Investigates whether emotional intelligenc e measured by the Swinburne University Emotional Intelligenc e Test predicted transformational , transactiona l and laissez-faire leadership styles measured by the multifactor leadershi p questionnair e in 110 senior level managers. Effective leaders were identifie d as those who reported transformationa l rather than transactiona l behaviours. Emotional intelligence correlate d highly with all components of transformationa l leadership, with the components of understandin g of emotions (external) and emotional management the best predictors of this type of leadershi p style. The utility of emotional intelligence testing in leadershi p selectio n and developmen t is discussed .
This study describes the development and validation of a new and improved body-image assessment tool, the BIAS-BD, based on known body dimensions. The scale's construction consisted of 17 male and 17 female contour-line drawings that used known anthropometric body dimensions of shoulder, chest, waist, hip breadth, thigh breadth, and upper leg breadth. The figural drawings correspond to a series of body weights ranging from 60% below the known average to 140% above average. Differences between figural drawings represented a 5% change in body weight. Participants were 207 undergraduates, including 66 men and 141 women, who selected drawings that reflected their perceived size and their ideal size. Retesting occurred after a 2-week interval and resulted in test-retest reliability values of r=.86 for actual perceived size, r=.72 for ideal size, and r=.76 for body dissatisfaction (p<.005). There were no significant differences in reliability values between genders. Mean differences in perceived size, ideal size, and body dissatisfaction between the two test administrations were small. Concurrent validity, measured as the correspondence between perceived and reported size, was r=.76 (p<.005). Participants slightly overestimated their perceived body size, with women overestimating significantly more. Unlike existing scales, the present scale uses figural drawings based on known body dimensions and has better reliability and validity. It avoids several problems inherent in existing scales, including scale coarseness, the presence of ethnic facial and body features, and the lack of documented reliability and validity values.
Advances in artificial intelligence for image processing hold great promise for increasing the scales at which ecological systems can be studied. The distribution and behavior of individuals is central to ecology, and computer vision using deep neural networks can learn to detect individual objects in imagery. However, developing computer vision for ecological monitoring is challenging because it needs large amounts of human-labeled training data, requires advanced technical expertise and computational infrastructure, and is prone to overfitting. This limits application across space and time. One solution is developing generalized models that can be applied across species and ecosystems. Using over 250,000 annotations from 13 projects from around the world, we develop a general bird detection model that achieves over 65% recall and 50% precision on novel aerial data without any local training despite differences in species, habitat, and imaging methodology. Fine-tuning this model with only 1000 local annotations increases these values to an average of 84% recall and 69% precision by building on the general features learned from other data sources. Retraining from the general model improves local predictions even when moderately large annotation sets are available and makes model training faster and more stable. Our results demonstrate that general models for detecting broad classes of organisms using airborne imagery are achievable. These models can reduce the effort, expertise, and computational resources necessary for automating the detection of individual organisms across large scales, helping to transform the scale of data collection in ecology and the questions that can be addressed.
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