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

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

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

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

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

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

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

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

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

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

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

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

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

Image Credits: AI Generated

DOI: 10.1016/j.isci.2026.117951

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

Adam Engst:

Apple has significantly improved dictation in iOS 27 by adding a few capabilities for editing text from the Voice Control system. In other words, Apple has finally done some of the things I requested in “How iOS and macOS Dictation Can Learn from Voice Control’s Dictation” (31 August 2020). Remember, dictation is what you get when you tap the microphone button on the keyboard, while Voice Control is an all-encompassing system that you turn on in Settings > Accessibility > Voice Control. The problem with leaving Voice Control on permanently is that it’s constantly listening and thus inserting text when you don’t want that, and at times I have felt that its dictation isn’t as good.

Most notably, when you’re using iOS’s regular dictation, you can now change the case of words and phrases by saying “Capitalize phrase” or “Lowercase phrase.” Incorrect capitalization of words drives me up the wall. Equally useful is the ability to fix misheard or misspoken words by saying “Replace phrase1 with phrase2.” Most other Voice Control commands aren’t available in dictation, but you can say “Select phrase” and “Undo that.”

[…]

Unfortunately, while Apple added some of Voice Control’s capabilities to dictation in iOS 27, it didn’t extend that effort to macOS 27 Golden Gate. Put simply, when you use dictation in macOS, you don’t have access to any text-editing commands.

Previously:

• Apple’s 2026 Accessibility Feature Preview
• 2025 Apple Vision Accessibility Report Card

Milt Windler, one of the four leaders of Mission Control who in 1970 directed the round-the-clock efforts to bring the Apollo 13 astronauts back to Earth safely, has died at the age of 94.

Windler died on Thursday, surrounded by his loving family, according to a brief notice posted online.

Named a NASA flight director in 1968 after working on recovery operations during the Mercury and Gemini programs, Windler also led a shift of flight controllers for the Apollo 8 first circumlunar flight and Apollo 10 lunar landing dress rehearsal, as well as the second, third and fourth moon landings — Apollo 12, Apollo 14 and Apollo 15. He then served as a flight director for all three crewed missions to the United States' first space station, Skylab.

Read full article

Comments

© NASA

O iPhone 17 segue sendo o smartphone perfeito para a grande maioria dos usuários que procuram um telefone mais recente da Apple.

Entre as suas especificações técnicas, podemos destacar a tela Super Retina XDR de 6,3 polegadas com ProMotion (até 120Hz), o conjunto de duas câmeras (ambas de 48 megapixels, com sistema Fusion) e o processador A19, além da presença do Controle da Câmera (Camera Control) e do botão de Ação (Action Button).

Se você está atrás desse modelo, hoje a Amazon está com uma promoção excelente! Trata-se da versão de 512GB, na cor preta. O aparelho está saindo por R$6.899 em até 12x no cartão de crédito.

iPhone 17 de 512GB

Parcelado: de R$9.799,00 por R$6.899,00 em até 12x
À vista no Pix: de R$8.819,10 por R$6.209,10

🛍️ Produto vendido e entregue pela Amazon💰https://t.co/tjVVUH9g0F

— MM Ofertas (@MMOfertas) October 21, 2026

Se levarmos em conta que o preço tabelado desse modelo aqui no Brasil é de R$9.799 nessa mesma modalidade de pagamento, estamos diante de um desconto de 30%.

Para quem preferir pagar à vista no Pix, o valor cai para R$6.209,10 — representando uma diferença também de 30% em comparação com o preço à vista da Apple Brasil, de R$8.819,10.

Aproveite! 📱

Fique sempre por dentro das ofertas!

Se você não é louco, é claro que gosta de economizar. E, para isso, o MacMagazine tem diversas opções para ajudar você a conseguir comprar o seu produto Apple pagando menos!

Você pode usar a nossa extensão para os navegadores Chrome/Opera/Edge, acompanhar as ofertas pelo MM Fórum, pelo Facebook, pelo X, pelo Threads, pelo Bluesky, pelo Mastodon ou por um canal no Telegram. Escolha a melhor opção para você — ou todas, para depois não se arrepender — e economize! 😉

NOTA DE TRANSPARÊNCIA: O MacMagazine recebe uma pequena comissão sobre vendas concluídas por meio de links deste post, mas você, como consumidor, não paga nada a mais pelos produtos/serviços ao usar os nossos links de afiliado.

via Tecnoblog

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

Take Control Books Publisher Joe Kissell continues our discussion of the OS27 releases, exploring Apple’s next-generation Siri and Apple Intelligence. Joe touches on including conversational context, on-screen awareness, cross-device continuity, and deeper access to personal information. He also explains where Apple’s generative AI falls short at the moment, why Image Playground isn’t up to his standards,, what users should expect from the new operating systems, and how Take Control’s new books cover the changes. (Part 2)

💾

The proposal ceases to be an inference from observations and known physics and becomes an appeal to the absurd.

Source

Apple (release notes, security, enterprise, developer, full installer, IPSW):

Explore what’s new for macOS 27.

The page is designed in an annoying way that requires sideways scrolling, and there doesn’t seem to be a PDF list of the new features like in previous years.

See also:

• Jason Snell
• Juli Clover
• Marcus Mendes
• William Gallagher
• Mr. Macintosh
• Reddit

Jeff Johnson:

There’s also a DEPRECATION NOTICE at the top of the man page that lists the diskutil replacements for hdiutil subcommands.

Patrick Wardle:

Fixed: launchd no longer supports loading launchd property list files with the quarantine extended attribute.

Dave Nanian:

[Even] though a bootable backup is properly configured to boot, they aren’t showing up as choices in either the startup disk preference pane or the startup picker. (FB24210103, reported quite a while ago, Apple friends.)

Previously:

• Golden Gate Release Candidate
• Preparing to Update to Golden Gate
• macOS 27 Public Beta
• Glow Leopard
• macOS 27 Golden Gate Announced
• macOS Tahoe 26

Update (2026-09-17): See also: Hacker News.

Andrew Cunningham (Hacker News):

Generative AI is unavoidable if you install macOS 27 Golden Gate. I’m being very literal: There used to be a toggle you could hit if you wanted to turn off Apple Intelligence and delete the gigabytes’ worth of AI models it would download to your disk, and now there isn’t. Apple Intelligence defines the Golden Gate release, in everything from its marketing to its features to the chips it runs on to the amount of space it takes on your disk.

[…]

More impressively, for apps that support it, the iPhone Mirroring window can now be resized dynamically while running apps. Apps will resize themselves to take up all the available space, and once the window is wide enough, you can even take advantage of multi-column iPad-style views for the apps that offer them.

[…]

For most Mac users, though, Golden Gate is a no-brainer update with few major downsides. It won’t hurt anything to wait until the 27.1 or 27.2 release if you’re already on macOS 15 Sequoia or macOS 26 Tahoe and you’re not in a hurry.

Juli Clover:

macOS Golden Gate: 10 Features to Try First

Paul Haddad:

Did Virtualization under 27 get hit with the slow stick? Trying to run a Mac VM on my M1 Ultra and it feels basically unusable. Getting Intel flashbacks.

Jeff Johnson:

In System Settings Privacy & Security, the Accessibility pane has been renamed to Device Control and Data Access.

Avi Drissman:

Have you been noticing that, starting with macOS 27, you have been consistently failing to SSH to a remote machine? Is it the case that the remote machine’s password ends with a shifted character?

There is a new setting: “Shift Return sends Meta Return” under Settings > Profiles > (profile) > Keyboard. This setting (enabled by default!) will cause a shift-return keypress to insert an ESC before the return is sent.

Rich Trouton:

As part of testing macOS Golden Gate, it was discovered by myself and others that MDM enrollment does not appear to completely work.

Adam Maxwell (tweet):

Golden Gate broke AuthorizationExecuteWithPrivileges such that spawned tasks no longer inherit PATH of the calling process[…]

Daniel Compton:

After upgrading to macOS 27 Golden Gate, my beloved copy of 1Password 7 started crashing every time I unlocked it.

[…]

If you’re planning ahead, you can disable Watchtower from Settings before you upgrade to macOS 27, but if you’ve already upgraded, then you’ll need to disable it with the following terminal command[…]

Update (2026-09-21): Benjamin Mayo:

Ever since updating to Golden Gate, my Mac now periodically makes a scratchy paper alert sound. No idea from what.

Chris Pepper:

Hopefully 1Password will upgrade v7 for OS 27 compatibility, but I can’t count on them fixing bugs in v7 — they’re pushing users to v8.

I found a ‘fix’ for this crash, but that command didn’t work (even after enabling Full Disk Access for Terminal so it could actually execute), and isn’t available on iPhone or iPad.

Nicholas Riley:

Golden Gate status: wow, the new menu extra/Control Center area is rough. Not just unpolished UI behaviors but multiple crashes. Hidden Bar is no longer functional and none of the replacements I found actually worked, while having about 100 features I don’t want.

System keyboard shortcuts also seem to be an area that weren’t well tested. My preferred screenshot shortcuts were ignored until I opened that section of System Settings, and some Siri thing created a conflict with Spotlight so I get both of them when I press ⇧⌘Space.

Previously:

• Golden Gate Liquid Glass

Update (2026-09-23): Jesse Squires:

macOS golden gate is still shockingly terrible in unexpected ways.

Steve Troughton-Smith:

I’m no math expert, but I’m pretty sure this doesn’t add up to anywhere near 16GB. I’ve only started seeing this dialog in the release build of macOS 27.0, despite running the betas all summer, so clearly there are some ghosts in this build

Mario Guzmán:

Okay… how do you

1. make everything super huge like the text and pictures… while
2. having way more empty space than you need AND STILL have to
3. truncate your text labels?

8 of the 9 text labels are truncated.

Can we please have designers who know how to design for DESKTOP software and NOT magazines/print?

Jeff Johnson:

There are a surprising number of behavioral bugs in Golden Gate. It's not just a superficial update. Apple is basically breaking AppKit.

Mark Lilback:

I reported a bug to Apple in May, found while building Alcove. Two OS releases later it still isn’t fixed. It has gotten worse with each one.

[…]

On the betas the whole window went blank after the Cmd-Tab: sidebar, content, detail pane, status bar, everything but the toolbar. On the 27.0 release it usually doesn’t wait for the Cmd-Tab. In 5 of 6 clean launches the window came up with only the toolbar drawn. Cmd-Tabbing away and back doesn’t fix it. It moves the sidebar’s first row up under the traffic-light buttons and leaves the rest of the window empty[…]

Jeff Johnson (Reddit):

Golden Gate Finder bug:

If you start dragging a file, the mouse scroll wheel stops working.

Jeff Johnson:

I’m seeing a bug on Golden Gate where main menu item titles are not displayed if the item is added to the menu after launch. Examples: the Safari Develop menu and the Profile menus of Chrome and Firefox.

Update (2026-09-24): Avi Drissman:

It’s bizarre that hiding a main menu item would hold the space rather then shift things over.

Jeff Johnson:

Search on Golden Gate is twice as good.

Update (2026-09-25): Ric Ford:

Randy Singer emailed us about printer and scanner failures following macOS 27 installation. Fortunately, there’s a fix: reinstalling Apple’s deprecated and removed Rosetta 2 emulation code[…]

Paul Kafasis:

We’re also already recommending upgrading your Mac to Golden Gate. The new OS includes several important audio improvements, and as a result, Golden Gate provides the best experience when using our apps. Below, you’ll find details on key bug fixes Apple included in MacOS 27.

pdfu (via Jeff Johnson):

Hidden behind a feature flag, the System Settings app can now be resized in any direction. You can make the app window narrower than the default too, or adjust the sidebar to get even more space.

Update (2026-09-28): Matt:

Steve Jobs would fire the entire company and replace them with AI if he was alive to see this

Collin Allen:

Turns out, Backblaze is half busted on macOS 27 and there’s no fix yet. Have to launch /Library/Backblaze.bzpkg/bzbmenu.app manually for now, it seems.

Update (2026-10-05): Karsten Kusche:

Kudos to 1Password for providing an update for version 7 on iOS and macOS to restore compatibility with this year’s operating systems!!

Joe Kissell discusses a new approach to Take Control Books’s coverage of Apple’s latest operating systems, including separate books for macOS Golden Gate, iOS 27 and iPadOS 27, plus a cross-platform guide to Siri AI and Apple Intelligence. Joe explains the challenges of documenting changing betas, evaluates Apple’s emphasis on polish and bug fixes, and examines Liquid Glass and lingering problems in Contacts. (Part 1) 

💾

In experiments in Thailand, Asian elephants (Elephas maximus) learned to ignore an enticing but ineffective route to food and quickly adapted when a familiar solution stopped working.

The post Elephants Show Talent for Resisting Impulses appeared first on Sci.News: Breaking Science News.

O iPhone 17 Pro (ainda) é o smartphone mais avançado da Apple! Entre as suas especificações técnicas, podemos destacar a tela Super Retina XDR de 6,3 polegadas, o conjunto de três câmeras (com um sistema Fusion de 48 megapixels nas três lentes) e o processador A19 Pro, além da presença do Controle da Câmera (Camera Control) e do botão de Ação (Action Button).

Mesmo com a nova linha de iPhones prestes a ser anunciada em poucas horas, é certeza de que eles chegarão muito mais caros que os modelos atuais, devido à escassez dos chips de memória.

Por isso, as grandes varejistas já começam a “queimar” o estoque dos modelos que devem sair de linha — caso do iPhone 17 Pro. Mesmo sendo descontinuado, esse smartphone certamente durará muitos e muitos anos graças às atualizações de software.

O Mercado Livre, por exemplo, está com uma promoção excelente para esse modelo! Trata-se da versão de 256GB, na cor azul-intenso. Ele está saindo por R$9.144,43 em até 10x no cartão de crédito.

iPhone 17 Pro de 256GB

Parcelado: de R$11.499,00 por R$9.144,43 em até 10x
À vista no Pix: de R$10.349,10 por R$8.229,99

💰Produto vendido pela loja oficial; aplique o cupom na página 🛒https://t.co/4NC0IAYhEl

— MM Ofertas (@MMOfertas) September 9, 2026

Se levarmos em conta que o preço tabelado desse modelo aqui no Brasil é de R$11.499 nessa mesma modalidade de pagamento, estamos diante de um desconto de 20%.

À vista no Pix, ele cai para R$8.229,99 — um desconto também de 20% em relação ao valor à vista da Apple, de R$10.349,10.

Para chegar a esses valores, basta aplicar o cupom que aparece na página do produto.

Aproveite! 📱

Fique sempre por dentro das ofertas!

Se você não é louco, é claro que gosta de economizar. E, para isso, o MacMagazine tem diversas opções para ajudar você a conseguir comprar o seu produto Apple pagando menos!

Você pode usar a nossa extensão para os navegadores Chrome/Opera/Edge, acompanhar as ofertas pelo MM Fórum, pelo Facebook, pelo X, pelo Threads, pelo Bluesky, pelo Mastodon ou por um canal no Telegram. Escolha a melhor opção para você — ou todas, para depois não se arrepender — e economize! 😉

NOTA DE TRANSPARÊNCIA: O MacMagazine recebe uma pequena comissão sobre vendas concluídas por meio de links deste post, mas você, como consumidor, não paga nada mais pelos produtos/serviços usando os nossos links de afiliado.

via Tecnoblog

O iPhone 17 Pro Max é o smartphone mais avançado da Apple! Entre as suas especificações técnicas, podemos destacar a tela Super Retina XDR de 6,9 polegadas, o conjunto de três câmeras (com um sistema Fusion de 48 megapixels nas três lentes) e o processador A19 Pro, além da presença do Controle da Câmera (Camera Control) e do botão de Ação (Action Button).

Mesmo com a nova linha de iPhones prestes a ser anunciada, é certeza de que eles chegarão muito mais caros que os modelos atuais, graças à escassez dos chips de memória. Portanto, pode ser uma boa ideia aproveitar as ofertas neste fim de ciclo — e com a certeza de que o iPhone 17 Pro Max ainda será suportado por muitos e muitos anos!

A Amazon está com uma promoção excelente! Trata-se da versão de 2TB, na cor prateada. Ela está saindo por R$12.949,30 em até 12x no cartão de crédito.

iPhone 17 Pro Max de 2TB

De R$18.499,00 por R$12.949,30 em até 12x

💰 Produto vendido e entregue pela Amazon 🤑https://t.co/k5pQJtT9Wy

— MM Ofertas (@MMOfertas) September 8, 2026

Se levarmos em conta que o preço tabelado desse modelo aqui no Brasil é de R$18.499 nessa mesma modalidade de pagamento, estamos diante de um desconto de 30%.

Aproveite! 📱

Fique sempre por dentro das ofertas!

Se você não é louco, é claro que gosta de economizar. E, para isso, o MacMagazine tem diversas opções para ajudar você a conseguir comprar o seu produto Apple pagando menos!

Você pode usar a nossa extensão para os navegadores Chrome/Opera/Edge, acompanhar as ofertas pelo MM Fórum, pelo Facebook, pelo X, pelo Threads, pelo Bluesky, pelo Mastodon ou por um canal no Telegram. Escolha a melhor opção para você — ou todas, para depois não se arrepender — e economize! 😉

NOTA DE TRANSPARÊNCIA: O MacMagazine recebe uma pequena comissão sobre vendas concluídas por meio de links deste post, mas você, como consumidor, não paga nada mais pelos produtos/serviços usando os nossos links de afiliado.

After five years in development, Free Stars: Children of Infinity—the official sequel to The Ur-Quan Masters by the old game's original creators—has released a free public demo. It's a "combat demo" in the style of UQM’s melee mode, where players can assemble a fleet from the game's roster of nearly 40 ships, then match those ships up in head-to-head arcade SpaceWar-style action. The demo can be found on Steam and GOG, and it's available for Windows, Mac, and Linux. (Console versions are coming, but they're not yet out.)

Pew pew! Credit: Pistol Shrimp Games

I have to present this news with a big disclaimer, though, because I've been part of the Children of Infinity writing team since 2021, working as a volunteer with the Pistol Shrimp Games crew and contributing NPC dialogue and some story bits. As a huge fan of the original games, it's been a dream opportunity. (Because I've been volunteering my time, I'm not financially involved and receive no compensation if you download the demo or, when it's eventually released, buy the full game.)

What's in the demo

Children of Infinity is a space adventure RPG, just like The Ur-Quan Masters (and its direct spiritual predecessors, Starflight and Starflight 2). What's been released today is just one part of the game—the arcade-style "melee" portion, which lets players jump into one-versus-one combat against another player or against a computer-controlled opponent. Each of the ships you can pick has its own maneuvering characteristics, weapons, and special abilities.

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© Pistol Shrimp Games

Machine vision is becoming a practical part of Apple’s U.S. factory training strategy, giving smaller manufacturers access to techniques that help production systems inspect what they make. The recent opening of the Houston manufacturing training center extends that effort into a working electronics production campus, while courses developed with Michigan State University introduce businesses to image analysis, machine learning, and automated quality checks.

For a manufacturer, the attraction can be straightforward: finding a missing component before a product leaves the line, identifying a surface defect before more material is added, or recognizing an assembly error while it can still be corrected cheaply.

These are manageable problems with measurable costs. They also offer an accessible starting point for businesses interested in AI but unsure where it belongs inside their operations.

Machine Vision Enters the Manufacturing Curriculum

The Apple Manufacturing Academy’s current course catalog includes Machine Learning with Vision, a course introducing machine learning and deep learning through practical activities using Apple devices. It sits alongside instruction in automation, quality fundamentals, manufacturing data, maintenance, and process improvement.

That placement gives the subject useful context. A camera can produce an image, but a factory needs a decision: accept the part, flag it for review, check its position, or investigate a process that may be drifting out of tolerance.

Machine vision connects image capture to those operational decisions. Some applications use established rules to measure dimensions or identify patterns. Others use machine learning to recognize acceptable variations and defects that are difficult to describe through fixed rules.

The academy’s public description confirms introductory activities with Apple devices. It does not identify a particular iPhone, Mac configuration, software framework, or industrial camera package that every participant must use.

For smaller businesses, the educational value is learning how to define an inspection problem before purchasing equipment. A company that understands what it needs to detect can have a much more productive conversation with an automation supplier.

Houston Brings Training Closer to Production

Apple opened its 20,000-square-foot Advanced Manufacturing Center on August 13. According to the Houston opening announcement, the free program offers small and medium-sized businesses instruction in advanced manufacturing, interactive labs, and experience with representative production equipment.

The first cohort worked with machine learning for quality control and advanced automation. Participants also explored production challenges, used a holographic table, and assembled and laser etched a product.

The location adds context: the center occupies the same Houston facility that builds advanced AI servers and is scheduled to begin Mac mini manufacturing this year. Its teaching program includes final assembly principles and design considerations for printed circuit board assembly.

Houston is the company’s second U.S. manufacturing learning site, following the Detroit academy established with Michigan State University in August 2025. By the Houston opening, Apple said the academy had helped nearly 1,000 workers, students, and entrepreneurs.

That figure measures people reached through the program. It should not be interpreted as 1,000 factories already operating AI inspection systems.

A Useful Inspection Starts With a Specific Problem

Imagine a small business assembling control panels. Each panel needs the same set of connectors, positioned correctly before the enclosure closes. A machine vision station could check for a missing connector while the assembly remains accessible.

The value comes from catching that error at the right moment. Discovering it after packaging creates extra handling and rework. Discovering it after delivery may involve a return, a service visit, and an unhappy customer.

A useful pilot would therefore begin with that single inspection point. The business could compare the existing process with the assisted one, recording missed defects, unnecessary rechecks, inspection time, and the cost of correcting mistakes.

The camera is only part of the exercise. Workers must agree on what constitutes a defect, collect representative examples, and decide what happens when the result is uncertain.

Those decisions draw heavily on experience already present on the factory floor. An operator may know that an unusual mark is harmless, while a barely visible change in alignment predicts a later assembly problem. Turning that knowledge into consistent inspection criteria is valuable work in its own right.

These are examples of how manufacturers could apply the training, rather than projects Apple has announced for individual participants.

Teaching Machine Vision to Handle Ordinary Variation

A demonstration can look impressive when every part arrives clean, centered, and evenly lit. Production introduces variation: different surface finishes, reflections, changing positions, and batches that look slightly different while remaining within specification.

The inspection system must distinguish those acceptable differences from actual failures. Otherwise, it can reject good products or allow defective ones to continue.

Industrial vision supplier Cognex’s deployment guidance recommends validating systems across product models, defect types, and manufacturing lines. It also describes an approach in which automated inspection handles preliminary checks while experienced inspectors review borderline cases.

For a business learning machine vision, that offers a realistic way to introduce the technology. Employees can compare its decisions with their own, investigate disagreements, and determine whether the results justify extending its role.

An inspection system also needs an owner after installation. Someone must understand when a product change requires new examples, when equipment needs attention, and when performance has slipped. Training that responsibility into the workforce helps prevent an initially successful project from becoming an unreliable fixture.

Smaller Manufacturers Are Already Applying the Training

The academy’s reach extends beyond companies producing consumer electronics. In its May update on participating manufacturers, Apple highlighted Block Imaging, a Michigan business that services and refurbishes medical imaging equipment.

The company hosted visitors during the academy’s Spring Forum and described applying techniques learned from Apple engineers and Michigan State experts to its operations. Apple reported that the academy had supported more than 150 American businesses at that point.

The announcement did not identify a particular machine vision deployment at Block Imaging or publish a percentage improvement attributable to visual inspection. It does provide an example of the training reaching businesses whose work differs substantially from building an iPhone.

That wider participation could benefit Apple indirectly. More capable domestic manufacturers can support equipment maintenance, tooling, components, and specialized production services. Participation itself does not establish a supplier relationship, but the skills can be used across customers and industries.

Access Without a Large Training Budget

The academy’s participation information says eligible small and medium-sized U.S. manufacturers can take courses free of charge. Online learning includes on-demand materials and interactive modules, while in-person sessions add workshops and more personalized instruction.

Digital badges are available for different topics, and completed course materials remain accessible for review. That gives a business room to train an employee around existing production commitments and return to the material when an actual project begins.

Free instruction does not remove the eventual cost of cameras, lighting, integration, maintenance, or employee time. It can help a manufacturer understand those costs before committing to a system.

The first proposal brought back to the factory might be modest: inspect one recurring defect at one station and record the results for several weeks. A supervisor can then weigh the time saved against the extra reviews, maintenance, and mistakes. That is the kind of evidence a business owner can use when deciding whether to install a second station.

The post Machine Vision Joins Apple’s U.S. Factory Training Push appeared first on AppleMagazine.

Our conversation with Kirk McElhearn about his new Take Control of AirPods continues, exploring the advanced capabilities of AirPods beyond simple audio playback, explaining listening modes, Conversation Awareness, head gestures, and how the Apple ecosystem enhances the experience. He also discusses why AirPods perform poorly as phone microphones, examines rumors of camera-equipped AirPods, and reflects […]

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Kirk McElhearn discusses his new guide to getting the most from Apple’s AirPods, Take Control of AirPods for Take Control Books, explaining why the deceptively simple earbuds deserve a dedicated manual. The conversation explores hearing assistance, noise cancellation, audio quality, controls, battery management, charging options, Bluetooth limitations, and choosing the right model. Kirk offers tips that unlock features many owners never realize are available.

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Joe Kissell introduces Take Control Live: Taming Big Tech, a four-session interactive course designed to help users understand privacy, security, subscriptions, ads, platform lock-in, and practical alternatives to major tech services. Rather than urging an all-or-nothing break from companies like Google, Meta, Amazon, Microsoft, or Apple, Joe will focus on realistic steps, live demos, Q&A, […]

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Jonathan Wells’s review was important, but it only scratches the surface of information in biology outside of the DNA.

Source

The Korea Aerospace Research Institute shared images that show the gash on the surface. It’s pretty small.