Scholars Junction - Mississippi State University Institutional Repository
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Alamo Plaza Hotel Courts, Jackson, Mississippi
No full textA color image of the Alamo Plaza Hotel Courts is freatured on this postcard. The Hotel is depicted as a white, Spanish style building with green and white awnings over the windows and green spaces between the driveways. The yellow section of the card beneath the image advertises the hotels amenities and various locations, including one in Jackson, Mississippi.https://scholarsjunction.msstate.edu/mss-lampton-images-ms-capitol/1539/thumbnail.jp
Evaluating the impact of biostimulants at variable nitrogen rates in corn production
No full textBiostimulants have garnered significant interest due to their potential to enhance crop productivity while optimizing nitrogen (N) uptake and nitrogen use efficiency (NUE). However, field research testing their efficacy in corn (Zea mays. L) production remains largely unexplored. Therefore, a field study was conducted in 2022 and 2023 in Mississippi (MS). A split plot design was implemented, with N rates as the main plot including 0 (control), 90, 180, 269 kg N ha−1 at Starkville, while Stoneville included an additional rate of 224 kg N ha−1. The subplot consisted of seven treatments, including a no biostimulant (check) and six microbial biostimulants (Source Corn®, Envita®, iNvigorate®, Blue N®, Micro AZ™, and Bio level phosN®) applied either as foliar at V4-V5 growth stages or in-furrow at planting. Nitrogen rates positively affected grain yield at all three site-years, whereas biostimulants effects on grain yield were only observed at one site (Stoneville 2022). Moreover, these differences only existed between six biostimulants and they were not significantly different from check plot with no biostimulant. Higher N rates reduced the efficiency of grain production in terms of NUE parameters and N uptake, showing a consistent inverse trend across all site years. This study observed minimal synergistic benefits of microbial biostimulants, despite evaluating their effectiveness alongside varied N rates. Further research testing diverse biostimulant categories with varied dosages and application timings is warranted to confirm their potential benefits for higher productivity and agricultural sustainability
Influence of seed-applied biostimulants on soybean germination and early seedling growth under low and high temperature stress
No full textBiostimulants are environment-friendly agricultural inputs that can improve plant health and yield potential under environmental stressors. Soybeans subjected to extreme temperatures during the growing seasons impacts plant health and performance. Uniform emergence and vigorous seedling establishment are the two traits during the early season that directly correlate with the final yield and are sensitive to abiotic stress. This study tested the effectiveness of seed-applied biostimulants in improving seed germination and emergence traits under different temperatures, low (15 °C, LT), optimum (25 °C, OT), and high (35 °C, HT), using three phenotyping methods such as the paper roll, growth pouch, and soil-based pot culture. Germination, emergence, and seedling growth were significantly accelerated under OT and HT compared to LT in both biostimulant-treated and untreated seeds. While seeds treated with biostimulants exhibited minor differences in germination, emergence, and growth traits under LT and HT compared to the OT. In the soil-based pot culture experiment, humic and fulvic acid-containing treatments extended the time to 50% emergence under LT. This delay was associated with a 13% increase in seedling biomass. A bacillus containing biostimulant improved seedling vigor by 7% under LT compared to untreated check. Notably, biostimulants containing bacterial strains, fulvic acid, and humic acid were found to have a role in reducing time to germination or emergence and enhancing seedling growth. However, the results obtained from different phenotyping methods were inconsistent, suggesting that the effects of biostimulants on germination and growth parameters may be more targeted rather than broad-spectrum. Future research is necessary to optimize application rates and fully explore their potential to mitigate the effects of stressors during the growing season
For Neurodivergent Marxism: Between Materialist Analysis and Escape from the Empire of Normality and Capitalism
No full textRobert Chapman’s Empire of Normality: Neurodiversity and Capitalism (2023) possesses extraordinary import for the emancipatory thinking and praxis of neurodivergent folk, but also of those of us who identify as disabled, chronically ill, crip, mad or even neurotypical. This is not only because Chapman’s work offers a fundamental contribution to debates initiated by recent Marxist engagements with the relationship between capitalism and health. It is also thanks to the intense theoretical innovation, historical analysis and inclusive politics that the book radiates. By dissecting the harms wrought by the Empire of Normality, Chapman’s Neurodivergent Marxism is an updated type of Marxism that simultaneously models updated ways of theorising against and beyond the capitalist Empire of Normality, and frames updated modes of class struggle. Despite originating close to neurodivergent power and neurodiversity theory, or because of this, it beckons all of us to follow, whether in workplaces, far from these, or in our own movements
Advanced monitoring of turbidity in the Mississippi Sound: A machine learning-based approach integrating unmanned aircraft systems, satellite observations, and land use analysis
No full textTurbidity is a vital indicator of water quality, influencing light penetration and the ecological function of coastal environments. Traditional monitoring methods often lack the spatial and temporal resolution needed for effective environmental management. This dissertation introduces a multi-scale framework that integrates Uncrewed Aircraft Systems (UAS), Autonomous Surface Vessels (ASV), satellite imagery, and machine learning to produce high-resolution turbidity maps and long-term trend analyses for the Mississippi Sound. Chapter I presents a fine-scale turbidity mapping method using UAS multispectral imagery calibrated with in situ ASV measurements. This hybrid UAS-ASV system enabled the generation of detailed turbidity estimates. Among various machine learning models tested on radiometer-derived sensor-specific remote sensing reflectance, the Support Vector Machine (SVM) performed best (R² = 0.943, RMSE = 0.454 NTU), effectively capturing nonlinear relationships between turbidity and remotely sensed data. Chapter II expands the analysis to broader spatial and temporal scales by developing a deep neural network (DNN) regression model. Trained on datasets from six ASV field campaigns and Landsat 8 and 9 surface reflectance imagery, the DNN outperformed RandomForest, SVM, and XGBoost models, achieving an R² of 0.864 and RMSE of 1.794 NTU. The model was then used to generate a 22-year turbidity time series (2002–2024), revealing seasonal trends and peak turbidity events in 2013, 2016, and 2020—linked to meteorological disturbances and Bonnet Carré Spillway openings. Chapter III investigates the influence of land use and land cover (LULC) changes on turbidity. Using harmonized annual LULC maps and spatial correlation analysis, the study found a significant negative correlation between barren land and turbidity at the HUC-10 watershed scale, while cropland and urban areas had minimal effects. Finer-resolution HUC-10 analysis revealed slightly stronger land–water relationships than coarser HUC-8 scales. As a preliminary study focused on a single LULC factor, this chapter provides a foundation for more comprehensive future research. This research underscores the value of integrating remote sensing with machine learning to monitor complex coastal systems. The proposed framework is adaptable across regions and scales, offering a flexible tool for water quality assessment, habitat restoration, and informed environmental decision-makin
The impact of need-based financial aid on degree completion for community college transfer students at a southern university
No full textThe purpose of this study was to examine the impact of need-based financial aid on degree completion and time to degree completion among community college transfer students. Transfer students often face barriers that influence persistence and graduation outcomes. This study addressed a gap in the literature by focusing on community college new transfer students at a southern university and assessing whether need-based financial aid influences both the likelihood of graduating and time to degree completion. Institutional data for the fall 2018 cohort (N = 2,074) were obtained from the university’s Information Technology Department and tracked through spring 2024. The analytic dataset included demographic variables (age, gender, race/ethnicity), academic measures (credits transferred, degree completion status, degree completion years), and financial characteristics (dependency status, receipt of need-based aid). Logistic regression was conducted to evaluate the effect of need-based aid on degree completion, and analysis of covariance (ANCOVA) was used to examine time to degree among students who graduated (n = 1,410). Findings revealed that receipt of need-based financial aid significantly increased the odds of degree completion, with students who received aid being over 57 times more likely to graduate compared to non-recipients, after controlling for covariates. Additional predictors of completion included credits transferred, age at entry, dependency status, and race/ethnicity, while gender was not significant. Results for time to degree completion showed no significant differences between aid recipients and non-recipients. Instead, time to degree was most strongly influenced by transfer credits, dependency status, gender, and ethnicity. Students who transferred more credits, were dependent, and belonged to certain demographic groups graduated more quickly, while older and independent students tended to take longer to complete. These findings suggest that while need-based aid plays an important role in ensuring degree completion, it does not shorten completion timelines. Policy and practice implications include expanding funding for need-based aid, strengthening credit transfer pathways, financial aid counseling in transfer services, and providing targeted advising for older, independent, and underrepresented students. Future research should extend these findings through qualitative perspectives on transfer student experiences and deeper analysis of how financial and academic policies interact to shape persistence and pace
Examining elementary teachers’ perceptions of differentiated mathematics instruction: A diffusion of innovation perspective
No full textThis study examined elementary teachers’ perceptions and adoption of Differentiated Mathematics Instruction (DMI) using Rogers’ (2003) Diffusion of Innovations theory as the guiding framework. The research aimed to (1) develop and validate the Differentiated Mathematics Instruction (DEMI) Adoption Scale, (2) investigate how teachers’ perceptions of DMI characteristics predict the frequency of its classroom use, and (3) explore the relationship between those perceptions and teachers’ adoption decision stages. A quantitative, cross-sectional survey design was employed with 118 elementary teachers from four U.S. states (California, Mississippi, New Jersey, and Florida). The DEMI Adoption Scale included five constructs (i.e., Relative Advantage, Compatibility, Complexity, Trialability, and Observability) adapted from Rogers’ innovation attributes. Confirmatory factor analysis (CFA) established a five-factor structure with ordinal alpha coefficients ranging from .58 to .97, demonstrating satisfactory internal consistency. Findings from ordinal logistic regression analyses revealed that Compatibility was the most consistent and significant predictor of both DMI use frequency and adoption decision stage, while Trialability predicted teachers’ progression through adoption stages. Complexity showed a marginal effect, becoming significant when overlapping predictors were removed, suggesting suppressor relationships among innovation characteristics. Group comparisons indicated differences by race, teaching experience, and education level, emphasizing the need for culturally responsive and differentiated professional development. This study contributes theoretically by refining the Diffusion of Innovations framework for pedagogical (non-technological) innovations and methodologically by validating the DEMI Adoption Scale as a reliable measure of teachers’ perceptions of DMI. Practically, it provides insights for designing professional development that emphasizes compatibility with teacher practice, hands-on trial opportunities, and supports for reducing perceived complexity. Recommendations for future research include longitudinal validation of the DEMI Scale, exploration of subgroup equity patterns, and mixed-methods studies connecting teacher perceptions with observed classroom practice
Examining relationships among teachers’ perceptions of school climate and culture and their sense of school belonging
No full textThe purpose of the study was to examine the relationships among teachers’ views of school climate and culture and their sense of belonging within a Mississippi school district. Using a quantitative survey research design, data were collected from 99 teachers during the 2024–2025 school year through the School Climate Survey and the School Belonging Scale Survey. The researcher sought to determine whether differences in the teachers\u27 perceptions of their schools’ climate and culture and their sense of belonging existed by gender and grade span, as well as whether significant relationships existed among the constructs of their schools’ climate and culture and the teachers’ sense of belonging. The findings revealed teachers who reported positive perceptions of their schools’ climate and culture also expressed a stronger sense of belonging. The additional analyses further showed differences across grade spans, with elementary teachers generally reporting more favorable perceptions than their secondary peers, and female teachers indicating slightly higher levels of connectedness and belonging than male teachers. The positive correlations among perceptions of school climate, culture, and belonging suggest as teachers feel safer, more supported, and respected, their sense of belonging increases as well. The results further identified specific dimensions, particularly staff connections and the physical environment, as critical factors influencing teachers’ sense of belonging. Schools characterized by strong collegial relationships, shared decision-making, and well-maintained facilities demonstrated high levels of teacher belonging. The findings emphasize the continued need to cultivate supportive and inclusive school environments in which teachers feel valued, respected, and connected. Such conditions foster greater professional engagement, collaboration, and commitment, ultimately enhancing the overall effectiveness of the school
Electrospun nanofiber creation with varying polymer-solvent combinations for air filtration
No full textThis study aims to analyze various polymer-solvent combinations for electrospun creation of nanofiber media and to determine the best-suited solution for lab-grade electrospinning. Suitable solutions allow for controlled morphology of fiber diameters and for efficiency of media creation. The principal variables are solvent, concentration, and voltage. Humidity and temperature play a secondary variable role. The solution must withstand an electric field of ≤30 kV and be insoluble in water to maintain desired morphology and structure. Manipulation of the solutions and variables allow for fiber diameter optimization and beading minimization. High temperature filter applications require efficiency in intense heat and preservation of media structure. Fiber diameters must be within the nanometer to micrometer range for filtration of small particles. Nanofiber beading causes inefficient filtration and must be avoided. These developments improve efficiency of filtration media within the electrospinning industry and potentially allow for optimization of air filtration within a nuclear setting