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Innovations in Education and Teaching International The Impact of Teachers' Assessment Literacy on the Willingness to use Student Peer Evaluation: A Mixed-Methods Study For Peer Review Only -Non-Anonymous PDF Cover Page
No full textThis study investigated the impact of TESL teachers' assessment literacy (TAL) on their willingness to use student peer evaluation (SPE) in China. Surveying 212 teachers from 26 Chinese universities, the research employed descriptive, multiple linear regression, and qualitative analysis. Results revealed that teachers possess a certain level of assessment literacy across six dimensions. These dimensions positively correlated with teachers' perceived usefulness, ease of use, and frequency of implementing SPE. Regression analyses showed that TAL significantly influences SPE practices. The order of influential dimensions is SPE data utilization> skills> awareness> objectives> knowledge> attitudes. However, assessment attitude is a critical factor affecting perceived ease and usefulness of peer evaluation. Notably, when predicting perceived usefulness, assessment attitude, awareness, and knowledge explained 83.4% of the variance. In the personal interviews, we explored the common obstacles of implementing SPE. The study offers valuable insights into enhancing SPE in TESL courses, highlighting the importance of TAL
Hanging by a Tār - A Geo-Temporal Survey of Strings Attached
Get PDFTār, the Persian term for string, is a common suffix of stringed musical instruments found across many cultures of Eurasia. From the Indian sitār to the Persian tār and all the way to the modern guitar, the history of the chordophone is as ancient as the human species. A common thread links the first ever bow and arrow to the shredding solos of Dimebag Darrell. The following extended program notes examines works of contrasting styles written or transcribed for the classical guitar from various periods in Western music performed during my graduate recital. Starting with Variations on Folies d'Espagne Op. 45, and Variations on a Theme by Handel, Op. 107 by Mauro Giuliani (1781-1829), a contemporary of Beethoven who was active during the transition from the Classical to the Romantic period, followed by an example of baroque counterpoint from the lute suite BWV 998 by Johann Sebastian Bach (1685-1750), and finally two of the most recognizable compositions from the Romantic era Asturias (Leyenda), Op. 47, No. 5 by Isaac Albéniz (1860-1909) and Francisco Tárrega's (1852-1909) Recuerdos de la Alhambr
Longfin Smelt in the San Francisco Bay
No full textThe longfin smelt is a small silvery fish native to estuarine environments along the Northern Pacific. It has suffered a historic decline of around 99.9% in the San Francisco Bay Area (Nobriga & Rosenfield, 2016), as a result of habitat loss, reduced freshwater flow, and competition against introduced species. As a result, it has been listed as a federally endangered species. However, a recently discovered population of spawning longfin smelt has the potential to provide direction on how to restore the population around Lower South San Francisco Bay (LSB). Surveys of LSB over the past 10 years have found an exponential population increase in populations of longfin smelt increasing from 98 in the 2014 water year to 2791 in the 2022 water year. Over 40% of the longfin smelt caught in the last five years has been caught within restored marshlands in the former salt ponds of A-19 and A-21. This indicates that marsh restoration is crucial for increasing the population of longfin smelt. Other factors influencing the longfin smelt population may include the El Nino/La Nina cycle, total precipitation, and changes to the watershed of Lower South San Francisco Bay, such as the removal of Anderson Dam in February 2020
Optimized DFT implementation
Get PDFThe Fourier Transform is a pivotal mathematical tool employed in signal processing and various engineering disciplines. To leverage this transform effectively, optimized implementations that facilitate faster computation and reduced resource utilization are crucial. Within the context of four projects, this study investigates strategies for memory and speed optimization of Discrete Fourier Transforms (DFT) in Field Programmable Gate Arrays (FPGA), with the hope of contributing to the development of efficient DFT implementations in FPGAs. The goal is to offer enhanced performance and reduced resource utilization for signal processing applications. Initially, the Xilinx DFT IP is implemented and analyzed to establish baseline performance metrics for speed and resource utilization. Subsequently, an optimized 8-point DFT module is developed, exploiting symmetries in the DFT's Twiddle Matrix. Comparative analysis reveals an average 78% reduction in FPGA block memory allocation and Look-Up Table (LUT) as memory usage, albeit at the cost of reduced computational speed. This implementation is suitable for applications requiring minimal footprint. Furthermore, a Fast Fourier Transform (FFT) module is created prioritizing memory footprint optimization, followed by speed enhancement. Notably, the FFT module supports N-point DFT calculations for N up to 64 with comparable memory utilization to the 8-point DFT module, demonstrating improved memory optimization for larger DFT sizes. A fourth project explores speed optimization, yielding an optimized FFT module capable of computing an 8-point DFT in merely 12 clock cycles ([N/2].log2N), surpassing the sample 8-point DFT module (64 clock cycles, N2) and the initial FFT module (24 clock cycles, N.log2N). These results show an 81% speed increase compared to the sample DFT module, and 50% speed increase compared to the initial FFT implementation
Food insecurity among Filipino Americans in Los Angeles County
No full textFood insecurity in the United States is a major public health concern that can lead to serious consequences for individuals' physical health and mental well-being, as well as society at large. It is a governance failure that warrants immediate attention and action, making it a public administration concern as well. Previous studies have demonstrated that there are racial disparities in food insecurity. One group that warrants special attention is Filipino Americans, who have been underrepresented in existing food insecurity studies. This research proposal seeks to identify socioeconomic challenges, such as low income, unemployment, and housing instability, within this community to help guide public administrators in designing or enhancing program and policy strategies aimed at addressing this problem. Through qualitative interviews, this study aims to identify socioeconomic issues that are prevalent among Filipino Americans in Los Angeles County, California. The findings of this study are expected to help support public administrators in identifying culturally appropriate solutions and interventions that improve food access for Filipino Americans. Furthermore, the solutions and interventions from this study can potentially be applied to the broader Asian American community
SURVEILLANCE ROBOT WITH ROTATING CAMERA CONTROLLED BY RASPBERRY PI
No full textThis project focuses on creating a versatile surveillance robot that combines a rotating motorized camera with Raspberry Pi control and ESP32-CAM integration to deliver a cost- effective solution. The system delivers real-time video streaming while offering advanced face recognition features together with remote control of camera movements which ensures full area coverage. This surveillance robot delivers scalable security solutions by combining the Raspberry Pi's computational power with the ESP32-CAM module's low-cost and low-power operation [1]. The system's design supports remote access through both web and mobile platforms which contributes to its functionality in both home and business environments. The ESP32-CAM module functions as the primary video capture and streaming unit through its integrated camera and Wi-Fi capabilities [2] which allows it to send live footage to the Raspberry Pi. The Raspberry Pi functions as the main processing unit by performing complex operations including face recognition and directing the movements of the motorized camera. The robot monitors large areas efficiently through a rotating camera mechanism which provides a wide field. This feature enables multiple uses including access control systems, detection solutions, and authorized personnel supervision. By processing video data directly on the Raspberry Pi device, users can eliminate constant transmission requirements and achieve quicker response times. The project aims to solve multiple problems by targeting latency reduction alongside power efficiency and data security. The power efficiency of the system results from incorporating low power components such as the ESP32 CAM and implementing effective power management strategies for the RaspberryPi. The system's modular structure provides flexibility for future upgrades by enabling the integration of extra sensors. The project showcases how the ESP32-CAM module and Raspberry Pi can combine to build a cost- efficient surveillance robot with a rotating motorized camera. Real-time video streaming combined with face recognition and remote-control capabilities makes the system an effective security enhancement tool while improving situational awareness The project establishes a basis for advancing intelligent surveillance systems by tackling essential problems like latency reduction, power optimization, and data protection
RTL Design and UVM RAL Verification of AMBA APB Protocol.
No full textThis research looks into how the UVM Register Abstraction Layer (RAL) can be used to check a simplified AMBA APB slave Interface. A reusable UVM testbench was made that includes a full RAL model in transactional-level test sequences. The model turned the operation of the APB signal level into read and write operations. Key parts are register-aware sequences and a mirrored model that checks that the predicted values are correct. This work shows how leveraging UVM RAL for clean, scalable register level verification can be useful in real life. It also lays the groundwork for applying similar methods with additional AMBA protocols or custom interconnects in GPU/CPU systems
Breast Cancer Prediction using Machine Learning Models
No full textBreast cancer is a major cause of deaths among women and therefore establishing the right tools to detect the disease at the right time is a clinical priority. A number of classical ways of diagnostics is associated with subjectivity and hits. This thesis tries to meet these challenges by developing, realizing and testing a practical and efficient machine learning framework to predict breast cancer. The research has been carried out based on the publicly not unique Wisconsin Breast Cancer Dataset (WBCD). A strict methodological pipeline was implemented: data preprocessing, feature selection based on correlation to avoid multicollinearity, and a comparison of four machine learning algorithms, such as K-Nearest Neighbors (KNN), Decision Tree (DT), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). The RF model performed best as the RF model had the best testing accuracy of 96.49%, precision of 98% on malignant cases, and recall of 93%, using 5-fold cross-validation and systematic hyperparameter tuning. The most important feature of the present work is a working end-to-end system, resulting to a real deployable model and a practical web interface with which users can interact. The RF model trained and the Data scaler therewith are serialized together with the joblib and placed into a Flask-based REST. This application offers the inter-active, real-time platform where the clinicians will enter the data about patients and will be provided with an immediate, data-based prognosis of the tumor status. This research will prove that machine learning has massive potential to support improved clinical decision-making, minimize diagnostic errors, and, ultimately, better patient outcomes in the oncology setting through the practical implementation of a complex predictive model
How do leadership styles affect classified employees within LAUSD?
No full textThis study examines how leadership styles influence classified employees' motivation, job satisfaction, and engagement in the Los Angeles Unified School District (LAUSD). While past research has focused on teachers and administrators, roles like office assistants and custodians remain underexplored. Using qualitative methods, including semi-structured interviews with 30-40 staff across ten schools and one regional office, this study investigates the effects of transformational, transactional, and laissez-faire leadership on employees' motivation, job satisfaction, and to increase engagement. Grounded in Public Service Motivation (PSM), Self-Determination Theory (SDT), and leadership frameworks, the research aims to identify key patterns through thematic analysis using a qualitative research approach. Findings are expected to show that transformational leadership boosts morale and intrinsic motivation, transactional leadership provides stability, and laissez-faire leadership leads to disengagement (Nielsen et al., 2019; Jeong, 2025; Zareen et al., 2014). Insights will inform leadership development and policies to improve staff retention and school climate in LAUSD and similar districts
Permanent supportive housing program impact on long-term housing retention among individuals experiencing chronic homelessness in California
No full textPermanent Supportive Housing is a "Housing First" model that combines affordable housing assistance with comprehensive services for individuals experiencing chronic homelessness. However, limited data on the long-term retention of housing throughout California is available to understand the success of this program in this state. This proposal aims to investigate the following research question through a quantitative approach: How does the Permanent Supportive Housing program impact long-term housing retention among individuals experiencing chronic homelessness in California? Researchers will utilize a survey format to gather data from a purposive sample of approximately 290 Permanent Supportive Housing service providers. This study aims to identify areas of success and opportunities for improvement in the implementation of Permanent Supportive Housing by engaging directly with program implementers to understand the impact of Permanent Supportive Housing on long-term housing retention among chronically homeless individuals