Service Providers’ Perceptions of Refugees’ Needs, Services and Service Delivery Barriers in Burlington, Vermont

This thesis investigates the behavioral, mental health, and social service needs of the refugee community in the Burlington area, and the services available for them. I explore what these services entail and how the various providers who work with the refugee community in the Burlington area provide these services based on the provider’s perceptions of the community’s behavioral, mental health, and social service needs. My research focused on seeking an understanding of how local service providers determine what services to provide to support the refugee community’s needs, as well as how providers think the refugee community perceives and uses theses services. This thesis examines what the providers believe works or does not work well to meet the refugees’ needs, as well as perceived barriers to and gaps in meeting these needs. Based on data I collected through ethnographic research, participant observation and interviews, I also describe similarities and differences among the providers. In conclusion, I identify potential strategies for overcoming the perceived barriers and gaps

Deep Learning for Plant Identification and Disease Classification from Leaf Images: Multi-prediction Approaches

Deep learning plays an important role in modern agriculture, especially in plant pathology using leaf images where convolutional neural networks (CNN) are attracting a lot of attention. While numerous reviews have explored the applications of deep learning within this research domain, there remains a notable absence of an empirical study to offer insightful comparisons due to the employment of varied datasets in the evaluation. Furthermore, a majority of these approaches tend to address the problem as a singular prediction task, overlooking the multifaceted nature of predicting various aspects of plant species and disease types. Lastly, there is an evident need for a more profound consideration of the semantic relationships that underlie plant species and disease types. In this paper, we start our study by surveying current deep learning approaches for plant identification and disease classification. We categorise the approaches into multi-model, multi-label, multi-output, and multi-task, in which different backbone CNNs can be employed. Furthermore, based on the survey of existing approaches in plant pathology and the study of available approaches in machine learning, we propose a new model named Generalised Stacking Multi-output CNN (GSMo-CNN). To investigate the effectiveness of different backbone CNNs and learning approaches, we conduct an intensive experiment on three benchmark datasets Plant Village, Plant Leaves, and PlantDoc. The experimental results demonstrate that InceptionV3 can be a good choice for a backbone CNN as its performance is better than AlexNet, VGG16, ResNet101, EfficientNet, MobileNet, and a custom CNN developed by us. Interestingly, empirical results support the hypothesis that using a single model can be comparable or better than using two models. Finally, we show that the proposed GSMo-CNN achieves state-of-the-art performance on three benchmark datasets.Comment: Jianping and Son are joint first authors (equal contribution

Machine Learning for Leaf Disease Classification: Data, Techniques and Applications

The growing demand for sustainable development brings a series of information technologies to help agriculture production. Especially, the emergence of machine learning applications, a branch of artificial intelligence, has shown multiple breakthroughs which can enhance and revolutionize plant pathology approaches. In recent years, machine learning has been adopted for leaf disease classification in both academic research and industrial applications. Therefore, it is enormously beneficial for researchers, engineers, managers, and entrepreneurs to have a comprehensive view about the recent development of machine learning technologies and applications for leaf disease detection. This study will provide a survey in different aspects of the topic including data, techniques, and applications. The paper will start with publicly available datasets. After that, we summarize common machine learning techniques, including traditional (shallow) learning, deep learning, and augmented learning. Finally, we discuss related applications. This paper would provide useful resources for future study and application of machine learning for smart agriculture in general and leaf disease classification in particular

Combined Arms Consulting

The Bush School of Government and Public Service graduate students at Texas A&M University worked with Combined Arms, Houston, from September 2019 through April 2020 to provide research-driven recommendations to substantiate the service model and facilitate expansion. The report consists of three components. First is a case summary that provides an objective assessment of the Combined Arms veteran service model, substantiated with academic literature, interviews with member organization, and the Combined Arms leadership team.Section two provides a detailed analysis of the services Combined Arms offers to the member organizations and explores which are perceived as the most beneficial. The report also investigates how Combined Arms’ 2019 budget aligns with the services that are valued by the member organizations. Section three provides information on veterans and the services available to veterans in Dallas, Tarrant, and Bexar counties as an initial exploration of the expansion opportunities in these regions

Improving Forensic Science Information Seeking

The goal of this presentation is to describe the findings from a survey of forensic science professionals from a variety of disciplines on how they search for information related to their occupation. The researchers aim to discover how libraries can serve as gateways to provide assistance when seeking information, such as scholarly materials.This research was funded by National Institutes of Justice grant 2016-R2-CX-0054. Opinions, points of view expressed in this research, and products discussed represent a consensus of the authors and do not necessarily represent the official position, policies, or endorsement of the United States Department of Justice, Office of Justice Programs, or the National Institute of Justice

Effects of drinking patterns on prospective memory performance in college students [pre-print]

OBJECTIVE: Traditional college students are at a critical juncture in the development of prospective memory (PM). Their brains are vulnerable to the effects of alcohol. METHOD: There were 123 third and fourth year college students, 19-23 years old, who completed the Self-Rating Effects of Alcohol (SREA), Modified Timeline Follow-back (TFLB), Brief Young Adult Alcohol Consequences Scale (BYAACS), and Alcohol Effects Questionnaire (AEQ) once per month on a secure online database, as reported elsewhere (Dager et al., 2013). Data from the 6 months immediately before memory testing were averaged. In a single testing session participants were administered the Mini International Neuropsychiatric Interview-Diagnostic and Statistical Manual for Mental Disorders-Fourth Edition-Text Revision (MINI-DSM-IV-TR), measures of PM (event-based and time-based), and retrospective memory (RM). Based on the average score of six consecutive monthly responses to the SREA, TLFB, and AEQ, students were classified as nondrinkers, light drinkers, or heavy drinkers (as defined previously; Dager et al., 2013). Alcohol-induced amnesia (blackout) was measured with the BYAACS. RESULTS: We found a relationship between these alcohol use classifications and time-based PM, such that participants who were classified as heavier drinkers were more likely to forget to perform the time-based PM task. We also found that self-reported alcohol-induced amnesia (blackouts) during the month immediately preceding memory testing was associated with lower performance on the event-based PM task. Participants\u27 ability to recall the RM tasks suggested the PM items were successfully encoded even when they were not carried out, and we observed no relationship between alcohol use and RM performance. CONCLUSION: Heavy alcohol use in college students may be related to impairments in PM. (PsycINFO Database Recor

Drivers of site fidelity in ungulates

1. While the tendency to return to previously visited locations—termed ‘site fidelity’—is common in animals, the cause of this behaviour is not well understood. One hypothesis is that site fidelity is shaped by an animal's environment, such that animals living in landscapes with predictable resources have stronger site fidelity. Site fidelity may also be conditional on the success of animals’ recent visits to that location, and it may become stronger with age as the animal accumulates experience in their landscape. Finally, differences between species, such as the way memory shapes site attractiveness, may interact with environmental drivers to modulate the strength of site fidelity. 2. We compared inter‐year site fidelity in 669 individuals across eight ungulate species fitted with GPS collars and occupying a range of environmental conditions in North America and Africa. We used a distance‐based index of site fidelity and tested hypothesized drivers of site fidelity using linear mixed effects models, while accounting for variation in annual range size. 3. Mule deer Odocoileus hemionus and moose Alces alces exhibited relatively strong site fidelity, while wildebeest Connochaetes taurinus and barren‐ground caribou Rangifer tarandus granti had relatively weak fidelity. Site fidelity was strongest in predictable landscapes where vegetative greening occurred at regular intervals over time (i.e. high temporal contingency). Species differed in their response to spatial heterogeneity in greenness (i.e. spatial constancy). Site fidelity varied seasonally in some species, but remained constant over time in others. Elk employed a ‘win‐stay, lose‐switch’ strategy, in which successful resource tracking in the springtime resulted in strong site fidelity the following spring. Site fidelity did not vary with age in any species tested. 4. Our results provide support for the environmental hypothesis, particularly that regularity in vegetative phenology shapes the strength of site fidelity at the inter‐annual scale. Large unexplained differences in site fidelity suggest that other factors, possibly species‐specific differences in attraction to known sites, contribute to variation in the expression of this behaviour. 5. Understanding drivers of variation in site fidelity across groups of organisms living in different environments provides important behavioural context for predicting how animals will respond to environmental change