Real-world data streams pose a unique challenge to the implementation of machine learning (ML) models and data analysis. A notable problem that has been introduced by the growth of Internet of Things (IoT) deployments across the smart city ecosystem is that the statistical properties of data streams can change over time, resulting in poor prediction performance and ineffective decisions. While concept drift detection methods aim to patch this problem, emerging communication and sensing technologies are generating a massive amount of data, requiring distributed environments to perform computation tasks across smart city administrative domains. In this article, we implement and test a number of state-of-the-art active concept drift detection algorithms for time series analysis within a distributed environment. We use real-world data streams and provide critical analysis of results retrieved. The challenges of implementing concept drift adaptation algorithms, along with their applications in smart cities, are also discussed.

Cities serve as vital hubs of economic activity and knowledge generation and dissemination. As such, cities bear a significant responsibility to uphold environmental protection measures while promoting the welfare and living comfort of their residents. There are diverse views on the development of smart cities, from integrating Information and Communication Technologies into urban environments for better operational decisions to supporting sustainability, wealth, and comfort of people. However, for all these cases, data are the key ingredient and enabler for the vision and realization of smart cities. This article explores the challenges associated with smart city data. We start with gaining an understanding of the concept of a smart city, how to measure that the city is a smart one, and what architectures and platforms exist to develop one. Afterwards, we research the challenges associated with the data of the cities, including availability, heterogeneity, management, analysis, privacy, and security. Finally, we discuss ethical issues. This article aims to serve as a “one-stop shop” covering data-related issues of smart cities with references for diving deeper into particular topics of interest.

Low grade sulfide ores are difficult to process due to their composite mineralogy and their fine grained dissemination with gangue minerals. Therefore, fine grinding of such ores becomes essential to liberate valuable minerals. In this research, selective flotation was carried out using two pitched blade turbine impellers with diameters of 6 cm and 7 cm to float copper and nickel. The main focus of this research was to generate optimum hydrodynamic conditions that can effectively separate nickel and copper from gangue minerals. In addition, we investigated the effects of superficial gas velocity, impeller speed, bubble size distribution, and bubble surface area flux on the flotation recovery and rate constant. The results demonstrated that a 7 cm impeller comparatively produced optimum hydrodynamic conditions that improved Cu-Ni recovery and the rate constant. The maximum copper and nickel recoveries in the 7 cm impeller tests were observed at 93.1% and 72.5%, respectively. However, a significant decrease in the flotation rate of nickel was observed, due to entrainment of nickel in copper concentrate and the slime coating of gangue minerals on the nickel particle surfaces.

In a post-pandemic world, remaining vigilant and maintaining social distancing are still crucial so societies can contain the virus and the public can avoid disproportionate health impacts. Augmented Reality (AR) can visually assist users in understanding the distances in social distancing. However, integrating external sensing and analysis is required for social distancing beyond the users' local environment. We present DistAR , an Android-based application for social distancing leveraging AR and smart sensing using on-device analysis of optical images and environment crowdedness from smart campus data. Our prototype is one of the first efforts to combine AR and smart sensing technologies to create a real-time social distancing application.