Detecting the interaction between urban elements evolution with population dynamics model.

Exploring the evolution of urban elements can improve understanding of the developmental process of city and drive such development into a better direction. However, the non-linearity and complexity of changes in urban elements have brought great challenges to understanding this process. In this paper, we propose a cross-diffusion partial differential equation based on ecological dynamics to simulate the evolutionary process of urban elements from the microscopic viewpoint.

Observer-Based Boundary Stabilization of Coupled Semilinear Reaction-Diffusion Neural Networks With Spatially Varying Coefficients via Event-Triggered Controller.

The aim of this article is to propose an observer-based event-triggered Robin boundary control strategy for the exponential stabilization of the coupled semilinear reaction-diffusion neural networks with spatially varying coefficients. Toward this aim, we design an observer to estimate the value of system states by using some of these system values as the available measurement. An observer-based event-triggered boundary stabilizer is then presented to exponentially stabilize the considered systems with the Zeno behavior being excluded.

Predicting aviation non-volatile particulate matter emissions at cruise via convolutional neural network.

Aviation emissions are the only direct source of anthropogenic particulate pollution at high altitudes, which can form contrails and contrail-induced clouds, with consequent effects upon global radiative forcing. In this study, we develop a predictive model, called APMEP-CNN, for aviation non-volatile particulate matter (nvPM) emissions using a convolutional neural network (CNN) technique. The model is established with data sets from the newly published aviation emission databank and measurement results from several field studies on the ground and during cruise operation.

Integrated Time-Fractional Diffusion Processes for Fractional-Order Chaos-Based Image Encryption.

The purpose of this paper is to explore a novel image encryption algorithm that is developed by combining the fractional-order Chua's system and the 1D time-fractional diffusion system of order α∈(0,1]. To this end, we first discuss basic properties of the fractional-order Chua's system and the 1D time-fractional diffusion system. After these, a new spatiotemporal chaos-based cryptosystem is proposed by designing the chaotic sequence of the fractional-order Chua's system as the initial condition and the boundary conditions of the studied time-fractional diffusion system.

Optimal vaccination and treatment policies for regional approximate controllability of the time-fractional reaction-diffusion SIR epidemic systems.

This paper addresses the optimal vaccination and treatment control problems for regional approximate controllability of a new spatiotemporal epidemic model that is developed by afterwards adding at the basic susceptible-infected-recovered (SIR) epidemic system with the Caputo time-fractional derivative of order α∈(0,1] and the diffusion term in each compartment. The obtained results can be used by policy-makers in any nation to optimally plan the limited vaccination and treatment resources ahead of ongoing outbreaks. Toward this aim the Sakawa-type controller is introduced, which is a finite-dimensional controller that prevents and controls the spreading of infection.

Subdiffusive Source Sensing by a Regional Detection Method.

Motivated by the fact that the danger may increase if the source of pollution problem remains unknown, in this paper, we study the source sensing problem for subdiffusion processes governed by time fractional diffusion systems based on a limited number of sensor measurements. For this, we first give some preliminary notions such as source, detection and regional spy sensors, etc. Secondly, we investigate the characterizations of regional strategic sensors and regional spy sensors.