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Apple explica como bloqueia rastreadores que capturam emails de usuários no WebKit

20 October 2026 at 15:50

Após causar um reboliço no setor de publicidade ao quebrar determinados serviços de entrega de anúncios com a versão 27 de seus sistemas operacionais, a Apple finalmente explicou as mudanças que realizou em um post no blog do WebKit.

A empresa afirma estar combatendo um método que ela classificou como “resolução de identidade”, em que empresas de publicidade de terceiros capturam o endereço de email dos usuários em sites quando eles fazem login ou assinam uma newsletter.

Bloqueio de Resolução de Identidade

Esse endereço pode ser usado para montar um perfil e direcionar anúncios online — uma evolução do rastreamento por cookies, cujo uso por terceiros já é bloqueado pelo Safari há seis anos.

Bloqueio de Resolução de Identidade

Com o lançamento recente dos novos sistemas, a Apple passou a bloquear, no nível do domínio, as solicitações de rede destinadas à infraestrutura dessas companhias — o que impede o envio do endereço de email dos usuários aos servidores delas.

Bloqueio de Resolução de Identidade

De acordo com o Business Insider, essa abordagem da Maçã vem agitando o mercado porque pode dificultar bastante o trabalho de anunciantes que dependem desses identificadores para segmentar campanhas e medir resultados.

Com isso, os anunciantes terão menor capacidade de alcançar públicos específicos na web e medir a eficácia de suas campanhas. Em consequência, os anunciantes poderão migrar para plataformas como Google e Meta, que já possuem perfis detalhados de seus usuários.

Planning permission for new Scottish AI datacentres suspended for up to a year

16 September 2026 at 19:28

MSPs vote for strict environmental impact assessments and will delay decisions until national strategy is developed

Holyrood has voted to pause planning applications for all new AI datacentres for up to a year until the Scottish government has developed a national strategy.

On Wednesday, MSPs backed a Scottish Labour motion to halt new schemes for up to 12 months while rules on hyperscale datacentres are developed in what could be a blow to the British government’s AI strategy.

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© Photograph: Murdo MacLeod/The Guardian

© Photograph: Murdo MacLeod/The Guardian

© Photograph: Murdo MacLeod/The Guardian

The complicated AI infrastructure market

15 September 2026 at 10:00

There is a quiet war happening underneath the AI boom, and most people are not paying attention to the right front. While headlines focus on which model is winning, which startup is raising money, or which chatbot is gaining users, a parallel race is playing out in data centers and corporate boardrooms across the world. The race is for AI infrastructure: the GPU clusters, power contracts, cooling systems, networking fabric, orchestration tools, and inference services that make modern AI actually work. The companies fighting in that race are called neoclouds.

Understanding the importance of neoclouds matters for the same reason understanding cloud computing mattered in 2008. The companies that first saw the shift to cloud and positioned themselves around it correctly ended up controlling a massive layer of the technology industry. The same dynamics are playing out in AI infrastructure right now. But the neocloud market is more complicated than it looks. Not all of these companies do the same thing. Grouping them together as if they are all just GPU rental businesses misses the point entirely. The more useful way to understand the space is to recognize that neoclouds are evolving into four distinct categories, each with a different focus, a different customer, and a different role in the AI stack.

What a neocloud actually is

Before getting into categories, it helps to establish what the term means. A neocloud is a cloud infrastructure provider designed specifically for AI workloads. That sounds simple, but it covers a lot of ground. General-purpose hyperscalers like AWS, Azure, and Google Cloud were engineered to support a wide variety of workloads across many industries. They built architecture around flexibility, breadth of services, and broad enterprise appeal. Neoclouds took a different approach. They built around one mandate: make AI compute as fast, dense, and scalable as possible.

That means designing facilities around high power density, integrating fast interconnects between GPUs, investing in liquid cooling and energy-aware cooling, and building orchestration around tools like Kubernetes and Slurm that are purpose-built for distributed AI training. It also means competing aggressively for access to the latest Nvidia accelerators, because in AI infrastructure, GPU access is often the single most important competitive advantage a company can have.

The result is a fragmented but deeply competitive market. Some neoclouds are focused on physical capacity. Some are focused on making GPU compute accessible to developers. Some are building enterprise-grade infrastructure that can compete directly with hyperscalers in specific AI domains. And some are focused on one of the most important long-term bets in AI: inference. Let’s take a closer look at the four categories of neoclouds.

Full-stack AI infrastructure

The first and most prominent category is full-stack AI infrastructure providers. These companies are trying to become the AWS of the AI era, yet they are built from the ground up around GPUs rather than general-purpose compute. Their goal is to own as much of the AI infrastructure experience as possible: raw GPU clusters, networking, storage, orchestration, managed Kubernetes, Slurm scheduling, private clusters, enterprise support, training workloads, and inference services.

The companies in this space include CoreWeave, Nebius, Lambda, Crusoe, Nscale, and Fluidstack. CoreWeave has emerged as the clearest example of this category. It built its identity around massive GPU clusters optimized for large-scale AI training, paired with Kubernetes-native orchestration and enterprise contracts worth billions. Nebius took a different path, combining deep engineering expertise with a sovereign and European-oriented strategy while expanding heavily into US data centers. Lambda started as a developer and researcher-friendly GPU cloud for machine learning but has moved deliberately upmarket, adding superclusters and private cluster services.

Developer and self-serve GPU clouds

The second category is developer and self-serve GPU clouds. These platforms are not competing for billion-dollar enterprise contracts. They are competing for developers, startups, researchers, and smaller teams who need GPU access quickly without negotiating long-term deals. The product experience here is self-serve: pick a GPU, spin up an instance, deploy a model, run a notebook, test a workload, or launch a small cluster.

The brands that fit here include Vultr, Runpod, Civo, Lambda, and Together AI. Vultr brings GPU compute into a broader cloud platform with a global footprint and self-service deployment. Runpod has built a loyal developer following by keeping the experience simple and fast. Civo targets developers who prefer a Kubernetes-first environment. Lambda still offers strong self-serve GPU instances and 1-Click Clusters alongside its enterprise offerings. Together AI goes beyond raw GPU rental to combine inference, fine-tuning, model hosting, and dedicated clusters in one developer-accessible platform.

This category matters because not every AI team needs thousands of GPUs and a dedicated account manager. A lot of important work happens at the research bench, the startup prototype stage, and the individual developer level. These clouds serve that market, and they often grow into larger accounts as teams scale.

Inference-first AI clouds

The third and increasingly important category is inference-first AI clouds. Training gets the dramatic headlines, but inference is where AI products actually live. Every chatbot response, coding assistant completion, voice agent interaction, and image generation request is inference. And unlike training, which happens in bursts, inference runs all day, every day, for every user.

The companies to watch include Together AI, Groq, CoreWeave Inference, Nebius Token Factory, Crusoe Managed Inference, and Nscale Serverless Inference. Together AI has built one of the clearest inference-first platforms in the market, especially for open source and open-weight models, while also supporting fine-tuning and dedicated GPU clusters. Groq stands apart because its identity is tied to ultra-fast inference running on its own language processing unit (LPU) architecture, which inverts the traditional GPU paradigm. CoreWeave Inference extends CoreWeave’s infrastructure story into serverless and dedicated model-serving. Nebius Token Factory and Crusoe Managed Inference represent how full-stack players are adding inference layers to monetize their GPU capacity differently. Nscale Serverless Inference is Nscale’s move into on-demand model serving without infrastructure management.

What makes this category so compelling is the economic direction. As AI moves from a handful of large frontier labs into millions of production applications, the demand for inference infrastructure will likely dwarf training demand. Companies that control inference capacity may end up controlling the more durable long-term revenue stream.

Data-center and capacity-heavy players

The fourth category is the most physically grounded. These are the companies that are building and controlling the real estate of AI. This goes beyond GPU clouds in the traditional sense. These companies are acquiring land, securing power contracts, designing data centers, developing campuses, deploying high-density racks, and racing to control capacity that genuinely cannot be replicated quickly.

The brands that fit here include Fluidstack, Applied Digital, Core Scientific, Voltage Park, Crusoe, and Nscale. Some of these companies also offer cloud services, but their strategic advantage is fundamentally about commanding physical infrastructure. Fluidstack specializes in delivering large-scale custom GPU cluster capacity and custom data center solutions for enterprises and AI labs. Applied Digital provides hosting infrastructure for AI and high-performance computing at scale. Core Scientific transitioned from crypto-mining data centers into AI infrastructure. Voltage Park is focused on large-scale GPU capacity for AI and research organizations.

Crusoe and Nscale appear in this category as well as in others, which gets to the heart of one of the most important points about the neocloud market overall. These categories are not clean boxes. Companies routinely sit across multiple categories, and competitive pressure is pushing most of them to expand their scope. Crusoe is both a cloud provider and a data-center developer. Nscale is both a cloud platform and a major infrastructure builder. The companies that ultimately win may be those that can span the four categories most effectively.

Why the category framing matters

Understanding these categories is not just an academic exercise. It has real consequences for how companies make buying decisions, how investors evaluate the space, and how technology leaders think about AI infrastructure strategy.

A company focused on physical capacity is playing a fundamentally different game than one selling managed inference APIs. They may appear in the same technology articles and compete for some of the same headlines, but their economics, customer relationships, and long-term differentiation look very different. From an enterprise buyer perspective, understanding which layer you are actually buying from matters before you sign a contract. Some neoclouds are building long-term platform relationships. Others are offering capacity to fill an immediate gap. The category determines which is which.

The neocloud market is young, fast-moving, and genuinely complicated. New entrants, mergers and acquisitions, and platform expansion are all reshaping the landscape in real time. But the underlying logic remains constant. AI infrastructure is becoming a critical layer of the technology stack, and whoever controls that layer will have enormous influence over how AI gets built, deployed, and monetized.

The companies in this market are not all the same. Paying attention to what they actually do, rather than just what they call themselves, is how you separate the ones that will shape AI’s future from the ones that were simply first to send a press release.

How heat and cooling methods alter basalt fiber concrete’s mechanical behavior

11 September 2026 at 11:56

When a tunnel catches fire, temperatures inside the concrete lining can soar to 800 or 900 degrees Celsius, and the emergency response almost always involves dousing the structure with water. A new study suggests that this familiar firefighting image—flames followed by a torrent of cooling water—may be doing far more damage to the concrete than engineers have typically accounted for. Researchers investigating basalt fiber-reinforced concrete have found that how a fire-damaged structure is cooled can be as important as how hot it got in the first place, with water quenching consistently inflicting greater strength loss than slow, natural cooling across every temperature they tested.

The research, published in Case Studies in Construction Materials, systematically examined how heating temperature, cooling regime, fiber content, and impact loading combine to determine whether concrete survives a fire with its load-bearing capacity intact. The team, led by Yang Liu and colleagues including Delin Li, Jinyue Dong, Yingda Zhang, Xianliang Zhou, and Qiong Zhou, chose basalt fiber-reinforced concrete, or BFRC, as their test material because of the fiber’s unusual combination of properties. Basalt fibers, drawn from volcanic rock, offer high tensile strength, thermal stability up to melting points between 1350 and 1450 degrees Celsius, and a cost profile far more attractive than premium alternatives such as carbon fiber. This makes them an appealing candidate for tunnel linings, where fire resistance and economy must be balanced.

The experimental design was exhaustive. The researchers prepared a C35-grade concrete mix—the grade commonly recommended for tunnel design—using ordinary Portland cement, medium sand, and continuously graded gravel between 5 and 10 millimeters. Into this base mix they incorporated chopped basalt fibers, 12 millimeters long with a monofilament diameter of 17 micrometers, at volume fractions of 0, 0.1, and 0.2 percent. The fibers were added in small batches during dry mixing to prevent agglomeration, and the fresh concrete was cast into cylindrical specimens measuring 50 millimeters in diameter and 25 millimeters in height, then cured under standard conditions at 20 degrees Celsius and 95 percent relative humidity.

The thermal assault began in a high-temperature electric oven capable of reaching 1200 degrees Celsius. Specimens were heated at a controlled rate of 5 degrees Celsius per minute to target temperatures of 200, 400, 600, and 800 degrees Celsius, then held at temperature for a full hour to ensure the heat penetrated the interior. Half the specimens were then removed and allowed to cool naturally in ambient air, mimicking a structure left to cool on its own after a fire. The other half were plunged immediately into a tank of tap water, replicating the thermal shock of firefighting operations. After cooling, the water-immersed specimens were conditioned in air for seven days to return them to a natural moisture state before testing.

The dynamic mechanical properties were then probed using a split Hopkinson pressure bar, the workhorse instrument for measuring how materials behave under the extreme strain rates produced by impacts and blasts. The apparatus used bars 75 millimeters in diameter, made of high-strength steel with an elastic modulus of 210 gigapascals, with a striker bar propelled by compressed gas striking an incident bar that transmitted stress waves through the specimen. Three impact velocities—3.5, 4.4, and 5.2 meters per second—were tested, achieved by varying the operating gas pressure, with three specimens per condition. A thin pulse shaper was pasted at the impact end of the incident bar to smooth out high-frequency oscillations, and stress equilibrium between the two faces of each specimen was verified from strain-gauge signals, confirming the reliability of the data. Fragments from each impact were sieved through meshes ranging from 4.75 millimeters down to 75 micrometers so that a fractal dimension could be calculated, providing a quantitative measure of how thoroughly each specimen shattered.

The visual evidence of thermal damage followed a familiar but telling progression. At 200 degrees Celsius, the concrete surfaces turned bluish-gray, with no cracking. By 400 degrees, the color shifted to yellowish-gray, still structurally sound. At 600 degrees, fine cracks appeared along with slight surface peeling, and by 800 degrees the specimens showed severe cracking, spalling, and edge breakage. Water-cooled specimens generally exhibited more pronounced surface damage than their naturally cooled counterparts, and at 800 degrees the concave shape at the start of the stress-strain curve—evidence of pre-existing microcracks being closed under compression—was especially marked in the water-quenched samples.

The strength data told a stark quantitative story. For naturally cooled specimens containing 0.2 percent basalt fiber, the dynamic peak stress at an impact velocity of 3.5 meters per second fell from 51.1 megapascals at room temperature to 18.6 megapascals at 800 degrees—a reduction of 63.6 percent. Water cooling made matters worse across the board: at the same impact velocity, the reductions reached 23.9 percent at just 200 degrees and climbed to 64.9 percent at 800 degrees. Averaged across the temperature range, each 100-degree increment cost naturally cooled specimens roughly 6.7 to 8.0 percent of their strength, while water-cooled specimens lost about 7.5 to 8.1 percent per 100 degrees. The gap between the two cooling regimes widened at higher impact velocities: at 5.2 meters per second and 800 degrees, the water-cooled specimens suffered 6.4 percent greater strength loss than the naturally cooled ones, whereas at the lowest velocity the difference was a mere 1.3 percent. The explanation lies in competing timescales—at slow loading rates, cracks have time to propagate and dissipate energy, blunting the effect of water-induced damage, but at high rates the loading outpaces the material’s energy dissipation, and the microcracks created by thermal shock propagate catastrophically.

Against this backdrop of degradation, the basalt fibers demonstrated genuine protective power. At every temperature from 200 to 800 degrees, specimens with 0.2 percent fiber content outperformed both plain concrete and the 0.1 percent mix. At 400 degrees and an impact velocity of 3.5 meters per second, the naturally cooled fiber-reinforced specimens reached peak stresses of 40.1 megapascals, some 16.9 percent above plain concrete, while at 200 degrees under water cooling the 0.2 percent mix achieved 38.8 megapascals—a remarkable 32.4 percent improvement over unreinforced concrete. The mechanism, the authors explain, is architectural: randomly distributed fibers form a three-dimensional network that binds tightly with the cementitious matrix, reduces internal pores and cracks, and bridges microcracks before they can coalesce into fracture surfaces. But this protection has a ceiling. Above roughly 600 degrees, the fibers begin to degrade and lose their reinforcing capability, and the fiber-matrix interface—already weakened by thermal mismatch between fiber and paste—becomes a liability during water quenching.

Fractal analysis of the impact debris added another layer of insight. The fractal dimension of the fragments rose steadily with temperature, from around 1.82 at 200 degrees to above 2.3 at 800 degrees under the highest loading rate, indicating progressively finer and more complex fragmentation. Water-cooled specimens consistently shattered into finer pieces than naturally cooled ones, particularly at 600 and 800 degrees, confirming that the steep thermal gradients during quenching seed dense populations of microcracks that later explode into fracture networks under impact.

Microstructural investigation grounded these observations in chemistry and physics. X-ray diffraction of specimens heated to 800 degrees showed the disappearance of calcium hydroxide peaks and weakened calcium carbonate signals, evidence that these phases had thermally decomposed—reactions that release vapor, increase porosity, and weaken the matrix. Scanning electron microscopy revealed the deterioration in vivid detail: an unheated specimen showed a dense microstructure with abundant calcium-silicate-hydrate gel and a compact interfacial transition zone around each aggregate, while specimens heated to 800 degrees contained no detectable C-S-H gel at all, their structure riddled with pores and cracks and hosting instead a dicalcium silicate-like phase. Comparing cooling regimes at 400 degrees, the naturally cooled sample remained relatively dense and rich in hydration products, whereas its water-quenched twin displayed obvious cracks at the interfacial transition zone and a loosened surrounding matrix—the fingerprint of thermal shock stress racing through the cooling material.

The practical implications reach well beyond the laboratory. Tunnel linings that survive a fire are routinely assessed and, if necessary, repaired, but this study shows that the assessment itself must account for the cooling history. A lining quenched by firefighting water may be significantly weaker than its peak temperature alone would suggest, particularly if it subsequently faces dynamic loads from traffic vibration, rockfall impacts, or secondary events. Conversely, the results give engineers a positive tool: specifying basalt fiber reinforcement at around 0.2 percent by volume can meaningfully bolster the impact resistance of fire-exposed concrete below 600 degrees, at a modest material cost. The authors frame their work as a theoretical foundation for post-fire safety assessment of tunnel linings and for the wider engineering adoption of basalt fiber-reinforced concrete—a reminder that in structural fire engineering, what happens in the minutes after the flames matter almost as much as the fire itself.

Subject of Research: Dynamic mechanical properties and deterioration mechanisms of basalt fiber-reinforced concrete after exposure to high temperatures under natural cooling and water cooling, investigated using split Hopkinson pressure bar testing, XRD, and SEM analysis.

Article Title: Dynamic mechanical properties of basalt fiber-reinforced concrete after exposure to high temperature and different cooling methods

Article References: Liu, Y., Li, D., Dong, J., Zhang, Y., Zhou, X., & Zhou, Q. (2026). Dynamic mechanical properties of basalt fiber-reinforced concrete after exposure to high temperature and different cooling methods. Case Studies in Construction Materials, 25, Article e06505. https://doi.org/10.1016/j.cscm.2026.e06505

Image Credits: AI Generated

DOI: 10.1016/j.cscm.2026.e06505

Keywords: basalt fiber-reinforced concrete, tunnel fire, high temperature exposure, water cooling, thermal shock, split Hopkinson pressure bar, dynamic compressive strength, fractal dimension, interfacial transition zone, XRD, SEM, microstructural deterioration

Cloud has a new bulk capacity market

11 September 2026 at 10:00

For 15 years, public cloud providers have defined the industry for most enterprises: on-demand, automatically metered services delivered over the open internet that give developers immediate access to storage, databases, compute, application development platforms, and AI capabilities. That model remains dominant because it is simple, well-instrumented, and universally supported. Yet an adjacent reality has always existed. Large-scale, off-market capacity deals where technology companies with surplus GPUs, storage, and compute sold blocks of capacity in bulk to other firms under nondisclosure agreements were once common. Those were cloud transactions in substance, since you were using somebody else’s servers. However, they lacked the automation, metering, and governance that define public cloud services, and they operated entirely outside the frameworks enterprises rely on for budgeting, compliance, and accountability.

A shadow market becomes visible

These arrangements are now becoming more visible and more formalized. Some are even openly auctioned. Meta’s entrance into the cloud capacity space, essentially offering its excess compute infrastructure to outside buyers, represents the maturation of a market that has existed in back rooms for years. Large, multiyear capacity commitments are no longer whispered about during boardroom lunches. They are being announced, financed, and tracked by analysts. This shift is creating a distinct layer in the cloud landscape, one that sits between traditional hyperscalers and true private clouds, and one that enterprises can no longer afford to ignore.

The practical implication is that the cloud market you thought you understood now has a parallel track. When a large enterprise needs maximum GPU capacity for model training or inference, it has more options than a year ago, and those options come with genuinely different trade-offs. You can go to a hyperscaler and pay the published rate for metered GPU as a service, receiving a fully managed platform with all the tools, governance, and integration you need. Or you can reach out to a technology provider that happens to have idle or excess GPU capacity and negotiate a bulk deal at a fraction of the published rate. The first option provides predictability and depth of service. The second offers raw cost savings if you have the operational capability to handle less-managed infrastructure.

This bifurcation is muddying procurement decisions. The gap in pricing between bulk off-market capacity and public cloud offerings can be substantial. Some analysts cite differentials ranging from 10 to 100 times. But headline GPU rates are only part of the evaluation. Bulk deals tend to come with minimal metering, limited monitoring, and a do-it-yourself culture around incident response and optimization. You are, in effect, purchasing wholesale energy. It is robust and capable, but it requires your own wiring and maintenance. Organizations accustomed to the instrumentation and service-level agreements of hyperscalers may find such a different operational reality a bit jarring. Those with mature cloudops teams may find the economics compelling.

Who benefits and who adjusts

The hyperscale incumbents will not be displaced overnight. They offer the complete ecosystem, not just GPU frameworks but development tools, hundreds of managed services, identity, compliance, and integration layers that are genuinely difficult to replicate. However, they are now facing a market where customers with intensive computational needs can source raw GPU capacity from alternative suppliers, particularly for bursty or cost-sensitive workloads. This is not necessarily a threat to hyperscaler dominance, but it is a complication. The hyperscalers will likely respond through partnerships, capacity-coverage agreements, and possibly acquisitions of promising capacity players. Effectively, they will become customers of the same off-market ecosystem they once ignored.

The cloud market is adding a new axis of choice: managed breadth versus specialized efficiency. Enterprises will increasingly construct hybrid operating models, keeping steady-state, security-sensitive, and toolchain-dependent workloads on hyperscaler platforms while offloading cost-intensive training runs and elastic inference to bulk-capacity providers. This is not a binary choice. It is an expanding continuum of sourcing options that demands more sophisticated procurement and architecture planning.

How to take advantage

Know your actual workload cost before you negotiate. If your AI workloads are compute-intensive, model training-heavy, or require burst capacity at unpredictable intervals, the economics of bulk capacity deals can be compelling. Run a pilot that captures total cost of ownership, including data movement, integration effort, operational overhead, and potential downtime during self-managed troubleshooting before you assume bulk is simply cheaper. Compare fully loaded costs, not GPU-hour sticker prices.

Build portability into your AI stack from the start. Bulk-capacity providers often lack the governance, compliance tools, and model life-cycle management enterprises need. Mitigate this risk by standardizing on containerized inference runtimes, open model formats, and CI/CD pipelines capable of deploying across multiple targets. This type of discipline turns vendor diversity into negotiating leverage rather than operational liability. It also ensures you can shift workloads as capacity markets fluctuate.

Treat capacity sourcing as a strategic procurement function. Bulk cloud capacity is not a niche quirk. It is becoming a recognized market that rewards supply-chain thinking. Diversify your supplier base, negotiate multi-vendor commitments where possible, and develop a reserve strategy for critical workloads. Keep your hyperscaler relationship for reliability and breadth, but maintain at least one secondary capacity arrangement for cost-sensitive or elastic workloads. The enterprises that treat capacity as a variable asset, rather than a fixed infrastructure decision will be better positioned as this market continues to expand and formalize.

The cloud market is no longer a simple fork between public and private. A defined middle ground of negotiated bulk capacity is now attracting billions in investment and reshaping how AI infrastructure costs are allocated. Enterprises that understand this landscape and build flexible, portable architectures that can span it will capture more value from AI investments than those who default to the familiar hyperscaler menu alone.

MacVoices #26244: Live! – Flock Cameras, Cyber Mercenaries, and the Data Center Backlash

By: Chuck
31 August 2026 at 20:04
Flock cameras have ignited a debate over surveillance, privacy, data collection, and public resistance. The MacVoices panel weighs in on the debate. They also discuss the risks of government-sponsored cyberattacks carried out by private companies, growing opposition to data centers over power and infrastructure demands, and the regulatory challenges surrounding their expansion.

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JPMorgan, Goldman And 38 Others Test Trading Of Tokenized Assets Across Wall Street

Using blockchain-based representations of real-world assets (securities, including equities and bonds), and operating under the SEC’s December 2025 No-Action Letter that covers Russell 1000 stocks and major ETFs, the titans of Wall Street put the pedal to the metal for 4 hours. The test was a successful proof of concept, and DTCC is planning to move toward a live October 2026 launch. The trading system will operational by the end of 2026. ⁃ Patrick Wood, Editor.

For four hours in July, some of the biggest players in finance put Wall Street’s long-running blockchain experiment to work across an ordinary trading day.

After years of tests with digital versions of securities, cash and trading networks, the focus has shifted to whether these technologies could work together. This time, nearly 40 firms were testing how tokenized assets could move through the institutions that keep modern markets moving.

Over blockchain networks, JPMorgan Chase & Co., Goldman Sachs Group Inc., Invesco Ltd. and Citadel Securities LLC traded stocks and Treasuries, posted collateral, met margin calls and transferred assets. Overseeing it all was the Depository Trust & Clearing Corp., which monitored the exercise from “war rooms” in New York and New Jersey as the firms completed dozens of transactions across various use cases.

The broader aim was to test whether tokenized versions of familiar assets can move through new blockchain systems without sacrificing the settlement, custody and risk controls of traditional markets.

“We tried to replicate the activities that occur in the market on a day to day,” said Nadine Chakar, DTCC’s global head of digital assets. “You don’t want tokenization to be done in isolation. You want it to be part of the daily workflow.”

That is the bet behind much of Wall Street’s tokenization push: not just longer operating hours as markets move toward 24/7 trading, but whether assets can move more freely across institutions, reducing trapped liquidity and some of the handoffs involved in moving collateral.

The July pilot was an attempt to test that proposition under something closer to real market conditions, bringing together exchanges, clearinghouses, banks and asset managers rather than limiting transactions to bilateral trades. Transactions included collateral pledges, margin calls, repo trades, delivery-versus-payment and transfers between blockchains using digital versions of stocks, exchange-traded funds and Treasuries on DTCC’s private network built using Besu and the Canton Network.

DTCC said it plans to move the service from limited tests toward ongoing use in October, adding another blockchain network, expanding support for Treasury corporate actions — coupon payment and Treasury maturity dates — and opening it to eligible participants beyond the July group.

DTCC’s role in the test was key because it maintains the official record for trillions of dollars of securities. If tokenized trades still have to be reconciled back to that traditional record at the DTCC, some of the promised efficiency disappears.

DTCC declined to provide transaction values or client-specific details. Chakar said “the purpose of the day was to simulate a representative snapshot of the market by including exchanges, clearing houses, buy-side and sell-side participants.”

Major hurdles remain, Chakar said, including adoption, regulatory alignment and integration with existing systems.

DTCC says Wall Street’s current tokenization push is different from previous attempts to deploy blockchain to financial markets because institutional demand has grown. The agency pointed to an industry working group that has nearly doubled in a few months to more than 100 members. Whether that matters commercially is a different question, said Vivian Fang, a finance professor at Indiana University.

“Many enterprise blockchain pilots over the past decade failed not because the technology didn’t work, but because they lacked the conditions needed for commercial adoption,” she said. “The ultimate test will be whether market participants meaningfully adopt tokenized securities and whether the platform delivers measurable economic value.”

Collateral Movement

Collateral offered one of the clearest tests of whether tokenization has economic value. In one transaction, DTCC tokenized an equity it held and deployed it as margin at CME Group Inc. within minutes. Tokenized collateral can settle almost instantly, potentially reducing trapped liquidity and allowing firms to put assets to work more efficiently, Chakar said.

JPMorgan, participating as a DTCC clearing member, separately converted securities collateral into digital tokens and used them to fund a margin call. The bank also tested tokenized equity collateral, including Invesco’s Nasdaq 100 ETF, ticker QQQ.

“Tokenization could reduce friction, improve collateral mobility and enable assets to be deployed more efficiently in support of margin requirements,” said Danny Hand, digital assets lead for prime financial services at JPMorgan.

There is a catch. JPMorgan can create tokenized collateral, but clearinghouses still have to accept it, custodians have to handle it and risk systems have to account for it. Broader adoption will depend in part on central counterparties recognizing tokenized collateral after DTCC expands the service.

That helps explain why early adoption may cluster around a handful of uses. Citigroup Inc. has projected tokenized assets could reach $5.5 trillion by 2030, rising to $8.2 trillion in a bullish scenario. Bank of America Corp. Global Research analysts expect early adoption to focus on collateral mobility, on-chain cash management and settlement of standardized financial instruments, with stocks and credit developing more gradually.

Legal clarity remains another obstacle, according to Noelle Acheson, author of the “Crypto Is Macro Now” newsletter. But DTCC is “in the relatively unique position of being able to move the industry forward by setting standards and working with global partners,” she noted. “That said, the transformation is likely to take about a decade.”

Even if those uses take hold, another problem remains: firms are building on blockchain networks that do not automatically communicate. Moving an asset between them still requires both systems to agree on what moved and who owns it.

The July transactions also tested whether separately built systems could work together. Digital Asset Holdings LLC’s Canton Network underpinned several of those trades.

“It was a real functioning secondary market set of activities that was enabled by market participants,” said Kelly Mathieson, the company’s chief business development officer.

For all the activity in July, the exercise was carefully choreographed. Participants spent months testing together in an intentionally narrow window and a relatively small amount of assets were converted into an onchain form. October begins the harder phase: whether the system gets used by a larger group during the course of everyday business.

“It was a massive step for the market and for the overall state of readiness for all participants come October,” Chakar said. “There’s nobody questioning anymore whether the technology works.”

Read full story here…

BCM Team Wins Inaugural Burroughs Wellcome Fund Grant for Climate-Health Research

11 August 2026 at 19:34

A multidisciplinary team from Baylor College of Medicine, Rice University and the University of Texas School of Public Health has received the inaugural Climate + Health Excellence award from the Burroughs Wellcome Fund. The five-year, $10 million award will support FORECAST, an ambitious program designed to investigate how climate change, extreme weather and environmental disruption influence the emergence and spread of infectious diseases. Led by infectious disease researcher Dr. Anthony Maresso, the project will combine wastewater surveillance, metagenomic sequencing, climate science, epidemiology and public education to create an early-warning system for pathogen activity across Texas.

FORECAST will build on Texas’s expanding wastewater-monitoring infrastructure, including the Texas Wastewater Environmental Biomonitoring Network, or TexWEB. The statewide network samples sewage from 15 Texas cities and analyzes biological material shed by communities. Unlike clinical surveillance, which depends on people seeking medical care and receiving diagnostic tests, wastewater epidemiology captures population-level signals from entire communities. Viral genetic fragments, bacterial DNA and other biological markers can appear in sewage before hospitals and clinics register a rise in patients, providing public health officials with valuable time to investigate and respond.

The program will use metagenomic surveillance, a technique that examines genetic material recovered from environmental samples rather than searching only for a predetermined pathogen. In conventional testing, laboratories typically use targeted molecular assays designed to detect a known virus or bacterium. Metagenomic sequencing can survey a much broader range of organisms, including variants and potentially unfamiliar pathogens. Researchers can then compare these signals with clinical data, geographic information and environmental measurements to determine whether changes in temperature, rainfall, flooding, drought or other conditions are associated with shifts in infectious disease activity.

The scientific goal is not simply to identify pathogens after they appear, but to uncover relationships between microbes and the environmental conditions that help them enter or circulate within human populations. Climate change can influence disease transmission through several pathways. Warmer temperatures may expand the geographic range or seasonal activity of mosquitoes and other vectors. Heavy rainfall and flooding can overwhelm sanitation systems and redistribute contaminants, while drought can alter water use and concentrate biological material. Changes in human behavior, animal movement and ecosystems may create additional opportunities for pathogens to cross species boundaries.

Maresso and his colleagues demonstrated during the COVID-19 pandemic that SARS-CoV-2 signals in wastewater could precede increases in clinically diagnosed infections. Viral RNA released in feces entered sewage systems, where researchers tracked changes in concentration over time. Although wastewater measurements cannot determine which individuals are infected, they can reveal whether pathogen activity is increasing or declining within a catchment area. The approach has since been extended to a wider range of targets, including avian influenza, measles, HIV and viruses associated with cancer. FORECAST aims to develop this surveillance capacity into a predictive climate-health platform.

Rice University climate scientist Dr. Sylvia Dee will help connect pathogen observations with weather and climate data. The team plans to examine whether specific environmental patterns consistently occur before increases in particular infectious agents. Such models could eventually function like a public health weather report, indicating when conditions are becoming more favorable for disease transmission. The researchers emphasize that predictive systems will require careful validation because the presence of pathogen genetic material does not necessarily indicate infectious virus, active transmission or a future outbreak. Interpreting the signal will require laboratory studies, clinical evidence and knowledge of local infrastructure.

The project will also investigate diseases transmitted by vectors such as mosquitoes. Dr. Peter Hotez and colleagues at the National School of Tropical Medicine will focus on vector-borne pathogen transmission and the growing health risks associated with changing climate conditions. Texas is particularly relevant for this work because it contains diverse ecological regions and experiences heat waves, hurricanes, flooding and rapid urban growth. These factors can affect mosquito habitats, human exposure and the movement of pathogens across communities. Integrating wastewater findings with vector surveillance could help reveal connections that would remain hidden if either system were used alone.

Education and public communication will form a central part of FORECAST. The team plans to create a “Middle to Medical” climate-health education track extending from middle school through medical training and professional development. Dr. Nancy Moreno, who developed Baylor’s BioEd online program, will lead educational efforts with Rice University’s Dr. Joseph Campana, director of the university’s Center for Environmental Studies and EcoStudio. The curriculum will explain how pathogens spread, how climate conditions alter disease risk and how scientific evidence can guide decisions by students, clinicians, policymakers and communities.

The award is particularly significant because FORECAST was selected as the sole funded project from 161 applications submitted to the new CHEX program. The initiative is intended to strengthen research, education and public engagement at the intersection of climate change and human health. Dr. Eric Boerwinkle, dean of the UT School of Public Health and a project co-investigator, will contribute access to large clinical datasets that can be used to evaluate wastewater and environmental signals against reported illnesses. The investigators say the work could improve outbreak preparedness while advancing the still-developing field of wastewater science.

By combining environmental sequencing with climate modeling and health education, FORECAST seeks to shift infectious disease surveillance from a largely reactive system toward earlier detection and prevention. Its findings may help officials recognize emerging threats before they produce widespread clinical disease, while also clarifying how environmental changes shape viral evolution, transmission and geographic spread. The researchers describe wastewater as an unusually candid public health resource: it aggregates biological information from a population without requiring individual testing. As climate pressures intensify, they argue, that collective signal could become an essential tool for protecting communities and preparing the next generation of scientists.

Subject of Research:
Climate-driven infectious disease emergence, wastewater epidemiology, metagenomic pathogen surveillance and climate-health education.

Article Title:
Texas Researchers Launch FORECAST Program to Link Climate Change With Emerging Viral Threats

Web References:
Baylor College of Medicine; Rice University; University of Texas School of Public Health; Burroughs Wellcome Fund; Texas Wastewater Environmental Biomonitoring Network (TexWEB).

References:
Information provided in the source news release concerning the FORECAST project, the Climate + Health Excellence award and TexWEB wastewater surveillance activities.

Keywords:
Wastewater epidemiology, viral surveillance, metagenomic sequencing, climate change, infectious diseases, pathogen emergence, public health, Texas, SARS-CoV-2, avian influenza, measles, vector-borne disease, climate health, epidemiology, wastewater science

Annenberg Survey Finds Sharp Rise in Opposition to Local Data Centers

11 August 2026 at 14:41

PHILADELPHIA — Public resistance to new data centers has risen sharply across the United States, even as Americans’ overall views of artificial intelligence remain largely unchanged, according to a nationally representative survey from the Annenberg Public Policy Center at the University of Pennsylvania. The findings reveal a growing divide between public attitudes toward AI as a technology and attitudes toward the physical infrastructure required to operate it. While many people continue to see potential benefits from AI, particularly in medical research, opposition increases substantially when the technology is associated with a large industrial facility being built near their homes.

The survey found that 61% of U.S. adults somewhat or strongly oppose the construction of new data centers in their communities. That figure is 12 percentage points higher than the 49% recorded in an earlier APPC survey conducted in February and March. Support fell during the same period, from 21% to 14%. The change comes as communities across the country debate the environmental, economic, and social consequences of rapidly expanding computing infrastructure used to train and operate advanced AI systems.

Data centers are specialized facilities filled with servers, networking equipment, cooling systems, backup power supplies, and security infrastructure. AI applications can require enormous amounts of computational power because they process vast datasets and perform calculations across billions of model parameters. Training a large AI model may require thousands of processors operating simultaneously, while everyday use also depends on data centers to generate responses, analyze images, translate languages, and carry out other tasks. These facilities can consume substantial amounts of electricity and water, making their construction a highly visible local issue even when the software they support is used remotely.

Opposition to local data centers crosses political boundaries. Majorities of Democrats, Republicans, and independents said they opposed new facilities in their areas, although the proportions differed: 69% of Democrats, 54% of Republicans, and 53% of independents expressed opposition. Resistance was strongest among adults younger than 30, reaching 70%, and declined to 57% among people 65 and older. The pattern is notable because younger adults are often assumed to be more comfortable with emerging technologies and are more likely to use AI-powered services in education, work, entertainment, and communication.

Despite the shift in views about local infrastructure, broader opinions about AI have remained stable. Thirty-nine percent of respondents said they expected AI’s impact on the United States over the next decade to be somewhat or very negative, compared with 18% who expected a positive impact. The result was statistically unchanged from the spring survey. In addition, 68% said the federal government has done “too little” to regulate AI, a view shared by majorities of Democrats, Republicans, and independents. The results suggest that concerns about AI regulation have become persistent rather than being driven solely by short-term news events.

Respondents distinguished sharply between areas where AI might produce public benefits and areas where it could create risks. Among 13 sectors examined, medical research and scientific discovery was the only category receiving a net-positive assessment, with anticipated benefits exceeding expected harms by 41 percentage points. AI systems are increasingly being used to identify patterns in biological data, predict molecular structures, assist with medical imaging, and help researchers screen potential treatments. These applications may accelerate work that would otherwise require years of laboratory experiments, although they still depend on human validation and carefully controlled clinical studies.

The most negative assessments involved personal privacy and data security, which registered a net score of minus 63 points. Children’s online safety followed at minus 50 points, while employment and jobs reached minus 46 points. These concerns reflect the technical and social consequences of deploying AI at scale. Models may be trained on or exposed to sensitive information, automated systems can make decisions that are difficult to explain, and workplace applications may alter or eliminate certain tasks. The survey does not measure whether these risks will materialize in specific industries, but it indicates that many Americans view them as more immediate than AI’s possible benefits outside medicine.

Personal experience with AI was associated with more favorable opinions about its national impact. Among respondents who said they had never used AI during the previous month, 54% expected its effect on the country to be negative. That proportion fell to 40% among light users and to 29% among those who used AI many times or almost every day. However, frequent use did not make people more supportive of data centers. Opposition to building new facilities near respondents’ homes remained essentially flat across different levels of AI use. This distinction suggests that familiarity may influence perceptions of digital tools, while concerns about electricity demand, water consumption, noise, traffic, land use, and local utility costs may remain regardless of personal experience with AI software.

“The people who use AI the most are the most optimistic about what it will do for the country, and that has been consistent across our surveys,” said Matt Levendusky, a University of Pennsylvania political science professor and the Stephen and Mary Baran Chair in the Institutions of Democracy at APPC. He said that optimism had clear limits, particularly when questions involved privacy, employment, or the construction of a data center nearby. The survey was conducted by SSRS for APPC’s Institutions of Democracy division among 1,320 U.S. adult citizens from June 16 through July 19, 2026. Respondents were surveyed primarily online, with a small telephone sample, and weighted to match population benchmarks. The margin of error for the full sample was plus or minus 3.5 percentage points, with larger margins for subgroups. Together, the findings indicate that the next phase of the AI debate may be shaped less by abstract promises about intelligent software than by negotiations over the highly physical systems that make it possible.

Subject of Research: People

Article Title: Opposition to Local Data Centers Rises Sharply as Views on AI Hold Steady

Web References: Annenberg Public Policy Center news release; Reuters; The New York Times; Associated Press

References: Annenberg Public Policy Center Institutions of Democracy national survey, June 16–July 19, 2026; APPC AI topline survey data; APPC survey methodology

Image Credits: Annenberg Public Policy Center

Keywords: Artificial intelligence, data centers, public opinion, AI regulation, medical research, privacy, data security, employment, social surveys, political science

How Low Gravity and Pressure Affect Space Manufacturing of Carbon-Fiber Structures

9 August 2026 at 04:58

A new study is challenging one of the most persistent assumptions in space manufacturing: that building advanced composite structures beyond Earth is simply a matter of moving terrestrial production methods into orbit. Research published in npj Advanced Manufacturing examines how two defining features of the space environment—reduced gravity and reduced atmospheric pressure—can alter the manufacture of carbon-fiber-reinforced structures, materials widely regarded as essential for future spacecraft, habitats and large orbital infrastructure.

Carbon-fiber-reinforced polymers, commonly known as CFRPs, combine strong, lightweight carbon fibers with a polymer resin that binds the fibers into a rigid composite. Their exceptional strength-to-weight ratio makes them attractive for launch vehicles and spacecraft, where every kilogram matters. Yet manufacturing these materials is a delicate process. Fibers must be positioned accurately, resin must penetrate the reinforcement evenly, and gases or trapped voids must be removed before the material cures. On Earth, gravity and atmospheric pressure quietly assist—or complicate—each of these steps.

In space, those familiar forces change dramatically. Reduced gravity can alter how liquid resin flows through a carbon-fiber structure, while low pressure can encourage dissolved gases and volatile compounds to expand or escape. Together, these effects may influence impregnation, consolidation, curing and the final mechanical performance of a composite part. The new work by R. Strecker, T. Rieger, A. Tabelander and colleagues focuses on understanding these interactions before manufacturers attempt to produce increasingly complex structures in orbit.

The central issue is void formation. A composite that appears solid can contain microscopic pockets of air or vapor between its fibers. These voids interrupt the transfer of stress through the material and can reduce strength, stiffness and fatigue resistance. During conventional manufacturing on Earth, pressure is often applied to compress the layers and drive out unwanted gases while resin fills the spaces between fibers. In a reduced-gravity environment, however, buoyancy-driven movement is weakened, and the pathways through which bubbles travel may change. A bubble that would rise and escape during terrestrial processing could remain trapped inside the structure.

Pressure reduction introduces another layer of complexity. Lower ambient pressure can help remove volatile substances and trapped gases, but it can also cause dissolved gases in the resin to expand. If the resin is not sufficiently mobile, expanding bubbles may become locked in place as the material cures. The balance between degassing and bubble growth therefore becomes a critical manufacturing parameter. The study’s importance lies in treating pressure and gravity not as isolated variables, but as environmental conditions that can interact throughout the production cycle.

Resin flow is equally important. Carbon-fiber reinforcements are made from tightly packed filaments, creating narrow channels through which resin must travel. On Earth, gravity can influence resin distribution, especially in vertically oriented or large components. In reduced gravity, capillary forces and surface tension become relatively more significant. These forces can help draw resin into small spaces, but they may also create uneven wetting patterns depending on the fiber architecture, resin viscosity and processing orientation. Such changes could affect whether a component cures uniformly from its surface to its interior.

The findings carry implications far beyond laboratory samples. Future space manufacturers may seek to fabricate beams, pressure-vessel elements, antenna supports, solar-array components and repair patches directly in orbit. Producing these parts where they are needed could reduce the need to launch bulky finished structures from Earth. It could also enable designs too large to fit inside a rocket fairing, including trusses and habitation modules assembled gradually in space. But the economic and engineering promise of this approach depends on reliable quality control. A component that looks flawless may still fail if its internal fiber alignment, resin distribution or void content is inconsistent.

The research also highlights why manufacturing hardware designed for Earth cannot automatically be considered space-ready. Pumps, heaters, vacuum systems, molds and curing chambers must all operate under conditions in which heat transfer, fluid movement and gas removal behave differently. Temperature control is particularly important because resin viscosity changes strongly with temperature. A resin that flows readily during terrestrial processing might move more slowly or unevenly in orbit, while excessive heating could accelerate curing before impregnation is complete. Space-based production systems will therefore need carefully coordinated control of pressure, temperature, resin chemistry and processing time.

For engineers, the study provides a foundation for developing manufacturing protocols tailored to reduced-gravity environments. Instead of copying Earth-based workflows, future systems may need staged pressure changes, specialized fiber preforms, modified resin formulations or active bubble-management techniques. Sensors could monitor pressure, temperature and resin flow in real time, allowing automated systems to detect defects before a part is finished. Non-destructive inspection, including imaging and ultrasonic testing, will also be essential for verifying the integrity of components that cannot easily be returned to Earth for analysis.

The broader message is that space manufacturing is not merely an extension of industrial production—it is a new manufacturing regime governed by different physics. Understanding how gravity and pressure reshape the behavior of fluids, gases and composite materials could determine whether orbital fabrication remains a promising experiment or becomes a practical industrial capability. By examining the production of carbon-fiber-reinforced structures under space-relevant conditions, Strecker, Rieger, Tabelander and their colleagues are helping identify the hidden variables that will decide how safely and efficiently humanity can build beyond Earth.

Subject of Research: Effects of reduced gravity and reduced atmospheric pressure on the in-space manufacturing of carbon-fiber-reinforced structures

Article Title: Effects of reduced gravity and reduced atmospheric pressure on in-space manufacturing of carbon-fiber-reinforced structures

Article References: Strecker, R., Rieger, T., Tabelander, A. et al. Effects of reduced gravity and reduced atmospheric pressure on in-space manufacturing of carbon-fiber-reinforced structures. npj Adv. Manuf. 3, 26 (2026). https://doi.org/10.1038/s44334-026-00104-w

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s44334-026-00104-w

Keywords: in-space manufacturing, carbon-fiber-reinforced polymers, reduced gravity, reduced atmospheric pressure, composite materials, resin flow, void formation, space technology

Microsoft Opens Largest India Data Center as AI Competition Intensifies

7 August 2026 at 19:53
Microsoft has opened its largest India cloud region in Hyderabad as it expands AI infrastructure and competes with Google and Amazon for enterprise demand.

Queensland and NT reject Labor’s push to ensure that power-hungry AI datacentres use renewable energy

29 July 2026 at 04:19

Federal-state stoush comes as rating agency warns that electricity bills could skyrocket

Queensland and the Northern Territory have rejected the federal government’s plans to mandate that AI datacentres use renewable power, rubbishing Anthony Albanese’s proposals to regulate the booming technology as “underdeveloped ideas that hand increased power to Canberra”.

The latest federal-state stoush on energy comes as S&P Global, the economic rating agency, warns that power use from datacentres could rise five-fold by 2035 to 10% of Australia’s total consumption, and that a mismatch between delivery timelines for datacentres and renewable projects could lead to energy bills skyrocketing.

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© Photograph: Bloomberg/Getty Images

© Photograph: Bloomberg/Getty Images

© Photograph: Bloomberg/Getty Images

Intel, AMD Pursue Long-Term China CPU Deals as AI Demand Surges

24 July 2026 at 17:00
Intel and AMD are reportedly negotiating long-term CPU supply agreements with Chinese customers as AI demand drives higher server processor prices and tighter supply.

Key Factors Driving Sustainable Stormwater Management Success

18 July 2026 at 22:18

A new study in npj Urban Sustainability points to the practical levers that determine whether stormwater systems actually deliver long-term sustainability. As cities expand and rainfall extremes intensify, green and nature-based infrastructure has been promoted to capture runoff, reduce flooding, and protect water quality. But the research warns that success is not automatic: design decisions and operational realities can make or break performance.

The authors, Sun, Randrup, Blecken and colleagues, analyze what they call “decisive factors” governing sustainable stormwater management. Their central message is that the most visible features—such as bioswales, permeable surfaces, or detention basins—only work when supported by the right planning assumptions and measurable hydrological outcomes.

A key technical theme is how stormwater must be matched to system capacity under changing conditions. The study emphasizes that system sizing, soil or media properties, and infiltration or storage behavior strongly influence whether the infrastructure can handle typical storms and rare, high-intensity events. When these factors are misestimated, treatment efficiency can collapse during peak flows.

Equally important are maintenance and lifecycle considerations. Sustainable stormwater is sensitive to clogging, sediment accumulation, vegetation health, and changes in catchment behavior. The research highlights the need for maintenance regimes that reflect likely failure modes, rather than generic schedules that ignore local hydrology and traffic or land-use impacts.

The paper also stresses the value of monitoring and model-informed decision-making. Reliable data on rainfall patterns, runoff routing, and pollutant loads allow managers to validate performance and adjust operations. This feedback loop helps prevent long-term degradation that otherwise remains invisible until issues become costly.

For cities seeking viral, widely transferable “what works” guidance, the authors’ findings offer a shortlist of priorities: realistic capacity planning, robust material characterization, design for multi-storm performance, and maintenance strategies tied to specific mechanisms. Together, these elements translate sustainability goals into systems that function under real weather and real urban change.

The study’s DOI is 10.1038/s42949-026-00448-4.

Subject of Research: Sustainable stormwater management / urban sustainability
Article Title: Decisive factors in sustainable stormwater management
Article References: Sun, Z., Randrup, T.B., Blecken, GT. et al. Decisive factors in sustainable stormwater management. npj Urban Sustain 6, 104 (2026). https://doi.org/10.1038/s42949-026-00448-4
Image Credits: AI Generated
DOI: https://doi.org/10.1038/s42949-026-00448-4

“This Upper Limit is an Illusion”: NASA Study Warns There May Be “No Limit” to How Solar Storms Impact Earth 

18 July 2026 at 14:40

Our modern society depends heavily on communications and power networks that are highly susceptible to interference from solar storms, making these volatile space weather events a paramount concern for scientists.

Now, it turns out there may be more to fear than previously realized. Scientists are discovering that there may be no ceiling to the devastating effects solar storms may have on Earth, overturning what little sense of security may exist as a mere mathematical anomaly.

In a recent paper published in Nature, a NASA-led team discovered that the potential impact of solar storms on our technological infrastructure could be substantially more severe than currently recognized.

Solar Storms Reconsidered

According to new research, any past limitations perceived regarding the impact of solar storms on our planet are purely an illusion, which arises from imprecise data collection.

Previous observations of electric currents in the Earth’s upper atmosphere during solar storm events suggested a ceiling to how far they could increase. Yet the new paper claims that uncertainties in solar wind data are falsely giving rise to this idea.

The researchers say that the distance of the spacecraft taking solar wind measurements is the issue. Those spacecraft are millions of miles closer to the Sun than the Earth is, meaning that by the time those winds reach Earth, they have weakened considerably. Additionally, instrument error and the effects of the local plasma and field conditions all come into play.

All this, the researchers say, creates a mismatch between the measured strength of the wind and its effects on our planet’s electrical currents.

Correcting For Distance

The new findings contain one significant difference from existing research: they relied on observation platforms much closer to Earth to collect data, which included spacecraft such as NASA’s MMS and THEMIS.

Launched in 2007, THEMIS consists of five satellites that target the Earth, rather than the Sun, to study our planet’s magnetosphere and auroras. MMS consists of four spacecraft that also study the Earth’s magnetosphere.

Existing charts show the impact of solar winds on Earth leveling off at a certain threshold, which scientists had believed was the maximum saturation possible. However, in new solar wind strength data collected much closer to Earth, more accurately reflecting power at the time of impact, this saturation point disappeared. Instead, this data revealed that there is no ceiling on how heavily solar storms can charge the Earth’s electrical currents.

Solar Storm Impacts

Another crucial element in the NASA-led research is the statistical concept of regression to the mean, which holds that extreme measurements often change as more data come in. In this case, as more data on solar storm impacts on Earth recorded from various distances were collected, the plating effect disappeared. The authors note that similar effects occur in other areas of research, such as climate studies and chronic medical pain.

These new findings present a major challenge to our understanding of space weather. Scientists will now need to conduct many further observations to determine if any such ceiling exists. The threat to our infrastructure may be severe, as satellite communication underpins our modern society, not to mention the power grid, which keeps our world running.

For now, NASA continues to monitor solar storms to prepare for their potentially dangerous effects on our infrastructure, and in light of the new research, the potential for future challenges ahead may be greater than previously realized.

The paper, “Regression to the Mean Can Explain Saturation of Geomagnetic Storms,” appeared in Nature on July 15, 2026.

Ryan Whalen covers science and technology for The Debrief. He holds an MA in History and a Master of Library and Information Science with a certificate in Data Science. He can be contacted at ryan@thedebrief.org, and follow him on Twitter @mdntwvlf.

Climate Change Alters Resource Adequacy Risks and Optimal Wind, Solar Siting

16 July 2026 at 14:40

Climate change is poised to reshuffle the reliability problem facing decarbonized power systems, according to a new study that links weather extremes, long-term climate projections, and real-world infrastructure constraints. As grids rely more heavily on wind and solar, they become sensitive not just to the amount of renewable capacity installed, but to where that capacity is sited relative to transmission lines, load centers, and the local meteorological patterns that govern output.

Using high-resolution climate projections at 12-km scale, the researchers examined how future conditions could alter both renewable generation and electricity demand across two distinct US regions: New England and Texas. They then coupled these climate inputs with an optimization model operating at the county level, allowing the team to test how system design choices—particularly spatial siting of wind and solar and the presence of transmission bottlenecks—affect long-run adequacy.

The results suggest a sharp increase in resource inadequacy risk by mid-century. In both regions, climate change can drive prolonged periods of low renewable generation, which occur when meteorological conditions reduce wind and solar output while demand simultaneously rises. For Texas, the study finds the frequency of inadequacy events could increase dramatically—up to fivefold—if planning proceeds using non-climate-informed assumptions.

Crucially, the work shows that these risks are not determined by aggregate capacity targets alone. Fine-scale siting decisions can lock the system into unfavorable “weather-resource–grid” pairings, amplifying periods when local supply falls short and transmission limits prevent imports from compensating.

Yet the study also offers a pathway to resilience. Climate-informed planning can mitigate the emerging adequacy threat at relatively modest cost. Texas, for example, can maintain near-zero marginal investment cost by shifting wind capacity westward, improving alignment with future wind patterns and reducing reliance on constrained transfers.

New England requires a different strategy. The region faces a higher investment need—about a 2.34% increase—achieved through targeted solar and transmission expansion closer to load centers. This approach helps buffer localized shortfalls during future, climate-driven demand and renewable output mismatches.

Overall, the study argues that achieving climate-resilient decarbonization means moving beyond simple build-out schedules. Instead, it calls for high-resolution, climate-aware spatial planning that reveals siting opportunities invisible to coarse models—turning reliability from a static design metric into a dynamic, region-specific forecast problem.

Subject of Research: Climate change and resource adequacy risks in wind/solar grids, including optimal renewable siting
Article Title: Climate change reshapes resource adequacy risks and optimal renewable energy siting in wind and solar energy systems
Article References: Qiu, L., Khorramfar, R., Wang, S. et al. Climate change reshapes resource adequacy risks and optimal renewable energy siting in wind and solar energy systems. Nat Energy (2026). https://doi.org/10.1038/s41560-026-02109-3
Image Credits: AI Generated
DOI: https://doi.org/10.1038/s41560-026-02109-3
Keywords:

São Paulo’s Bike Lane Expansion Boosts Cycling Among Residents

9 July 2026 at 06:16

In a groundbreaking decade-long study analyzing São Paulo’s urban landscape, researchers have revealed how the expansion of bike lanes and public parks profoundly influences physical activity and public health. Between 2014 and 2024, the city’s bike lane network surged from 242 to 743 kilometers, complemented by the addition of 15 municipal parks. This urban transformation was linked to increased transport-related cycling, emphasizing the critical role of accessible infrastructure within a 500-meter radius of residents’ homes.

The study’s multidisciplinary approach combined longitudinal surveys of 1,500 residents with geospatial datasets from the Geosampa platform, enabling precise correlation between environmental changes and behavior. Findings indicate that proximity to bike lanes significantly counteracts declining bicycle use trends, suggesting these facilities serve as protective factors promoting sustained physical activity. Such activity reduction directly translates to lower risks of cardiovascular disease, cancer, and improved mental health outcomes like reduced anxiety and depression.

Importantly, the research underscores active transportation infrastructure not only as a logistical planning tool but as a vital public health strategy. By reducing reliance on automobiles, these interventions diminish air pollution and traffic-related accidents, thereby contributing to climate change mitigation efforts. However, the study highlights uneven developments: wealthier neighborhoods received disproportionate infrastructure investments, calling for policy shifts to equip underserved zones with similar amenities.

Beyond infrastructural improvements, the researchers emphasize that sociocultural elements and affordability constraints also limit cycling uptake. Despite increased bike lanes, economic barriers such as the high cost of bicycles and gender disparities in cycling participation suggest that integrated approaches—encompassing recreational street design, bike-sharing expansions, and commuter support facilities—are necessary for meaningful behavioral shifts.

An illustrative testimony comes from a local photographer who transitioned from motorbike to bicycle commuting due to new bike bridges providing direct, safer routes. While embracing cycling’s growth, he voices concerns about road space allocation favoring cars and the emerging challenges of cyclists’ vulnerability amid the rise of faster electric bikes. Additionally, issues of public safety and bike theft remain barriers to broader adoption.

Overall, the findings reveal that São Paulo’s urban planning efforts over the past decade represent a promising model of how thoughtfully designed public open spaces can foster healthier, more sustainable cities. Still, ensuring equitable access and addressing multifaceted cultural and socioeconomic factors remain essential for realizing the full potential of active transport systems.


Subject of Research: Public open spaces and transport-related cycling in urban environments
Article Title: A decade-long study on public open spaces and transport-related cycling in the largest Brazilian city
News Publication Date: 13-Apr-2026
Web References: https://dx.doi.org/10.1016/j.jth.2026.102323
Image Credits: Phelipe Janning/Agência FAPESP

Keywords

Urban planning, Transportation, Public health, Cycling, Active transportation, São Paulo, Infrastructure, Mental health

Datacentres are a ticking timebomb. We must make sure AI’s benefits outweigh the costs | Nicki Hutley

8 July 2026 at 16:00

They suck up energy and water, and blast out heat. Just who is better off from all this investment – aside from tech bros?

The two great existential threats of our time – the climate crisis and AI – come hurtling together in the explosion of datacentres across Australia and around the world.

You can hardly avoid hearing about them these days, either with awed reverence of the promised benefits to humankind or with fear and anger given the implications for the climate, inflation, jobs and even housing affordability.

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© Photograph: Jono Searle/AAP

© Photograph: Jono Searle/AAP

© Photograph: Jono Searle/AAP

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