Design and Pilot Study for an Efficient High-Throughput Automated Computer-Vision Guided Intelligent De-Calyxing Machine for Post-Harvest Strawberry Processing

Strawberries harvested for processing as frozen fruits are currently de-calyxed manually in the field. This process requires the removal of the stem cap with green leaves (i.e. the calyx) and incurs many disadvantages when performed by hand. Not only does it necessitate the need to maintain cutting tool sanitation, but it also increases labor time and exposure of the de-capped strawberries before in-plant processing. This leads to labor inefficiency and decreased harvest yield. By moving the calyx removal process from the fields to the processing plants, this new practice would reduce field labor and improve management and logistics, while increasing annual yield. As labor prices continue to increase, the strawberry industry has shown great interest in the development and implementation of an automated calyx removal system. In response, this dissertation describes the design, operation, and performance of a full-scale automatic vision-guided intelligent de-calyxing (AVID) prototype machine. The AVID machine utilizes commercially available equipment to produce a relatively low cost automated de-calyxing system that can be retrofitted into existing food processing facilities. This dissertation is broken up into five sections. The first two sections include a machine overview and a 12-week processing plant pilot study. Results of the pilot study indicate the AVID machine is able to de-calyx grade-1-with-cap conical strawberries at roughly 66 percent output weight yield at a throughput of 10,000 pounds per hour. The remaining three sections describe in detail the three main components of the machine: a strawberry loading and orientation conveyor, a machine vision system for calyx identification, and a synchronized multi-waterjet knife calyx removal system. In short, the loading system utilizes rotational energy to orient conical strawberries. The machine vision system determines cut locations through RGB real-time feature extraction. The high-speed multi-waterjet knife system uses direct drive actuation to locate 30,000 psi cutting streams to precise coordinates for calyx removal. Based on the observations and studies performed within this dissertation, the AVID machine is seen to be a viable option for automated high-throughput strawberry calyx removal. A summary of future tasks and further improvements is discussed at the end.

Credible Commitments and Post-Conflict Refugee Return: A Statistical and Network Analysis

I examine determinants of refugee return after conflicts. I argue that institutional constraints placed on the executive provide a credible commitment that signals to refugees that the conditions required for durable return will be created. This results in increased return flows for refugees. Further, when credible commitments are stronger in the country of origin than in the country of asylum, the level of return increases. Finally, I find that specific commitments made to refugees in the peace agreement do not lead to increased return because they are not credible without institutional constraints. Using data on returnees that has only recently been made available, along with network analysis and an original coding of the provisions in refugee agreements, statistical results are found to support this theory. An examination of cases in Djibouti, Sierra Leone, and Liberia provides additional support for this argument.

Development and Application of Predictive Models for Survival, Growth, and Death of Enteric Pathogens in Leafy Greens Supply Chain

Leafy greens are essential part of a healthy diet. Because of their health benefits, production and consumption of leafy greens has increased considerably in the U.S. in the last few decades. However, leafy greens are also associated with a large number of foodborne disease outbreaks in the last few years. The overall goal of this dissertation was to use the current knowledge of predictive models and available data to understand the growth, survival, and death of enteric pathogens in leafy greens at pre- and post-harvest levels. Temperature plays a major role in the growth and death of bacteria in foods. A growth-death model was developed for Salmonella and Listeria monocytogenes in leafy greens for varying temperature conditions typically encountered during supply chain. The developed growth-death models were validated using experimental dynamic time-temperature profiles available in the literature. Furthermore, these growth-death models for Salmonella and Listeria monocytogenes and a similar model for E. coli O157:H7 were used to predict the growth of these pathogens in leafy greens during transportation without temperature control. Refrigeration of leafy greens meets the purposes of increasing their shelf-life and mitigating the bacterial growth, but at the same time, storage of foods at lower temperature increases the storage cost. Nonlinear programming was used to optimize the storage temperature of leafy greens during supply chain while minimizing the storage cost and maintaining the desired levels of sensory quality and microbial safety. Most of the outbreaks associated with consumption of leafy greens contaminated with E. coli O157:H7 have occurred during July-November in the U.S. A dynamic system model consisting of subsystems and inputs (soil, irrigation, cattle, wildlife, and rainfall) simulating a farm in a major leafy greens producing area in California was developed. The model was simulated incorporating the events of planting, irrigation, harvesting, ground preparation for the new crop, contamination of soil and plants, and survival of E. coli O157:H7. The predictions of this system model are in agreement with the seasonality of outbreaks. This dissertation utilized the growth, survival, and death models of enteric pathogens in leafy greens during production and supply chain.

The role of new information and communication technologies (ICTs) in information and communication in science. A conceptual framework and empirical study

Problem This dissertation presents a literature-based framework for communication in science (with the elements partners, purposes, message, and channel), which it then applies in and amends through an empirical study of how geoscientists use two social computing technologies (SCTs), blogging and Twitter (both general use and tweeting from conferences). How are these technologies used and what value do scientists derive from them? Method The empirical part used a two-pronged qualitative study, using (1) purposive samples of ~400 blog posts and ~1000 tweets and (2) a purposive sample of 8 geoscientist interviews. Blog posts, tweets, and interviews were coded using the framework, adding new codes as needed. The results were aggregated into 8 geoscientist case studies, and general patterns were derived through cross-case analysis. Results A detailed picture of how geoscientists use blogs and twitter emerged, including a number of new functions not served by traditional channels. Some highlights: Geoscientists use SCTs for communication among themselves as well as with the public. Blogs serve persuasion and personal knowledge management; Twitter often amplifies the signal of traditional communications such as journal articles. Blogs include tutorials for peers, reviews of basic science concepts, and book reviews. Twitter includes links to readings, requests for assistance, and discussions of politics and religion. Twitter at conferences provides live coverage of sessions. Conclusions Both blogs and Twitter are routine parts of scientists' communication toolbox, blogs for in-depth, well-prepared essays, Twitter for faster and broader interactions. Both have important roles in supporting community building, mentoring, and learning and teaching. The Framework of Communication in Science was a useful tool in studying these two SCTs in this domain. The results should encourage science administrators to facilitate SCT use of scientists in their organization and information providers to search SCT documents as an important source of information.

The Influence of Pre-Migration Factors and Post-Migration Climate of the Receiving Community on the Psychological Distress of Latino Immigrants

Over forty million foreign-born residents currently live in the United States. Latinos make up the largest population of immigrants living in the U.S. Previous research suggests that Latino immigrants often experience pre-migration stressors, such as traumatic experiences, political upheaval, and unplanned migration. These stressors may have a negative impact on immigrants’ post-migration mental health. Research also suggests that the post-migration climate of the receiving community may inform the connection between pre-migration experiences and post-migration mental health. The current study examined the relationship between Latino immigrants’ reasons for migration, migration planning, and pre-migration experience of political and/or interpersonal violence, and post-migration symptoms of psychological distress. In addition to examining the effect of these pre-migration factors, the current study also examined the community “climate” experienced by Latino immigrants post-migration by assessing the influence of three post-migration factors: 1) community support and engagement, 2) discrimination, and 3) employment. The study was a secondary analysis of data collected for the National Latino and Asian American Study, which focused on the mental health and service utilization of Latinos and Asian Americans. Participants included 1,629 Latino immigrants from across the United States. Results indicated that pre-migration experience of political and/or interpersonal trauma, post-migration experience of discrimination, and female sex were positively associated with psychological distress. Post-migration employment was negatively associated with psychological distress. In addition, discrimination modified the association between unplanned migration and psychological distress; the relationship between unplanned migration and psychological distress decreased for participants who reported more discrimination. Furthermore, employment modified the association between political and/or interpersonal trauma and psychological distress; the connection between trauma and psychological distress increased among those who reported having less employment. Recommendations for further research were presented. Policy and clinical practice implications were discussed, particularly given the current climate of high anti-immigrant sentiment and hostility in the U.S.