Minimal S U ( 3 ) × S U ( 3 ) symmetry breaking patterns
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The existence of closed loops of degeneracies in crystals has been intimately connected with associated crystal symmetries, raising the following question: What is the minimum symmetry required for topological character, and can one find an example? Triclinic CaAs 3 , in the space group P 1¯ with only a center of inversion, has been found to display, without need for tuning, a nodal loop of accidental degeneracies with topological character, centered on one face of the Brillouin zone that is otherwise fully gapped. The small loop is very flat in energy, yet is cut four times by the Fermi energy, a condition that results in an intricate repeated touching of inversion related pairs of Fermi surfaces at Weyl points. Spin-orbit coupling lifts the fourfold degeneracy along the loop, leaving trivial Kramers pairs. Here, with its single nodal loop that emerges without protection from any point group symmetry, CaAs 3 represents the primal “hydrogen atom” of nodal loop systems.
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The first-ever measurement of energy correlators within inclusive jets produced in heavy-ion collisions, revealed by the CMS Collaboration, shows a clear enhancement at large angles relative to the proton-proton (p-p) baseline. However, interpreting this enhancement is complicated due to selection bias from energy loss, which also distorts the energy correlator heavy-ion to p-p ratio in the hadronization region, hindering our understanding of parton/hadron dynamics in a colored medium. In this Letter, we introduce a new ratio of energy correlator observables that removes the leading effects of selection bias from the two-point energy correlator spectrum (E2C). PYTHIA and HERWIG simulations show that the impact of selection bias in the E2C is reduced by an order of magnitude, while sensitivity to any other medium modifications is retained. This quantity can be obtained directly from the experimental measurements presented by CMS, as illustrated in an accompanying note [C. Andres and J. Holguin, arXiv:2409.07526].
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X-ray diffraction data were obtained at the National Synchrotron Light Source from insulin and lysozyme crystals that were densely deposited on three types of surfaces suitable for serial micro-crystallography: MiTeGen MicroMeshes™, Greiner Bio-One Ltd in situ micro-plates, and a moving kapton crystal conveyor belt that is used to deliver crystals directly into the X-ray beam. 6° wedges of data were taken from ~100 crystals mounted on each material, and these individual data sets were merged to form nine complete data sets (six from insulin crystals and three from lysozyme crystals). Insulin crystals have a parallelepiped habit with an extended flat face that preferentially aligned with the mounting surfaces, impacting the data collection strategy and the design of the serial crystallography apparatus. Lysozyme crystals had a cuboidal habit and showed no preferential orientation. Preferential orientation occluded regions of reciprocal space when the X-ray beam was incident normal to the data-collection medium surface, requiring a second pass of data collection with the apparatus inclined away from the orthogonal. In addition, crystals measuring less than 20 µm were observed to clump together into clusters of crystals. Clustering required that the X-ray beam be adjusted to match the crystal size to prevent overlapping diffraction patterns. No additional problems were encountered with the serial crystallography strategy of combining small randomly oriented wedges of data from a large number of specimens. Lastly, high-quality data able to support a realistic molecular replacement solution were readily obtained from both crystal types using all three serial crystallography strategies.
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The global climate crisis demands urgent action to mitigate global warming. Using renewable energy sources, such as solar and wind power, for electricity generation is crucial. This shift from centralized to distributed power systems, however, brings challenges, including voltage fluctuations and renewable energy curtailment. The rapid growth of the electric vehicle (EV) industry adds complexity, increasing overall electricity demand and straining the power supply during peak charging times. This paper proposes a scheduling strategy for EV aggregators to reduce renewable energy curtailment and stabilize grid operation by strategically scheduling EV charging. Using Multi -Agent Transport Simulation (MATSim), a traffic simulation tool, EV driving data in Denver, Colorado, USA, were modeled. The EV aggregator adjusts charging fees based on locational marginal prices, encouraging EVs to charge at different stations according to pricing. Simulations on an IEEE 33-bus system with distributed energy resources and EV charging stations validate the proposed algorithm, demonstrating its effectiveness in reducing curtailment by 12.55% and stabilizing grid operation.
In this paper, optimization of the winding AC resistance of the flexible printed circuit board (FPCB) wire windings is performed. A one-dimensional model of the FPCB winding is introduced and an equation for the FPCB winding AC resistance and its low and medium frequency approximation are derived. The approximate FPCB winding AC resistance equation is used to derive optimum thickness of the FPCB winding conductor thickness at which minimum of winding AC resistance (global optimum) is achieved. The Finite Element Method analysis and experimental verification of derived equations (winding resistance, impedance, power loss, and temperature measurements) is performed in order validate derived model and winding resistance equation.
Geographically annotated social media is extremely valuable for modern information retrieval. However, when researchers can only access publicly-visible data, one quickly finds that social media users rarely publish location information. In this work, we provide a method which can geolocate the overwhelming majority of active Twitter users, independent of their location sharing preferences, using only publicly-visible Twitter data. Our method infers an unknown user's location by examining their friend's locations. We frame the geotagging problem as an optimization over a social network with a total variation-based objective and provide a scalable and distributed algorithm for its solution. Furthermore, we show how a robust estimate of the geographic dispersion of each user's ego network can be used as a per-user accuracy measure which is effective at removing outlying errors. Leave-many-out evaluation shows that our method is able to infer location for 101, 846, 236 Twitter users at a median error of 6.38 km, allowing us to geotag over 80% of public tweets.