University of Nebraska System

Active canopy crop sensor commercialization offers producers the ability to vary nitrogen applications in real time based on crop reflectance measures. However, adoption of active canopy crop sensors has been limited due to inconsistent results, potential yield losses, and lack of information from field-scale trials under different management strategies. Therefore, the purpose of this study was to evaluate the capability of active crop canopy sensor system (OptRx™, Ag Leader, Ames, IA) in field trials on nine non-irrigated maize (Zea mays L.) sites in eastern Nebraska, where rainfall often limits yield (2019–2020). The sensor-based N management treatments were compared to each site’s grower treatment, examining the effects of N base rate, in-season application timing, and spatial variability technology performance. The sensor-based N management reduced N application by an average of 38.7 ± 20.8 kg Nha -1 without a yield penalty in 77% of the sites (n = 9). The base rate of N applied prior to the in-season, sensor-based application rate in-season, and timing of the in-season application influenced the N use efficiency (NUE) of the sensor-based N management approach. Partial factor productivity of N was improved by 16.8 ± 8.4 kg grain kg N−1 relative to growers’ current management. In terms of profit, 35% of sites demonstrated a profit advantage in sensor-based treatments. Field-scale research demonstrates that active canopy sensors can improve nitrogen management efficiency and profitability. These findings highlight the importance of evaluating active canopy crop sensors under variable field conditions to optimize sensor-based N management strategies.

There is a growing consensus that transportation construction projects have an influence beyond the provision of reliable infrastructure and significantly impact socioeconomic advancements. This is reflected in recent global infrastructure development initiatives, which encompass diverse objectives related not only to technical aspects but also nontechnical ones. However, it is challenging to synthesize diverse and sometimes competing objectives in program development. Well-established frameworks or formulas supporting the synthesis and delivery of objectives do not always exist. Ex post empirical analysis of program development outcomes, rather than planning documents, is scarce. In this study, the authors applied an explainable machine learning method, specifically, a zero-inflated negative binomial model with two classification parts, supported by a genetic algorithm, the extreme gradient boosting algorithm, and SHapley Addictive explanation values, on the project inventory supported by discretionary transportation programs of Canadian federal governments over an eight-year period. The project records were mapped to the three categories of community attributes corresponding to the three objectives of the Canadian infrastructure initiative: resilient infrastructure; economic growth; and equity promotion. The results reveal a dichotomous reality: Although transportation equity could be a significant factor in rural areas, the communities where most Canadians live host major transportation construction projects because of factors related to transportation assets and local economics. Bridge improvement is a focal theme, and a vibrant local economy is generally favored. Percentages of senior citizens and children might adjust the level of development. It is anticipated that the findings will provide policy decision-makers, transportation asset owners, and community leaders with a realistic understanding of the realities of discretionary transportation program development, thereby informing evidence-based decision-making in the future.

ABSTRACT This study presents a comprehensive analytical investigation of an innovative consolidation approach that integrates electro‐osmotic consolidation with surcharge and vacuum preloading (EOC‐SP‐VPM) to overcome the persistent challenges of marine clay, including poor drainage, prolonged consolidation, and excessive settlement, which were inadequately addressed by conventional single or dual‐method techniques. Compared to existing models, which often overlook critical interactions and multi‐physical factors, this work develops two‐dimensional analytical solutions to rigorously describe the consolidation process under the combined effects of electro‐osmosis, surcharge, and vacuum preloading. The solutions, derived using equal strain theory, Darcy's law, and fluid mass conservation principles, incorporate fluid‐current coupling effects, radial and vertical vacuum pressure attenuation, anisotropic permeability variations, and time‐dependent loading schemes. These factors were typically neglected in prior studies but essential for real‐world applicability. These advancements enable precise predictions of excess pore water pressure dissipation and ultimate settlement, offering superior accuracy compared to traditional methods. Advanced series transformation techniques resolved the governing equations, and validation against prior analytical results and experimental data confirmed the model robustness. The results demonstrate that the integrated EOC‐SP‐VPM approach significantly enhances consolidation efficiency by elucidating the interdependencies between electrokinetic, mechanical, and hydraulic mechanisms. By bridging gaps in existing theoretical frameworks, this study results can optimize the loading sequence of combined consolidation methods and assist in designing consolidation parameters, providing practical suggestions for engineering applications.

Nickel aluminum bronze (NAB) powders coated with reduced graphene oxide (RGO) were successfully fabricated using additive manufacturing (AM) via laser powder bed fusion. The resulting samples were characterized using a suite of complementary techniques, including x-ray diffraction, scanning electron microscopy, energy-dispersive x-ray spectroscopy, nanoindentation, x-ray computed tomography, and electron backscatter diffraction. These methods were employed to reveal the micro- and nanoscale features within the fine dendritic microstructure. The as-fabricated microstructures were observed to deviate significantly from typical as-cast configurations. In the AM RGO-coated NAB specimens, the formation of the κI phase was notably suppressed and is attributed to the rapid cooling rates inherent to the AM process. Comparatively, the AM RGO-coated NAB samples exhibited a more refined microstructure with a significantly reduced volume fraction of intermetallic particles compared to their cast NAB and uncoated AM-NAB counterparts. Mechanical assessment through nanoindentation revealed that the RGO-coated parts possess a higher modulus and hardness relative to cast NAB, with an enhancement of over 30%. These enhancements in mechanical properties are attributed to the microstructural advantages conferred by AM processes, including diminished segregation, uniform precipitation distribution, and reduced grain size.

This work addresses a critical knowledge gap in understanding how WhiB-like (Wbl) family proteins regulate gene expression and confer virulence and antibiotic resistance in Mycobacterium tuberculosis . WhiB-like proteins are widely distributed in Actinobacteria, the phylum that includes several deadly human pathogens and important antibiotic producers. By identifying the previously unrecognized structural motif that is highly conserved in the Wbl family and mediates Wbl interaction with the core transcriptional machinery, this work advances our understanding of the evolution, function and underlying mechanism of transcriptional regulation by this unique family of transcription factors in Actinobacteria and associated phages. These insights will be instrumental in developing new strategies to combat tuberculosis and other diseases caused by related pathogens.

Current management research indicates that enhanced access to political power puts firms in a superior competitive position. This is because such access enables firms to leverage government-controlled resources in their competitive endeavors. However, insights from the behavioral theory of the Firm (BToF) suggest that easy and abundant access to resources, such as those that accompany firm’s access to political power, may induce complacency in a firm's strategic behavior. Expanding on these insights, we contend that a firm's proximity to political power facilitates convenient and ample access to government resources. Consequently, we argue that this accessibility leads managers to become complacent in pursuing competitive actions. Our theoretical model and empirical tests contribute to the literature on business-government relationships and the literature on BToF.

Root hairs are considered important for rhizosphere formation, which affects root system functioning. Through interactions with soil microorganisms mediated by root exudation, root hairs may affect the phenotypes and growth of young plants. We tested this hypothesis by integrating results from two experiments: (1) a factorial greenhouse seedling experiment with Zea mays B73-wt and its root-hairless mutant, B73-rth3, grown in live and autoclaved soil, quantifying 15 phenotypic traits, seven growth rates, and soil microbiomes and (2) a semi-hydroponic system quantifying root exudation of maize genotypes. Possibly as compensation for lacking root hairs, B73-rth3 seedlings allocated more biomass to roots and grew slower than B73-wt seedlings in live soil, whereas B73-wt seedlings grew slowest in autoclaved soil, suggesting root hairs can be costly and their benefits were realized with more complete soil microbial assemblages. There were substantial differences in root exudation between genotypes and in rhizosphere versus non-rhizosphere microbiomes. The microbial taxa enriched in the presence of root hairs generally enhanced growth compared to taxa enriched in their absence. Our findings suggest the root hairs' adaptive valueextends to plant-microbe interactions mediated byroot exudates, affecting plant phenotypes, and ultimately, growth.

Classifying weight ranges is essential in fish farms since they are sold according to the fingerlings’ minimum size, uniformity, and maximum size. Therefore, evaluating the batches’ quality is fundamental, which analyzes the growth rate, feed conversion, and survival rate, among others. This paper aims to classify weight ranges for fingerlings of Pintado Real using supervised learning techniques. For this purpose, 60 images were captured, containing a 10-cent Real coin and a ruler next to each fingerling. The software was designed to extract attributes from the images and then serve as inputs for Weka’s training algorithms. The J48 algorithm obtained a performance 76.66% in the accuracy metric, and the ANOVA shows no statistical difference. This result is promising since the dataset of images taken from varying distances is a situation that is often common in this type of collection, and the proposed software takes care of standardizing the scale.