Multifunctional iron nanowires selectively obliterate cancer cells with a triple-punch combination attack.
Prediction method could help personalize cancer treatments and reveal new drug targets.
A better mathematical understanding of how big waves form could lead to better prediction of tsunami impacts.
The psychology of human creativity helps artificial intelligence imagine the unseen.
A machine learning method has identified highly elusive amino acid sequences involved in cell morphogenesis and adhesion and in diseases like cancer.
Combining silicon with a highly elastic polymer backing produces solar cells that have record-breaking stretchability and high efficiency.
Chaos could help put cyberhackers out of business with a patterned silicon chip that will be uncrackable even in the future.
Better prediction of extreme winds at sparsely observed locations could help optimize the design of wind farms.
Factors influencing the tolerance of barley to saline soils have been uncovered using an advanced robust statistical technique.
What do an electrical engineer, an organic chemist, a materials scientist and a cell biologist all have in common? They invent and improve applications at the interface of biology and electronics.
New methods for training machine learning models are quicker and more accurate than current approaches, previously considered state-of-the-art.
A magnetic skin that is safe and comfortable to wear could open the door to a wide range of wireless, remotely controlled applications.
A deep learning tool could help in structure-based drug discovery.
Asrar Damdam is setting up her own biotech company in Silicon Valley while pursuing a Ph.D. at KAUST
Stretchy, see-through, silver nanowire sheet combines optical transparency with excellent electrical conductivity.
Areas without earthquake monitoring systems could model the tremors by using data from local landslides.
Bridging the knowledge gap in artificial intelligence requires an embedding function that helps step between different types of "thinking."
Deep analysis of the way information is shared among parallel computations increases efficiency to accelerate machine learning at scale.
An inexpensive passive cooling technology could be used to cool buildings in cities, reducing energy consumption.
A more accurate and efficient method of capturing the local factors that lead to extreme rainfall enables better flood prediction across larger regions.
More accurate detection of hotspot clusters provides new insights into the behavior of air pollution.
Linking disease pathogens to clinical signs and symptoms through a database could support research into the molecular mechanisms of infectious diseases.
A deep learning model improves the ability to identify genes potentially involved in disease.
Understanding the situations when artificial intelligence can fail is critical for application of future autonomous vehicles and medical diagnostics.
Professors Maria Antónia da Conceição Abrantes Amaral Turkman and
Kamil Feridum Turkman, from the University of Lisbon used the
INLA methodology and specifically SPDE's (using R-INLA), to do the official
risk assessment for forest fire prediction in Portugal for 2018.
The spatial variation in different air pollution components helps identify possible targets for pollution control.
Macroalgae is shown to be a major global contributor to carbon sequestration.
Overlaying two film layers patterned with a nanoscale array can manipulate the propagation of light to create a powerful ultrathin lens.
Ruthenium oxide is used to integrate energy-storing microsupercapacitors and thin-film electronics at the transistor level.
Pyramidal graphs resulting from statistical analyses of EEG recordings can improve our understanding of epileptic seizures.
Computer simulation accurately captures the beguiling motion of a liquid magnetic material.
Exploring the links between natural climate cycles and the sea-surface temperature of the Red Sea reveals a cooling trend during the next few decades.
KAUST researchers Anna Fruehstueck, Dr. Ibraheem Alhashim, and Prof. Peter Wonka have developed a novel technique to generate images of realistic and highly detailed texture maps using deep neural networks. The texture images synthesized by their system TileGAN can be of gigapixel size and are created by seamlessly merging smaller texture blocks into a single large image. The underlying neural networks are trained using high-resolution images such as detailed satellite imagery, maps and famous paintings.
A universal query engine for big data that works across computing platforms could accelerate analytics research.
Powerful computer simulations are revealing new insights into water exchanges between the Red Sea and the Gulf of Aden.
A remote sensing algorithm offers better predictions of Red Sea coral bleaching and can be fine tuned for use in other tropical marine ecosystems.
Protein found in gut-dwelling pathogens changes shape under human body temperatures, leading to toxins responsible for diarrheal disease.
The Formula 1 race track is the ultimate testbed for a KAUST researcher's latest work.
A new kind of congenital disorder caused by a group of mutations in a gene previously linked only to a rare progressive brain disease has been discovered by an international team co-led by Saudi investigators.
A powerful statistical tool could significantly reduce the burden of analyzing very large datasets.
World-class computing facilities at KAUST enable researchers to tackle complex questions in climate science and oceanography.
Mangroves, seagrass meadows, seaweeds and saltmarshes, the forests of the sea, perform a vital and often overlooked role in storing carbon and mitigating the effects of climate change.
Marine plastic pollution accumulates in mangrove forests and is a danger to sea life.
Automatic detection of uncharacteristic data sequences could change the way data is processed and analyzed.
Low-cost, stretchy sensors can be assembled inside the lid of a drug container to help monitor patient safety.
Modeling the 3D structure of Red Sea eddies shows how transport of energy and biochemical materials influences circulation patterns in the Red Sea.
Highlighting sustainability and the work of female researchers was a strong focus of the international KAUST-US NSF electronics conference.
Floating paper cubes containing low-cost, inkjet printed electronic sensors can wirelessly monitor floods in real time.
Metallic nanostructures absorb light better than any other known structures.
Study of the mismatch between spatial environmental data and a commonly used statistical analysis suggests simpler statistics are sufficient in many cases.