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Understanding impedance spectra of a PEM fuel cell via the distribution of transport times
No full textOperation Pulp on Kubernetes - User Recap for German research center
No full textWithin the HPC operations, we have been using Pulp since 2022 for operating a central repository that is mainly used to distribute packages among HPC and internal systems. This talk gives a recap on the last 3 years of operations and highlights workflows, gains and challenges during operation
Hamiltonian dynamics
No full textHamiltonian dynamics describes the evolution of conservative physical systems. Originally developed as a generalization of Newtonian mechanics, it represents a core component of any undergraduate physics curriculum. What is not so widely recognized is that the ideal (i.e. conservative) form of the governing equations used in dynamical meteorology are also Hamiltonian dynamical systems. This chapter explains how this is so, and some of the consequences that follow from this fact. It is important to be able to connect theoretical results across the hierarchy of various models used in dynamical meteorology, from the simplest to the most complex. Hamiltonian dynamics is what allows one to do precisely that
Data-driven modeling of polymer electrolyte fuel cells: Towards predictive analytics with explainable artificial intelligence
No full textEffect of cold sintering temperatures on the microstructure and mechanical properties of BaZr0.7Ce0.2Y0.1O3-δ proton conductors
No full textRNA fitness prediction with sparse physics based models - A way to explore the sequence space
No full textThe field of medicine uses macromolecules as a means of therapeutic intervention. Consequently, the functional attributes of these novel molecules are assuming greater significance. To complement the wet-lab experiments, we have devised a series of statistical physics based models that are capable of predicting the fitness of RNA molecules based on one- and two-point mutation scans. The experimental data were employed as training data to fit models of increasing complexity, commencing with an additive model and concluding with a model that accounts for global and local epistasis. The models were validated using fitness data from scans with higher order mutations of the wild-type. In contrast to conventional AI algorithms, the parameters of our models were designed for direct interpretation. In examining more distant sequences, we can distinguish the corresponding RNA family from random sequences with a high degree of accuracy. Moreover, the models facilitate interpretations of evolutionary processes and the significance of epistatic terms. Our model can be used to create a fitness landscape far beyond the experimental sequence space, thus identifying promising RNA molecules. Furthermore, the extension to the entire sequence space can be used as a blueprint for other molecules, providing a novel avenue for questions in biomolecular design
Warm conveyor belt uplift as source for long-range transported biomass burning aerosol in the extratropical lowermost stratosphere over Europe
No full textQUEST: A New Technical Committee for Quantum Earth Science and Technology [Technical Committees]
No full textIn November 2024, recognizing the expanding capabilities of quantum technologies and their growing potential to address geoscience and remote sensing (RS) applications and challenges, the IEEE Geoscience and Remote Sensing Society (GRSS) established the Quantum Earth Science and Technology (QUEST) Technical Committee. QUEST is developing a collaborative, international venue for students, young professionals, scientists, and industry stakeholders who conduct interdisciplinary research at the intersection of quantum-based methods, geosciences, and RS, with the aim of advancing knowledge, fostering cross-disciplinary collaboration, and promoting the adoption of quantum-based solutions in these fields. Through various dissemination activities, QUEST will facilitate knowledge exchange across disciplines, enable collaborative projects with industry and academia, and support the development of innovative methodologies to address fundamental Earth observation (EO) applications such as environmental monitoring, climate modeling, and complex geospatial data analysis
