Researchers have uncovered a critical mechanism behind battery failure in solid-state batteries, offering new insights that could help unlock safer, longer-lasting energy storage technologies. Every time a smartphone is charged or an electric vehicle is plugged in, billions of lithium ions move through a battery to store energy. Future devices could perform far better with [...]
In a groundbreaking advancement for geriatric health assessment, researchers have unveiled a novel nonexercise approach to estimate cardiorespiratory fitness in older adults by utilizing seismocardiography (SCG). This innovative technology harnesses the subtle vibrations produced by cardiac activity, enabling precise monitoring of cardiovascular function without the need for physical exertion. As aging populations increase globally, the demand for accessible and reliable methods to evaluate heart and lung fitness has never been higher, making this development not only timely but also revolutionary.
Cardiorespiratory fitness traditionally requires exercise testing, involving treadmill or bicycle protocols that may pose risks or be impractical for elderly individuals with limited mobility or chronic conditions. The novel system employing SCG bypasses these challenges, offering a safe and feasible alternative that significantly minimizes patient burden. SCG captures micro-vibrations generated by the heart during each beat using sensitive accelerometers placed on the chest, translating mechanical cardiac events into quantitative data reflective of cardiovascular performance.
The study, led by Sundberg, Gates, and Boraxbekk among others, meticulously validated the use of SCG signals against established measures of cardiorespiratory fitness, such as VO2 max testing, across a diverse cohort of older adults. Their results demonstrated remarkable correlation, confirming that SCG can serve as a reliable proxy for exercise-based evaluations. This breakthrough is poised to transform clinical practice by facilitating widespread cardiovascular screening and monitoring in geriatric populations without exposing them to physical strain.
One of the pivotal technological advances underpinning this approach is the enhancement of signal processing algorithms that can accurately decipher the complex mechanical patterns of heart activity from SCG data. These algorithms filter noise and artifacts, isolating key features such as timing intervals between cardiac events that are indicative of cardiac output and fitness levels. By employing machine learning techniques, the research team optimized predictive models that link SCG metrics with cardiorespiratory capacity.
Beyond the impressive technical accomplishments, the application of SCG for fitness estimation aligns perfectly with the principles of personalized medicine. By providing continuous, noninvasive monitoring methods, clinicians can tailor interventions and lifestyle recommendations based on real-time fitness metrics, facilitating proactive management of cardiovascular health. This could substantially reduce the incidence of cardiovascular events, improve quality of life, and enhance longevity in aging adults.
Importantly, this methodology opens new avenues for remote health monitoring. The compact sensors utilized for SCG are lightweight, unobtrusive, and capable of wireless transmission, enabling integration with mobile health platforms. This may empower elderly individuals to measure their cardiorespiratory fitness regularly from the comfort of their homes. Such democratization of health data can lead to earlier detection of functional decline, prompt clinical interventions, and reduced healthcare costs.
From a physiological perspective, seismocardiography reveals intricate interactions governing heart mechanics, including myocardial contractility and valve function, which are directly tied to oxygen delivery and utilization — the cornerstone of cardiorespiratory fitness. This depth of insight surpasses conventional pulse or blood pressure monitoring, offering a richer dataset for comprehensive cardiovascular assessment. The capacity to obtain these insights passively without exercise expends minimal energy, crucial for frail patients.
The research also emphasizes the feasibility of SCG in monitoring dynamic changes in fitness over time. Whereas exercise-based testing reflects a snapshot requiring considerable effort and clinical resources, SCG’s accessibility permits serial measurements that track progression or regression in cardiorespiratory health. This longitudinal view enhances clinicians’ ability to evaluate treatment efficacy and adjust care plans appropriately.
Moreover, SCG technology exemplifies the convergence of biomechanics, biomedical engineering, and data science, epitomizing modern healthcare innovation. The fusion of robust sensors with advanced computational methods has produced a tool that transcends standard diagnostic modalities, offering a more comprehensive and patient-friendly option. This interdisciplinary synergy highlights the potential for future wearable technologies to expand their role in chronic disease management.
Addressing limitations, the study carefully acknowledges challenges such as variability in SCG signal quality due to anatomical differences and external factors. However, by employing adaptive algorithms and calibration protocols, the researchers have mitigated many of these issues, ensuring consistent performance across diverse patient profiles. Ongoing refinements aim to further enhance accuracy and user-friendliness, making broad clinical adoption realistic.
The implications of this research extend beyond geriatric cardiology, potentially influencing rehabilitation programs, sports science, and preventive medicine. With further validation, SCG could monitor athletes’ fitness without exhaustive testing or assist in evaluating pulmonary function in respiratory diseases. Its versatility underscores the broad applicability of biomechanical sensing in health monitoring paradigms.
In conclusion, the integration of seismocardiography for the nonexercise estimation of cardiorespiratory fitness in older adults marks a paradigm shift in cardiovascular health assessment. By circumventing the constraints of traditional exercise testing, this technology enables safe, efficient, and insightful evaluations that can be performed repeatedly and remotely. As the elderly demographic surges worldwide, the deployment of such innovative methods will be pivotal in optimizing healthcare delivery and promoting healthier aging.
The promise of SCG transcends mere technological novelty; it represents a compassionate approach to health evaluation that respects the physical limitations and safety of vulnerable populations. Empowering healthcare providers with precise, noninvasive tools enriches clinical decision-making and enhances patient outcomes. Looking ahead, continued research and integration with digital health ecosystems will likely establish seismocardiography as a cornerstone in the management of cardiovascular fitness.
Ultimately, the study spearheaded by Sundberg and colleagues not only broadens scientific understanding but also provides a tangible blueprint for implementing cutting-edge sensing technologies in everyday clinical practice. The synergy of engineering excellence, clinical insight, and patient-centric design encapsulates the future of medical innovation — one that prioritizes accessibility, safety, and actionable intelligence for all.
Subject of Research: Nonexercise estimation of cardiorespiratory fitness in older adults using seismocardiography.
Article Title: Seismocardiography for nonexercise estimation of cardiorespiratory fitness in older adults.
Article References:
Sundberg, M., Gates, A.T., Boraxbekk, C.J., et al. Seismocardiography for nonexercise estimation of cardiorespiratory fitness in older adults. BMC Geriatr (2026). https://doi.org/10.1186/s12877-026-07771-6
Image Credits: AI Generated
Does a thought-experiment about US ascendancy in the technology say as much about AI jitters as it does about the reality?
It’s 2031 and the US and China are about to tear Europe into pieces.
The US ploughed vast sums into datacentres and the EU did not. China built robots and Europe did not. American companies “restructured” their workflows around AI and fired people, while EU workers went on long lunch breaks and handed over administrative tasks to the AI model Claude.
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© Photograph: Hannibal Hanschke/EPA
© Photograph: Hannibal Hanschke/EPA
© Photograph: Hannibal Hanschke/EPA
Apart from effort to electrify, there were geopolitical tensions around climate science and the 1.5C goal at pre-Cop31 climate talks
Electrifying the world – with electric vehicles, electric heating and cooling, and modernised heavy industry – could be the next biggest step towards phasing out fossil fuels, replacing the 80% of global energy that still comes from hydrocarbons. As using electrical energy is much more efficient than combustion, the move would save billions of dollars for consumers and businesses – global energy demand could be halved, according to one estimate.
For decades, electrification has been a nerdish backwater of global climate action. But in the last two weeks, at preparatory talks in Bonn before the forthcoming UN Cop31 climate summit, the subject finally took centre stage.
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© Photograph: Filip Singer/EPA
© Photograph: Filip Singer/EPA
© Photograph: Filip Singer/EPA
Harvard researchers found that adding a little randomness to robot movements can prevent gridlock and dramatically boost efficiency in crowded swarms. Imagine a swarm of robots sent into a tight space to handle an urgent job, such as cleaning up an oil spill or assembling complex equipment. At first, adding more robots speeds things up. [...]A single transistor that behaves like a brain cell in the deep freeze could help unlock the next generation of quantum computers and space exploration systems. Researchers at the University of Hong Kong (HKU) have developed a new type of brain-inspired electronic hardware that can operate at temperatures close to absolute zero. The breakthrough could [...]
Researchers developed a brain-controlled gaming system that learns from the brain’s natural wiring, enabling fast BCI training and potentially transforming medicine, mental health, and human-computer interaction. It may not be long before video game controllers become optional. Researchers at Yale University have developed a new brain-computer interface (BCI) that allows people to play video games [...]Wealth tax criticized by billionaires and Gavin Newsom would levy a one-time 5% tax on residents worth over $1bn
A popular proposal in California to impose a wealth tax on billionaires has gained enough signatures to qualify for the ballot in November, state officials announced on Wednesday.
The news is set to intensify an already heated debate around the tax, which has pitted tech moguls and the state’s governor, Gavin Newsom, against the labor union backing the measure.
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© Photograph: Jae C Hong/AP
© Photograph: Jae C Hong/AP
© Photograph: Jae C Hong/AP
The popular framing of algorithmic recommendation, repeated across most contemporary writing on consumer technology, is that personalised suggestions surface preferences the consumer already has. The algorithm watches what someone does, identifies a pattern, and offers more of what the pattern predicts they will like. On this account, the algorithm is a useful but essentially neutral tool, one that accelerates discovery without changing what the consumer would have chosen if left to themselves.
The peer-reviewed experimental evidence accumulated over the past twenty years has substantially complicated this picture.
What the controlled studies have repeatedly shown is that algorithmic recommendations do not merely reflect existing preferences. They actively shape them. The brain treats the recommendation itself as a piece of information, an informational anchor, that influences subsequent judgment regardless of whether the underlying algorithm is accurate or not. The same effect persists when the recommendations are deliberately randomised, when they are intentionally incorrect, and when the consumer has been explicitly told that the recommendations may be unreliable. The mechanism is not driven by the algorithm being right. The mechanism is driven by the algorithm being present.
The foundational peer-reviewed study of this effect was published in 2018 by Gediminas Adomavicius at the University of Minnesota, with Jesse Bockstedt at the University of Arizona, Shawn Curley at Minnesota, and Jingjing Zhang at Indiana University, in the journal Information Systems Research. The team designed three controlled experiments to test whether personalised recommendation ratings produced by a recommender system would influence the prices that participants would be willing to pay for digital music tracks.
The first experiment showed participants algorithmic recommendation ratings for a series of songs. The ratings were presented as if they had been personalised to each participant by a recommender system trained on their listening history. The ratings were, in fact, randomly assigned. Participants were then asked how much they would be willing to pay for each song. The team found that the randomly assigned ratings substantially altered the prices participants were willing to pay, even when the team controlled for the participants’ own underlying preferences and demographics. Higher random ratings produced higher willingness-to-pay figures. Lower random ratings produced lower figures. The shift was statistically robust across the sample.
The second experiment used actual system-generated recommendations from a real recommender system, but the team intentionally perturbed the recommendations by introducing controlled errors. Participants saw recommendations that were partially based on their preferences but deliberately shifted away from what an unperturbed algorithm would have produced. The team observed substantially the same effect. The participants’ willingness to pay tracked the perturbed recommendations rather than their underlying preferences.
The third experiment reduced preference uncertainty by giving participants substantial time and information to develop firm views about the songs before they were exposed to the recommendation ratings. The hypothesis the team tested was that participants who had already made up their minds about the music would be less susceptible to algorithmic influence. The hypothesis did not hold. Even participants with stable, well-developed preferences showed the same willingness-to-pay shifts in response to randomly assigned algorithmic recommendations.
The Adomavicius team’s interpretation, supported by the broader peer-reviewed literature on cognitive anchoring effects, was that the recommendation operates as a numerical anchor that the brain uses as a starting point for subsequent judgment. The classic anchoring effect, identified by Daniel Kahneman and Amos Tversky in the 1970s and replicated in thousands of subsequent studies, shows that arbitrary numerical values presented before a judgment systematically shift the judgment toward the anchor, even when the people making the judgment know the anchor is irrelevant.
Algorithmic recommendations function as anchors in the same way. A recommendation of four out of five stars sets a numerical reference point that influences the consumer’s subsequent valuation of the product, regardless of whether the four-star recommendation reflects anything meaningful about the consumer’s actual preferences. The consumer who has been told a song is worth four stars, by what they believe to be an accurate personalised algorithm, will offer a price closer to what a four-star song commands than they would have offered in the absence of the recommendation. The shift occurs whether the four-star rating is generated by an accurate algorithm, an inaccurate algorithm, or a random number generator.
The implication is that the source of the algorithmic influence is not the accuracy of the algorithm. The source is the presence of the algorithm. The consumer is being influenced by a numerical informational anchor that has been generated by a system the consumer trusts to be measuring their preferences. Whether the system is actually doing that is, on the peer-reviewed evidence, largely beside the point.
The peer-reviewed experimental findings sit alongside a substantial body of industry data on the actual scale of algorithmic influence in major consumer platforms. Amazon’s recommendation engine, on figures the company has publicly disclosed and that have been independently analysed by McKinsey and Company since 2013, drives approximately 35 per cent of all purchases made on the platform. The figure has held roughly steady for over a decade, despite extensive evolution in the underlying algorithms and substantial growth in Amazon’s product catalogue.
YouTube’s chief product officer Neal Mohan disclosed at the Consumer Electronics Show in January 2018 that approximately 70 per cent of all viewing time on YouTube comes from videos selected by the platform’s recommendation algorithm rather than from videos that users have actively searched for. The figure has been widely confirmed by subsequent industry analysis and remains the standard reference for the role of algorithmic recommendation in video discovery. YouTube now has approximately 2.5 billion monthly users.
The figures across other major platforms follow similar patterns. Pew Research Center has found that approximately 81 per cent of YouTube users say they at least occasionally watch recommended videos, with 15 per cent reporting that they do so regularly. The recommendation algorithms on TikTok and Instagram measure user engagement to the millisecond and adjust the content stream in real time based on which videos a user has lingered on, replayed, or scrolled past quickly. The effect of all this, on the available evidence, is that the majority of the content the average person encounters in the major consumer platforms is selected by algorithms rather than chosen by the person consuming it.
A 2019 peer-reviewed article by Daniel Susser at Pennsylvania State University, Beate Roessler at the University of Amsterdam, and Helen Nissenbaum at Cornell Tech, published in the Georgetown Law Technology Review, argued that the systematic deployment of algorithmic influence in digital platforms now meets the philosophical definition of manipulation rather than persuasion. The distinction matters because manipulation, in the philosophical literature, is defined as the deliberate influence of someone’s beliefs, desires, or actions through means that bypass their rational agency. Persuasion, by contrast, addresses someone’s rational agency directly by giving them reasons to change their beliefs or actions.
The Susser team’s argument was that algorithmic recommendations, as currently deployed, systematically bypass the rational agency of consumers. The consumer is not given reasons why a particular product is being recommended. The consumer is not told what factors the algorithm has weighted, or how heavily, or what alternatives have been excluded. The consumer is presented with a numerical or categorical anchor and expected to make a decision in response to it. The cognitive mechanism the recommendation activates, on the Adomavicius experimental evidence, is the anchoring effect, which is precisely the kind of non-rational decision-making bias that the philosophical literature has long identified as the substrate of manipulation.
The Susser argument has not produced consensus. Other scholars have argued that the line between manipulation and persuasion is blurry, that consumers retain meaningful agency to ignore or reject algorithmic recommendations, and that the consumer welfare benefits of algorithmic curation outweigh the autonomy costs. The peer-reviewed dispute is genuinely ongoing. What is not in dispute is that the cognitive mechanisms identified by the Adomavicius team operate in ways the consumer is generally unaware of, and that they produce substantial behavioural effects that the consumer would not necessarily endorse if they were aware of them.
Several methodological caveats apply to the literature described above.
The Adomavicius experimental work has been conducted primarily in laboratory settings using digital music as the stimulus. The generalisability of the findings to other product categories, other recommendation contexts, and the open-ended environment of actual consumer life is plausible but not perfectly established. The effect sizes observed in the lab may be larger or smaller than the effect sizes that obtain in everyday digital commerce.
The industry figures for Amazon and YouTube are derived from corporate disclosures rather than from independent peer-reviewed measurement. The 35 per cent and 70 per cent figures are widely cited and broadly consistent with the available academic analyses, but they should be treated as informed estimates rather than as precise scientific measurements.
The Susser 2019 manipulation framework is a philosophical argument rather than an empirical finding. The conceptual definition of manipulation, and the application of that definition to algorithmic recommendation systems, remain genuinely contested in the peer-reviewed legal and philosophical literature. The argument is strong but is not the only available position.
The anchoring mechanism identified by the Adomavicius team is one of several cognitive effects that have been proposed to explain algorithmic influence on consumer behaviour. Other proposed mechanisms include social proof effects, decision fatigue and the deferral of judgment to perceived authority, identity-reinforcement effects in which recommendations mirror and amplify the consumer’s existing self-concept, and choice architecture effects in which the structure of the recommendation environment itself shapes which options the consumer considers. The relative contributions of these mechanisms are not yet fully understood.
Several things follow from the peer-reviewed evidence on algorithmic influence on consumer choice that are worth saying clearly.
The first is that the popular framing of algorithmic recommendation as a neutral discovery tool is not supported by the available experimental evidence. The recommendation is not a neutral observation of what the consumer would have chosen. It is an active intervention in the consumer’s decision-making process, operating through cognitive anchoring mechanisms that are substantially independent of the recommendation’s accuracy. The consumer who receives a recommendation is being measurably influenced by the fact of the recommendation, not just by its content.
The second is that the scale of algorithmic influence in modern consumer platforms is substantial. Approximately 35 per cent of Amazon purchases, approximately 70 per cent of YouTube viewing time, and the majority of content consumption on TikTok, Instagram, and the major streaming platforms now come from algorithm-driven recommendation rather than from user-initiated search. The figure means that, for most consumers in most digital environments, what they encounter is selected by algorithms rather than by them.
The third is that the cognitive mechanism producing the algorithmic influence is one the consumer is generally not aware of, and one the consumer cannot easily resist even when they have been told the recommendation may be unreliable. The Adomavicius team’s third experiment, in which participants with firm pre-existing preferences still showed the influence effect, suggests that informed scepticism about algorithms does not, by itself, neutralise the cognitive mechanisms by which algorithms shape decisions.
The fourth, on the strongest current reading of approximately twenty years of peer-reviewed evidence in behavioural economics, decision psychology, and the philosophy of digital technology, is that the consumer who believes their choices in algorithmically curated environments are predominantly their own is, on the available evidence, mistaken in a measurable way.
The choices are partly theirs.
They are also partly the algorithm’s.
The peer-reviewed evidence has not yet established the precise weighting, but it has established that the second contribution is larger than the popular framing has assumed.
The post Approximately 35 per cent of all purchases made on Amazon are driven by its recommendation engine, approximately 70 per cent of all viewing on YouTube comes from algorithm-driven recommendations, and peer-reviewed evidence has shown that even randomly assigned recommendations measurably alter what consumers are willing to pay for a product appeared first on Space Daily.
A new material, inspired by the frustratingly powerful strength of a tangled ball of staples, could represent the future of flexible, adaptable design.
Published in the Journal of Applied Physics, material scientists and engineers from the University of Colorado Boulder’s Paul M. Rady Department of Mechanical Engineering revealed an interlocking particle design that can be shaped into rigid structures, and then return to a pile of loose pieces with only a targeted movement or vibration.
The particles, the team says, could potentially be used as a building material with high strength and flexibility, capable of reshaping into new forms on command.
The difficulty of separating tangled metal, such as a cluster of standard office staples, intrigued the researchers enough to investigate how interlocking particles could be used in recyclable construction. Such techniques are found in nature, as birds weave together found objects to create strong structures to protect their young.
On the level of particles, interlocking such elements is called “entanglement,” and is a relatively new focus for material scientists.
“We’ve been playing around with the idea of building blocks and geometry for many years, but we started looking at interlocking, entangled particles only recently,” said Professor Francois Barthelat, the leader of the Laboratory for Advanced Materials & Bioinspiration.
“We are excited about the combination of properties we can get out of these systems, and we believe this technology has the potential to go in many directions,” Barthelat said.
While entanglement occurs naturally, as in the combination of soft protein and hard minerals that interlock to form bones, producing it artificially requires a careful understanding of how these interactions work. According to the team, particle shape is the most foundational element to natural entanglement.
“Let’s take sand as an example,” says PhD student Youhan Sohn. “Sand is smooth and convex-shaped, meaning it cannot interlock from grain to grain.”
“However, we found that if we change the shape of a grain of sand, we can drastically affect its behavior and mechanical properties, including the particle’s ability to link with other particles,” Sohn adds.
With shape decided as the researcher’s primary focus, they began running computer models, called Monte Carlo simulations, to observe how various potential shapes would interact, aiming to identify the optimal entanglement geometry. The shape their research settled on was remarkably similar to that of a staple: a particle with two legs.
After achieving success in the digital realm, the researchers fabricated the shape in the real world to test its behavior under practical conditions. During these tests, the team discovered that their optimized shape contained even more beneficial qualities than they had initially understood.
Conventional materials typically experience a trade-off between toughness and tensile strength. Typically, materials that are extremely tough and can absorb large amounts of energy are also quite brittle, lacking the ability to stretch or bend. However, this staple-like particle design exhibits an unusually high amount of both properties simultaneously.
“Our entangled granular material using the staple-like particle demonstrates both high strength and toughness at the same time,” said PhD student Saeed Pezeshki.
Additionally, they discovered that applying different vibrational patterns would change the particles’ degree of entanglement, allowing the team to assemble and disassemble a shape instantly. Applying a light vibration caused the pieces to interlock, whereas a heavy vibration untangled them as a pile of loose particles.
“No binder is required, so particles can be rapidly assembled in an infinite number of shapes, which can be reconfigured with appropriate mechanical stimuli, making them attractive for lightweight and reversible materials and structures and aggregate architectures,” the authors recently wrote.
“The fundamental understanding of entanglement gained in this project is also relevant to other physical systems, including biological structures, ‘living’ entangled matter, robotic materials, as well as colloidal assemblies,” the authors add.
“It’s a strange material because it’s obviously not a liquid. However, it’s also not quite solid,” Barthelat said. “This opens new and intriguing engineering possibilities.”
“Handling a bundle of these entangled particles feels very remote and exotic,” he added.
The paper, “Combined Effects of Particle Geometry and Applied Vibrations on the Mechanics and Strength of Entangled Materials,” appeared in the Journal of Applied Physics on April 14, 2026.
Ryan Whalen covers science and technology for The Debrief. He holds an MA in History and a Master of Library and Information Science with a certificate in Data Science. He can be contacted at ryan@thedebrief.org, and follow him on Twitter @mdntwvlf.
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