Tyler O'Neal, Staff Editor HEALTH

University of Copenhagen's new supercomputer model sows doubt about the composition of 70 percent of our universe

Researchers the world over have long believed that 70 percent of the universe is composed of dark energy, a substance that makes it possible for the universe to expand at an ever-increasing rate. But in a new study, University of Copenhagen researchers 

Until now, researchers have believed that dark energy accounted for nearly 70 percent of the ever-accelerating, expanding universe.

For many years, this mechanism has been associated with the so-called cosmological constant, developed by Einstein in 1917, that refers to an unknown repellant cosmic power.

But because the cosmological constant--known as dark energy--cannot be measured directly, numerous researchers, including Einstein, have doubted its existence--without being able to suggest a viable alternative. 

Until now. In a new study by researchers at the University of Copenhagen, a model was tested that replaces dark energy with dark matter in the form of magnetic forces.

"If what we discovered is accurate, it would upend our belief that what we thought made up 70 percent of the universe does not actually exist. We have removed dark energy from the equation and added in a few more properties for dark matter. This appears to have the same effect upon the universe's expansion as dark energy," explains Steen Harle Hansen, an associate professor at the Niels Bohr Institute's DARK Cosmology Centre.

Photo: Getty Images

The universe expands no differently without dark energy

The usual understanding of how the universe's energy is distributed is that it consists of five percent normal matter, 25 percent dark matter, and 70 percent dark energy.

In the UCPH researchers' new model, the 25 percent share of dark matter is accorded special qualities that make the 70 percent of dark energy redundant.

"We don't know much about the dark matter other than that it is a heavy and slow particle. But then we wondered--what if the dark matter had some quality that was analogous to magnetism in it? We know that as normal particles move around, they create magnetism. And, magnets attract or repel other magnets--so what if that's what's going on in the universe? That this constant expansion of dark matter is occurring thanks to some sort of magnetic force?" asks Steen Hansen.

Supercomputer model tests dark matter with a type of magnetic energy

Hansen's question served as the foundation for the new supercomputer model, where researchers included everything that they know about the universe--including gravity, the speed of the universe's expansion, and X, the unknown force that expands the universe.

"We developed a model that worked from the assumption that dark matter particles have a type of magnetic force and investigated what effect this force would have on the universe. It turns out that it would have exactly the same effect on the speed of the university's expansion as we know from dark energy," explains Steen Hansen. In 1572, the Danish physicist Tycho Brahe discovered this supernova called Stella Nova. By measuring the distance from this supernova and other novas, researchers later on concluded, that the universe is expanding constantly and with accellerating speed. Photo: NASA/CXC/SAO

However, there remains much about this mechanism that has yet to be understood by the researchers.

And it all needs to be checked in better models that take more factors into consideration. As Hansen puts it: "Honestly, our discovery may just be a coincidence. But if it isn't, it is truly incredible. It would change our understanding of the universe's composition and why it is expanding. As far as our current knowledge, our ideas about the dark matter with a type of magnetic force and the idea about dark energy are equally wild. Only more detailed observations will determine which of these models is the more realistic. So, it will be incredibly exciting to retest our result."

Surrey scientists show how the early death of nerve cells is crucial to form healthy brains

Tyler O'Neal, Staff Editor HEALTH

Computer scientists at the University of Surrey have created a ground-breaking model that could improve our understanding of developmental disorders such as autism.

Scientists have long tried to better understand how the cerebral cortex and its layers develop, with pathologies such as autism, schizophrenia, and epilepsy linked to this process.

In a paper published by the journal Cerebral Cortex, scientists from Surrey, Newcastle University, and Nottingham University detail how they developed and used a computational model to simulate cell division, cell migration, and apoptosis (cell death) in the hope of understanding how these processes affect the development of the brain.

With their computer model's help, the researchers reproduced a wide number of cerebral structures to study - from rats to macaques, to humans.

The team also observed that slight changes in how cells perform division and apoptosis lead to the development of cortical structures found in neurodevelopmental disorders such as autism, polymicrogyria, and subcortical band heterotopia.

Dr. Roman Bauer, Engineering and Physical Sciences Research Council Research Fellow and lead author of the study from the University of Surrey, said: "We are working towards a comprehensive computational model of the cerebral cortex and how it develops - taking into account how neurons behave and organize themselves in our brains. It is clear to us that computational models have a crucial role to play in helping us to comprehensively understand the complex biological processes that lead to developmental disorders."

Marcus Kaiser, Professor of Neuroinformatics at the University of Nottingham and senior author of the study, said: "A large proportion of nerve cells dies before birth, but it was unclear why these cells are just born to die at such an early stage. The team's results showed that cell death plays an essential role in developing the brain, as it influences the thickness of the cortex's layers, variety and layer cell density."

SwRI to develop AI for integrated corridor management traffic solutions for Tennessee DOT

Tyler O'Neal, Staff Editor HEALTH

Software will use machine learning to optimize performance of regional transportation corridors

Southwest Research Institute, in collaboration with Vanderbilt University, is developing machine learning algorithms to help the Tennessee Department of Transportation (TDOT) coordinate traffic management and incident response along portions of Interstate 24 in the rapidly growing Nashville region.

The project will use artificial intelligence to enhance an integrated corridor management (ICM) system, using software and systems to promote smart mobility and improve collaboration among various transportation agencies.

"SwRI's ICM solutions fuse data across freeways, surface streets and transit systems to help balance traffic flow and improve performance of the entire corridor," said Samantha Blaisdell, a program manager at SwRI.

SwRI's Intelligent Systems Division and Vanderbilt University will develop an Artificial Intelligence-based ICM Decision Support System (DSS) through a TDOT grant funded by the U.S. Department of Transportation.

Integrated corridor management is making its way out of the laboratory and hitting the road following two decades of research led by the Federal Highway Administration (FHWA). ICM systems manage freeways and arterial roadways with dynamic lane control, speed harmonization, traffic signal control, ramp metering, demand management and other strategies. Deployment, however, has been limited by reliance on conventional traffic simulation modeling, which can be cost prohibitive due to the time and resources required to develop and maintain traffic models.

The project will use artificial intelligence in the place of simulation models to learn from and mimic operator behavior and decision making. This will enable quicker accident response and mitigation, rerouting traffic around problem areas quickly and efficiently, and ensuring state and local agency collaboration.

"SwRI's TDOT research aims to overcome the roadblocks of ICM traffic modeling by using artificial intelligence algorithms to speed up the analysis of traffic," said Clay Weston, an SwRI project manager leading the project. "After training the system using traffic patterns, the algorithms will be able to recommend alternative routes in real-time, taking advantage of high-capacity urban roads and surface streets."

The SwRI-led decision tool will have several applications, such as traffic signal coordination on underutilized roads to ease congestion on highways. State transportation operations staff will use the decision tool to evaluate and recommend traffic management strategies for real-time diversion routing. Using a web interface, the DSS will integrate into the state's management center, the public agency owned ActiveITS™ and other regional intelligent transportation systems (ITS).

The project is part of a bigger TDOT initiative known as the I-24 Smart Corridor, a 28-mile stretch of Interstate 24 with corresponding arterial roadways in the municipalities of Nashville, La Verne, Smyrna, and Murfreesboro. In addition to improving coordination, the ICM DSS tool will help meet I-24 Smart Corridor project goals to increase travel time reliability and multimodal mobility while reducing congestion associated with incidents such as collisions.

"Integrated corridor management is gaining interest as the ITS community deploys smart mobility solutions to solve old congestion problems using new technology, especially when investments in physical infrastructure may not be feasible," said Blaisdell. "We are excited to be part of this evolution with the forward-looking ITS professionals at TDOT."