Wednesday, January 22, 2025

 ANOTHER FIND FROM THE MUSEUM STORAGE ROOM

Fossil discovery in the Geiseltal Collection: Researchers identify unique bird skull





Martin-Luther-Universität Halle-Wittenberg
Diatryma skeleton 

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Reconstruction of the complete skeleton of a Diatryma

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Credit: Uni Halle / Markus Scholz




Around 45 million years ago, a 4.6 feet-tall (1.40 metres) flightless bird called Diatryma roamed the Geiseltal region in southern Saxony-Anhalt. An international team of researchers led by the Martin Luther University Halle-Wittenberg (MLU) and the Senckenberg Research Institute and Natural History Museum in Frankfurt report on the bird's fully preserved skull in the scientific journal "Palaeontologia Electronica". The fossil was unearthed in the 1950s in a former lignite mining area in the Geiseltal in Germany. It was initially misclassified and thus led a shadowy existence until its rediscovery. The only other place that a similar skull fossil has been found is the USA.

The Geiseltal Saxony-Anhalt is located south-west of Halle and was a lignite mining area until 1993. Numerous exceptionally well-preserved animal fossils have been unearthed here. The Geiseltal Collection at MLU comprises 50,000 fossils and is considered a national heritage asset. These fossils offer unique insights into the evolution of animals and the Eocene Epoch around 45 million years ago. At that time, the Geiseltal was a warm, tropical swamp. Ancient horses, early tapirs, large land crocodiles as well as giant tortoises, lizards and numerous birds lived here. Some of the latter were flightless and the largest of these was Diatryma, a herbivore with a gigantic beak which stood around 4.6 feet high. 

For many years no one knew that an almost completely preserved skull of Diatryma was part of the collection. "The find was initially misidentified as a crocodile skull," says Michael Stache, a geological preparator at MLU’s Central Repository of Natural Science Collections. Stache came across the fossil again by chance several years ago. He realised the mistake and got down to work, restoring and then analysing the piece of skull. He combined the fossil with another object from the collection, reconstructing an almost entire skull. Dr Gerald Mayr, a researcher at the Senckenberg Institute, examined the find more closely and realised its importance: the skull clearly belonged to a Diatryma. Only one other fully preserved skull is known to exist in the world and is housed in the American Museum of Natural History in the USA. 

"This shows once again that many of the most interesting discoveries in palaeontology occur in museum collections. Just a few years ago, nobody would have thought that the Geiseltal Collection would contain such surprises," says Gerald Mayr. Michael Stache also reports that there is great scientific interest in the fossils. Researchers from Germany and abroad come to MLU on a regular basis to investigate the objects. "This research expands our understanding of the Eocene Epoch in the Geiseltal even though the excavations were completed long ago," says Michael Stache. Up until ten years ago, for example, it was assumed that Diatryma hunted prehistoric horses in the Geiseltal. More recent investigations have found that the bird was, in fact, a herbivore. 

There are around 40 specimens of the bird in the Geiseltal Collection. "Diatryma was probably a rare guest in the Geisetal. Otherwise, there would probably be more fossils," concludes Stache. 

Study: Mayr G, Mourer-Chauviré C, Bourdon E, and Stache M. Resurrecting the taxon Diatryma: A review of the giant flightless Eocene Gastornithiformes (Aves), with a report of the first skull of DiatrymaPalaeontologia Electronica (2024). doi: 10.26879/1438

The almost complete skull fossil can be found in the Geiseltal Collection.

Credit

Uni Halle / Michael Stache

 

Grass surfaces drastically reduce drone noise making the way for soundless city skies



University of Bristol




The findings, published today in Scientific Reports, show, for the first time, how porous ground treatments can mitigate noise and optimise propellor performance.

Lead author Dr Hasan Kamliya Jawahar from the University of Bristol’s aeroacoustic group managed by Professor Mahdi Azarpeyvand was able to demonstrate that porous ground treatments, can significantly reduce noise by up to 30 dB in low-mid frequencies and enhance thrust and power coefficients compared to solid ground surfaces. This suggests that treating roofs of building, landing pads and vertiports with porous surfaces like grass or mosses will reduce noise when drone is landing.

Dr Kamliya Jawahar based in Bristol’s Faculty of Science and Engineering explained: “It was known that ground effects influence propeller performance and noise, particularly during take-off and landing.

“While noise issues are well-documented, solutions tailored to urban environments are limited.

“I drew inspiration from natural porous materials, such as vegetation, known for their noise-damping properties. This led to exploring engineered porous surfaces as a potential solution to reduce noise and improve aerodynamics.”

The team conducted experiments in an anechoic chamber using a pusher propeller mounted above a ground plane. The ground was alternated between solid and porous treatments with varying porosity and thickness. Microphones placed in both near-field and far-field locations captured acoustic data, while a six-axis load cell measured aerodynamic forces. By comparing results across configurations, they were able to calculate how porous surfaces influence noise and performance under ground-effect conditions.

Dr Kamliya Jawahar said: “Vegetation is known to function as a natural porous medium, where its structural complexity and material properties such as foliage density and moisture content contribute to its noise absorption capabilities.

“It has been widely used in environmental noise reduction strategies such as roadside barriers and urban green spaces but this is the first time it is being investigated for futuristic Urban Air Mobility.”

The noise reduction effect of porous ground treatments stems from their ability to modify and manage the flow dynamics near the ground. When a propeller operates close to a porous surface, the porous material absorbs some of the energy from the flow impingement reducing the velocity of the tangential wall jet—a high-speed outwash of air along the ground—thereby mitigating the aerodynamic interactions that contribute to noise.

Additionally, the porous structure traps portions of the impinging flow, reducing its reflection back towards the propeller. This minimizes the re-ingestion of disturbed airflows into the propeller, which are a significant source of tonal and broadband noise. The reduction in reflected turbulence and the stabilized hydrodynamic pressure field help decrease both tonal and broadband noise emissions, resulting in quieter operations. These effects are particularly pronounced in ground effect conditions.

These findings can be applied to UAM operations by enabling quieter and more efficient vehicle designs. They also support the development of noise-reducing vertiport surfaces, fostering greater community acceptance and compliance with urban noise regulations.

“Our research demonstrates that innovative porous landing surfaces can drastically reduce noise from drones and air taxis, paving the way for quieter and more sustainable urban skies,” added Dr Kamliya Jawahar.

 

Paper:

‘Porous ground treatments for propeller noise reduction in ground effect’ by Hasan Kamliya Jawahar, Liam Hanson, Md. Zishan Akhter and Mahdi Azarpeyvand in Scientific Reports.

 

Extent of microfibre pollution from textiles to be explored at new research hub


A newly established research hub in North East England will explore the extent and environmental impact of microfibre loss from textiles.



Northumbria University

L-R: Dr Kelly Sheridan and Dr Alana James, pictured in the FibER Hub 

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L-R: Dr Kelly Sheridan and Dr Alana James, pictured in the FibER Hub

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Credit: North News/Northumbria University




A newly established research hub in North East England will explore the extent and environmental impact of microfibre loss from textiles.

Microfibre shedding from clothing during machine washing and drying is well known, with the tiny fibres causing harm to wildlife and the environment when they enter soil, air and waterways.

Located on Northumbria University’s campus in the centre of Newcastle, the Fibre-fragmentation and Environment Research Hub (FibER Hub) is the result of a collaboration between the University and The Microfibre Consortium (TMC) and will extensively test a wide variety of  fabrics to determine the level of microfibre loss under different conditions and the associated environmental impacts.

Recent research has shown that the clothes we wear are shedding microfibres throughout their entire lifespan, from textile manufacture through to everyday wear. Even microfibres from fabrics considered ‘natural’, such as cotton, can have a negative impact on the environment, as manufacturing processes introduce chemical dyes and finishes to the fabric so that it is no longer in its natural state.

Based in the Northumbria School of Design, Arts and Creative Industries, the FibER Hub features state-of-the-art equipment which will allow researchers to understand exactly what and how much fibre a fabric sheds at each stage of its lifespan.

In recent years, efforts have focused on quantifying microfibre loss from domestic laundering. This new collaboration will build on existing knowledge and compliment these learnings through the exploration of additional environmental settings in which textiles shed fibres.

It is hoped that the research will inform the development of more sustainable textiles in the future, with targeted interventions throughout the lifespan to reduce shedding rates. 

Work on this topic is being led by The Microfibre Consortium (TMC), a science-led nonprofit organisation which is convening the global textiles sector through The Microfibre 2030 Commitment and Roadmap.

TMC connects academic research with the reality of commercial supply chain production to facilitate science-led change within the industry. It is the first and only organisation that is fully focused on this issue and works on behalf of its 95 signatories, which include global brands and retailers, suppliers, and NGOs.

The FibER Hub has been developed as part of the IMPACT+ project – a multi-disciplinary network of academics and industry experts, set up to challenge the way environmental impact is measured and assessed across the fashion and textile industries.

Established in 2023, the project is funded through UK Research and Innovation’s circular fashion and textile programme NetworkPlus, and includes academics from Northumbria University, King’s College London and Loughborough University, covering a variety of expertise, such as water, air and soil pollution, forensic science, design, and big data.

Working alongside them are representatives from global fashion brands including Barbour, Montane, and ASOS; sustainable clothing companies Agogic and This is Unfolded; campaign groups Fashion Revolution and WRAP; and the Northern Clothing and Textile Network, Newcastle City Council and Newcastle Gateshead Initiative. 

Northumbria’s Dr Alana James is Principal Investigator for the project and said: “This strategic partnership reflects the core aim of the IMPACT+ Network by focusing on microfibres as an overlooked and unmeasured environmental pollutant.”

“Interdisciplinary collaboration with design and environmental science will enable our research to reduce fibre shedding at the root cause, whilst implementing these insights directly within an industry setting.” 

Dr Kelly Sheridan is Chief Executive Officer of TMC and an Associate Professor in Forensic Science at Northumbria. Her research focuses on textile fibres and fibre fragmentation.

She said: “The FibER Hub collaboration enables TMC to draw on the interdisciplinary skills and technical capabilities of Northumbria and the IMPACT+ team to expand our knowledge offering to our signatory community.”

“Through this collaboration, the TMC research team will provide direction to relevant research informed by industry needs, to go beyond what is possible today and create robust, wide ranging and comprehensive lifespan data on fibre fragmentation.”

Find out more about the IMPACT+ project or get in touch if you are interested in collaborating with the project team.

 

Why are most companies failing to benefit from AI? It’s about the people not the tech, says new study



Successful uptake of new technology is a matter of emotions — and with 4 in 5 companies saying they’re failing to capitalise on its potential, managers need to know how to deal with them, say researchers from Aalto University.



Aalto University

Natalia Vuori 

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Natalia Vuori

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Credit: Jaakko Kahilaniemi / Aalto University



 

AI has the potential to enhance decision-making, spark innovation and help leaders boost employees’ productivity, according to recent research. Many large companies have invested accordingly, in the form of both funding and effort. Yet despite this, studies show that they are failing to achieve the expected benefits, with as many as 80 percent of companies reporting a failure to benefit from the new technology.

‘Often employees fail to embrace new AI and benefit from it, but we don’t really know why,’ says Assistant Professor Natalia Vuori from Aalto University. Our limited understanding stems partly from the tendency to study these failings as limitations of the technologies themselves, or from the perspective of users’ cognitive judgments about AI performance, she says.

‘What we learned is that success is not so much about technology and its capabilities, but about the different emotional and behavioural reactions employees develop towards AI — and how leaders can manage these reactions,” says Vuori. 

Her research team followed a consulting company of 600 employees for over a year as it attempted to develop and implement the use of a new artificial intelligence tool. The tool was supposed to collect employees’ digital footprints and map their skills and abilities, ultimately building a capabilities map of the company. The results were supposed to streamline the team selection process for consulting projects, and the whole experiment was, in fact, a pilot for AI software they hoped to offer their own customers.

After almost two years, the company buried the experiment — and the proposed product. So what happened?

It turns out, although some staff believed that the tool performed well and was very valuable, they were not comfortable with AI following their calendar notes, internal communications and daily dealings. As a result, employees either stopped providing information altogether, or they started manipulating the system by feeding it information they thought would benefit their career path. This led to the AI becoming increasingly inaccurate in its output, feeding a vicious cycle as users started losing faith in its abilities.

‘Leaders couldn’t understand why the AI usage was declining. They were taking a lot of action to promote the tools and so on, trying to explain how they use the data, but it didn’t help,’ says Vuori, who believes this case study reflects a common pattern when it comes to AI uptake, and tech adoption generally. 

The team is now collecting data on the use of Microsoft’s widely used Copilot AI software, which is so far yielding similar findings.

 

What should leaders do?

 

Researchers found that people fell into the same four groups in terms of their reaction to the new technology. Distinguishing between cognitive trust; whether a person believes the technology performs well, and emotional trust; their feelings towards the system, the groups were: full trust, full distrust, uncomfortable trust and blind trust.

People in the first group had high trust both on the cognitive and emotional level, whereas people in the second group scored low on both. Uncomfortable trust signified high cognitive trust but low emotional trust, and vice versa for blind trust.

The less people trusted the tool emotionally, the more they restricted, withdrew or manipulated their digital footprint, and it was particularly notable that this held true even if they had cognitive trust in the technology. 

The findings give companies the chance to strategise a more successful approach to AI uptake. 

“AI adoption isn’t just a technological challenge — it’s a leadership one. Success hinges on understanding trust and addressing emotions, and making employees feel excited about using and experimenting with AI,” says Vuori. “Without this human-centered approach, and strategies that are tailored to address the needs of each group, even the smartest AI will fail to deliver on its potential.” 

The research findings were published in the Journal of Management Studies on 22 January:

It's Amazing – But Terrifying!: Unveiling the Combined Effect of Emotional and Cognitive Trust on Organizational Member' Behaviours, AI Performance, and Adoption

 

New AI technology helps scientists detect which pollutants in England’s lakes are most harmful to life, and identify species which are at highest risk



University of Birmingham




Scientists can now identify the most harmful pollutants present in UK waters that are having the biggest impact on biodiversity thanks to pioneering AI technology developed at the University of Birmingham, a new study published in Environmental DNA has revealed.

The new technology allowed the team of scientists to analyse water and biofilm samples from 52 freshwater lakes across the country, efficiently and effectively sifting through reams of complex data to find key links between the presence of pollutants and biodiversity loss. The data concluded that insecticides and fungicides were the main factors affecting biodiversity, along with 43 other physico-chemical factors, including heavy metals and alkalinity.

Lead author of the study Dr Niamh Eastwood explained: “Up until now, DNA-based methods have been used to estimate changes in indicator species, or species groups (e.g. diatoms), but have tended to focus on individual  environmental factors like temperature or pH, overlooking the complex interaction between biodiversity and environmental change. This narrow approach is now insufficient to address the complexities of a world facing multiple stressors and rapidly emerging threats to water and wildlife. The results from our study highlighted the severe impact that insecticides and fungicides from agricultural runoff have on aquatic ecosystems. It is clear that these chemicals are harming many more species than those which they are intended for, which makes them of great concern.”

Senior author Professor Luisa Orsini added: “Protecting biodiversity is more important than ever. Effective conservation goes beyond looking at how single environmental factors affect individual species. Instead, it requires understanding of how these factors interact with climate and other environmental changes to drive overall biodiversity loss. Our innovative, data-driven approach embraces the complexity of natural systems, while providing actional targets for regulators. By analysing vast amounts of data, we can uncover which environmental factors have the greatest impact on sensitive species. This insight is key to developing targeted, effective conservation strategies that can address the root causes of biodiversity decline and help preserve our planet's ecosystems. With this approach, we aim to pave the way for smarter, science-backed conservation efforts that safeguard the natural world for future generations”.

Dr. Jiarui Zhou, a senior author of the study, highlighted the transformative power of artificial intelligence in tackling environmental challenges. " This study utilises advanced statistical learning to integrate complex multimodal datasets, showcasing how AI-powered approaches can revolutionise environmental science," Dr. Zhou explained. "By enabling the prioritisation of species for conservation and identifying the chemicals most harmful to biodiversity, this approach opens new pathways for protecting our natural world. This breakthrough showcases how cutting-edge technology can drive practical solutions in conservation and environmental protection, setting the stage for a healthier, more sustainable planet”.

Arron Watson, co-author of the study, emphasised the practical implications of the research, stating: "Our study highlighted the harmful effects of chemicals banned shortly after our study, providing confidence in the approach to uncover harmful substances. This approach could also be used to detect chemicals that still cause harm to biodiversity even after their use is discontinued, due to their persistence in the environment"

This groundbreaking work underscores the importance of proactive measures in chemical regulation and demonstrates the long-lasting impact harmful substances can have on ecosystems. By identifying and addressing these threats, this research supports stronger, data-driven strategies for safeguarding biodiversity and protecting the environment.

ENDS

For media enquiries please contact Press Office, University of Birmingham, tel: +44 (0)121 4142772: email: pressoffice@contacts.bham.ac.uk

Notes to editor:

  • The University of Birmingham is ranked amongst the world’s top 100 institutions. Its work brings people from across the world to Birmingham, including researchers, teachers and more than 8,000 international students from over 150 countries.
  • Research paper Unveiling Landscape-Level Drivers of Freshwater Biodiversity Dynamics published today in Environmental DNA10.1002/edn3.70058 ​

 

 

 

AI enables a major innovation in glacier modelling and offers groundbreaking simulation of the last Alpine glaciation





University of Lausanne
AI enables a major innovation in glacier modelling and offers groundbreaking simulation of the last Alpine glaciation 

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Much more in line with field observations, the new results show that the ice was thinner than in previous models.

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Credit: UNIL




Scientists at the University of Lausanne (UNIL) have used AI to massively speed up computer calculations and simulate the last ice cover in the Alps. Much more in line with field observations, the new results show that the ice was thinner than in previous models. This innovative method opens the door to countless new simulations and predictions linked to climate upheavals. The research is published in Nature Communications.

25,000 years ago, the Alps were covered by a layer of ice up to 2 kilometers thick. For almost 15 years, this glaciation has been put into perspective by 3D digital models, based on climate reconstructions, thermodynamics and ice physics. However, these models have sparked debate in the scientific community, as until now there has not been a full  correspondence between these simulations and the physical traces - rocks, moraines, etc. - found in the field, particularly erosion lines, which bear witness to past ice thicknesses.

A team of scientists from the University of Lausanne (UNIL) have just solved this persistent problem. For the first time, they have used artificial intelligence to massively boost their new glacial evolution model, generating a large series of simulations of unprecedented accuracy: they correspond much more closely to the physical traces left on the ground. Their results show an average ice cover 35-50% thinner than in previous reference simulations. Model resolution has been increased from two kilometers to 300 meters, and it is only thanks to this precision that it is possible to describe the complex topography of the Alps numerically.

In line with the current state of scientific knowledge, based on field observations, it shows, for example, that certain peaks such as the Matterhorn and Grand Muveran were clearly protruding from the ice during the Ice Age. This breakthrough is published in Nature Communications.

The research is significant in more ways than one. Firstly, the ability to correctly model the glacial past is essential to understanding our environment.  For over 2 million years, the Earth has experienced alternating glacial and warm cycles, which have profoundly shaped the landscape in which we live. The new model now corresponds much more closely to the evidence left on the ground following the retreat of the glaciers, and make it possible to better quantify many natural phenomena, such as glacial erosion, which has largely contributed to sculpting the relief of the Alps.

On the other hand, the innovative methodology used in this research marks a new era in numerical modelling. “By using recent technology, and applying it to the last major glaciation in the Alps, we can finalize a 17,000-year simulation at very high resolution (300 m) in 2.5 days, whereas such spatial resolution would have taken 2.5 years to calculate using traditional methods, which are also extremely costly and energy-intensive”, explains Tancrède Leger, researcher at UNIL's Faculty of Geosciences and Environment (FGSE), and first author of the study.

With this approach, the model first learns about the physics of ice flow, using Deep Learning methods. It then receives data on the climate of the period (temperature, precipitation, etc.), to calculate ice supply and melt.

Deep learning calculations are then performed not by the traditional central processing unit (CPU), but via a GPU (or graphics processing unit), which enables numerous operations to be performed in parallel, boosting the computer's computing power phenomenally.

“It's as if we once had six Ferraris at our disposal, and now we have ten thousand small cars. We've gone from very large machine clusters to a simple 30 cm graphics card,” illustrates Guillaume Jouvet, FGSE professor behind the AI model and co-first author of the study. “We're not doing anything new, but we're doing it a thousand times faster, making it possible to achieve resolutions that were not even considered before”.

This progress will enable new research to be launched. In particular, a new SNSF-funded project is about to get underway to use this revolutionary method to better predict the impact of the melting Greenland and Antarctic ice sheets on global sea level rise .

Source : Tancrède P. M. Leger, Guillaume Jouvet, Sarah Kamleitner, Jürgen Mey, Frederic Herman, Brandon D. Finley, Susan Ivy-Ochs, Andreas Vieli , Andreas Henz & Samuel U. Nussbaumer, A data-consistent model of the last glaciation in the Alps achieved with physics-driven AINature Communications, 2025.

The research was performed in the framework of the SNSF-funded project RECONCILE in collaboration with the University of Zurich, using the IGM model (https://github.com/jouvetg/igm) developed at UNIL.

More simulations on: Youtube