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
Author(s): Mark Buchanan
A new technique for generating solid fibers from a liquid jet is easier to model—and thus easier to control—than past methods.
[Physics 19, 137] Published Fri Oct 02, 2026
Author(s): Michael Schirber
A carbon nanotube foam springs back to its initial state, regardless of the speed of the compression, a property that could lead to a new type of shock absorber.
[Physics 19, 129] Published Fri Sep 18, 2026
New research led by University of the Witwatersrand and University of Southern California paleoanthropologists sheds more light on one of the most debated transitions in human evolution: when our ancestors stopped regularly climbing trees and committed fully to walking on two legs.
The post Fossil Evidence Suggests Early Homo Had Abandoned Tree-Climbing by 1.8 Million Years Ago appeared first on Sci.News: Breaking Science News.
The transplanted cells formed circuits, reached the spinal cord and responded to injury.
iPhone 18 Pro is at the center of What Holds Us, a new photography exhibition built around three very different approaches to image-making. Alex Prager, Jack Davison, and Jimmy Chin shot the entire project on Apple’s latest Pro iPhone, using the device in environments ranging from a Los Angeles soundstage to a London warehouse and the high-altitude terrain of British Columbia.
Curated by Kathy Ryan, the former longtime director of photography for The New York Times Magazine, the exhibition examines connection through touch, shared experience, physical environments, and human relationships.
It also gives Apple a more ambitious showcase for the iPhone 18 Pro camera than a conventional product demonstration. Rather than presenting standardized comparisons or individual sample images. What Holds Us asks three established photographers to work within their own visual languages while using the phone as their sole camera.
The exhibition opens in New York City on September 18 and runs through September 20 before traveling to London and Shanghai in the coming months.
The project works partly because the three photographers have little stylistically in common.
Jimmy Chin took iPhone 18 Pro into the Bugaboo Mountains of southeastern British Columbia, working around snow, ice, steep climbing terrain, and winds reaching as high as 70 miles per hour.
His series focuses on trust and connection within climbing, with veteran mountaineer Conrad Anker appearing throughout the photographs. Some images place Anker as a small figure against vast mountain landscapes, while others move much closer to rock, snow, and glacial textures.
Chin used both the 48MP Fusion Ultra Wide and Telephoto cameras to shift between those extremes. The practical appeal was also straightforward: carrying a phone instead of a full conventional camera setup reduced the equipment required in an environment where weight and mobility affect every decision.
Jack Davison took almost the opposite approach. Shooting inside a former sugar warehouse in London, he created intimate portraits built around people interacting with clay and chalk.
Davison is known for high-contrast black-and-white imagery, and his contribution emphasizes texture, shadows, highlights, skin, and physical contact with the material. Some subjects appear partly covered in clay, while others manipulate it directly, leaving fingerprints and marks across the compositions.
For Davison, the smaller camera changed the relationship between photographer and subject. Without a large camera constantly positioned in front of his face, he could maintain more eye contact and work at closer range.
His use of slower shutter speeds, RAW capture, and multiple lenses also illustrates how Apple increasingly wants the Pro iPhone camera to function as a configurable photographic tool rather than simply a computational point-and-shoot system.
Alex Prager’s section places iPhone 18 Pro inside a much more elaborate production environment.
Working on a Los Angeles soundstage, Prager created color-heavy scenes filled with crowds, confetti, staged city elements, costumes, and loosely choreographed movement. Some images use high viewpoints to turn groups of people into a continuous visual pattern, while tighter photographs isolate individual figures within the same constructed world.
The visual approach resembles film production more than spontaneous mobile photography, which gives the project another test for the phone. The camera has to preserve detail across numerous faces, costumes, props, highlights, and areas of movement while operating within scenes deliberately designed for large-format presentation.
Prager also treated the camera’s immediacy as part of the creative process rather than simply a convenience. The ease of moving between perspectives and capturing repeated moments allowed the scenes to evolve through improvisation instead of being locked into a single carefully prepared frame.
That approach aligns with a recurring theme across the exhibition: the phone becomes less intrusive without requiring the photographers to abandon their existing visual standards.
For Apple, it is a useful distinction. Professional photography campaigns built around smartphones can easily become exercises in technical proof, where the central question is whether a phone can imitate equipment already available to photographers. What Holds Us is more interested in how a smaller camera changes the way an artist works.
The exhibition arrives alongside one of Apple’s most significant physical camera changes in years.
iPhone 18 Pro introduces variable aperture on its 48MP Fusion Main camera. The lens can operate at ƒ/1.48, ƒ/1.8, ƒ/2.8, or ƒ/4.0, changing both light intake and depth of field.
A wider ƒ/1.48 aperture is designed for stronger low-light performance, while narrower settings can retain more detail across different planes of an image. Apple also lets photographers select aperture settings manually through new Pro controls in the Camera app.
Those controls extend to shutter speed and white balance, with a histogram available for monitoring exposure. ProRAW remains available for photographers who want greater flexibility during post-production.
The rear camera system also includes 48MP Fusion Ultra Wide and 48MP Fusion Telephoto cameras, providing focal lengths from 13mm at the wide end through an optical-quality 8x option.
What Holds Us does not reduce the exhibition to those specifications, but the technical changes help explain why Apple chose photography, rather than video, for this particular campaign. Variable aperture affects choices that photographers traditionally make during capture, especially around available light and how much of a scene remains in focus.
Apple has spent years using Shot on iPhone campaigns to demonstrate camera quality through finished work rather than charts and controlled tests. What Holds Us pushes that format closer to a conventional curated exhibition.
Ryan connects the three bodies of work through the idea of human presence, but each artist interprets it differently. Chin finds it through dependence between climbers in hostile landscapes. Davison uses physical contact between people and clay. Prager builds populated fictional environments in which individual expressions emerge from larger crowds.
The images therefore function as an exhibition first and a hardware showcase second, even though the production is closely tied to the launch of iPhone 18 Pro.
That balance may be the most effective part of the campaign. Apple does not need to demonstrate that a modern iPhone can produce a technically competent photograph. The more interesting challenge is showing where its smaller size, computational processing, lens flexibility, RAW workflow, and new manual controls can alter the photographer’s decisions.
New York visitors will get the first opportunity to see how those images hold up away from a phone or computer display when What Holds Us opens September 18. After its three-day New York run, Apple says the exhibition will continue to London and Shanghai, giving the iPhone 18 Pro photography project a physical life beyond the launch campaign.
The post iPhone 18 Pro exhibition puts three photographers behind Apple’s new camera system appeared first on AppleMagazine.
Paleontologists have described a previously unknown species of mosasaur -- a group of extinct marine reptiles that patrolled Cretaceous seas alongside dinosaurs on land -- based on fossils unearthed in Egypt’s Dakhla Oasis.
The post Paleontologists Uncover New Mosasaur Species in Egypt’s Western Desert appeared first on Sci.News: Breaking Science News.
The GeForce RTX 5090 is the first thing that comes to mind when you think of the best graphics cards. However, you'd better pray the serial number on the inside of the metal bracket does not fade over time, since that could be a reason for Nvidia to reject warranty claims. At least one Redditor has shared a case of Nvidia reportedly denying a warranty claim because the bracket's serial number was unreadable.
The Redditor Willing-Avocado-4830, based in the UK, began experiencing black-screen crashes with a GeForce RTX 5090 Founders Edition graphics card under heavy load. The user sent the card to Nvidia for a warranty claim. After 30 days of radio silence, the owner finally received an email stating that their RMA request was rejected with no apparent explanation for the denial.
Seeking clarification, the user reached out to Nvidia for further information. According to the Redditor's recount, Nvidia support purportedly rejected the RMA because the serial number on the graphics card's metal bracket had faded. Like many gamers, Willing-Avocado-4830 claims never to have modified or tampered with the graphics card after installation. The fading serial number does not appear to be an isolated incident. Several other owners have reported similar issues in the Reddit discussion.
The metal bracket on graphics cards is susceptible to dust buildup, oxidation, and corrosion over time. This can cause it to look dirty, stained, or sometimes slightly discolored compared to when it was brand new. These same phenomena could have affected the serial number's legibility, especially since it is printed with ink onto the bracket's surface rather than etched for better durability. For comparison, the last-generation GeForce RTX 4090 Founders Edition had its serial number laser-etched onto the metal bracket.
My RTX 5090 FE RMA was rejected because the physical serial number is no longer readable — has anyone experienced this?from r/nvidia
One Redditor said that someone in the Nvidia Discord channel recommended cleaning the area with isopropyl alcohol to restore the serial number's visibility. However, that may not work if the ink is no longer present, as previous reports suggest the hot air expelled by the Founders Edition cooler could be erasing the serial number.
Surprisingly, Nvidia rejected a warranty claim solely because of a faded serial number on the graphics card’s bracket. The same serial number is clearly printed on a sticker on Nvidia's modular PCIe connector, which the chipmaker's technicians can verify when disassembling the GeForce RTX 5090. Moreover, you can pull the serial number from the graphics card through the command prompt with Nvidia SMI (System Management Interface), assuming the card is still working, which, in this case, it is. Willing-Avocado-4830 still had the original packaging and the original invoice from Nvidia U.K., so it would be easy to cross-reference the serial numbers.
Redditor hawok reportedly sent their GeForce RTX 5090 Founders Edition for RMA two months ago and encountered a similar situation. The serial number on the metal bracket was only partially visible. However, customer support seemingly accepted the RMA after the user sent photographs of the packaging that corroborated the serial number. It is unclear what Nvidia's policy is, or whether the chipmaker handles each case differently. We have reached out to Nvidia for clarification.
Some Redditors have suggested taking a photograph that shows the graphics card's serial number next to the packaging's serial number. Others jokingly recommend putting a piece of tape over the serial number. It might not be a bad idea, but you would need heat-resistant tape, something like Kapton tape.
GeForce RTX 5090 stock has practically disappeared from the U.S. market, with very few units starting at $6,500, 3.25X over the Blackwell flagship's MSRP. You cannot blame GeForce RTX 5090 owners for worrying. They already have a lot on their plates, dealing with the 16-pin power connector melting woes, and now also have to worry about fading serial numbers.
At last, Joe (Zoe Saldaña) has been rescued in Lioness season 3. Frankly, that's an episode earlier than expected.
Still, it seems as though there's more to be solved. The timing of her rescue suggests there's more to the CIA's strategy than we've been able to give proper attention to (at least, I really hope that's the case), with Joe saying she can potentially "never" return home.
But does anybody really know what they're talking about? And when does Lioness season 3 episode 8 arrive on Paramount+?
Lioness season 3 episode 8 will drop on one of the world's best streaming services in the US and Canada on Sunday, September 20 at 12am PT / 3am ET.
Here's when it will be released in other nations:
Lioness season 3 will have a total of eight episodes, with new entries airing weekly. That gives us the following schedule:
We're living in a drought of bold and brave storytelling. By that, I mean that almost everything we consume on our favorite streaming services plays it safe, looks the same, or lacks so much substance that you can't really say anything about it other than "it's fine".
MobLand, however, has been bucking that trend, and season 2 continues to build on an authenticity that's light years ahead of anything else. Why? At large, it's because MobLand trusts its audience to be able to think critically.
You've probably read reports of streamers modifying their scripts and plots to accommodate 'second screen' viewers — subscribers who've put something on in the background while doing something else. Without naming any names, this is starting to result in bland, unambitious storytelling that spoon-feeds you information unnecessarily.
But MobLand season 2 shows us just how fulfilling the results can be when creators and distributors alike are prepared to disrupt popular industry trends. The rich, complex layers to the central storyline feel never-ending, forcing you to play close attention to every second of dialogue and action.
The Harrigan family, as always, are unhinged. Conrad's (Pierce Brosnan) feud with Kevin (Paddy Considine) ramps up a notch, embroiling new 'fixer' Frankie (Johnny Flynn) in their mess as family arch-rival Kat (Janet McTeer) tries to take control of London from a distance.
This leaves Harry (Tom Hardy) having to clean up even more mess than before, particularly considering his and Jan's (Joanne Froggatt) impending divorce. On top of that, daughter Gina (Teddie Allen) has gone missing.
If you think you understand, you don't — between the Harrigans and us, it's the blind leading the blind. But that makes tuning into Paramount+ this week all the more enriching... because everyone is prepared to take a risk.
There's something so wonderfully hypnotic about watching a Dame and a former James Bond shower us with See You Next Tuesdays like it's no big deal (I counted, and Conrad says this four times within the first six minutes of episode 1). But such gimmicks aren't what makes MobLand season 2 so entertaining.
As I've touched on above, it all comes down to the construction. Having a whip-smart script, edgy editing and a soundtrack so defiant that Spotify would ask a listener with the same taste if they were okay, doesn't automatically give you a good show — all elements of production need to be working in complete synergy.
There will be no spoilers in this review, so I need you to trust me when I say that some scenes had all of their creative elements working in such beautiful harmony that I spiritually ascended. Never before have I wanted to be involved in organized crime, but throw me into an action sequence with dynamically rhythmic edits and Firestarter by The Prodigy as the soundtrack and I'd seriously consider it.
If I had to described MobLand season 2 in one word, it would be relentless. From the onslaught of family issues to the duplicity and deceit within them, nothing ever lets up. It's a mental scramble to keep up with everything that happens in a single episode (let alone the entire season); Guy Ritchie has created the most violent workout for the mind possible.
Throw yourself into it at full-throttle and you'll never want the grip of MobLand to let you go. It's not worth having any favorites — they'll either die or deceive you — but that in itself is a fitting lesson in trust. You stick out the ride and try to survive, and become pleasantly addicted to the process.
It goes without saying that our ensemble cast is, as always, on top form. If there was any truth to the alleged behind-the-scenes drama involving Tom Hardy, all it's done is fuel the fire of some exceptional television. Harry's constant need to live on his wit's end is often amusing, while Conrad's slowly declining grip on reality never loses its gravitas.
Maeve (Helen Mirren) needed no improvement, really, but I've particularly enjoyed her power-dressing wardrobe this time around. In direct contrast is Bella (Lara Pulvey), who is the most fretful and unguarded we've ever seen her. Seraphina (Mandeep Dhillon) is on the way up the food chain, but her own worst enemy.
It's a social study in how complicated humans are while intent on making their own problems infinitely worse, then. But a particular shoutout must go to our most important newcomer, "prick on a scooter" Frankie Cooper.
Without giving too much away, he's brought into's Harry's orbit to act on behalf of the terrifyingly aloof Kat, all while dressed like the third Gallagher brother if he used the Lime app. With a chillingly monotone voice, and eyes that seem like they're constant dissociated from the world around him, he's the first genuinely troubling threat to Harry since the show began.
Frankie's relationship to everyone around him is just another fascinating dimension to behold, revealing a collective fear and vulnerability among the rest of the characters that we've not yet seen. From beginning to end, Ritchie's thought process is truly masterful.
Thank God MobLand season 3 is already a done deal. Thank God we get to experience more of this addictive high. If you thought The Gentlemen season 2 was good, you ain't seen nothing yet.
Paleontologists have identified a new species of ancient, distant mammal relative from two fossil specimens found decades apart in the Manda Beds of Tanzania.
The post New Dicynodont Species from Tanzania Suggests Younger Age for Rocks Tied to Earliest Dinosaurs appeared first on Sci.News: Breaking Science News.
iCloud+ oferece Apple TV, Apple Arcade e Apple Music Select sem custo extra em mais de 100 países; entenda.
O post iCloud+ ganha Apple TV, Apple Arcade e Apple Music Select em 100+ países apareceu primeiro em Blog do Edivaldo - Informações e Notícias sobre Linux.
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