Cognia (education)

The incorporation of o-MAX phases, characterized by out-of-plane atomic arrangements, extends the MAX phase family with valuable new members, driven by their superior thermo-mechanical properties, which are suitable for high-temperature applications. In this research, three novel o-MAX phases, Mo2A2AlC3 (A = Zr, Nb, Ta), have been explored, and their structural geometries, electronic properties, mechanical behavior, thermodynamic properties, and optical response have been comprehensively investigated for the first-time using density functional theory (DFT). The formation energy (EF), phonon dispersion curve (PDC), and elastic constants (Cij) were used to validate the stability of the predicted compounds. A study of the band structure, density of states (DOS), and charge density mapping revealed the metallic nature and the presence of mixed bonding within Mo2A2AlC3 (A = Zr, Nb, Ta) phases. Mechanical properties were thoroughly evaluated by calculating elastic constants, elastic moduli, fracture toughness, machinability, and hardness. Variations in mechanical properties were explained through Mulliken population analysis and charge density mapping. Poisson's ratio, Pugh ratio, and Cauchy pressure values indicate a brittle nature for these materials. Additionally, elastic anisotropy was examined through 2D and 3D diagrams of stiffness moduli and established anisotropy indicators. The Debye temperature (Theta D), Gr & uuml;neisen parameter, lattice thermal conductivity (kph), minimum lattice thermal conductivity (Kmin), melting point (Tm), and thermal expansion coefficient (TEC) were studied, making these compounds promising candidates for next-generation thermal barrier coating (TBC) applications. The optical properties were thoroughly analyzed, with reflectivity spectra (exceeding 44%), which implies that these materials can efficiently reduce solar heating across various energy ranges.

Monitoring volcanoes involves a variety of data sources and methods to maintain complete continuity of coverage. Global navigation satellite system (GNSS) and interferometric synthetic aperture radar (InSAR) are commonly used complementary methods to assess the deformation state of a volcano as magma migrates beneath the surface. The amount of data these methods produce, however, is growing rapidly beyond human analysis capabilities and is becoming difficult to manage. Here, we create a novel multimodal deep learning framework to ingest InSAR and GNSS data simultaneously and classify the deformation state of the system. We apply this methodology to Mauna Loa, Hawai‘i given its wealth of InSAR and GNSS data as well as its propensity to deform on multiple timescales. Our model performs with high accuracy and is able to identify both slow and fast deformation from 2015 to 2023. The multimodal nature of our model also allows us to identify the presence of atmospheric noise in InSAR data. Furthermore, we employ explainability algorithms to show that our model is making decisions for the right reasons and to connect complex black-box machine learning mappings to current real-world geodetic interpretations of the Mauna Loa magmatic system.

The Citizen CATE 2024 next-generation experiment placed 43 identical telescope and camera setups along the path of totality during the total solar eclipse (TSE) on 8 April 2024 to capture a 60-minute movie of the inner and middle solar corona in polarized visible light. The 2024 TSE path covered a large geographic swath of North America and we recruited and trained 36 teams of community participants ("citizen scientists") representative of the various communities along the path of totality. Afterwards, these teams retained the equipment in their communities for ongoing education and public engagement activities. Participants ranged from students (K12, undergraduate, and graduate), educators, and adult learners to amateur and professional astronomers. In addition to equipment for their communities, CATE 2024 teams received hands-on telescope training, educational and learning materials, and instruction on data analysis techniques. CATE 2024 used high-cadence, high-dynamic-range (HDR) polarimetric observations of the solar corona to characterize the physical processes that shape its heating, structure, and evolution at scales and sensitivities that cannot be studied outside of a TSE. Conventional eclipse observations do not span sufficient time to capture changing coronal topology, but the extended observation from CATE 2024 does. Analysis of the fully calibrated dataset will provide deeper insight and understanding into these critical physical processes. We present an overview of the CATE 2024 project, including how we engaged local communities along the path of totality, and the first look at CATE 2024 data products from the 2024 TSE.

The complex regulatory dynamics of biological networks can be succinctly captured using discrete logic models. Previous work has shown that global optimization schemes are well suited for finding state transition logic and kinetic model parameters, in what are typically large and irregular solution spaces where even relatively small networks represent search spaces exceeding 1040 possible solutions. Powerful computing strategies are required to make this modeling practical for in silico pharmaceutical research. Here, we present a benchmark study quantifying the speedup achieved using a GPU framework in the regulatory logic modeling of two biological networks spanning an order of magnitude increase in complexity and several orders of magnitude in search space size. GPU implementation resulted in a 33%–57% reduction in wall time over multi-thread CPU and a 33%–1866% increase over serial CPU while also delivering better quality solutions in many cases. Evaluation on an even larger pathogen-host immune regulatory network suggests that migration to GPU may support convergence in less than 12 h for established models of immune signaling and less than 6 days for networks describing neural transmission in animals. These results offer a first scalability benchmark for regulatory logic modeling of biological networks, making the latter a potentially attractive approach for practical in silico hypothesis generation.

The classical problem of Other Minds has dogged philosophers for millennia; asking if we have any way to truly understand the experience of another mind. We know our own intrinsic experience by acquaintance, but can only ever hope to possess an extrinsic description of another's, with the two separated by an acquaintance gap. Structural approaches aim to characterise experience in terms of a mathematical structure, and promise a 'Rosetta Stone'; that is, a principled method to translate the contents of experience into a mathematical structure. In this chapter, we examine two such approaches - the Qualia Structure Paradigm (Qstr) and Integrated Information Theory (IIT) - to ask what, if anything, they allow us to infer about another mind should they possess the Rosetta Stone they seek. Qstr proceeds inter-phenomenally; aiming to exhaustively characterise an experience by its relations to all other experiences and providing a necessary condition on the sameness of experience. IIT proceeds intra-phenomenally; conjecturing that a single experience is accounted for by the cause-effect structure unfolded from a substrate in a state, an explanatory identity that is both necessary and sufficient for the sameness of experience. While neither approach can cross the acquaintance gap to another's mind, they provide a common formal medium through which minds may be compared and in turn reduce the size of this gap. We identify the strength of inference with levels of structural correspondence in Category Theory. These descend from isomorphism through strong and weak adjunction, to a principled limit where inference runs out. We conclude that these structural approaches and the existence of their respective Rosetta Stones would not solve the problem of Other Minds, but instead provide a system of principled constraints on what we may infer, which is far more than what has been justifiable before.