Polytechnic University of Catalonia

About: Polytechnic University of Catalonia is a education organization based out in Barcelona, Spain. It is known for research contribution in the topics: Finite element method & Population. The organization has 16006 authors who have published 45325 publications receiving 949306 citations. The organization is also known as: UPC - BarcelonaTECH & Technical University of Catalonia.

A general formulation for raindrop size distribution

Abstract: A general phenomenological formulation for drop size distribution (DSD), written down as a scaling law, is proposed. It accounts for all previous fitted DSDs. As a main implication of the expression proposed, the integral rainfall variables are related by power functions and agree with experimental evidence. Additional consequences are also analyzed. From this formulation there follows a general methodology for scaling all data in a unique plot, leading to more robust fits of the DSD. An illustrative example on real data is provided.

Evaluation of the Diurnal Cycle in the atmospheric boundary layer over land as represented by a variety of single-column models: The second GABLS Experiment

Abstract: We present the main results from the second model intercomparison within the GEWEX (Global Energy and Water cycle EXperiment) Atmospheric Boundary Layer Study (GABLS). The target is to examine the diurnal cycle over land in today’s numerical weather prediction and climate models for operational and research purposes. The set-up of the case is based on observations taken during the Cooperative Atmosphere-Surface Exchange Study-1999 (CASES-99), which was held in Kansas, USA in the early autumn with a strong diurnal cycle with no clouds present. The models are forced with a constant geostrophic wind, prescribed surface temperature and large-scale divergence. Results from 30 different model simulations and one large-eddy simulation (LES) are analyzed and compared with observations. Even though the surface temperature is prescribed, the models give variable near-surface air temperatures. This, in turn, gives rise to differences in low-level stability affecting the turbulence and the turbulent heat fluxes. The increase in modelled upward sensible heat flux during the morning transition is typically too weak and the growth of the convective boundary layer before noon is too slow. This is related to weak modelled near-surface winds during the morning hours. The agreement between the models, the LES and observations is the best during the late afternoon. From this intercomparison study, we find that modelling the diurnal cycle is still a big challenge. For the convective part of the diurnal cycle, some of the first-order schemes perform somewhat better while the turbulent kinetic energy (TKE) schemes tend to be slightly better during nighttime conditions. Finer vertical resolution tends to improve results to some extent, but is certainly not the solution to all the deficiencies identified.

Some factors controlling the injectability of calcium phosphate bone cements.

Abstract: The injectability of four calcium phosphate bone cements (CPBCs) was measured using a commercial disposable syringe. It varied considerably with the cement powder composition, with the liquid/powder ratio, with the time after starting the mixing of liquid and powder, with the accelerator concentration (% Na2HPO4), and with the ageing time of the cement powder which was prepared by milling. The injectability test could be used to determine accurately the dough time of CPBCs. Relations between the setting time and the cohesion time are discussed.

Machine learning for network automation: overview, architecture, and applications [Invited Tutorial]

Abstract: Networks are complex interacting systems involving cloud operations, core and metro transport, and mobile connectivity all the way to video streaming and similar user applications.With localized and highly engineered operational tools, it is typical of these networks to take days to weeks for any changes, upgrades, or service deployments to take effect. Machine learning, a sub-domain of artificial intelligence, is highly suitable for complex system representation. In this tutorial paper, we review several machine learning concepts tailored to the optical networking industry and discuss algorithm choices, data and model management strategies, and integration into existing network control and management tools. We then describe four networking case studies in detail, covering predictive maintenance, virtual network topology management, capacity optimization, and optical spectral analysis.

Localization and delocalization of light in photonic moiré lattices

Abstract: Moire lattices consist of two superimposed identical periodic structures with a relative rotation angle. Moire lattices have several applications in everyday life, including artistic design, the textile industry, architecture, image processing, metrology and interferometry. For scientific studies, they have been produced using coupled graphene-hexagonal boron nitride monolayers1,2, graphene-graphene layers3,4 and graphene quasicrystals on a silicon carbide surface5. The recent surge of interest in moire lattices arises from the possibility of exploring many salient physical phenomena in such systems; examples include commensurable-incommensurable transitions and topological defects2, the emergence of insulating states owing to band flattening3,6, unconventional superconductivity4 controlled by the rotation angle7,8, the quantum Hall effect9, the realization of non-Abelian gauge potentials10 and the appearance of quasicrystals at special rotation angles11. A fundamental question that remains unexplored concerns the evolution of waves in the potentials defined by moire lattices. Here we experimentally create two-dimensional photonic moire lattices, which-unlike their material counterparts-have readily controllable parameters and symmetry, allowing us to explore transitions between structures with fundamentally different geometries (periodic, general aperiodic and quasicrystal). We observe localization of light in deterministic linear lattices that is based on flat-band physics6, in contrast to previous schemes based on light diffusion in optical quasicrystals12, where disorder is required13 for the onset of Anderson localization14 (that is, wave localization in random media). Using commensurable and incommensurable moire patterns, we experimentally demonstrate the two-dimensional localization-delocalization transition of light. Moire lattices may feature an almost arbitrary geometry that is consistent with the crystallographic symmetry groups of the sublattices, and therefore afford a powerful tool for controlling the properties of light patterns and exploring the physics of periodic-aperiodic phase transitions and two-dimensional wavepacket phenomena relevant to several areas of science, including optics, acoustics, condensed matter and atomic physics.