Scaled unscented transform Gaussian sum filter: theory and application

In this work we consider the state estimation problem in nonlinear/non-Gaussian systems. We introduce a framework, called the scaled unscented transform Gaussian sum filter (SUT-GSF), which combines two ideas: the scaled unscented Kalman filter (SUKF) based on the concept of scaled unscented transform (SUT), and the Gaussian mixture model (GMM). The SUT is used to approximate the mean and covariance of a Gaussian random variable which is transformed by a nonlinear function, while the GMM is adopted to approximate the probability density function (pdf) of a random variable through a set of Gaussian distributions. With these two tools, a framework can be set up to assimilate nonlinear systems in a recursive way. Within this framework, one can treat a nonlinear stochastic system as a mixture model of a set of sub-systems, each of which takes the form of a nonlinear system driven by a known Gaussian random process. Then, for each sub-system, one applies the SUKF to estimate the mean and covariance of the underlying Gaussian random variable transformed by the nonlinear governing equations of the sub-system. Incorporating the estimations of the sub-systems into the GMM gives an explicit (approximate) form of the pdf, which can be regarded as a "complete" solution to the state estimation problem, as all of the statistical information of interest can be obtained from the explicit form of the pdf ... This work is on the construction of the Gaussian sum filter based on the scaled unscented transform

Architectural Education in the Arab World and Its Role in Facing the Contemporary Local and Regional Challenges

This research discusses the academic architectural education and its role in facing the challenges caused by the Arab revolutions that commenced in 2011, also known as “The Arab Spring”. It begins with a review of the most important issues of the present Arab world and continues by demonstrating the role of architecture in developing society and improving the quality of life. It also explores the appropriate architecture for the current phase, which must be design-based with a humanitarian-social dimension and respect to both “sustainability” and “participation”. Subsequently, the need to review the academic architectural education is addressed so that it convoys the current developments of society and becomes able to shape and create architects qualified in the field of the desired architecture. This may be done by introducing new resolutions related to social architecture, launching the Design/Build studio alongside traditional architecture studios, and indulging students in the field of participatory design. In addition, architectural research of students and their graduation projects should be directed to address the local issues, aiming to serve society and promote its development. Finally, recommendations to universities and higher education institutes are raised so that they can act their intended role in the development and improvement of their societies

Effects of Investor Sentiment Using Social Media on Corporate Financial Distress

The mainstream quantitative models in the finance literature have been ineffective in detecting possible bankruptcies during the 2007 to 2009 financial crisis. Coinciding with the same period, various researchers suggested that sentiments in social media can predict future events. The purpose of the study was to examine the relationship between investor sentiment within the social media and the financial distress of firms Grounded on the social amplification of risk framework that shows the media as an amplified channel for risk events, the central hypothesis of the study was that investor sentiments in the social media could predict t he level of financial distress of firms. Third quarter 2014 financial data and 66,038 public postings in the social media website Twitter were collected for 5,787 publicly held firms in the United States for this study. The Spearman rank correlation was applied using Altman Z-Score for measuring financial distress levels in corporate firms and Stanford natural language processing algorithm for detecting sentiment levels in the social media. The findings from the study suggested a non-significant relationship between investor sentiments in the social media and corporate financial distress, and, hence, did not support the research hypothesis. However, the model developed in this study for analyzing investor sentiments and corporate distress in firms is both original and extensible for future research and is also accessible as a low-cost solution for financial market sentiment analysis

Deconstructivism: Translation From Philosophy to Architecture

There has always been a significant interaction between architecture and the human sciences, such as philosophy, psychology, and sociology. Intellectual and especially philosophical currents of thought have influenced architecture at the time that it was created. This research article examines the study of the philosophical current of “deconstruction” and its relation to deconstructivist architecture. First, the research explains the basic principles of this philosophy, which began with the work of Jacques Derrida. Next, it defines the basic terms and vocabulary of this philosophy. Then, this research identifies the deconstruction concepts that were transferred to architecture and became the basis of deconstructivist architectural styles. Deconstructivist projects and buildings initially seem to be fragmented and lack any visual logic; however, they are unified under the principles and concepts of deconstruction philosophy. The “transfer” of the concepts of deconstruction to architecture was not direct and literal; some concepts were modified and renamed to suit architecture. Moreover, iconic deconstructivist architects were not committed to all concepts of this philosophy; they were known to focus on one or two concepts in deconstruction and make them fundamental principles of their personal styles in architecture. Peter Eisenman focused on the concepts of presentness and trace, Daniel Libeskind concentrated on the concept of absence, and Frank Gehry focused on binary oppositions and free play. Finally, a deconstructivist architect is not as free as a reader or a philosopher; not all that one can do or apply in language and philosophy can be done and applied in architecture

A Bayesian Consistent Dual Ensemble Kalman Filter for State-Parameter Estimation in Subsurface Hydrology

Ensemble Kalman filtering (EnKF) is an efficient approach to addressing uncertainties in subsurface groundwater models. The EnKF sequentially integrates field data into simulation models to obtain a better characterization of the model's state and parameters. These are generally estimated following joint and dual filtering strategies, in which, at each assimilation cycle, a forecast step by the model is followed by an update step with incoming observations. The Joint-EnKF directly updates the augmented state-parameter vector while the Dual-EnKF employs two separate filters, first estimating the parameters and then estimating the state based on the updated parameters. In this paper, we reverse the order of the forecast-update steps following the one-step-ahead (OSA) smoothing formulation of the Bayesian filtering problem, based on which we propose a new dual EnKF scheme, the Dual-EnKF$_{\rm OSA}$. Compared to the Dual-EnKF, this introduces a new update step to the state in a fully consistent Bayesian framework, which is shown to enhance the performance of the dual filtering approach without any significant increase in the computational cost. Numerical experiments are conducted with a two-dimensional synthetic groundwater aquifer model to assess the performance and robustness of the proposed Dual-EnKF$_{\rm OSA}$, and to evaluate its results against those of the Joint- and Dual-EnKFs. The proposed scheme is able to successfully recover both the hydraulic head and the aquifer conductivity, further providing reliable estimates of their uncertainties. Compared with the standard Joint- and Dual-EnKFs, the proposed scheme is found more robust to different assimilation settings, such as the spatial and temporal distribution of the observations, and the level of noise in the data. Based on our experimental setups, it yields up to 25% more accurate state and parameters estimates