Orbital ferromagnetism in interacting few-electron dots with strong spin-orbit coupling

We study the ground state of $N$ weakly interacting electrons (with $N\le 10$) in a two-dimensional parabolic quantum dot with strong Rashba spin-orbit coupling. Using dimensionless parameters for the Coulomb interaction, $\lambda\lesssim 1$, and the Rashba coupling, $\alpha\gg 1$, the low-energy physics is characterized by an almost flat single-particle dispersion. From an analytical approach for $\alpha\to \infty$ and $N=2$, and from numerical exact diagonalization and Hartree-Fock calculations, we find a transition from a conventional unmagnetized ground state (for $\lambda<\lambda_c$) to an orbital ferromagnet (for $\lambda>\lambda_c$), with a large magnetization and a circulating charge current. We show that the critical interaction strength, $\lambda_c=\lambda_c(\alpha,N)$, vanishes in the limit $\alpha\to \infty$.Comment: 15 pages, 9 figures; (v2) more discussion added, fig.8 correcte

Adopting and incorporating crowdsourced traffic data in advanced transportation management systems

The widespread availability of internet and mobile devices has made crowdsourced reports a considerable source of information in many domains. Traffic managers, among others, have started using crowdsourced traffic incident reports (CSTIRs) to complement their existing sources of traffic monitoring. One of the prominent providers of CSTIRs is Waze. In this dissertation, first a quantitative analysis was conducted to evaluate Waze data in comparison to the existing sources of Iowa Department of Transportation. The potential added coverage that Waze can provide was also estimated. Redundant CSTIRs of the same incident were found to be one of the main challenges of Waze and CSTIRs in general. To leverage the value of the redundant reports and address this challenge, a state-of-the-art cluster analysis was implemented to reduce the redundancies, while providing further information about the incident. The clustered CSTIRs indicate the area impacted by an incident and provide a basis for estimating the reliability of the cluster. Furthermore, the challenges with clustering CSTIRs were described and recommendations were made for parameter tuning and cluster validation. Finally, an open-source software package was offered to implement the clustering method in near real-time. This software downloads and parses the raw data, implements clustering, tracks clusters, assigns a reliability score to clusters, and provides a RESTful API for information dissemination portals and web pages to use the data for multiple applications within the DOT and for the general public. With emerging technologies such as connected vehicles and vehicle-to-infrastructure (V2I) communication, CSTIRs and similar type of data are expected to grow. The findings and recommendations in this work, although implemented on Waze data, will be beneficial to the analysis of these emerging sources of data

An Investigation of the Performance of the ANN Method for Predicting the Base Shear and Overturning Moment Time-Series Datasets of an Offshore Jacket Structure

The primary purpose of the current study was to investigate the performance of artificial neural networks to predict the time series of the water surface level (WSL), base shear, and overturning moment using two types of ANN models: Nonlinear Autoregressive models with exogenous inputs (NARX) and Nonlinear Autoregressive models (NAR). After determining the suitable model, NARX, the possibility of predicting the time series of the base shear and the overturning moment data was investigated by considering the water surface level and time as the multivariable model inputs. A jacket model with a height of 4.55m was fabricated and tested in the 402m-long wave flume of the NIMALA marine laboratory. The jacket was tested at the water depth of 4m and subjected to random waves with a JONSWAP energy spectrum. Three input wave heights were chosen for the tests: 20cm, 23cm, and 28cm. The findings showed that using the NARX neural network is a convenient method to predict the base shear and overturning moment values based on the water surface level data as input values. Finally, after suitable neural network determination, using the NARX neural network, the correlation value (R) for calculating water surface level (WSL), Base Shear, and Overturning Moment were obtained as 0.994, 0.97, and 0.94, respectively

Mobile Robot Online Motion Planning Using Generalized Voronoi Graphs

In this paper, a new online robot motion planner is developed for systematically exploring unknown environ¬ments by intelligent mobile robots in real-time applications. The algorithm takes advantage of sensory data to find an obstacle-free start-to-goal path. It does so by online calculation of the Generalized Voronoi Graph (GVG) of the free space, and utilizing a combination of depth-first and breadth-first searches on the GVG. The planner is equipped with components such as step generation and correction, backtracking, and loop handling. It is fast, simple, complete, and extendable to higher spaces

Investigación de los factores efectivos sobre el empoderamiento orientado al empleo de las mujeres jefas de hogares rurales en Ilam, Irán

El propósito de este estudio es investigar los factores que afectan el empoderamiento orientado al empleo de las mujeres jefas de hogar rurales en Ilam. En términos de seguimiento y grado de control, se trata de una investigación de campo y en términos de método de obtención de hechos y procesamiento de datos, es un estudio de encuesta y en términos de recolección de datos es descriptivo-correlacional. La población estadística de este estudio es 14156 mujeres jefas de hogar rurales en Ilam. El tamaño de la muestra se estableció para 374 personas utilizando Krejcie y Morgan Table. En este estudio, debido a tener una lista de los nombres de todas las mujeres estudiadas, se utilizó un método de muestreo sistemático. La herramienta de recolección de datos en este estudio fue un cuestionario elaborado por investigadores cuya validez aparente y validez de contenido fueron proporcionadas a través de un panel de expertos. La fiabilidad del cuestionario también fue confirmada por el alfa de Cronbach. Los resultados del análisis de regresión mostraron que las variables de dimensión psicológica, dimensión social, dimensión cultural y la dimensión técnica de las competencias profesionales tienen el mayor impacto en el empoderamiento de las mujeres rurales, es decir, explican el 52% de los cambios en el empoderamiento. variable de mujeres