A KAUST research team is using cellphone mobility data, Google searches, and social media to model and predict COVID-19 spread. The models aim to forecast cases in the coming weeks and inform resource allocation, including hospital beds and medical staff. The team is using aggregated and anonymized data from cellphone companies to respect people's privacy. Why it matters: Integrating real-time digital data with epidemiological modeling can improve the speed and effectiveness of public health responses in the region and globally.
Christopher Fabian, co-founder of UNICEF’s Innovation Unit, spoke at KAUST about using data and technology to improve lives. He highlighted how IoT and wearables can connect remote populations in developing countries with their governments. The talk emphasized using data to include unaccounted populations. Why it matters: The discussion reinforces KAUST's commitment to leveraging technology for global development and aligns with Saudi Arabia's broader goals for digital transformation.
KAUST researchers propose using tethered unmanned aerial vehicles (TUAVs) with cellphone antennas to address public concerns about EMF exposure from mobile networks. The TUAVs would receive signals, reducing users' uplink exposure and employing low power 'green antennas' that do not radiate EMF. A network of ground stations would provide power and broadband data links to the TUAVs. Why it matters: The system could allow the development of 6G mobile systems to continue while decreasing EMF exposure, and the team has already applied for a U.S. patent for their proposal, indicating significant commercial potential.
Prof. Chun Jason Xue from the City University of Hong Kong presented research on optimizing mobile memory and storage by analyzing mobile application characteristics, noting their differences from server applications. The research explores system software designs inherited from the Linux kernel and identifies optimization opportunities in mobile memory and storage management. Xue's work aims to enhance user experience on mobile devices through mobile application characterization, focusing on non-volatile and flash memories. Why it matters: Optimizing mobile systems based on the unique characteristics of mobile applications can significantly improve device performance and user experience in the region.
KAUST, in collaboration with KSU and KFUPM, is working on a project initiated by the Saudi Communications, Space & Technology Commission (CST) to expand mobile communication coverage in remote areas of the Kingdom. The study explores utilizing the sub-700 MHz ultrahigh frequency (UHF) band, potentially reassigning it from television broadcast to mobile telecommunication networks. This band's long wavelength radio waves can travel further and penetrate obstacles more easily, reducing network infrastructure costs. Why it matters: This initiative could bridge the digital divide in Saudi Arabia by providing affordable mobile connectivity to underserved communities.
This article discusses the evolution of mobile extended reality (MEX) and its potential to revolutionize urban interaction. It highlights the convergence of augmented and virtual reality technologies for mobile usage. A novel approach to 3D models, characterized as urban situated models or “3D-plus-time” (4D.City), is introduced. Why it matters: The development of MEX and 4D.City could significantly enhance user experience and analog-digital convergence in urban environments, offering new possibilities for human-computer interaction.
KAUST and EPFL Blue Brain Project researchers propose a new theory about a 'secret language' used by cells for internal communication regarding the external world. Using a computational model, they suggest that metabolic pathways can code details about neuromodulators that stimulate energy consumption. The model focuses on astrocytes and their cooperation with neurons in fueling the brain. Why it matters: This suggests a new avenue for understanding information processing in the brain and how cells contribute to the energy efficiency of brains compared to computers.
This article discusses the application of uncertain time series (UTS) approach to manage and analyze big traffic data for high-resolution vehicular transportation services. The study addresses challenges such as data sparseness, decision-making among multiple UTSs, and future forecasting with spatio-temporal correlations. Jilin Hui, previously a Research Associate at the Inception Institute of Artificial Intelligence (UAE), is applying this approach to solve problems related to increased congestion, greenhouse gas emissions, and reduced air quality in urban environments. Why it matters: The application of AI techniques to traffic management could significantly improve urban mobility and environmental sustainability in the GCC region and beyond.