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1. A Century of Nonlinearity in the Geosciences

   https://doi.org/10.1029/2019EA000599  

   Ghil, Michael ( July 2019 , Earth and Space Science)

   This paper provides a thumbnail sketch of the evolution of nonlinear ideas in the mathematics and physics of the geosciences, broadly construed, over the last hundred or so years. It emphasizes the mathematical concepts and methods and outlines simple examples of how they were, are, and maybe will be applied to the solid Earth—that is, the crust, mantle, and core—and its fluid envelopes—that is, the atmosphere and oceans.

    

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2. The multiplicity of a singularity in a vexillary Schubert variety

   https://doi.org/10.1016/j.aim.2023.109366  

   Anderson, David ; Ikeda, Takeshi ; Jeon, Minyoung ; Kawago, Ryotaro ( December 2023 , Advances in Mathematics)
3. The conformation of a semiflexible filament in a random potential

   Slepukhin, Valentin M. ; Grill, Maximilian J. ; Muller, Kei W. ; Wall, Wolfgang A. ; Levine, Alex J. ( April 2019 , Physical review and Physical review letters index)

   Motivated by the observation of the storage of excess elastic free energy -- prestress in cross linked semiflexible filament networks, we consider the problem of the conformational statistics of a single semiflexible polymer in a quenched random potential. The random potential, which represents the effect of cross linking to other filaments is assumed to have a finite correlation length and mean strength. We examine the statistical distribution of curvature in the limit that the filaments are much shorter than their thermal persistence length. We compare our theoretical predictions to finite element Brownian dynamics simulations. Lastly we comment on the validity of replica field techniques in addressing these questions. 

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4. The maximum multiplicity of a generator in a reduced word

   Gaetz, Christian ; Gao, Yibo ; Jiradilok, Pakawut ; Nenashev, Gleb ; Postnikov, Alexander ( January 2021 , Seminaire lotharingien de combinatoire)
5. Exploring the Predictability of Temperatures in a Scaled Model of a Smarthome

   https://doi.org/10.3390/s21186052  

   Burns, Thomas ; Fichthorn, Gregory ; Ling, Jason ; Zehtabian, Sharare ; Bacanlı, Salih S. ; Bölöni, Ladislau ; Turgut, Damla ( September 2021 , Sensors)

   In modern smarthomes, temperature regulation is achieved through a mix of traditional and emergent technologies including air conditioning, heating, intelligent utilization of the effects of sun, wind, and shade as well as using stored heat and cold. To achieve the desired comfort for the inhabitants while minimizing environmental impact and cost, the home controller must predict how its actions will impact the temperature and other environmental factors in various parts of the home. The question we are investigating in this paper is whether the temperature values in different rooms in a home are predictable based on readings from sensors in the home. We are also interested in whether increased accuracy can be achieved by adding sensors to capture the state of doors and windows of the given room and/or the whole home, and what type of machine learning algorithms can take advantage of the additional information. As experimentation on real-world homes is highly expensive, we use ScaledHome, a 1:12 scale, IoT-enabled model of a smart home for data acquisition. Our experiments show that while additional data can improve the accuracy of the prediction, the type of machine learning models needs to be carefully adapted to the number of data features available. 

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