In March 2023, Southern California experienced heavy storms after almost 20 years of drought. Sixteen of these came from long, narrow regions of concentrated water vapor moving through the air, known as atmospheric rivers. During droughts, groundwater supplies up to 60% of the total water to the Greater Los Angeles area, so researchers think that stormwater from the 2023 events could have recharged groundwater reserves for future use. However, scientists lack adequate tools to assess how much groundwater is in the subsurface.
To overcome this challenge, researchers from California and Texas developed a new method to study deep groundwater. California gets a large number of earthquakes, so it has over 450 stations around the state to monitor the associated vibrations, referred to as seismic waves. To study how much the 2023 storm recharged the groundwater in the Greater Los Angeles area, the team used a tool called seismic-DI, which employs these seismic waves to estimate how much groundwater is stored at different depths.
Seismic-DI works by measuring how fast seismic waves move through the Earth, known as their seismic velocity. The researchers explained that the Greater Los Angeles area is a very crowded city, so it produces an abundance of seismic waves. They measured changes in the seismic velocities of these underground waves to see where the groundwater is full of water, or saturated, versus partially full, or unsaturated. They used 20 years of these data to produce graphs that record changes in groundwater storage at different depths, known as hydrographs.
To validate the accuracy of seismic-DI, the researchers also compared their seismic hydrographs with groundwater thickness data from a NASA mission known as the Gravity Recovery and Climate Experiment, or GRACE. GRACE consists of 2 satellites that measure changes in Earth’s gravity, which reflect surface water and groundwater storage. Both their seismic hydrographs and the GRACE groundwater data demonstrated that the Greater Los Angeles region experienced an increase in groundwater storage from stormwater in the winter, and a reduction in groundwater storage during the dry summer.
The team’s hydrographs showed that the 2023 storms saturated the Greater Los Angeles area groundwater down to 35 meters (114 feet), but it was abnormally dry at 200 meters (656 feet), and at a severe drought at 450 meters (1,476 feet). The GRACE data similarly demonstrated that the subsurface is in severe drought. Combined, these data indicated the 2023 storms helped the Greater Los Angeles area recover up to 90% of its groundwater at depths of 50 meters (164 feet) since the 2006-2022 drought. However, it recovered only about 40% at 75 meters (246 feet) and 20% to 30% at 100 meters (328 feet).
They concluded that the 2023 atmospheric river events recharged only shallow groundwater reserves in the Los Angeles area. As a result, they suggested future researchers consider deep groundwater reserves for a more complete assessment of total regional water deficits. While this study demonstrated that seismic-DI can successfully reveal spatial changes in groundwater and drought at depth, the team acknowledged that contributing factors like the amount, rate, and spatial variability of groundwater reserves remain targets of future work.
The post Using seismic stations to track groundwater resources appeared first on Sciworthy.
Frost is one of the most punishing adversaries of tea cultivation. A single cold snap can scorch tender leaves, derail an entire season’s harvest, and degrade the delicate chemistry that gives tea its value. For growers and breeders, however, judging how badly a field has been damaged has long been a stubbornly subjective exercise: visual ratings lump continuous injury into crude categories, and laboratory assays destroy the very tissue they measure. A new study published in Plant Methods by Zhengtong He of Shihezi University, Zhiwei Chen of the Tea Research Institute of Shandong Academy of Agricultural Sciences, and colleagues offers a way out, pairing hyperspectral imaging with a purpose-built deep learning model to read frost damage directly from the way leaves reflect light.
The core problem the team confronted is that frost injury is not a binary event but a continuum. Leaves exposed to sub-zero temperatures suffer a cascade of overlapping stresses: photosynthetic pigments degrade, the machinery of photosynthesis falters, and cell membranes and walls break down. Any single measurement captures only a sliver of that cascade. To capture the whole picture, the researchers collected hyperspectral data together with eight key physicochemical indicators from tea plants growing across natural frost damage gradients in the field, rather than in the artificial conditions of a growth chamber. From these multidimensional physiological responses they constructed a composite measure, the tea cold stress response index, or TCSRI, which expresses frost damage severity as a continuous value instead of a coarse visual class.
Building TCSRI is a meaningful advance in itself. Traditional frost assessment often relies on ordinal scales, where an observer assigns a score of mild, moderate, or severe. Such scales throw away information and make it difficult to track recovery or compare cultivars with fine precision. By integrating several physiological dimensions into one index, the team created a target variable that reflects the plant’s actual internal state. The challenge then became predicting that index, and the individual chemical indicators behind it, from spectral data alone, without touching or destroying a single leaf.
Hyperspectral sensing is well suited to this task in principle. Across hundreds of narrow wavelength bands, plant leaves encode a wealth of information: chlorophyll absorption in the visible range, water and structural features in the near infrared. But the data are high-dimensional, with far more spectral channels than samples, a regime that invites overfitting, especially in field studies where collecting ground-truth measurements is laborious. Conventional chemometric approaches, from partial least squares regression to standard machine learning pipelines, often struggle to separate genuine physiological signals from noise, sensor drift, and the confounding effects of leaf surface properties and illumination variation.
The researchers’ answer is a model they call STAMPformer, short for spectral-prior-guided multi-view attention pooling. Its architecture reflects a deliberate design philosophy: rather than letting a neural network rediscover everything from scratch, the model is steered by what spectroscopists already know about how plants respond to stress. At its front end, a parameter-free attention convolution extraction module jointly models both the raw reflectance spectra and their first-order derivatives. Derivative spectra are a classic spectral prior: they accentuate subtle local features such as absorption peaks and shoulders that are easily drowned out in raw reflectance, while suppressing broad baseline drift and noise. By processing both views together, the module sharpens the local spectral signatures that carry physiological meaning.
On top of this extraction stage sits the second innovation, a multi-view attention pooling module. Deep networks processing spectral sequences produce long streams of features, but not all wavelengths matter equally for a given prediction. The pooling module adaptively aggregates sequence features drawn from different feature subspaces, learning to amplify the responses that are most diagnostic of cold stress and to down-weight the rest. This attention mechanism acts like a learned spotlight, and because it operates across multiple subspaces, it can capture complementary patterns that a single attention head might miss. The result is a compact representation tailored to the prediction task at hand, whether that is estimating a specific chemical indicator or the composite TCSRI.
The performance numbers are striking. Across all nine prediction tasks, spanning the eight physicochemical indicators and the TCSRI itself, STAMPformer achieved coefficients of determination above 0.87 and ratios of performance to deviation above 2.80, thresholds generally regarded as indicating reliable quantitative prediction. In other words, from a leaf’s reflectance fingerprint alone, the model could reconstruct the plant’s internal frost-stress state with an accuracy that approaches laboratory measurement, while remaining entirely non-destructive. The team did not stop at headline metrics. Ablation experiments systematically removed each proposed module to confirm that both the attention convolution extraction and the multi-view pooling contributed measurably to the gains, and paired statistical tests across multiple random seeds ensured the improvements were not artifacts of a lucky initialization.
Interpretability was a further priority, and here the study delivers something rarer than raw accuracy. Applying SHAP analysis, a technique that attributes each prediction to the contributions of individual input features, the researchers identified the wavelengths the model relied on most heavily. These key spectral regions correspond to physiologically meaningful processes: pigment degradation, photosynthetic impairment, and the disruption of cellular structure. That alignment between machine attention and plant biology matters on two fronts. It gives spectroscopists confidence that the model has learned genuine stress signatures rather than spurious correlations, and it hands plant physiologists a data-driven map of which parts of the spectrum track which facets of cold injury, potentially guiding future sensor design and breeding assays.
Crucially, the model also proved itself on data it had never seen. Independent external validation, using samples not involved in training, demonstrated that STAMPformer’s predictive ability generalizes beyond its original dataset, addressing one of the most common failure modes of machine learning in agricultural sensing. For a field where models are often tuned and tested on the same plots, this external check is an important credibility marker, suggesting the framework could be deployed across different farms, seasons, and frost events rather than remaining a laboratory curiosity.
The practical implications reach well beyond tea. An accurate, non-destructive, continuous measure of cold damage opens the door to rapid post-frost field surveys, letting growers quantify losses within hours instead of waiting for symptoms to develop or sending samples to a lab. For breeders screening for cold-tolerant germplasm, a spectral readout of TCSRI could accelerate the evaluation of hundreds of candidate lines without destructive sampling. And in the emerging vision of the smart tea garden, where drones and handheld spectrometers roam the rows, a model like STAMPformer could feed real-time frost maps into irrigation, shading, and recovery-management decisions. The authors frame their work as supporting frost monitoring, cold-tolerant germplasm evaluation, and intelligent garden management, and the architecture’s reliance on general spectral priors rather than tea-specific tricks suggests the approach could be adapted to other crops facing abiotic stress. As climate variability makes late-season frosts increasingly erratic, tools that turn light reflected from a leaf into a precise physiological diagnosis may become as essential to the tea garden as the thermometer.
Subject of Research: Hyperspectral deep learning prediction of continuous frost damage severity in tea plants
Article Title: STAMPformer: spectral-prior-guided multi-view attention learning for hyperspectral prediction of continuous frost damage severity in field-grown tea plants
Article References: He, Z., Chen, Z., Ding, Z., Wang, Z., Zhang, F., Zhao, Y., & Dong, C. (2026). STAMPformer: spectral-prior-guided multi-view attention learning for hyperspectral prediction of continuous frost damage severity in field-grown tea plants. Plant Methods. https://doi.org/10.1186/s13007-026-01597-4
Image Credits: AI Generated
DOI: 10.1186/s13007-026-01597-4
Keywords: tea plant, frost damage, hyperspectral imaging, deep learning, attention mechanism, cold stress index, non-destructive prediction, SHAP analysis, precision agriculture, spectral priors, plant physiology, smart farming
Apple (Hacker News, MacRumors):
Our solution hinges on splitting the Apple Reference Image process into two phases: creating a secure digital negative, and developing that negative into a reference image. Each phase receives our strongest protections.
[…]
Apple Reference Image provides both a lower bound and an upper bound on capture time from Apple’s cryptographic timestamp service, and we guarantee the photo was taken between the two bounds. On a regular heartbeat, the device requests a cryptographic timestamp token, and retains the most recent one it has received. Globally this happens on average every 15 minutes, though the interval depends on local network conditions. This provides a proven lower bound timestamp for the photographic capture. After capture, the device requests a second timestamp to use as an upper bound, and both timestamps are embedded and signed with the sensor data.
[…]
When the user chooses to create a reference image, the device uploads the digital negative to PCC, which runs the processing steps needed to render the image — including demosaicing, tone mapping, and compression — in a highly secure, private, and verifiable environment. Experts can verify that PCC doesn’t alter a digital negative during development: they can examine the software that does the work.
[…]
Finally, as no security system is perfect, we created a revocation system that can revoke individual photos, as well as all photos from a specific sensor.
[…]
We built Apple Reference Image to avoid using an explicit, public credential for photographers, and to avoid even implicit public association between different photos taken by the same sensor. The final reference image is instead signed by Apple’s signing service, after validation by PCC.
Previously:
Half a year after Iranian drone strikes knocked out multiple Amazon data centers, the US tech company has acknowledged the permanent loss of some customer data that was hosted in Bahrain and the United Arab Emirates.
Amazon Web Services was “unable to restore access to the resources and data” hosted in some of the war-damaged data centers, according to an AWS dashboard update posted on September 15. The development was first reported by Reuters and suggests that the Iranian strikes inflicted catastrophic damage on the data centers.
Customer data was irretrievably lost in one of three AWS availability zones in the United Arab Emirates region, specifically the mec1-az2 availability zone. Each availability zone is serviced by one or more Amazon data centers.
© Planet Labs PBC
Half a year after Iranian drone strikes knocked out multiple Amazon data centers, the US tech company has acknowledged the permanent loss of some customer data that was hosted in Bahrain and the United Arab Emirates.
Amazon Web Services was “unable to restore access to the resources and data” hosted in some of the war-damaged data centers, according to an AWS dashboard update posted on September 15. The development was first reported by Reuters and suggests that the Iranian strikes inflicted catastrophic damage on the data centers.
Customer data was irretrievably lost in one of three AWS availability zones in the United Arab Emirates region, specifically the mec1-az2 availability zone. Each availability zone is serviced by one or more Amazon data centers.
© Planet Labs PBC
Xcode 27 includes Swift 6.4 and SDKs for iOS 27, iPadOS 27, tvOS 27, watchOS 27, macOS 27, and visionOS 27. Xcode 27 supports on-device debugging in iOS 17 and later, tvOS 17 and later, watchOS 10 and later, and visionOS. Xcode 27 requires a Mac running macOS Tahoe 26.6 or later.
Is this new in Xcode 27? Pretty sure it as and I approve whole heartedly! I really hate setting up Xcode settings more than once.
Building with Xcode 27 doesn’t work because of some intransparent XCStrings error… and I even can’t check with Xcode 26 anymore because Apple decided to not let me run it on macOS 27…
After all these years Apple has finally broken cmd-e in Xcode 27.
Previously:
iPhone 18 Pro arrives with Apple concentrating its biggest changes in three areas: camera control, sustained performance, and battery life. The new Pro models retain familiar 6.3-inch and 6.9-inch display sizes, but introduce a variable-aperture main camera, the 2-nanometer A20 Pro chip, redesigned thermal hardware, and a smaller Dynamic Island.
The lineup consists of iPhone 18 Pro and iPhone 18 Pro Max, starting at $1,199 and $1,299 in the U.S., respectively. Both begin with 256GB of storage and are available in black, silver, glacier, and a new burgundy finish. Preorders opened September 12, with retail availability beginning September 18 in more than 65 countries and regions.
Rather than treating the Pro models as a cosmetic update, Apple has focused on changes that become more noticeable during demanding use, particularly photography, video work, gaming, AI processing, and long sessions away from a charger.
The centerpiece is a new 48MP Fusion Main camera with a physical variable aperture, a first for the iPhone.
Six laser-cut aperture blades can adjust the amount of light reaching the sensor while also changing depth of field. The camera supports four aperture settings: ƒ/1.48, ƒ/1.8, ƒ/2.8, and ƒ/4.0.
At the wider end, the camera can gather more light in darker scenes. A narrower aperture can keep more people or background detail in focus, giving photographers another tool beyond computational depth effects and Portrait mode.
Apple also gives users direct access to those settings through new Pro controls in the Camera app. Aperture, shutter speed, and white balance can be adjusted manually, while a histogram provides a more conventional way to monitor exposure.
The feature is paired with a new main-camera sensor and updated computational imaging pipeline. Apple says the combination improves low-light performance and detail, while Photographic Styles gain texture and grain controls for additional control over an image’s appearance.
Video receives several additions as well. Cinematic effects can now be applied after recording to footage captured at up to 60 frames per second, while Time-lapse can record in 4K with Dolby Vision HDR. Audio Mix has also been updated with improved voice processing and the ability to isolate music.
For photographers who want more evidence of how an image originated, Apple is introducing Apple Reference Image. In Reference mode, the main camera signs sensor data, which Private Cloud Compute uses to create an unalterable reference version that can be compared with an edited image in Photos. Apple is also preparing support for the SynthID standard as another way of identifying AI-generated or altered content.
A20 Pro is built using a 2-nanometer manufacturing process and contains a six-core CPU, seven-core GPU, and dual 16-core Neural Engine.
Apple says memory bandwidth is 50% higher than on A19 Pro, while GPU performance can be up to 40% faster. The Neural Engine provides 32 cores in total, doubling the AI processing capability of the previous Pro chip.
The silicon change is closely tied to a redesigned cooling system. Apple has rearranged the chip package so that the silicon die sits beside the memory rather than beneath it in the thermal path, allowing A20 Pro to connect more directly to a next-generation vapor chamber.
That vapor chamber has three times the surface area of the previous generation and uses new materials to move heat away from the processor more effectively. Apple claims the design can deliver up to a 40% gain in sustained performance compared with iPhone 17 Pro.
That distinction is more relevant than peak benchmark numbers for workloads such as extended gaming sessions, video editing, computational photography, and local AI models, where heat can force a phone to reduce performance over time.
Connectivity has also moved further toward Apple-designed silicon. The N1 wireless chip supports Wi-Fi 7, Bluetooth 6, and Thread, while the new C2 modem succeeds C1X. Apple says C2 uses 15% less energy while offering faster uploads and adds mmWave support in the U.S.
Battery improvements are another major part of the new hardware.
Apple rates eSIM-only versions of iPhone 18 Pro for up to 36 hours of video playback, while iPhone 18 Pro Max reaches as much as 45 hours. Apple says Pro Max receives the largest battery-life increase ever delivered in an iPhone.
Some of that gain comes from using the space previously occupied by a physical SIM tray. In markets receiving eSIM-only models, including the U.S., Canada, Japan, Mexico, Saudi Arabia, and the UAE, the larger internal battery provides an additional two hours of rated video playback.
Charging has accelerated as well. iPhone 18 Pro can reach 50% charge in approximately 15 minutes using a supported wired setup, while wireless charging reaches 50% in around 30 minutes. Apple says five minutes of wired charging on iPhone 18 Pro Max can provide roughly seven hours of video playback.
Both devices use aluminum unibody construction with Ceramic Shield 2 on the front and Ceramic Shield on the back. Storage options run from 256GB through 2TB.
The iPhone 18 Pro weighs 211 grams, while iPhone 18 Pro Max weighs 249 grams. Display sizes remain 6.3 inches and 6.9 inches, respectively.
The hardware launches alongside iOS 27, which places Apple Intelligence and the new Siri AI more prominently across the system.
Siri AI can use personal context from information stored across apps, respond to onscreen content, perform actions in supported apps, and handle more open-ended questions. A new Siri mode in Camera lets users ask questions and take action based on objects or information visible through the viewfinder.
Photo tools include Spatial Reframing, Extend, and an upgraded Clean Up feature, while Safari can monitor selected webpages for changes such as a product restock or price adjustment.
The Dynamic Island has also been physically reduced and redesigned. It can display up to three Live Activities simultaneously while continuing to support Face ID.
Apple released iOS 27 on September 14, while Siri AI is rolling out initially as a beta in English, with additional language support planned. iPhone 18 Pro and iPhone 18 Pro Max reach stores September 18, giving Apple a Pro lineup whose most substantial additions are less about a new exterior shape and more about physical camera control, heat management, and how long the hardware can sustain demanding workloads.
The post iPhone 18 Pro adds variable aperture, A20 Pro, and longer battery life appeared first on AppleMagazine.
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