University of Nebraska at Kearney

The photocycloaddition (PCA) of chalcones represents an important reaction pathway for accessing substituted cyclobutanes, which is a molecular framework with utility in synthetic chemistry, materials science, and medicine. In the past, our group has demonstrated the utility of the large cavity of γ-CD as a container for encapsulating two photo reactants for directing the PCA of several classes of aryl alkenes with high stereo- and regioselectivity: the cavitand-mediated photodimerization (CMP) approach. The CMP of chalcones reported in this work further demonstrates the effectiveness of this approach as high yields of dimers were observed in the photoreactions, while they were non-reactive in the solid state and yielded only the isomerization product in homogeneous media. The γ-CD CMP of chalcones yielded predominantly dimerized products in very good to high yields (>70%), composed of a mixture of three dimers in different proportions with syn HH as the major product. Computational analysis of the ground state complex structures revealed a strong correlation between the stability of the complex and predominance of the stereoisomer in the mixture. Further insights were deduced from temperature-dependence studies, which showed a shift in dimer selectivity tending towards a single stereoisomer.

The adoption of artificial intelligence (AI) chatbots in higher education promises improved student support and administrative efficiency. Our objective is to offer insights to institutions in effectively adopting AI chatbots, tailored to the needs of both students and faculty. Using secondary survey data from United States higher education, we conducted a moderated mediation analysis to examine the mediation effect of perceived resources/support on the relationship between user role and perceived efficiency of AI chatbots, moderated by technological tool usage. The results revealed that perceived resources/support availability plays a mediating role in shaping how both students and faculty perceive the efficiency of AI chatbots, with students reporting greater efficiency when resources and support are more readily available. This mediation effect becomes marginally stronger as the usage of technological tools increases. While both student and faculty groups demonstrate openness to using AI chatbots, their perceptions of benefits and challenges towards AI chatbots differ significantly.

Using sintered neodymium–iron–boron (NdFeB) cube magnets and additively manufactured polylactic acid (PLA) and steel frames, we investigate hybrid manufacturing approaches for enhancing magnetic fields in cylindrical Halbach arrays while reducing reliance on critical rare-earth materials. Nested Halbach geometries with eight discrete dipolar segments were selected to balance magnetic performance with construction simplicity. The inner Halbach ring consisted of eight N48 NdFeB cube magnets, while the outer ring employed either additional NdFeB cubes or hybrid configurations incorporating additively manufactured steel elements. Experimentally, introducing a second Halbach ring of eight NdFeB cubes increased the lateral magnetic field by only ∼5%, indicating strong geometric and finite-length limitations in discretized arrays. In contrast, the axial magnetic field increased by ∼27% when 16 soft-magnetic steel cubes were incorporated into the outer ring. Notably, the lateral field enhancement achieved using eight NdFeB cubes combined with 16 steel cubes was comparable to that obtained using a full outer ring of 24 NdFeB magnets, despite substantially reduced permanent-magnet content. Ansys Maxwell Multiphysics simulations show that finite array thickness, magnet segmentation, alignment tolerances, and contour-driven demagnetization effects associated with discrete magnet geometry and array boundaries strongly suppress idealized field enhancements. These results demonstrate the potential of hybrid manufacturing to achieve competitive magnetic performance using lightweight, application-specific nested Halbach array architectures while reducing the criticality of high-grade permanent magnet materials.

California produces approximately 80% of the world’s almonds [Prunus dulcis (Miller) D. A. Webb]. In May of 2024, yellow spots and orange to dark red-brown blotches, were detected in an almond orchard (Nonpareil, Monterey, and Fritz cvs.) in Merced County. Similar symptoms were observed during June of 2024 from 12 additional orchards in Fresno, Madera, Merced, San Joaquin, and Stanislaus counties. Disease incidence in orchards varied from a few infected leaves to nearly 100%. Symptoms were characteristic of red leaf blotch caused by the fungal pathogen Polystigma amygdalinum. Leaves from all orchards were collected and taken to the laboratory in sealed resealable bags to conduct morphological and DNA analyses to identify the pathogen involved. For molecular identification, we used a quantitative real-time PCR detection method, and a primer pair (PamyI2F4/PamyI2R2) previously developed for P. amygdalinum (Zúñiga et al. 2018). Following direct DNA extraction from necrotic leaf tissues, the assay confirmed the detection of P. amygdalinum in symptomatic leaves. In addition, four lesions on separate leaves from different orchards were selected for genomic DNA extraction and sequencing of the internal transcribed spacer region (ITS) using ITS1F/ITS4 primers (Gardes and Bruns 1993) and a segment of the 28S region of the large subunit (LSU) ribosomal RNA gene using LR5 and LR0R primers (Guterres et al. 2018). Resulting sequences (ITS: GenBank Accession PV491257-PV491260; LSU: GenBank Accession PV487836-PV487836) were compared to reference sequences (Habibi et al. 2015; Guterres et al. 2018) using BLAST analysis. The ITS sequences of the Californian isolates were 100% identical to at least one of P. amygdalinum reference strains MH205935 or KC756362; LSU sequences were at least 99.46% similar to that of P. amygdalinum reference strain (KM111540.1). Morphological analyses were done by mounting sections of necrotic leaves on microscopic slides for observation under a light microscope (Leica DM2000 LED Microscope, Wetzlar, Germany). Conidiomata consisted of globose pycnidia, 80-120 μm diam. Conidia were hyaline, aseptate, filiform, 22.1 to 36.9 × 1.6 to 2.5 μm (n = 60). Perithecia formed in fallen leaves during January and were flask-shaped and contained clavate asci with eight ascospores. Asci were 95.4 to 129.5 × 18.3 to 25.1 μm. Ascospores were aseptate, ovoid to cylindrical with one end round and one end slightly acute, sometimes slightly constricted in the middle, hyaline, and measured 15.2 to 20.7 × 6.1 to 8.4 μm. Pathogenicity tests were conducted on potted, 1-year-old almond trees (cv. Monterey and Nonpareil) under greenhouse conditions. An ascospore suspension of 1 × 104 ascospores·ml-1 was obtained by crushing fallen almond leaves that contained mature perithecia of P. amygdalinum. The emerging leaflets from 10 plants of each cultivar were spray-inoculated with the ascospore suspension and then covered with plastic bags for 48hrs. After 40 days, typical symptoms of red leaf blotch appeared on leaves of all inoculated plants for each cultivar and no symptoms occurred in the control plants. Koch’s postulates were satisfied after confirming P. amygdalinum from symptomatic leaves through molecular identification as described above. This is the first detection of P. amygdalinum from almond in California, the United States, and North America. These findings improve our knowledge of the etiology and biology of red leaf blotch of almond in California.

Abstract Peppermint ( Mentha piperita ) is a perennial herb valued for its menthol‐rich oil and requires high nitrogen (N) inputs for its irrigated production. Optimizing N management can reduce nitrous oxide (N 2 O) emissions, a potent greenhouse gas associated with fertilizer N input. A 2‐year experiment (2022–2023) was conducted in western Nebraska to evaluate the effects of N fertilizer sources (urea and polymer‐coated urea; PCU) applied at different rates on peppermint yield and N 2 O emissions. Application rates were lower in 2022 than in 2023 due to transplanting and herbicide injury issues. Therefore, dry matter yield was lower in 2022 (3.38–3.84 Mg ha −1 ) than in 2023 (7.56–14.11 Mg ha −1 ). In 2023, PCU at the highest rate (332 kg N ha −1 ) had a greater peppermint dry matter yield than all other treatment combinations except for urea at the same rate. In 2023, yield did not vary with N source, except at the low rate, where PCU had a greater yield (12.14 Mg ha −1 ) than urea (9.31 Mg ha −1 ). In both years, urea had greater N 2 O emissions than PCU, except for the lowest N rate (34 kg N ha −1 ) in 2022. Nitrous oxide emissions varied by N rates for urea but not for PCU. Fertilizer‐induced emission factors (FIEF) were within the range of the Intergovernmental Panel on Climate Change (IPCC) disaggregated emission factor of 0.5% (0.0%–1.1%) for dry climates. Nitrogen source‐specific FIEF disaggregation might narrow the current IPCC uncertainty range.

Background : Various summary metrics have been proposed to standardize the processing of raw accelerometer data including the ActiGraph Counts (AC), Activity Index (AI), Euclidean Norm Minus One (ENMO), Mean Amplitude Deviation (MAD), Monitor-Independent Movement Summary Unit (MIMS), and Rate of Change Acceleration Movement (ROCAM). Methods : Data were from the Free-Living Activity Study for Health, in which participants ( N = 78, age: 23.2 ± 8.4 years, 64% female, 87% white, body mass index: 24.4 ± 3.5 kg/m 2 ) wore an ActiGraph wGT3X-BT monitor on each wrist, plus GENEActiv and Axivity AX3 monitors (one on each wrist, counterbalanced for equal placements on dominant and nondominant sides). Participants wore devices for 24 hr and reported their physical activity via the Activities Completed over Time in 24 hr web survey. Summary metrics were obtained using open-source methods. The agreement was evaluated by equivalence testing (reference, ActiGraph) and Bland–Altman plots. The mean absolute percent error was calculated to assess relative individual errors. Results : The AC, AI, and MIMS yielded 10% statistical equivalence across all brands. Trends were less consistent for ENMO, MAD, and ROCAM. Lowest overall mean absolute percent error was seen for MIMS (13.2 ± 16.7%), with higher values for AI (22.7 ± 50.5%), AC (25.1 ± 146.9%), ENMO (44.1 ± 79.2%), MAD (45.3 ± 66.3%), and ROCAM (98.5 ± 115.1%). Similar patterns were observed within specific intensities segmented by the Activities Completed over Time in 24 hr. Conclusion : The results support the use of MIMS for device-agnostic physical behavior monitoring, whereas interdevice agreement was weaker for the other summary metrics.

Purpose This study investigates the comparative effectiveness of peer-reviewed feedback and AI-enhanced feedback, specifically through Grammarly, in graduate-level online action research courses. Grounded in constructivist and sociocultural learning theories, the study explores how students perceive and apply feedback from either peers or an AI-based tool across dimensions such as feedback characteristics, application and writing quality outcomes. Design/methodology/approach A convergent parallel mixed-methods design was employed with 39 graduate students who selected their preferred feedback mechanism. Data collection included surveys, feedback characteristics, revision tracking and rubric-based assessments. Statistical tests assessed feedback application and writing improvements. Findings Findings showed that AI feedback via Grammarly was more effective in addressing surface-level issues such as grammar and clarity, while peer feedback more often engaged with higher-order concerns like organization and originality. Both feedback types contributed to improved writing quality, with peer feedback yielding greater gains. Grammarly was chosen for this study due to its wide availability in higher education and institutional support at the time of data collection in early 2023, before the widespread classroom integration of generative AI tools like ChatGPT. While the results suggest complementary strengths between human and AI feedback, limitations include the narrow scope of AI used and reliance on feedback frequency as a proxy for feedback quality. Originality/value This study uniquely compares peer and AI feedback in graduate writing, allowing students to choose their preferred method. The findings highlight the strengths of both approaches and emphasize the need for a balanced integration of AI and human feedback in academic settings. This research contributes to AI-assisted education by offering insights into optimizing feedback strategies for enhanced learning outcomes.