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In this paper we focus on a critical component of the city: its building stock, which holds much of its socio-economic activities. In our case, the lack of a comprehensive database about their features and its limitation to a surveyed subset lead us to adopt data-driven techniques to extend our knowledge to the near-city-scale. Neural networks and random forests are applied to identify the buildings' number of floors and construction periods' dependencies on a set of shape features: area, perimeter, and height along with the annual electricity consumption, relying a surveyed data in the city of Beirut. The predicted results are then compared with established scaling laws of urban forms, which constitutes a further consistency check and validation of our workflow. [ABSTRACT FROM AUTHOR]

To simulate the motion of neutrophils and their morphodynamics in response to chemical cues, we construct a model based on the phase-field method utilizing a description with a free-energy functional and associated dynamics which captures the basic features of the phenomenon. We additionally incorporate spatial sensing by introducing an auxiliary field which depicts the polymerization of the region of the cell facing the highest concentration of the chemical attractant. [ABSTRACT FROM AUTHOR]

Chemotropism is the action of targeting a part of the cell by means of chemical mediators and cues, and subsequently delimiting the pathway that it should undertake. In a neural cell, this initiates axonal elongation. Herein we model this growth, where chemotropic forcing leads the axon, by a phase field method utilizing two dynamical fields assigned respectively to the cell and to its leading edge. Additionally we quantify the condition for the retraction of the axon which takes place when the cell fails to form a synaptic connection. [ABSTRACT FROM AUTHOR]

We analyze the morphology of the modern urban skyline in terms of its roughness properties. This is facilitated by a database of 10 7 building heights in cities throughout the Netherlands which allows us to compute the asymptotic height difference correlation function in each city. We find that in cities for which the height correlations display power-law scaling as a function of distance between the buildings, the corresponding roughness exponents are commensurate to the Edwards-Wilkinson and Kardar-Parisi-Zhang equations for kinetic roughening. Based on analogy to discrete deposition models, we argue that these two limiting classes emerge because of possible height restriction rules for buildings in some cities. [ABSTRACT FROM AUTHOR]

We explore the scaling of cities' solar potentials with their number of buildings and reveal a latent dependence between the solar potential and the length of the corresponding city's road network. This scaling is shown to be valid at the grid and block levels and is attributed to a common street length distribution. Additionally, we compute the buildings' solar potential correlation function and length in order to determine the set of critical exponents typifying the urban solar potential universality class. [ABSTRACT FROM AUTHOR]

We construct a phase-field model for collective cell migration based on a Ginzburg-Landau free-energy formulation. We model adhesion, surface tension, repulsion, coattraction, and polarization, enabling us to follow the cells' morphologies and the effect of their membranes fluctuations on collective motion. We were able to measure the tissue surface tension as a function of the individual cell cortical tension and adhesion and identify a density threshold for cell-sheet formation. [ABSTRACT FROM AUTHOR]

Urban expansion, driven by population and economic growth, has been a major contributor to increased levels of energy consumption across the globe. Beirut, Lebanon's capital, is no exception in facing a surge in its demand for electricity as it expands. However, with frequent power outages, underpinning a largely problematic power sector, Beirut's demand for electricity is becoming a real hurdle that impedes the city's economic growth and development. This paper introduces a near-city-scale building energy model, BEirut Energy Model BEEM, which estimates the building stock's electricity consumption in two different districts in Beirut. The methodology uses rule-based expert data for an archetypal classification of the buildings based on their functions and periods of construction with their corresponding attributes including the number of floors, number of apartments, and bimonthly electricity consumption to generate a 3D model for 3630 buildings coupled to the hourly weather conditions and topographic map, which is then simulated in EnergyPlus. The predicted consumption of 2311 buildings is then calibrated with actual available metered data, to adapt the model to Beirut's occupancy and users' behaviors. Calibrated results are mapped to reveal the spatiotemporal distribution of energy peak demands which provide insights for future interventions. An analysis of the spatial distribution of electricity use demonstrates a spatial clustering that underlies urban energy demand which can be used for smart grid zoning. [ABSTRACT FROM AUTHOR]