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1. The Cost of Quantum Yield

   https://doi.org/10.1021/acs.oprd.3c00167  

   Swierk, John R. (July 2023, Organic Process Research & Development)

   The quantum yield (QY) of a photocatalytic reaction significantly influences its performance, as reactions with low QYs require more intense light sources and longer illumination times to achieve efficient reaction rates. Unfortunately, the importance of QY is often overlooked in the design of photocatalytic reactions for small-molecule synthesis, leading to potential cost implications and reduced productivity. This study examines various photochemical reactor designs from the literature to estimate photon flux and light generation costs and investigates the impact of QY on both cost and productivity. The findings reveal substantial penalties in cost and productivity when QYs are low. For instance, external QYs below 1% can result in significant light generation costs and maximum productivities of less than 1 mol of product per day. Moreover, the study highlights that high QYs have a larger effect on potential productivity than high product yields. By optimizing for QY instead of product yield, kinetic and revenue modeling for the photoredox-mediated synthesis of ceralasertib demonstrate the potential for generating hundreds of thousands of dollars in additional revenue per day. Overall, this work emphasizes the need for increased consideration of QY in the design of photocatalytic reactions. 

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2. Extending yield-stress fluid paradigms

   https://doi.org/10.1122/1.5003841  

   Nelson, Arif Z.; Bras, Rafael E.; Liu, Jingping; Ewoldt, Randy H. (January 2018, Journal of Rheology)
3. Global spatially explicit yield gap time trends reveal regions at risk of future crop yield stagnation

   https://doi.org/10.1038/s43016-023-00913-8  

   Gerber, James S.; Ray, Deepak K.; Makowski, David; Butler, Ethan E.; Mueller, Nathaniel D.; West, Paul C.; Johnson, Justin A.; Polasky, Stephen; Samberg, Leah H.; Siebert, Stefan; et al (January 2024, Nature Food)

   Abstract Yield gaps, here defined as the difference between actual and attainable yields, provide a framework for assessing opportunities to increase agricultural productivity. Previous global assessments, centred on a single year, were unable to identify temporal variation. Here we provide a spatially and temporally comprehensive analysis of yield gaps for ten major crops from 1975 to 2010. Yield gaps have widened steadily over most areas for the eight annual crops and remained static for sugar cane and oil palm. We developed a three-category typology to differentiate regions of ‘steady growth’ in actual and attainable yields, ‘stalled floor’ where yield is stagnated and ‘ceiling pressure’ where yield gaps are closing. Over 60% of maize area is experiencing ‘steady growth’, in contrast to ∼12% for rice. Rice and wheat have 84% and 56% of area, respectively, experiencing ‘ceiling pressure’. We show that ‘ceiling pressure’ correlates with subsequent yield stagnation, signalling risks for multiple countries currently realizing gains from yield growth. 

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4. Differential electron yield imaging with STXM

   https://doi.org/10.1016/j.ultramic.2020.113198  

   Hubbard, William A.; Lodico, Jared J.; Ling, Xin Yi; Zutter, Brian T.; Yu, Young-Sang; Shapiro, David A.; Regan, B.C. (March 2021, Ultramicroscopy)
5. Do Software Security Practices Yield Fewer Vulnerabilities?

   https://doi.org/10.1109/ICSE-SEIP58684.2023.00032  

   Zahan, Nusrat; Shohan, Shohanuzzaman; Harris, Dan; Williams, Laurie (May 2023, IEEE)

   Due to the ever-increasing number of security breaches, practitioners are motivated to produce more secure software. In the United States, the White House Office released a memorandum on Executive Order (EO) 14028 that mandates organizations provide self-attestation of the use of secure software development practices. The OpenSSF Scorecard project allows practitioners to measure the use of software security practices automatically. However, little research has been done to determine whether the use of security practices improves package security, particularly which security practices have the biggest impact on security outcomes. The goal of this study is to assist practitioners and researchers in making informed decisions on which security practices to adopt through the development of models between software security practice scores and security vulnerability counts. To that end, we developed five supervised machine learning models for npm and PyPI packages using the OpenSSF Scorecard security practices scores and aggregate security scores as predictors and the number of externally-reported vulnerabilities as a target variable. Our models found that four security practices (Maintained, Code Review, Branch Protection, and Security Policy) were the most important practices influencing vulnerability count. However, we had low R2 (ranging from 9% to 12%) when we tested the models to predict vulnerability counts. Additionally, we observed that the number of reported vulnerabilities increased rather than reduced as the aggregate security score of the packages increased. Both findings indicate that additional factors may influence the package vulnerability count. Other factors, such as the scarcity of vulnerability data, time to implicate security practices vs. time to detect vulnerabilities, and the need for more adequate scripts to detect security practices, may impede the data-driven studies to indicate that practice can aid in reducing externally-reported vulnerabilities. We suggest that vulnerability count and security score data be refined so that these measures can be used to provide actionable guidance on security practices. 

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