Longitudinal airflow can sharply suppress the drifting and ablation of the electrical arc formed between a train’s pantograph and overhead contact wire, according to a modeling and experimental study of pantograph–catenary systems under low-pressure conditions. The researchers built a magnetohydrodynamic model that coupled electric, magnetic, thermal, fluid and arc-motion dynamics, then tested the method in a vacuum chamber with copper and carbon electrodes. In experiments conducted at atmospheric pressure, increasing the airflow to 45 metres per second reduced the measured arc drift height from 27.6 millimetres to zero and shortened the arc-root distance from 51.3 to 3.9 millimetres. The same airflow setting also produced progressively less carbon-electrode damage, supporting the proposal as a possible current-collection control method for railways operating at high altitude.

The need for such control arises because the pantograph–catenary system carries the electrical energy used by electric trains, while the stability of its contact strongly affects operating safety and efficiency. Lower atmospheric pressure presents a particularly difficult environment. The study notes that reduced air density and breakdown-field strength can lower the ignition threshold for pantograph–catenary arcs, increase their frequency and duration, and enlarge the region affected by arcing. Repeated arcing can cause irreversible high-temperature ablation and electrical wear on contact wires and pantograph strips. It can also generate broad-band electromagnetic interference that may affect onboard signalling and traction-control systems, with potential consequences including insulator flashover and disruption of the traction network. These background risks motivate efforts to understand and restrain the spatial movement of arcs in plateau environments.

Existing arc models include the Cassie and Mayr black-box approaches, which support analysis of interactions between arcs and traction-network circuits but do not represent an arc’s changing spatial shape. Magnetohydrodynamic models offer a more physical alternative by coupling electromagnetic, flow and thermal fields. Previous work represented crosswind effects on pantograph–catenary arcs and examined how decreasing pressure affects arc-root jumping, in which the root forms at a new location after the elongated column reaches a critical length. Other studies have shown that magnetic fields can stabilize welding and switching arcs. Airflow regulation, however, has been investigated more extensively in welding than in pantograph–catenary systems, whose dissimilar electrodes, high operating speed, open environment, changing electrode gap and constrained vehicle installation space make their arc dynamics more complicated.

The new model represents an arc initially 6 millimetres long and 2 millimetres in diameter between a copper anode and a carbon cathode. It includes the surrounding air, both electrodes and a central longitudinal-airflow channel beneath the copper electrode, including a 1.5-millimetre control-airflow inlet. A separate inlet represents train-induced airflow and was set to 40 metres per second. Calculations covered pressures of 101, 80 and 60 kilopascals, longitudinal airflow from zero to 80 metres per second and a total simulation time of 25 milliseconds. Air properties were adjusted with pressure, while the electrodes were assigned distinct densities, resistivities, thermal conductivities and heat capacities. The governing equations represented electrical potential and current, magnetic-vector potential, mass and momentum conservation, energy conservation, viscous dissipation, Joule heating and net volumetric radiation.

The calculations use several explicit simplifications. Arc ignition is omitted, and the plasma is treated as being in local thermodynamic equilibrium. Flow in the air and arc domains is assumed laminar and incompressible, while metal vapour generated by electrode evaporation and phase change is excluded. These assumptions make it possible to examine large-scale arc motion and temperature distribution but prevent the simulation from directly calculating electrode mass loss, crack width or crater area. Copper was assigned the anode and carbon the cathode; a Gaussian distribution represented current entering through the copper electrode, and the bottom of the carbon electrode was grounded. The air boundary was treated as magnetically insulated, and the initial temperatures were set to 300 kelvin for the air and electrodes and 5,000 kelvin for the arc domain. The experimental apparatus was separate from the numerical model and provided direct physical measurements of damage.

Without longitudinal airflow, the simulated arc passed through two stages. During the first 10 milliseconds, its column elongated continuously, bent under airflow influence, and developed greater drift height and arc-root distance. From 10 to 25 milliseconds, the column and root distance fluctuated through repeated shortening and elongation. The authors associate this behaviour with the electrical and mechanical limits of the arc: once a column reaches a critical length, the voltage and energy available between the electrodes may no longer sustain continuous burning, and a shorter path of lower resistance may form on the electrode surface. The resulting root “jump” shortened the column before airflow stretched it again. Arc development became faster and both drift height and root distance increased as pressure fell. Temperature briefly exceeded 10,000 kelvin and reached a maximum within roughly two to three milliseconds before fluctuating, while arc voltage rose as the column lengthened and resistance increased.

Longitudinal airflow changed these trajectories in the simulations. As speed increased, drift height, root distance and temperature all declined, with the greatest reductions occurring at lower airflow speeds before the curves began to level off. At 101 kilopascals, the calculated drift height fell from 37.5 millimetres without control airflow to zero at 45 metres per second, while root distance decreased from 57.3 to 3.7 millimetres and temperature dropped from 12,900 to 8,650 kelvin. The airflow needed to eliminate drift was 45, 40 and 35 metres per second at pressures of 101, 80 and 60 kilopascals, respectively; speeds required to bring root distance below 5 millimetres were 45, 40 and 30 metres per second. The authors attribute the stronger response at lower pressure to reduced plasma density, which weakens the arc’s inherent rigidity, Lorentz force and thermal buoyancy, allowing airflow load to become a more dominant influence on its motion.

The temperature response also revealed a practical plateau. The researchers defined the optimal airflow speed as the point after which increasing speed by 5 metres per second changes arc temperature by less than 3 per cent. At atmospheric pressure this occurred at 40 metres per second, compared with 30 metres per second at 60 kilopascals. The reported temperature reduction was 33.8 per cent at 101 kilopascals and 38.1 per cent at 60 kilopascals. Airflow strengthened convective heat removal, which lowered plasma temperature and, in turn, increased deionization and reduced Joule heating. Once thermal exchange approached saturation and the minimum temperature required for self-sustaining arc combustion was approached, further increases in speed produced little additional cooling. The model therefore indicates that the airflow speeds required for control decline as ambient pressure falls.

Laboratory testing used a vacuum chamber capable of controlling pressure with an accuracy of plus or minus 100 pascals, a constant-current supply rated from zero to 150 amperes, an arc igniter, a fan with continuously adjustable airflow from zero to 60 metres per second, a high-speed camera and an oscilloscope. Airflow at the electrode outlet was measured with an anemometer accurate to plus or minus 0.5 metres per second. The camera operated at 6,400 frames per second with an exposure of five microseconds, while each two-second arc test was repeated five times and the mean was reported. Tests at 101 kilopascals used airflow speeds of zero, 20, 30 and 45 metres per second. Without control airflow, high-speed images showed increasing arc drift. At 45 metres per second, drift was virtually absent during the final development phase and root distance was substantially shortened, with observed morphologies broadly consistent with the numerical results.

The experiments also found reductions in carbon-electrode ablation that increased with airflow speed. Microscopy showed shorter ablation areas, smaller craters and narrower crack propagation as the controlled airflow rose. The maximum crack width was 66.7 micrometres without airflow and decreased at higher speeds, while analytical weighing showed progressively less mass loss from the carbon electrode. The proposed mechanism is that stronger airflow promotes arc-root slippage, shortening the time the root remains at one point, while convective cooling lowers temperature and local energy density. These are experimental observations rather than direct outputs of the simulation: because the model omits electrode evaporation and metal-vapour transport, it cannot quantitatively predict ablation rates or near-electrode energy balances. The study therefore demonstrates reduced damage under its controlled laboratory conditions, not a complete first-principles calculation of all processes responsible for electrode wear or a proven field performance level in operating plateau railways.

Subject of Research: Experimental and numerical evaluation of airflow control of pantograph–catenary arcs at low pressure.

Article Title: Research on regulation method for suppression of pantograph-catenary arc by airflow in low-pressure environment

Article References: Xu, Z., Wang, S., Dong, K., & Gao, G. (2026). Research on regulation method for suppression of pantograph-catenary arc by airflow in low-pressure environment. iScience, 29(11), Article 117951. https://doi.org/10.1016/j.isci.2026.117951

Image Credits: AI Generated

DOI: 10.1016/j.isci.2026.117951

Keywords: pantograph–catenary systems, railway electrification, electric arcs, low-pressure environments, longitudinal airflow, magnetohydrodynamic modelling, electrode ablation, high-speed rail, plateau railways, arc suppression, Research, regulation

While Windows overclockers have spent years lazily sliding UI controls to adjust frequency curves and tweak voltages inside Ryzen Master, Linux users have largely had to treat their CPU tuning like an archaeological dig into the opaque veil of UEFI/BIOS firmware. Well, rejoice, Linux users, as AMD has officially published a fresh set of patches for the Linux kernel introducing AOD (AMD Overclocking Driver) support for Ryzen processors, Phoronix reports.

Instead of forcing developers to reverse-engineer closed-source firmware hooks or rely on messy, machine-specific ACPI workarounds, this new kernel driver is a direct pipeline to AMD's platform adjustments. By exposing core tuning capabilities straight through the standard sysfs interface, AMD is laying down an official, upstreamed foundation for software-level CPU management.

Right out of the gate, the driver handles platform-level SMT toggling, memory clock visibility, and power tunables. That's right: you can now toggle Simultaneous Multithreading (SMT) dynamically through this driver interface. That works through the CPU hotplugging mechanism and acts as a clean alternative to passing the old 'nosmt' boot parameter to the kernel. The new driver also allows real-time monitoring of memory clocks, and initial power controls are being wired up, too.

Now, if you're expecting a polished, one-click official AMD application equivalent to Ryzen Master for Linux, just pump the brakes. AMD is following its open-source amdgpu graphics driver playbook here, building the kernel plumbing and then leaving the user-space presentation layer entirely up to the community. That means you're going to have to wait for utilities like CoreCtrl to build out interfaces for this new driver, but it shouldn't be long now before that work gets underway in earnest.

This approach is arguably a better fit for the Linux philosophy anyway. Because the controls are being exposed via clean kernel sysfs nodes, it won't be long before independent developers integrate these knobs into powerhouse open-source monitoring and tuning suites, or even write dedicated desktop environment extensions specifically for the purpose of tweaking Ryzen processors.

The new patches are currently winding their way through review on the mailing lists, meaning mainstream kernel integration is on the horizon. For a DIY enthusiast who loves squeezing every last MHz out of silicon while maintaining a fully open-source software stack, this is easily the most exciting hardware-enablement milestone I've seen in a long time.

Prime Minister Andy Burnham announced a campaign against listening to music or videos without headphones on public transportation. We asked London commuters to weigh in.

More than 11 percent of the GPUs at Meta’s Prometheus supercluster in New Albany, Ohio, are either dead or sitting idle waiting to step in when the next drops out, Zuckercorp revealed this week. The massive pool of redundant GPUs is just one of several measures the hyperscaler has taken to keep its AI training runs on track. Prometheus is among Meta's largest compute deployments to date. Powered by Nvidia's Grace Blackwell and (soon) Vera Rubin rack systems, Meta says the supercluster will eventually scale to a gigawatt of capacity spanning about a million accelerators. But as anyone who has worked on a large scale compute cluster understands, whether for LLM training or traditional HPC workloads, more chips inevitably means more problems. In an eight-GPU node, the chance of having a GPU fail is relatively low. In a 100,000-GPU cluster, failures are unavoidable. Meta is building something that's 10x larger and requires distributed training runs across multiple datacenters. Keeping the entire thing humming long enough to reach the next checkpoint becomes a major headache. In a blog post published this week, Meta offered a unique look at the work it's done to keep failures from derailing the development of models like Muse Spark or its most recent release Muse Wonder. Idle GPUs are the cost of continuity When Meta began the first phase of its Prometheus deployment in New Albany, the company says, it was experiencing roughly 1,000 interruptions per day for every 8,000 GPUs. Interruptions don't mean GPU failure. They simply mean something crashed, failed, or timed out, forcing the training run to stop and roll back to the last known good checkpoint. Meta claims in the year since it's been able to cut down the number of interruptions by 99 percent, to about 11 per day per 8,000 GPUs. One of the ways it's done this is by keeping some of Prometheus' GPUs in reserve. The cluster is currently powered by a combination of GB200 and GB300 systems, each with 72 GPUs per NVLink domain. However, only 64 of them are ever in use at any given moment. The other eight are essentially hot spares. Meta did this because a single failed GPU would take out an entire rack and interrupt the training run. Now, when a GPU fails, one of the spares can slot in and take its place. While this saves time — Meta doesn't have to drop the entire rack from the cluster or wait for someone to swap the faulty component to start training again — a failed GPU would still result in an interruption, albeit a shorter one. Parallel resiliency To mitigate this, Meta took advantage of the fact that large scale training jobs usually use data parallelism, where multiple copies of the model are resident within the cluster. "When a fault can be isolated to a data-parallel model replica, we reduce the impact of a hardware failure by allowing a single faulty replica to be taken offline, while the remaining cluster repeats that step and continues productive training," Meta explained. "The compromised slice simply restarts on available spare hosts and resynchronizes, converting a potential total system halt into a minor, temporary reduction in aggregate throughput." In other words, Meta's training runs only get interrupted if the fault exceeds the data parallel domain. When this happens, the company has implemented a comprehensive checkpointing scheme, which you can think of a bit like auto-save or a revision history. That way if something does fail, Meta doesn't have to start over from scratch. "Our distributed checkpointing architecture asynchronously saves the model state — including gradients and optimizer data — to multi-tier storage," the company wrote. NVLink growing pains Meta says one of the most difficult challenges was making the NVLink fabrics used to connect 72 GPUs behave like one great big accelerator. Earlier H100 and H200-based systems featured just eight GPUs per NVLink domain, which meant the links were far shorter and less numerous. But this functionality was expanded nine-fold with Nvidia's Blackwell generation. "With over 600,000 NVLink connections, even marginal signal degradation triggers frequent NVLink recovery cycles," the company wrote. To overcome this, Meta had to implement a continuous telemetry system and conduct L11 diagnostic burns to isolate weak links before the systems were put into production. Power was another challenge. Ramping up and down tens of thousands of GPUs for a training run can introduce massive spikes in power consumption called transients, which can overwhelm power supply equipment. According to Meta, by focusing on the stability and reliability of its cluster, it was able to achieve more than 85 percent "goodput" — a measure of useful throughput — for pretraining and more than 90 percent for reinforcement learning on its latest model Muse Wonder. The company's ambitions don't stop here. As it works its way toward a million GPUs, Meta hopes to get its interruption rate down to just two per day for every 8,000 GPUs, or about 100,000 GPU-hours mean time between interrupts. ®

In hot weather, trees “sweat” to cool down by releasing water from their leaves or needles and then replacing it with water from the soil. But during long periods of high temperatures, trees may release more water than they can replace. To strengthen forests against heat stress and other environmental hazards, scientists have suggested adding fungi to the soil. 

Soil fungi living on tree roots are called ectomycorrhizal fungi. They help trees absorb water by expanding their root networks, and trees help the fungi by providing them with sugars for energy. This beneficial partnership is known as symbiosis. Scientists at Fujian Agriculture and Forestry University in China tested whether symbiosis with ectomycorrhizal fungi could help the pine trees Pinus massoniana survive warmer temperatures. 

For their experiment, the researchers grew pine seedlings with and without ectomycorrhizal fungi in a greenhouse under different temperature conditions. First, they grew fungi called Cenococcum geophilum for 45 days on a gel-like substance called agar. At the same time, they grew the pine seedlings for 30 days, then transferred them into pots with natural soil. 

After 15 days in the pots, the researchers spread the gel-like agar containing the fungi over small 1 x 1 centimeter (cm) areas of the pine root tips to introduce the fungi to the seedlings, a process called inoculation. They assumed that the fungal network would expand from the starting area to colonize new root tips as the seedlings grew. The team’s inoculations included 6 different C. geophilum strains and 1 fungi-free control. To simulate heat stress, the researchers replicated each of the 7 inoculations at 25°C (77°F) and 35°C (95°F). They repeated each inoculation condition in 3 pots with 3 seedlings, for a total of 126 seedlings. 

After 60 days, the researchers harvested the pine seedlings and used a microscope to count how many of their root tips had fungi. They divided the number of root tips with fungi by the total number of root tips to calculate a colonization rate. They considered seedlings with a colonization rate of 80% or higher to have successfully formed a symbiotic relationship with the fungi. Then, the researchers analyzed seedlings with an 80% colonization rate to investigate their symbiotic partnerships.

The team observed that pine seedlings grown under heat stress at 35°C were 24% to 59% smaller than those grown at 25°C, and their needles were yellow instead of a healthy green. However, the seedlings that formed a symbiotic relationship with the ectomycorrhizal fungi were greener and 80% heavier than the control seedlings. 

Investigating the trees further, the team measured 3 chemical compounds related to plant stress, including malondialdehyde (MDA), proline, and nitric oxide. Plants release MDA when damaged, so high MDA levels mean a plant is struggling. In contrast, plants release proline and nitric oxide to repair cells or hold water, so high amounts of these compounds mean a plant is healing.

To measure MDA and proline levels in the pine needles, the researchers mixed them with different acids that change color depending on the sample’s MDA or proline content. To measure nitric oxide, the researchers mixed the needles with a protein that converts the compound into a chemical that changes color. They found that inoculated seedlings at 35°C had about 30% to 40% less MDA, 15% to 30% more proline, and 150% more nitric oxide in their roots than the control seedlings. 

Finally, the researchers measured changes in gene activity in the inoculated pine seedlings. They identified active genes by analyzing RNA molecules from the pine seedling roots with a process called RNA sequencing or transcriptomics. They found that inoculating the seedlings with ectomycorrhizal fungi activated more genes related to root strength, water retention, cell defense, and general plant protection than were activated in the control seedlings. 

By combining chemical analyses with growth measurements, the team concluded that a symbiotic partnership with the ectomycorrhizal fungi helped the pine seedlings survive heat stress. They demonstrated that through prolonged heat, inoculated seedlings maintained greener needles and lower levels of stress chemicals. They suggested that ectomycorrhizal fungi could be an ecological tool to help trees stay hydrated in a warming world. They recommended that future researchers conduct field trials with inoculated trees to translate these findings into forest management strategies. 

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Researchers have followed up on the discovery of a rare binary system containing a white dwarf and a brown dwarf. This unusual pairing offers valuable insights into the physics of atmospheres subjected to strong radiation.

Odd Couple

In 2025, researchers searching through observations from the Transiting Exoplanet Survey Satellite (TESS) spotted an eclipsing binary system in which a brown dwarf orbits the white dwarf J0404+1112 every 2.93 hours. Only about a dozen white dwarf–brown dwarf binaries have been confirmed so far, and each newfound system offers clues to how substellar or planetary companions survive a star’s transition from main-sequence star to swollen red giant to blazing white dwarf.

Because the two objects orbit one another so closely, this remarkable system also offers researchers an opportunity to study the dynamics and chemistry of an atmosphere blasted with high-energy radiation, with important implications for close-in brown dwarfs and giant planets.

Insights from New Observations

Based on early data from TESS and SPECULOOS, Julien de Wit (Massachusetts Institute of Technology) and collaborators posited that the J0404+1112 system contained an unusually puffy low-mass white dwarf and a 25-Jupiter-mass brown dwarf. However, the initial data couldn’t fully constrain the white dwarf’s properties, leaving degeneracies between its temperature, radius, and other factors.

Now, the team has obtained new photometry from the Gran Telescopio Canarias and the United Kingdom Infra-Red Telescope, plus spectroscopy from the Gran Telescopio Canarias, to get a better sense of the system. These observations allowed them to break the degeneracies in the white dwarf’s properties, showing it to be an 0.60-solar-mass white dwarf that has been cooling for more than 10 million years to a present-day temperature of 28,000K. The brown dwarf also appears to be more massive than initially thought, weighing in at roughly 40 Jupiter masses.

Like Night and Day

Because the system’s orbit is almost exactly edge on from our point of view, the white dwarf is completely eclipsed as the much larger brown dwarf passes in front of it. This allows the contributions from the two components to be separated, and for the emission from the brown dwarf’s nightside to be measured. The authors constrained the brown dwarf’s nightside temperature to roughly 1750K. Its dayside temperatures are likely to be significantly higher — potentially around 3750K at the point where the white dwarf is directly overhead — but more data are needed to constrain the dayside temperature of this world.

The estimated day–night temperature difference for this brown dwarf places it in the realm of hot and ultra-hot Jupiters. However, it’s subjected to more ultraviolet radiation than a typical hot or ultra-hot Jupiter orbiting a main-sequence star. This makes J0404+1112 a valuable system for disentangling the interconnected effects of temperature, radiation environment, atmospheric composition, and other factors on hot or ultra-hot Jupiters and other strongly irradiated brown dwarfs. We can certainly expect to learn more about this rare system as new data are collected!

Citation

“J0404+1112: A 3 hr Eclipsing White Dwarf–Brown Dwarf Probing Multiple Atmospheric Regimes,” J. de Wit et al 2026 ApJL 1009 L40. doi:10.3847/2041-8213/ae9a97

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While our solar system sports one Jupiter-like planet, exoplanet discoveries have revealed that giant planets come in a few different forms in systems with varying architectures. A recent study has explored how the mass–eccentricity relationship in warm Jupiters is shaped by dynamical interactions between planets.

Jupiter-like Planet Eccentricities

Exoplanet surveys have uncovered three different Jupiter-like planet populations across the galaxy: hot, warm, and cold Jupiters. Much like our own, cold Jupiters orbit on long periods, far away from their host stars. Conversely, hot Jupiters zip around their host stars on extremely short-period orbits. Somewhere in between, warm Jupiters orbit their host stars with periods from 10 to 365 days.

These giant planets have proven to be deeply entwined with the makeup of their host systems, and their orbital properties have been used to test and trace planet formation pathways. For example, long-period cold Jupiters discovered in radial velocity surveys have exhibited a positive correlation between planetary mass and orbital eccentricity. This mass–eccentricity relationship, as previous studies have suggested, is likely the result of a process known as planet–planet scattering, in which dynamical interactions between planets in the same system cause significant and chaotic changes to their orbits.

Does this relationship extend to shorter-period giant planets as well? Hot Jupiters are heavily impacted by tidal interactions with their host stars, circularizing their orbits and erasing dynamical imprints on their eccentricities. Warm Jupiters, on the other hand, orbit far enough away for their eccentricities to preserve clues about their energetic histories. Recently, thanks to the Transiting Exoplanet Survey Satellite (TESS), astronomers have identified a positive correlation between planetary mass and eccentricity in a growing sample of warm Jupiters, but the exact driver of this trend has yet to be uncovered.

Planets Scattering Planets

To test if the positive mass–eccentricity correlation in warm Jupiters is consistent with planet–planet scattering, Jiayin Dong (University of Illinois at Urbana-Champaign) constructed a sample of 85 warm Jupiters from the NASA Exoplanet Archive. This carefully selected sample, dominated by discoveries from TESS and Kepler, included planets with masses between 0.3 and 15 Jupiter masses and orbital periods between 10 and 365 days. In conjunction with this sample, the authors built a planet–planet scattering framework to assess the range of properties anticipated for scattered warm Jupiter systems.

Comparing the observed trends in their sample with their theory-based expectations, Dong and team showed that planet–planet scattering naturally explains the mass–eccentricity relation of warm Jupiters. In their framework, warm Jupiters originate in compact, multiplanet systems, and as the systems evolve, gravitational interactions between planets excite orbital eccentricities and inclinations, leading to close encounters. These encounters can cause planets to collide or to be ejected from the system entirely.

The mass–eccentricity relation of warm Jupiters, the authors suggested, represents a transition from collision-dominated interactions among low-mass planets to ejection-dominated interactions among high-mass planets. For massive enough systems, scattering can excite eccentricities high enough to trigger the high-eccentricity migration responsible for moving hot Jupiters to their close-in orbits. Overall, this study suggests that warm Jupiters are not a distinct planet species, but instead may represent the shorter-period counterparts of cold Jupiters whose orbits are shaped by the same dynamical processes. Future observations expanding the sample of warm Jupiters and discoveries of additional planet companions will further uncover the links between hot, warm, and cold Jupiter systems.

Citation

“Planet–Planet Scattering Explains the Mass–Eccentricity Relation of Warm Jupiters,” Jiayin Dong et al 2026 ApJL 1007 L29. doi:10.3847/2041-8213/ae930e

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Electric vehicles (EVs) use high-powered rechargeable batteries to store electrical energy that powers their motors. Most EV batteries use a specific mixture of metals to hold the battery’s charge, called nanoscale lithiated nickel manganese cobalt oxides, aka NMC materials. NMC materials can store a large amount of energy, making them ideal for batteries, but the metals themselves can be toxic in soil and water if the batteries are not properly recycled.  

NMC batteries leave behind a specific combination of toxic metals, so researchers can study how bacteria respond to this waste when it enters the environment. By understanding how bacteria adapt to toxic metals, scientists hope to predict how new technologies like EV batteries will affect the environment. 

In 2019, a team of researchers from the University of Minnesota reported that a common soil bacterium called Shewanella oneidensis can rapidly evolve resistance to NMC materials, but they didn’t know how. True beneficial gene evolution usually develops over thousands of generations, but Shewanella oneidensis adapted much more quickly. The team recently conducted a follow-up study to determine the genetic mechanisms behind this bacterium’s unique metal resistance.

They started by analyzing the complete DNA sequences of 33 Shewanella colonies that were resistant to either pure NMC waste or individual metals like nickel and cobalt, using a process called whole-genome sequencing. They expected to find DNA mutations in genes that control how bacteria take up and expel toxic metals that would give the resistant Shewanella an advantage in surviving NMC metal toxicity. However, they didn’t find any DNA mutations in metal-related genes. Instead, they found that a specific DNA region was duplicated 10 to 15 times in all 33 NMC-resistant or metal-resistant colonies. This result suggested to the researchers that a temporary adaptation, not true gene evolution, was responsible for the bacteria’s metal resistance. 

The researchers wanted to know more about the duplicated DNA region in metal-resistant Shewanella, so they focused on 2 gene duplication mechanisms that might serve as temporary adaptations in Shewanella. The first mechanism directly duplicates DNA regions to create extra copies of a gene, via a process called gene duplication amplification. The second mechanism moves DNA segments, called mobile genetic elements, into different DNA sections to create extra copies of a gene. 

To test whether either of these mechanisms was causing the gene duplication in Shewanella, the team grew the bacteria in different liquid media containing only cobalt, only nickel, or cobalt and nickel together. The researchers used a technique that measures the exact amount of DNA in a given sequence, called quantitative polymerase chain reaction (qPCR), to calculate how many duplicated DNA regions were in each genome sequence. They found that neither cobalt nor nickel alone triggered DNA duplication. But the bacteria grown in the media with cobalt and nickel together had 8 additional copies of the DNA region. This suggested that something about the combination of nickel and cobalt triggered gene duplication. 

To determine whether this gene duplication only occurred with nickel and cobalt, the team substituted copper and zinc for nickel in the growth media. They exposed Shewanella cells to different combinations of these metals using a grid pattern in a 96-well plate, a technique called a checkerboard assay. Then they repeated the qPCR process to measure the duplicated DNA region. They found that no other combinations of metals triggered gene duplication, suggesting that nickel and cobalt are especially toxic together, and that the gene duplication responds to this combined toxicity.

Finally, the researchers identified the function of the duplicated DNA region. They found that one of the genes within the region turned on a special protein that pushes metal ions and other toxins out of the cell, called an efflux pump protein. The team hypothesized that this efflux pump protein was responsible for removing cobalt and nickel from the bacteria’s cells. To test this theory, they grew modified Shewanella cells without the efflux pump gene in media containing cobalt, nickel, or cobalt and nickel together. They found that the modified bacteria grew well in the nickel-only treatment, but didn’t grow in the cobalt-only or combined treatments, which indicated that the efflux pump protein is necessary for Shewanella to resist cobalt toxicity. 

Based on their experiments, the team concluded that nickel and cobalt together trigger a gene duplication event in Shewanella oneidensis. This gene duplication event increases the number of efflux proteins in Shewanella that can expel cobalt and underlies its unique NMC resistance. They suggested that future researchers study temporary adaptations similar to gene duplication in bacteria to track how they respond to electronic waste and other new technologies.

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In March 2023, Southern California experienced heavy storms after almost 20 years of drought. Sixteen of these came from long, narrow regions of concentrated water vapor moving through the air, known as atmospheric rivers. During droughts, groundwater supplies up to 60% of the total water to the Greater Los Angeles area, so researchers think that stormwater from the 2023 events could have recharged groundwater reserves for future use. However, scientists lack adequate tools to assess how much groundwater is in the subsurface.

To overcome this challenge, researchers from California and Texas developed a new method to study deep groundwater. California gets a large number of earthquakes, so it has over 450 stations around the state to monitor the associated vibrations, referred to as seismic waves. To study how much the 2023 storm recharged the groundwater in the Greater Los Angeles area, the team used a tool called seismic-DI, which employs these seismic waves to estimate how much groundwater is stored at different depths. 

Seismic-DI works by measuring how fast seismic waves move through the Earth, known as their seismic velocity. The researchers explained that the Greater Los Angeles area is a very crowded city, so it produces an abundance of seismic waves. They measured changes in the seismic velocities of these underground waves to see where the groundwater is full of water, or saturated, versus partially full, or unsaturated. They used 20 years of these data to produce graphs that record changes in groundwater storage at different depths, known as hydrographs.

To validate the accuracy of seismic-DI, the researchers also compared their seismic hydrographs with groundwater thickness data from a NASA mission known as the Gravity Recovery and Climate Experiment, or GRACE. GRACE consists of 2 satellites that measure changes in Earth’s gravity, which reflect surface water and groundwater storage. Both their seismic hydrographs and the GRACE groundwater data demonstrated that the Greater Los Angeles region experienced an increase in groundwater storage from stormwater in the winter, and a reduction in groundwater storage during the dry summer. 

The team’s hydrographs showed that the 2023 storms saturated the Greater Los Angeles area groundwater down to 35 meters (114 feet), but it was abnormally dry at 200 meters (656 feet), and at a severe drought at 450 meters (1,476 feet). The GRACE data similarly demonstrated that the subsurface is in severe drought. Combined, these data indicated the 2023 storms helped the Greater Los Angeles area recover up to 90% of its groundwater at depths of 50 meters (164 feet) since the 2006-2022 drought. However, it recovered only about 40% at 75 meters (246 feet) and 20% to 30% at 100 meters (328 feet).

They concluded that the 2023 atmospheric river events recharged only shallow groundwater reserves in the Los Angeles area. As a result, they suggested future researchers consider deep groundwater reserves for a more complete assessment of total regional water deficits. While this study demonstrated that seismic-DI can successfully reveal spatial changes in groundwater and drought at depth, the team acknowledged that contributing factors like the amount, rate, and spatial variability of groundwater reserves remain targets of future work. 

The post Using seismic stations to track groundwater resources appeared first on Sciworthy.

More than 90,000 tons of radioactive waste are stored on-site at nuclear power plants awaiting permanent disposal. Nuclear waste releases unstable forms of uranium and other elements, called radionuclides. Long-term exposure to radionuclides can cause cancer, so environmental safety regulations require that nuclear waste be made inaccessible for at least 1 million years. Thus, researchers are evaluating possible ways to store this waste permanently. 

One proposal is to store nuclear waste thousands of feet below ground in rock formations called deep geological repositories, or DGRs, to seal in radionuclides. Scientists can’t see into the future, so instead they look to the past to determine how well DRGs could work. A team of researchers from Canada recently analyzed uranium deposits that have existed for billions of years as an analog for how nuclear waste behaves deep underground.

Past researchers found that uranium deposits in ideal DGR conditions, with their protective casings intact, will stay contained for more than a billion years. However, these conditions require a stable geological environment. This team instead focused on uranium deposits in non-ideal conditions. They studied the Canadian Kiggavik uranium deposits, which occur in cracked bedrock approximately 500 meters (1,600 feet) below the surface. The team claimed these conditions are similar to DRGs with broken protective casings.

The team knew from previous work that most of the uranium minerals in the Kiggavik deposits are more than 274 million years old, but about 10% are less than 1 million years old. They found that these younger uranium minerals occurred alongside clay minerals surrounding the older uranium minerals and within open fractures. The researchers interpreted the older uranium as the source material of the younger uranium because of their proximity. They concluded that water must have infiltrated the deposits and interacted with older minerals, transporting uranium and forming new, younger minerals.

To determine the source of water infiltration, the researchers compared the chemistry of clay minerals formed alongside the younger uranium with modern snow in the Kiggavik region. They looked at the number of neutrons, or isotopes, in hydrogen atoms within the clay minerals, and found heavy values matching those of fresh water. These values indicated that snow or glacial meltwater was the source, rather than underlying bedrock or seawater. 

To determine when the water infiltrated over the last million years, the researchers used uranium-thorium dating to find when the radionuclides were mobilized. This dating method shows when uranium was removed from the original minerals by measuring how much its concentration was reduced by water infiltration. They found that the dates of these radionuclide mobilizations clustered into 5 separate water-infiltration events that occurred from 36 to 470 thousand years ago. 

Comparing these dates with existing climate records, the researchers found that the water infiltration events overlapped with periods when glaciers covered the ground surface. Normally, water can’t travel thousands of feet underground. But, like a hydraulic press, a glacier pushes water at its base downward. If the water encountered cracks in the ground, the pressure could inject it to the depth of the uranium deposit.

Next, the researchers wanted to measure how quickly radionuclides left the deposit. They measured the maximum distance between younger and older uranium minerals and used the age of the most recent water-infiltration event to estimate the fastest speed at which the radionuclides could have moved. They determined this maximum speed to be 0.0014 millimeters (0.000055 inches) per year. At that rate, they calculated it would take 360 million years for uranium radionuclides to travel 500 meters from a DGR at the same depth as the Kiggavik uranium deposits to the surface. This timescale far exceeds the necessary 1 million years for nuclear waste containment.

The team concluded that if glaciers were to return to Kiggavik, the uranium deposits wouldn’t experience substantial radionuclide movement, even in contact with glacial meltwater. Similarly, if a DGR has broken protective casings, radionuclides would be moving too slowly to reach the surface even if water leaked into it. They suggested their findings reduce constraints on viable DGR sites and could help ensure the long-term secure storage of nuclear waste.

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Astronomers and astrobiologists use a wide range of tools in their search for extraterrestrial life. Probes, satellites, and rovers take photographs and collect data on the geology and chemistry of planets and moons in the solar system. Radio telescopes sweep the sky, looking for signals from intelligent civilizations. And telescopes like the JWST can observe the atmospheres of distant exoplanets, potentially finding ones similar to Earth’s. 

A limitation of these methods is that they assume life on other worlds is fundamentally similar to life on Earth, requiring the same balance of molecules in the atmosphere, producing the same chemicals for their metabolic processes, or developing technologies analogous to those humans make. While researchers use these assumptions as baseline markers to look for, assuming such similarities might lead them astray. Scientists looking for life using too narrow a set of parameters might miss life unlike Earthlings. Or they might mistake coincidentally similar geological, chemical, or astronomical processes for having been made by living things.

So, to complement techniques that assume alien life resembles life on Earth, scientists also consider approaches that detect signs of life while making as few assumptions as possible about what it could look like, known as agnostic biosignatures. One of these life signs with few assumptions is complexity. Though it can be difficult to quantify precisely, living things tend to create more complex systems than nonliving things. A team of researchers recently tested whether living and nonliving worlds could be distinguished solely by examining the complexity of the light-reflection patterns from their surfaces.

For this, the team treated Earth and Mars, similar planets but one with life and one without, as stand-ins for exoplanets around other stars. They collected data on the light reflected off both planets’ surfaces from a space station monitoring Earth, known as the Deep Space Climate Observatory or DSCOVR, and an orbiter around Mars observing its atmosphere, known as the Hope Probe. 

The team identified 3 nearly identical wavelengths of light that these machines observe from their respective planets. From the machine databases, they collected measurements of how much light each planet reflected, called its reflectance, at these 3 wavelengths over 486 local days. By using only individual wavelengths of light rather than full photographs, the team claimed that, if their test worked, researchers could apply it to distant exoplanets that cannot be photographed in detail.

Without looking for any particular chemical or signal, the team compared these measured reflectances using a series of statistical tests to determine the variation and randomness in their reflected light, which they considered representative of the complexity of the planets’ surfaces. The researchers found that of the 27 complexity metrics they tested, Earth scored higher than Mars on 25, Mars scored higher than Earth on 1, and the planets scored the same on 1. So, even though both planets have rocky surfaces with atmospheres, weather, and cyclical changes, the one with life is measurably more intricate and dynamic, and the researchers found this difference detectable even with a small stream of light at great distances.

The researchers claimed their test demonstrated that complexity could be a valid agnostic biosignature, with caveats. One is that Earth is quite distinct from Mars, in that it has oceans and a relatively thick atmosphere. This means that processes like making and dispersing clouds and the sharp contrast between land and water could add complexity to Earth’s measurements without direct ties to living things. 

They suggested 2 possible explanations for how these complicating factors relate to life on Earth. Either life allows Earth to have these factors, or Earth is inherently complex regardless of life, and therefore it can’t be used to assess how life leads to measurable complexity. To say which is the case, one would have to replicate this test with a lifeless copy of Earth. However, the team argued that Earth’s biosphere is unlikely to be wholly unrelated to its water cycle.

To bolster the validity of their methods, the team plans to expand their sample of planets to include Venus, Saturn’s moon Titan, and simulated exoplanets with modeled biochemistry and atmospheres. If the model holds up, their methodology could represent a new category of mission to find alien life, with long searches for variations in reflected light uncovering properties of distant worlds that would otherwise go unnoticed.

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Waterfalls play an essential role in river development and landscape evolution. Researchers are particularly interested in how abrupt changes in the steepness of a river channel, also called knickpoints, develop. Knickpoints vary in size and can be as small as your index finger or as large as Niagara Falls. Researchers from Virginia hypothesized that waterfalls, a type of knickpoint, can influence erosion when a river channel is cut into solid rock, called bedrock. 

The researchers wondered whether fractures and other pre-existing weaknesses can influence how water removes rock slabs from a river channel, via an erosional process called plucking. Fractures can form in many different orientations, but the researchers focused on 3. Imagine a flat tabletop that represents a riverbed. Fractures that are parallel to the flat tabletop are called horizontal fractures. If you tip the table so the low end points downstream, that’s the direction of downstream-dipping fractures. If you tip the table so the low end points upstream, that’s the direction of upstream-dipping fractures. 

First, the researchers tested how fracture orientation changed erosion before an established knickpoint. They used a man-made channel, called a flume, to simulate a natural river system. To set up the experiment, they glued layers of small tiles about the size of an adult’s thumbnail to a plastic frame and put it in the flume. Then, they placed a layer of unglued tiles upstream of the knickpoint. 

To begin the experiment, the researchers incrementally increased the amount of water flowing through the flume every 5 minutes until they reached a maximum flow or all the loose tiles were “eroded.” They considered a tile to be “eroded” once it was transported over the knickpoint and downstream. The researchers measured the water depth at a fixed point upstream of the knickpoint, the number of tiles eroded through plucking, and the volume of water. They repeated this 8 times for each fracture orientation.

The team found differences in the amount and timing of erosion between the 3 orientations. In particular, they saw that erosion began at lower water speeds for horizontal beds than for tilted beds. Based on the observed erosion rates, they concluded that horizontal beds are the most susceptible to erosion, while upstream-dipping beds are the most resistant, and downstream-dipping beds fall between the 2. 

The researchers then tested how a knickpoint would develop and influence water flow if all of the tiles were unglued and free to move. They individually stacked almost 5,000 unglued tiles across the channel. Before starting the experiment, they filled the flume slowly with water to submerge all of the tiles without moving them. Then, they increased the water’s speed to start erosion. Instead of collecting measurements as they did in the first experiment, the researchers filmed this experiment and observed how the knickpoint shape changed and how plucking occurred. 

The researchers observed that the plucked tiles in this experiment formed piles downstream of the knickpoint in a process known as armoring. This armor of tiles, which represents loose rocks near a waterfall in a natural setting, prevents erosion of the underlying river bed. Only when the water transports the armor downstream can it erode the bedrock and develop a knickpoint.

Unlike the first experiment, researchers found no differences in erosion between fracture orientations in this experiment, but did observe differences in armoring. They found that upstream-dipping beds had the least armoring because water quickly transported piles of tiles near the waterfall downstream. Downstream-dipping beds had the most armoring, and their eroded tiles took longer to move downstream. 

The team concluded that waterfalls will erode at different rates based on their fracture orientation and armoring processes. The orientation of bedrock fractures also impacts the shape of the waterfall and the river channel around it. So the next time you see a large waterfall, consider that it may have started as a small fracture the size of your thumbnail.

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Neutral mesons, the fleeting particles that endlessly oscillate between matter and antimatter forms, have just acquired a strikingly new mathematical lens. In a theoretical study published in The European Physical Journal C, physicist Swarup Sangiri of the Indian Institute of Technology Kharagpur has constructed a fresh class of geometric objects, called connected sequential Bargmann invariants, that encode how two different decay channels of an entangled meson pair relate to one another inside the projective geometry of quantum states. The work extends an earlier framework and shows that when decay-projected states are wired directly into a cyclic chain of quantum overlaps, the resulting quantities behave in a fundamentally different way near the boundary where CP violation switches off.

To appreciate why this matters, it helps to recall what makes neutral meson systems so special. Particles such as the neutral kaon, the D meson, and the two neutral B mesons are produced as flavor eigenstates but propagate as mixtures of heavy and light eigenstates, conventionally written as combinations of the meson and its antiparticle with coefficients p and q. When the magnitudes of p and q differ, the two propagation eigenstates are no longer orthogonal, and this nonorthogonality is the hallmark of indirect CP violation, the subtle asymmetry between matter and antimatter that was famously discovered in kaons in 1964. Because any physical observable must be independent of arbitrary phase conventions attached to quantum states, the natural language for describing interference in these systems is one of rephasing-invariant quantities.

Bargmann invariants provide exactly that language. For a closed sequence of quantum states, the nth-order Bargmann invariant is the product of overlaps taken around the cycle, and it remains unchanged under independent phase redefinitions of each state. Its phase is a geometric phase, the kind of holonomy familiar from the Berry phase, accumulated along a closed loop in the space of rays. Earlier work had applied this machinery to correlated neutral meson decays, constructing third-order invariants that probe single-channel interference and fourth-order invariants that correlate two decay channels through the heavy and light propagation eigenstates. Those constructions linked the geometric picture to conventional flavor observables, including Jarlskog-type structures, without introducing any new phenomenological parameters.

The new study asks a deceptively simple question: what happens if the two decay-projected states themselves are connected directly inside the cyclic overlap chain? When one meson of an entangled pair, produced for example in the decay of a phi or Upsilon resonance, decays into a channel f, its partner is projected into a conditional state determined by the decay amplitudes. A second channel g prepares a different conditional state. The connected sequential fourth-order Bargmann invariant follows the loop from the heavy eigenstate to the f-conditioned state, then directly to the g-conditioned state through their mutual overlap, then to the light eigenstate, and finally back to the heavy one. The crucial new ingredient is the overlap between the two conditional states, a quantity built purely from the decay amplitudes that measures how similar the two post-selected meson states are in projective Hilbert space.

This change in connectivity has dramatic consequences for how the geometric quantities scale as CP violation becomes small. The previously studied disconnected fourth-order invariant, when normalized by the product of two third-order invariants, produced a ratio that blows up quadratically, scaling as the inverse square of the mixing asymmetry, the difference between the squared magnitudes of p and q. The connected sequential ratio, by contrast, carries only a single factor of that asymmetry in its denominator, so its enhancement near the CP-conserving limit is merely linear. Physically, replacing one propagation-mediated link with a direct overlap between the projected states removes one power of the heavy-light suppression, placing the two classes of geometric correlation in distinct scaling classes.

A second new ratio compares the sequential invariant directly with the disconnected one. This comparison ratio behaves in the opposite manner: it is proportional to the mixing asymmetry itself, vanishing in the exact CP-symmetric limit. Its behavior is governed by a competition between two effects, the shrinking of the heavy-light overlap on one side and the possible alignment of interference phases between the two decay channels on the other. When the conditional states prepared by the two channels become geometrically aligned, the suppression can be weakened, revealing that the connected structure is sensitive not only to mixing asymmetry but also to the relative coherence of the decay projections.

To make these statements precise, the analysis is carried out in terms of the standard rephasing-invariant interference parameters lambda, the products of the mixing ratio q over p and the ratios of decay amplitudes to their CP-conjugates. A systematic small-asymmetry expansion, with the squared magnitudes of p and q written as one half plus or minus a small parameter, shows that the sequential ratio depends explicitly on the phase difference between the two channels through the numerator structure. This dependence has no analogue in the disconnected construction, where the channels communicate only through the propagation eigenstates. It means the connected sequential geometry probes the relative orientation of the decay-conditioned rays themselves, a piece of interference information that conventional observables do not isolate on their own.

The geometric interpretation is perhaps the most elegant part of the story. Both the disconnected and the sequential fourth-order invariants involve the same four rays, the heavy and light propagation eigenstates and the two decay-projected states, yet they define different closed paths through projective Hilbert space. A Bargmann invariant depends not just on which states participate but on how they are stitched together, so rewiring the cycle produces a genuinely different geometric correlation. In the CP-conserving limit the heavy and light eigenstates become orthogonal, one link of every cycle collapses, and the entire cyclic structure degenerates, which is why the invariants vanish there. The formalism thus ties the very existence of these CP-sensitive geometric objects to the nonorthogonality of the propagation eigenstates.

Crucially for experiment, none of this requires new physics inputs. The sequential invariant and both ratios can be written entirely in terms of the mixing parameters and decay amplitudes already extracted from time-dependent flavor analyses at flavor factories such as the KLOE experiment with entangled kaons and the BABAR and Belle measurements with entangled B mesons. The framework simply reorganizes the same accessible information into cyclic geometric quantities. The neutral kaon system, with its small indirect CP violation and long-lived interference phenomena, is flagged as a particularly natural arena where the linear enhancement of the sequential ratio could be pronounced, with candidate channels including two-pion, semileptonic, and multi-pion final states. The B meson systems, including channels like B decays to J/psi kaons and to pion pairs, offer rich phase-dependent interference structures, while the D system, where both mixing and CP violation remain tiny in the Standard Model, could serve as a sensitive testing ground for small deviations from exact symmetry.

The study stops short of claiming a new dynamical observable. The author is careful to note that the sequential construction does not add an extra propagation stage in the manner of conventional cascade CP analyses; it is a reorganization of overlap geometry within a single meson system. Even so, the result establishes a hierarchy of Bargmann invariant structures, from single-channel triangles through disconnected quadrilaterals to directly connected sequential cycles, each capturing a different layer of CP-sensitive interference. As precision flavor physics continues to squeeze ever tighter constraints on the origins of matter-antimatter asymmetry, geometric tools of this kind may reveal correlation patterns hiding in plain sight within data that experiments already collect.

Subject of Research: Geometric Bargmann invariant structures and CP-sensitive interference in correlated neutral meson systems

Article Title: Connected sequential Bargmann invariants and (\mathcal CP~)-sensitive geometric correlation structures in neutral meson systems

Article References: Sangiri, S. (2026). Connected sequential Bargmann invariants and $$\mathcal CP~$$-sensitive geometric correlation structures in neutral meson systems. The European Physical Journal C, 86(9), Article 1056. https://doi.org/10.1140/epjc/s10052-026-16308-5

Image Credits: AI Generated

DOI: 10.1140/epjc/s10052-026-16308-5

Keywords: Bargmann invariants, neutral mesons, CP violation, geometric phase, quantum interference, meson mixing, projective Hilbert space, kaons, B mesons, rephasing invariance, entangled meson pairs, flavor physics

Every time a power grid operator decides how much electricity each generator should produce, a quiet balancing act takes place. Burning less fuel saves money, but it often means allowing more pollution, and cutting emissions usually drives costs up. Engineers have long searched for a way to satisfy both goals at once, a challenge known as combined economic emission dispatch, or CEED. Now, a team of researchers has turned to one of the ocean’s most graceful feeders, the manta ray, for inspiration, and the result is an optimization algorithm that outperforms a crowded field of competitors on both cost and cleanliness.

The study, published in the journal Results in Engineering by Yu-Feng Sun, Jie-Sheng Wang and colleagues, describes an enhanced version of the Manta Ray Foraging Optimization algorithm, or MRFO. The original algorithm, first introduced a few years ago, mimics the cooperative hunting strategies that manta rays use to harvest plankton. Because plankton drifts unpredictably with seasons and weather, manta rays have evolved three distinct foraging behaviors: chain foraging, in which animals line up in arcs so that each one catches food missed by the one ahead; spiral foraging, where the group coils around a dense patch of food; and somersaulting, in which individuals flip back and forth around a promising spot. Translated into mathematics, these behaviors become rules that move a population of candidate solutions around a search space, gradually converging on the best answer.

What makes the CEED problem so stubborn is its mathematical character. The relationship between a generator’s output and its fuel consumption follows a cubic curve, and the emissions of carbon dioxide, nitrogen oxides and sulfur dioxide each follow their own cubic functions. The combined objective is nonlinear, nonconvex and discontinuous, riddled with local minima that can trap classical techniques such as lambda iteration or gradient-based methods. To make matters harder, the problem is genuinely multi-objective, so the researchers converted it into a single objective by applying price penalty factors that fold the cost of each pollutant into one total figure in dollars per hour.

Constraint handling is where many algorithms stumble. Traditional approaches add a fixed penalty whenever a solution violates the power balance requirement, the equation demanding that total generation minus transmission losses equals demand. But a fixed penalty is a blunt instrument. Set it too low and the algorithm happily returns infeasible schedules; set it too high and the search becomes so restricted that it cannot explore properly. Finding the right value through trial and simulation is time-consuming and inefficient. The team’s answer was to make the penalty itself dynamic, drawing on five different mathematical functions: sinusoidal, hyperbolic tangent, arctangent, linear and hyperbolic secant. Each function is raised to an order that changes during the run, so the penalty strength adapts to both the size of the violation and the stage of the optimization. Early on, mild penalties preserve exploration; later, penalties tighten to push solutions toward feasibility.

The second major innovation targets the heart of the MRFO algorithm. In the original design, a parameter called C governs the switch between exploration, casting the net wide across the search space, and exploitation, homing in on the best solution found so far. This parameter rises monotonically from zero to one, a predictable march that can leave the algorithm poorly balanced at certain stages. The researchers replaced it with a chaotic switching factor, drawn from nine well-known chaotic maps including Chebyshev, logistic, tent, sine, Gauss and singer maps, combined with an exponential adjustment function. The result is a parameter that still trends upward but oscillates along the way, periodically reopening the search space and preventing the population from settling prematurely into a local optimum.

To find out which chaotic map worked best, the team ran all nine enhanced variants, named C1MRFO through C9MRFO, through the CEC-2022 benchmark suite of twelve test functions, using a population of thirty and five hundred iterations averaged over thirty independent runs. The Chebyshev-based variant, C1MRFO, emerged as the overall winner, ranking first among the nine and beating the original MRFO, which finished last. Wilcoxon rank-sum tests confirmed that most of the improvements were statistically significant, with p-values below the conventional 0.05 threshold. When C1MRFO was pitted against a roster of state-of-the-art metaheuristics, including the dhole optimization algorithm, evolutionary mating algorithm, dung beetle optimizer, snow geese algorithm, particle swarm optimization, grey wolf optimizer, sparrow search algorithm and marine predators algorithm, it achieved the best final rank, taking first place on many of the test functions while keeping computational times competitive.

The real test, however, came when the algorithm was applied to actual dispatch problems. The researchers simulated a six-unit power system under four load demands of 150, 175, 200 and 225 megawatts. Across all four scenarios, C1MRFO produced the lowest comprehensive total cost, which combines fuel cost with the weighted penalties for carbon dioxide, nitrogen oxide and sulfur dioxide emissions. At 150 megawatts, for example, it reached a total cost of about 10,137 dollars per hour, edging out the grey wolf optimizer and clearly beating algorithms such as the arithmetic optimization algorithm, Harris hawks optimization, whale optimization algorithm, sine cosine algorithm, particle swarm optimization and the lambda iteration method. The pattern held at every load level, with the margin over the weakest classical method growing to thousands of dollars per hour at 225 megawatts.

The dynamic penalty functions delivered a further, striking gain. When the sinusoidal penalty function was applied with the parameter order set to dy5, the total cost at 150 megawatts dropped from roughly 10,137 to 9,065.71 dollars per hour, and similar reductions appeared at the higher loads, reaching 14,621.45 dollars per hour at 225 megawatts. Comparing the five penalty shapes, the sinusoidal function consistently produced the lowest minimum costs, beating the hyperbolic tangent, arctangent, linear and hyperbolic secant alternatives by margins ranging from hundreds to nearly two thousand dollars per hour depending on the load. Notably, when the penalty order was fixed rather than dynamic, performance was worst across the board, confirming that the adaptivity of the penalty, not merely its presence, is what matters.

To verify that each improvement contributes on its own, the team conducted an ablation study on a much larger twenty-unit system with a total demand of 2,500 megawatts. The results formed a clear hierarchy: the original MRFO achieved 61,835.16 dollars per hour, the chaos-enhanced C1MRFO reached 61,826.11, and the fully improved version combining chaos with the optimal sinusoidal dynamic penalty, sin(dy5)MRFO, achieved the best figure of 61,807.36 dollars per hour. All three outperformed the phasor particle swarm optimization benchmark, which came in at 61,895.57 dollars per hour. The stepwise improvement demonstrates that the two enhancements are not redundant but superimpose their benefits, and that the framework scales to systems far larger than the six-unit case.

The authors are careful to note where the work goes next. The current approach converts the multi-objective problem into a single objective through price penalty factors, but future versions could adopt Pareto-based multi-objective optimization to generate full sets of non-dominated trade-off solutions. The test cases also assume deterministic demand, so extending the algorithm to stochastic dispatch with wind and solar integration is another goal, as is combining the dynamic penalty with feasibility-first constraint-handling strategies. For now, the study offers a compelling demonstration that borrowing the foraging logic of a filter-feeding fish, perturbing it with chaos, and teaching it to calibrate its own punishments can squeeze meaningful savings and emission reductions out of one of power engineering’s hardest scheduling problems.

Subject of Research: An improved manta ray foraging optimization algorithm with chaotic switching factors and dynamic penalty functions for solving the combined economic emission dispatch problem in power systems.

Article Title: Manta ray foraging optimization algorithm with dynamic penalty function and chaos switching factor to solve combined economic emission dispatch problem

Article References: Sun, Y.-F., Wang, J.-S., Zhang, X.-Y., Li, Y.-X., Guan, X.-Y., & Liu, X. (2026). Manta ray foraging optimization algorithm with dynamic penalty function and chaos switching factor to solve combined economic emission dispatch problem. Results in Engineering, 32, Article 113383. https://doi.org/10.1016/j.rineng.2026.113383

Image Credits: AI Generated

DOI: Not provided

Keywords: manta ray foraging optimization, combined economic emission dispatch, metaheuristic algorithms, chaotic maps, dynamic penalty function, power system optimization, emission reduction, fuel cost minimization, CEC-2022 benchmarks, swarm intelligence, constraint handling, Results in Engineering

Metal ions are everywhere in biology and the environment, and telling them apart quickly and cheaply has long been a challenge for chemists. A new open-access review published in Discover Chemistry by M. S. Ajay Kumar, Pradeep Bhaskar, and B. Anindita of Presidency University in Bengaluru takes a fresh look at one of the most versatile tools in the detection toolbox: organic fluorescent probes. Rather than simply cataloguing probes and their detection limits, the review argues that the real key to building better sensors lies in understanding why different metal ions produce fundamentally different fluorescence responses, from brilliant turn-on emission to complete quenching.

The stakes are high. Essential ions such as copper, zinc, and iron participate in enzymatic catalysis, cellular signaling, redox regulation, and gene expression, but when their balance is disrupted they can contribute to oxidative stress, metabolic disorders, and neurodegenerative diseases. Toxic heavy metals such as mercury and lead offer no biological benefit at all and are hazardous even at trace levels because of their persistence, bioaccumulation, and long biological half-lives. Conventional analytical techniques like atomic absorption spectroscopy and inductively coupled plasma mass spectrometry deliver excellent accuracy, but they demand expensive instruments, trained personnel, and complex sample preparation, making them poorly suited for real-time or on-site detection. Fluorescent probes, which convert metal-ligand interactions into measurable optical signals, promise something different: fast, simple, and biologically compatible sensing.

The central insight of the review is that probe performance depends not only on the fluorophore but on the compatibility between the target ion and the sensing mechanism. Paramagnetic ions such as Cu2+ and Fe3+ usually promote fluorescence quenching through non-radiative decay, intersystem crossing, and charge-transfer pathways. Diamagnetic Zn2+, by contrast, more readily supports turn-on emission through suppression of photoinduced electron transfer, known as PET, and chelation-induced rigidification of the molecular framework. Soft heavy metals such as Hg2+ and Pb2+ occupy a middle ground: they bind strongly to carefully selected donor atoms, yet their heavy-atom character can trigger spin-orbit coupling that quenches emission, so designers must balance strong binding against signal reliability.

The authors walk through the major photophysical mechanisms one by one. PET-based probes contain an electron-rich donor unit that quenches the excited fluorophore in the free state; when a metal ion coordinates to the donor, the electron-transfer pathway is blocked and fluorescence returns. This OFF-ON switching works beautifully for closed-shell d10 ions like Zn2+ but often fails for paramagnetic targets, where suppressing PET merely opens the door to stronger quenching routes such as ligand-to-metal charge transfer. Intramolecular charge transfer, or ICT, systems integrate the binding site directly into the conjugated framework, producing wavelength shifts that enable ratiometric sensing, a more reliable readout than simple intensity changes because it reduces dependence on probe concentration and excitation conditions. Twisted intramolecular charge transfer adds a conformational dimension: metal coordination can restrict torsional motion, suppressing non-emissive twisted states and boosting emission.

Chelation-enhanced fluorescence and its counterpart, chelation-enhanced quenching, are presented not as separate mechanisms but as opposite outcomes of the same coordination event. When metal binding locks a flexible ligand into a rigid, planar geometry, non-radiative decay is suppressed and brightness increases; when the bound ion instead introduces intersystem crossing or electron transfer, the same binding event extinguishes the signal. Excited-state intramolecular proton transfer, or ESIPT, offers another elegant route: photoexcitation drives a proton from a donor group to a nearby acceptor, generating a keto tautomer with a much larger Stokes shift. Metal ions can either disrupt the hydrogen bond required for this process or stabilize it, giving designers a tunable switch between enol and keto emission bands.

The review also highlights through-bond energy transfer, an emerging mechanism in which excitation energy moves along a covalently linked donor-acceptor backbone rather than through space as in FRET. Because TBET does not require strict spectral overlap or precise donor-acceptor distances, it can generate large pseudo-Stokes shifts and support ratiometric, multi-channel detection. A representative Hg2+-responsive cassette shows weak emission in the OFF state but strong acceptor fluorescence once metal binding enhances electronic communication between the chromophores. The authors caution, however, that TBET systems demand precise molecular architecture and often struggle with aggregation-induced quenching in water.

Computational chemistry receives a notably critical treatment. Density functional theory and time-dependent DFT are now standard in probe studies, but the authors argue their value is frequently squandered on decorative HOMO-LUMO diagrams. Used properly, ground-state DFT can confirm binding sites, coordination geometry, and structural rearrangements such as rigidification or ring opening, while TD-DFT can assign whether an observed transition is locally excited, charge-transfer, or ligand-to-metal in character. The review stresses that a smaller HOMO-LUMO gap alone proves nothing; orbital localization, charge-transfer direction, and experimental fluorescence output must all be correlated. Method choice matters too: hybrid functionals like B3LYP suit organic fluorophores, while effective core potential basis sets such as LANL2DZ are needed for heavy metals, and long-range corrected functionals like CAM-B3LYP or omega-B97XD better describe charge-transfer states.

Metal-by-metal, the review distills design lessons from dozens of representative probes. For Cu2+, quenching dominates because of its paramagnetic d9 configuration, with peptide-based probes reaching detection limits as low as 1.5 nanomolar, while rhodamine ring-opening and ratiometric platforms offer turn-on and quantitative alternatives. Iron sensing is even harder because Fe3+ is a hard, highly charged, paramagnetic d5 ion that promotes ligand-to-metal charge transfer, so most probes are turn-off or colorimetric. Zn2+ is the friendliest target: ratiometric and turn-on probes routinely achieve low-nanomolar detection in seconds, though discriminating Zn2+ from its fellow d10 ion Cd2+ remains the field’s stubborn challenge. For Hg2+ and Pb2+, the standout performers combine aggregation-induced emission, near-infrared output, and ratiometric readout, with some systems reporting detection limits down to the picomolar range in water, food, and living cells.

The application landscape is shifting rapidly from bench-top demonstrations to real-world deployment. Recent probes have been validated in tap water, groundwater, river water, milk, soil, fruit juice, and plant tissues, with recovery values typically between 90 and 102 percent. Smartphone-assisted detection, 3D-printed sensing devices, and paper test strips are turning molecular recognition into field-ready tools. In bioimaging, ratiometric probes have tracked intracellular Cu2+ in HeLa cells, an AIE probe with a 130-nanometer Stokes shift visualized Hg2+ in A549 cells and in the roots of rice plants, and a peptide-based self-assembling system detected Pb2+ in groundwater and living cells below 10 nanomolar. Nitrogen and sulfur co-doped carbon dots have even combined metal sensing with logic-gate behavior in a single platform.

Significant hurdles remain before fluorescent probes fulfill their promise. Absolute selectivity in complex matrices is still elusive, because borderline and soft ions such as Pb2+, Hg2+, and Cd2+ exhibit overlapping coordination behavior. Quantitative accuracy inside cells is compromised by probe distribution, intracellular heterogeneity, and interference from endogenous biomolecules like glutathione. Many probes lack reversibility, subcellular targeting, or the long-term stability needed for continuous monitoring. The review’s closing message is clear and likely to shape the next generation of research: successful probes must be mechanism-guided rather than structure-driven, matching the electronic personality of each metal ion with the right signal-transduction pathway, and validated not under ideal laboratory conditions but in the messy, competitive environments where metal ions actually matter.

Subject of Research: Organic fluorescent probes for detecting metal ions through mechanism-guided photophysical design and bioimaging

Article Title: Organic fluorescent metal ion probes for understanding design rules photophysical mechanisms and bioimaging applications

Article References: Ajay Kumar, M. S., Bhaskar, P., & Anindita, B. (2026). Organic fluorescent metal ion probes for understanding design rules photophysical mechanisms and bioimaging applications. Discover Chemistry, 3(1), Article 472. https://doi.org/10.1007/s44371-026-00857-5

Image Credits: AI Generated

DOI: 10.1007/s44371-026-00857-5

Keywords: fluorescent probes, metal ion sensing, photoinduced electron transfer, intramolecular charge transfer, ESIPT, chelation-enhanced fluorescence, aggregation-induced emission, DFT calculations, zinc detection, mercury detection, lead detection, bioimaging

Buildings are responsible for an enormous share of global electricity demand, and the way their air conditioners, lights, and heating systems are scheduled can make the difference between a grid under strain and one running smoothly. For years, engineers have tried to optimize these schedules with evolutionary algorithms and reinforcement learning, but the problem becomes brutally hard when dozens of buildings share a single electrical network and must be coordinated simultaneously. A new study published in Discover Artificial Intelligence proposes a strikingly different approach: instead of searching step by step for the best schedule, a generative artificial intelligence model learns to paint one directly out of pure noise, guided by a map of how the buildings are physically wired together.

The framework, called DMCBO, short for Diffusion-driven Multi-objective Collaborative Optimization for Building Clusters, was developed by Yalan Zheng of Zhengzhou Technical College and Taixiang Yin of North China University of Water Resources and Electric Power. Their central insight is that the joint scheduling of equipment settings and load distribution across an entire cluster of buildings can be treated as a high-dimensional data sample, much like an image. Diffusion models, the same family of generative techniques behind modern text-to-image systems, work by progressively corrupting data with Gaussian noise and then learning to reverse that corruption. The researchers reformulated multi-objective building optimization as exactly this kind of conditional denoising process: starting from random noise, the model strips away noise step by step until a complete, Pareto-optimal collaborative schedule for every piece of equipment in every building emerges.

Technically, the forward diffusion process is a fixed Markov chain that adds tiny amounts of Gaussian noise to real scheduling data over one hundred steps, with the noise coefficient increasing linearly from 0.0001 to 0.02 until the original data becomes indistinguishable from standard Gaussian noise. A neural network is then trained to predict the noise that was added at each step, given the noisy schedule and a conditional encoding vector describing the current situation. Once trained, the model can generate fresh solutions at inference time by sampling pure noise and running the reverse process, refining it over one hundred denoising steps into a schedule tailored to the prevailing weather, occupancy, and grid conditions. Because this generation is a gradient-based learning process rather than an iterative search, it avoids the local optima traps that plague traditional multi-objective evolutionary algorithms and the reward oscillations that destabilize deep reinforcement learning.

What makes the system genuinely cluster-aware is its use of graph neural networks. The researchers model the building cluster as a graph mapped onto a radial distribution microgrid topology, where nodes represent buildings and edges encode physical constraints such as maximum line thermal capacities and transformer limits. A two-layer graph convolutional network passes messages between neighboring buildings, fusing spatial-topological dependencies and network constraints into a single graph embedding. This embedding is concatenated with an adjustable preference weight vector to form the condition that steers the denoising trajectory. In effect, when the model generates a control strategy for one building, it simultaneously perceives the state of its neighbors and the limits of the shared electrical infrastructure, suppressing systemic coordination errors from 0.18 down to 0.06 in the team’s experiments.

The multi-objective aspect is handled through an elegant scalarization trick. Each objective, such as energy consumption, operating cost, or thermal comfort, is normalized to a dimensionless value between zero and one using Min-Max normalization based on historical operational bounds. The normalized objectives are then blended into a single scalar condition via a weight vector whose components sum to one. During training, these weights are sampled from a uniform Dirichlet distribution, exposing the model to the entire space of possible preferences so it learns the distribution of the full Pareto front. At deployment, a decision maker can slide the weight vector like an adjustment knob, instantly generating schedules that emphasize energy saving, comfort, or a balanced compromise, all within the same trained model.

The denoising backbone itself is a U-Net adapted to process one- and two-dimensional building scheduling tensors. Diffusion time steps are embedded through sinusoidal positional encodings, while the graph-derived condition is injected into every residual block via Adaptive Layer Normalization. A bottleneck layer with multi-head self-attention captures long-range dependencies across the entire daily scheduling horizon, and symmetric skip connections preserve high-resolution information during reconstruction. The authors trained the model for 300 epochs on dual NVIDIA Tesla V100 GPUs, and the loss curves dropped from 0.75 to below 0.03 with training and validation curves closely coinciding, indicating no overfitting. As the reverse diffusion step count decreased from 90 to 10 during analysis, the noise-prediction error distribution became sharply concentrated at zero, demonstrating extremely high denoising precision at the end of generation.

Validation was carried out on the open-access Building Data Genome Project 2 dataset, which supplied real hourly weather records, operational schedules, and annual electricity, cooling, and heating load profiles across diverse climate zones. Because that dataset provides metered energy data rather than physical building models, the team used United States Department of Energy Commercial Reference Buildings as templates and calibrated them in EnergyPlus, a high-fidelity thermodynamic simulation platform, to create a digital twin consistent with the real data. All results were benchmarked against a rule-based control strategy with static thermostat setpoints of 25 degrees Celsius in summer and 22 degrees in winter, and every experiment was repeated ten independent times to confirm statistical significance.

The headline numbers are impressive. In high-demand summer conditions, DMCBO achieved an average energy-saving rate of 28.6 percent and cut peak load by 38.5 percent, shaving the afternoon peak from roughly 560 kilowatts down to about 440 kilowatts and saving approximately 142.80 US dollars per day. Cumulative daily power savings exceeded 1,000 kilowatt-hours, compared with 400 kilowatt-hours for a traditional collaborative strategy and just 120 kilowatt-hours without coordination. In winter, peak shaving and valley filling reached about 39 and 42 percent respectively, far above the 22 and 25 percent of conventional methods. When the solution sets were projected into objective space, DMCBO dominated the baselines: it reached a hypervolume indicator of 0.95 within 12,000 function evaluations, versus 0.85 for NSGA-II and 0.70 for MOPSO, while keeping operating costs between 140 and 160 dollars against more than 210 dollars for deep reinforcement learning and 260 to 300 dollars for a GAN-based generative approach.

Robustness and scalability proved equally compelling. Under injected stochastic disturbances mimicking sudden weather changes and occupancy swings, the framework confined power fluctuations to a narrow band of plus or minus 5 kilowatts, whereas the deep reinforcement learning baseline swung between minus 15 and plus 20 kilowatts. Scaling from 5 to 50 buildings, the global energy-saving rate actually rose from 28.5 to 31.2 percent, because larger clusters unlock more load-complementary opportunities that the graph encoder can safely exploit within encoded grid boundaries. Computation time grew to 9.8 seconds at the 50-building scale, but that remains comfortably within the 15-minute control horizon used by real building management systems, and the hypervolume index slipped only slightly to 0.88. Ablation experiments confirmed that both the graph neural network module and the choice of 100 diffusion steps, which yields a 50-millisecond inference latency, were essential to this balance of speed, accuracy, and stability.

The authors are candid about limitations. The framework has so far been validated only in simulation, and EnergyPlus simplifies sub-minute mechanical actuation latencies that real HVAC hardware would introduce; they propose cascading PID loops and predictive safety margins to absorb those delays in practice. Data quality, generalization to residential buildings and extreme climates, the computational cost of scaling to city-sized networks, and the black-box nature of the generative process all remain open challenges, with explainable AI and transfer learning flagged as future work. Still, the architecture is designed to slot into standard building management systems as a supervisory layer, transmitting setpoints over protocols such as BACnet or Modbus. If the simulation gains survive contact with physical buildings, the idea of generating optimal energy schedules from noise, guided by a graph of the grid itself, could become a powerful new tool in the push toward decarbonized cities.

Subject of Research: Conditional diffusion models with graph neural networks for multi-objective energy optimization in building clusters

Article Title: Diffusion model-driven multi-objective collaborative optimization for building energy management using graph neural networks

Article References: Zheng, Y., & Yin, T. (2026). Diffusion model-driven multi-objective collaborative optimization for building energy management using graph neural networks. Discover Artificial Intelligence, 6(1), Article 1383. https://doi.org/10.1007/s44163-026-02140-z

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02140-z

Keywords: diffusion models, graph neural networks, building energy management, multi-objective optimization, EnergyPlus, smart buildings, Pareto optimization, deep learning, energy efficiency, HVAC control, microgrids, generative AI

Faced with growing opposition to datacenters from residents who don't want the bit barns in their backyards, Amazon plans to invest more than $1 billion over the next five years to indoctrinate the public. The company announced the investment in a whataboutism-packed blog post aimed at convincing readers datacenters are not as bad as they've been led to believe, and that if the common folk don't get in line and support their development, the nation could be left in the dust. The program aims to invest in education, job training, local infrastructure improvements, and sustainability initiatives in communities home to Amazon datacenters. "This build out is so important geopolitically that there are widespread reports of various countries intentionally seeding misinformation in the US about datacenters to trick us into slowing down," the e-tailer turned cloud titan wrote. "Right now there are over 100 datacenter moratoriums being considered across the country. If these measures are enacted, the US could be writing its own losing ticket to this race, and the consequences would last generations." If Jeff Bezos's cash cow is to be believed, datacenters will transform the US economy much like the buildout of the highway system did in the '50s. But that can't happen, Amazon argues, if Americans block the development of these bit barns over misguided fears. The blog post highlights four such datacenter fears, which Amazon claims are "myths," despite plenty of reporting to suggest otherwise. Who needs water? The first "myth" is the fact that datacenters consume an excessive share of water. Bit barns commonly employ evaporative coolers which use water to reject heat generated by the facility into the atmosphere. These cooling towers use less energy than dry coolers or refrigerant-based systems but remove water from local watersheds as a consequence. According to Amazon, citizens should really be more worried about the water consumed by golf courses, which it claims use 200x more water than all of Amazon's datacenters. That argument might be more convincing if people didn't like golf courses. But they do and the whole lesser of two evils argument works a lot better when one of those evils isn't extremely popular. Amazon also attempted to dispel claims that a typical datacenter consumes five million gallons a day, arguing that its facilities are much more efficient using just 13,000 gallons a day, the equivalent of 42 US households. But this leaves out an important detail. It is possible to build datacenters that don't consume large quantities of water, but these facilities often consume large sums of power as a consequence. Most gas, nuclear, coal, and anything else that uses steam to turn a turbine also consumes water. In other words, nearly every large-scale power-generation method uses water either directly or indirectly. And despite Amazon's attempts to downplay the impact of datacenters, there's good reason for those living nearby to be worried. Earlier this year, it was reported that a datacenter, which we'll note was not operated by Amazon, has quietly sucked up nearly 30 million gallons of water and the only reason anyone discovered the issue was that local residents started complaining of unusually low water pressure. We totally won't let datacenters drive up your power bill or poison your air Amazon also attempted to dispel fears that datacenters could leave neighboring residents picking up the tab for their massive power consumption. The company argues that rates are going up because the US grid is old, and that responsible bit barn builders are working with utilities to ensure they're paying their fair share for the additional capacity and infrastructure upgrades required to support them. What wasn't addressed is why folks might be worried about rising rates. One of the reasons that utilities and plant operators are so conservative is that they've been burned by speculative markets before. If new power plants are built to meet demand that never materializes, ratepayers could get stuck with the bill. Amazon and others agreeing to pay higher rates might help, but only if they're consuming an appreciable amount of the capacity they convinced utilities to build. An empty datacenter doesn't pull a lot of juice. Speaking of power, Amazon also attempted to downplay the impact of datacenters on air quality due to their reliance on diesel backup generators. "The truth is the datacenter generators almost never run," the company wrote. Amazon also argued that these generators are subject to stringent permitting requirements which dictate how long they can run for. That might be more reassuring if datacenters in power constrained regions weren't getting permission after the fact to exceed those limits. And, as we saw with Elon Musk's Colossus supercomputer, temporary gas turbines can be deployed when the local grid can't support the load. That hasn't sat well with residents of Boxtown, Tennessee, while the NAACP is suing xAI over allegedly unpermitted turbines in Mississippi. Amazon can argue it's got a better track record than most, but that doesn't mean the community's concerns are misplaced. But think of the taxes Finally, Amazon argues that folks don't realize how big of an economic impact datacenters have on their community. "Datacenter operators are often the single largest taxpayer in a community, with upfront payments routinely exceeding entire annual municipal budgets in the first year of construction," the company claimed. "For example, in St. Joseph County, Indiana, the taxes Amazon will pay to the county are estimated to exceed $3 billion." The subtext is that, by enacting datacenter moratoriums, communities are walking away from billions in tax dollars. Then again, if the tax revenue from these facilities is so great, it raises the question why Amazon needs to spend more than $1 billion over five years sweetening the deal for communities hosting its bit barns. ® Updated Oct 3 at 1447 GMT to reflect the fact that Amazon actually said it would spend more than $1 billion.