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- Creators: Schubert, Franz, 1797-1828
ContributorsSchubert, Franz, 1797-1828 (Composer)
ContributorsSchubert, Franz, 1797-1828 (Composer)
ContributorsSchubert, Franz, 1797-1828 (Composer)
ContributorsSchubert, Franz, 1797-1828 (Composer)
Description
Salad bars are promoted as a means to increase fruit and vegetable consumption among school-age children; however, no study has assessed barriers to having salad bars. Further, it is not known if barriers differ across school level. This cross-sectional study investigated the barriers to having salad bars across school level among schools without salad bars in Arizona (n=177). Multivariate binominal regression models were used to determine differences between the barriers and school level, adjusting for years at current job, enrollment of school, free-reduced eligibility rate and district level clustering. The top five barriers were not enough staff (51.4%), lack of space for salad bars (49.7%), food waste concerns (37.9%), sanitation/food safety concerns (31.3%), and time to get through the lines (28.3%) Adjusted analyses indicated two significant differences between barriers across school level: time to get through lines (p=0.040) and outside caterer/vendor (p=0.018) with time to get through lines reported more often by elementary and middle school nutrition managers and outside caterer/vendor reported most often by high school nutrition managers. There were several key barriers reported and results indicate that having an outside vendor/caterer for their meal programs and time to get through the service lines varied across school level. High schools report a higher percent of the barrier outside caterer/vendors and elementary and middle schools report a higher percent of the barrier time to get through the lines. Results indicate that research determining the approximate time it takes students to get through salad bar lines will need to be considered. More research is needed to determine if the barrier time to get through the service lines is due to selection of food items or if it is due to the enrollment size of the lunch period. Future research interventions may consider investigating food safety and sanitation concerns of middle school nutrition managers. Findings may be used to guide ways to decrease barriers in schools without salad bars.
ContributorsKebric, Kelsey (Author) / Bruening, Meg (Thesis advisor) / Ohri-Vachaspati, Punam (Committee member) / Adams, Marc (Committee member) / Arizona State University (Publisher)
Created2016
Description
Objectives
This cross-sectional study sought to assess the eating and physical activity behaviors among in-state and out-of-state college freshmen attending Arizona State University and to determine if social connectedness mediated the relationship between residency status and eating and physical activity behaviors.
Methods
College freshmen from two dormitories were recruited for participation from Arizona State University’s Tempe campus. A 128-item survey assessing demographics, college life, eating and physical activity behaviors, and social connectedness was administered. In addition, participants completed up to three days of dietary recall. Multivariate linear regression models, adjusting for age, gender, race, ethnicity, highest parental education, dormitory, Pell grant status, number of dietary recalls, and availability of a weekend day of dietary recall were used to assess the relationships between residency status, social connectedness, and eating and physical activity behaviors.
Results
No associations were observed between residency status and calories, grams and percentage of calories from fat, and added sugar. There was a statistically significant association between residency status and moderate-to-vigorous physical activity (MVPA). In-state students reported 21 minutes less per day of MVPA than out-of-state students did (β=-20.85; 95% CI=-30.68, -11.02; p<0.001). There was no relationship between residency status and social connectedness. Social connectedness and eating and physical activity behaviors were not associated. Social connectedness did not mediate the relationship between residency status and eating and physical activity behaviors.
Conclusions
In-state and out-of-state students differed in their MVPA; however, this relationship was not mediated by social connectedness. Further studies are needed to confirm the relationship between MVPA and residency status. In addition, more studies are needed to assess the relationship between social connectedness and MVPA.
This cross-sectional study sought to assess the eating and physical activity behaviors among in-state and out-of-state college freshmen attending Arizona State University and to determine if social connectedness mediated the relationship between residency status and eating and physical activity behaviors.
Methods
College freshmen from two dormitories were recruited for participation from Arizona State University’s Tempe campus. A 128-item survey assessing demographics, college life, eating and physical activity behaviors, and social connectedness was administered. In addition, participants completed up to three days of dietary recall. Multivariate linear regression models, adjusting for age, gender, race, ethnicity, highest parental education, dormitory, Pell grant status, number of dietary recalls, and availability of a weekend day of dietary recall were used to assess the relationships between residency status, social connectedness, and eating and physical activity behaviors.
Results
No associations were observed between residency status and calories, grams and percentage of calories from fat, and added sugar. There was a statistically significant association between residency status and moderate-to-vigorous physical activity (MVPA). In-state students reported 21 minutes less per day of MVPA than out-of-state students did (β=-20.85; 95% CI=-30.68, -11.02; p<0.001). There was no relationship between residency status and social connectedness. Social connectedness and eating and physical activity behaviors were not associated. Social connectedness did not mediate the relationship between residency status and eating and physical activity behaviors.
Conclusions
In-state and out-of-state students differed in their MVPA; however, this relationship was not mediated by social connectedness. Further studies are needed to confirm the relationship between MVPA and residency status. In addition, more studies are needed to assess the relationship between social connectedness and MVPA.
ContributorsNelson, Stephanie A. (Stephanie Anne), 1958- (Author) / Bruening, Meg (Thesis advisor) / Ohri-Vachaspati, Punam (Committee member) / Whisner, Corrie (Committee member) / Arizona State University (Publisher)
Created2016
Description
Cyber-Physical Systems (CPS) are being used in many safety-critical applications. Due to the important role in virtually every aspect of human life, it is crucial to make sure that a CPS works properly before its deployment. However, formal verification of CPS is a computationally hard problem. Therefore, lightweight verification methods such as testing and monitoring of the CPS are considered in the industry. The formal representation of the CPS requirements is a challenging task. In addition, checking the system outputs with respect to requirements is a computationally complex problem. In this dissertation, these problems for the verification of CPS are addressed. The first method provides a formal requirement analysis framework which can find logical issues in the requirements and help engineers to correct the requirements. Also, a method is provided to detect tests which vacuously satisfy the requirement because of the requirement structure. This method is used to improve the test generation framework for CPS. Finally, two runtime verification algorithms are developed for off-line/on-line monitoring with respect to real-time requirements. These monitoring algorithms are computationally efficient, and they can be used in practical applications for monitoring CPS with low runtime overhead.
ContributorsDokhanchi, Adel (Author) / Fainekos, Georgios (Thesis advisor) / Lee, Yann-Hang (Committee member) / Sarjoughian, Hessam S. (Committee member) / Shrivastava, Aviral (Committee member) / Arizona State University (Publisher)
Created2017
Description
Designers employ a variety of modeling theories and methodologies to create functional models of discrete network systems. These dynamical models are evaluated using verification and validation techniques throughout incremental design stages. Models created for these systems should directly represent their growing complexity with respect to composition and heterogeneity. Similar to software engineering practices, incremental model design is required for complex system design. As a result, models at early increments are significantly simpler relative to real systems. While experimenting (verification or validation) on models at early increments are computationally less demanding, the results of these experiments are less trustworthy and less rewarding. At any increment of design, a set of tools and technique are required for controlling the complexity of models and experimentation.
A complex system such as Network-on-Chip (NoC) may benefit from incremental design stages. Current design methods for NoC rely on multiple models developed using various modeling frameworks. It is useful to develop frameworks that can formalize the relationships among these models. Fine-grain models are derived using their coarse-grain counterparts. Moreover, validation and verification capability at various design stages enabled through disciplined model conversion is very beneficial.
In this research, Multiresolution Modeling (MRM) is used for system level design of NoC. MRM aids in creating a family of models at different levels of scale and complexity with well-formed relationships. In addition, a variant of the Discrete Event System Specification (DEVS) formalism is proposed which supports model checking. Hierarchical models of Network-on-Chip components may be created at different resolutions while each model can be validated using discrete-event simulation and verified via state exploration. System property expressions are defined in the DEVS language and developed as Transducers which can be applied seamlessly for model checking and simulation purposes.
Multiresolution Modeling with verification and validation capabilities of this framework complement one another. MRM manages the scale and complexity of models which in turn can reduces V&V time and effort and conversely the V&V helps ensure correctness of models at multiple resolutions. This framework is realized through extending the DEVS-Suite simulator and its applicability demonstrated for exemplar NoC models.
A complex system such as Network-on-Chip (NoC) may benefit from incremental design stages. Current design methods for NoC rely on multiple models developed using various modeling frameworks. It is useful to develop frameworks that can formalize the relationships among these models. Fine-grain models are derived using their coarse-grain counterparts. Moreover, validation and verification capability at various design stages enabled through disciplined model conversion is very beneficial.
In this research, Multiresolution Modeling (MRM) is used for system level design of NoC. MRM aids in creating a family of models at different levels of scale and complexity with well-formed relationships. In addition, a variant of the Discrete Event System Specification (DEVS) formalism is proposed which supports model checking. Hierarchical models of Network-on-Chip components may be created at different resolutions while each model can be validated using discrete-event simulation and verified via state exploration. System property expressions are defined in the DEVS language and developed as Transducers which can be applied seamlessly for model checking and simulation purposes.
Multiresolution Modeling with verification and validation capabilities of this framework complement one another. MRM manages the scale and complexity of models which in turn can reduces V&V time and effort and conversely the V&V helps ensure correctness of models at multiple resolutions. This framework is realized through extending the DEVS-Suite simulator and its applicability demonstrated for exemplar NoC models.
ContributorsGholami, Soroosh (Author) / Sarjoughian, Hessam S. (Thesis advisor) / Fainekos, Georgios (Committee member) / Ogras, Umit Y. (Committee member) / Shrivastava, Aviral (Committee member) / Arizona State University (Publisher)
Created2017
Description
Fruit and vegetable consumption among school children falls short of current recommendations. The development of Public-Private Partnerships (PPP), which combine the resources of government entities with the resources of private entities, such as businesses or not-for-profit agencies, has been suggested as an effective approach to address a number of public health concerns, including inadequate fruit and vegetable consumption. The United States Department of Agriculture's (USDA) Fresh Fruit and Vegetable Program (FFVP) provides fruits and vegetables as snacks at least twice per week in low-income elementary schools. In addition to increasing fruit and vegetable consumption behaviors at school, children participating in the FFVP have been found to make more requests for fruits and vegetables in grocery stores and at home, suggesting the impact of the program extends beyond school settings. The purpose of this multicase study was to describe key stakeholders' perceptions about creating PPPs between schools and nearby retailers to cross-promote fruits and vegetables in low-income communities, using the FFVP. Semi-structured interviews were conducted with participants from three cases groups: grocery store/produce managers (n=10), district FFVP personnel (n=5) and school FFVP personnel (n=12). Data were analyzed using a directed content analysis approach using constructs from the Health Belief Model, including benefits, barriers, strategies, and motivation. While findings varied by case group, key benefits of creating a PPP included the potential to increase store sales, to enhance public relations with the community, and to extend the impact of the FFVP to settings outside of schools. Barriers included offering expensive produce through the FFVP, time/labor-associated costs, and needing approval from authorities and supervisors. Strategies for developing a PPP included using seasonal produce and having clear instructions for teachers and staff. Stakeholders reported being motivated to create a PPP by the potential to improve health outcomes in children and by wanting to help the community. Both objective and subjective measures were suggested to measure the success of such a partnership. Finally, the educational component of the USDA's Supplemental Nutrition Assistance Program (SNAP-Ed) has the potential to serve as a catalyst for organizing a PPP between FFVP-participating schools and nearby grocery stores.
ContributorsGruner, Jessie (Author) / Ohri-Vachaspati, Punam (Thesis advisor) / Evans, Browynne (Committee member) / Bruening, Meg (Committee member) / Tasevska, Natasha (Committee member) / Hekler, Eric (Committee member) / Arizona State University (Publisher)
Created2017
Description
While techniques for reading DNA in some capacity has been possible for decades,
the ability to accurately edit genomes at scale has remained elusive. Novel techniques
have been introduced recently to aid in the writing of DNA sequences. While writing
DNA is more accessible, it still remains expensive, justifying the increased interest in
in silico predictions of cell behavior. In order to accurately predict the behavior of
cells it is necessary to extensively model the cell environment, including gene-to-gene
interactions as completely as possible.
Significant algorithmic advances have been made for identifying these interactions,
but despite these improvements current techniques fail to infer some edges, and
fail to capture some complexities in the network. Much of this limitation is due to
heavily underdetermined problems, whereby tens of thousands of variables are to be
inferred using datasets with the power to resolve only a small fraction of the variables.
Additionally, failure to correctly resolve gene isoforms using short reads contributes
significantly to noise in gene quantification measures.
This dissertation introduces novel mathematical models, machine learning techniques,
and biological techniques to solve the problems described above. Mathematical
models are proposed for simulation of gene network motifs, and raw read simulation.
Machine learning techniques are shown for DNA sequence matching, and DNA
sequence correction.
Results provide novel insights into the low level functionality of gene networks. Also
shown is the ability to use normalization techniques to aggregate data for gene network
inference leading to larger data sets while minimizing increases in inter-experimental
noise. Results also demonstrate that high error rates experienced by third generation
sequencing are significantly different than previous error profiles, and that these errors can be modeled, simulated, and rectified. Finally, techniques are provided for amending this DNA error that preserve the benefits of third generation sequencing.
the ability to accurately edit genomes at scale has remained elusive. Novel techniques
have been introduced recently to aid in the writing of DNA sequences. While writing
DNA is more accessible, it still remains expensive, justifying the increased interest in
in silico predictions of cell behavior. In order to accurately predict the behavior of
cells it is necessary to extensively model the cell environment, including gene-to-gene
interactions as completely as possible.
Significant algorithmic advances have been made for identifying these interactions,
but despite these improvements current techniques fail to infer some edges, and
fail to capture some complexities in the network. Much of this limitation is due to
heavily underdetermined problems, whereby tens of thousands of variables are to be
inferred using datasets with the power to resolve only a small fraction of the variables.
Additionally, failure to correctly resolve gene isoforms using short reads contributes
significantly to noise in gene quantification measures.
This dissertation introduces novel mathematical models, machine learning techniques,
and biological techniques to solve the problems described above. Mathematical
models are proposed for simulation of gene network motifs, and raw read simulation.
Machine learning techniques are shown for DNA sequence matching, and DNA
sequence correction.
Results provide novel insights into the low level functionality of gene networks. Also
shown is the ability to use normalization techniques to aggregate data for gene network
inference leading to larger data sets while minimizing increases in inter-experimental
noise. Results also demonstrate that high error rates experienced by third generation
sequencing are significantly different than previous error profiles, and that these errors can be modeled, simulated, and rectified. Finally, techniques are provided for amending this DNA error that preserve the benefits of third generation sequencing.
ContributorsFaucon, Philippe Christophe (Author) / Liu, Huan (Thesis advisor) / Wang, Xiao (Committee member) / Crook, Sharon M (Committee member) / Wang, Yalin (Committee member) / Sarjoughian, Hessam S. (Committee member) / Arizona State University (Publisher)
Created2017