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- Language: English
- Creators: Ira A. Fulton Schools of Engineering
To understand the role communication and effective management play in the project management field, virtual work was analyzed in two phases. Phase one consisted of gaining familiarity within the field of project management by interviewing three project managers who discussed their field of work, how it has changed due to Covid-19, approaches to communication and virtual team management, and strategies that allow for effective project management. Phase two comprised a simulation in which 8 ASU student volunteers were put into scenarios that required completing and executing a given project. Students gained project experience through the simulation and had an opportunity to reflect on their project experience.
The current study examines the effects that college students' personal characteristics, such as age, sex, gender, or race/ethnicity, have on students’ perceptions of perceived victim blameworthiness. This study also examines how college students’ perceptions of blameworthiness change after being exposed to real life sexual assault vignettes that tap into issues surrounding rape myths. Specifically, I assess blameworthiness perceptions surrounding rape myths regarding clothing, drinking, and various situational characteristics. Blameworthiness perceptions were examined through a survey with pre-test and post-test questions that occurred before and after the student reviewed different sexual assault vignettes. Descriptive statistics show that the majority of college students, after being introduced to the vignettes, reduced their blameworthiness beliefs. Results from the regression analysis show that few individual characteristics are associated with changes in blameworthiness beliefs. Overall, these findings suggest that exposure to sexual assault vignettes have an effect on how individuals perceive victim blameworthiness.
As the world’s population exponentially grows, more food production is required. This increasing food production currently has led to the un-sustainable production of chemical fertilizers and resultant overuse. A more sustainable option to enhance food production could be the use of fertilizer derived from food waste. To address this, we investigated the possibility of utilizing a fertilizer derived from food waste to grow hydroponic vegetables. Arugula (Eruca sativa) ‘Slow Bolt’ and lettuce (Lactuca sativa) ‘Cherokee’ and ‘Rex’ were cultivated using indoor deep-flow hydroponic systems at 23 ºC under a photosynthetic photon flux density of 170 µmol∙m−2∙s−1 with an 18-hour photoperiod. Plant nutrient solutions were provided by food waste fertilizer or commercial 15:5:20 NPK fertilizer at the identical electrical conductivity (EC) of 2.3 mS·cm–1. At the EC of 2.3 mS·cm–1, chemical fertilizer contained 150 ppm N, 50 ppm P, and 200 ppm K, while food waste fertilizer had 60 ppm N, 26 ppm P, and 119 ppm K. Four weeks after the nutrient treatments were implemented, compared to plants grown with chemical fertilizer, lettuce ‘Rex’ grown with food waste fertilizer had four less leaves, 27.1% shorter leaves, 68.2% and 23.1% less shoot and root fresh weight, respectively. Lettuce ‘Cherokee’ and arugula grown with food waste fertilizer followed a similar trend with fresh shoot weights that were 80.1% and 95.6% less compared to the chemical fertilizer, respectively. In general, the magnitude of reduction in the plant growth was greatest in arugula. These results suggest that both fertilizers were able to successfully grow lettuce and arugula, although the reduced plant growth with the food waste fertilizer in our study is likely from a lower concentration of nutrients when we considered EC as an indicator of nutrient concentration equivalency of the two fertilizer types.
SYSTEMA NERVOSUM is an interdisciplinary personal narrative on design, music, and identity. The project is composed of eleven parts, each addressing the themes of interconnection, the power of the human body, internal and external misunderstanding, and fear. The goal of SYSTEMA NERVOSUM was to create a body of work that reflected the very essence of creative and interdisciplinary thinking.
In a COVID-19 world, student engagement has suffered drastically as organizations and universities shifted to an online format. Yet, there is still an opportunity and a space for digital content creation to bridge the gap in a virtual and hybrid university lifestyle. This project looks at how student groups can still engage students at ASU Tempe through digital content creation and which tools to use to enter the space.
Drosophila melanogaster has been established as a model organism for investigating the developmental gene interactions. The spatio-temporal gene expression patterns of Drosophila melanogaster can be visualized by in situ hybridization and documented as digital images. Automated and efficient tools for analyzing these expression images will provide biological insights into the gene functions, interactions, and networks. To facilitate pattern recognition and comparison, many web-based resources have been created to conduct comparative analysis based on the body part keywords and the associated images. With the fast accumulation of images from high-throughput techniques, manual inspection of images will impose a serious impediment on the pace of biological discovery. It is thus imperative to design an automated system for efficient image annotation and comparison.
Results
We present a computational framework to perform anatomical keywords annotation for Drosophila gene expression images. The spatial sparse coding approach is used to represent local patches of images in comparison with the well-known bag-of-words (BoW) method. Three pooling functions including max pooling, average pooling and Sqrt (square root of mean squared statistics) pooling are employed to transform the sparse codes to image features. Based on the constructed features, we develop both an image-level scheme and a group-level scheme to tackle the key challenges in annotating Drosophila gene expression pattern images automatically. To deal with the imbalanced data distribution inherent in image annotation tasks, the undersampling method is applied together with majority vote. Results on Drosophila embryonic expression pattern images verify the efficacy of our approach.
Conclusion
In our experiment, the three pooling functions perform comparably well in feature dimension reduction. The undersampling with majority vote is shown to be effective in tackling the problem of imbalanced data. Moreover, combining sparse coding and image-level scheme leads to consistent performance improvement in keywords annotation.