Interpreting Housing Prices with a MultidisciplinaryApproach Based on Nature-Inspired Algorithms and Quantum Computing

Current technology still does not allow the use of quantum computers for broader and individual uses; however, it is possible to simulate some of its potentialities through quantum computing. Quantum computing can be integrated with nature-inspired algorithms to innovatively analyze the dynamics of the real estate market or any other economic phenomenon. With this main aim, this study implements a multidisciplinary approach based on the integration of quantum computing and genetic algorithms to interpret housing prices. Starting from the principles of quantum programming, the work applies genetic algorithms for the marginal price determination of relevant real estate characteristics for a particular segment of Naples’ real estate market. These marginal prices constitute the quantum program inputs to provide, as results, the purchase probabilities corresponding to each real estate characteristic considered. The other main outcomes of this study consist of a comparison of the optimal quantities for each real estate characteristic as determined by the quantum program and the average amounts of the same characteristics but relative to the real estate data sampled, as well as the weights of the same characteristics obtained with the implementation of genetic algorithms. With respect to the current state of the art, this study is among the first regarding the application of quantum computing to interpretation of selling prices in local real estate markets

Earthquake Ground Motion Simulation using Novel Machine Learning Tools

A novel method of model-independent probabilistic seismic hazard analysis(PSHA) and ground motion simulation is presented and verified using previously recorded data and machine learning. The concept of “eigenquakes” is introduced as an orthonormal set of basis vectors that represent characteristic earthquake records in a large database. Our proposed procedure consists of three phases, (1) estimation of the anticipated level of shaking for a scenario earthquake at a site using Gaussian Process regression, (2) extraction of the eigenquakes from Principal Component Analysis (PCA) of data, and (3) optimal combination of the eigenquakes to generate time-series of ground acceleration with spectral ordinates obtained in phase (1). The benefits of using a model-independent method of PSHA and ground motion simulation, particularly in large urban areas where dense instrumentation is available or expected, are argued. The effectiveness of the proposed methodology is exhibited using eight scenario examples for downtown areas of Los Angeles and San Francisco where it is shown that no dependency on specific ground motion prediction equations or processes of selection and scaling would be needed in our procedure. Furthermore, PCA allows systematic analysis of large databases of ground motion records that are otherwise very difficult to handle by conventional methods of data analysis. Advantages, disadvantages, and future research needs are highlighted at the end

Emerging Trends in Mechatronics

Mechatronics is a multidisciplinary branch of engineering combining mechanical, electrical and electronics, control and automation, and computer engineering fields. The main research task of mechatronics is design, control, and optimization of advanced devices, products, and hybrid systems utilizing the concepts found in all these fields. The purpose of this special issue is to help better understand how mechatronics will impact on the practice and research of developing advanced techniques to model, control, and optimize complex systems. The special issue presents recent advances in mechatronics and related technologies. The selected topics give an overview of the state of the art and present new research results and prospects for the future development of the interdisciplinary field of mechatronic systems

CMB-S4 Science Book, First Edition

This book lays out the scientific goals to be addressed by the next-generation ground-based cosmic microwave background experiment, CMB-S4, envisioned to consist of dedicated telescopes at the South Pole, the high Chilean Atacama plateau and possibly a northern hemisphere site, all equipped with new superconducting cameras. CMB-S4 will dramatically advance cosmological studies by crossing critical thresholds in the search for the B-mode polarization signature of primordial gravitational waves, in the determination of the number and masses of the neutrinos, in the search for evidence of new light relics, in constraining the nature of dark energy, and in testing general relativity on large scales

Local Methods for the Cosmic Microwave Background

The standard tools for analyzing the Cosmic Microwave Background (CMB), a key component for making cosmological inferences, are usually of global sampling type. Such a methodological bias may preclude the development of important techniques for cosmology. This thesis develops local, real-space tools for CMB analysis which may be complemented using harmonic space techniques or provide useful signal diagnostics on their own. Particularly, finite-difference schemes for performing local derivatives are investigated. One can define derivatives which extract the primordial polarization modes from the measured CMB Stokes parameters by constructing real scalar and pseudo-scalar fields. The detection of a primordial curl-like (‘B’-mode) CMB polarization signal would imply the existence of a background of primordial gravitational waves, the ‘smoking gun’ signal of an inflationary cosmology. On an obscured (masked) sky, the gradient-like (‘E’-mode) signal leaks into the B-mode signal when the standard harmonic E/B signal decomposition is performed — using local techniques instead, this leakage can be reduced since the masked region is not sampled from. An algorithm and a software package are developed for just such a calculation. Furthermore, differencing errors in the presence of discontinuous signals are utilized to produce the ‘Laplacian-difference’ method, which enhances pathological and discontinuous signals. Such signals, in the absence of systematics, might reveal the presence of cosmic defects. The scalar and pseudo-scalar fields produced will feature self-coupled modetransfer due to masking; the mode-transfer matrices are related to the optimal apodization schemes for extracting power spectra. The transfer matrices for various spectral operations on scalar fields are presented, which for the polarization spectra provide important computational advantages over the direct utilization of the E- and B-modes

Optimization for Decision Making II

In the current context of the electronic governance of society, both administrations and citizens are demanding the greater participation of all the actors involved in the decision-making process relative to the governance of society. This book presents collective works published in the recent Special Issue (SI) entitled “Optimization for Decision Making II”. These works give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and the application of optimization techniques to support decisions are particularly complex and a wide range of optimization techniques and methodologies are used to minimize risks, improve quality in making decisions or, in general, to solve problems. In addition, a sensitivity or robustness analysis should be done to validate/analyze the influence of uncertainty regarding decision-making. This book brings together a collection of inter-/multi-disciplinary works applied to the optimization of decision making in a coherent manner

Optimización del diseño estructural de pavimentos asfálticos para calles y carreteras

gráficos, tablasThe construction of asphalt pavements in streets and highways is an activity that requires optimizing the consumption of significant economic and natural resources. Pavement design optimization meets contradictory objectives according to the availability of resources and users’ needs. This dissertation explores the application of metaheuristics to optimize the design of asphalt pavements using an incremental design based on the prediction of damage and vehicle operating costs (VOC). The costs are proportional to energy and resource consumption and polluting emissions. The evolution of asphalt pavement design and metaheuristic optimization techniques on this topic were reviewed. Four computer programs were developed: (1) UNLEA, a program for the structural analysis of multilayer systems. (2) PSO-UNLEA, a program that uses particle swarm optimization metaheuristic (PSO) for the backcalculation of pavement moduli. (3) UNPAVE, an incremental pavement design program based on the equations of the North American MEPDG and includes the computation of vehicle operating costs based on IRI. (4) PSO-PAVE, a PSO program to search for thicknesses that optimize the design considering construction and vehicle operating costs. The case studies show that the backcalculation and structural design of pavements can be optimized by PSO considering restrictions in the thickness and the selection of materials. Future developments should reduce the computational cost and calibrate the pavement performance and VOC models. (Texto tomado de la fuente)La construcción de pavimentos asfálticos en calles y carreteras es una actividad que requiere la optimización del consumo de cuantiosos recursos económicos y naturales. La optimización del diseño de pavimentos atiende objetivos contradictorios de acuerdo con la disponibilidad de recursos y las necesidades de los usuarios. Este trabajo explora el empleo de metaheurísticas para optimizar el diseño de pavimentos asfálticos empleando el diseño incremental basado en la predicción del deterioro y los costos de operación vehicular (COV). Los costos son proporcionales al consumo energético y de recursos y las emisiones contaminantes. Se revisó la evolución del diseño de pavimentos asfálticos y el desarrollo de técnicas metaheurísticas de optimización en este tema. Se desarrollaron cuatro programas de computador: (1) UNLEA, programa para el análisis estructural de sistemas multicapa. (2) PSO-UNLEA, programa que emplea la metaheurística de optimización con enjambre de partículas (PSO) para el cálculo inverso de módulos de pavimentos. (3) UNPAVE, programa de diseño incremental de pavimentos basado en las ecuaciones de la MEPDG norteamericana, y el cálculo de costos de construcción y operación vehicular basados en el IRI. (4) PSO-PAVE, programa que emplea la PSO en la búsqueda de espesores que permitan optimizar el diseño considerando los costos de construcción y de operación vehicular. Los estudios de caso muestran que el cálculo inverso y el diseño estructural de pavimentos pueden optimizarse mediante PSO considerando restricciones en los espesores y la selección de materiales. Los desarrollos futuros deben enfocarse en reducir el costo computacional y calibrar los modelos de deterioro y COV.DoctoradoDoctor en Ingeniería - Ingeniería AutomáticaDiseño incremental de pavimentosEléctrica, Electrónica, Automatización Y Telecomunicacione

Structural optimization in steel structures, algorithms and applications

L'abstract è presente nell'allegato / the abstract is in the attachmen