Sex-Positive Curricula: An Investigation of the Relationship Between Physical Fitness, Self-Concept and Sexual Functioning

Despite the well-recognized benefits of exercise, Americans are gaining weight in astounding proportions and levels of physical activity are on the decline. The purpose of this study was to investigate a relationship between physical fitness, self-concept and sexual health. There is a dearth of knowledge on this relationship specifically in the context of sex-negative curricula, which is the dominate discourse in the United States. One hundred and thirty-three participants between the ages of 18 - 50 volunteered for fitness testing and data collection. Physical fitness was assessed through body fat, resting metabolic rate, cardiovascular endurance, muscular strength, muscular endurance and flexibility. Self-reported exercise was measured using the International Physical Activity Questionnaire. Self-concept was measured by the Six Factor Self-Concept Scale, which presented a total self-concept score and as six individual concepts of self (likability, morality, task accomplishment, giftedness, power and vulnerability). Additionally, sexual function was measured by Derogatis Interview for Sexual Functioning and presented as both an aggregate score and five separate constructs of sexual functioning (fantasy/cognition, arousal, orgasm, behavior/experience, and drive/desire). Questions pertaining to sexual partners, sex education, and demographic information were also included. The results of the General Linear Model indicated significant relationships between physical fitness, self-concept and total sexual functioning. The sexual behavior/experience of men was predicted by body fat percentage and flexibility. In women, behavior/experience was predicted by body fat percentage and arousal was predicted by cardiovascular endurance. Total self-concept was related to muscular endurance. When men were isolated in the analysis, likability was positively related to sexual behavior/experience, and task accomplishment was inversely related to sexual behavior/experience. In women, giftedness was related to cognition/fantasy, arousal, orgasm and total sexual functioning. No relationships were found between physical fitness and the number of sexual partners in men; however, both muscular strength and the power self-concept were significantly related to number of sexual partners in women. As a result of these findings, women may be inclined to exercise to improve arousal and sexual functioning. Furthermore, educators should note the findings of a positive relationship between physical and psychological health and sexual well-being because they provide support for the development and adoption of sex-positive curricula that incorporate potential benefits of sexual activity.

The Relationships between Elementary School Principals' Emotional Intelligence, Leadership Style and School Culture

The role of the principal in school settings and the principal’s perceived effect on student achievement have frequently been considered vital factors in school reform. The relationships between emotional intelligence, leadership style and school culture have been widely studied. The literature reveals agreement among scholars regarding the principal’s vital role in developing and fostering a positive school culture. The purpose of this study was to explore the relationships between elementary school principals’ emotional intelligence, leadership style and school culture. The researcher implemented a non-experimental ex post facto research design to investigate four specific research hypotheses. Utilizing the Qualtrics Survey Software, 57 elementary school principals within a large urban school district in southeast Florida completed the Emotional Quotient Inventory (EQ-i), and 850 of their faculty members completed the Multifactor Leadership Questionnaire (MLQ Form 5X). Faculty responses to the school district’s School Climate Survey retrieved from the district’s web site were used as the measure of school culture. Linear regression analyses revealed significant positive associations between emotional intelligence and the following leadership measures: Idealized Influence-Attributes (β = .23, p = < .05), Idealized Influence-Behaviors (β = .34, p = < .01), Inspirational Motivation (β = .39, p = < .01) and Contingent Reward (β = .33, p = < .01). Hierarchical regression analyses revealed positive associations between school culture and both transformational and transactional leadership measures, and negative associations between school culture and passive-avoidant leadership measures. Significant positive associations were found between school culture and the principals’ emotional intelligence over and above leadership style. Hierarchical linear regressions to test the statistical hypothesis developed to account for alternative explanations revealed significant associations between leadership style and school culture over and above school grade. These results suggest that emotional intelligence merits consideration in the development of leadership theory. Practical implications include suggestions that principals employ both transformational and transactional leadership strategies, and focus on developing their level of emotional intelligence. The associations between emotional intelligence, transformational leadership, Contingent Reward and school culture found in this study validate the role of the principal as the leader of school reform.

Simple approximate MAP inference for Dirichlet processes mixtures

The Dirichlet process mixture model (DPMM) is a ubiquitous, flexible Bayesian nonparametric statistical model. However, full probabilistic inference in this model is analytically intractable, so that computationally intensive techniques such as Gibbs sampling are required. As a result, DPMM-based methods, which have considerable potential, are restricted to applications in which computational resources and time for inference is plentiful. For example, they would not be practical for digital signal processing on embedded hardware, where computational resources are at a serious premium. Here, we develop a simplified yet statistically rigorous approximate maximum a-posteriori (MAP) inference algorithm for DPMMs. This algorithm is as simple as DP-means clustering, solves the MAP problem as well as Gibbs sampling, while requiring only a fraction of the computational effort. (For freely available code that implements the MAP-DP algorithm for Gaussian mixtures see http://www.maxlittle.net/.) Unlike related small variance asymptotics (SVA), our method is non-degenerate and so inherits the “rich get richer” property of the Dirichlet process. It also retains a non-degenerate closed-form likelihood which enables out-of-sample calculations and the use of standard tools such as cross-validation. We illustrate the benefits of our algorithm on a range of examples and contrast it to variational, SVA and sampling approaches from both a computational complexity perspective as well as in terms of clustering performance. We demonstrate the wide applicabiity of our approach by presenting an approximate MAP inference method for the infinite hidden Markov model whose performance contrasts favorably with a recently proposed hybrid SVA approach. Similarly, we show how our algorithm can applied to a semiparametric mixed-effects regression model where the random effects distribution is modelled using an infinite mixture model, as used in longitudinal progression modelling in population health science. Finally, we propose directions for future research on approximate MAP inference in Bayesian nonparametrics.

Transferability of environmental favourability models in geographic space: The case of the Iberian desman (Galemys pyrenaicus) in Portugal and Spain

Transferring distribution models between different geographical areas may be problematic, as the performance of models outside their original scope is hard to predict. A modelling procedure is needed that gets the gist of the environmental descriptors of a distribution area, without either overfitting to the training data or overestimating the species’ distribution potential.We tested the transferability power of the favourability function, a generalized linear model, on the distribution of the Iberian desman (Galemys pyrenaicus) in the Iberian territories of Portugal and Spain.We also tested the effects of two of the main potential constraints on model transferability: the analysed ranges of the predictor variables, and the completeness of the species distribution data. We modelled 10 km×10km presence/absence data from Portugal and Spain separately, extrapolated each model to the other country, and compared predictions with observations. The Spanish model, despite arguably containing more false absences, showed good predictive ability in Portugal. The Portuguese model, whose predictors ranged between only a subset of the values observed in Spain, overestimated desman distribution when transferred.We discuss possible reasons for this differential model behaviour, and highlight the importance of this kind of models for prediction and conservation applications

Portuguese ecotypes of Opuntia ficus-indica differ in biomass production

The Opuntia ficus-indica (L.) Miller is a species from the Cactaceae family with the center of origin and domestication in central Mexico. This species introduction in the Iberia Peninsula occurred, probably, by the end of the 15th century, after the discovery of America, spreading later throughout the Mediterranean basin. In Portugal, O. ficus-indica is located, usually, with a typical ruderal behavior, at the edge of roads and paths. In Portugal, as in other Mediterranean regions, inlands areas are under severe draught during extensive summers, in particular, and global warming is expected to affect them deeply in the near future. O. ficus-indica, by its morpho-physiological characteristics and multiple economic uses, represent an alternative crop for those regions. Sixteen Portuguese O. ficus indica ecotypes and two ‘Italian’ cultivars ("Gialla" and "Bianca") were evaluated for plant vigor and biomass production, by nondestructive methods, in the two years following planting. Biomass production and plant vigor were measured by estimating cladode number, cladode area and fresh weight per plant. Linear models to predict the area of cladodes and fresh weight per plant were previously established using a biometric analysis of 180 cladodes. It was not possible to establish an accurate linear model for dry matter using non-destructive estimation. Significant differences were found among populations in the studied biomass-related parameters, and different groups were unfolded. A group of four Portuguese ecotypes outperformed in terms of biomass production, comparable with the “Gialla” cultivar. This group could be used to start a breeding program with the objective of deploy material for animal feeding, biomass and fruit production. Nevertheless, the ‘Gialla’ cultivar showed the best performance, achieving the highest biomass related parameters, not surprisingly for it is an improved plant material.

Dry grinding technology for automotive gears manufacturing: process modeling and optimization

The following thesis focused on the dry grinding process modelling and optimization for automotive gears production. A FEM model was implemented with the aim at predicting process temperatures and preventing grinding thermal defects on the material surface. In particular, the model was conceived to facilitate the choice of the grinding parameters during the design and the execution of the dry-hard finishing process developed and patented by the company Samputensili Machine Tools (EMAG Group) on automotive gears. The proposed model allows to analyse the influence of the technological parameters, comprising the grinding wheel specifications. Automotive gears finished by dry-hard finishing process are supposed to reach the same quality target of the gears finished through the conventional wet grinding process with the advantage of reducing production costs and environmental pollution. But, the grinding process allows very high values of specific pressure and heat absorbed by the material, therefore, removing the lubricant increases the risk of thermal defects occurrence. An incorrect design of the process parameters set could cause grinding burns, which affect the mechanical performance of the ground component inevitably. Therefore, a modelling phase of the process could allow to enhance the mechanical characteristics of the components and avoid waste during production. A hierarchical FEM model was implemented to predict dry grinding temperatures and was represented by the interconnection of a microscopic and a macroscopic approach. A microscopic single grain grinding model was linked to a macroscopic thermal model to predict the dry grinding process temperatures and so to forecast the thermal cycle effect caused by the process parameters and the grinding wheel specification choice. Good agreement between the model and the experiments was achieved making the dry-hard finishing an efficient and reliable technology to implement in the gears automotive industry.

Towards the circularity of outdoor lighting systems

In recent years, there has been increasing attention to lighting energy efficiency, due to economics - lower energy costs - and environmental reasons - maninduced climate change. Driven by strict energy-efficiency requirements, the lighting industry started to replace the traditional lamps with LED lighting solutions, ignoring the limits of their maintenance and recycling. Faced with an increasing global population, rising resource consumption and associated negative environmental impacts, shifting from a traditional economic linear model to a more sustainable paradigm of growth is now becoming increasingly urgent. Whereas the topic of circular economy has been widely investigated in literature in the past, little attention has been reserved for the different evaluation tools to assess and improve product circularity and how companies can become more resource-efficient. Hence, the present thesis investigates the implementation of a circular economy in the lighting industry through the use of circularity indicators and ecodesign strategies. Concerning the real luminaire products, the role of the luminaire in the circular economy and recycling industry is explored, highlighting the limits of their End-of-life process. The main conclusions of the thesis reveal the significance of initial product development, reuse, remanufacturing and repair strategies in a transition towards a circular economy.

Skew-normal linear calibration: a Bayesian perspective

In this paper, we present a Bayesian approach for estimation in the skew-normal calibration model, as well as the conditional posterior distributions which are useful for implementing the Gibbs sampler. Data transformation is thus avoided by using the methodology proposed. Model fitting is implemented by proposing the asymmetric deviance information criterion, ADIC, a modification of the ordinary DIC. We also report an application of the model studied by using a real data set, related to the relationship between the resistance and the elasticity of a sample of concrete beams. Copyright (C) 2008 John Wiley & Sons, Ltd.

Parameter recovery for a skew-normal IRT model under a Bayesian approach: hierarchical framework, prior and kernel sensitivity and sample size

Item response theory (IRT) comprises a set of statistical models which are useful in many fields, especially when there is an interest in studying latent variables (or latent traits). Usually such latent traits are assumed to be random variables and a convenient distribution is assigned to them. A very common choice for such a distribution has been the standard normal. Recently, Azevedo et al. [Bayesian inference for a skew-normal IRT model under the centred parameterization, Comput. Stat. Data Anal. 55 (2011), pp. 353-365] proposed a skew-normal distribution under the centred parameterization (SNCP) as had been studied in [R. B. Arellano-Valle and A. Azzalini, The centred parametrization for the multivariate skew-normal distribution, J. Multivariate Anal. 99(7) (2008), pp. 1362-1382], to model the latent trait distribution. This approach allows one to represent any asymmetric behaviour concerning the latent trait distribution. Also, they developed a Metropolis-Hastings within the Gibbs sampling (MHWGS) algorithm based on the density of the SNCP. They showed that the algorithm recovers all parameters properly. Their results indicated that, in the presence of asymmetry, the proposed model and the estimation algorithm perform better than the usual model and estimation methods. Our main goal in this paper is to propose another type of MHWGS algorithm based on a stochastic representation (hierarchical structure) of the SNCP studied in [N. Henze, A probabilistic representation of the skew-normal distribution, Scand. J. Statist. 13 (1986), pp. 271-275]. Our algorithm has only one Metropolis-Hastings step, in opposition to the algorithm developed by Azevedo et al., which has two such steps. This not only makes the implementation easier but also reduces the number of proposal densities to be used, which can be a problem in the implementation of MHWGS algorithms, as can be seen in [R.J. Patz and B.W. Junker, A straightforward approach to Markov Chain Monte Carlo methods for item response models, J. Educ. Behav. Stat. 24(2) (1999), pp. 146-178; R. J. Patz and B. W. Junker, The applications and extensions of MCMC in IRT: Multiple item types, missing data, and rated responses, J. Educ. Behav. Stat. 24(4) (1999), pp. 342-366; A. Gelman, G.O. Roberts, and W.R. Gilks, Efficient Metropolis jumping rules, Bayesian Stat. 5 (1996), pp. 599-607]. Moreover, we consider a modified beta prior (which generalizes the one considered in [3]) and a Jeffreys prior for the asymmetry parameter. Furthermore, we study the sensitivity of such priors as well as the use of different kernel densities for this parameter. Finally, we assess the impact of the number of examinees, number of items and the asymmetry level on the parameter recovery. Results of the simulation study indicated that our approach performed equally as well as that in [3], in terms of parameter recovery, mainly using the Jeffreys prior. Also, they indicated that the asymmetry level has the highest impact on parameter recovery, even though it is relatively small. A real data analysis is considered jointly with the development of model fitting assessment tools. The results are compared with the ones obtained by Azevedo et al. The results indicate that using the hierarchical approach allows us to implement MCMC algorithms more easily, it facilitates diagnosis of the convergence and also it can be very useful to fit more complex skew IRT models.

Bayesian change-point analysis in linear regression model with scale mixtures of normal distributions

In this thesis, we consider Bayesian inference on the detection of variance change-point models with scale mixtures of normal (for short SMN) distributions. This class of distributions is symmetric and thick-tailed and includes as special cases: Gaussian, Student-t, contaminated normal, and slash distributions. The proposed models provide greater flexibility to analyze a lot of practical data, which often show heavy-tail and may not satisfy the normal assumption. As to the Bayesian analysis, we specify some prior distributions for the unknown parameters in the variance change-point models with the SMN distributions. Due to the complexity of the joint posterior distribution, we propose an efficient Gibbs-type with Metropolis- Hastings sampling algorithm for posterior Bayesian inference. Thereafter, following the idea of [1], we consider the problems of the single and multiple change-point detections. The performance of the proposed procedures is illustrated and analyzed by simulation studies. A real application to the closing price data of U.S. stock market has been analyzed for illustrative purposes.

A Hierarchical Bayesian Model for Measuring Motion Adaptation

Previous research has shown that motion imagery draws on the same neural circuits that are involved in perception of motion, thus leading to a motion aftereffect (Winawer et al., 2010). Imagined stimuli can induce a similar shift in participants’ psychometric functions as neural adaptation due to a perceived stimulus. However, these studies have been criticized on the grounds that they fail to exclude the possibility that the subjects might have guessed the experimental hypothesis, and behaved accordingly (Morgan et al., 2012). In particular, the authors claim that participants can adopt arbitrary response criteria, which results in similar changes of the central tendency μ of psychometric curves as those shown by Winawer et al. (2010).

An empirical hierarchical Bayesian unification of occupational exposure assessment methods.

In occupational exposure assessment of airborne contaminants, exposure levels can either be estimated through repeated measurements of the pollutant concentration in air, expert judgment or through exposure models that use information on the conditions of exposure as input. In this report, we propose an empirical hierarchical Bayesian model to unify these approaches. Prior to any measurement, the hygienist conducts an assessment to generate prior distributions of exposure determinants. Monte-Carlo samples from these distributions feed two level-2 models: a physical, two-compartment model, and a non-parametric, neural network model trained with existing exposure data. The outputs of these two models are weighted according to the expert's assessment of their relevance to yield predictive distributions of the long-term geometric mean and geometric standard deviation of the worker's exposure profile (level-1 model). Bayesian inferences are then drawn iteratively from subsequent measurements of worker exposure. Any traditional decision strategy based on a comparison with occupational exposure limits (e.g. mean exposure, exceedance strategies) can then be applied. Data on 82 workers exposed to 18 contaminants in 14 companies were used to validate the model with cross-validation techniques. A user-friendly program running the model is available upon request.