18525 research outputs found
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Merrily Hart, Rachel Klayman, and Julie Roberts, Interview, 2024
Get PDFThroughout 2024, the city of Ann Arbor celebrated its 200th anniversary with community events, festivals, and art displays that highlighted its rich culture and history. To cap off the celebrations, in December 2024 the EMU Center for Oral History Research invited community members to Skyline High School to share what makes Ann Arbor special. In this interview, mother Merrily Hart and daughters Rachel Klayman and Julie Roberts talk about growing up in Ann Arbor close to family, attending the University of Michigan, and finding their ways back to each other after leaving.https://commons.emich.edu/oral_histories/1209/thumbnail.jp
Researching the effectiveness of an online COVID-19 educational module among community health nursing students
No full textPurposeThe purpose of this research study was to determine the effectiveness of an innovative online COVID-19 educational module among community health nursing students.
DesignMixed-methods study.
MethodsThe sample (N = 86) consisted of prelicensure and postlicensure community health nursing students, who completed a pretest, COVID-19 educational intervention, and posttest.
FindingsThe majority of participants’ scores increased from pretest to posttest, and most participants strongly agreed that the COVID-19 educational module was effective. Strategies to address vaccine hesitancy, information learned and found most helpful, and plans for application and utilization of this knowledge were revealed.
ConclusionsAn online COVID-19 community health nursing educational intervention was effective at improving participants’ knowledge, confidence, and attitudes regarding COVID-19.
Clinical EvidenceOnline COVID-19 community health nursing education was an effective strategy for increasing preparation for this pandemic and the format can be useful to utilize for future public health emergencies
Rare legumes are missing mutualists, but herbivory and environmental filtering are more important determinants of reintroduction success
No full textSoil microbial mutualists like rhizobia bacteria can promote the establishment of rare, late-successional legumes. Despite restoration efforts, these mutualists are often absent in the microbiome. Therefore, restoring this mutualism by directly inoculating rare legumes with rhizobia mutualists may increase plant establishment. We inoculated seedlings of Amorpha canescens, Dalea purpurea, and Lespedeza capitata with three strains of species-specific rhizobia each to investigate how this mutualism would promote growth in the field and in the greenhouse. Because many herbaceous plants are vulnerable to herbivory, we used exclosures for half of our field transplantations to prevent mammalian herbivory. We did not find that rhizobia bacteria directly promoted the growth of our legumes in the field but rather that herbivory and environmental conditions overwhelmed the effects of the rhizobia. Of the plants transplanted, only 17.78% of 180 survived to the end of the growing season, all of which were protected from herbivory. Survival at the end of the growing season was also greater in the northern, drier end of the field site. In the second growing season, plants were more likely to survive in the exclosure treatment, while only four recovered in the open treatment. In the greenhouse, we found increased nodulation with inoculations, supporting the hypothesis that species-specific mutualists are absent from restoration sites. Though several recent studies have shown that restoring mutualistic interactions has the potential to dramatically improve the outcomes of ecological restoration, our results show that protecting rare species from herbivory after transplantation might achieve greater gains in establishment
Immigrants\u27 entrepreneurial intentions: Acculturation-based socio-psychological lens
No full textThe past few decades have witnessed an increase in individuals leaving their country of origin, settling, and pursuing venture creation opportunities in different parts of the world. However, despite anecdotal evidence, little theoretical attention has focused on understanding how exposure to another culture shapes the entrepreneurial intent of immigrant individuals. We integrate insights from acculturation, entrepreneurship, and social identity literature to illustrate how acculturation impacts immigrants’ entrepreneurial cognitions and motivations, influencing their perceived feasibility and desirability of starting new ventures. We bring to the fore the under-researched yet critically important psychological, cognitive, and social factors underlying immigrant entrepreneurship. We contribute to the acculturation and entrepreneurship literature, advancing implications for migrant policymaking
CoRRE Trait Data: A dataset of 17 categorical and continuous traits for 4079 grassland species worldwide
No full textIn our changing world, understanding plant community responses to global change drivers is critical for predicting future ecosystem composition and function. Plant functional traits promise to be a key predictive tool for many ecosystems, including grasslands; however, their use requires both complete plant community and functional trait data. Yet, representation of these data in global databases is sparse, particularly beyond a handful of most used traits and common species. Here we present the CoRRE Trait Data, spanning 17 traits (9 categorical, 8 continuous) anticipated to predict species’ responses to global change for 4,079 vascular plant species across 173 plant families present in 390 grassland experiments from around the world. The dataset contains complete categorical trait records for all 4,079 plant species obtained from a comprehensive literature search, as well as nearly complete coverage (99.97%) of imputed continuous trait values for a subset of 2,927 plant species. These data will shed light on mechanisms underlying population, community, and ecosystem responses to global change in grasslands worldwide
The future of childhood maltreatment research: Diversity and equity-informed perspectives for inclusive methodology and social justice
No full textA long-standing practice in clinical and developmental psychology research on childhood maltreatment has been to consider prospective, official court records to be the gold standard measure of childhood maltreatment and to give less weight to adults’ retrospective self-reports of childhood maltreatment, sometimes even treating this data source as invalid. We argue that both formats of assessment – prospective and retrospective – provide important information on childhood maltreatment. Prospective data drawn from court records should not necessarily be considered the superior format, especially considering evidence of structural racism in child welfare. Part I overviews current maltreatment definitions in the context of the developmental psychopathology (DP) framework that has guided maltreatment research for over 40 years. Part II describes the ongoing debate about the disproportionalities of minoritized children at multiple decision-making stages of the child welfare system and the role that racism plays in many minoritized families’ experience of this system. Part III offers alternative interpretations for the lack of concordance between prospective, official records of childhood maltreatment and retrospective self-reports, and for the differential associations between each format of data with health outcomes. Moving forward, we recommend that future DP research on childhood maltreatment apply more inclusive, diversity and equity-informed approaches when assessing and interpreting the effects of childhood maltreatment on lifespan and intergenerational outcomes. We encourage future generations of DP scholars to use assessment methods that affirm the lived experiences of individuals and families who have directly experienced maltreatment and the child welfare system
Exploring low-level statistical features of n-grams in phishing URLs: A comparative analysis with high-level features
No full textPhishing attacks are the biggest cybersecurity threats in the digital world. Attackers exploit users by impersonating real, authentic websites to obtain sensitive information such as passwords and bank statements. One common technique in these attacks is using malicious URLs. These malicious URLs mimic legitimate URLs, misleading users into interacting with malicious websites. This practice, URL phishing, presents a big threat to internet security, emphasizing the need for advanced detection methods. So we aim to enhance phishing URL detection by using machine learning and deep learning models, leveraging a set of low-level URL features derived from n-gram analysis. In this paper, we present a method for detecting malicious URLs using statistical features extracted from n-grams. These n-grams are extracted from the hexadecimal representation of URLs. We employed 4 experiments in our paper. The first 3 experiments used machine learning with the statistical features extracted from these n-grams, and the fourth experiment used these grams directly with deep learning models to evaluate their effectiveness. Also, we used Explainable AI (XAI) to explore the extracted features and evaluate their importance and role in phishing detection. A key advantage of our method is its ability to reduce the number of features required and reduce the training time by using fewer features after applying XAI techniques. This stands in contrast to the previous study, which relies on high-level URL features and needs pre-processing and a high number of features (87 high-level URL-based features). So our technique only uses statistical features extracted from n-grams and the n-gram itself, without the need for any high-level features. Our method is evaluated across different n-gram lengths (2, 4, 6, and 8), aiming to optimize detection accuracy. We conducted four experiments in our study. In the first experiment, we focused on extracting and using 12 common statistical features like mean, median, etc. In the first experiment, the XGBoost model achieved the highest accuracy using 8-gram features with 82.41%. In the second experiment, we expanded the feature set and extracted an additional 13 features, so our feature count became 25. XGBoost in the second experiment achieved the highest accuracy with 86.40%. Accuracy improvement continued in the third experiment, we extracted an additional 16 features (character count features), and these features increased XGBoost accuracy to 88.15% in the third experiment. In the fourth experiment, we directly fed n-gram representations into deep learning models. The Convolutional Neural Network (CNN) model achieved the highest accuracy of 94.09% in experiment four. Also, we applied XAI techniques, SHapley Additive exPlanations (SHAP), and Local Interpretable Model-agnostic Explanations (LIME). Through the explanation provided by XAI methods, we were able to determine the most important features in our feature set, enabling a reduction in feature count. Using fewer features (4, 7, 10, 13, 15), we got good accuracy compared to the 41 features used in experiment three and reduced the models\u27 training times and complexity. This research aimed to enhance phishing URL detection by using machine learning and deep learning models, leveraging a set of low-level URL features derived from n-gram analysis. Our findings show the importance of using minimal statistical features to identify malicious URLs. Notably, the use of CNN had a great advancement, achieving an accuracy rate of 94
