MPE: a mobility pattern embedding model for predicting next locations

World Wide Web - The wide spread use of positioning and photographing devices gives rise to a deluge of traffic trajectory data (e.g., vehicle passage records and taxi trajectory data), with each...     

A mineralogical approach to evaluating laboratory scale acid rock drainage characterisation tests

Effective management of the risks associated with acid rock drainage (ARD) requires the ability to identify material with a potential to generate ARD reliably. With the increasing prevalence of quantitative mineralogy (Quantitative XRD, auto-SEM), opportunity exists to use mineralogy at all stages in ARD characterisation and prediction. This study uses a mineralogical approach across the head grade samples and the residues obtained under leach conditions of several common ARD characterisation tests (Acid Neutralising Capacity, Net Acid Generation), as well as the University of Cape Town (UCT) biokinetic test to evaluate the extent to which acid-neutralising minerals react. The results show the contribution of the carbonates to the acid neutralising capacity, as well as the partial dissolution of intermediate weathering silicate minerals such as chlorite and mica.     

Lagrangian relaxation hybrid with evolutionary algorithm for short-term generation scheduling

Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.