A St John Ambulance volunteer who met Princess Anne after gaining national recognition has admitted possession of indecent images of children. Elliot Brooke, 19, was honoured at the Young Achievers’ Reception in 2024, an annual event which celebrates the exceptional contributions of young St John Ambulance members. The event was attended by The Princess Royal, who has served as St John’s Commandant-in-Chief (Youth) for many years. Brooke, of Rowan Road in Martham, appeared at Norwich Magistrates’ Court facing four charges involving images of children aged as young as under 10 after being arrested in March 2025. He pleaded guilty to three offences of making indecent images including 45 category A, the most serious, 48 category B and 31 category C. The teenager also admitted possessing a prohibited image of a child. Adjourning his sentencing until September 9, District Judge Matthew Bone said: "The offences you have admitted are serious and they attract a guideline starting sentence of 12 months in custody. "However they also say I should take into account other mitigating factors." He ordered a pre-sentence report to look at options including a suspended prison sentence, committal to the crown court or a community order. Brooke was made subject to interim notification requirements and bail conditions including restrictions on his internet use. The court heard there was also evidence that Brooke had messaged children though he has not been charged with any communications offence. He had been aged 16 and 17 at the time and the messages had been to other teens, one of whom has been charged with similar offences, the court was told. His defence solicitor said he had a number of conditions including autism spectrum disorder. Brooke was previously nominated for a Young Diamonds award, a special regional recognition created by the Norfolk Lieutenancy and the
Aug 3, 2026 · via edp24.co.uk
Windows 11 supports numerous AI capabilities, but the best experiences require a Copilot+ PC, a new class of AI PC that includes a powerful and efficient Neural Processing Unit (NPU). Copilot+ PCs can run sophisticated AI workloads locally–on-device–against Small Language Models (SMLs), so these features don’t require an Internet connection or a paid, cloud-based AI service. Granted, you don’t need a Copilot+ PC to use many of the AI features that Windows 11 offers. Those AI features that rely on cloud-hosted Large Language Models (LLMs) are available to all Windows 11 users, albeit with a few limitations: - Connectivity. Your PC must be online to access these AI features. - Subscription. Most of the cloud-based AI features in Windows 11 have usage limits unless you pay for a Microsoft 365 Personal, Family, or Premium subscription. With a Copilot+ PC, you get the best of both worlds: All the AI features that Windows 11 offers plus all the local AI features that are unique to AI PCs and Copilot+ PCs. Windows 11, AI PCs, and Copilot+ PCs Understanding which AI-based Windows 11 features work for everyone and which require AI PCs or Copilot+ PCs can be confusing. So it may be helpful to understand what’s available depending on the type of PC you have. AI features in Windows 11 Windows 11 includes a growing set of AI features integrated throughout the system and in its in-box apps. Many of these features provide so-called generative AI capabilities, meaning they can be used to create new content or edit existing content. The in-box apps in Windows 11 ship with an extensive and always growing set of AI features and functionality that’s available to all users. Some key examples include: - Copilot in Windows 11. This conversational AI-powered assistant brings the ever-improving productivity and
Aug 3, 2026 · via thurrott.com
New California law will change the internet forever as ‘digital fingerprints’ added See more of our coverage in your search results. Add The California Post on Google California is putting a digital leash on AI fakes. A landmark new state law taking effect this week will require artificial intelligence companies to add invisible “digital fingerprints” to AI-generated images, videos and audio, giving users a way to determine whether the content they’re seeing was made by a human — or a machine. Written by Bay Area lawmakers, it will require AI companies to embed difficult-to-remove data known as “provenance” into their creations, allowing people to check a file’s origins through content verification tools. The goal is to make the internet a little less of a Wild West. “While AI has some benefits, we’ve seen AI content used for election misinformation for scams and abusive deepfakes,” state Sen. Josh Becker, D-Menlo Park, recently told KQED. The concern comes as AI-generated political content has already begun making waves in The Golden State. As previously reported by The Post, two California Democrats recently urged Congress to crack down on AI-generated campaign ads that could impersonate candidates or political organizations as the technology becomes increasingly sophisticated. US Sen. Adam Schiff and Rep. Ro Khanna (D-Fremont) are pushing a new crackdown that would stop fake candidate impersonations from hijacking federal campaigns. The push came after California political campaigns embraced AI-generated ads. This included Republican gubernatorial candidate Steve Hilton’s videos attacking Gov. Gavin Newsom, former Vice President Kamala Harris and Democratic nominee Xavier Becerra — as well as former Los Angeles mayoral candidate Spencer Pratt’s viral “Pratt Man” campaign video, which racked up millions of views with a Hollywood-style AI parody. Schiff warned that AI-fueled political deception crosses party lines. “AI-generated fraudulent advertising which uses the likeness
Aug 3, 2026 · via nypost.com
Figures Abstract This study presents a pythagorean fuzzy set-based framework combined with contrast-limited adaptive histogram equalization (CLAHE) for low-light color image enhancement. Low-light color images often suffer from poor contrast, reduced visibility, and brightness distortion. To address these challenges, the proposed method employs pythagorean fuzzy sets to model pixel-level uncertainty more flexibly than conventional fuzzy representations. The method first transforms the input image into a pythagorean fuzzy image using a parameterized nonlinear membership mapping and then applies adaptive CLAHE to enhance contrast while preserving color fidelity. Experimental results on benchmark datasets demonstrate that the proposed approach improves visual quality and achieves competitive quantitative performance compared with existing enhancement techniques. Performance is evaluated using entropy, absolute mean brightness error, contrast improvement index, correlation coefficient, structural similarity index, and the natural image quality evaluator. The results highlight the effectiveness of integrating pythagorean fuzzy uncertainty modeling with adaptive contrast enhancement for low-light color image processing. Citation: S UM, S J (2026) Enhancing low contrast color images via pythagorean interval-valued fuzzy sets and CLAHE. PLoS One 21(8): e0354362. https://doi.org/10.1371/journal.pone.0354362 Editor: Peng Wu, Anhui University, CHINA Received: September 19, 2025; Accepted: July 7, 2026; Published: August 3, 2026 Copyright: © 2026 S., S.. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: The datasets used in this study are publicly available from the Figshare repository at DOI: 10.6084/m9.figshare.27192921, and all relevant data are presented within the paper. Funding: The author(s) received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. 1. Introduction Image enhancement plays a critical role in improving visual perception for computer vision, pattern recognition, and digital image
Aug 3, 2026 · via journals.plos.org
A translation of a mass shooter’s cryptic journal in the days after an attack. A public records review that revealed failures to install flood warning systems in Central Texas. An exposé of American technology companies’ complicity in building the Chinese surveillance state. An audit of the SEC’s crypto lawsuits that showed weakening enforcement under the second Trump administration. On May 4, the Pulitzer Prizes recognized these stories among the winners and finalists across 15 journalism categories. The reporters behind each of these stories also disclosed using AI to the judging committee. Ultimately, five award winners and three finalists this year disclosed AI adoption in their submissions — the most since the disclosure requirement was added in 2024. For the past two years, I’ve spoken to Pulitzer-recognized reporters about how they used AI in their reporting. Both years, generative AI took a back seat to more conventional machine learning technologies, like using embedding models to produce complex data visualizations and pattern recognition models to analyze satellite imagery in conflict zones. This year, though, generative AI tools and commercial large language models (LLMs) were more commonly used, largely to speed up the process of combing through document dumps. “To state the obvious, perhaps, AI is here to stay,” Marjorie Miller, the administrator of the Pulitzer Prizes, told me. “The industry [used to be] far more apprehensive about AI tools than it is today, with a clearer understanding now of what uses might be appropriate — data collection and analysis, for example — and when it might not, such as in writing and editing stories in any format that might be considered for a Pulitzer Prize.” Miller cautioned that as AI evolves, reporters will need to “ensure and reassure” the Pulitzers that submissions are ultimately produced by human beings, even when AI is
Aug 3, 2026 · via niemanlab.org
Figures Abstract With the extensive applications of satellite image data in environmental monitoring and geographic surveying and mapping, the amount of data has increased rapidly, which brings great challenges for transmitting and storing these images. However, when processing high-resolution and multi-spectral satellite data, existing image compression methods often lead to low compression efficiency or poor reconstruction image quality due to its insufficient generalization ability. In this paper, we propose an end-to-end deep learning image compression framework for visible infrared imaging radiometer suite (VIIRS) satellite imagery. The framework consists of an analysis transform encoder, a synthesis transform decoder, a hybrid training-testing quantizer and a probability model for entropy coding. A cumulative distribution function (CDF) is constructed to compute the discrete likelihood of quantized latent symbols under a Gaussian-mixture entropy model. It is integrated with a checkerboard context structure and a VIIRS-oriented block-processing pipeline. Finally, we conduct systematic experiments based on NASA VIIRS multi-spectral datasets. Experimental results show that the proposed method achieves 0.51 ± 0.04 bpp, 38.39 ± 0.72 dB PSNR and 0.973 ± 0.007 SSIM. Relative to ELIC and the Transformer-CNN baseline, it reduced bpp by 13.6% and 10.5% and improved PSNR by 0.78 dB and 0.61 dB, respectively. The framework can compress the data volume to approximately 1.5%−4% of the original size, corresponding to an average compression ratio of about 30:1. In order to meet the processing requirements of high-resolution satellite images, we further propose a block compression strategy, which divides large-size images into sub-blocks of 256 × 256 pixels for independent compression, and realizes complete image reconstruction through decompression and splicing technology. Citation: Gao Y, Xu H, Huang W, Bai H, Peng B, Xu H (2026) An end-to-end deep learning image compression method for satellite images based on entropy model. PLoS One 21(8): e0355234. https://doi.org/10.1371/journal.pone.0355234 Editor: Hashmat Fida,
Aug 3, 2026 · via journals.plos.org
Liberal California city makes dramatic U-turn on police surveillance, poll shows See more of our coverage in your search results. Add The California Post on Google San Francisco police have expanded their use of cameras, drones and other surveillance tech to nab criminals — and most residents of the famously liberal city are good with it, a new poll shows. Seventy-two percent of those surveyed in a poll by GrowSF, a San Francisco political advocacy group, said they somewhat or strongly support the use of cameras and drones after a 911 call to assist in “active investigations.” A strong majority — 66% of those surveyed — even backed real-time video monitoring of crime hotspots such as Tenderloin drug markets “to help deter crime and hold criminals accountable.” Currently, police are only allowed to monitor live footage in limited circumstances. Similar numbers of San Francisco residence said they support using surveillance tech for other uses, such as preventing illegal dumping (67%) and “monitoring areas with high drug-use” to help prevent overdoses (69%). The GrowSF poll suggests San Franciscans broadly support giving cops more leeway in catching suspects: 66% support giving police tasers, while an overwhelming 75% are in favor of police dispersing drug markets. “By many measures, San Francisco is safer, and its quality of life is stronger than it has been in decades, thanks in part to modern public-safety technology,” said Steven Bacio, co-founder of GrowSF, in a statement. “The message is clear: residents want city leaders to continue modernizing the police department and deploying every effective tool available to keep them and their families safe,” he added. The new survey appears to reflect a sharp turnaround in police surveillance sentiment in the Bay Area city, which became the first city in the US to ban the use of facial
Aug 3, 2026 · via nypost.com
Apple eyes facial recognition for smart home hub Apple is reportedly developing a Siri-powered smart home hub that uses facial recognition to identify household members and display personalized content, according to Bloomberg. The device is expected to feature a seven-inch display that recognizes who is looking at the screen. It could also estimate a person’s distance from the device and adjust the size and type of information displayed accordingly. Once it recognizes a household member, the hub could display personalized information such as calendars, notes and other user-specific content. Bloomberg also reports that Apple is developing a higher-end version of the hub with a nine-inch display and a robotic arm that can move the screen. The hub would support FaceTime video calls, smart home controls, security camera monitoring, photo viewing, music playback and an intercom function. It is reportedly designed to serve as the primary visual interface for Apple’s connected home ecosystem, allowing users to control HomeKit-enabled devices including smart locks, speakers, lighting and security accessories. Bloomberg reports that Apple could release the hub between October 2026 and early 2027. The company is also said to be working on a separate home security camera to compete with Amazon’s Ring products. The reported expansion of facial recognition into the home comes as Apple continues to face scrutiny over how it collects and manages biometric data. Illinois lawsuit raises questions on control of face biometric data Apple’s smart home devices are expected to launch as the company faces a certified class action in Illinois over the facial grouping feature in its Photos app. The lawsuit centers on the Photos app feature previously known as People and now called People & Pets. Plaintiffs allege Apple scans facial biometrics to group photographs of the same individual, then synchronizes that biometric information across devices through
Aug 3, 2026 · via biometricupdate.com
LEO Technologies extends prison surveillance from calls to voices, faces, and behavior LEO Technologies has launched a pair of AI products that expand correctional surveillance beyond the content of incarcerated people’s communications, allowing agencies to identify speakers through voice biometrics and continuously analyze camera feeds for movements, relationships, and behavior the company says may signal an emerging threat. Verus Voice AI is designed to detect when someone uses another incarcerated person’s personal identification number to place a call. The launches represent a significant broadening of correctional monitoring. Instead of requiring an investigator to search calls or review recorded video after an incident, LEO Technologies is marketing Verus as a continuously operating intelligence system that can connect a person’s communications with their physical movements and institutional history. Voice AI addresses a longstanding weakness in prison telephone systems. Calls are generally attributed to the PIN entered before they are placed, but PINs and telephone access can be shared, traded, coerced, or stolen. That can allow one person to use another’s account, obscuring who is communicating with people outside the facility. Verus Voice AI analyzes the speaker rather than relying on the account or device. The company says its agentic AI continuously develops voice identities across monitored calls and alerts investigators when the detected speaker does not appear to match the person assigned to the PIN. The system is intended to identify possible PIN sharing, identity fraud, and unauthorized communications across large quantities of recorded call data. The product can also be used as an investigative voice seeker whose name or account information is unknown. The company describes the capability as a way to “surface hidden voices” and connect individuals across different conversations. A suspected mismatch can then be cross-referenced through the larger Verus platform against the person’s previous calls and facility history.
Aug 3, 2026 · via biometricupdate.com
New support allows desktop PCs and docked laptops to use TPM-protected biometric authentication. Key Takeaways: Microsoft is making passwordless sign-ins more secure and accessible for desktop users by extending Windows Hello Enhanced Sign-in Security (ESS) to compatible external fingerprint readers. This update enables hardware-backed biometric authentication on more Windows 11 devices, which helps protect sensitive data from sophisticated cyberattacks. Windows Hello Enhanced Sign-in Security (ESS) is an advanced security feature in Windows 11 that strengthens biometric sign-ins by creating a protected pathway between users’ fingerprint reader or facial recognition camera and the operating system. ESS uses hardware-backed protections, such as the Trusted Platform Module (TPM) and Virtualization-Based Security (VBS), to isolate sensitive biometric data and authentication processes from potential malware and unauthorized access. This capability makes it harder for an attacker with elevated privileges to intercept, manipulate, or spoof biometric credentials. Previously, devices with built-in fingerprint sensors could take advantage of enhanced Sign-in Security (ESS). However, users with external USB fingerprint readers often couldn’t access the same level of protection. Consequently, desktop users and those with docked laptops had to choose between the convenience of biometric sign-in and the stronger security safeguards provided by ESS. Microsoft’s latest update (KB5101684) addresses this gap by allowing compatible external fingerprint readers to work within the ESS framework. This release brings secure, passwordless authentication to a much wider range of Windows 11 devices. “Windows Hello Enhanced Sign-in Security (ESS) now supports external fingerprint sensors. With this update, this secure sign-in option extends beyond devices with integrated fingerprint sensors to desktop PCs and other Windows 11 PCs, including Copilot+ PCs. To get started, connect a supported ESS fingerprint reader,” Microsoft explained. Microsoft has started rolling out this optional update to PCs running Windows 11 versions 25H2 and 24H2. Users can get started with fingerprint authentication
Aug 3, 2026 · via petri.com
Dive Brief: - Wegmans is piloting new, smaller shopping carts at two upstate New York stores, the grocery company confirmed with Grocery Dive. - The new carts are lighter, quieter, easier to maneuver and feature conveniences like cup and phone holders. They’re also better suited for larger items and family-pack purchases. - The carts are still undergoing updates based on initial feedback that Wegmans received from shoppers and employees. Dive Insight: At a time when most grocers are testing smart carts — and about a year after Wegmans itself confirmed it was piloting the tech-forward carts — the New York grocer is taking a more classic approach to improving the in-store customer experience. Wegmans first rolled out the redesigned carts to its Pittsford and Penfield, New York, locations in mid-July, the grocer wrote in emailed comments. This month, Wegmans is introducing a limited number of redesigned small carts at both stores based on feedback from shoppers and workers and said it will continue gathering customer feedback to evaluate next steps. The carts, which are manufactured in the U.S., aim to improve the shopping experience as well as better support the store team, Wegmans said, noting that its previous small cart design was more than 15 years old.
Aug 3, 2026 · via grocerydive.com
< The research team. From left: Sangyun Chung (KAIST, first author of the MAD study and co-first author of the DNA study); Yong Man Ro (KAIST, corresponding author); Youngjoon Yu (KAIST, co-first author of the DNA study) > Multimodal large language models (MLLMs), which process multiple types of sensory information such as text, images, and audio at the same time, are rapidly expanding the range of applications for artificial intelligence (AI). However, in real-world environments, these models can misinterpret the physical characteristics of sensors, mistakenly identify objects, or claim to hear sounds that are not actually present simply because a certain object appears in a video. These errors are known as hallucinations. A KAIST research team has developed a new technology that corrects such information confusion and physical misperceptions in AI. KAIST (President Choongsik Bae) announced on the 31st of July that a research team led by Professor Yong Man Ro from the School of Electrical Engineering has developed two core technologies that overcome the tendency of existing large language models to rely too heavily on ordinary camera (RGB) images and enable AI to suppress cross-modal hallucinations that occur when different sensory inputs become mixed. < Figure 1. Examples of vision sensor-related questions and responses by recent. It fails to understand the core principle of thermal imaging, incorrectly attributing brightness to reflected sunlight rather than emitted heat. (Courtesy of KAIST) > The first technology developed by the research team is the Diverse Negative Attributes (DNA) optimization method, which helps AI accurately understand the physical characteristics of special camera sensors such as thermal, depth, and X-ray sensors. Existing AI models often failed to understand the physical meaning of such images, for example by mistaking bright areas in thermal images for simple light reflection. The research team built VS-TDX, the first comprehensive
Aug 3, 2026 · via miragenews.com
New guidance explains consent requirements, statutory exceptions and governance expectations The Office of the Australian Information Commissioner (OAIC) has published updated guidance for organisations covered by the Australian Privacy Principles (APPs) that are considering using facial recognition technology (FRT) in high-volume, publicly accessible physical spaces such as retail shopfronts. The revised guidance implements the findings of the Administrative Review Tribunal (ART) in the Bunnings Group Limited matter, which concerned the retailer’s use of facial recognition technology in 62 of its stores between 2018 and 2021. The ART’s findings confirmed that there is a high bar for using facial recognition technology in Australia. In a media release, the OAIC explained that the new guidance makes clear that the Privacy Act neither prohibits nor expressly permits facial recognition technology. Instead, entities must demonstrate that any use complies with the APPs. It confirms that biometric templates and facial images used for automated identification are considered sensitive information and generally attract stronger privacy protections. The OAIC states that entities should adopt a "privacy by design" approach before introducing facial recognition technology. It recommends conducting a privacy impact assessment at the outset of any project to identify, manage and minimise privacy risks, and encourages entities to publish the assessment where possible. Businesses operating facial recognition systems across multiple premises should assess whether each location presents different privacy or security considerations rather than relying on a single blanket assessment. The updated guidance also provides more detailed direction on the lawful collection of biometric information. It says entities must generally obtain an individual's valid consent unless a narrow statutory exception applies. The OAIC emphasises that prominent signage alone will not normally constitute consent, while implied or opt-out consent should rarely be relied upon for the collection of sensitive information. Where businesses seek to rely on an exception
Aug 3, 2026 · via thelawyermag.com
Portuguese cork producer M.A.Silva developed its Nobeltech corks with Bionic Eye, an integrated AI inspection system designed to evaluate each stopper across multiple quality parameters. The technology embeds machine learning into the traditional cork production process to improve consistency, reliability and traceability at scale. Bionic Eye assesses TCA risk, mechanical integrity and sealing performance through imaging and pattern recognition, completing 12 independent inspections before each cork is classified. The system can detect structural cracks, clay contamination, lenticel exposure and insect holes while learning from newly identified defects. Every inspection is recorded, creating a detailed audit trail for each stopper. For winemakers, AI-selected Nobeltech corks offer greater consistency in oxygen transmission rates and long-term ageing performance, reducing bottle-to-bottle variability. By applying machine learning to a natural closure, M.A.Silva combines traditional cork production with measurable quality assurance. AI-Powered Inspection Systems M.A.Silva Introduced Its Bionic Eye Nobeltech Corks Trend Themes - AI Quality Inspection — Machine learning embedded into production lines creates new value in defect prediction, classification accuracy and scalable quality assurance for natural materials. - Traceable Packaging Components — Digital audit trails for individual closures expand transparency across supply chains and support premium product verification in regulated or reputation-sensitive markets. - Smart Natural Materials — Traditional materials enhanced with imaging and pattern recognition can compete with synthetic alternatives through improved consistency, reliability and performance data. Industry Implications - Wine and Spirits — Consistent closure performance and reduced bottle variability strengthen premium ageing claims while supporting more predictable product experiences. - Packaging Technology — Sensor-driven inspection systems introduce differentiated packaging components that combine functional performance with measurable quality records. - Industrial Automation — AI-enabled visual inspection platforms broaden automation opportunities in material-specific manufacturing environments where defects are irregular and evolving.
Aug 3, 2026 · via trendhunter.com
Can generative models also be trained end-to-end? The core turns out to be a for loop. In 2012, AlexNet ended an era with a landslide victory. Before that, image recognition relied on manually designed hierarchical feature extraction pipelines; AlexNet proved a fact that was repeatedly verified later: handing over the entire task to the model for self-learning end-to-end almost always outperforms the carefully designed phased pipelines by humans. From image classification to object detection and then to image segmentation, behind every leap of deep learning is the same logic: let it learn the whole task in one go. There has always been only one exception: generative models. The most powerful and scalable generative models today (whether autoregressive or diffusion models) are not end-to-end. During training, they only learn to predict "one small step", but during inference, they need to unfold this step repeatedly hundreds or thousands of times like a recurrent network. Training and inference do not use the same sampling method. This discrepancy brings up an old problem: the error of each step is fed into the next step, the input gradually drifts away from the distribution seen during training, and errors accumulate layer by layer. Academically, this is called "exposure bias". In other words, "end-to-end performs better" — this core experience of deep learning — has never been truly applied to generative models for more than a decade. Recently, a paper from UIUC and Harvard University attempts to fill this last piece of the puzzle. The authors named this new paradigm Explorative Modeling, abbreviated as XM. Its idea is so simple that it seems almost naive, but it points to a bold conclusion: In addition to parameters and data, generative models actually have a third amplifiable dimension. Project website: https://explorative-modeling.github.io Paper link: https://arxiv.org/abs/2607.27372 Code repository: https://github.com/alexiglad/XM Root of
Aug 3, 2026 · via eu.36kr.com
Space artificial intelligence (AI) corporations TelePIX said on the 3rd that it has completed verification using a space situational awareness camera launched in Jun. last year to estimate a satellite's attitude and compare it with the actual satellite bus's measured values. The space situational awareness camera is a deep-space navigation device co-developed by TelePIX and optical solution corporations LK Samyang. It consists of a wide-field-of-view camera capable of capturing a broad area and an image-processing algorithm, and calculates a satellite's position and attitude based on the locations of stars and planets photographed in space. According to TelePIX, the camera has carried out imaging missions in orbit for more than a year since launch and has continuously secured images sufficient to identify stars. In a recent test, it checked whether the function to estimate changes in the satellite's attitude using only captured images operates normally. As a result of the test, the attitude changes calculated from the star observation images showed a trend similar to the values measured by the satellite bus. The angular velocity, which represents the satellite's rotation speed, also quantitatively matched. The camera identifies individual stars by comparing the positions and brightness of stars captured in the image with a celestial information databases, the "star catalog." It then calculates which direction the camera and the satellite are facing based on the arrangement of the confirmed stars. A test was also conducted to see whether the star recognition function is maintained even in environments where a bright celestial body is captured together. The footage shot in Apr. this year included the moon, but the company said it was still able to continue identifying surrounding stars even when part of the image was saturated by moonlight. This camera applies commercial off-the-shelf (COTS) components rather than parts specially manufactured for exclusive
Aug 3, 2026 · via biz.chosun.com
5 Reds Prospects Earn Major Recognition After Breakout Season In this story: The Cincinnati Reds may have a weaker farm system, ranking 25th in Baseball America's latest ranking, but they had several top prospects contribute to the team's Complex League team advancing to the Arizona Complex League championship series. According to multiple MiLB.com writers, the Reds had five players make their All-Complex League All-Star team: Jesus Colina, Aaron Watson, Jirvin Morillo, Iker Redona, and Steele Hall. Steele Hall Has Been Every Bit As Advertised The Reds selected Steele Hall with their first-round selection in the 2025 draft; he was just 17 years old at the time. He made his professional debut this season, and he answered many questions that were asked about his game. "While Hallâs swing can get a little long at times in games," MLB Pipeline wrote about Hall, "heâs shown in batting practice that he has the ability to be quick to the ball and to drive it gap-to-gap. Heâs a free swinger who will need to refine his approach, particularly in terms of recognizing secondary stuff. The power arrived in his first season. He slashed .288/.387/.504 with 10 home runs, 18 doubles, and his speed was on display with one of his home runs being an inside-the-park home run, two triples, and 25 stolen bases. Hall was promoted at the conclusion of the ACL season to Single-A Daytona, where he went 1-2 with three walks, two RBIs, and a stolen base in his second game. Jirvin Morillo is Ascending Through The Prospect Rankings Morillo debuted this season as the Reds' 29th-ranked prospect. He is now ranked eighth. This season, he slashed .321/.449/.652 with 13 home runs, 13 doubles, five triples, and 53 RBI. He was promoted at the conclusion of the ACL season. Morillo has more
Aug 3, 2026 · via si.com
IAS Residential Fellow Dr Ojwang Alice Achieng delivers a seminar on their research, fully titled "Carbo AI: An Artificial Intelligence-Based Photographic Application to Support Dietary and Lifestyle Education for the Management of Type 2 Diabetes" - Type 2 diabetes is a rapidly growing global health challenge, with dietary management central to its prevention and control. However, estimating the carbohydrate content of meals remains difficult, particularly for locally consumed foods. Carbo AI is an artificial intelligence-powered mobile application that uses photographic image recognition, computer vision, and machine learning to identify foods, estimate portion sizes, calculate carbohydrate content, and provide instant dietary guidance. The project was led by the Human Nutrition and Dietetics Team in the Department of Health and Biomedical Sciences, Technical University of Kenya, in collaboration with experts in computer science, medical physics, and software engineering. Development was supported by Amazon Web Services (AWS) cloud infrastructure and KENET's High-Performance Computing (HPC) platform. Carbo AI aims to improve carbohydrate literacy, strengthen diabetes self-management, enhance nutrition counselling, and demonstrate the potential of interdisciplinary AI-driven innovations to improve health outcomes. Arrivals from 11:45 am for a 12:00 noon start. For those joining in-person, lunch will be served after the seminar from 1:00pm. This event is hybrid format, please use the required booking button at the bottom of the page to choose either in-person or online attendance. (Please note that in-person spaces are limited and booking is required, so we can manage numbers for catering and also the space in the seminar room) By booking a place at this event, attendees agree to behave in a respectful manner such that everyone feels comfortable contributing as they wish. The IAS reserves the right to eject anyone who does not abide by this policy. IAS events are typically recorded, minus any Q&A sessions at the end,
Aug 3, 2026 · via lboro.ac.uk
U.S. Air Force Lt. Col. Melissa Forsyth, left, commander of the 174th Attack Wing Communications Squadron, presents Senior Master Sgt. Jason Hanavan with his promotion order after promoting him during a ceremony at Hancock Field Air National Guard Base, Syracuse, New York, June 8, 2026. Hanavan was promoted to senior master sergeant in recognition of his leadership, professionalism and continued service to the New York Air National Guard. (U.S. Air National Guard photo by Tech. Sgt. Duane Morgan)
| Date Taken: | 06.08.2026 |
| Date Posted: | 08.02.2026 09:04 |
| Photo ID: | 9810044 |
| VIRIN: | 260608-Z-PJ168-1035 |
| Resolution: | 5218x3472 |
| Size: | 3.37 MB |
| Location: | SYRACUSE, NEW YORK, US |
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This work, Forsyth promotes Hanavan to senior master sergeant [Image 4 of 4], by TSgt Duane Morgan, identified by DVIDS, must comply with the restrictions shown on https://www.dvidshub.net/about/copyright.
Aug 2, 2026 · via dvidshub.net
Here’s a new one: smart glasses. “Smart” this, “smart” that. Once “smart” described humans; now, products. The 21st century, awash with smart products, is the smartest century ever. Smart product history is traceable all the way back to 1867: a remotely controlled toy boat. That’s prehistory, really. History proper begins a century later (1968) with Echo IV, a proto home computer that could switch appliances on and off, write a household budget, control home temperature, regulate the kids’ TV access and so on. It was great science but bad marketing: too big, too awkward, it didn’t fit. It was a harbinger, not a hit. The hits of the 21st century were born in the late 20th. Astonishing at first, they are now commonplace. Novelty wears off quickly. The ubiquitous smartphone aside, there are smart cars, smart cards, smart locks, smart doorbells, smart refrigerators, smart saltshakers, smart just about anything. Embryonic smart glasses were Echo IV’s contemporaries, and shared its fate – their time was not yet. It’s now. Beware, warns Spa magazine (July 21-28). They’re dangerous. They range in price from 12,000 to 100,000 yen. The cheapest model displayed on Amazon offers “newest HD camera and speaker, 200 W image stabilization camera glass, video recording, dual microphone, video/ photography, automatic transmission, ENC call noise reduction, audio recording, real-time translation, AI voice assistant, AI image recognition, camera, music player, video recording” – one very smart pair of spectacles! They join the growing ranks of wearable technology – rings, watches, shirts, socks, jackets, trousers – that count, measure and/ or compute steps, heart rate, breathing, sleep, body temperature, foot pressure – everything countable, measurable and computable, and no doubt we’re the healthier for it, though one might venture the suggestion that there are enough things to worry about without all this data
Aug 2, 2026 · via japantoday.com