Digital Commons @ Southern University and A&M College
Not a member yet
607 research outputs found
Sort by
Southern University and A&M College 2024-2026 Graduate Catalog
No full textSouthern University and A&M University 2024-2026 Graduate Cataloghttps://digitalcommons.subr.edu/subr_ccat/1056/thumbnail.jp
Analyzing the Capacities of Cotton Production in Sudan
Full text linkIn the political economy of the nation of Sudan, cotton ranks significantly high as an essential component of the revenue generation sources. Considering the significance of the crop in the country, cotton has not only been exported to various countries, but some portions of the produce are also used up internally. With that also came, large scale cultivation of the cotton produce on vast swaths of land over the years amidst demands and fluctuations in the marketplace. Given the effects of cotton production on the environment from the use of chemicals and widespread water use, there came changes in the lager agricultural structure that coincided with variations in yield levels, land size and production. Yet, as very little has been done to assess the changing trends in cotton land use in Sudan, no one has bothered to examine the extent and nature of cotton land use and the potentials under a mixscale approach. Accordingly, the paper focusses on cotton land use change, with emphasis on the issues, trends, environmental analysis, impacts, and factors using descriptive statistical techniques connected to Geographic Information Systems (GIS). Through the applications of mixscale model, the study not only showed rising changes in cotton production and yield, but most of the land use indices and the others posted variations as well. While the changes are attributed to a host of socio-economic, ecological, and political elements located within the local farm system, the GIS mappings of the trends point to the gradual dispersion of cotton land use indices spread across different points in space in the study area. There are also widespread impacts in the form of intense water usage, pollution from chemicals, the loss of land and degradation. Along these lines, despite the efforts of the institutions, the paper proffered solutions ranging from education, monitoring to the design of regional and national cotton land use information system
Self-healing of macroscopic cracks in concrete by cellulose fiber carried microbes
Full text linkThis research introduces a new approach to healing millimeter scale cracks in concrete using Lysinibaccilus Sphaericus Bacteria (LSB) encapsulated in cellulose fibers. Cracking in concrete, particularly macroscopic cracking, can cause premature structural failure and reduce its lifespan, which is a critical industry challenge. While bacteria encapsulated in cellulose fibers have been used to heal cement mortar, the studies are limited to heal much narrower cracks. In this study, we integrate LSB, known for its strong biocalcification abilities, with the protective environment of cellulose fibers, which are renewable and sustainable, for healing millimeter scale cracks in ordinary cement concrete. To understand the healing process, we firstly used a 3D-printed polymeric scaffold for preliminary observations of calcite precipitation, demonstrating the potential of bacteria-induced calcification in a controlled environment before applying these insights to concrete. We then studied the self-healing of concrete. Through mechanical testing, we identified the optimal concentration of cellulose fiber as 0.45 % by volume of mortar. Approximately 2.38 × 108 bacteria were immobilized in each gram of cellulose fibers. With cellulose fiber encapsulated LSB, the test results show up to 25 % increase in compressive strength and split tensile strength. After crack healing, the self-healing concrete still has higher mechanical strength than the undamaged control concrete. Particularly, the self-healing concrete was able to heal cracks up to 2.5 mm wide in fully wet environments and 1.5 mm wide in wet-dry conditions. This research also highlights the resilience of bacteria carried by cellulose fibers against harsh environmental conditions, including high temperatures at 160 ◦C, ensuring the durability and applicability of the proposed self-healing concrete in diverse climates. Integrating cellulose fibers encapsulated LSB into concrete represents a significant breakthrough in addressing the perennial problem of concrete cracking, offering a promising avenue for constructing durable and maintenance-free structures
LUC Awards Hall of Fame
No full textSouthern University and A & M College, John B. Cade Library received the following awards at the LUC (Louis Users Conference for the following:
2024: Leader in Librarianship
Dean of Libraries, Dawn Kight
This award recognizes a library administrator who has shown leadership skills, guidance, and innovative ideas within their library. 2024 Library of the Year
This award recognizes a LOUIS member library demonstrating excellence in at least one of these areas: service to community, innovation in developing community programs, a dramatic increase in library usage, or leadership in creating programs that can be emulated by other libraries. Best Outreach
Outreach and social media programs require organization, planning, and time. If successful, these programs lead to an increase in appreciation for, understanding of, and use of the library. The Best Outreach award recognizes the time and work that a person or persons put into an event for their library.
2024: Eddie Hughes, Vanissa Ely, Quiana Wright, Maya Banks, Rena Darensbourg, Angela Proctor, Ashley Weir-Matthews, Melanie Haynes, Edwia Richardson, Southern University and A&M College 2023 Timely Librarianship
The Timely Librarianship Award recognizes a librarian or group of librarians over the past year who has completed a significant achievement, contribution, project, or goal related to a theme related to the circumstances of the year leading up to LUC.
2022 (Theme: Promoting Wellness): Rena Darensbourg, Maya Banks, Charlotte Henderson, Vanissa Ely, Cheryl Taylor, Christopher Russell, Dawn Kight, and Maletta Payne, Southern University and A&M Colleg
Bioaugmentation with Tetrasphaera to Improve Biological Phosphorus Removal from Anaerobic Digestate of Swine Wastewater
Full text linkTetrasphaera-enhanced biological phosphorus removal (T-EBPR) was developed by augmenting conventional EBPR (C-EBPR) with Tetrasphaera to improve phosphorus removal from anaerobic digestate of swine wastewater. At influent total phosphorus (TP) concentrations of 45 to 55 mg/L, T-EBPR achieved effluent TP concentration of 4.17 ± 1.02 mg/L, 54% lower than that in C-EBPR (8.98 ± 0.76 mg/L). The enhanced phosphorous removal was presumably due to the synergistic effect of Candidatus Accumulibacter and Tetrasphaera occupying different ecological niches. Bioaugmentation with Tetrasphaera promoted the polyphosphate accumulation metabolism depending more on the glycolysis pathway, as evidenced by an increase in intracellular storage compounds of glycogen and polyhydroxyalkanoates by 0.87 and 0.34 mmol C/L, respectively. The enhanced intracellular storage capacity was coincidentally linked to the increase in phosphorus release and uptake rates by 1.23 and 1.01 times, respectively. These results suggest bioaugmentation with Tetrasphaera could be an efficient way for improved phosphorus removal from high-strength wastewater
Southern University Walking Bridge
No full texthttps://digitalcommons.subr.edu/bob_images/1006/thumbnail.jp
Trees on the bluff 3
No full textThis photo is from the Christopher Russel Collection.https://digitalcommons.subr.edu/bob_images/1009/thumbnail.jp
Relative Genetic Homogeneity within a Phenotypically diverse group; a case of the Lake Tana Labeobarbus (Cyprinidae) Species Flock, Ethiopia
Full text linkThe Lake Tana Labeobarbus species flock represents one of the world’s most famous examples of lacustrine species radiations. Previous studies of this group have resulted in the description of at least 15 species based on their differences in functional morphology and definition of two clades (lacustrine and riverine spawning clades) based on life history traits. A total of 166 fish representing 14 Labeobarbus species were genotyped using 10 lineage-specific hexaploid microsatellite loci. Six of these loci were developed for this study based on DNA sequence contigs derived from a microsatellite-enriched genomic library of Labeobarbus intermedius from Lake Tana; the remaining four loci were obtained from a previous study. The genotypes of the 10 loci were analyzed to examine genetic diversity and population structure within Lake Tana Labeobarbus. Overall mean allelic richness (NA) was 17.6 alleles per locus and observed (Ho) and expected (He) heterozygosities were 0.84 ± 0.14 and 0.73 ± 0.09, respectively, across all Lake Tana Labeobarbus samples examined. Our analyses reveal that there is little genetic differentiation among species (FST = 0.020–0.099; only 10 of 91 species comparisons were significant), but moderate differentiation (FST = 0.11, p \u3c 0.05) between lacustrine and riverine spawning populations. Relative to previous phylogenetic hypotheses, our phenetic analysis employing the R-based Analysis of Phylogenetics and Evolution (APE) program seems to perform marginally better in revealing lineages within Lake Tana Labeobarbus. Herein, our results are compared to a previous microsatellite-based study of the same populations
Advancing flame retardant prediction: A self-enforcing machine learning approach for small datasets
Full text linkImproving the fireproof performance of polymers is crucial for ensuring human safety and enabling future space colonization. However, the complexity of the mechanisms for flame retardant and the need for customized material design pose significant challenges. To address these issues, we propose a machine learning (ML) framework based on substructure fingerprinting and self-enforcing deep neural networks (SDNN) to predict the fireproof performance of flame-retardant epoxy resins. Our model is based on a comprehensive understanding of the physical mechanisms of materials and can predict fireproof performance and eliminate the needs for properties descriptors, making it more convenient than previous ML models. With a dataset of only 163 samples, our SDNN models show an average prediction error of 3% for the limited oxygen index (LOI). They also provide satisfactory predictions for the peak of heat release rate PHR and total heat release (THR), with coefficient of determination (R2) values of 0.87 and 0.85, respectively, and average prediction errors less than 17%. Our model outperforms the support vector model SVM for all three indices, making it a state-of-the-art study in the field of flame retardancy. We believe that our framework will be a valuable tool for the design and virtual screening of flame retardants and will contribute to the development of safer and more efficient polymer materials
Beauty on the Bluff: The Minidome at Night
No full textPhotographs are from the Christopher Russell Collection.https://digitalcommons.subr.edu/bob_images/1013/thumbnail.jp