Findings of a study, published in PeerJ, provide new insights into how pterosaurs managed to take flight despite reaching sizes far larger than modern animals. The research sheds new light on the flight initiating jumping ability of these animals, some of which had wingspans of over ten meters.

The study, carried out by scientists at the University of Bristol, Liverpool John Moores University, Universidade Federal do ABC and the University of Keele, follows years of analysis and modeling of how muscles interact with bones to create movement in other animals and is now being used to start answering the question of how the largest flying animals known managed to get off the ground.

The team created the first computer model for this kind of analysis of a pterosaur to test three different ways pterosaurs may have taken off: a vertical burst jump using just the legs like those used by primarily ground-dwelling birds, a less vertical jump using just the legs more similar to the jump used by birds that fly frequently, and a four-limbed jump using its wings as well in a motion more like the take-off jump of a bat.

By mimicking these motions, the researchers aimed to understand the leverage available to push the animal into the air.

“Larger animals have greater challenges to overcome in order to fly making the ability of animals as large as pterosaurs to do so especially fascinating,” Dr. Ben Griffin, the lead author of the study, said.

“Unlike birds which mainly rely on their hindlimbs, our models indicate that pterosaurs were more likely to rely on all four of their limbs to propel themselves into the air.”

This study examines one of the long-standing questions about the underlying biomechanics of the pterosaur. This research not only enhances the understanding of pterosaur biology but also provides broader insights into the limits and dynamics of flight in large animals. By comparing pterosaurs with modern birds and bats, the study highlights the remarkable evolutionary solutions to the challenge of powered flight.

An Arkansas researcher has developed the first high-resolution forest canopy cover dataset for an entire state, providing valuable insights for forest management and conservation to a major economic sector in Arkansas.

“I had this vision of creating something that we can rely on,” said Hamdi Zurqani, assistant professor for the College of Forestry, Agriculture and Natural Resources at the University of Arkansas at Monticello and researcher with the Arkansas Agricultural Experiment Station. “No data of this kind existed before for an entire state. Usually, people only create similar data for site-specific projects.”

The 1-meter measurements are unique. Until now, the most common forest measurements and datasets have come from satellite imagery at 30-meter spatial resolution, said Zurqani, who conducts research as part of the Arkansas Forest Resources Center, a partnership between the University of Arkansas System Division of Agriculture and UAM. The experiment station is the research arm of the Division of Agriculture.

Forest canopy cover measures the coverage of tree crowns from an aerial view. It shows how much a forest’s uppermost layer of branches, leaves and vegetation forms a continuous cover over the ground. This detailed information is crucial for tracking forest health, as canopy cover is essential for carbon sequestration, wildlife habitat and water regulation.

Zurqani says accurate mapping of tree coverage helps scientists monitor and manage forest resources effectively, ensuring the sustainability of these ecosystems. This information can also assist with wildfire risk assessments, tracking forest health threats from pests and climate, and urban planning.

Zurqani’s research was published in the journal Remote Sensing Applications: Society and Environment. The article was titled “High-resolution forest canopy cover estimation in eco-diverse landscape using machine learning and Google Earth Engine: Validity and reliability assessment.“

According to the latest Arkansas Agricultural Profile, forests cover 57% of the state, and timber was one of the state’s top commodities in 2021 with about $409 million in cash farm receipts.

Machine learning

To create the Arkansas forest canopy cover dataset, Zurqani used machine learning techniques and the Google Earth Engine.

Machine learning is a branch of artificial intelligence that allows computers to “learn” from data and improve their performance over time without being programmed. Machine learning algorithms identify patterns in data, make predictions and adapt to new information.

The Google Earth Engine is a cloud-based platform designed for processing and analyzing large-scale geospatial data. It provides access to a vast repository of satellite imagery and geospatial datasets.

Zurqani’s research utilized high-resolution National Agriculture Imagery Program aerial imagery to apply and test his methods.

The National Agriculture Imagery Program, administered by the United States Department of Agriculture, captures high-resolution aerial imagery of agricultural areas during the growing season. The imagery is used for monitoring crop conditions, assessing land use changes, and supporting various agricultural and environmental applications.

Room for growth

A finer spatial resolution of Arkansas forests provides a more accurate assessment of canopy structure and composition. Zurqani says this precision is essential for monitoring changes in forest dynamics, identifying vulnerable areas and implementing targeted conservation strategies. Zurqani hopes his 1-meter dataset could become the new standard for measuring forest canopy cover.

“So, in the future, we can use this dataset to cover all forest areas and see which trees are healthy and which ones are diseased,” Zurqani said. “Because it’s high-resolution imagery, we can detect the location of the trees within urban areas.”

There are 502 cities and 75 counties in Arkansas, according to the U.S. Census Bureau, and Zurqani said he evaluated forests and tree-covered areas within those cities and counties. While initially focused on the state of Arkansas, Zurqani envisions expanding this innovative approach to cover all 50 states.

“The studies demonstrate that machine learning and cloud computing technologies can produce reliable, high-resolution forest cover datasets,” Zurqani said. “These methods can be applied to other regions globally, enhancing forest management and conservation efforts worldwide.”

The prevalence of ticks in Scotland will increase by a quarter under the most optimistic climate change scenario, according to new modeling by mathematicians at the University of Stirling.

The paper “GIS-ODE: linking dynamic population models with GIS to predict pathogen vector abundance across a country under climate change scenarios” was published in the Journal of The Royal Society Interface.

Ticks are tiny spider-like creatures usually found in grassy and wooded areas that can spread viral and bacterial infections, including Lyme disease.

If global temperatures are limited to 1°C by 2080, the prevalence of ticks will increase by 26%, but under the 4°C temperature rise scenario, the number of ticks will almost double—a 99% increase—by 2080.

Only the highest peaks in Scotland will remain too cold for maintaining tick populations if temperatures rise by 4°C, according to the research.

World leaders promised in 2015 to try to limit the long-term temperature rise to 1.5°C to help avoid the most damaging impacts.

Mathematicians in the University of Stirling’s Faculty of Natural Sciences developed a new model which predicts tick density under varying climate change scenarios and produced maps which show which areas of Scotland will be worst affected.

Professor Rachel Norman, who led the study, said, “The model predicted an increase in tick densities and a spread of tick distribution over Scotland for all climate warming scenarios by 2080.

“The strength of these predicted increases in tick density varied depending on the habitat. While woodland habitats were predicted to experience the highest absolute increases, the largest proportional increases were predicted for the slopes of mountains, known as montane habitats.

“Many of these areas that were predicted to be tick-free under recent climatic conditions were predicted to become warm enough to allow sustained tick populations by 2080.”

Pioneering approach

Professor Norman and her team developed a powerful tool that is dynamic and mechanistic, yet mathematically relatively simple, so it can be adopted by non-specialists. In the future, it could be adapted to predict disease risk.

Professor Norman said, “Scotland is an ideal country for pioneering this approach as the issue of ticks and tick-borne disease risk is of increasing concern with reported increases in tick abundance and Lyme disease incidence.

“This modeling has allowed us to identify which geographic areas and habitats might be particularly vulnerable to increased tick densities owing to climate warming.

“While we developed the approach to predict tick densities over Scotland, it could be easily used for other areas and other vector species, and pathogens could be added to the model, enabling predictions of disease risk.

“Indeed, this methodology could be used more broadly to understand the dynamic response of populations over time to a variety of environmental changes and provides a neat new method in the modeling toolbox for researchers to choose from.”

More than 8,000 man-made satellites orbit planet Earth today, many of which were launched into space decades ago. Repairing and maintaining the proper operation of these satellites is not always easy and often requires carefully planned and targeted interventions.

A common satellite maintenance operation is known as “detumbling.” This process entails stabilizing and manipulating the orientation of satellites that start to uncontrollably spin (i.e., tumble) in space.

Researchers at the University of New Mexico (UNM) recently introduced a new adaptive method to detumble non-rigid satellites with unknown motion dynamics. Their proposed approach was outlined in a paper posted on the arXiv preprint server and is set to be presented at the IEEE Conference on Decision and Control (CDC 2024) held December 16–19 in Milan, Italy.

“Our paper is based on our current space research funding—SURI,” Longsen Gao, first author of the paper, now a third-year Ph.D. student in AgMan Lab as one of the core researchers in this project, told Tech Xplore.

“At UNM, we aim to leverage our multi-robot systems (i.e., space tugs, space servicing multi-arm robotic systems, etc.), which incorporate novel controller designs, to complete complicated servicing and repairing manipulation tasks aimed at fixing malfunctioning space systems, such as satellites, solar panels, rigid or non-rigid parts of space systems, etc.”

The key objective of the recent study by Gao and his colleagues was to devise an effective method to detumble non-rigid satellites. Such a method could have important ramifications, as tumbling is a key cause of satellite malfunction, and most existing detumbling solutions are only applicable to rigid satellites.

“There is no other previous research that considers the detumbling problem for non-rigid satellites,” Gao said. “Almost all detumbling efforts consider satellites as rigid bodies, simplifying their structure and ignoring potential issues that could arise due to variations in their stiffness.

“Our research reaches beyond this, tackling the non-rigid satellite detumbling problem using two space tugs, one holding the base of the satellite and another holding the malfunctioning solar panel, to transfer the whole satellite module into a two-link chain as a non-rigid system, ceasing the unknown motion of the satellite.”

The detumbling method introduced by this team of researchers is adaptive, in the sense that it can be applied to various non-rigid objects irrespective of their properties. In other words, the approach does not require prior knowledge of a satellite (e.g., its mass, inertia, center of mass, shape, stiffness, etc.) to successfully detumble it.

“Most importantly, our method ensures that the multi-agent system can detumble the non-rigid satellite without knowing its grasping position relative to the center of mass of the satellite, which is also a breakthrough compared with SOTA adaptive control methods,” Gao explained.

The approach introduced by the researchers entails the use of two robotic systems that attach to the satellite in different locations. These systems apply the force and torque necessary to stabilize the satellite, ceasing its motion and thus returning it to its proper functioning.

“Our adaptive detumbling method deals with the most challenging problem in SOTA detumbling research, namely that most existing approaches ignore the non-rigid structure of the satellite during the detumbling process,” Gao said. “We consider this a serious and common issue that may happen on satellites when they are working in space.”

The new detumbling approach introduced by Gao and his colleagues could soon be improved and tested further in real experiments, especially in zero-gravity environments. Notably, while the method was designed with the detumbling of non-rigid satellites in mind, it could also be applied to other objects with non-rigid body structures, thus it could potentially be used to tackle other maintenance and repair jobs.

“In our next studies focusing on space servicing and repairs, we will continue exploring how to leverage robotics systems to implement dexterous manipulation work during repairing and servicing tasks,” Gao added.

“We plan to combine learning-based methods (e.g., neural networks, machine learning and deep learning) with our control system design to boost the performance on our control algorithm. We are also focusing on robust adaptive MPC algorithms designs that could be applied to space servicing and repairing tasks, which could improve the effectiveness and robustness of our method.”

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