1,213 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2023-2024

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    An examination of the verbal behaviour of intergroup discrimination

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    This thesis examined relationships between psychological flexibility, psychological inflexibility, prejudicial attitudes, and dehumanization across three cross-sectional studies with an additional proposed experimental study. Psychological flexibility refers to mindful attention to the present moment, willing acceptance of private experiences, and engaging in behaviours congruent with one’s freely chosen values. Inflexibility, on the other hand, indicates a tendency to suppress unwanted thoughts and emotions, entanglement with one’s thoughts, and rigid behavioural patterns. Study 1 found limited correlations between inflexibility and sexism, racism, homonegativity, and dehumanization. Study 2 demonstrated more consistent positive associations between inflexibility and prejudice. And Study 3 controlled for right-wing authoritarianism and social dominance orientation, finding inflexibility predicted hostile sexism and racism beyond these factors. While showing some relationships, particularly with sexism and racism, psychological inflexibility did not consistently correlate with varied prejudices across studies. The proposed randomized controlled trial aims to evaluate an Acceptance and Commitment Therapy intervention to reduce sexism through enhanced psychological flexibility. Overall, findings provide mixed support for the utility of flexibility-based skills in addressing complex societal prejudices. Research should continue examining flexibility integrated with socio-cultural approaches to promote equity

    Broadband Coherent Anti-Stokes Raman Spectroscopy: A Comprehensive Approach to Analyzing Crystalline Materials

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    Broadband Coherent Anti-Stokes Raman scattering (B-CARS) is an advanced Raman spectroscopy technique used to investigate the vibrational properties of materials. B-CARS combines the spectral sensitivity of spontaneous Raman scattering with the enhanced signal intensity of coherent Raman techniques. While B-CARS has been successfully applied in biomedicine for ultra-fast imaging of biological tissue, its potential in solid-state physics remains largely unexplored. This work delves into the challenges and adaptations necessary to apply B-CARS to crystalline materials and shows its potential as a powerful tool for high-speed, hyperspectral investigations. The theoretical part of this work covers inelastic light-matter scattering fundamentals and the signal generation process of B-CARS, with special attention given to the so-called Non-Resonant Background (NRB). This sample-unspecific signal amplifies the B-CARS intensity but also distorts the shape and position of the measured spectral peaks. A reliable NRB correction becomes crucial to retrieve precise spectral parameters containing information on the investigated material's crystallographic structure, defect density, and stress distribution. The first results chapter presents a practical guideline for an optimized workflow of sample preparation, measurement procedure, and data analysis. The influences of sample surfaces, focus positioning, and polarization sensitivity are discussed. The successful NRB removal is achieved by adapting an algorithm initially designed for biomedical purposes. The second chapter involves a transnational Round Robin investigating the same set of materials using different experimental setups. The influences of laser source, detection range, and transmission vs. epi detection are explored to optimize the experimental parameters. This work showcases applications such as high-speed, hyperspectral imaging of ferroelectric domain walls in LiNbO3, demonstrating the potential of B-CARS in the cutting-edge field of domain wall engineering. Additionally, imaging and polarization-sensitive measurements are shown for MoO3 flakes, paving the way for B-CARS investigations of 2D materials. The final chapter presents advanced techniques, such as Three-Color CARS and Time-Delay CARS, applied to crystalline materials. Three-Color CARS is especially promising, as it enhances the signal intensity for low-frequency Raman modes, which are particularly interesting for solid-state physics compared to the usual large-shift modes investigated in biomedical research. Meanwhile, Time-Delay CARS is sensitive to relaxation processes of vibrational and NRB states, enabling experimental NRB removal and lifetime measurements. Additionally, a neural network-based NRB removal method is presented, eliminating the need for a prior NRB spectrum and offering rapid computation. In summary, this work demonstrates the successful implementation of B-CARS for crystalline materials and provides a comprehensive guideline for the optimal experimental setup, workflow, and data processing. The application of B-CARS for imaging bulk crystalline materials, ferroelectric domain walls, and 2D structures shows promising possibilities for future research

    E-learning usage from a social constructivist learning approach: perspectives of Iraqi Kurdistan students in social studies classrooms

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    Background: Many schools in the Iraqi Kurdistan Region have incorporated information and communication technologies (ICT) into their environments. However, the results have shown that computer usage has had only a minimal effect on the classroom environment and learning outcomes. This minimal effect could be attributed to the teaching and learning of social studies subjects, which often rely on a traditional vision of teaching and an excessive inclusion of facts and dates in school textbooks. Consequently, students feel compelled to memorize all the information to pass tests. Yet, merely employing technology alongside traditional teaching and assessment approaches, such as lecturing or having students study in isolation without any form of collaborative learning, does not foster the development of students' higher-order thinking skills. It's time to revitalize school curricula and teaching practices to embrace a more contemporary, open-minded approach to social science education. This approach should incorporate a social constructivist perspective with technology to better instill international moral values such as democracy, respect for differences, and learning to live harmoniously with others. Aim: This cross-sectional study aims to investigate the impact of a social constructivist learning approach on the acceptance of technology and its influence on perceived e-learning outcomes among students in the Iraqi Kurdistan Region. Additionally, this study examines the differences in the effects of the social constructivist learning approach and dimensions of technology acceptance on perceived e-learning outcomes between students studying social studies in Arabic and those studying social studies in English. Setting and participants: Data were gathered from both public and private schools in Erbil governorate, situated in northern Iraq and affiliated with the Ministry of Education-Iraqi Kurdistan Regional Government. To select participants, a random sampling technique was employed, encompassing students in grades 8 through 12 of both genders. The data were obtained through a self-administered paper-based questionnaire. Instruments: Data were collected using a social constructivist learning environment survey (personal relevance, critical voice, shared control, uncertainty, student negotiation), dimensions of the attitude toward technology (attitude toward technology use, perceived usefulness, feeling ease of use, learning facility condition, and subjective norms), some additional external variables (investigation, respect for difference, student economic ability, and perceived e-learning outcomes), and socio-demographic data. Conclusion: This study is intended to emphasize the significance of employing constructivist pedagogy to enhance the technology acceptance model and improve learning outcomes. The findings of the study showed that a social constructivist learning environment had a favorable influence on perceived e-learning outcomes as well as ease of use, perceived usefulness, investigation, and respect for difference. Attitude towards technology use and perceived usefulness are contributory factors to the positive perceived e-learning outcomes. Furthermore, feeling ease of use technology has a positive effect on both attitude towards technology use and perceived usefulness. Perceived usefulness also has a direct positive impact on attitudes towards technology use. Finally, students’ technological experience is positively correlated with feeling ease of use but not with perceived usefulness. Additionally, regarding the comparison between students studying social studies in Arabic and those in English, the findings demonstrated that students studying social studies in English showed stronger positive effects from the social constructivist learning environment on their perceived e-learning outcomes. Conversely, students studying social studies in Arabic demonstrated a more potent positive effect of perceived usefulness on their attitudes towards technology. Moreover, the positive impact of an attitude towards technology use on perceived e-learning outcomes was more pronounced among the Arabic students compared to their English counterparts. Additionally, the influence of the learning facility on the perceived ease of use, as well as the perceived usefulness of technology, differed between the two groups. The English group experienced a more substantial positive impact. However, there was no significant difference observed in the effect of feeling ease of use on attitudes towards technology use between the English and Arabic student groups. Furthermore, no significant difference was observed in the effect of perceived usefulness on the social constructivist learning environment for either group. The findings from this research are expected to contribute to the development of effective and efficient counseling and support intervention programs. These programs can play a crucial role in transforming teachers

    Supporting the executability of R markdown files

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    R Markdown files are examples of literate programming documents that combine R code with results and explanations. Such dynamic documents are designed to execute easily and reproduce study results. However, little is known about the executability of R Markdown files which can cause frustration among its users who intend to reuse the document. This thesis aims to understand the executability of R Markdown files and improve the current state of supporting the executability of those files. Towards this direction, a large-scale study has been conducted on the executability of R Markdown files collected from GitHub repositories. Results from the study show that a significant number of R Markdown files (64.95%) are not executable, even after our best efforts. To better understand the challenges, the exceptions encountered while executing the files are categorized into different categories and a classifier is developed to determine which Markdown files are likely to be executable. Such a classifier can be utilized by search engines in their ranking which helps developers to find literate programming documents as learning resources. To support the executability of R Markdown files a command-line tool is developed. Such a tool can find issues in R Markdown files that prevent the executability of those files. Using an R Markdown file as an input, the tool generates an intuitive list of outputs that assist developers in identifying areas that require attention to ensure the executability of the file. The tool not only utilizes static analysis of source code but also uses a carefully crafted knowledge base of package dependencies to generate version constraints of involved packages and a Satisfiability Modulo Theories (SMT) solver (i.e., Z3) to identify compatible versions of those packages. Findings from this research can help developers reuse R Markdown files easily, thus improving the productivity of developers. [...

    Undergraduate Catalog of Studies, 2022-2023

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    Automated riverbed composition analysis using deep learning on underwater images

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    The sediment of alluvial riverbeds plays a significant role in river systems both in engineering and natural processes. However, the sediment composition can show high spatial and temporal heterogeneity, even on river-reach scale, making it difficult to representatively sample and assess. Conventional sampling methods are inadequate and time-consuming for effectively capturing the variability of bed surface texture in these situations. In this study, we overcome this issue by adopting an image-based deep-learning (DL) algorithm. The algorithm was trained to recognise the main sediment classes in videos that were taken along cross sections underwater in the Danube. A total of 27 riverbed samples were collected and analysed for validation. The introduced DL-based method is fast, i.e. the videos of 300–400 m long sections can be analysed within minutes with continuous spatial sampling distribution (i.e. the whole riverbed along the path is mapped with images in ca. 0.3–1 m2 overlapping windows). The quality of the trained algorithm was evaluated (i) mathematically by dividing the annotated images into test and validation sets and also via (ii) intercomparison with other direct (sieving of physical samples) and indirect sampling methods (wavelet-based image processing of the riverbed images), focusing on the percentages of the detected sediment fractions. For the final evaluation, the sieving analysis of the collected physical samples were considered the ground truth. After correcting for samples affected by bed armouring, comparison of the DL approach with 14 physical samples yielded a mean classification error of 4.5 %. In addition, based upon the visual evaluation of the footage, the spatial trend in the fraction changes was also well captured along the cross sections. Suggestions for performing proper field measurements are also given; furthermore, possibilities for combining the algorithm with other techniques are highlighted, briefly showcasing the multi-purpose nature of underwater videos for hydromorphological assessment.</p

    Reflections on Philanthropy for Social Justice A New Era of Giving

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    Philanthropy in India is growing steadily, with a surge in funds and practice advancements. The question remains, how can this redistribution of wealth be effectively harnessed to achieve transformative social change and more inclusive development? In A New Era of Giving, thought leaders from India and abroad share their insights and perspectives on the challenges and issues to be addressed to make a shift from a charitable model of support to an approach that prioritises social justice

    Optical Measurement of Airborne Particles on Unmanned Aircraft

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    Aerosols and clouds are persistent causes of uncertainty in climate and weather models, which is due to their heterogeneous suspension and occurrence within the atmosphere, and complex interactions which are chaotic and exist on small scales. Unmanned aerial vehicles (UAVs) have grown in popularity, and are becoming more commonly used for general atmospheric measurement, particularly measurement of aerosols and clouds. This thesis presents and evaluates a synergy between two UAVs, a multi-rotor: the UH-AeroSAM octocopter and a fixed-wing: the FMI-Talon, and an optical particle instrument: the Universal Cloud and Aerosol Sounding System. Computational fluid dynamics with Lagrangian particle tracking (CFD-LPT) was used as a tool for the characterisation of the velocity fields and particle trajectories around both UAVs. In both instances CFD-LPT was used to develop an operational envelope, with particular attention to angle of attack constraints and size distribution perturbation, for the UAV – instrument synergy. UCASS was the first open path instrument to be used on a UAV, and a good case has been made for its continued use, particularly on fixed-wing UAVs, which exhibit less complex aerodynamics and superior stability in the induced sampling airflow through the instrument
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