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Moving Physical Therapy Forward with IoT

Moving-Physical-Therapy-Forward-with-IoT---AI-exo-skeloton

You went for a run and sprained your ankle or pulled something in your back while lifting the couch to find the remote. With millions of injuries per year, it is likely that at some point, you’ll need to see a physical therapist. Roughly 8.6 million injuries happen a year, with many of them requiring long-term therapy care1. Some of the most common injuries that require physical therapy (PT) are soft tissue injuries like sprains, strains, or tears, with many of them occurring during sports or exercise. But as people age, wear and tear on their bodies might lead to the breakdown of various muscles and tendons. 

Devices that are part of the Internet of Things (IoT) ecosystem are increasingly used in care for customized plans and treatments, remote monitoring, and increased accessibility. IoT aims to move the world of physical therapy forward by reducing pain, allowing for increased access to exercises, and continuous remote monitoring of data and movement. 

How IoT is Changing Physical Therapy 

IoT devices are making their way into every aspect of healthcare, from fitness trackers to medical wearables to biometrics for early detection of diseases and diagnostics, and physical therapy is no exception. The goal of physical therapy is to promote, maintain, or restore health through treatments like targeted exercises, massages, movement, and more. Physical therapy aims to restore functional movement, and patients typically go through treatment plans that take weeks or months. 

IoT is changing how people get treated with customized plans, monitoring, accessibility, and more accurate treatment programs. 

Remote monitoring 

While physical therapy sessions often happen in person, many treatment plans require at-home exercises and stretching or could even replace being physically present for an appointment. Home care is just as important as in-person sessions, and IoT wearable devices such as wearable sleeves with sensors can help monitor patients’ daily movement and encourage accountability. Through user consent, these devices can send the patient health vitals or range of motion information that doctors can use to track their patients’ progress and even adjust exercises as necessary.  

Increased accessibility 

A full physical therapy plan is often an intensive time commitment. Some individuals might not be able to take off work or live in a rural area where commuting to a therapy center isn’t feasible. Wearables can help reduce these barriers by providing access that works around the patient’s schedule. Improved accessibility also increases the likelihood a patient stays committed to their treatment plan and makes a full recovery because they can do it from the comfort of their homes. 

Customized, accurate treatment plans 

IoT devices can allow for highly individualized physical therapy plans through the use of wearable sensors that capture minute details of a patient’s movement. For example, many smartwatches and smart bands these days can measure small nuances in gait, balance, and range of motion with higher accuracy than the human eye. With thousands of data points aggregated into actionable insights for physical therapists, providers can create more customized, targeted plans that improve patient outcomes and speed recovery. 

Like many other healthcare sectors, physical therapy is seeing a massive transformation in the adoption of IoT and physical therapy. 

Moving-Physical-Therapy-Forward-with-IoT---VR

Virtual and augmented reality 

Virtual reality (VR) and augmented reality (AR) are not only helping manage pain in physical therapy, but they are also creating more immersive experiences for rehabilitation. VR and AR create engaging, unique experiences for patients to try out exercises, and they’re also used to facilitate remote supervision. 

Gamification 

Research shows gamification leads to positive outcomes, and with physical therapy, the desired outcome is often a speedier recovery. This might come in the form of reward challenges or even video games in motor rehabilitation care for clinical conditions like Parkinson’s or cerebral palsy2

Real-world Applications 

Take a look at some of the companies innovating physical therapy technology through the use of wearable devices or sensors. 

Ekso Bionics 

Ekso Bionics created exoskeleton technology to help patients regain mobility after strokes and spinal cord injuries or after a diagnosis of multiple sclerosis3. Utilizing their devices, patients can improve their walking gait and balance through robotic exoskeletons. 

Cipher Skin 

Cipher Skin’s motion and biometrics tracking technology connects in-clinic therapy with at-home care and improves remote patient monitoring4. The wearable devices use advanced sensor technology in “smart sleeves” and chest motion sensors to capture motions. Real-time insights and a continuous feed of patient data are sent to providers, allowing physical therapists to adjust treatments. 

Cleveland Clinic research 

In this research study out of Cleveland Clinic, virtual reality was used with an omnichannel treadmill to create an immersive grocery store experience5. Not only was it helpful in identifying early-stage Parkinson’s, but it also helped to train patients to improve their walking gait in a natural setting. 

Outlook of IoT for Physical Therapy 

As IoT devices, virtual reality headsets, and healthcare wearable devices become more popular, costs will likely decrease and make these endpoint intelligence technologies more accessible for more clinics, providers, and patients. The global IoT market is expected to grow 23% from 2022 to 2030, highlighting the need for flexible hardware devices connecting people and data worldwide6. IoT in healthcare alone is expected to hit $348 billion in market size by 2030, and as VR and AR continue to develop, more wearables will integrate these technologies7

Artificial intelligence will also continue to improve these battery-powered, energy-efficient IoT devices with data visualizations, trends and predictive analysis, and more. 

How Ambiq is Contributing 

For over a decade, Ambiq has pioneered ultra-low powered System on Chips (SoCs) that extend the processing capability and battery life in a variety of smart devices. With a growing market for smart medical devices, manufacturers developing IoT devices for physical therapy can create more powerful wearables that offer remote monitoring, increased accessibility to patients, better treatment plans, and more positive outcomes. Learn more about Ambiq products here

Sources: 

1 Sports and Recreation-Related Injuries Top 8.6 Million Annually | January 4, 2017 

2 Gamification in Musculoskeletal Rehabilitation | October 27, 2022 

3 Ekso Bionics | 2023 

4 Cipher Skin | 2023 

5 The Immersive Cleveland Clinic Virtual Reality Shopping Platform for the Assessment of Instrumental Activities of Daily Living | July 28, 2022 

6 IoT Market size growing with a CAGR of 23.10%: Growth Outlook from 2022 to 2030, projecting market trends analysis by Application, Regional Outlook, and Revenue | December 19, 2023 

7 IoT in Healthcare Market Size & Share Analysis – Growth Trends & Forecasts (2024 – 2029) | 2023 

Benefits-of-Computing-AI-at-the-Endpoint---central-devices

In an era dominated by interconnected technologies, integrating Artificial Intelligence (AI) and computing on local Internet of Things (IoT) — connected devices is revolutionizing how we perceive and interact with smart devices. This integration, often called local device AI or endpoint AI computing, is an increasingly popular framework for efficiently collecting and processing data independent of the Cloud. With IOT and smart device adoption estimated to increase from 15.1 billion to 34.6 billion over the next decade1, endpoint AI shows promise for enhancing the power and intelligence of these devices. 

Here, we’ll explore the intricacies of endpoint AI, including its benefits, challenges, and the promising outlook for enabling intelligent devices everywhere. 

What Is Endpoint AI? 

With an AI approach, developers deploy AI algorithms and models directly on local devices, including sensors or IoT devices, which then collect and process data locally. This convergence of on-device intelligence and machine learning (ML) capabilities provides new grounds for the potential to unlock new use cases and applications. 

Benefits of Computing on a Local Device 

This departure from traditional cloud-based approaches brings intelligence closer to the source of data, providing many advantages. 

Enhanced Privacy and Data Security 

Implementing AI on local devices empowers users with greater control over their data. Processing sensitive information locally alleviates privacy concerns and reduces the likelihood of data exposure to external networks. By moving AI to local devices, organizations responsible for maintaining the safety of their customers’ personally identifiable information (PII) are better protected. 

On the devices, this data isn’t exposed to the servers of cloud service providers and other third parties, ensuring better compliance with local and international regulations around data protection. This approach mitigates the risks associated with data breaches and unauthorized access, providing a robust solution for applications requiring heightened security. 

Faster Processing 

The benefits of cloud-based AI come at a steep cost. Language models like GPT-3, the system upon which OpenAI built ChatGPT, require tremendous compute power to process. OpenAI had to activate traffic management strategies like a queuing system and slowing down queries to handle the surge in demand after ChatGPT’s launch2. This incident highlights how compute power is becoming a bottleneck, limiting the advancement of AI models. 

Local device AI significantly reduces latency, leading to faster processing times. Applications benefit from quicker response times, enabling real-time decision-making, especially in critical scenarios such as autonomous vehicles or smart home devices. Decentralized processing also means that insights are generated in real-time with less latency than if the device had to send data to the Cloud to be processed and listen for a response. 

Benefits-of-Computing-AI-at-the-Endpoint---smartwatch

Enhanced User Experience 

Reduced latency and improved processing speed provide a seamless and responsive user experience. Real-time feedback becomes possible, resulting in higher user satisfaction and engagement. Endpoint AI can process and analyze user data locally to create personalized experiences without relying on centralized servers3

This leads to more responsive and tailored services, such as personalized recommendations and content delivery for cases like shopping lists, fitness apps, and meal recommendations. This type of personalized AI can more effectively captivate users and increase their engagement with content and experiences that resonate with their interests. 

Reduced Dependence on the Cloud 

While the Cloud has enormous power for collecting and processing data, it is susceptible to threats like hacking or outages and may not be available in areas with limited internet access4. Reducing reliance on the cloud with endpoint AI devices not only enhances their performance but also promotes their security from external threats. This also increases their resilience in scenarios with limited internet connectivity, particularly significant for applications in remote areas or environments with intermittent network access. 

Challenges of AI on Local Devices 

Some barriers exist that may limit the full potential of endpoint AI. These include hardware limitations, memory requirements, and power restrictions. AI models, particularly deep learning models, often demand substantial memory resources. Local devices often have constrained processing power, posing a challenge in implementing resource-intensive AI algorithms. AI computations are also power-intensive, impacting the battery life of local devices. 

Mobile devices are more restricted around computing resources, memory, storage, and power consumption. As a result, on-device models need to be much smaller than their server counterparts, which can make them less powerful5. Striking a balance between functionality and resource consumption is a key challenge in designing AI-enabled devices. 

The Outlook of AI on Local Devices 

Despite these challenges, ongoing advancements in hardware design and optimization techniques are steadily overcoming obstacles. Technologies such as edge computing and energy-efficient processors pave the way for more efficient local device AI implementations6

The benefits of AI and computing on local IoT-connected devices are reshaping the technological landscape, offering improved privacy, security, speed, and user experiences. While challenges exist, continuous research and innovation are overcoming these obstacles. The outlook for deploying AI on local devices appears promising, enabling a new era of intelligent devices seamlessly integrated into our daily lives. 

How Ambiq is Contributing 

Since 2010, Ambiq has successfully helped smart devices perform complex inferencing tasks such as AI at the endpoint, with their ultra-low powered semiconductor solutions. The revolutionary subthreshold power optimization technology (SPOT) platform has helped solve the power constraints smart device manufacturers run into when developing sophisticated and power-hungry features. As a result, developers can expect smooth performance with a battery life that goes for days, weeks, or months on a single charge. See more applications of Ambiq

Sources 

1 Edge AI | October 7, 2023 

2 Compute power is becoming a bottleneck for developing AI. Here’s how you clear it. | March 17, 2023 

3 Getting personal with on-device AI | October 11, 2023 

4 Challenges of Privacy in Cloud Computing | December 2022 

5 Why On-Device Machine Learning | 2024 

6 Interview With Scott Hanson – Founder and CTO at Ambiq | January 4, 2024 

Putting-AI-in-the-Driver’s-Seat-for-Traffic-Management---Intersection

How many hours do you spend in traffic a year? Probably more than you realize. While a few minutes every day might not seem like a lot at the moment, it adds up over time. According to transportation analytics firm INRIX, the average U.S. motorist spent 51 hours in traffic in 20221. That’s more than an entire workweek. The same study found that traffic jams cost U.S. drivers more than $81 billion during 2022. Long story short, traffic is a major drain on consumers’ time and resources. 

The good news? Innovations in the Internet of Things (IoT) and artificial intelligence (AI) have paved the way for new solutions that would introduce smarter traffic mitigation and make the roads safer. From accident detection to traffic light management, AI offers the transportation industry a range of opportunities to beat the traffic. In this article, we’ll outline how the intersection of AI and IoT can optimize road operations and ultimately give people their time and money back. 

Tech Innovations for Traffic Optimization 

While the use of AI in traffic management is still in its early stages, a range of applications already allows for real-time monitoring and predictive analytics. Below, we outline key tech innovations that are making traffic innovation possible. 

Flow Sensors 

Traffic management has traditionally operated on a fixed schedule. Now, AI-powered sensors can be deployed in streets to monitor demand and adapt traffic signals based on shifts to optimize the flow. These sensors ultimately reduce congestion during peak times by prioritizing high-traffic roads. 

Predictive Monitors 

With mounds of historical and real-time data, traffic monitors can be trained to understand traffic patterns and predict traffic flow at a given time. This can be used to forecast future conditions so that traffic personnel can better allocate resources, optimize routes, and adjust traffic signals. 

Incident Monitors 

AI-powered monitors can be deployed to watch for and identify traffic incidents, including accidents, speeding, or blockages. If detected, this data can be sent to the correct parties to dispatch support and resolve hold-ups more quickly. 

Players Leading AI-Powered Traffic Management 

So, what companies are fueling this innovation…and how? Below, we examine several leaders in AI-powered traffic management and their real-world applications. 

INRIX 

INRIX offers instantaneous real-time traffic conditions, pinpointing traffic speeds in different lanes and delivering accurate ETAs for any road in the world, including interstates, country roads, and intersections 2. Additionally, they leverage vast datasets to provide insights into problem areas and trends in traffic conditions that can help users get back more of their time or allow the transportation aspect of businesses to run more profitably. 

Canon 

Canon, one of the top manufacturers of consumer cameras, is leading the trend in support of computer vision technology that can be used in traffic mitigation with its complementary metal oxide semiconductor (CMOS) sensors3. The Canon CMOS sensors are highly sensitive and power-accurate imaging for traffic management automation. They can provide clarity in different light conditions and with varying speeds of objects, which can help governments calibrate their traffic signal coordination for ramp meters and red-light cameras. 

Putting-AI-in-the-Driver’s-Seat-for-Traffic-Management---Traffic-Camera

Miovision 

Miovision collects multimodal traffic data and delivers actionable, real-time insights for urban grids and corridors4. This second-by-second response to dynamic conditions eliminates driver stops, reduces wait time, and improves travel speeds. The result is less congested roads and even reduced CO2 emissions as there won’t be as many automobiles on the road. 

Challenges in AI-Powered Traffic Management 

With any new technology, there are challenges to widespread adoption that can only be addressed with time. Let’s examine challenges in AI-powered traffic management. 

Privacy 

Privacy must remain at the forefront of all traffic management technology to ensure industry and consumer buy-in. Data and imaging collected on the roads must be anonymous and protected, including compliance with global privacy regulations. 

Energy Efficiency 

Like many battery-powered IoT devices, traffic monitors and sensors have limitations when it comes to energy efficiency. To process large amounts of data, these devices require significant processing power and, thus, battery power. Implementing these technologies shouldn’t require several battery changes throughout the week. 

Accuracy 

As with any device placed outdoors, there’s a high likelihood that external factors, such as earthquakes, storms, and extreme weather conditions, can impact device accuracy and, ultimately, the intelligence of those devices. Additionally, these devices are tracking moving objects, which presents an accuracy challenge within itself. Continued development and processing power could help offset some of these constraints. 

The Future of AI-Powered Traffic Management 

The intersection of AI and IoT is already facilitating easier and faster coordination across the range of systems contributing to traffic operations. As adoption increases, these enhanced sensors and monitors will ultimately be fully interoperable, allowing for greater optimization down the line. 

How Ambiq is Contributing 

Sophisticated sensors and monitors that can reduce the dread of driving in traffic is a win for motorists, granted these devices can overcome the energy challenges associated with them. Fortunately, Ambiq, specializes in creating ultra-low power system-on-chips (SoCs) that provide long-lasting battery life and processes complex data with greater energy efficiency. Continuous monitoring is possible, and these innovations will allow cities to become more effective in managing traffic conditions for drivers.  

Sources: 

1 Cities where motorists lose the most time and money | August 2023 
2 Real Time Traffic Data [Powered by Artificial Intelligence] | INRIX | 2023 
3 Why Choose Canon CMOS Sensors | Canon USA | 2023 
4 Miovision TrafficLink – Smart cities start with smart signals | 2023 

Revolutionizing-Recycling-with-AI---Robot-with-trashbag

Recycling, when done effectively, can significantly impact environmental sustainability by conserving valuable resources, contributing to a circular economy, reducing landfill waste, and cutting energy used to produce new materials. However, the initial progress of recycling in nations like the United States has largely stalled to a current rate of 32 percent1 due to problems around consumer knowledge, sorting, and contamination.  

Artificial intelligence (AI), machine learning (ML), robotics, and automation aim to increase the effectiveness of recycling efforts and improve the country’s chances of reaching the Environmental Protection Agency’s goal of a 50 percent recycling rate by 2030. Let’s look at common recycling problems and how AI could help. 

What Is Contamination in Recycling? 

As one of the biggest problems facing effective recycling programs, contamination happens when consumers place materials into the wrong recycling bin (such as a glass bottle into a plastic bin). Contamination can also occur when materials aren’t cleaned properly before the recycling process. 

Today’s recycling systems aren’t designed to deal well with contamination. According to Columbia University’s Climate School, single-stream recycling—where consumers place all materials into the same bin leads to about one-quarter of the material being contaminated and therefore worthless to buyers2

Industry insiders also point to a related contamination problem sometimes referred to as aspirational recycling3 or “wishcycling,4” when consumers throw an item into a recycling bin, hoping it will just find its way to its correct location somewhere down the line. 

This, unfortunately, rarely happens. Here’s why: 

Recycling Breaks Down 

When the number of contaminants in a load of recycling becomes too great, the materials will be sent to the landfill, even if some are suitable for recycling, as it costs extra money to sort out the contaminants. Recycling materials have value aside from their benefit to the planet. Contamination reduces or eliminates the quality of recyclables, giving them less market value and further causing the recycling programs to suffer or resulting in increased service costs. 

In addition, Americans throw nearly 300,000 tons of shopping bags away each year5. These can later wrap around the parts of a sorting machine and endanger the human sorters tasked with removing them. When consumers throw non-recyclable materials into sorting bins, they can also expose workers to hazardous waste, vector-borne diseases, and other dangerous items. 

How AI Could Help 

Fortunately, several researchers, startups, and manufacturers are developing innovations fueled by AI to improve the effectiveness of recycling programs. 

Pello Cuts the Plastic 

Pello Systems has created a system of sensors and cameras to help recyclers reduce contamination by plastic bags6. The system uses AI, ML, and advanced algorithms to identify plastic bags in photos of recycling bin contents and provide facilities with high confidence in that identification. 

By identifying and removing contaminants before collection, facilities save vendor contamination fees. They can improve signage and train employees and consumers to reduce the number of plastic bags in the system. 

TrashBot Cleans Up 

The TrashBot, by Clean Robotics, is a smart “recycling bin of the future” that sorts waste at the point of disposal while providing insight into proper recycling to the consumer7. Through AI, ML, robotics, and computer vision, the Trashbot diverts each deposited item into its proper bin inside, assigning contaminated items to landfill bins or organics into their corresponding bin. 

Trashbot also uses a consumer-facing screen that provides real-time, adaptable feedback and custom content reflecting the item and recycling process. In addition to this educational feature, Clean Robotics says that Trashbot provides data-driven reporting to its users and helps facilities boost their sorting accuracy by 95 percent, compared to the typical 30 percent of conventional bins. 

Revolutionizing-Recycling-with-AI----Plastic-analysis

Oscar Sorts It Out 

Intuitive AI, a Canadian startup, has introduced Oscar Sort, an AI-driven, intuitive, “smart recycling assistant” trained to identify a broad spectrum of beverage and food containers8. Consumers simply point their trash item at a computer screen, and Oscar will tell them if it’s recyclable or compostable. 

Adaptable to existing waste and recycling bins, Oscar Sort can be customized to local and facility-specific recycling rules and has been installed in 300 locations, including university cafeterias, sports stadiums, and retail stores. 

AMP Cortex Doubles the Picks 

AMP Robotics has built a sorting innovation that recycling programs could place further down the line in the recycling process. Their AMP Cortex is a high-speed robotic sorting system guided by AI9

AMP’s AI platform uses computer vision to recognize patterns of specific recyclable materials within the typically complex waste stream of folded, smashed, and tattered objects. Their robots perform physical tasks of sorting, picking, and placing materials to achieve what they say has a 99 percent accuracy and 80 picks per minute (the average human makes roughly 40 picks per minute.) 

The Outlook of AI in Recycling Management 

As AI continues to make strides in recycling management, the outlook is promising: 

AI-driven sensors and robotics will provide real-time data analytics, enabling recycling facilities to make data-based decisions for process optimization. This will likely expand into other areas, such as predictive maintenance, supply chain optimization, and adaptive recycling strategies. The widespread adoption of AI in recycling has the potential to contribute significantly to global sustainability goals, reducing environmental impact and fostering a more circular economy. 

As innovators continue to invest in AI-driven solutions, we can anticipate a transformative impact on recycling practices, accelerating our journey towards a more sustainable planet. 

How Ambiq is Contributing 

Utilizing key technologies like AI to take on the world’s larger problems such as climate change and sustainability is a noble task, and an energy consuming one. Performing AI and object recognition to sort recyclables is complex and will require an embedded chip capable of handling these features with high efficiency. 

Ambiq creates a wide range of system-on-chips (SoCs) that support AI features and even has a start in optical identification support. Implementing sustainable recycling practices should also use sustainable technology, and Ambiq excels in powering smart devices with previously unseen levels of energy efficiency that can do more with less power. Learn more about the various applications Ambiq can support

Sources 

1 America Recycles Day | 2023
2 Recycling in the U.S. Is Broken. How Do We Fix It? | March 13, 2020&
3 The Waste of Aspirational Recycling | February 6, 2023
4 What Is Wishcycling? Two Waste Experts Explain | January 21, 2022
5 Recycling Statistics | 2023
6 Pello Systems | 2023
7 Clean Robotics | 2023
8 Intuitive AI | 2023
9 AMP Robotics | 2023

Ambiq®, a global leader in ultra-low-power semiconductor solutions that deliver a multifold increase in energy efficiency, is pleased to announce that Fumihide Esaka, CEO of Ambiq, has been named a recipient of the prestigious Entrepreneur 100 Award 2023, presented by the Association of Trade and Commerce (ATC). This award recognizes Esaka’s outstanding contributions and achievements as a trailblazing entrepreneur within the business community. 

The Entrepreneur 100 Award is an annual accolade that acknowledges Singapore’s emerging entrepreneurs of the year who have demonstrated exemplary leadership, innovation, and dedication to their respective industries. The award is highly competitive and serves as a benchmark for excellence in entrepreneurship. 

Ambiq, led by Fumihide Esaka, stood out among a remarkable pool of applicants due to our unwavering commitment to innovation, business excellence, and profound impact on the semiconductor industry and its community.  

“This recognition would not be possible without the dedication and vision of everyone within Ambiq working tirelessly to deliver transformative technology that redefines what we know about energy efficiency in endpoint devices,” said Fumihide Esaka. “More, I am impressed with the progress Ambiq has made since we’ve expanded our presence in Singapore.  The high level of innovation has been a great addition and allows us to operate at an efficiency we’ve never seen before.”  

Earlier this year, Ambiq expanded its operation in Singapore to accelerate the development of its integrated circuit design, a move that strengthens its commitment and investments in recruiting regional talent. 

About the Association of Trade & Commerce (Singapore)  

The Association of Trade & Commerce (Singapore) (ATC) is a community organization that was established to congregate and represent Singapore’s businesses and entrepreneurs across major industries and trade sectors, on both local and international scale. ATC advocates actively for the interests of enterprises, focusing in the areas of development, capabilities, sustainability and community building. For all the people across the businesses that we represent, ATC is positioned as a trusted advocate, partner and network; working with the community to develop and improve businesses, the society and people’s lives. 

About Ambiq   

Ambiq’s mission is to develop the lowest-power semiconductor solutions to enable intelligent devices everywhere by developing the lowest-power semiconductor solutions to drive a more energy-efficient, sustainable, and data-driven world. Ambiq has helped leading manufacturers worldwide develop products that last weeks on a single charge (rather than days), while delivering a maximum feature set in compact industrial designs. Ambiq’s goal is to take Artificial Intelligence (AI) where it has never gone before in mobile and portable devices, using Ambiq’s advanced ultra-low power system on chip (SoC) solutions. Ambiq has shipped more than 230 million units as of October 2023. For more information, visit www.ambiq.com

Contact 

Charlene Wan 

VP of Branding, Marketing and Investor Relations 

[email protected] 

+1.512.879.2850 

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Ambiq’s CEO Fumihide Esaka Wins the Singapore Entrepreneur 100 Award 
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