Author Archives: Krystian Weissgerber

30 January 2023
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Business

Novel Carbon Capture System Turns Carbon Into Methanol

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Source: Eric Francavilla, Pacific Northwest National Laboratory, https://www.pnnl.gov/news-media/scientists-unveil-least-costly-carbon-capture-system-date

Carbon capture and storage (CCS) systems are a promising technology for reducing carbon dioxide (CO2) emissions from power plants and industrial facilities. CO2 is one of the main greenhouse gases responsible for global warming, and reducing emissions from these sources is crucial for achieving global climate goals.

There are two main types of carbon capture systems: post-combustion and pre-combustion. Post-combustion capture involves capturing CO2 after it has been released from the combustion process, while pre-combustion capture captures CO2 before it is released.

Post-combustion capture systems use various technologies, such as amine solvents, to capture CO2 from the flue gas produced by power plants and industrial facilities. The captured CO2 is then compressed and transported to a storage site, typically an underground geological formation. One of the main advantages of post-combustion capture is that it can be retrofitted to existing power plants, allowing for a relatively quick deployment of the technology.

Pre-combustion capture systems, on the other hand, capture CO2 before it is released by converting fossil fuels into a mixture of hydrogen and CO2. The CO2 is then separated and captured, while the hydrogen is used as a fuel. Pre-combustion capture systems are typically more efficient than post-combustion systems, but they require the construction of new power plants or the retrofitting of existing ones.

The cost of carbon capture is a major barrier to the widespread deployment of the technology. However, costs have been decreasing in recent years, and it is expected that they will continue to decrease as the technology is further developed and deployed. Additionally, government policies, such as carbon pricing and subsidies, can help to reduce costs and encourage the deployment of carbon capture systems.

Source: Andrea Starr, Pacific Northwest National Laboratory, https://www.pnnl.gov/news-media/scientists-unveil-least-costly-carbon-capture-system-date

A recent breakthrough post-combustion carbon capture system unveiled by the researchers from the Pacific Northwest National Laboratory promises to cut those costs significantly by converting the captured carbon into methanol. Methanol being one of the most produced chemicals in the world, utilized in the production of many everyday items such as plastics, paints, construction materials and biofuels.

The sales from the created methanol can generate meaningful revenues for power plants, further decreasing the costs of running carbon capture systems. PNNL scientists believe that this form of carbon recycling, can boost the development and adoption of CCS around the world. The new system is intended to work with the flue gas emitted by coal, gas and, biomass power plants.

Innovation in the carbon capture sector is essential in the fight against climate change. The International Energy Agency (IEA) estimates that CCS could reduce CO2 emissions by up to 10 Gt by 2050, which is equivalent to about one-fifth of the total emissions reduction needed to meet the Paris Agreement targets.

The new carbon capture system created by Pacific Northwest National Laboratory researchers is a promising technology that could play a vital role in the fight against climate change. Its high selectivity, stability, and lower cost of production make it an attractive option for large-scale carbon capture and storage. With further research and development, this technology could become a vital tool in the effort to reduce carbon emissions and slow down the effects of global warming.

Bibliography:

Bane, Brendan. “Scientists Unveil Least Costly Carbon Capture System to Date.” PNNL. Published January 23, 2023. https://www.pnnl.gov/news-media/scientists-unveil-least-costly-carbon-capture-system-date

IEA. “Carbon capture, utilisation and storage.” IEA. Accessed January 25, 2023. https://www.iea.org/fuels-and-technologies/carbon-capture-utilisation-and-storage

IEA. “Pathway to critical and formidable goal of net-zero emissions by 2050 is narrow but brings huge benefits, according to IEA special report.” IEA. Published May 18, 2021. https://www.iea.org/news/pathway-to-critical-and-formidable-goal-of-net-zero-emissions-by-2050-is-narrow-but-brings-huge-benefits

Kothandaraman, Jotheeswari et al. “Integrated Capture and Conversion of CO2 to Methanol in a Post-Combustion Capture Solvent: Heterogeneous Catalysts for Selective C-N Bond Cleavage.” Advanced Energy Materials. Published October 3, 2022. https://onlinelibrary.wiley.com/doi/10.1002/aenm.202202369

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22 January 2023
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Sharing economy, Software

The Environmental Impact of Computational Science

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Source: Kingston Technology https://www.kingston.com/pl/blog/servers-and-data-centers/4-things-data-centers-can-learn-from-hpc

Computational science has revolutionized the way we understand and interact with the world around us. From simulating weather patterns to modeling the behavior of subatomic particles, computational methods have enabled scientists to make breakthroughs that would have been impossible just a few decades ago. However, as our reliance on computational methods has grown, so too has the carbon footprint of this field.

According to a recent study published in the journal Nature Climate Change, the carbon footprint of computational science is much larger than previously thought. The study estimates that the energy consumption of high-performance computing (HPC) centers, which are used to run large-scale simulations and analyses, is equivalent to that of a small country. In fact, the study estimates that the carbon footprint of HPC centers is larger than that of the entire aviation industry.

The reason for this large carbon footprint is the enormous amount of energy required to power and cool the thousands of computer processors and storage devices that make up HPC centers. These machines use a tremendous amount of electricity to run, and even more to cool, which generates significant amounts of greenhouse gases.

The study’s authors also point out that the carbon footprint of computational science is likely to grow in the future as the demand for HPC services increases. This is due in part to the growing amount of data that scientists need to process and analyze, as well as the increasing complexity of simulations and models.

Loïc Lannelongue, the author of the study was curious about the environmental impact of his own research. Together with Jason Grealey from the University of Melbourne, they decided to look further into ways of calculating the carbon footprint of HPC centers. They expected to find an already established carbon footprint calculator online. Which to their surprise, they did not. It lead them to believe that the environmental impact of HPC centers is a phenomon that many scholars aren’t aware of or are not taking into consideration when utilizing HPC centers for their work.

Source: Texas Advanced Computing Center https://cen.acs.org/physical-chemistry/computational-chemistry/Computational-scientists-look-lessons-learned/99/i28

Inspired to fill this gap in the scientific community, Lannelongue created Green Algorithms. A website that encourages other researchers to be mindful of the carbon footprint of computational science by imparting knowledge on how to reduce your impact while also providing a high performance computing carbon footprint calculator for anyone to access. Lannelongue doesn’t hope to stop researchers from using HPC centers, but instead to be more mindful of their impact. For which the calculator is a perfect tool for.

While the carbon footprint of computational science may seem daunting, on the green algorithms website Lannelongue provides steps that can be taken to reduce it. One approach is to make HPC centers more energy efficient by using more efficient processors and storage devices, as well as by using more efficient cooling systems. Additionally, scientists can also reduce their carbon footprint by using cloud-based HPC services, which allow them to access large amounts of computing power without the need to build and maintain their own HPC centers.

Another approach is to use distributed computing methods, which allow scientists to tap into the computing power of thousands of individual computers, rather than relying on a single HPC center. This can significantly reduce the energy consumption and carbon footprint of computational science.

In conclusion, computational science plays a vital role in advancing our understanding of the world around us, but it also has a significant carbon footprint. It is important for scientists, policymakers, and other stakeholders to be aware of this footprint and to take steps to reduce it. By making HPC centers more energy efficient, using cloud-based HPC services, and using distributed computing methods, we can continue to make breakthroughs in science while also reducing our impact on the environment.

Bibliography:

Brierley, Craig. “Big data’s hidden cost: The carbon footprint of computational science.” TechXplore. Published January 20, 2023. https://techxplore.com/news/2023-01-big-hidden-carbon-footprint-science.html

Green Algorithms. “Carbon Footprint Calculator”. Green Algorithms Accessed January 22, 2023. https://www.green-algorithms.org/

Lannelongue, Loïc. “Carbon footprint, the (not so) hidden cost of high performance computing.” BCS. Published October 14, 2021. https://www.bcs.org/articles-opinion-and-research/carbon-footprint-the-not-so-hidden-cost-of-high-performance-computing/

10 January 2023
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AI, Open Source, Programming languages, Software

Deepmind’s AlphaCode Satisfactory in a Programming Competition

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Source: Maciek905/Dreamstime stock image

AI code generation systems are a type of artificial intelligence technology that is capable of automatically generating code. These systems have the potential to revolutionize the way software is developed, making it faster and more efficient.

One of the main benefits of AI code generation systems is their ability to save time. These systems can analyze a given problem and automatically generate a solution in the form of code. This can significantly reduce the amount of time it takes for developers to write code from scratch. Additionally, these systems can often generate code that is more efficient and optimized than code written by humans, which can lead to faster and more reliable software.

Another benefit of AI code generation systems is their ability to improve the accuracy and reliability of code. By analyzing a problem and generating a solution, these systems can help eliminate human error that can lead to bugs and other issues in software. This can help reduce the time and resources needed for debugging and testing, which can save money and improve the overall quality of the software.

One of the main challenges of AI code generation systems is their reliance on data. These systems need large amounts of data to learn and generate code, which can be a problem if the data is not available or is of poor quality. Additionally, these systems are only as good as the algorithms and models they are based on, and it can be difficult to design and train these models to generate high-quality code.

Despite these challenges, there has been significant progress in the development of AI code generation systems in recent years. One example is the development of “neural machine translation” systems, which are capable of automatically translating text from one language to another. These systems have been able to achieve impressive levels of accuracy, and they have been widely adopted in a variety of industries.

Another example is the development of “auto-coding” systems, which are capable of generating code for a variety of programming languages. These systems have the potential to significantly reduce the time and effort required to develop software, and they are being explored by a number of companies and organizations.

Examining the abilities of AI code generation systems can be tricky. One means of doing so is to place the system in a programming competition against regular human programmers. A recent experiment of that kind was performed by Deepmind. Deepmind, a subsidiary of Alphabet Inc. is a trailblazing artificial intelligence research laboratory. The experiment was carried out with the use of its AlphaCode deep learning algorithm. AlphaCode converts user input into functioning code by first rewriting it as an action plan. It transforms it into set steps and finally turns it into fully working code. AlphaCode achieved an ‘average’ rating in the competition. A promising acceleration for AI code generation systems.

Overall, AI code generation systems have the potential to revolutionize the way software is developed. These systems can save time and improve the accuracy and reliability of code, and they have already made significant progress in a number of areas. However, there are still challenges to be addressed in terms of data availability and model design, and it will be interesting to see how these systems continue to evolve and improve in the coming years.

Bibliography:

DeepMind. “Competitive programming with AlphaCode.” Deepmind. Published December 8, 2022. https://www.deepmind.com/blog/competitive-programming-with-alphacode

Li, Yujia et al. “Competition-level code generation with AlphaCode.” Science. Published December 8, 2022. https://www.science.org/doi/10.1126/science.abq1158

Kolter, J. Zico. “AlphaCode and “data-driven” programming.” Science. Published December 8, 2022. https://www.science.org/doi/10.1126/science.add8258

Deepmind. “AlphaCode Attention Visualization.” Deepmind. Accessed January 9, 2023. https://alphacode.deepmind.com/

23 December 2022
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Business, Devices, Sharing economy

Drone Transplant Delivery in Mass Just Around The Corner

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Source: Getty Images

Drones have revolutionized the way we think about transportation and delivery systems. These unmanned aerial vehicles (UAVs) have the ability to fly over traffic and other obstacles, making them an efficient and cost-effective option for delivering goods. In recent years, drones have even been used to deliver life-saving transplants to patients in need.

One of the main advantages of using drones to deliver transplants is the speed at which they can travel. In emergency situations, every minute counts and traditional modes of transportation can be slowed down by traffic and other delays. Drones, on the other hand, can bypass these obstacles and reach their destination quickly and efficiently.

Another advantage of using drones to deliver transplants is their ability to reach remote or hard-to-access locations. Many transplant recipients live in rural areas or areas that are difficult to reach by car or ambulance. Drones can fly over these obstacles and deliver the transplant to the patient in a timely manner.

Source: University of Maryland Medical School

There are also cost benefits to using drones to deliver transplants. Traditional methods of transportation, such as ambulances and helicopters, can be expensive and may not always be available. Drones, on the other hand, can be deployed quickly and at a lower cost.

Despite the many benefits of using drones to deliver transplants, there are also some challenges that need to be addressed. One of the main challenges is ensuring the safety of the transplant during the delivery process. Transplants are often fragile and can be damaged during transit. To address this issue, some companies are developing specialized drones with temperature-controlled compartments and other features to ensure that the transplant stays safe and viable during the delivery process.

When it comes to that matter, a recent proof-of-concept flight organized by a team of researchers from Toronto General Hospital Research Institute, Techna, University Health Network and Unither Bioelectronics has displayed the feasibility of overcoming these challenges.

Source: Unither Bioelectronics, Bromont QC

The flight was taken by a Chinese made M600 Pro drone, a commercial device of a price of only 600$. The team exchanged some parts for ones that provide improved connectivity. While also attaching external apparatus like a parachute, lights, GPS trackers, cameras and a recovery system. Lastly they connected the transplant secure box on the bottom of the drone.

Then, after testing the drone 400 times they considered it ready for the real test. The drone was tasked with delivering a donated lung from Toronto Western Hospital to Toronto General Hospital. The two kilometer flight was a success and the organ was implanted into the patient in need.

However, there’s more challenges this innovation still faces. One of them are regulatory hurdles. While many countries have regulations in place for drones, there are still some legal and regulatory issues that need to be addressed before drones can be widely used to deliver transplants. For example, some countries have strict rules about drones flying over certain areas or at certain altitudes, which can make it difficult to use drones for transplant delivery.

Despite these challenges, the use of drones to deliver transplants is a promising area of research and development. In the future, it is likely that drones will play a larger role in the delivery of transplants and other medical supplies. As technology continues to advance and regulatory issues are addressed, we may see drones becoming a more common sight in our skies, delivering life-saving transplants to patients in need.

Bibliography:

Yirka, Bob. “Proof-of-concept drone flight delivers transplant lung to patient in Toronto.” Tech Xplore. Published December 22, 2022. https://techxplore.com/news/2022-12-proof-of-concept-drone-flight-transplant-lung.html

Sage, T. Andrew et al. “Testing the delivery of human organ transportation with drones in the real world.” Science Robotics. Published December 21, 2022. https://www.science.org/doi/10.1126/scirobotics.adf5798#tab-contributors

Freeman, David. “A drone just flew a kidney to a transplant patient for the first time ever. It won’t be the last.” NBC News. Published May 3, 2019. https://www.nbcnews.com/mach/science/drone-just-flew-kidney-transplant-patient-first-time-ever-it-ncna1001396

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10 December 2022
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AI, Security

Blinding Autonomous Vehicles with Lasers

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Source: Sara Rampazzi/University of Florida

Autonomous vehicles, a medium of transport that many believe will become the standard in the future. In recent years, the progress in the development of this type of technology has risen exponentially. Self-driving cars are already superior to humans in certain abilities that affect the safety of transportation. Primarily their faster reaction time. They also do not suffer from hindered focus due to tiredness, distractions or use of alcohol like we do.

However, autonomous vehicles are still inferior to human drivers when it comes to recognizing the situation on the road. There are three main sensor systems through which self-driving cars collect the information regarding the space around them: camera, radar, and LiDAR systems.

LiDAR stands for Light Detection and Ranging. It works by sending a laser light, capturing the reflected light from objects around it and then, calculating the distance to said objects thanks to the recorded time of flight of the laser light. Its widely believed to be crucial in making cars fully autonomous in the future.

Thanks to researchers from the University of Florida, the University of Michigan and the University of Electro-Communications in Japan this sensor can be improved through their discovery of an alarming error that can be maliciously caused by third parties.

Source: Sara Rampazzi/University of Florida

They were first to discover that a laser light aimed at a LiDAR system can imitate the systems own reflected LiDAR laser creating a blind spot in its virtual map of objects. The created blind spot can erase obstacles, crossing pedestrians or even other vehicles from its view. It is not hard to imagine how that could lead to tragic consequences on the road.

In their experiment they setup the third-party laser 4.5 meters from the theoretical road aimed at a stationary LiDAR sensor on top of a vehicle. A person walked in front of the vehicle providing data for the system. After analyzing the LiDARs map of objects data, they were able to find that the sensor had no problem with detecting the pedestrian at the start. But, as soon as he walked into the 8 ° range of the attack region he was immediately removed from the systems view. Upon leaving the range of the blind spot the sensor was again able to detect the pedestrian in its view.

The attack can be recreated by others without much difficulty with the tracking of the LiDAR system on top the car being the hardest part. The choice of a laser that can emulate the sensors reflected lights is not much trouble as LiDAR sensor producers publish their sensors technicalities publicly.

As troubling as this discovery may seem. I am personally grateful that this type of vulnerability has been exposed that early into the development of self-driving cars. Giving us plenty of time for improvement before they freely roam our streets. A future where movie villains could just aim a laser at a car approaching their target is not something that any of us would want to be a part of.

Bibliography:

Yuen, Desmond. “Can You React Faster than a Self-Driving Car on 5G Networks?” MEDIUM. Published January 30, 2021. https://medium.com/predict/making-roads-safer-with-self-driving-cars-and-5g-c1e28526362c

University of Florida. “Laser attack blinds autonomous vehicles, deleting pedestrians and confusing cars” TechXplore. Published October 31, 2022. https://techxplore.com/news/2022-10-laser-autonomous-vehicles-deleting-pedestrians.html

Cao, Yulong. Bhupathiraju, S. Hrushikesh. Naghavi, Pirouz. Sugawara, Takeshi. Mao, Z. Morley. Rampazzi, Sara. „You Can’t See Me: Physical Removal Attacks on LiDAR-based Autonomous Vehicles Driving Frameworks.” arXiv. Last revised October 27, 2022. https://arxiv.org/abs/2210.09482

Synopsys. “LiDAR.” Synopsys. Accessed December 9, 2022. https://www.synopsys.com/glossary/what-is-lidar.html