North Dakota State University

We prove that for a smooth convex body K ⊂ ℝd, d ≥ 2, with positive Gauss curvature, its homothety with a certain associated convex body implies that K is either a ball or an ellipsoid, depending on the associated body considered.

Amino acid identification is crucial across various scientific disciplines, including biochemistry, pharmaceutical research, and medical diagnostics. However, traditional methods such as mass spectrometry require extensive sample preparation and are time-consuming, complex and costly. Therefore, this study presents a pioneering Machine Learning (ML) approach for automatic amino acid identification by utilizing the unique absorption profiles from an Elliptical Dichroism (ED) spectrometer. Advanced data preprocessing techniques and ML algorithms to learn patterns from the absorption profiles that distinguish different amino acids were investigated to prove the feasibility of this approach. The results show that ML can potentially revolutionize the amino acid analysis and detection paradigm.

In this critical discourse analysis, I explored the language used in “campus update” emails sent to faculty, staff, and students to better understand how university presidents communicate via email to institutional stakeholders. Specifically, I explored the use of effective stance, epistemic stance, and intersubjectivity in this corpus of emails from higher education presidents in 2020 and 2021, because their use can indicate a speaker’s commitment to what they are saying as well as their accountability and responsibility for their words (Marín-Arrese, 2009, Studies on English Modality. In honour of Frank R. Palmer, 111, (pp. 23–52). Peter Lang; 2011a, Critical Discourse Studies in Context and Cognition, 193–223; 2011b, Discourse Studies, 13(6), 789–797; 2015, Discourse Studies, 17(2), 210–225). Altogether, I sought to develop a better understanding of what and how university presidents communicate to faculty, students, and staff and, notably, how that informed and illuminated power in higher education.

The common bean weevil [Acanthoscelides obtectus (Say)] (AO) is the most important post-harvest pest of common bean (Phaseolus vulgaris L.) worldwide. Identifying sources of AO resistance and resistance loci is critical to developing resistant varieties. The Andean Diversity Panel (ADP) comprised of landraces, breeding lines and varieties from various countries and breeding programs is a major genetic and breeding resource of common bean. There are no previous efforts to understand the extent of genetic variability in the ADP for AO resistance and genetic basis of that possible resistance. The objectives of this study were to: (i) identify Andean genotypes resistant to AO, and (ii) identify genomic regions associated with AO resistance. The ADP (n = 476) was evaluated for resistance to AO. Number of perforations on the seed and percentage of damaged seed were recorded and used as metrics for AO resistance. The ADP was genotyped with 24,772 SNPs and genome-wide association analysis was conducted. Significant variability for AO resistance was observed in the ADP. A total of 15 accessions in variable market classes showed AO resistance. These 15 accessions can be used as sources of resistance to enhance AO resistance in specific market classes of common bean. Genomic regions on Pv03 and Pv07 with coefficient of determination (R²) of 14.2% and 17.1%, respectively, were significantly associated with AO resistance. These two identified genomic regions if validated can be used in combination with the Arcelin, Phytohemaglutinin and Apha-amylase (APA) locus on Pv04 to offer durable resistance to AO.

Over the past two decades, the rising demand for sustainable materials has sparked significant interest in developing plant‐based or bio‐based polymers. This study explores an innovative approach to synthesizing eco‐friendly polyamide‐graphene composites using maleinized cottonseed oil (MACSO) as a sustainable precursor. The composites were produced through a room‐temperature catalyst‐free ring opening of MACSO with 4,4′‐Diaminodiphenylmethane (DDM) and graphenamine, followed by high‐temperature curing. The impact of graphenamine content on the composites' structural, thermo‐mechanical, surface, and anticorrosion properties was thoroughly examined. Notably, the composite containing 3 wt% graphenamine demonstrated superior thermal stability, glass transition temperature ( T g ), mechanical toughness, and corrosion resistance. This research represents a unique example of using anhydride‐grafted vegetable oil for amide bond formation in the final polymer, highlighting an economically viable approach to sustainable polyamide synthesis. Furthermore, using MACSO as a bio‐based modifier aligns with the principles of green chemistry, offering a sustainable alternative to traditional petrochemical‐based additives.

Magnetic nanoparticles (NPs) are gaining significant interest in the field of biomedical functional nanomaterials because of their distinctive chemical and physical characteristics, particularly in drug delivery and magnetic hyperthermia applications. In this paper, we experimentally synthesized and characterized new Fe3O4-based NPs, functionalizing its surface with a 5-TAMRA cadaverine modified copolymer consisting of PMAO and PEG. Despite these advancements, many combinations of NP cores and coatings remain unexplored. To address this, we created a new data set of NP systems from public sources. Herein, 11 different AI/ML algorithms were used to develop the predictive AI/ML models. The linear discriminant analysis (LDA) and random forest (RF) models showed high values of sensitivity and specificity (>0.9) in training/validation series and 3-fold cross validation, respectively. The AI/ML models are able to predict 14 output properties (CC50 (μM), EC50 (μM), inhibition (%), etc.) for all combinations of 54 different NP cores classes vs. 25 different coats and vs. 41 different cell lines, allowing the short listing of the best results for experimental assays. The results of this work may help to reduce the cost of traditional trial and error procedures.

Introduction Load carriage is an inherent part of tactical operations. Critical speed (CS) has been associated with technical and combat-specific performance measures (e.g., loaded running). The 3-min all-out exercise test provides estimates of CS and the maximal capacity to displace the body (Dʹ) at speeds above CS. The current study investigated the contributions of CS, Dʹ, lean body mass (LBM), thigh lean mass (TLM), and lower body isokinetic strength and endurance parameters related to load carriage time trials (LCTTs). Methods Twenty-two Reserve Officers’ Training Corps cadets (6 = females, age = 20.82 ± 1.59 years) underwent various assessments that included a running 3-minute all-out test to determine CS and Dʹ, isokinetic knee extension (KE) muscle strength and endurance, body composition assessed by dual-energy X-ray absorption, and two 21-kg LCTTs of 400 and 3,200 m, respectively. Pearson’s product-moment correlations investigated relationships between selected predictor variables. Stepwise multiple linear regression analyses were used to determine the relationship between variables that predicted LCTT performance. Results Significant correlations were as follows: LBM and CS (r = 0.651, P < .001), KE endurance work and CS (r = 0.645, P < .001), TLM and CS (r = 0.593, P < .05), and KE peak torque and CS (r = 0.529, P < .05). The stepwise regression analyses indicated that CS and LBM contributed significantly to predicting 3,200-m LCTT (F [2,19] = 81.85, R2 = 0.90, P < .001) with standardized β coefficients (−0.723 and −0.301, respectively). Thigh lean mass contributed significantly to predicting the 400-m LCTT (F [1,20] = 46.586, R2 = 0.70, P < .001) with a standardized β coefficient (−0.836). Conclusion The results of this study highlight that CS and LBM were the best predictors of the 3,200-m LCTT, and TLM was the best predictor of the 400-m LCTT. The findings of this study support that CS and LBM, including TLM, are important in predicting load carriage task completion in the time trial tasks.

As mazes are typically complex, cluttered stimuli, solving them is likely limited by visual crowding. Thus, several aspects of the appearance of the maze – the thickness, spacing, and curvature of the paths, as well as the texture of both paths and walls – likely influence the performance. In the current study, we investigate the effects of perceptual aspects of maze design on maze-solving performance to understand the role of crowding and visual complexity. We conducted two experiments using a set of controlled stimuli to examine the effects of path and wall thickness, as well as the style of rendering used for both paths and walls. Experiment 1 finds that maze-solving time increases with thicker paths (thus thinner walls). Experiment 2 replicates this finding while also showing that maze-solving time increases when mazes have wavy walls, which are likely more crowded, rather than straight walls. Our findings imply a role of both crowding and figure/ground segmentation in mental maze solving and suggest reformulating the growth cone models.

Horizontal transfer of genetic material in eukaryotes has rarely been documented over short evolutionary timescales. Here, we show that two retrotransposons, Shellder and Spoink, invaded the genomes of multiple species of the melanogaster subgroup within the last 50 years. Through horizontal transfer, Spoink spread in D. melanogaster during the 1980s, while both Shellder and Spoink invaded D. simulans in the 1990s. Possibly following hybridization, D. simulans infected the island endemic species D. mauritiana (Mauritius) and D. sechellia (Seychelles) with both TEs after 1995. In the same approximate time-frame, Shellder also invaded D. teissieri, a species confined to sub-Saharan Africa. We find that the donors of Shellder and Spoink are likely American Drosophila species from the willistoni, cardini, and repleta groups. Thus, the described cascade of TE invasions could only become feasible after D. melanogaster and D. simulans extended their distributions into the Americas 200 years ago, likely aided by human activity. Our work reveals that cascades of TE invasions, likely initiated by human-mediated range expansions, could have an impact on the genomic and phenotypic evolution of geographically dispersed species. Within a few decades, TEs could invade many species, including island endemics, with distributions very distant from the donor of the TE.

In recent years, Large Language Models (LLMs) have emerged as a transformative development in artificial intelligence (AI), drawing significant attention from industry and academia. Trained on vast datasets, these sophisticated AI systems exhibit impressive natural language processing and content generation capabilities. This paper explores the potential of LLMs to address key challenges in personal finance, focusing on the United States. We evaluate several leading LLMs, including OpenAI's ChatGPT, Google's Gemini, Anthropic's Claude, and Meta's Llama, to assess their effectiveness in providing accurate financial advice on topics such as mortgages, taxes, loans, and investments. Our findings show that while these models achieve an average accuracy rate of approximately 70%, they also display notable limitations in certain areas. Specifically, LLMs struggle to provide accurate responses for complex financial queries, with performance varying significantly across different topics. Despite these limitations, the analysis reveals notable improvements in newer versions of these models, highlighting their growing utility for individuals and financial advisors. As these AI systems continue to evolve, their potential for advancing AI-driven applications in personal finance becomes increasingly promising.