There are over 250,000 reinforced concrete bridges in the U.S. many of which do not have a load rating on record nor the plans required to perform the calculations. The U.S. Army owns and maintains hundreds of these bridges throughout the U.S. This dissertation describes the development of multiple regression models to estimate the load rating of reinforced concrete bridges. An exploratory data analysis of the 2017 NBI data was performed for the selection of a representative data sample. The data was found to have multiple errors and required significant processing in order to extract a reliable sample for modeling. After processing, a data sample of 31,112 bridges remained, providing sufficient sample for model training and testing. A six-variable model (Model A) was determined to provide the best performance while maintaining a desired low level of complexity. The model was tested by comparing the percentage of cases that fell within its 95% prediction interval, which resulted in 94.9% of the real values falling within the prediction interval. Given the concerns that arose of the quality of the 2017 NBI data during its exploration, as built-drawings from 50 slab bridges throughout the U.S. were collected. With these drawings a new data sample was generated by calculating the load rating of each bridge. Availability of the as-built drawings provided the opportunity to investigate other variables not available in the 2017 NBI, most notably the slab thickness. This data sample was significantly smaller than the previous one, therefore a repeated 10old cross-validation approach was taken to evaluate model performance. It was determined that a five-variable model (Model B) provided the best trade-off between complexity and performance. Model B performed significantly better than Model A due to the inclusion of the slab thickness variable. The models presented in this dissertation provide a valuable tool for reinforced concrete bridge owners tasked with the assigning a load rating when no structural plans are available helping.

The purpose of this study was to examine the use of instructional design when considering the principles of cognitive load theory in traditional classroom instruction. The treatment group (N=31) was designed with a focus on two principles of cognitive load theory—multiple representation and contiguity. The multiple representation principle included text and pictures rather than spoken words and contiguity presented words and pictures simultaneously through multimedia video. The control group (N=26) did not focus on cognitive load theory principles and was discussion or text only—no multimedia video. This study was conducted using a pretest and posttest control group design and demographic survey. The participants consisted of students from an undergraduate computer applications class that was used to meet computer literacy requirements. The same instructor taught both instructional design methods in the traditional classroom setting. The major finding that there was a significant difference in achievement based on the instruction mode (integration of video vs. no integration of video) was constant across all variables in favor of the treatment group. Again, the control group had a mean posttest score of 80.58, and the treatment group had a mean posttest score of 84.48. The groups were significantly different based on the posttest exam, t = 3.28, p < .01. The treatment group scored significantly higher than the control group. The research also examined the relationships among the posttest scores and the various demographic variables. No meaningful relationships were identified. All associations were in the very low (less than .20) or low (.20-.39) level.

Load imbalance is a major source of performance degradation in parallel scientific applications. Load balancing increases the efficient use of existing resources and improves performance of parallel applications running in distributed environments. At a coarse level of granularity, advances in runtime systems for parallel programs have been proposed in order to control available resources as efficiently as possible by utilizing idle resources and using task migration. At a finer granularity level, advances in algorithmic strategies for dynamically balancing computational loads by data redistribution have been proposed in order to respond to variations in processor performance during the execution of a given parallel application. Algorithmic and systemic load balancing strategies have complementary set of advantages. An integration of these two techniques is possible and it should result in a system, which delivers advantages over each technique used in isolation. This thesis presents a design and implementation of a system that combines an algorithmic fine-grained data parallel load balancing strategy called Fractiling with a systemic coarse-grained task-parallel load balancing system called Hector. It also reports on experimental results of running N-body simulations under this integrated system. The experimental results indicate that a distributed runtime environment, which combines both algorithmic and systemic load balancing strategies, can provide performance advantages with little overhead, underscoring the importance of this approach in large complex scientific applications.

Scientific and engineering problems are often large, complex, irregular and data-parallel. The performance of many parallel applications is affected by factors such as irregular nature of the problem, the difference in processor characteristics and runtime loads, the non-uniform distribution of data, and the unpredictable system behavior. These factors give rise to load imbalance. In general, in order to achieve high performance, dynamic load balancing strategies are embedded into solution algorithms. Over time, a number of dynamic load balancing algorithms have been implemented into software tools and successfully used in scientific applications. However, most of these dynamic load balancing tools use an iterative static approach that does not address irregularities during the application execution, and the scheduling overhead incurred is high. During the last decade, a number of dynamic loop scheduling strategies have been proposed to address causes of load imbalance in scientific applications running in parallel and distributed environments. However, there is no single strategy that works well for all scientific applications, and it is up to the user to select the best strategy and integrate it into the application. In most applications using dynamic load balancing, the load balancing algorithm is directly embedded in the application, with close coupling between the data structures of the application and the load balancing algorithm. This typical approach leads to two disadvantages. First, the integration of each newly developed load balancing algorithm into the application needs to be performed from scratch. Second, it is unlikely that the user has incorporated the optimal load balancing algorithm into the application. Moreover, in a certain application (of various problem sizes and number of processors), it is difficult to assess in advance the advantage of incorporating one load balancing algorithm versus another. To overcome these drawbacks, there is a need for developing an application programming interface (API) for dynamic load balancing scientific applications using the recently developed dynamic loop scheduling algorithms. This thesis describes the design and development of such an API, called ProLAS, which is scalable, and independent of data structures of a host application. ProLAS performance is evaluated theoretically and experimentally (after being used in scientific applications). A qualitative and quantitative analysis of ProLAS is presented by comparing its performance with the state of the art technology in dynamic load balancing tools (e.g. CHARM++ library) for parallel applications. The analysis of the experimental results of using ProLAS in a few scientific aplications indicate that it consistently outperforms the existing technology in dynamic load balancing.

The purpose of this study was to determine the quality and microbial load of blueberries at different maturity stages, to develop the effective microbial recovery method and to compare sanitation postharvest treatments on microbial counts of blueberries. The soluble solids and the sugar to acid ratio increased, while pH and TA decreased with maturity. Ripe berries had lower yeast and mold counts (YMC) at other maturity stages, but there were no differences on aerobic plate counts (APC). The medium pH was lower for stomaching and blending than hand massaged samples. This leads to higher recovery of microorganisms by massaging. Sodium hypochlorite at 400 ppm was effective in reducing APC but not YMC. Acidified sodium chloride was very effective, lowering APC and YMC below detectable level. All sanitation treatment did not influence sensory attributes of blueberries.

Parallel and distributed environments are used to solve large scientific and engineering problems that often are irregular and data parallel. However, performance of many parallel applications is affected by computation overheads, communication time and load imbalance. Among these factors, load imbalance is caused by the irregular nature of the problem, its algorithm, the difference in processor characteristics, and runtime loads. A number of applications achieve load balancing by one-time assignment of task. However, a number of applications have workloads that are unpredictable, and vary over the course of their execution. For such type of applications, load balancing is achieved by dynamic assignment of tasks at runtime. A large group of scientific applications has parallel loops as major source of concurrency. However, due to the irregular execution times of the loops, it is difficult to achieve optimal performance without dynamic load balancing. There are number of dynamic load balancing tools and libraries have been developed for different kind of applications. However these libraries fail to address all three degradation factors i.e. problem, algorithmic, and systemic. In this thesis a dynamic load balancing library called LB_Migrate is presented which addresses the degradation factors in application with parallel loops. The library provides a range of dynamic scheduling techniques and data migration strategies to achieve effective load balancing. It is designed to be independent of the host application data structure hence providing the flexibility to be used with different applications. The analysis of the experimental results using LB_Migrate with different applications indicates consistent performance improvement, and low overhead cost by the use of the library.

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