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• The FBI’s missive follows three previous ones in as many years
• Statement is aimed at educating businesses and warding off domestic collaborators
• Suggested remedies include employing endpoint protection on computer systems and checking applications for “typos and unusual nomenclature”

The FBI has claimed North Korean IT workers are extorting US companies which have hired them by leveraging their access to steal source code.

In a statement, the agency warned domestic and international firms employees turned threat actors, “facilitate cyber-criminal activities and conduct revenue-generating activity” using stolen data “on behalf of the regime.”

It recommended endpoint protection, and monitoring network logs to identify where data has been compromised across “easily accessible means” like shared internal drives and cloud storage drives.

FBI guidance on remote hiring processes

The FBI also recommended a litany of actions that all amount to taking care to know who you’re hiring, which sounds like good practice even if you’re not especially worried about unwittingly hiring a threat actor.

It recommended stringent identity verification processes throughout the recruitment process and cross-checking applicants’ details against that of others in the pile, and across different HR systems.

It also claimed these applicants are using AI tools to obfuscate their identities, but, if true, offered little advice to counter them beyond conducting recruitment processes in person; which isn’t always possible.

The agency also suggested recruiters ask applicants “soft questions” about their whereabouts and identity, but we’d suggest that this is good practice all round too.

North Korean IT workers have been a target of the FBI for some time, having released separate guidance in 2022, 2023, and 2024. In the latter, it expressed concern that US-based individuals were, knowingly or unknowingly, helping facilitate state-sponsored threat actors by setting up US-based infrastructure such as front addresses and businesses.

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Garmin Instinct 3: One minute review

As I begin our Garmin Instinct 3 review testing, I know this watch has a lot to live up to. After all, the Garmin Instinct 2 received a rare, coveted five stars in our official review. With a variety of sizes (from 40mm to the Instinct 2X Solar at 50mm), solar-powered and non-solar versions, a smorgasbord of fun colorways, and - my personal favorite - a hybrid Garmin Instinct Crossover, there truly was an Instinct 2 for everyone.

The Instinct 3 runs along very much the same lines, with a selection of cool colorways, a variety of sizes, and two key screen options. The smallest 40mm size is called the Instinct E, and is most reminiscent of the Instinct 2: there’s only the MIP (Memory in Pixel) option with no solar charging and no flashlight. In 45mm and 50mm sizes, you can choose between a bright, smartwatch AMOLED screen and a classic Garmin MIP screen equipped with Garmin’s Power Glass solar charge technology, offering ‘unlimited’ battery life in bright enough conditions, if you’re out and about with it exposed to sunlight for several hours.

It only really makes a big difference during endurance exercise on sunny days, when it can slow down the GPS mode’s drain on the battery life, but it’s nice to have. We got the AMOLED 45mm version to test in its cool, minty Neotropic colorway, but I do have a soft spot for the digital watch-esque MIP screens that the best Garmin watches used to use as standard. As is, this feels more like a Garmin Forerunner 265, with limited map capabilities, although the case design is still stereotypically Instinct.

The Instinct series has never offered the full-color topographical maps more premium watches in Garmin’s range have, and I was wondering if the AMOLED screen meant we’d finally see topographical maps on an Instinct, but there are no maps or touchscreen here. I suppose Garmin has to distinguish between the ranges and keep the Instinct’s price down somehow. I have recorded two workouts with the Instinct 3 so far – a run and a boxing session – and it handled both with accuracy, and was as intuitive to use as the Instinct 2. It was easy for me to replace my daily Instinct Crossover with this with barely three minutes of setup, seamlessly taking over with my sleep tracking, Body Battery and Training Readiness scores from my old watch.

Overall? As a pre-existing Instinct fan, I’m loving it. It’s fast, it’s bright, it looks fun, and most importantly, it’s useful. Yes, it’s chunky and plasticky, and if you don’t like the old design, you won’t like this one either. But it’s not a sleek metal communications tool: it’s a toy, and it’s best played with outside.

Garmin Instinct 3: Price and availability

(Image credit: Future / Stephen Warwick)

• Instinct E starts at $299 / £259 / AU$549
• 45mm Instinct 3 starts at $399 / £349 / AU$829.
• 50mm Instinct 3 starts at $449 / £429 / AU$929

The Garmin Instinct 3 is available now on Garmin’s website and other retailers, slightly confusingly with five different prices, depending on which model you pick.

The cheapest Instinct E is available now for $299 / £259 / AU$549. The 45mm watch starts at $399 / £349 / AU$729 for the MIP Solar screen, rising to $449 / £389 / AU$829 for the AMOLED screen option. The largest 50mm watch with MIP Solar screen is available from $449 / £429 / AU$829, rising to $499 / £449 / AU$929 for the AMOLED screen option.

Garmin Instinct 3: Specifications

(Image credit: Future / Stephen Warwick)

Below are the specifications for our review unit, the 45mm AMOLED Instinct 3.

Garmin Instinct 3: Design

(Image credit: Future / Matt Evans)

• Chunky polymer casing
• Choice of screens
• Intuitive, unchanged Garmin layout

The Instinct 3 builds closely on the design legacy of the Garmin Instinct 2, and the casing looks mostly the same. It’s got loads of colors, it’s roughly the same thickness, roughly the same weight (with just a gram of difference between the 45mm Instinct 3 versus the 45mm Instinct 2), and it’s still got five buttons and similar bezel indentations. The main differences are the metal accents, the new AMOLED screen option, and the flashlight, neither of the latter are available on the smallest Instinct E.

The new AMOLED screen is clear and bright, and seems at first glance to have a faster refresh rate than slightly older AMOLEDs like the Garmin Epix Pro 2 and Garmin Forerunner 965, although I’ll have to confirm that’s the case in my full review. I like the default watch face layout, although this is easily changed, and navigating around the watch with the five-button control interface is intuitive for me (a regular Garmin user) even if my Apple Watch-wearing fitness writer, Stephen Warwick, was offended at the lack of touchscreen.

While Garmin’s Forerunner, Venu and Fenix watches have adopted the hybrid of buttons and touchscreen, it’s another feature missing from the Instinct 3 to keep it affordable. The same could be said of the chunky ‘fiber-reinforced polymer’ (read that as ‘thick plastic’) casing, with slim metal accents the only thing separating it from being too childish for some users. Metal screws give it a rugged edge.

Functionally, the heart rate and skin temperature sensor array has been given a slight upgrade, with the watch now sporting the more accurate Elevate Gen 5 instead of Gen 4. The universal Garmin proprietary charger remains the same, only with a newer USB-C cable instead of USB-A, as is standard with most Garmins now.

Garmin Instinct 3: Features

(Image credit: Future / Matt Evans)

• Almost all of Garmin’s wellness features
• New torch is useful
• Basic directional maps

For the uninitiated, opening a Garmin watch and scrolling down with the ‘down’ button provides a list of wellness, navigational, and quality-of-life widgets. Outdoor-focused widgets range from the weather, sunrise and sunset times, and a compass shortcut to your sleep tracking information. Wellness stuff includes your last completed workout, sleep, Body Battery and your Training Readiness score. As a watch tailored to the great outdoors, all the functionalities you expect can be found here and the list can be customized as usual to suit your needs. Climbing a mountain? Stick the altimeter at the top. A keen marathon runner? Choose the ‘Race Calendar’ widget.

It’s fun, intuitive and acts like the watchOS 11 widget stack, but less flashy. I found most of the usual Garmin stuff there, but some premium features like Endurance Score and Hill Score were missing. They would have been nice to have. As previously mentioned, there’s no topographical maps functionality either, just basic directional guidance, but there have never been maps on an Instinct watch, and most of the time directions are all you need. You can, however, still retrace your steps with the ‘TracBack’ feature, along with a slew of other navigational tools.

Hardware-wise, the new torch is very useful: it comes in four intensities and a less dazzling red option, just like the torch on the Garmin Fenix 8. I’ve already used the red light on an evening run and the white light to help me stumble my way to the bathroom at six am. In the morning, you’ll get a daily digest, Garmin’s ‘Morning Report’ detailing information on your sleep, the weather, your Training Readiness, and a motivational message. Garmin’s been doing this for a while and it’s refined the feature over the last couple of years. Its long battery life means you won’t shy away from wearing it to bed.

• Our performance section, final scores and verdict will be added after around two weeks of testing – watch this space!

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• QNAP said it addressed six flaws in its Hybrid Backup Sync tool
• The flaws stemmed from rsync, an open-source file syncing tool
• Users are advised to update their HBS immediately

QNAP has addressed half a dozen vulnerabilities affecting its Hybrid Backup Sync (HBS) software.

In a security advisory, the company noted the vulnerabilities were discovered in rsync, an open source file synchronization tool used to transfer and sync files between systems. It supports local and remote operations via SSH, and minimizes data transfer with incremental updates. Many backup solutions use rsync, including Duplicity, Bacula, Rclone, and others.

HBS is a data backup and disaster recovery solution that supports local, remote, and cloud storage services.

Arbitrary code execution

The bugs are tracked as CVE-2024-12084, CVE-2024-12085, CVE-2024-12086, CVE-2024-12087, and CVE-2024-12088, and affect HBS 3 Hybrid Backup Sync 25.1.x. QNAP said they could have been used to run malicious code remotely against unpatched Network Attached Storage (NAS) endpoints. Apparently, threat actors would only need anonymous read access to vulnerable servers, in order to exploit the flaws.

"When combined, the first two vulnerabilities (heap buffer overflow and information leak) allow a client to execute arbitrary code on a device that has an Rsync server running," CERT/CC said when rsync 3.4.0 was released. "The client requires only anonymous read-access to the server, such as public mirrors. Additionally, attackers can take control of a malicious server and read/write arbitrary files of any connected client."

To secure their systems, administrators are advised to update their HBS 3 Hybrid Backup Sync to version 25.1.4.952, by logging into QTS or QuTS hero as an admin, opening App Center and searching for HBS 3 Hybrid Backup Sync, and clicking on the Update button.

According to BleepingComputer, there are currently more than 700,000 IP addresses with exposed rsync servers, but it’s difficult to determine how many can be exploited.

Via BleepingComputer

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• Pre-orders should go live for AMD’s RX 9070 GPUs on March 23, according to one US retailer
• There are a couple of reasons why this is likely wrong, though
• We’ve also seen more strangeness around the previously rumored January 23 launch for RDNA 4, as an ad campaign appears to have misfired

As if enough weirdness hasn’t already surrounded AMD’s RDNA 4 GPU launch – it’s been an odd affair indeed – something at Team Red appears to have misfired on the marketing side again, and we’ve also caught a mention of a possible new release date for the RX 9070 graphics cards.

However, I think the date shared by a US retailer is off the mark, and just a placeholder, so let’s deal with that first.

The story here is that B&H Photo previously posted what it believed to be the date that gamers would be able to pre-order RX 9070 models, namely January 23 – but of course that didn’t happen.

Instead, AMD made an announcement to let us know that the RX 9070 graphics cards won’t arrive until March (still within the official Q1 2025 timeframe that was previously promised, it’s worth noting).

Following that, VideoCardz spotted that B&H Photo now has a date of March 23 for pre-orders on RDNA 4 graphics cards – and the retailer still has this date on its site (it hasn’t been taken down yet).

What’s strange about this is that March 23 is a Sunday, so it seems highly unlikely that a sales milestone (pre-orders, or indeed the actual on-sale date) would be on a weekend. Surely, AMD would wait until the Monday (March 24) if this was the plan.

Also, bearing in mind that the previous date B&H posted was January 23, it feels like what might have happened here is that a staff member working on the product listings just replaced the name of the month in the date, and left the day the same (though why an update was posted at all to the RDNA 4 listings, when nothing’s official, is a good question).

Hence my conclusion that this looks very much like a placeholder date, given the way this has happened, and the additional fact that it’s a Sunday.

(Image credit: Gorodenkoff / Shutterstock) Reddit campaign wasn’t canceled?

The second oddity that backs up some previous rumors – and the initial January date B&H attached to RX 9070 pre-orders – is that some AMD ads have popped up, going by reports, claiming that you can ‘play now’ with the new RDNA 4 GPUs.

This was again noticed by VideoCardz, and it happened yesterday, on January 23, so one theory is that this was a date AMD had indeed planned for something big to happen, either a launch or reveal. (If you can ‘play now’ though, then the GPUs would surely be on sale). Team Red had a bunch of marketing stuff scheduled for Reddit (as flagged up here) around this, which accidentally still went ahead somehow (with these ads appearing in a few different countries in Europe, seemingly being published in German, Italian, and Polish).

The not-unreasonable conclusion folks are reaching is that AMD did have something planned for January 23, especially as there were a lot of rumors this was the case (albeit some mentioned January 24, though it was all in the same ballpark of the week we find ourselves in now). But all this has now been put off to March, although officially, as mentioned, AMD has never said anything concrete for the RX 9070 launch outside of it being Q1 2025.

The rumors around RDNA 4 have taken some very strange turns indeed, then, but the good news is that it seems these next-gen graphics cards will be worth waiting for. Even if we do have to hang on until later in March, rather than seeing the RX 9070 GPUs emerge earlier in the month – and really, I wouldn’t be swayed at all by the new B&H pre-order date for all the reasons mentioned.

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All four hostages are female Israeli soldiers. They are expected to be released Saturday in accordance with a ceasefire agreement with Israel that's halted fighting in the Gaza war since last weekend.

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Now that Netflix has unveiled which titles will be leaving in February 2025, take this as your sign to get a head start on catching the best Netflix movies before it's too late. But while Netflix doesn't tend to remove a huge amount of titles each month (which is a good thing), there are always one or two gems thrown into the mix that I know I'll miss dearly. As for this month, those movies are Pearl (2022) and Stand By Me (1986), but there are plenty new additions on the way to make up for it, looking at everything coming to Netflix in February.

When it comes to Netflix series, I find that these are less prone to being axed from Netflix's library. This month, only two TV shows (Brooklyn Nine-Nine and The Mindy Project) are being removed compared to over 30 movies, so with that said, you can't say that Netflix isn't one of the best streaming services when it comes to TV content.

Everything leaving Netflix in February 2025

Leaving on February 1

Cocaine Cowboys 2 (movie)
Plus One (movie)
Run All Night (movie)

Leaving on February 11

The Fast and the Furious (movie)
2 Fast 2 Furious (movie)
The Fast and the Furious: Tokyo Drift (movie)
Fast Five (movie)
Fast & Furious 6 (movie)
The Pope's Exorcist (movie)

Leaving on February 14

The Catcher Was a Spy (movie)
White Boy (movie)

Leaving on February 15

47 Meters Down: Uncaged (movie)
Blackhat (movie)
Pearl (movie)

Leaving on February 20

Book Club (movie)
Southpaw (movie)

Leaving on February 21

All Good Things (movie)

Leaving on February 24

U Turn (movie)

Leaving on February 25

Brooklyn Nine-Nine seasons 1-2 (TV show)

Leaving on February 28

21 Bridges (movie)
A Haunted House (movie)
A Haunted House 2 (movie)
Aloha (movie)
The Angry Birds Movie (movie)
Blended (movie)
Cinderella Man (movie)
Due Date (movie)
Green Lantern (movie)
Inception (movie)
Legends of the Fall (movie)
Little (movie)
The Mindy Project seasons 1-6 (TV show)
Oblivion (movie)
The Other Guys (movie)
Scooby-Doo (movie)
Scooby-Doo 2: Monsters Unleashed (movie)
Sixteen Candles (movie)
Stand by Me (movie)
Without a Paddle (movie)

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Frank Scheufens, Product Manager at PNY Technologies explains the benefits and uses of Digital Twins in this second installment.

Operating Digital Twins: virtually manage, monitor and optimize physical assets

A digital twin operates through a cycle of data collection, transmission, analysis, and user interaction. The process begins with data collection, where physical assets are fitted with sensors that monitor various parameters—such as temperature, pressure, or movement. This data is then transmitted to the digital twin via cloud or edge computing, allowing the digital model to update in real-time. Advanced algorithms and analytics are applied to the incoming data, helping the twin predict future states, identify anomalies, and provide actionable insights.

The real power of a digital twin lies in its interactivity. Operators can interact with the model to test hypothetical scenarios, diagnose problems, and adjust operations without affecting the actual physical asset. This simulation capability, coupled with real-time data, provides a dynamic environment for experimentation and optimization.

The role of AI in Digital Twin projects

Artificial intelligence (AI) plays a crucial role in maximizing the potential of digital twins. AI’s ability to handle massive datasets, identify patterns, and make predictions allows digital twins to go beyond basic monitoring. In many cases, machine learning algorithms are integrated into the twin to enable predictive analytics—forecasting potential failures or maintenance needs before they occur. This predictive capability helps organizations shift from reactive to proactive maintenance, reducing costs and avoiding unexpected downtime.

AI can also help with anomaly detection, identifying unusual behavior patterns in the data. In industries like manufacturing or aviation, these patterns might indicate a need for maintenance or signal a potential issue. Additionally, AI algorithms can simulate various scenarios within the digital twin, allowing organizations to optimize processes and test changes without risking real-world consequences. In some advanced applications, AI even enables the digital twin to make autonomous decisions, adjusting operations based on the analyzed data.

Challenges in Digital Twin projects

While digital twins offer immense benefits, they also come with challenges that need to be managed carefully. One of the most significant is data management. The vast quantities of data generated by sensors can be overwhelming, requiring robust storage, processing, and analysis capabilities. Integrating a digital twin with existing, sometimes outdated, systems can also be challenging. Legacy infrastructure isn’t always compatible with modern digital twins, necessitating extensive adjustments and integrations. Another challenge is the high initial cost. Developing a digital twin, especially for complex systems, involves significant investment. This includes installing sensors, building models, integrating AI capabilities, and ensuring cybersecurity. The initial expense can be a barrier for smaller organizations or those with tight budgets.

Cybersecurity is another critical concern. Digital twins rely on a constant flow of data, often transmitted through the internet or other networks. This data transmission can become a vulnerability, exposing the twin to potential cyber threats if not adequately secured. Keeping a digital twin accurate over time also requires continuous maintenance. Models must be regularly calibrated and adjusted to reflect changes in the physical object they represent, which can be a demanding and time-consuming process.

Why Digital Twins require special platforms

Developing and managing a digital twin demands a specialized platform due to the unique requirements of real-time data processing, integration, and visualization.

A dedicated platform offers several benefits:

• Data integration: These platforms can aggregate data from various sources—IoT devices, cloud databases, and other systems—seamlessly. This allows the digital twin to have a holistic view of the physical system.
• Advanced analytics: Many platforms include built-in analytics and machine learning tools, making it easier to apply AI to data analysis and predictions. • Scalability: A specialized platform can grow as the digital twin project expands, whether it’s adding more assets, increasing data points, or integrating additional processes.
• Security: Given the importance of data protection, dedicated platforms often include robust security features to safeguard sensitive information.
• Visualization & interaction: Digital twin platforms provide tools for visualizing the model and interacting with it in a meaningful way, such as through 3D interfaces or augmented reality.

These platforms streamline the complex process of building and maintaining a digital twin, ensuring the project is efficient, scalable, and secure.

Types of Digital Twins: from components to processes

Digital twins come in various forms, each catering to different aspects of a physical system:

1. Component Twins: These focus on individual parts or components of a system, like an engine or a pump. Component twins are often used to monitor the health and performance of critical parts, predicting failures before they occur.
2. Asset Twins: These model entire assets, such as a vehicle or a machine. Asset twins are used to track the overall health, efficiency, and lifecycle of the asset, providing valuable insights for maintenance and upgrades.
3. System Twins: System twins represent groups of assets working together, like a factory production line. They help optimize workflows, identify bottlenecks, and improve overall efficiency.
4. Process Twins: These are the most comprehensive, simulating entire processes or operations. Process twins allow organizations to see how different components and systems interact over time, providing insights that can streamline complex workflows and improve productivity. Each type of digital twin offers a unique perspective, from the granular level of individual parts to the broad view of entire processes.

Security considerations for Digital Twins

Security is a top priority when it comes to digital twins, as they involve constant data exchanges between the physical and digital realms. The level of security varies based on implementation, but several challenges need to be addressed:

• Data transmission: With data continuously flowing from the physical object to the digital twin, encryption is crucial. End-to-end encryption ensures that data remains secure during transmission, protecting it from interception.
• Integration points: Digital twins are often connected to other systems, like cloud platforms or analytics tools. Each connection point can be a potential vulnerability, so it’s vital to secure them with proper authentication and encryption measures.
• IoT device security: Many digital twins rely on IoT devices to gather real-time data, and these devices can be targets for cyberattacks. Ensuring they have up-to-date firmware, secure communication channels, and strong access controls is essential.

A multi-layered security approach, regular updates, and proactive monitoring are key to ensuring that digital twins remain secure while handling sensitive data.

Conclusion

Digital twins are revolutionizing industries by creating a bridge between the physical and digital worlds. They provide a dynamic, real-time view of physical systems, offering unprecedented opportunities for optimization, prediction, and innovation. However, they also come with challenges, from high initial costs to cybersecurity concerns. With the right platform, tools, and security measures in place, digital twins can offer significant advantages, helping organizations navigate the complexities of modern technology and stay ahead of the competition. As AI and IoT technologies continue to advance, the capabilities and applications of digital twins are only expected to grow, making them an integral part of the future of smart systems and connected industries.

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This article was produced as part of TechRadarPro's Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro

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In today's rapidly advancing technological landscape, digital transformation is a key driver of innovation across many industries. One of the most impactful technologies leading this revolution is the digital twin. This is a real-time virtual model that replicates a physical object, system, or process. It continuously receives data from its physical twin, creating a dynamic, up-to-date digital replica. This allows users to monitor, simulate, and enhance the object or system without directly interacting with the real-world version.

While the concept of a digital twin isn’t entirely new, its application has exploded due to progress in Internet of Things (IoT) devices, artificial intelligence (AI), machine learning, and big data. Across industries such as manufacturing, healthcare, smart cities, and aerospace, digital twins are helping businesses operate more efficiently, reduce costs, and improve decision-making.

This comprehensive guide explains what digital twins are, how they are used in different industries, and how they are built, while also examining their potential impact on future technology.

What is a Digital Twin?

A digital twin is a digital replica of a physical object or system that is kept in sync with its real-world counterpart. This virtual model collects real-time data through sensors, cameras, and IoT devices, providing an accurate representation of the object’s current state. A digital twin is more than a 3D model—it is a dynamic, data-driven simulation that evolves as the physical object changes over time.

One of the key features of a digital twin is its ability to simulate future scenarios. By using historical and real-time data, digital twins can model various conditions and outcomes, allowing businesses to foresee challenges, predict system failures, and optimize operations. These simulations are incredibly useful for making informed decisions without physically testing every possible outcome, saving both time and money.

The origins and evolution of Digital Twins

The idea of creating digital replicas of physical objects goes back to the early days of space exploration. NASA engineers used physical models and simulators to monitor and diagnose issues with spacecraft that were too far away to inspect directly. These early models laid the groundwork for the development of the digital twin concept.

But it wasn’t until the convergence of IoT, big data, and AI technologies that digital twins became a practical tool for mainstream use. Today, digital twins are more sophisticated than ever before. They can process vast amounts of data in real time, enabling detailed simulations and advanced analytics. As digital twins continue to evolve, they are playing a pivotal role in the Fourth Industrial Revolution (Industry 4.0), transforming sectors from manufacturing to urban planning.

What are Digital Twins used for?

Digital twins are incredibly versatile and have found applications across many industries. Below are some of the primary use cases for these tools and how they are transforming different sectors.

Manufacturing

Manufacturing is one of the largest users of digital twin technology. In this sector, digital twins are used to optimize production lines, monitor machinery, and improve product designs. By creating digital replicas of factory equipment and processes, manufacturers can simulate different production scenarios, spot inefficiencies, and predict potential breakdowns. For example, a car manufacturer might create a digital twin of an assembly line to test how adding a new robotic arm will affect workflow. By running simulations, the manufacturer can fine-tune the process to ensure the robotic arm integrates seamlessly, leading to more efficient working.

Additionally, digital twins are invaluable for predictive maintenance. By collecting data on the condition of machines—such as temperature, vibration, or pressure—they can predict when a machine is likely to break down. This allows manufacturers to schedule maintenance at the right time, cutting unplanned downtime and extending the lifespan of equipment.

Healthcare

Digital twin technology is making strides in healthcare, where it is used to model individual patients, medical devices, and biological systems. Personalized healthcare, in particular, benefits from digital twins. By creating a virtual model of a patient’s body or organ, doctors can simulate different treatment options to determine the best course of action.

For example, heart surgeons may use a digital twin of a patient’s heart to plan and simulate a procedure before performing it. This allows them to visualize the surgery and plan for potential complications, improving the chances of successful surgery.

Similarly, pharmaceutical companies use digital twins to simulate how drugs interact with the human body. This helps them develop new treatments more quickly and efficiently by testing drug reactions virtually before conducting human trials.

Medical device manufacturers also leverage digital twins to design and test products like pacemakers, joint replacements, or diagnostic machines. By using digital twins, they can ensure that devices perform optimally within the body before they are ever implanted or used.

Smart cities and urban planning

Digital twins are now playing an increasingly important role in the development of smart cities. City planners and local authorities are using digital twins to create virtual models of urban infrastructure and services, such as transport systems, energy grids, and waste management. With real-time data collected from sensors placed throughout an urban area, digital twins can help cities monitor traffic flow, energy usage, and pollution. This data allows town planners to test different strategies for improving transport networks, reducing congestion, and lowering energy consumption.

For example, local government could use a digital twin of its public transport system to simulate the impact of rerouting buses or adding new train lines. By running these models, planners can identify the best ways to cut travel times and improve service without disrupting the real-world network.

Digital twins are also instrumental in disaster response planning. By modelling how a city would be affected by natural disasters such as floods, earthquakes, or fires, emergency workers can develop better contingency plans and improve their ability to manage crises in real time.

Aerospace

In the aerospace industry, digital twins are widely used to improve aircraft design, production, and maintenance. By creating digital replicas of airplanes, engines, and other components, aerospace engineers can simulate how different factors—such as extreme weather, air pressure, or mechanical stress—will affect an aircraft over time.

One of key aspects of digital twins in aerospace is their ability to inform predictive maintenance. For example, digital twins of jet engines collect data on performance metrics like temperature, pressure, and vibration. Using this data, engineers can predict when parts are likely to wear out or malfunction, allowing airlines to perform maintenance before a problem occurs. This reduces the risk of in-flight issues and lowers operational costs. Airlines are also using digital twins to simulate flight conditions and boost fuel efficiency. By modelling various flight paths, weather conditions, and aircraft configurations, digital twins help pilots and airlines reduce fuel consumption, leading to both cost savings and environmental benefits.

Energy

Digital twins have found significant applications in the energy sector, where they are used to monitor and enhance the performance of power plants, wind farms, and solar energy systems. By creating digital replicas of these systems, operators can simulate different conditions—such as changes in weather or energy demand—and finetune operations accordingly. For example, wind farm operators use digital twins to track the performance of individual turbines. The digital twin collects data on wind speed, turbine rotation, and power output, providing insights into each turbine’s efficiency. This data helps operators identify underperforming turbines and adjust to maximize energy production.

In the case of power plants, digital twins can monitor critical elements, such as generators, cooling systems, and pipelines. By predicting when components are likely to fail, digital twins enable operators to carry out preventive maintenance, cutting downtime and improving the reliability of the energy grid.

Digital twins also help energy companies manage grid stability. With real-time data on energy consumption and generation, they allow operators to balance supply and demand more efficiently, preventing blackouts and reducing energy waste.

How to build a Digital Twin

Creating a digital twin involves several steps, from data collection to simulation and analysis. Below is a detailed explanation of each phase in the development process.

Data collection

The first step in building a digital twin is collecting data from the physical object or system. This is typically gathered using sensors, IoT devices, and control systems that measure key parameters such as temperature, pressure, speed, and vibration. In some cases, historical data may also be used to model how the object has performed over time.

For example, if you are creating a digital twin of a factory production line, you would install sensors on the machines to track their performance, energy consumption, and maintenance needs. The more data is collected, the more accurate and detailed the digital twin will be.

Create the digital model

Once the data is collected, the next step is to create a digital model of the object or system. This is often built using 3D modelling software or computer-aided design (CAD) tools. The complexity of the model will depend on the nature of the object being reproduced. For some applications, a simple 3D model might suffice, while for others, a highly detailed, physics-based simulation may be needed.

For instance, a digital twin of a wind turbine would not only include a 3D model of the turbine blades but also a simulation of how the blades interact with different wind speeds and environmental conditions.

Real-time data connection

To keep the digital twin updated, it must be connected to its physical twin through real-time data transmission. This connection ensures that the digital version evolves as the physical object changes or moves through different operating conditions.

For example, in smart cities, sensors placed throughout an urban area feed data back to the digital twin, which continuously updates itself in line with real-time conditions like traffic flow, air quality, or energy consumption.

In many cases, real-time data is transmitted via IoT platforms and processed in the cloud. Advanced AI and machine learning algorithms are often used to analyze this data, providing insights into how the physical object is performing and predicting future outcomes.

Simulation and optimization

Once the digital twin is live and connected to its physical equivalent, it can be used for simulations and optimizations. By testing different scenarios and variables on the digital model, users can identify areas which can be finetuned without affecting the real-world object. For instance, a digital twin of a factory machine could help simulate how different workloads or production speeds impact overall efficiency. Based on the findings of these models, factory managers can adjust operations to reduce bottlenecks, save energy, or cut downtime.

Digital twins can also be used for scenario planning, helping organisations to model how changes—such as new laws or market conditions—will impact their operations. By running these simulations, businesses can prepare for potential challenges and make more informed strategic decisions.

Continuous updates and maintenance

To remain accurate, digital twins must be constantly updated with real-time data and information about the physical object’s condition. This includes tracking wear and tear, repairs, and upgrades. Regular updates ensure that the digital twin remains a reliable tool for monitoring and simulation.

In the aerospace industry, digital twins of aircraft engines are regularly updated to reflect the engine’s current condition and usage history. These updates allow engineers to make accurate predictions about future maintenance needs and performance.

Understanding Digital Twins vs. traditional simulations

At the core, a digital twin is a digital replica of a physical object, system, or process, continuously updated with real-time data. Unlike traditional simulations, which run under set parameters and aren’t connected to the real world, digital twins are dynamic. They reflect the current state of their physical counterpart, using data gathered from sensors and other sources. This connection allows digital twins to provide accurate insights, predict future behaviors, and make real-time decisions.

For example, a simulation might help design a new product, testing it virtually under various conditions. However, once the simulation ends, it’s static. A digital twin, on the other hand, remains active, continuously mirroring the physical product’s lifecycle. This ongoing connection enables businesses to manage assets, troubleshoot issues, and optimize operations more effectively than a traditional simulation could.

Building a Digital Twin: timeframes and considerations

The timeline to create a digital twin depends heavily on the complexity of the object or system being modeled. For a simple asset, like a single machine or piece of equipment, a digital twin could be developed in a few weeks to a few months. The process involves setting up data collection through sensors, building the digital model, and validating its accuracy. For more complex systems—like an entire manufacturing plant or a smart city—development can take from 6 months to a year, or even longer. These large-scale digital twins require extensive data integration, advanced simulations, and rigorous testing to ensure they mirror their physical counterparts accurately.

Creating a digital twin isn’t just about initial setup; it’s an ongoing effort. A digital twin must be updated and maintained to keep it in sync with the physical world. This involves continuous data collection, periodic calibration, and refining the model to reflect any changes in the real-world object.

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