Character cars : How computer technology enhances learning in terms of arts ideas and arts skills and proceses in a year 7 male visual arts education program

\u27The possibilities that the technology can offer are seemingly endless and remain to be fully explored in [visual] art education. (Callow. 2001. p,43) The aim of this research is to investigate whether the integration of Visual Arts Technology Tools (TECH-TOOLS) into Traditional Visual Arts Programs (TRAD-[\u27ROG) enhance the students\u27 learning in terms of Arts Ideas (AI) and Arts Skills and Processes (ASP) and whether it is a cost effective option for Western Australian primary schools. To determine whether it is worth the inclusion of TECH-TOOLS in terms of enhancing learning. this research will statistically state whether the combination of TECH-TOOLS and Traditional Visual Arts Media (TRAD-MEDIA) enhance the expressive outcomes of Year Seven boys\u27 artwork. The comparative case study method has been chosen as the most suitable method to enable the Researcher to establish the impact that combining TECH-TOOLS with TRAD-MEDIA have upon Year Seven boys\u27 artwork. The Control group only used TRAD-MEDIA and the Experimental group used both TRAD-MEDIA and TECH-TOOLS to create a piece of artwork based on the chosen theme, Character Cars. There were 23 students in the Control group and 24 students in the Experimental group, however not all students attempted or completed the task for reasons which will be explained in Chapter Four. Each group was involved in three sequenced activities based on the chosen theme, with the second activity varying only according to the media used to complete the task. Combinations of quantitative and qualitative methods have been used in this research. To present quantitative data which provides insights into whether Visual Arts (VA) teachers should be combining TECH-TOOLS with TRAD-MEDIA in their Visual Arts Programs (VAP), each piece of artwork was assessed and analysed using descriptive analysis of the data. Each participant completed a written feedback form outlining their attitudes, feelings and thoughts about their artwork and the media that they used. The Researcher and an independent Visual Arts Education (V AE) expert also took anecdotal records during the VA activities with the aim of recording the participants\u27 involvement and enjoyment of the activities. This study is significantly different from the current research in this area u!; it will: provide quantitative data which will demonstrate Whether the combination of TECH-TOOLS and TRAD-MEDlA enhances students\u27 artwork; link the relevant literature and findings of this study to the Western Australian primary school context; provide links to the Western Australian Curriculum Council\u27s Curriculum Framework; and comment on the influence of gender in VAE. All of these factors contribute to the uniqueness of this study

Latent variable models for understanding user behavior in software applications

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 147-157).Understanding user behavior in software applications is of significant interest to software developers and companies. By having a better understanding of the user needs and usage patterns, the developers can design a more efficient workflow, add new features, or even automate the user's workflow. In this thesis, I propose novel latent variable models to understand, predict and eventually automate the user interaction with a software application. I start by analyzing users' clicks using time series models; I introduce models and inference algorithms for time series segmentation which are scalable to large-scale user datasets. Next, using a conditional variational autoencoder and some related models, I introduce a framework for automating the user interaction with a software application. I focus on photo enhancement applications, but this framework can be applied to any domain where segmentation, prediction and personalization is valuable. Finally, by combining sequential Monte Carlo and variational inference, I propose a new inference scheme which has better convergence properties than other reasonable baselines.by Ardavan Saeedi.Ph. D

Deciphering the Immune Evolution Landscape of Multiple Myeloma Long-Term Survivors Using Single Cell Genomics

Multiple myeloma (MM) is a malignant bone marrow (BM) disease characterized by somatic hypermutation and DNA damage in plasma cells; leading to the overproduction of dysfunctional malignant myeloma cells. Accumulation of myeloma cells has direct and indirect effects on the BM and other organs. Despite the development of new therapeutic options; MM remains incurable and only a small fraction of patients experiences long-term survival (LTS). The past has shown that ultimately all patients still relapse; leading to the hypothesis that a state of active immune-surveillance is required to control the residual disease. To understand the long-term survival phenomenon and its link to the immune-phenotypes in MM disease; we collected paired bone marrow samples from 24 patients who survived for about 7 to 17 years after Autologous Stem Cell Transplant (ASCT), with a high plasma cell infiltration in the BM (median 49.5%) at diagnosis time. Response assessment according to the International Myeloma Working Group (IMWG) revealed that 15 patients were in complete remission (CR), whereas 9 patients were in non-complete remission (non-CR) that had tumor cells which remained stable over recent years. We performed single-cell RNA-seq sequencing on more than 290,000 bone marrow cells from 11 patients before treatment (BT) and in LTS, as well as three healthy controls using 10x Genomics technology. I developed a computational approach using the state-of-the-art single cell methods, statistical inference and machine learning models to decipher the bone marrow immune cell types and states across all clinical groups. I performed in-depth analyses of the bone marrow immune microenvironment across all captured cell types, and provided the global landscape of cellular states across all clinical groups. In this work, I defined new cellular states, marker genes, and gene signatures associated with the patients’ clinical and survival states. Additionally, I defined a new myeloid population termed Myeloma-associated Neutrophils (MAN) cells and a T cell exhaustion population termed Aberrant Memory Cytotoxic (AMC) CD8+ T cells in newly diagnosed Multiple Myeloma patients. Moreover, I propose new therapeutic targets CXCR3 and NR4A2 in AMC CD8+ T cells, which could be further investigated to reverse the T cell exhaustion state in newly diagnosed MM patients. Furthermore, I defined new prognostic markers in the CD8+ T cell compartment which could be predictive for the global disease state. Finally, I propose that MM long-term survivors go through a complex and evolving immune landscape and acquire cellular states in a stepwise manner. Furthermore, I propose the Continuum Immune Landscape (CIL) Model which explains the immune landscape of MM patients before and after long-term survival. Additionally, I introduced the Disease-State Trajectories (DST) hypothesis regarding the disease-associated dysregulated cellular states in MM context, which could be generalized into other tumor entities and diseases

Optimizing the AI Development Process by Providing the Best Support Environment

The purpose of this study is to investigate the development process for Artificial inelegance (AI) and machine learning (ML) applications in order to provide the best support environment. The main stages of ML are problem understanding, data management, model building, model deployment and maintenance. This project focuses on investigating the data management stage of ML development and its obstacles as it is the most important stage of machine learning development because the accuracy of the end model is relying on the kind of data fed into the model. The biggest obstacle found on this stage was the lack of sufficient data for model learning, especially in the fields where data is confidential. This project aimed to build and develop a framework for researchers and developers that can help solve the lack of sufficient data during data management stage. The framework utilizes several data augmentation techniques that can be used to generate new data from the original dataset which can improve the overall performance of the ML applications by increasing the quantity and quality of available data to feed the model with the best possible data. The framework was built using python language to perform data augmentation using deep learning advancements

Separator fluid volume requirements in multi-infusion settings

INTRODUCTION. Intravenous (IV) therapy is a widely used method for the administration of medication in hospitals worldwide. ICU and surgical patients in particular often require multiple IV catheters due to incompatibility of certain drugs and the high complexity of medical therapy. This increases discomfort by painful invasive procedures, the risk of infections and costs of medication and disposable considerably. When different drugs are administered through the same lumen, it is common ICU practice to flush with a neutral fluid between the administration of two incompatible drugs in order to optimally use infusion lumens. An important constraint for delivering multiple incompatible drugs is the volume of separator fluid that is sufficient to safely separate them. OBJECTIVES. In this pilot study we investigated whether the choice of separator fluid, solvent, or administration rate affects the separator volume required in a typical ICU infusion setting. METHODS. A standard ICU IV line (2m, 2ml, 1mm internal diameter) was filled with methylene blue (40 mg/l) solution and flushed using an infusion pump with separator fluid. Independent variables were solvent for methylene blue (NaCl 0.9% vs. glucose 5%), separator fluid (NaCl 0.9% vs. glucose 5%), and administration rate (50, 100, or 200 ml/h). Samples were collected using a fraction collector until <2% of the original drug concentration remained and were analyzed using spectrophotometry. RESULTS. We did not find a significant effect of administration rate on separator fluid volume. However, NaCl/G5% (solvent/separator fluid) required significantly less separator fluid than NaCl/NaCl (3.6 ± 0.1 ml vs. 3.9 ± 0.1 ml, p <0.05). Also, G5%/G5% required significantly less separator fluid than NaCl/NaCl (3.6 ± 0.1 ml vs. 3.9 ± 0.1 ml, p <0.05). The significant decrease in required flushing volume might be due to differences in the viscosity of the solutions. However, mean differences were small and were most likely caused by human interactions with the fluid collection setup. The average required flushing volume is 3.7 ml. CONCLUSIONS. The choice of separator fluid, solvent or administration rate had no impact on the required flushing volume in the experiment. Future research should take IV line length, diameter, volume and also drug solution volumes into account in order to provide a full account of variables affecting the required separator fluid volume

2016-17 Graduate Bulletin

After 2003 the University of Dayton Bulletin went exclusively online. This copy was downloaded from the University of Dayton\u27s website in March 2018.https://ecommons.udayton.edu/bulletin_grad/1047/thumbnail.jp

The Nature of Self-Regulation, Scaffolding, and Feedback in a Computer-Based Developmental Mathematics Classroom

This study looks at what aspects of a computer-based course are key to success and building understanding in mathematics. Three students enrolled in the Independent Study section of Developmental Mathematics at the University are interviewed, and several other students observed and surveyed throughout a semester in the course. Their responses are analyzed in terms of their perceptions of learning and understanding mathematics; confidence, motivation, and interest in mathematics; and self-regulation and one's ability to keep up with the online mathematics course. Each of the three interviewee's interviews are analyzed individually in a case-study format and discussed individually based on patterns seen. These interviews are used to address how these online courses are set up, how students proceed in such courses, and what makes students successful in such courses