A research paper proposes a smart waste management system called TUHR for Makkah, Saudi Arabia, leveraging IoT and AI to handle waste accumulation during the annual pilgrimage. The system uses ultrasonic sensors to monitor waste levels and gas detectors to identify harmful substances, alerting authorities when containers are full or hazards are detected. The proposed system aligns with Saudi Vision 2030 by promoting sustainability and improving public health through optimized waste management.
KAUST Professor Muhammad Mustafa Hussain is working to democratize electronics and make advanced technology accessible. His research focuses on creating flexible, stretchable, and reconfigurable electronics that are cost-effective and easy to use. Hussain also teaches a course at KAUST where students develop electronics solutions to everyday problems. Why it matters: This initiative could empower individuals globally by providing access to affordable and user-friendly electronic devices for various applications.
This paper introduces a hybrid deep learning and machine learning pipeline for classifying construction and demolition waste. A dataset of 1,800 images from UAE construction sites was created, and deep features were extracted using a pre-trained Xception network. The combination of Xception features with machine learning classifiers achieved up to 99.5% accuracy, demonstrating state-of-the-art performance for debris identification.
Averda and KAUST have signed a 10-year contract to provide sustainable waste management solutions, targeting zero-waste goals by 2025. Averda will manage solid and hazardous waste, recycling, sorting, and disposal, building on a partnership since 2015 that has already recycled over 3,500 tonnes of waste. The new facility will include an all-electric fleet of waste collection vehicles and advanced sorting technology to divert waste from landfills. Why it matters: This partnership showcases a commitment to environmental sustainability and sets a precedent for technology-driven waste management solutions in Saudi Arabia.
KAUST researchers are exploring thin-film device technologies using materials like printable organics and metal oxides for a greener Internet of Things (IoT). They propose wirelessly powered sensor nodes using energy harvesters to reduce reliance on batteries, which are costly and environmentally harmful. Large-area electronics, printed on flexible substrates, offer a more eco-friendly alternative to silicon-based technologies due to solution-based processing and lower production temperatures. Why it matters: This research contributes to a more sustainable and environmentally friendly IoT ecosystem, aligning with global efforts to reduce electronic waste and energy consumption.
MBZUAI's Qirong Ho and colleagues are developing an Artificial Intelligence Operating System (AIOS) for decarbonization, aiming to reduce energy waste in AI development. The AIOS focuses on improving communication efficiency between machines during AI model training, as inefficient communication leads to prolonged tasks and increased energy consumption. This system addresses the high computing power demands of large language models like ChatGPT and LLaMA-2. Why it matters: By optimizing energy usage in AI development, the AIOS could significantly reduce the carbon footprint of AI technologies in the region and globally.
KAUST researchers have developed a technology to convert spoiled dairy and fruit beverages into valuable short-chain and medium-chain carboxylic acids (SCCAs and MCCAs). These acids can be used for animal feed, aviation fuel, and pharmaceuticals, with SCCAs valued at $300 per ton and MCCAs having 10x higher value. A pilot study is underway at KAUST, utilizing over 500 liters of waste per week from regional companies. Why it matters: This innovation supports Saudi Arabia's goal to eliminate 90% of landfill waste by 2040 and promotes a circular economy by transforming food waste into high-value products.