14878 research outputs found
Sort by
The Viability of Using Recycled Rubber for Load Bearing Structures
No full textThe objective of this research project was to investigate the feasibility of using recycled rubber as a component of load bearing structures. There have been several studies into using recycled rubber as an aggregate in concrete, which shows promising results. Additionally, a team of students at Oakland University researched and tested if crumb rubber can be used to make a rail tie. Multiple design variations were configured with two different binder ratios (11% and 12%) and different insert shapes, which were tested to find what combination performs the best and can handle actual railway loads based on American Railway Engineering and Maintenance-of-Way Association (AREMA) standards. A variety of potential structural applications where recycled rubber can be used were investigated and a determination of best practices was recorded
Structural Characteristics of Articular Cartilage in the Early Detection of Post-Traumatic Osteoarthritis by Microscopic Imaging Techniques
No full textCartilage is a specialized form of connective tissue that provides support and cushioning to adjacent tissues in the body. Cartilage is of three types: Hyaline, fibrocartilage, and elastic. The articular cartilage is a hyaline type and is the most found throughout the animal and human bodies. Articular cartilage is composed of a dense extracellular matrix (ECM) with specialized cells, and chondrocytes, which are sparsely distributed. The ECM is primarily made up of collagen, proteoglycan, water, non-collagenous proteins, and glycoproteins. The components of the ECM are subject to change in the disease state, especially in osteoarthritis. As a result of the complex and unique nature of the articular cartilage, early detection, treatment, and repair pose a challenge in clinic. Imaging techniques such as magnetic resonance imaging (MRI) has been used in the noninvasive evaluation of the cartilage structure, and polarized light microscopy (PLM) allows the examination of the molecular organization at optical resolution.The first project in this dissertation aimed to study the structural characteristics of the articular cartilage in the patella and the fibrocartilage of the suprapatella in the knee joint. This was achieved quantitatively using µMRI and PLM at both low and high resolutions. The second project in this dissertation aimed to compare the structures between the immature and mature articular cartilage of the femur and humerus qualitatively and quantitatively using µMRI and PLM. The third project in this dissertation was aimed at the structural characteristics of the articular cartilage in the disease state. Specifically qualitative and quantitative characteristics from traumatized joints (post-traumatic osteoarthritis) were studied using µMRI and PLM at high resolutions. These studies confirmed the ability of µMRI and PLM to examine the cartilage structure quantitatively and qualitatively in a healthy state and in a diseased state. The ability to study the microscopic anatomy of cartilage and pathology (osteoarthritis) in the early stage will contribute to the treatment and early diagnosis of arthritis
Their Side. A Hybrid Documentary Project
No full textWhat began as a hybrid documentary project about decision making, became a journey that led to unrealized truth about the consequences that our actions have on those around us. "Their Side" explores the custody battle between two parents and the lasting effects on their children as they learn to cope with the truth and find closure on their own terms. In this critical analysis and reflection, the audience will receive an inside look into the complications and process that led to the final cut of the film
Humanizing Literacy Coaching
No full textThe Humanity of Literacy Coaching
Literacy coaching has the potential to center humanizing professional learning pedagogies–promoting equity, disrupting oppression, and recognizing the complex humanity of teachers. This potential can be realized through the use of deep reflection to support teachers’ awareness of what guides their behavior and further strengthened by complex supportive relationships with literacy coaches. These humanizing coaching practices not only re-humanize teachers but can influence changes to literacy instruction. Yet, humanizing approaches are often overtaken by more behavioristic approaches in literacy coaching models and the urgency of pandemic-related acceleration pervading schools. In this article, I share the findings of a case study in which I, as a literacy coach, explored the relationship between elements of a humanizing model of literacy coaching, including complex relational and reflective work, and a teacher’s willingness to change her literacy instruction. Implications are shared on the potential of utilizing a conceptual framework guided by Maslow’s (1943) theory of humanism and Korthagen's (2004) onion model could influence teachers' willingness to change and humanize professional learning.
The Collaborative Literacy Coaching Framework for Transformation
Literacy coaching is professional learning designed to provide teachers with
supportive partnerships as they enhance their instruction (L’Allier et al., 2010). However, this enhancement requires teachers to make changes to long-standing practices. To prepare for change, teachers must have the psychological safety and time to explore their beliefs, values, and identities and how these factors influence their willingness to change (Dewey, 1933). Literacy coaches can prepare teachers for this work by using The Collaborative Literacy Coaching Framework for Transformation, which focuses on the cultivation of relationships, the examination of intrapersonal factors, the acknowledgment of their instructional impact, and the need to plan for change. I will share the framework and the stories of three teachers who were better prepared for change while working within it
A Comparison of Learning Anxiety and Coping Mechanisms Utilized by University Students in Virtual and In-Person Courses
No full textThe present study examined anxiety-inducing aspects of virtual learning for university students, and coping strategies that students use to cope with those aspects of virtual learning. These measures were compared to those of in-person learning, which students may generally be more accustomed to. It was hypothesized that students would more often utilize emotion-focused coping strategies in response to feelings of anxiety caused by virtual learning, and that virtual learning would be more anxiety-inducing. Additionally, we investigated students’ ratings of effectiveness of coping mechanisms in terms of reducing feelings of anxiety. The study involved 147 Oakland University students of at least 18 years of age responding to a 22-question survey involving demographic questions and Likert scale questions regarding anxiety-inducing characteristics of virtual learning, coping strategies, and their effectiveness. Findings show that
although students find in-person learning to be most anxiety-inducing, they are still most willing to participate in an in-person learning environment. Additionally, students utilize a variety of coping mechanisms for in-person and virtual learning, although more of them are problem- focused
A Different World: an Examination of the Relationship between Student Involvement and Student Well-Being in Black and White Students at a Predominantly White Institution
No full textThe purpose of the study was to compare the relationship between student involvement and student well-being in Black and White students at a Predominately White Institution. Research has shown that students who are involved in organizations have more positive collegiate experiences. The primary areas of the study focused on student involvement, student well-being, and student racialized experiences and the relationship with anxiety, self-esteem, depression and psychological stress. Ultimately, this study examined the factors that contributed positively or negatively to the experiences of students on campus and provided recommendations for increasing the emotional well-being of Black students at Predominantly White Institutions. The methods for this study used a quantitative, cross-sectional approach. Data collection involved survey data which explored relationships between variables and the testing of differences between groups for significance. More specifically, it used descriptive statistics, cross tabulations (chi-square), one-way ANOVA, correlations between continuous measures and compared the size of correlations. The key findings of this study were that the majority of students were involved in at least one organization on campus. Both races of students stated their reasons for joining were due to enjoyment in the organizations, positive feelings of connectedness, sense of belonging, and celebration of cultural traditions. Additionally, students reported to have lower levels of depression and anxiety when they were involved in organizations on campus. This finding suggests that it is not the quality or number of organizations in which students are involved that impacts their emotional well-being; rather, it is the quality of experiences. Students indicated that negative experiences with microaggressions resulted in stress, anxiety, and depression, and a decrease in self-esteem. Furthermore, students reported having higher levels of self-esteem when they felt integrated within the university communit
Knowledge Net: An Automated Clinical Knowledge Graph Generation Framework for Evidence Based Medicine
No full textTo practice the evidence-based medicine, clinicians are interested to find the most suitable research for the clinical decision making. The use of knowledge graphs (KGs) and Neuro-Symbolic methods to integrate and analyze complex and heterogeneous healthcare data is critical to enable evidence-based treatment in clinical decision support systems (CDSS). Healthcare generates a vast amount of data, including electronic health records (EHRs), medical images, genetic information, research papers, and clinical guidelines. Neuro-symbolic AI can leverage its neural network component to process unstructured data, while using symbolic reasoning to interpret the data and make logical inferences. It also enables a deeper understanding of patient data, leading to more accurate diagnoses, personalized treatment plans, and improved patient outcomes. By incorporating symbolic reasoning, Neuro-Symbolic AI systems can provide explanations for their outputs, making them more transparent and interpretable. To enable Neuro-Symbolic AI in healthcare, large-scale KGs play a pivotal role as it can integrate heterogeneous and big healthcare data including medical ontologies, clinical guidelines, drug databases, patient records, and research literature.The existing KG construction frameworks are not fully automated and predominantly carried out using manual or semi-automated approach, requiring substantial effort and expertise. The challenges encompass identifying knowledge sources, disambiguating concepts in context, enriching semantics, determining relationships, and conducting inferential reasoning. Automating the extraction of coherent knowledge and constructing KGs from diverse data forms remains a longstanding goal in AI research. Also, the current frameworks for constructing KGs fail to generate KGs that provide relevant information for evidence-based practitioners. This is because the organization of constructed subgraphs is neither topic-specific nor evidence-based PICO (Participants/Problem P, Intervention-I, Comparison C, Outcome O) query-friendly. These KGs, built through manual or semi-automated processes, are incapable of adapting to new domains and incorporating the constantly changing information into their knowledge base. Consequently, they gradually lose relevance over time and miss out on important evidence. Thus, ignoring temporal information and failing to incorporate dynamic nature of entities and relations can lead to erroneous information extraction and suboptimal decision-making. This dissertation proposes fully automated knowledge graph curation framework to curate information and create KG of different clinical domains by employing concept extraction, semantic enrichment, optimized clustering using Neuro-Symbolic approach, and state of art Recurrent Neural Networks (RNNs) with BioBERT based encoded representation to categorize PICO elements and predict relationships between concepts using huge corpus of publicly available literature on COVID-19 and cerebral aneurysms. The evaluation shows that the proposed framework achieves significant improvement over baseline models and has 93 , and 82 accuracy on aneurysm and COVID data set respectively for PICO classification. The Neuro-Symbolic clustering approach outperforms traditional baseline models by 43 and achieves average precision of 88 across all identified clusters. Also, the relationship extraction module has an accuracy of 96 with precision and recall being 92 , and 90 respectively. The incorporation of domain-specific and language models has proven to enhance the performance of machine learning models, particularly in the context of Neuro-Symbolic clustering, PICO classification, and relation extraction. The integration of deep learning and symbolic reasoning techniques has demonstrated significant improvements in clustering performance, especially in biomedical research domains. The utilization of the BioBERT embedded layer and LSTM model has notably boosted the accuracy of PICO classification tasks by 11 for both the COVID-19 dataset and cerebral aneurysm dataset. Furthermore, when BioBERT is combined with Bi-LSTM and CNN, the performance of the RE model also experiences substantial enhancements. Future work will focus on parallelizing the data processing pipeline to enhance the efficiency and scalability of the knowledge graph framework, while also developing an interactive user interface for visualization. Additionally, efforts will be dedicated to extending the frameworks application across diverse domains such as the food supply chain, dietary recommendations, agriculture, and fisheries, addressing unique challenges and expanding its impact. This expansion aims to advance multiple industries and leverage the potential benefits of the approach in various domains
Feasibility of Integrating Cognitive and Motor Functions into a New Balance Tracking System (BTrackS) Training Protocol
No full textMany studies have shown that combining cognitive and motor tasks into one dual-task activity can prove to be beneficial in improving many gait parameters and balance. However, gaps in knowledge within this area of study still exist. Dr. Daniel Goble, professor at Oakland University, added a new cognitive-motor application to his Balance Tracking System (BTrackS). The present study had 17 adult participants complete this task three times each one week apart. The task involves standing on a force plate while looking at a screen displaying the plate, a yellow dot to represent the participant’s center of pressure (COP) location, and letters for memorization to trigger the motor response. The system collects average response time, fastest response time, and accuracy. The statistical analyses included a one-way analysis of variance, Tukey's honestly significant difference, t-test, and intraclass correlation. Results showed that participants’ average response time improved over three trials with an overall difference between the trial average response time means. Good reliability can be seen when looking at average response times between sessions one and two while excellent reliability can be seen when looking at the average response times between sessions two and three. There is poor reliability in the results on accuracy. It can be interpreted that improvements can be made over three trials on this new BTrackS cognitive-motor application when testing young, healthy adults with no balance impairments. The results also suggest that a practice trial would be beneficial
