results showed that the proposed method is more accurate and robust than four state-of-the-art segmentation methods. The proposed method resulted in rates of 98.14%, 98.82%, 86.46% and 99.81% in accuracy, sensitivity, specificity and precision, respectively, after testing with 1200 sperms. The proposed algorithm is expected to be implemented in analysing sperm motility because of the robustness and capability of this algorithm.
Project description:The high-dimensional search space involved in markerless full-body articulated human motion tracking from multiple-views video sequences has led to a number of solutions based on metaheuristics, the most recent form of which is Particle Swarm Optimization (PSO). However, the classical PSO suffers from premature convergence and it is trapped easily into local optima, significantly affecting the tracking accuracy. To overcome these drawbacks, we have developed a method for the problem based on Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization (H-MCPSO). The tracking problem is formulated as a non-linear 34-dimensional function optimization problem where the fitness function quantifies the difference between the observed image and a projection of the model configuration. Both the silhouette and edge likelihoods are used in the fitness function. Experiments using Brown and HumanEva-II dataset demonstrated that H-MCPSO performance is better than two leading alternative approaches-Annealed Particle Filter (APF) and Hierarchical Particle Swarm Optimization (HPSO). Further, the proposed tracking method is capable of automatic initialization and self-recovery from temporary tracking failures. Comprehensive experimental results are presented to support the claims.