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Asteraceae: Heliopsis helianthoides
No full textHeliopsis helianthoides is a perennial herbaceous plant that grows from a fibrous root system with creeping rhizomes that form dense clumps or colonies over time. The stems are erect, branching and typically reach 60 to 150 cm tall. Leaves are opposite or alternate, ovate to lanceolate, measuring 7 to 15 cm long and 3 to 7 cm wide. The leaf margins are serrated with coarse teeth, and the surface is rough to the touch. Leaves have pointed tips and tapering bases, attaching via short petioles. The plant has a bushy habit. Flowering occurs from mid to late summer (July–September). Inflorescences consist of bright yellow, daisy-like flower heads about 4–7 cm across. Each flower head is surrounded by several series of green, lanceolate to ovate involucral bracts about 10 to 15 mm long, often with fine hairs. The ray florets number about 10 to 20, with petals that are oblong to spatulate, measuring 2.5 to 4 cm long and 5 to 10 mm wide, with smooth edges. Disc florets are tubular, perfect (bisexual), about 3 to 5 mm long, with five lobes at the corolla tip. Stamens are fused into a tube around the style, which divides into two stigmatic branches. The fruit is a small, dry achene, 3 to 4 mm long, with no pappus or with a pappus of short hairs, that matures in late summer. Smooth oxeye is native to South Dakota and commonly found in prairies, open woodlands, and along roadsides and disturbed sites across the state.https://openprairie.sdstate.edu/nativeplant/1315/thumbnail.jp
Fabaceae: Medicago sativa
No full textMedicago sativa is a perennial herbaceous plant growing 30 to 100 cm tall, often forming dense stands. It has a deep, extensive taproot system that allows it to access deep soil moisture. The stems are erect to ascending, slender, and typically glabrous or sparsely hairy. Leaves are alternate and trifoliate, with three oval to oblong leaflets measuring about 1.5 to 4 cm long and 0.8 to 2 cm wide; leaflets have smooth or slightly serrated margins and a fine, soft pubescence on the surface. Leaflets are petiolulate with short stalks about 2 to 5 mm long, and stipules are small and lanceolate at the base of the leaf stalks. The inflorescence is a raceme or spike-like cluster of 10 to 40 pea-shaped flowers blooming from late spring through summer (May–August). Each flower is subtended by a calyx of five sepals fused at the base into a tubular cup about 5 to 8 mm long, with five pointed lobes that are lanceolate to ovate and often covered with fine hairs. Flowers are about 10 to 15 mm long. The corolla’s standard petal is broadly ovate and measures approximately 9 to 14 mm long and 7 to 10 mm wide; the wing petals are about 8 to 12 mm long and 3 to 5 mm wide; the keel, formed by two fused petals, is about 8 to 13 mm long and 3 to 5 mm wide. Flowers are typically purple to violet, sometimes pink or white. Flowers have ten stamens, nine of which are fused, and a superior ovary. The fruit is a spiral coiled pod about 2 to 4 cm long containing several seeds, maturing from late summer to early fall. Alfalfa is native to southwestern Asia but widely cultivated and naturalized in North America, including South Dakota. Primarily used as a forage crop, it is occasionally found naturalized along roadsides and disturbed sites.https://openprairie.sdstate.edu/nativeplant/1329/thumbnail.jp
Semantic Think-on-Graph (SemToG) : Enhancing GraphRAG through Semantic Community Detection
No full textGraph-based Retrieval-Augmented Generation (GraphRAG) enhances large language models (LLMs) by grounding their reasoning in structured knowledge graphs, making them more reliable for multi-hop reasoning and factual QA. A central mechanism in Think-on-Graph systems such as ToG[14] and FastToG[7] is community detection, which groups locally related nodes into compact subgraphs so that LLMs can reason over focused, information-rich neighborhoods instead of traversing the entire graph. However, these methods rely purely on structural connectivity, often scattering semantically related entities across different communities and weakening the evidence provided to the LLM. We propose Semantic Think-on-Graph (SemToG), a semantic-aware extension of FastToG[7] that integrates node and relation embeddings directly into community detection through relation-query similarity, entity relevance, hop-aware propagation, and answer-type intent. By combining structural connectivity with semantic alignment, SemToG constructs query-relevant communities that deliver more coherent evidence for LLM reasoning. We evaluate SemToG on six benchmark datasets : CWQ [15], WebQSP[19], QALD[12],ZSRE[13], TREx [3], and Creak [10]-measuring answer accuracy and reasoning steps. SemToG achieves consistent gains of 2-5% accuracy over FastToG[7] while requiring fewer community-expansion steps, demonstrating improved retrieval precision, semantic consistency, and computational efficiency. These results highlight the significance of incorporating semantic-aware community formation into GraphRAG pipelines and point toward more robust and contextually aligned knowledge-grounded LLM systems
Generative AI in the Workplace: Adoption Patterns, Innovation Attributes, and Equity Implications at BHSSC
No full textThis case study explores generative artificial intelligence (GenAI) adoption patterns, innovation attributes, and equity implications within a diverse educational cooperative in western South Dakota. A mixed methods approach was employed to analyze adoption trends, perceived productivity differentials, and the significance of key innovation characteristics to 40 employees representing a range of divisions, roles, and levels of digital competence. Employees with lower self-reported digital competence and higher average age reported disproportionately higher perceived productivity gains from GenAI tools. This may suggest GenAI may level the playing field for aging individuals who have previously felt marginalized by rapid technological change in the workplace. Using an extended model of the Innovation Diffusion Theory (IDT) framework, this study also finds support that the attributes of relative advantage, ease of use, result demonstrability, and trialability are significant predictors of GenAI adoption. This has important implications for less digitally confident individuals as well as their employers as it may indicate a path toward unlocking latent productivity potential in the aging workforce
Dairy & Food Science News, March 2025
Full text linkMessage from the Department HeadFaculty & Staff UpdatesDepartment Faculty Earn AwardsMidwest Regional Dairy Challenge Competition hosted by SDSUSmall-Scale Dairy Processing ProjectRecruitment Updates & OpportunitiesRecent ActivityAlumni Mentorship ProgramSDSU Food Science Research in the NewsStudent UpdatesEvents & Calendarhttps://openprairie.sdstate.edu/dairy_news/1001/thumbnail.jp
Modeling Area Deprivation Index Using Non-Gaussian Fixed Rank Kriging
No full textSocioeconomic disparities shape health outcomes across the U.S., with the Area Deprivation Index (ADI) serving as a key measure of community-level disadvantage. Predicting ADI using Social Determinants of Health (SDOH) like income and health-care access allows for targeted interventions. However, traditional models often ignore spatial patterns, limiting accuracy. This study compares conventional and spatial models to improve ADI prediction. Tract-level ADI and SDOH data were analyzed using six predictors selected via stepwise AIC: median income and distances to five healthcare facility types. Five models were tested: Linear Regression, GAM, GAMLSS, Gaussian FRK, and Poisson FRK. FRK models use low-rank basis functions to efficiently capture complex spatial dependencies in large datasets. Model performance was evaluated using R², RMSE, MAE, AIC, and cross-validation. Linear regression performed worst; GAM and GAMLSS improved results by modeling non-linearity. Gaussian FRK enhanced spatial prediction but oversmoothed local detail. Poisson FRK delivered the best accuracy, capturing both regional and local deprivation patterns. Based on our findings spatial models are essential for analyzing geographically structured data. They capture spatial dependence and distributional complexity, improving prediction and interpretation, unlike traditional methods. Their use supports more accurate location-specific insights in public health and beyond
Integrating Invasion Risk and Habitat Suitability to Guide Invasive Species Management in Wetlands, Lakes, And Rivers
No full textInvasive species disrupt ecosystems by altering habitats, competing with native species, and disrupting food webs. The northern Great Plains is an ecologically important region with many wetlands, lakes, and rivers that are under threat from two invasive species, Bighead (Hypophthalmichthys nobilis) and Silver Carp (H. molitrix; bigheaded carp). These large planktivores change the abundance and composition of phytoplankton and zooplankton communities which alters prey for native mussels, fishes, and, potentially, waterfowl. Bigheaded carps have invaded a portion of the Northern Great Plains, and preventing further spread is a primary management goal. My work assists these efforts by providing tools to improve preventative management actions for bigheaded carp. While prevention is critical, monitoring all waterbodies for early introductions is impractical. My second chapter aims to help managers prioritize surveillance monitoring locations by predicting habitat suitability for bigheaded carp across 53 waterbodies (wetlands, lakes, and rivers) in the region. I used an individual-based model that predicted bigheaded carp survival and growth based on observed environmental conditions. These predictions helped categorize waterbodies from very high to low risk, guiding managers in prioritizing surveillance efforts and ensuring efficient use of resources. Once high-risk locations are identified, managers still need to know where within a waterbody to sample because invasive individuals are often rare and patchy in occurrence. Since individuals commonly seek patches of high-quality habitat, my third chapter used a modeling approach to predict growth rate potential as a fine-scale habitat quality metric. I then assessed the extent to which the location of high-quality habitat changed throughout the year and assessed whether this occurred more in certain habitat types. The location of high-quality habitat patches was consistent through time for wetlands and lakes, whereas these locations were quite variable throughout the year in rivers. These results suggest that it might be beneficial for sampling protocols to be customized for particular habitat types. Overall, these results will assist management efforts to contain bigheaded carp populations by providing data-driven information about habitat suitability that can be used to target management efforts to the most at-risk locations and to efficiently use management resources
SDSU Data Science Symposium, 2025
No full texthttps://openprairie.sdstate.edu/ds_symposium_2025_gallery/1005/thumbnail.jp
SDSU Data Science Symposium, 2025
No full texthttps://openprairie.sdstate.edu/ds_symposium_2025_gallery/1011/thumbnail.jp
SDSU Data Science Symposium, 2025
No full texthttps://openprairie.sdstate.edu/ds_symposium_2025_gallery/1031/thumbnail.jp