Responding to the Needs of Early Literacy Teachers: Designing Online Professional Development to Improve Writing Instruction for Multilingual Learners

While the population of multilingual learners (MLs) in the United States has grown rapidly in recent years, many teachers feel unprepared to teach them. The professional development (PD) teachers receive is often misaligned with their particular needs and experiences. This design-based research study examined the development and impact of online PD designed with teacher input to improve writing instruction for young MLs. Data were collected over twelve months from 25 teachers in six Southeastern U.S. school districts. Researchers evaluated the impact of the PD on teachers’ knowledge and self-efficacy for teaching writing to MLs. Findings highlight how teachers made instructional moves to support the linguistic assets of their students and shifted their beliefs toward more culturally responsive perspectives. These shifts were facilitated by the core PD elements of content focus, active learning, coherence, and duration. The study highlights a need for soliciting iterative feedback on PD from teachers themselves, revising PD based on ongoing feedback, and providing practical opportunities to apply new learning

Multilingual Voices: Transforming Professional Development Based on Student Perceptions of Literacy Learning

Acknowledging student perceptions of academic learning has been shown to increase the effectiveness of classroom instruction. However, existing research on multilingual learners (MLs) often focuses on their pedagogical and linguistic needs, overlooking their perceptions of literacy learning. This research, part of a large-scale, design-based research (DBR) project, explores MLs\u27 perceptions of literacy learning to increase the capacity of instruction through professional development to classroom teachers in a high-needs school district. Using grounded theory coding, we analyzed MLs\u27 responses from a self-efficacy survey to gain insight into their perceptions of literacy learning. Those findings were used to inform DBR modifications, including creating a writing module, a JEDI (Justice, Equity, Diversity, and Inclusion) Escape Room, and a course for ML caregivers. By centering the voices of MLs, this study provides actionable implications for teachers and researchers, contributing to a deeper understanding of literacy education. Our findings emphasize the importance of considering student perceptions in curriculum and instructional design to meet the needs of diverse learners

The New American Farmer – Extension Engagement with Urban Agriculture and Food Systems

Extension’s evolving role in urban food production will require intensive reflexivity and ongoing collaboration. Extension educators around the country have already made progress in engaging with both the social and horticultural sides of urban agriculture. Designed appropriately, urban food systems hold the potential for healthy food access, community and environmental resilience, and economic prosperity (Rangarajan & Riordan 2019). Moving forward, we offer recommendations for Extension staff to apply within their institutions and beyond. Specifically, we urge Extension to prioritize the following: 1) mediate the rural/urban dichotomy, 2) tackle structural and institutional power dynamics, and 3) intensify strategies for community resilience

Optimizing Compression Efficiency with Adaptive Quantization Bit Depths

Large-scale scientific instruments and applications generate massive amounts of data, lead- ing to significant challenges in data transfer and storage for analysis. This constitutes a major bottleneck to workflow efficiency and scientific throughput. Lossy compression offers a solution to these storage challenges in increasingly complex systems and services. Error-bounded lossy compression allows users to limit the error introduced during the compression process according to a user-defined metric and achieves significantly higher compression ratios than lossless compression for floating-point data. However, certain data types and compression configurations hinder the attainment of large compression ratios. To address the need for improved compression ratios in hard-to-compress data, this work introduces adaptive error quantization—a novel approach to lossy compression. Adaptive error quantization further refines the quantization of the error between the true value and the reconstructed value. When the original quantized error is excessively large or can be significantly improved, this method dynamically refines the quantized error by adding additional layers of quantization. This process enhances the accuracy of the reconstructed value while incurring minimal overhead. Adaptive error quantization provides a powerful tool for scientists to fine-tune the error introduced in lossy-compressed scientific datasets while simultaneously improving the compression ratio

Life Cycle Assessment Approach for Energy Usage in Animal Rendering Plants

The animal rendering industry plays a crucial role in transforming waste products from livestock production and meat processing facilities into valuable and economically beneficial products, promoting both resource efficiency and sustainability. This study uses a life cycle assessment (LCA) approach to evaluate energy usage and greenhouse gas emissions in animal rendering plants, focusing on electricity and natural gas as the main energy inputs for plant operation. This research examines three distinct rendering facilities, each processing different raw materials, to quantify the environmental impacts of rendering operations. Site visits, data collection, and real-time monitoring were used to capture energy use across key unit processes, such as cooking, milling, fat separation, odor control, and wastewater treatment. The findings indicate significant energy consumption in cooking and fat separation processes, primarily fueled by natural gas and electricity. The main cooking processes use almost all the natural gas at the rendering facilities. The allocation of electricity usage for unit operations varied among the three rendering plants studied. CO2 emissions from these processes contribute substantially to the carbon footprint, with Scope 1 and Scope 2 emissions quantified for each facility. Additionally, the study incorporates an upgraded carbon footprinting tool based on a previous model developed by Gooding (2012), allowing for the quantification of carbon emissions based on the raw material and production data for a rendering plant. Recommendations are provided for energy-saving opportunities, such as integrating renewable energy (e.g., biomass, solar) and implementing real-time data loggers to optimize operational efficiency. The study underscores the need for targeted strategies to reduce energy use and emissions in rendering plants, positioning these facilities for enhanced sustainability in the face of growing environmental regulations. This analysis serves as a foundational resource for future research and provides actionable insights for other rendering plants aiming to improve their environmental performance

Unsupervised Moving Object Segmentation with Atmospheric Turbulence

Moving object segmentation in the presence of atmospheric turbulence is a highly challenging task due to the irregular and time-varying distortions induced by the atmospheric turbulence. This thesis presents an unsupervised approach for segmenting moving objects in videos affected by such atmospheric turbulence. The proposed methodology is grounded in a detect-then-grow scheme: the algorithm begins by identifying a small set of moving object pixels (seed points) with high confidence and progressively expanding a foreground mask from these seed points to segment all moving objects. The proposed approach capitalizes on rigid geometric consistency across video frames to disentangle different types of motion, leveraging the Sampson distance to initialize seedling pixels. To ensure the spatio-temporal consistency of the generated masks, the proposed algorithm employs spatial grouping loss and temporal consistency loss during the refinement phase. Unlike traditional methods, the proposed approach is fully unsupervised and does not require any labeled training data, making it highly adaptable to real-world applications where labeled data may be scarce. To evaluate the effectiveness of the proposed method, this thesis introduces and releases the Dynamic Object Segmentation in Turbulence (DOST) dataset, the first real-captured long-range turbulent video dataset with ground-truth moving object segmentation masks. The proposed algorithm demonstrates strong accuracy and robustness across varying turbulence strengths. Moreover, it can effectively handle multiple moving objects in dynamic scenes. Comparative results on DOST show that the proposed approach outperforms existing state-of-the-art methods in terms of accuracy and robustness under both normal and severe turbulence conditions. This demonstrates the potential of our framework for long-range video analysis in applications like surveillance, environmental monitoring, and remote sensing

Urban Residents’ Perceptions of Rural People and Places: Data from Texas

In this study, we used data drawn from a 2021 online survey to assess urban Texas residents’ perceptions of rural people and places. We also evaluated how respondents’ views of rurality vary depending upon their direct personal experiences in rural Texas, their participation in selected rural Texas-focused activities, and their sociodemographic characteristics. Descriptive statistics revealed that urban Texans tend to agree with positive depictions of rurality and disagree with negative images. Multiple regression analysis showed that direct personal experiences in rural Texas, participation in rural Texas-focused activities, and certain sociodemographic characteristics were associated with pro-rural perceptions. These findings are compared with the results of previous studies. Suggestions for future research are provided

Who Cares for the Caregiver? Exploring Extension’s Role in Informal Caregiving Support

Family caregivers provide invaluable service by taking on the responsibility of caring for older adults in the United States. With a growing aging population, the need for family caregivers and demands on their time and resources will continue to increase. Although a variety of caregiving resources and support services exist, family caregivers often lack the awareness, time, transportation, or financial resources to connect to services. There is a critical need to address the barriers that prevent caregivers from accessing services. In this commentary, we highlight barriers and make recommendations for improving conversations, research, and practice to meet this growing problem

Audience Preferences for Extension Forestry Zoom Webinars

The Washington State University Extension Forestry program switched to all online programming in 2020 in response to the COVID-19 pandemic. Over 2,000 people participated in our webinars, providing an opportunity to survey a large audience about their webinar preferences. We found that people prefer webinars that are approximately an hour long and offered in the evening or late morning. Participants placed high importance on having a recording available, but they had mixed views on including video of the instructor speaking. Participants found online delivery to be successful and had a strong preference for online programming in the future