RESUMEN
Traffic simulations are valuable tools for urban mobility planning and operation, particularly in large cities. Simulation-based microscopic models have enabled traffic engineers to understand local transit and transport behaviors more deeply and manage urban mobility. However, for the simulations to be effective, the transport network and user behavior parameters must be calibrated to mirror real scenarios. In general, calibration is performed manually by traffic engineers who use their knowledge and experience to adjust the parameters of the simulator. Unfortunately, there is still no systematic and automatic process for calibrating traffic simulation networks, although some methods have been proposed in the literature. This study proposes a methodology that facilitates the calibration process, where an artificial neural network (ANN) is trained to learn the behavior of the transport network of interest. The ANN used is the Multi-Layer Perceptron (MLP), trained with back-propagation methods. Based on this learning procedure, the neural network can select the optimized values of the simulation parameters that best mimic the traffic conditions of interest. Experiments considered two microscopic models of traffic and two psychophysical models (Wiedemann 74 and Wiedemann 99). The microscopic traffic models are located in the metropolitan region of São Paulo, Brazil. Moreover, we tested the different configurations of the MLP (layers and numbers of neurons) as well as several variations of the backpropagation training method: Stochastic Gradient Descent (SGD), Adam, Adagrad, Adadelta, Adamax, and Nadam. The results of the experiments show that the proposed methodology is accurate and efficient, leading to calibration with a correlation coefficient greater than 0.8, when the calibrated parameters generate more visible effects on the road network, such as travel times, vehicle counts, and average speeds. For the psychophysical parameters, in the most simplified model (W74), the correlation coefficient was greater than 0.7. The advantage of using ANN for the automatic calibration of simulation parameters is that it allows traffic engineers to carry out comprehensive studies on a large number of future scenarios, such as at different times of the day, as well as on different days of the week and months of the year.
RESUMEN
This paper presents an ab initio investigation, performed in the framework of density functional theory, on the properties of functionalized few-layer silicene nanosheets, denoted as Si2X2 bilayers and Si4X2 trilayers with X = B, N, Al, and P. Searching for stable phases, we computed the structural, energetic, thermodynamic, dynamic, elastic, and electronic properties of those systems in several stacking configurations, labeled as AA', AB, AA'A'', and ABC. The results revealed that AA'-Si2N2, AB-Si2N2, AA'-Si2P2, and AB-Si2P2 bilayers, as well as ABC-Si4B2, ABC-Si4Al2, AA'A''-Si4P2, and ABC-Si4P2 trilayers are all dynamically stable, based on their respective phonon dispersion spectra. Particularly, there is scarce literature regarding functionalized trilayer silicene systems and, in this work, we found four new nanosheet systems with interesting physical properties and promising applications. Additionally, according to their standard enthalpies of formation and by exploring their electronic properties, we established that those structures could be experimentally accessed, and we discovered that those silicene nanosheets are indirect band gap semiconductors when functionalized with N or P atoms and metallic with B or Al ones. Finally, we envision potential applications for those nanosheets in alkali-metal ion batteries, van der Waals heterostructures, UV-light devices, and thermoelectric materials.
RESUMEN
The discovery of a pressure induced iron-related spin crossover in Mg((1-x))Fe(x)O ferropericlase (Fp) and Mg-silicate perovskite, the major phases of Earth's lower mantle, has raised new questions about mantle properties which are of central importance to seismology. Despite extensive experimental work on the anomalous elasticity of Fp throughout the crossover, inconsistencies reported in the literature are still unexplained. Here we introduce a formulation for thermoelasticity of spin crossover systems, apply it to Fp by combining it with predictive first principles density-functional theory with on-site repulsion parameter U calculations, and contrast results with available data on samples with various iron concentrations. We explain why the shear modulus of Fp should not soften along the crossover, as observed in some experiments, predict its velocities at lower mantle conditions, and show the importance of constraining the elastic properties of minerals without extrapolations for analyses of the thermochemical state of this region.