It’s possible that I shall make an ass of myself. But in that case one can always get out of it with a little dialectic. I have, of course, so worded my proposition as to be right either way (K.Marx, Letter to F.Engels on the Indian Mutiny)
Thursday, June 19, 2025
Hope is the key to a meaningful life, according to new research
University of Missouri researchers demonstrate that boosting hope could be a game-changer for mental health and resilience
Hope isn’t just wishful thinking — it’s a powerful emotional force that gives our lives meaning. Now, a new groundbreaking study from the University of Missouri shows it may be even more essential to well-being than happiness or gratitude.
For years, psychology has tied hope to goal-setting and motivation. But a team of researchers led by Megan Edwards and Laura King from Mizzou’s Department of Psychological Sciences is challenging that idea, showing that hope stands apart as one of the strongest positive emotions that directly fosters a sense of meaning.
"Our research shifts the perspective on hope from merely a cognitive process related to goal attainment to recognizing it as a vital emotional experience that enriches life's meaning," said Edwards, who earned a doctorate at Mizzou and is now a postdoctoral scholar at Duke University. “This insight opens new avenues for enhancing psychological well-being.”
Using six studies with more than 2,300 participants from diverse backgrounds, the team analyzed a range of emotions, including amusement, contentment, excitement and happiness. The findings consistently demonstrated that only hope consistently predicted a stronger sense of meaning.
Experiencing meaning in life is a central aspect of psychological functioning, predicting a host of important outcomes, such as happiness, better quality relationships, better physical health and higher income, King, a Curators’ Distinguished Professor of Psychological Sciences, said.
“Experiencing life as meaningful is crucial for just about every good thing you can imagine in a person's life,” King said. “This cornerstone of psychological functioning is not a rare experience — it is available to people in their everyday lives and hope is one of the things that make life feel meaningful.”
How to cultivate more hope in daily life
Since finding meaning in life enhances everything from self-care to relationships and daily routines, the researchers suggest simple ways to build hope each day.
One key approach is to pay attention to and appreciate positive moments — even small ones. While we often think about future milestones, simply noticing when things are going well can foster hope.
Another strategy is to seize opportunities even in chaotic times. When life feels uncertain, recognizing and seizing small opportunities can create a sense of forward momentum.
Additionally, it helps to appreciate growth and potential, both in yourself and others. Recognizing ongoing progress can inspire thoughts of a positive future.
Engaging in caring and nurturing activities is another way to cultivate hope. Just as tending to children or planting trees can symbolize future possibilities, investing time in activities that nurture growth can reinforce a hopeful mindset.
And when things feel bleak, it’s important to remember that nothing is permanent. Situations can change — and hope begins with the belief that they will.
What’s next
King believes their findings may only scratch the surface of hope’s full impact.
Future research will explore the power of hope in especially difficult times, Edwards said. The goal is to develop strategies that help people stay hopeful and find meaning, even when facing adversity.
In a world-first discovery, researchers have shown that Australia’s iconic Bogong moth uses constellations of stars and the Milky Way to navigate hundreds of kilometres across the country during its annual migration – making it the first known invertebrate to rely on a stellar compass for long-distance travel.
The landmark study, published today (Thursday 19 June) in Nature, reveals how this unassuming nocturnal moth combines celestial navigation with Earth’s magnetic field to pinpoint a specific destination it has never visited before: the cool alpine caves of the Snowy Mountains, where it hibernates for the summer.
Led by an international team of scientists from Lund University, the Australian National University (ANU), the University of South Australia (UniSA) and other global institutions, the research sheds new light on one of nature’s great migration mysteries, involving approximately four million moths each year.
“Until now, we knew that some birds and even humans could use the stars to navigate long distances, but this is the first time that it’s been proven in an insect,” says Lund University Professor of Zoology, Eric Warrant, who is also a Visiting Fellow at the ANU and an Adjunct Professor at UniSA.
“Bogong moths are incredibly precise. They use the stars as a compass to guide them over vast distances, adjusting their bearing based on the season and time of night.”
Each spring, billions of Bogong moths (Agrotis infusa) emerge from breeding grounds across southeast Australia and fly up to 1000 kilometres to a small number of caves and rocky outcrops in the Australian Alps.
The moths lie dormant in the cool, dark shelters throughout summer, and in autumn make the return journey to breed and die.
Using sophisticated flight simulators and brain recordings in controlled, magnetically neutral environments, the researchers tested how moths orient themselves under different sky conditions.
When presented with natural starry skies and no magnetic field, they consistently flew in the correct migratory direction for the season – southward in spring, northward in autumn.
When the starry skies were rotated 180 degrees, the moths reversed direction accordingly, but when the stars were scrambled, their orientation vanished.
“This proves they are not just flying towards the brightest light or following a simple visual cue,” says Prof Warrant. “They’re reading specific patterns in the night sky to determine a geographic direction, just like migratory birds do.”
Interestingly, when stars were obscured by clouds, the moths maintained their direction using only the Earth’s magnetic field. This dual compass system ensures reliable navigation even in variable conditions.
The team also delved into the neurological basis of this behaviour, identifying specialised neurons in the moth’s brain that respond to the orientation of the starry sky. These cells, found in brain regions responsible for navigation and steering, fire most strongly when the moth is facing southwards.
“This kind of directional tuning shows that the Bogong moth brain encodes celestial information in a surprisingly sophisticated way. It’s a remarkable example of complex navigational ability packed into a tiny insect brain.”
Researchers say the discovery could inform technologies in robotics, drone navigation, and even conservation strategies for species threatened by habitat loss or climate change.
Bogong moth populations have declined sharply in recent years, promoting their listing as vulnerable.
The study underscores the importance of protecting migratory pathways and the dark skies these moths rely on.
“This is not just about a moth ̶ it’s about how animals read the world around them,” says Prof Warrant. “The night sky has guided human explorers for millennia. Now we know that it guides moths, too.”
Co-author Professor Javaan Chahl, a remote sensing engineer from the University of South Australia, made headlines in August 2024 using the discoveries from a previous study led by Lund University involving dung beetles, who use the Milky Way as a reference point to roll balls of dung in straight lines. Prof Chahl’s team modelled the same technique used by dung beetles to develop an AI sensor for robot navigation in low light.
The Nature paper 'Bogong moths use a stellar compass for long-distance navigation at night' is authored by researchers from Europe, the UK, China, Australia, Canada and Australia. DOI: 10.1038/s41586-025-09135-3
When cement is mixed with water, sand and gravel, it becomes concrete – the most widely used building material in the world. However, the production of cement releases large amounts of carbon dioxide. Researchers at PSI are using artificial intelligence and computational modelling to develop alternative formulations that should be more climate-friendly.
The cement industry produces around eight percent of global CO₂ emissions – more than the entire aviation sector worldwide. Researchers at the Paul Scherrer Institute PSI have developed an AI-based model that helps to accelerate the discovery of new cement formulations that could yield the same material quality with a better carbon footprint.
The rotary kilns in cement plants are heated to a scorching 1,400 degrees Celsius to burn ground limestone down to clinker, the raw material for ready-to-use cement. Unsurprisingly, such temperatures typically can't be achieved with electricity alone. They are the result of energy-intensive combustion processes that emit large amounts of carbon dioxide (CO₂). What may be surprising, however, is that the combustion process accounts for less than half of these emissions, far less. The majority is contained in the raw materials needed to produce clinker and cement: CO₂ that is chemically bound in the limestone is released during its transformation in the high-temperature kilns.
One promising strategy for reducing emissions is to modify the cement recipe itself – replacing some of the clinker with alternative cementitious materials. That is exactly what an interdisciplinary team in the Laboratory for Waste Management in PSI’s Center for Nuclear Engineering and Sciences has been investigating. Instead of relying solely on time-consuming experiments or complex simulations, the researchers developed a modelling approach based on machine learning. “This allows us to simulate and optimise cement formulations so that they emit significantly less CO₂ while maintaining the same high level of mechanical performance,” explains mathematician Romana Boiger, first author of the study. “Instead of testing thousands of variations in the lab, we can use our model to generate practical recipe suggestions within seconds – it's like having a digital cookbook for climate-friendly cement.”
With their novel approach, the researchers were able to selectively filter out those cement formulations that could meet the desired criteria. “The range of possibilities for the material composition – which ultimately determines the final properties – is extraordinarily vast,” says Nikolaos Prasianakis head of the Transport Mechanisms Research Group at PSI, who was the initiator and co-author of the study. “Our method allows us to significantly accelerate the development cycle by selecting promising candidates for further experimental investigation.” The results of the study were published in the journal Materials and Structures.
The right recipe
Already today, industrial by-products such as slag from iron production and fly ash from coal-fired power plants are already being used to partially replace clinker in cement formulations and thus reduce CO₂ emissions. However, the global demand for cement is so enormous that these materials alone cannot meet the need. “What we need is the right combination of materials that are available in large quantities and from which high-quality, reliable cement can be produced,” says John Provis, head of the Cement Systems Research Group at PSI and co-author of the study.
Finding such combinations, however, is challenging: “Cement is basically a mineral binding agent – in concrete, we use cement, water, and gravel to artificially create minerals that hold the entire material together,» Provis explains. “You could say we're doing geology in fast motion.” This geology – or rather, the set of physical processes behind it – is enormously complex, and modelling it on a computer is correspondingly computationally intensive and expensive. That is why the research team is relying on artificial intelligence.
AI as computational accelerator
Artificial neural networks are computer models that are trained, using existing data, to speed up complex calculations. During training, the network is fed a known data set and learns from it by adjusting the relative strength or “weighting” of its internal connections so that it can quickly and reliably predict similar relationships. This weighting serves as a kind of shortcut – a faster alternative to otherwise computationally intensive physical modelling.
The researchers at PSI also made use of such a neural network. They themselves generated the data required for training: “With the help of the open-source thermodynamic modelling software GEMS, developed at PSI, we calculated – for various cement formulations – which minerals form during hardening and which geochemical processes take place,” explains Nikolaos Prasianakis. By combining these results with experimental data and mechanical models, the researchers were able to derive a reliable indicator for mechanical properties – and thus for the material quality of the cement. For each component used, they also applied a corresponding CO₂ factor, a specific emission value that made it possible to determine the total CO₂ emissions. “That was a very complex and computationally intensive modelling exercise,” the scientist says.
But it was worth the effort – with the data generated in this way, the AI model was able to learn. “Instead of seconds or minutes, the trained neural network can now calculate mechanical properties for an arbitrary cement recipe in milliseconds – that is, around a thousand times faster than with traditional modelling,” Boiger explains.
From output to input
How can this AI now be used to find optimal cement formulations – with the lowest possible CO₂ emissions and high material quality? One possibility would be to try out various formulations, use the AI model to calculate their properties, and then select the best variants. A more efficient approach, however, is to reverse the process. Instead of trying out all options, ask the question the other way around: Which cement composition meets the desired specifications regarding CO₂ balance and material quality?
Both the mechanical properties and the CO₂ emissions depend directly on the recipe. “Viewed mathematically, both variables are functions of the composition – if this changes, the respective properties also change,” the mathematician explains. To determine an optimal recipe, the researchers formulate the problem as a mathematical optimisation task: They are looking for a composition that simultaneously maximises mechanical properties and minimises CO₂ emissions. “Basically, we are looking for a maximum and a minimum – from this we can directly deduce the desired formulation,” the mathematician says.
To find the solution, the team integrated in the workflow an additional AI technology, the so-called genetic algorithms – computer-assisted methods inspired by natural selection. This enabled them to selectively identify formulations that ideally combine the two target variables.
The advantage of this “reverse approach”: You no longer have to blindly test countless recipes and then evaluate their resulting properties; instead you can specifically search for those that meet specific desired criteria – in this case, maximum mechanical properties with minimum CO₂ emissions.
Interdisciplinary approach with great potential
Among the cement formulations identified by the researchers, there are already some promising candidates. “Some of these formulations have real potential,” says John Provis, “not only in terms of CO₂ reduction and quality, but also in terms of practical feasibility in production.” To complete the development cycle, however, the recipes must first be tested in the laboratory. “We're not going to build a tower with them right away without testing them first,” Nikolaos Prasianakis says with a smile.
The study primarily serves as a proof of concept – that is, as evidence that promising formulations can be identified purely by mathematical calculation. “We can extend our AI modelling tool as required and integrate additional aspects, such as the production or availability of raw materials, or where the building material is to be used – for example, in a marine environment, where cement and concrete behave differently, or even in the desert,” says Romana BoigerNikolaos Prasianakis is already looking ahead: “This is just the beginning. The time savings offered by such a general workflow are enormous – making it a very promising approach for all sorts of material and system designs.”
Without the interdisciplinary background of the researchers, the project would never have come to fruition: “We needed cement chemists, thermodynamics experts, AI specialists – and a team that could bring all of this together,” Prasianakis says. “Added to this was the important exchange with other research institutions such as EMPA within the framework of the SCENE project.” SCENE (the Swiss Centre of Excellence on Net Zero Emissions) is an interdisciplinary research programme that aims to develop scientifically sound solutions for drastically reducing greenhouse gas emissions in industry and the energy supply. The study was carried out as part of this project.
Text: Paul Scherrer Institute PSI/Benjamin A. Senn
About PSI
The Paul Scherrer Institute PSI develops, builds and operates large, complex research facilities and makes them available to the national and international research community. The institute's own key research priorities are in the fields of future technologies, energy and climate, health innovation and fundamentals of nature. PSI is committed to the training of future generations. Therefore about one quarter of our staff are post-docs, post-graduates or apprentices. Altogether PSI employs 2300 people, thus being the largest research institute in Switzerland. The annual budget amounts to approximately CHF 460 million. PSI is part of the ETH Domain, with the other members being the two Swiss Federal Institutes of Technology, ETH Zurich and EPFL Lausanne, as well as Eawag (Swiss Federal Institute of Aquatic Science and Technology), Empa (Swiss Federal Laboratories for Materials Science and Technology) and WSL (Swiss Federal Institute for Forest, Snow and Landscape Research).
A cement chemist, a mathematician and an engineer – and more: The team at PSI brings together expertise from a range of disciplines. It is only thanks to this interdisciplinary approach that the researchers were able to develop their AI-supported optimisation approach. Pictured (from left to right): John Provis, Romana Boiger, and Nikolaos Prasianakis.
Today, all non-Africans are known to have descended from a small group of people that ventured into Eurasia after around 50 thousand years ago. However, fossil evidence shows that there were numerous failed dispersals before this time that left no detectable traces in living people.
In a paper published in Nature this week, new evidence for the first time explains why those earlier migrations didn’t succeed. A consortium of scientists led by Prof. Eleanor Scerri of the Max Planck Institute of Geoanthropology in Germany, and Prof. Andrea Manica of the University of Cambridge has found that before expanding into Eurasia 50 thousand years ago, humans began to exploit different habitat types in Africa in ways not seen before.
“We assembled a dataset of archaeological sites and environmental information covering the last 120 thousand years in Africa. We used methods developed in ecology to understand changes in human environmental niches, the habitats humans can use and thrive in, during this time,” says Dr Emily Hallett of Loyola University Chicago, co-lead author of the study.
“Our results showed that the human niche began to expand significantly from 70 thousand years ago, and that this expansion was driven by humans increasing their use of diverse habitat types, from forests to arid deserts,” adds Dr Michela Leonardi of London’s Natural History Museum, the study’s other lead author.
“This is a key result.” explains Professor Manica, “Previous dispersals seem to have happened during particularly favourable windows of increased rainfall in the Saharo-Arabian desert belt, thus creating ‘green corridors’ for people to move into Eurasia. However, around 70,000-50,000 years ago, the easiest route out of Africa would have been more challenging than during previous periods, and yet this expansion was sizeable and ultimately successful.”
Many explanations for the uniquely successful dispersal out of Africa have been made, from technological innovations to immunities granted by admixture with Eurasian hominins. However, no technological innovations have been apparent, and previous admixture events do not appear to have saved older human dispersals out of Africa.
Here the researchers show that humans greatly increased the breadth of habitats they were able to exploit within Africa before the expansion out of the continent. This increase in the human niche may have been a result of a positive feedback of greater contact and cultural exchange, allowing larger ranges and the breakdown of geographic barriers.
“Unlike previous humans dispersing out of Africa, those human groups moving into Eurasia after ~60-50 thousand years ago were equipped with a distinctive ecological flexibility as a result of coping with climatically challenging habitats,” says Prof. Scerri, “This likely provided a key mechanism for the adaptive success of our species beyond their African homeland.”
The research was supported by funding from the Max Planck Society, European Research Council and Leverhulme Trust.
Humans learned to thrive in a variety of African environments before their successful expansion into Eurasia roughly 50,000 years ago.
Credit
Ondrej Pelanek and Martin Pelanek
Humans learned to thrive in a variety of African environments before their successful expansion into Eurasia roughly 50,000 years ago.
Physicists at the University of Liège have succeeded in sculpting the surface of water by exploiting surface tension. Using 3D printing of closely spaced spines, they have combined menisci to create programmed liquid reliefs, capable of guiding particles under the action of gravity alone. This is a promising advance for microscopic transport and sorting, as well as marine pollution control.
Have you ever tried tilting a liquid in a glass? It's completely impossible. If you tilt the glass, the surface of the liquid will automatically return to the horizontal ... except for a small - barely visible - curvature that forms near the edge of the glass. This curvature is called a meniscus. And this meniscus is due to capillarity, a force acting on a millimetre scale and resulting from the surface tension of the liquid. What would happen if we could create lots of little menisci over a large surface? What if these small reliefs could add up to form slopes, valleys, or even entire landscapes ... liquid? This is exactly what scientists from the GRASP laboratory at the University of Liège, in collaboration with Brown University (USA), have succeeded in doing.
Drawing on its experience in the field of liquids, and more specifically of liquid interfaces, and with access to cutting-edge 3D printing equipment, the GRASP team set about printing several 'models', several playgrounds, in an attempt to validate their theory: 3D printing conical spines close enough together to deform the surface of water on a large scale. As we know, each spike creates a meniscus around itself," explains physicist Megan Delens. Following this logic, this means that if we align them well and they are close enough together, we should see a sort of giant meniscus appear, resulting from the superposition and addition of each individual meniscus." The team found that ' by modifying each spine individually, the surface of the liquid no longer remains flat, but forms a kind of "programmed" liquid landscape. "Programmed" because it is by modifying the height or distance between the spines that the researchers have been able to design liquid interfaces that follow all sorts of topographies: inclined planes, hemispheres, but that also draw much more complex shapes. For example, they have succeeded in creating the Atomium in Brussels in liquid relief!*
A motorway for bubbles and microparticles
But that's not all. This method also offers a new way of moving and sorting floating objects such as marbles, droplets or plastic particles," explains Professor Nicolas Vandewalle, physicist and director of the lab. When the liquid surface slopes, the lighter objects rise thanks to Archimedes' thrust , and the denser ones sink under the action of their own weight, as if they were sliding down a hill of water". This completely passive approach could be used in micromanipulation, particle sorting or even cleaning liquid surfaces, for example, to capture microplastics or oil droplets on the surface of water.
Future research could look at more advanced ways of making the small tips move, for example by using materials that react to magnetic fields or that can change shape." The idea would be to be able to control the shape of the liquid surface in real time. These advances would make this method even more useful for developing innovative new technologies in microfluidics," concludes Megan Delens."
Physicists have succeeded in sculpting the surface of water by exploiting surface tension. Using 3D printing of closely spaced spines, they have combined menisci to create programmed liquid reliefs, capable of guiding particles under the action of gravity alone. This is a promising advance for microscopic transport and sorting, as well as marine pollution control
