Zinc oxide nanoparticles (ZnO-NPs) hold promise as novel fertilizer nutrients for crops. However, their ultra-small size could hinder large-scale field application due to potential for drift, untimely dissolution or aggregation. In this study, urea was coated with ZnO-NPs (1%) or bulk ZnO (2%) and evaluated in wheat (Triticum aestivum L.) in a greenhouse, under drought (40% field moisture capacity; FMC) and non-drought (80% FMC) conditions, in comparison with urea not coated with ZnO (control), and urea with separate ZnO-NP (1%) or bulk ZnO (2%) amendment. Plants were exposed to ≤ 2.17 mg/kg ZnO-NPs and ≤ 4.34 mg/kg bulk-ZnO, indicating exposure to a higher rate of Zn from the bulk ZnO. ZnO-NPs and bulk-ZnO showed similar urea coating efficiencies of 74-75%. Drought significantly (p ≤ 0.05) increased time to panicle initiation, reduced grain yield, and inhibited uptake of Zn, nitrogen (N), and phosphorus (P). Under drought, ZnO-NPs significantly reduced average time to panicle initiation by 5 days, irrespective of coating, and relative to the control. In contrast, bulk ZnO did not affect time to panicle initiation. Compared to the control, grain yield increased significantly, 51 or 39%, with ZnO-NP-coated or uncoated urea. Yield increases from bulk-ZnO-coated or uncoated urea were insignificant, compared to both the control and the ZnO-NP treatments. Plant uptake of Zn increased by 24 or 8% with coated or uncoated ZnO-NPs; and by 78 or 10% with coated or uncoated bulk-ZnO. Under non-drought conditions, Zn treatment did not significantly reduce panicle initiation time, except with uncoated bulk-ZnO. Relative to the control, ZnO-NPs (irrespective of coating) significantly increased grain yield; and coated ZnO-NPs enhanced Zn uptake significantly. Zn fertilization did not significantly affect N and P uptake, regardless of particle size or coating. Collectively, these findings demonstrate that coating urea with ZnO-NPs enhances plant performance and Zn accumulation, thus potentiating field-scale deployment of nano-scale micronutrients. Notably, lower Zn inputs

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Construction time and time overruns for infrastructure projects have been frequently studied; however, the construction time of power plants has not been studied. This lack of study is problematic, as more renewable energy power plants, such as wind and solar, are planned for many jurisdictions. Accurately estimating the construction time of a power plant will assist construction planning, budget estimates, and policy development encouraging the use of more renewable sources. The construction times of utility scale power plants in Canada were studied using publicly available data. Multiple linear regression analysis techniques were applied to the data to generate construction time estimation functions for all power plants together, and for individual technologies. The analyses reveal that construction time is sensitive to jurisdiction and the decade of construction, indicating that decisions made by individual Canadian provincial governments at different times had statistically significant impacts on construction time. The analyses also indicated that construction time is a strong function of installed capacity, independent of technology. This finding suggests that large solar or wind energy facilities will encounter longer construction times similar to large hydroelectric facilities.

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Hiring is a high-stakes decision-making process that balances the joint objectives of being fair and accurately selecting the top candidates. The industry standard method employs subject-matter experts to manually generate hiring algorithms; however, this method is resource intensive and finds sub-optimal solutions. Despite the recognized need for algorithmic hiring solutions to address these limitations, no reported method currently supports optimizing predictive objectives while complying to legal fairness standards. We present the novel application of Evolutionary Many-Objective Optimization (EMOO) methods to create the first fair, interpretable, and legally compliant algorithmic hiring approach. Using a proposed novel application of Dirichlet-based genetic operators for improved search, we compare state-of-the-art EMOO models (NSGA-III, SPEA2-SDE, bi-goal evolution) to expert solutions, verifying our results across three real world datasets across diverse organizational positions. Experimental results demonstrate the proposed EMOO models outperform human experts, consistently generate fairer hiring algorithms, and can provide additional lift when removing constraints required for human analysis.