Researchers created a simulation of a deep-sea sponge and how it responds to and influences the flow of water. The work revealed a profound connection between the sponge's structure and function, shedding light on both the basket sponge's ability to withstand the dynamic forces of the surrounding ocean and its ability to create a vortex within the body cavity "basket." These properties may help for the design of ships, planes and skyscrapers of the future.
Researchers are the first to model COVID-19 completion versus cessation in clinical trials using machine learning algorithms and ensemble learning. They collected 4,441 COVID-19 trials from ClinicalTrials.gov to build a testbed with 693 dimensional features created to represent each clinical trial. These computational methods can predict whether a COVID-19 clinical trial will be completed or terminated, withdrawn or suspended. Stakeholders can leverage the predictions to plan resources, reduce costs, and minimize the time of the clinical study.
Alexandria, Va., USA - Muthuthanthrige Cooray, Tohoku University, Sendai, Japan, presented the oral session "Oral and General Health Associations Using Machine Learning Prediction Algorithms" at the virtual 99th General Session & Exhibition of the International Association for Dental Research (IADR), held in conjunction with the 50th Annual Meeting of the American Association for Dental Research (AADR) and the 45th Annual Meeting of the Canadian Association for Dental Research (CADR), on July 21-24, 2021.
For the first time an autonomously flying quadrotor has outperformed two human pilots in a drone race. The success is based on a novel algorithm that was developed by researchers of the University of Zurich. It calculates time-optimal trajectories that fully consider the drones' limitations.
A content recommendation system based on the user's brain model would be ideal for targeted advertising. Creating such a brain model, however, is computationally expensive. In a new study, researchers from Japan propose and validate a machine learning scheme to infer a user's brain model from their profile with high accuracy while optimizing the information collection cost using a feature selection technique, providing hope for its real-world application following further optimizations.
Although photovoltaic systems constitute a promising way of harnessing solar energy, power grid managers need to accurately predict their power output to schedule generation and maintenance operations efficiently. Scientists from Incheon National University, Korea, have developed a machine learning-based approach that can more accurately estimate the output of photovoltaic systems than similar algorithms, paving the way to a more sustainable society.
Uber and Lyft are popular on-demand ways to travel, but does that mean trains and buses are a thing of the past? Travelers prefer different modes of transportation at different times. So how can all these modes co-exist and do so successfully? New research in the INFORMS Journal Transportation Science has created a model and an algorithm to redistribute transit resources based on commuter preferences resulting in millions in savings.
Hydropower has massive potential as a source of clean electricity, and the Indus basin can be a key player in fulfilling long-term energy storage demands across Africa, Asia, Europe, and the Middle East. IIASA researchers explored the role the Indus basin could play to support global sustainable development.
New machine learning technology, developed by a multi-disciplinary team based at University of California, Berkeley, has devised a machine learning system to tap the problem-solving potential of satellite imaging. The low-cost, easy-to-use technology could bring satellite image access and analytical power to researchers and governments worldwide.
New research from the Annenberg School for Communication at the University of Pennsylvania found that social influencers are unlikely to change a person's behavior by example. To stimulate a shift in people's thinking, target small groups of people in the outer edge or fringe of a network.