Projecting future populations of urban agglomerations around the world and through the 21st century

Future population projections of urban agglomerations furnish essential input for development policies and sustainability strategies. Here, working within the Shared Socioeconomic Pathways (SSPs) and using a simple urban-growth model, we estimate population trends throughout the 21st century for ~20,000 urban agglomerations in 151 countries. Our results suggest that urban growth in this century will produce increasingly concentrated cities, some growing to enormous sizes. We also demonstrate that, although detailed urbanization trajectories differ for different SSP scenarios, in all cases, the largest projected agglomerations of the future are more populous than the largest agglomerations today. Our projection strategy advances urban-population research by producing urban-size projections—for agglomerations around the world—that correctly obey empirically observed distribution laws. Although our method is very simple and omits various aspects of urbanization, it nonetheless yields valuable insight into long-term SSP-specific urbanization trends to inform discussion of sustainable urban policies.

In Pursuit of Security and Influence

Chapter 8 focuses on the United Arab Emirates (UAE), led by Mohamed bin Zayed, and its newly assertive power projection strategy that includes establishment naval and military bases on the Red Sea, in the chokepoints of the Bab al-Mandab one of the most important global shipping lanes and the Gulf of Aden and its littoral territory—the arid nations of Djibouti, Eritrea, and Somalia to achieve dual geostrategic and economic goals including the acquisition of operational and management rights over ports and economic zones. The theoretical framework used in this chapter to asses foreign policy change employs simultaneous levels of inquiry taking into account regional and systemic effects, domestic influences, and the role of leadership adding to the literature on international relations and the Middle East in two distinct ways (1) by addressing understudied theoretical questions concerning foreign policy change in small states and (2) exploring the nature and motivations of the emerging trend of securitizing waterspace and shipping lanes in the Middle East.

Forecasting stock price index movement using a constrained deep neural network training algorithm

The prediction of stock index movement is considered a rather significant objective in the financial world, since a reasonably accurate prediction has the possibility of gaining profit in stock exchange, yielding high financial benefits and hedging against market risks. Undoubtedly, the area of financial analysis has been dramatically changed from a rather qualitative science to a more quantitative science which is also based on knowledge extraction from databases. During the last years, deep learning constitutes a significant prediction tool in analyzing and exploiting the knowledge acquired from financial data. In this paper, we propose a new Deep Neural Network (DNN) prediction model for forecasting stock exchange index movement. The proposed DNN is characterized by the application of conditions on the weights in the form of box-constraints, during the training process. The motivation for placing these constraints is focused on defining the weights in the trained network in more uniform way, by restricting them from taking large values in order for all inputs and neurons of the DNN to be efficiently exploited and explored. The training of the new DNN model is performed by a Weight-Constrained Deep Neural Network (WCDNN) training algorithm which exploits the numerical efficiency and very low memory requirements of the L-BFGS (Limited-memory Broyden-Fletcher-Goldfarb-Shanno) matrices together with a gradient-projection strategy for handling the bounds on the weights of the network. The performance evaluation carried out on three popular stock exchange indices, demonstrates the classification efficiency of the proposed algorithm.

Forecasting Economy-Related Data Utilizing Weight-Constrained Recurrent Neural Networks

During the last few decades, machine learning has constituted a significant tool in extracting useful knowledge from economic data for assisting decision-making. In this work, we evaluate the performance of weight-constrained recurrent neural networks in forecasting economic classification problems. These networks are efficiently trained with a recently-proposed training algorithm, which has two major advantages. Firstly, it exploits the numerical efficiency and very low memory requirements of the limited memory BFGS matrices; secondly, it utilizes a gradient-projection strategy for handling the bounds on the weights. The reported numerical experiments present the classification accuracy of the proposed model, providing empirical evidence that the application of the bounds on the weights of the recurrent neural network provides more stable and reliable learning.

Sliding mode control for uncertain switched systems subject to state and input delays

This paper considers sliding mode control for a class of uncertain switched systems with state and input delays. In the control systems, each subsystem is not required to share the same input channel, which is usually assumed in previous works. By employing a weighted sum of the input matrices, a common sliding surface is designed. The sliding surface includes a predictor to compensate for the input delay of the control systems. To guarantee the asymptotic stability of the sliding motion, a switching signal based on the min-projection strategy is proposed. Moreover, it is shown that the state trajectories can be driven onto the specified sliding surface despite the presence of state and input delays and external disturbances. Finally, a numerical example is given to demonstrate the effectiveness of the proposed method.

Workspace analysis and verification of cable-driven parallel mechanism for wind tunnel test

Cable-driven parallel mechanism is a special kind of parallel robot in which traditional rigid links are replaced by actuated cables. This provides a new suspension method for wind tunnel test, in which an aircraft model is driven by a number of parallel cables to fulfil 6-DOF motion. The workspace of such a cable robot is limited due to the geometrical and unilateral force constraints, the investigation of which is important for applications requiring large flight space. This paper focuses on the workspace analysis and verification of a redundant constraint 6-DOF cable-driven parallel suspension system. Based on the system motion and dynamic equations, the geometrical interference (either intersection between two cables or between a cable and the aircraft) and cable tension restraint conditions are constructed and analyzed. The hyperplane vector projection strategy is used to solve the aircraft’s orientation and position workspace. Moreover, software ADAMS is used to check the workspace, and experiments are done on the prototype, which adopts a camera to monitor the actual motion space. In addition, the system construction is designed by using a built-in six-component balance to measure the aerodynamic force. The results of simulation and tests show a good consistency, which means that the restraint conditions and workspace solution strategy are valid and can be used to provide guidance for the cable-driven parallel suspension system’s application in wind tunnel tests.