Gong’s current research focuses on API-driven geospatial analysis methods that support near real-time and on-demand data access.

Maynooth University PhD researcher Chao Gong has spent more than 10 years as a professional in the geospatial data analysis area, with a background in national-level surveying and mapping work in China.

During his time working in China, Gong was involved in managing large-scale geospatial databases and supporting infrastructure, land use and environmental projects.

Currently, he is pursuing a PhD in geographic information system (GIS) and remote sensing at Maynooth, while also working as a GIS specialist with Quarry Consulting, based out of Co Mayo.

His most recent work focuses on automating spatial analysis workflows and integrating real-time and on-demand data access into GIS systems.

“Over time, my research has evolved from traditional GIS processing towards more dynamic, API-driven approaches that aim to improve efficiency and bridge the gap between academic research and real-world applications,” he says.

What inspired you to become a researcher?

I was first introduced to GIS during my undergraduate studies, and I was immediately fascinated by how spatial data could be used to understand and interpret the real world.

However, a more defining moment came later during my professional work. I was involved in projects where large volumes of geospatial data had to be processed manually, often requiring significant time and effort before any meaningful analysis could begin.

I remember thinking that the real challenge was not only analysing data, but accessing and managing it efficiently. That realisation stayed with me and became a turning point. It made me see that improving how we work with spatial data could have just as much impact as the analysis itself.

That was when I became interested in exploring new approaches, which ultimately led me towards research.

Can you tell us about the research you’re currently working on?

My current research focuses on developing API-driven geospatial analysis methods that support near real-time and on-demand data access.

I have developed a QGIS-based tool that integrates local spatial data with live web services such as WFS and ArcGIS APIs. This allows users to perform proximity analysis without relying on downloading complete datasets in advance, which is a common limitation in traditional GIS workflows.

The tool has been applied in collaboration with Quarry Consulting to support environmental analysis tasks in real-world industry contexts.

By incorporating techniques such as coordinate system standardisation, spatial indexing and caching, the system improves both efficiency and performance while maintaining analytical reliability.

This work reflects a broader shift in GIS towards more scalable, flexible and data-efficient workflows.

In your opinion, why is your research important?

Traditional geospatial analysis often relies on downloading and processing entire datasets, which can be time-consuming and inefficient, particularly as data volumes continue to grow.

My research addresses this by enabling on-demand access to a relevant subset of spatial data through APIs, allowing users to retrieve the information they need when they need it.

This approach can significantly reduce data transfer and processing overhead, making spatial analysis more efficient and accessible. It is particularly relevant in contexts where timely and reliable information is critical, such as environmental monitoring, planning and infrastructure development.

What commercial applications do you foresee for your research?

This research has strong potential across several sectors, including environmental compliance and monitoring, infrastructure planning and site selection, engineering and construction projects and spatial risk assessment.

By enabling faster and more efficient access to relevant spatial data, organisations can improve decision-making while reducing operational costs and technical barriers.

What are some of the biggest challenges you face as a researcher in your field?

One of the main challenges is balancing near real-time data access with analytical reliability. Ensuring that results remain robust while working with dynamic and distributed data sources is not always straightforward.

Another challenge lies in integrating heterogeneous data sources, which often differ in format, quality and coordinate systems.

Additionally, translating research outputs into practical tools that can be effectively adopted by industry requires bridging the gap between technical innovation and real-world usability.

Are there any common misconceptions about this area of research?

A common misconception is that having access to more data automatically leads to better analysis.

While larger datasets can be valuable, they do not necessarily result in better outcomes if they are not relevant or efficiently managed. In many real-world scenarios, the key challenge is identifying and accessing the right data at the right time, rather than processing all available data.

Traditional workflows often involve downloading complete datasets, even when only a small portion is required. My research focuses on addressing this inefficiency by enabling API-based, on-demand access to a relevant subset of data, allowing analysis to be more targeted and efficient.

In this sense, it is not simply about having more data, but about having better access to the data that matters.

What are some of the areas of research you’d like to see tackled in the years ahead?

I would like to see further research in areas such as near real-time geospatial data processing, integration between cloud-based GIS and desktop systems and the combination of GIS with AI and machine learning for more automated analysis.

In particular, developing scalable and user-friendly GIS systems that can be easily deployed across both research and industry environments will be an important direction for the future.

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Looking for the most recent Mini Crossword answer? Click here for today’s Mini Crossword hints, as well as our daily answers and hints for The New York Times Wordle, Strands, Connections and Connections: Sports Edition puzzles.

Need some help with today’s Mini Crossword? Once you solve it, look at the letters in the drop shapes — they spell out a word related to that shape. Read on for all the answers. And if you could use some hints and guidance for daily solving, check out our Mini Crossword tips.

If you’re looking for today’s Wordle, Connections, Connections: Sports Edition and Strands answers, you can visit CNET’s NYT puzzle hints page.

Read more: Tips and Tricks for Solving The New York Times Mini Crossword

Let’s get to those Mini Crossword clues and answers.

Mini across clues and answers

1A clue: Unit of scissors, underwear or AirPods
Answer: PAIR

5A clue: Rabbit relative
Answer: HARE

6A clue: The circled letters in this puzzle, e.g.
Answer: DROPS

8A clue: Justice Sotomayor
Answer: SONIA

9A clue: Letters in a Big Apple address
Answer: NYNY

Mini down clues and answers

1D clue: High degrees, for short
Answer: PHDS

2D clue: Quarterback Rodgers
Answer: AARON

3D clue: A hardware store with a broken doorknob is an example of it
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The new model arrives June 16 for $400

Take a look at my Philips 5000 Series Dual Basket Air Fryer XXL Steam review and you’ll know that I’ve taken a shine to having steam cooking in my air fryer. I still use this two-drawer steam fryer (I like calling it that), giving me the ability to steam only or steam and air fry (steamfry) at the same time. I’m miffed that steaming is restricted to one basket only, though.

As good as it is, the non-stick coating in the larger basket (which is used more often) is beginning to come off and, importantly, I really need a more compact option as I live in a small apartment with barely any countertop or cabinet storage.

Which is why Philips’ newer 5000 Series Airfryer with Steam is so appealing to me as an upgrade — and it’s back to its lowest price of AU$374.25 on Amazon, which isn’t all that much more than the dual-basket’s discounted price of AU$349.

Admittedly the single basket of the newer model might be a deal breaker for some, but its 7.2L capacity makes up for not having two baskets — I don’t use the smaller basket in my air fryer anyway, so I personally wouldn’t miss it much.

The compactness of the single-basket Philips 5000 Series air fryer with steam isn’t the only reason I’m considering the upgrade. As I’ve already mentioned, the disappearance of the non-stick coating of the baskets in my two-drawer version is worrying me. I’m not too concerned that it’s toxic — it’s not — but the ceramic coating used in the newer model would just give me a little more peace of mind that it’s safer and longer lasting.

Then there’s the design of the new Philips air fryer compared to the dual basket — it looks so much more premium with the metallic trim and the little cooking window. I will admit I’m not too keen on the water reservoir on the side as it detracts from the appliance’s symmetry, but that’s me nitpicking. I’d very much prefer it if Philips had figured out a spot on the top or on the rear, but the vents at the back of the machine would make the latter a difficult task.

Moreover, my biggest complaint of only one of the two baskets having steam in the older model is taken care of here — it is, after all, a single-basket air fryer.

If the Philips 4000 Series Stacked air fryer had a steam functionality, I’d probably go for it over the single-basket 5000 Series, only so I still get the two 5L drawers. But I’ll take the ability to steam over the dual baskets any day as it adds versatility to the air fryer, making it better value.

Reallusion, the 3D animation software company behind iClone and Character Creator, has launched AI Studio, a production platform that pairs traditional 3D scene-building with generative AI video models. The centrepiece is a direct integration with ByteDance’s Seedance 2.0, currently the top-ranked AI video model on the Artificial Analysis leaderboard.

The pitch is straightforward. AI video generators like Seedance, Google’s Veo 3, and Runway’s Gen-4 can produce impressive footage from text prompts, but they struggle with precision. Complex character motion, camera choreography, and spatial continuity break down when the AI is working from language alone. Objects warp, perspectives shift, and directors have limited control over what actually appears on screen.

Reallusion’s answer is a hybrid workflow. Artists build their scene in iClone, a real-time 3D animation tool, setting camera paths, character positions, skeletal motion, and lighting. That 3D data then serves as what the company calls a “precision control layer” for the AI model. Seedance 2.0 handles the visual rendering, textures, and cinematic quality, while the 3D scene provides the spatial structure. The artist retains directorial control. The AI handles execution.

Seedance 2.0 is well suited to this approach. ByteDance designed it with strong spatial intelligence, meaning it can interpret exact scene layouts, camera paths, and skeletal data without the guesswork that plagues other models. It generates clips up to 15 seconds in length with camera choreography and motion dynamics that feel intentional rather than random. China’s AI video industry has moved faster than any other market on production tooling, and Seedance reflects that momentum.

AI Studio is not limited to a single engine. Reallusion has built it as a multi-model platform, consolidating Flux and Nano Banana for image generation alongside Kling AI, Veo 3, Wan, LTX, and Scail for video. Users can switch between models depending on the shot, choosing one for photorealism and another for stylised animation. The idea is to give studios the flexibility to use whichever model best fits each scene rather than locking into a single provider.

The timing matters. OpenAI shut down Sora in April after the video tool peaked at one million users and reportedly cost $1 million per day to operate. The shutdown rattled creators who had built workflows around it and underscored the risk of depending on a single AI platform. The AI-animated film Critterz missed its Cannes market debut as a direct consequence.

Reallusion is positioning AI Studio as a more stable alternative. Because the 3D scene data lives locally in iClone, the creative work is not lost if a particular AI model is discontinued or repriced. The 3D assets, motion data, and camera setups remain usable. Only the rendering layer changes. That is a meaningful difference for studios investing in long-term production pipelines.

The company, founded in 1993 with R&D centres in Taiwan and offices in Silicon Valley, Canada, Germany, and Japan, has spent decades building tools for real-time 3D character animation. iClone and Character Creator are used in game development, film pre-visualisation, and virtual production. AI Studio extends that ecosystem into generative video without abandoning the 3D skill set that existing users have invested years in developing.

Adobe has taken a similar approach with its Firefly AI Assistant and Project Graph, integrating generative models into existing creative software rather than replacing it. The pattern across the industry is converging: the most useful AI creative tools are not standalone generators but hybrid systems that augment professional workflows.

Whether AI Studio gains traction will depend on whether the hybrid model delivers on its promise. Pure AI video generation is improving rapidly, and each new model narrows the gap between what a text prompt can produce and what a 3D-controlled pipeline delivers. Reallusion is betting that the gap will never fully close, that professional filmmakers will always need spatial precision, repeatable camera setups, and frame-level control that language-driven generation cannot guarantee.

For an AI video market in flux, where the leading model changes every few months and platforms can vanish overnight, a tool that keeps the creative decisions in the artist’s hands rather than the model’s weights is a bet on stability over spectacle. Whether that bet pays off will depend on how many filmmakers prefer control to convenience.

People buy video walkie-talkies to facilitate short-distance communication among children and / or family members. Aaron Christophel recognized an opportunity to bring DOOM onto one of these low-cost gadgets. These devices have small color screens, built-in cameras, microphones, speakers, and rechargeable batteries. Models retail online for between fifteen and twenty euros and rely on the TXW818 system on a chip to function. This chip has processing capacity comparable to some wireless modules and supports external memory, as well as four megabytes of PSRAM.

Christophel began by closely inspecting the electronics, noting that the devices differed: some had two megabytes of external flash memory, while others had four. Stuffing chip markings and scrambling flash memory is a typical practice that manufacturers use to make it difficult to meddle with their goods. Not to mention the specific tools required to delve deep into things: a USB to UART converter, a Blue Pill board posing as a J-Link clone, and a box called a profiler that allows you to monitor power consumption as devices perform different things.

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The majority of the project included reverse engineering. Christophel dumped the original firmware and examined it with Ghidra, an app that is quite useful for this type of task. The software development kit that came with the device did not include drivers for the screen or camera, so Christophel had to create his own. Things only got tougher from there, since the stock firmware disabled the debug interface immediately after booting up. Christophel needed to find a means to keep it on, so he devised a mechanism that allows him to maintain access to the debug capabilities by connecting specific capacitors and communicating with the flash chip at startup.

With that resolved, Christophel began work on a new firmware, which did a variety of cool things such as initialize the screen and make the buttons functional. He also created a function that allows the device to recognize the amount of flash memory it has on board and alter its settings accordingly. Things got a lot more complicated when he attempted to port the game DOOM over. The original game data file alone takes up approximately one and a quarter megabytes, which was a problem for devices with only two megabytes of flash. Christophel had to reduce the file size to five hundred kilobytes. When the device boots, the firmware unpacks this data into PSRAM, which is exactly what is required to run the game.

The gameplay is rather straightforward, with you using the walkie-talkie buttons to move ahead and backward, as well as to turn around. When things begin rolling, an entertaining visual effect appears in the center of the screen, which is simply the front camera feed with the player’s face staring back at themself. Given the limited hardware, the performance is adequate, and the little screen does an excellent job of displaying the action, allowing you to have some fun with the device. Christophel has made the whole source code available on GitHub, so if you’re intrigued, you can now build the binaries and try it for yourself.
[Source]

Summary: China has launched a national ID system for humanoid robots, assigning each a 29-character code that tracks it from production to recycling. Over 28,000 robots across 200 models already have IDs. The system logs real-time performance data including joint wear, battery status, and AI training history.

China has launched a national identification system for humanoid robots. The Humanoid Full Lifecycle Management Service Platform, built by the Humanoid Robotics and Embodied Intelligence Standardization committee under the Ministry of Industry and Information Technology, assigns each robot a unique 29-character digital code that follows it from the factory floor to the scrapyard.

The code captures everything: manufacturer, product model, serial number, hardware specifications, AI capability level, software training history, and production records. It is modelled on China’s 18-character national citizen ID system but adds 11 extra characters to cover operational data specific to machines. More than 28,000 robots across 200 models have already been assigned an ID through the platform, which was launched by the Hubei province’s Humanoid Robotics Innovation Center in May.

This is not a static registry. The platform functions as a live digital record that tracks maintenance history, work environments, and real-time performance metrics including mechanical joint wear rates, battery degradation, and movement precision. When something goes wrong, the system is designed to enable rapid fault detection. When a robot is decommissioned, the ID follows it through recycling.

The scale of China’s humanoid industry explains why regulators moved now. The country has more than 100 humanoid robot manufacturers. Investment in robotics and embodied intelligence in 2025 exceeded the full-year 2024 total by the end of May, with China pouring $3.4 billion into new robotics ventures, 42 per cent more than the United States and five times Europe’s total. Shanghai has issued China’s first provincial plan for embodied intelligence, pairing R&D support with shared infrastructure for compute, testing, pilot production, and financing.

The robots are already showing up in the real world. A humanoid named Lightning completed the Beijing E-Town Half-Marathon in 50 minutes and 26 seconds earlier this year, beating the human world record by nearly seven minutes while navigating the 21-kilometre course autonomously. China’s State Grid Corporation plans to deploy 8,500 robots, including humanoids and robot dogs, for power grid operations. Tea farms in Hubei province have begun field trials with humanoid workers ahead of the 2026 World Robot Games.

The ID system addresses a governance gap that is widening as deployment accelerates. Without a standard way to track who built a robot, what software it runs, where it has been deployed, and how it has performed, liability becomes murky. If a humanoid injures a worker or damages property, regulators need a chain of information that connects the incident to a specific machine, its manufacturer, and its operational history. The ID code provides that chain.

There is a broader regulatory context. China has moved faster than any other country on AI governance frameworks, from algorithmic recommendation rules in 2022 to generative AI regulations in 2023 to deepfake and synthetic content rules in 2024. The robot ID system extends that approach to physical AI, treating humanoid robots as entities that require lifecycle oversight in the same way that vehicles, medical devices, and industrial equipment do.

The comparison to citizen IDs is deliberate but imperfect. Robots are not citizens. They do not have rights. The ID system is an industrial standard, not a legal status. But the structural parallel, a unique identifier issued by the state that tracks an entity across its entire existence, raises questions that other countries will eventually need to answer. As humanoid robots move from factories into hospitals, homes, and public spaces, who is responsible for what they do? The ID code does not answer that question, but it creates the informational infrastructure to start.

The United States and Europe have no equivalent system. The EU AI Act classifies AI systems by risk level but does not require individual identification of physical robots. The US has no federal framework for humanoid robot registration. China’s approach to AI governance has consistently prioritised state visibility into how the technology is deployed, and the robot ID system is the latest extension of that philosophy.

For manufacturers, the system creates both obligations and opportunities. Compliance means submitting detailed technical data for every unit produced. But it also means that a robot with a clean lifecycle record, well-maintained, regularly updated, deployed within its rated capabilities, carries a verifiable history that could function as a quality signal for buyers. In a market with 100-plus manufacturers and no dominant brand, standardisation is a competitive tool as much as a regulatory one.

The question for the rest of the world is not whether China’s approach is right but whether it is early. If humanoid robots become as common as China’s industrial policy intends, every country will need a way to track them. China is building that system while the rest of the world is still debating whether the robots are ready. The 28,000 units already in the database suggest the debate may be beside the point.

Google’s Quick Share is the kind of feature you don’t think about until the day you need it and your phone simply doesn’t have it. Huawei device owners live in that reality permanently, given that they don’t have access to Google Play services, and so does anyone running the Chinese regional build of Android. 

However, a developer with the handle Kyujin-cho just published an open-source Android app called Bada on GitHub that seems to solve exactly this problem. It does so by implementing Google’s own Quick Share protocol from scratch, circumventing the lack of Google Play Services.

What does Bada actually do?

Once Bada is installed on a device that lacks Quick Share, it becomes fully interoperable with any Quick Share-equipped Android device nearby on the same Wi-Fi network. The same four-digit PIN confirmation process that users already know shows up on both the sending and receiving sides. 

Using the app, people can send files from any Android app (via the system share sheet), receive files to a specific folder, and even send entire folders, with the directory structure intact. Like Quick Share, the app supports Wi-Fi LAN as the transfer route, with BLE-based identification for devices running on stock Android and Samsung’s One UI.

Testing has already confirmed that Bada works with Galaxy S26 Ultra and Z Fold 7 over BLE GATT bootstrap. NearDrop on macOS and Quick Share on Windows are listed as targets; however, they remain untested.

Should you actually use it right now?

According to Android Authority’s hands-on testing, the app experience isn’t exactly seamless when sharing files from a Quick Share device to a Bada device. Windows transfers completely failed. 

The project sits at 10 GitHub stars and one fork, which is still early-project territory by all means. The codebase is open-source, meaning anyone with the technical know-how can verify what it’s actually doing with their files. 

The app itself confirms that transfers still use Quick Share’s encryption method. The developer explicitly targets interoperability with NearDrop and Windows Quick Share for the near future. 

In my opinion, Bada won’t replace Quick Share for most people, but for Huawei users, along with the Chinese Android users Google quietly left behind, or for any other Android user whose phone doesn’t ship with Quick Share out of the box, it’s the closest thing to a real solution anyone has bothered to build.

You could argue, and people have, that the top gay dating apps are now optimized for monetization and juicing engagement loops. Increasingly overrun with bots, they are at times even devoid of actual connection.

Grindr, with its 15 million monthly active users, is drowning in ads while pushing expensive upsells on users. (In February, as part of its “gAI” overhaul, the company announced a new premium monthly subscription tier for $500.) Sniffies was beloved by cruisers until the seismic reaction in April to Match Group’s $100 million investment sparked concerns that another queer space could get absorbed into a larger dating conglomerate.

As public backlash against popular queer apps continues to mount, a batch of tech entrepreneurs are scrambling to meet the demand by doubling down on privacy-conscious, community-driven alternatives.

Calum Bowden, who posts under the internet persona @donjackoghue, launched MeetMarket in March. Currently only available as a web app, MeetMarket includes all the core features of your typical hookup app—a customizable profile, a grid of nearby users—with one major difference. It was built on a decentralized identity system, meaning MeetMarket doesn’t store users’ emails, passwords, or personal information. Users store everything on their device, giving them full control and ownership over their data and how it’s shared. Messages on the platform are end-to-end encrypted, and Bowden says it will always be ad-free, even for nonpaying members. (A monthly membership costs €12, or $13.99.)

“Decentralization and data privacy make a lot of sense for queer people in general, and especially in hostile legal environments or in the US right now, where you don’t really know what digital platforms actually have your best interest in mind,” says the 34-year-old PhD student in Berlin who studies the sociology of technology and organization.

Within the first 48 hours of MeetMarket’s launch on March 24, over 12,000 people had signed up, and some 60,000 people have used it since. The app averages 5,000 weekly visitors, according to Bowden, though there is not a lot of concurrent activity in the same cities. “It’s become more social than necessarily driving an immediate hookup.” But casual encounters do still happen, he says. “The Midwest bottom jockeys are eating meet market up,” one user noted on X.

Bowden didn’t anticipate public sentiment would sour on Sniffies just a few weeks after his launch. Still, the timing of it couldn’t have been more serendipitous. “When Sniffies announced their investment from Match Group, I was like, how are they fueling my fire?” he asks. “This is exactly the model that venture capital leads to. This is exactly why these economic models for technology are so bad, because they basically force the gentrification of a digital platform.” Sniffies did not immediately respond to a request for comment.

A self-described “utopian conspirator,” Bowden is the cofounder of Trust, a nonprofit that operates as a kind of incubator to prototype ideas “as a critique of technology and the status quo,” he says. With MeetMarket, he wanted to create an app that gave users more agency over their experience without cheapening it.

It can sometimes seem like Big Dating wants people to believe that it is the only answer to cure their romantic woes—Bumble CEO Whitney Wolfe Herd recently told Axios that there isn’t much longevity in niche apps—but the opposite is proving just as true, as people seek out more specificity and intention in their online dating experience.

“Gay men have tribes, subcultures, aesthetics, and different ways they want to be seen,” says Justin Finnegan, a 35-year-old software engineer in Toronto who last year created Chunkr, a gay hookup app that has resonated with the bear community despite originally being for all gay men.