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1. MBSE Online Professional Development Learning Modules Data

   https://doi.org/10.4231/3DR2-VY15  

   Douglas, Kerrie; Huang, Wanju; Li, Tiantian; Marcos, Leonardo; Huang, Wanju (January 2025, Purdue University Research Repository)

   <p>The data was downloaded and captured through MBSE online learning modules. Deidentified learners&#39; activities within the modules, such as clickstreams and assignments, were captured in the data/</p> <p>&bull; All files here are student submissions to one or more of the modules in the MBSE program.</p> <p>&bull; All user data has either been removed or redacted from the submission.</p> <p>&bull; &ldquo;Andrew Hurt&rdquo; is not a student and none of these files came from him. He is the person who did the redaction.</p> <p>&bull; The naming structure of the files is as follows: [Module number]-[Module Offering Date]-[Submission Number]-[Part Number of Submission]. Example: M5-040422-S1-Part3. This file is from Module 5, which was offered on April 4, 2022, it is submission 1, and part 3 of submission 1.</p> <p>&bull; Note that submissions within or between modules are not necessarily connected to specific students. So &ldquo;Submission 1&rdquo; from module 5 is not the same user as &ldquo;Submission 1&rdquo; from module 6.</p> <p>&bull; Not all submissions have multiple parts.</p> <p>&bull; No .mdzip files (proprietary MagicDraw software files) have been included in this list.</p> <p>&bull; If a module or folder in the module is missing content from a particular offering, it is because either no one submitted anything or because the file was a .mdzip file and was not downloaded.</p> <p>&nbsp;</p> 

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2. ChemML : A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data

   https://doi.org/10.1002/wcms.1458  

   Haghighatlari, Mojtaba; Vishwakarma, Gaurav; Altarawy, Doaa; Subramanian, Ramachandran; Kota, Bhargava U.; Sonpal, Aditya; Setlur, Srirangaraj; Hachmann, Johannes (January 2020, WIREs Computational Molecular Science)

   Abstract ChemMLis an open machine learning (ML) and informatics program suite that is designed to support and advance the data‐driven research paradigm that is currently emerging in the chemical and materials domain.ChemMLallows its users to perform various data science tasks and execute ML workflows that are adapted specifically for the chemical and materials context. Key features are automation, general‐purpose utility, versatility, and user‐friendliness in order to make the application of modern data science a viable and widely accessible proposition in the broader chemistry and materials community.ChemMLis also designed to facilitate methodological innovation, and it is one of the cornerstones of the software ecosystem for data‐driven in silico research. This article is categorized under:Software > Simulation MethodsComputer and Information Science > ChemoinformaticsStructure and Mechanism > Computational Materials ScienceSoftware > Molecular Modeling 

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3. Global meta-analysis shows pervasive phosphorus limitation of aboveground plant production in natural terrestrial ecosystems

   https://doi.org/10.1038/s41467-020-14492-w  

   Hou, Enqing; Luo, Yiqi; Kuang, Yuanwen; Chen, Chengrong; Lu, Xiankai; Jiang, Lifen; Luo, Xianzhen; Wen, Dazhi (January 2020, Nature Communications)

   Abstract Phosphorus (P) limitation of aboveground plant production is usually assumed to occur in tropical regions but rarely elsewhere. Here we report that such P limitation is more widespread and much stronger than previously estimated. In our global meta-analysis, almost half (46.2%) of 652 P-addition field experiments reveal a significant P limitation on aboveground plant production. Globally, P additions increase aboveground plant production by 34.9% in natural terrestrial ecosystems, which is 7.0–15.9% higher than previously suggested. In croplands, by contrast, P additions increase aboveground plant production by only 13.9%, probably because of historical fertilizations. The magnitude of P limitation also differs among climate zones and regions, and is driven by climate, ecosystem properties, and fertilization regimes. In addition to confirming that P limitation is widespread in tropical regions, our study demonstrates that P limitation often occurs in other regions. This suggests that previous studies have underestimated the importance of altered P supply on aboveground plant production in natural terrestrial ecosystems. 

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4. A simple proof of reflexivity and separability of N^{1,p} Sobolev spaces

   https://doi.org/10.54330/afm.127419  

   Alvarado, Ryan; Hajłasz, Piotr; Malý, Lukáš (October 2022, Annales Fennici Mathematici)

   We present an elementary proof of a well-known theorem of Cheeger which states that if a metric-measure space \(X\) supports a \(p\)-Poincaré inequality, then the \(N^{1,p}(X)\) Sobolev space is reflexive and separable whenever \(p\in (1,\infty)\). We also prove separability of the space when \(p=1\). Our proof is based on a straightforward construction of an equivalent norm on \(N^{1,p}(X)\), \(p\in [1,\infty)\), that is uniformly convex when \(p\in (1,\infty)\). Finally, we explicitly construct a functional that is pointwise comparable to the minimal \(p\)-weak upper gradient, when \(p\in (1,\infty)\). 

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5. Centers of Sylow subgroups and automorphisms

   https://doi.org/10.1007/s11856-020-2064-2  

   Glauberman, George; Guralnick, Robert; Lynd, Justin; Navarro, Gabriel (September 2020, Israel Journal of Mathematics)

   Suppose that p is an odd prime and G is a finite group having no normal non-trivial p′-subgroup. We show that if a is an automorphism of G of p-power order centralizing a Sylow p-group of G, then a is inner. 

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