Membership in ACM includes a subscription to Communications of the ACM (CACM), the computing industry's most trusted source for staying connected to the world of advanced computing. Digital twins (DTs) are virtual replicas of components, assets, systems, or processes, linked to their real-world counterparts, continuously updating their states and simulating their behavior in real-time, as illustrated in Figure 1 . They are adopted for monitoring, predicting, and optimizing the performance of diverse systems, bridging the gap between design, testing and deployment. Significant efforts are being devoted across Arab R&D institutions to export technology tackling challenges that are not only pertinent to the region, but also of global importance, e.g., energy, sustainability, disaster management, healthcare, and urbanization, among many others. For instance, Khalifa University, UAE, is pioneering research into optical wireless communication using DTs.

Hewlett Packard Enterprise announced that King Abdullah University of Science and Technology (KAUST) selected HPE to build its next-generation supercomputer, Shaheen III, to deliver state-of-the-art supercomputing and artificial intelligence (AI) capabilities for advancing research in fields such as food, water, energy and the environment. "Powered by AMD EPYC processors, Shaheen III will enable new discoveries that will have regional and global impacts across climate, clean energy and tectonic plate modeling, all made possible by the collaboration between KAUST scientists and HPE." Supercomputing capacity has become increasingly vital to global innovation, industry competitiveness and economic growth. From accelerating vaccine discovery to fight a pandemic, advancing clean energy systems to increase sustainability, to enabling new possibilities in AI, supercomputing is a core technology to solving the world's most challenging scientific and engineering problems. Shaheen III, set to be 20 times faster than KAUST's existing system, will be the most powerful supercomputer in the Middle East to address critical areas that have a societal and environmental impact. Built by HPE, the world's leading supercomputer provider, the new Shaheen III system will revolutionize KAUST's ability to process vast amounts of data at immense speed and scale, enabling its users to unlock discoveries that it could not have before, and realize new potentials for AI.

KAUST scientists have used deep learning algorithms to accelerate the examination of thousands of years old, giant, stone rectangles in the Saudi desert. "An international study showed that the huge, mysterious stone structures known as'Mustatil' (Arab word for'Rectangle') in northwestern Saudi Arabia, are among the oldest archeological ruins in the world," Saudi Minister of Culture, Prince Badr bin Abdullah bin Farhan, said in a tweet in 2021. These historic sites, which are around 7,000 years old, bewildered researchers and scientists who have long sought to determine their nature and the reasons behind their construction. A recent study by the University of Cambridge suggested that these huge structures, comprising chambers, entrances, and seats, are more complicated than expected. For quicker results, researchers at the King Abdullah University of Science and Technology (KAUST) have used an artificial intelligence network to carry out a detailed geological survey in the region, which hasn't been sufficiently studied so far.

According to a new KAUST study, machine learning approaches can achieve an assumption-free analysis of epidemic case data with amazingly good prediction accuracy and the flexibility to incorporate new data dynamically. Yasminah Alali, an intern in KAUST's 2021 Saudi Summer Internship (SSI) program, developed a proof of concept that reveals a possible alternative to traditional parameter-driven mechanistic models by removing human bias and assumptions from analysis, revealing the underlying story of the data. Using publicly released COVID-19 incidence and recovery data from India and Brazil, Alali leveraged her experience working with artificial intelligence models to design a framework to fit the characteristics and time-evolving nature of epidemic data in collaboration with KAUST's Ying Sun and Fouzi Harrou. To create an effective Gaussian process regression (GPR) based model for forecasting recovered and confirmed COVID-19 cases in two significantly impacted countries, India and Brazil, the researchers first used Bayesian optimization to modify the Gaussian process regression (GPR) hyperparameters. However, the time dependency in the COVID-19 data series is ignored by machine learning models.

Machine learning techniques can provide an assumption-free analysis of epidemic case data with surprisingly good prediction accuracy and the ability to dynamically incorporate the latest data, a new KAUST study has shown. The proof of concept developed by Yasminah Alali, a student in KAUST's 2021 Saudi Summer Internship (SSI) program, demonstrates a promising alternative approach to conventional parameter-driven mechanistic models that removes human bias and assumptions from analysis and shows the underlying story of the data. Working with KAUST's Ying Sun and Fouzi Harrou, Alali leveraged her experience working with artificial intelligence models to develop a framework to fit the characteristics and time-evolving nature of epidemic data using publicly reported COVID-19 incidence and recovery data from India and Brazil. "My major at college was artificial intelligence, and I previously worked on a medical project using various ML algorithms," says Alali. "Working with Professor Sun and Dr Harrou during my internship, we considered whether the Gaussian Process Regression method would be useful for predicting pandemic spread because it gives confidence intervals for the predictions, which can greatly assist decision-makers." Accurate forecasting of cases during a pandemic is essential to help mitigate and slow transmission.

David Keyes is a professor of applied mathematics and computational science and director of the Extreme Computing Research Center at the King Abdullah University of Science and Technology, Saudi Arabia.

The establishment of King Abdullah University of Science and Technology (KAUST) in 2009 was the fulfillment of a lifelong dream of its founder, the late King Abdullah of Saudi Arabia. His vision for the university was deeply rooted in the historical and cultural contexts of the Middle East. He intended the university to be seen as a revival of the old "house of wisdom," which was a premier institution of learning in Baghdad from the 9th century until the 13th century. Starting as a private library of the fabled Caliph Harun Al-Rasheed, it developed quickly into the 9th century equivalent of a research laboratory and a university. The house of wisdom was the birthplace of algebra and was a milieu where many developments took place in various fields of science and humanities.

Membrane separations have long been recognized as energy-efficient processes with a rapidly growing market. In particular, organic solvent nanofiltration (OSN) technology has shown considerable potential when applied to various industries, such as petrochemicals, pharmaceuticals and natural products. The energy consumed by these industries accounts for 10 to 15 percent of the world's entire energy consumption. Nevertheless, difficulties in predicting the separation performance of OSN membranes have hindered smooth transition from lab discovery to industry implementation. Predicting the performance of membranes is a challenging task because of the complex nature of solvent, solute and membrane interactions.

At the 2019 Supercomputing Conference in Denver, Colorado, global supercomputer leader Cray, a Hewlett Packard Enterprise company, announced that the King Abdullah University of Science and Technology (KAUST) in Saudi Arabia has selected a Cray CS-Storm 500NX supercomputer to support innovation in the university and the nation through a strategic artificial intelligence (AI) initiative. The added power of the GPU-accelerated CS-Storm system provides KAUST researchers greater computational capabilities to drive positive and significant outcomes in the university's core research areas of global significance: food, water, energy, the environment, and digitalization. From helping to build smart cities to developing AI algorithms that think like scientists, KAUST is the largest research center in the Middle East that brings together faculty, researchers and graduate students to leverage the interconnectedness of science and engineering. With an 8:2 ratio of GPUs to CPUs, the new Cray CS-Storm 500NX supercomputer will meet KAUST's most demanding computing requirements for production scalability, while also delivering a low total cost of ownership. Engineered for the convergence of modeling, simulation and analytics, the CS-Storm fast-started KAUST's AI journey and provides researchers and scientists the highly-advanced supercomputing and software capabilities required to analyze large volumes of data for rapid insight, where simulation alone is unsatisfactory for predicting real-world outcomes.

I used to see the young people in the city mostly spending their time at the eateries, playing games online, driving out to the desert. Coffee shops were just a place to drink coffee or spend some time chilling out. That scenario is slowly changing. A new group of youngsters are emerging in Jeddah who love music, art, fashion as well as creating their own startups. Many of these youngsters are women.