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Cloud Computing IaaS In Life Science Market Size, Scope, Revenue, Opportunities and Growth by 2028 Shanghaiist – Shanghaiist

New Jersey, United States Verified MarketResearch recently released a new report titled Cloud Computing IaaS In Life Science Market Insights, Size, Forecast to 2028. The report is prepared using primary and secondary research methodologies that provide an accurate and concise understanding of the Cloud Computing IaaS In Life Science market. Analysts used a top-down and bottom-up approach to evaluate the segments and give a fair assessment of their impact on the Cloud Computing IaaS In Life Science market. The report provides a market overview that briefly describes the market status and key segments. It also mentions the top players in the Cloud Computing IaaS In Life Science market.

The Cloud Computing IaaS In Life Science market research report includes SWOT analysis and Porters Five Forces Analysis which help to deliver the accurate evolution of the market. These market measurement tools help to identify drivers, restraints, weaknesses, Cloud Computing IaaS In Life Science market opportunities, and threats. The research report provides figures on the global market as well as figures on the regional markets and their segments.

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The Cloud Computing IaaS In Life Science research report begins with an executive summary that offers a brief overview of the market. It names the leading segments and players that will shape the market in the coming years. The executive summary provides an unbiased view of the market. In the following chapters, the research report on the Cloud Computing IaaS In Life Science market focuses on Engines. He explains the demographic shifts that are likely to impact demand and supply in the Cloud Computing IaaS In Life Science market. It addresses regulatory reforms that are expected to change the outlook. In addition, the researchers discussed the actual source of the request to analyze its nature.

The report also highlights the restraints in the Cloud Computing IaaS In Life Science market. The analysts have discussed the details and highlighted the factors that are likely to hamper the growth of the market in the coming years. The changing lifestyles, tax policies, and purchasing power of different economies have been studied in detail. The report makes a good case for how these limitations if properly evaluated, can be turned into opportunities.

Key Players Mentioned in the Cloud Computing IaaS In Life Science Market Research Report:

Cleardata Networks Dell Inc., Global Net Access (GNAX), Carecloud Corporation, Vmware Carestream Health IBM Corporation, Iron Mountain Athenahealth, Inc. and Oracle Corporation.

Cloud Computing IaaS In Life ScienceMarket Segmentation:

Cloud Computing IaaS In Life Science Market, By Component

Software Hardware Services

Cloud Computing IaaS In Life Science Market, By Application

Nonclinical Information Systems (NCIS) Clinical Information Systems

Cloud Computing IaaS In Life Science Market, By Deployment Model

Private Cloud Public Cloud Hybrid Cloud

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For Prepare TOC Our Analyst deep Researched the Following Things:

Report Overview:It includes major players of the Cloud Computing IaaS In Life Science market covered in the research study, research scope, market segments by type, market segments by application, years considered for the research study, and objectives of the report.

Global Growth Trends:This section focuses on industry trends where market drivers and top market trends are shed light upon. It also provides growth rates of key producers operating in the Cloud Computing IaaS In Life Science market. Furthermore, it offers production and capacity analysis where marketing pricing trends, capacity, production, and production value of the Cloud Computing IaaS In Life Science market are discussed.

Market Share by Manufacturers:Here, the report provides details about revenue by manufacturers, production and capacity by manufacturers, price by manufacturers, expansion plans, mergers and acquisitions, and products, market entry dates, distribution, and market areas of key manufacturers.

Market Size by Type:This section concentrates on product type segments where production value market share, price, and production market share by product type are discussed.

Market Size by Application:Besides an overview of the Cloud Computing IaaS In Life Science market by application, it gives a study on the consumption in the Cloud Computing IaaS In Life Science market by application.

Production by Region:Here, the production value growth rate, production growth rate, import and export, and key players of each regional market are provided.

Consumption by Region:This section provides information on the consumption in each regional market studied in the report. The consumption is discussed on the basis of country, application, and product type.

Company Profiles:Almost all leading players of the Cloud Computing IaaS In Life Science market are profiled in this section. The analysts have provided information about their recent developments in the Cloud Computing IaaS In Life Science market, products, revenue, production, business, and company.

Market Forecast by Production:The production and production value forecasts included in this section are for the Cloud Computing IaaS In Life Science market as well as for key regional markets.

Market Forecast by Consumption:The consumption and consumption value forecasts included in this section are for the Cloud Computing IaaS In Life Science market as well as for key regional markets.

Value Chain and Sales Analysis:It deeply analyzes customers, distributors, sales channels, and value chain of the Cloud Computing IaaS In Life Science market.

Key Findings:This section gives a quick look at the important findings of the research study.

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Verified Market Research is a leading Global Research and Consulting firm that has been providing advanced analytical research solutions, custom consulting and in-depth data analysis for 10+ years to individuals and companies alike that are looking for accurate, reliable and up to date research data and technical consulting. We offer insights into strategic and growth analyses, Data necessary to achieve corporate goals and help make critical revenue decisions.

Our research studies help our clients make superior data-driven decisions, understand market forecast, capitalize on future opportunities and optimize efficiency by working as their partner to deliver accurate and valuable information. The industries we cover span over a large spectrum including Technology, Chemicals, Manufacturing, Energy, Food and Beverages, Automotive, Robotics, Packaging, Construction, Mining & Gas. Etc.

We, at Verified Market Research, assist in understanding holistic market indicating factors and most current and future market trends. Our analysts, with their high expertise in data gathering and governance, utilize industry techniques to collate and examine data at all stages. They are trained to combine modern data collection techniques, superior research methodology, subject expertise and years of collective experience to produce informative and accurate research.

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Cloud Computing IaaS In Life Science Market Size, Scope, Revenue, Opportunities and Growth by 2028 Shanghaiist - Shanghaiist

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Global Healthcare BPO Market Trajectory & Analytics Report 2022: Inherent Advantages of Cloud Computing Make "The Cloud" a Game Changer…

DUBLIN--(BUSINESS WIRE)--The "Healthcare BPO - Global Market Trajectory & Analytics" report has been added to ResearchAndMarkets.com's offering.

Global Healthcare BPO Market to Reach $441.8 Billion by 2026

Amid the COVID-19 crisis, the global market for Healthcare BPO estimated at US$311.7 Billion in the year 2022, is projected to reach a revised size of US$441.8 Billion by 2026, registering a compounded annual growth rate (CAGR) of 9.3% over the analysis period.

While traditional services offered by BPOs comprised call centers, member processing, and claims adjudication, new-age BPO providers are re-evolving the service offerings and are creating completely integrated service models that are fully branded by the underwriting insurance company. Some of the innovative offerings of the BPO today include alternate payment services, claims modernization, novel plan design strategy, and analytics.

The United States represents the largest regional market for Healthcare BPO and is projected to reach US$167.7 Billion by 2026. China is expected to spearhead growth and emerge as the fastest growing regional market with a CAGR of 10.6% over the analysis period. The market globally is anticipated to post a significant growth over the short-term owing to the mounting pressure to cut healthcare delivery costs and consolidation of incorporated healthcare systems. The market growth is expected to be propelled by implementation of stringent regulations along with PPACA and clinical trial protocols.

Fueled by these factors, the healthcare BPO market is forecast to reflect an impressive growth rate over the short term. Moreover, patent cliffs faced by various pharmaceutical companies and mandatory enactment of ICD-10 codes within the US are expected to further provide the required impetus to the market.

On the other hand, concerns regarding service quality coupled with apprehensions about losing control over outsourcing are likely to remain key inhibitors in the coming years. The rise in number of individuals getting insured is further driving healthcare organizations to outsource certain services to address the demand for effective healthcare management.

The consolidation of healthcare providers and growing pressure to minimize healthcare costs is further driving the demand for healthcare outsourcing services.

What`s New for 2022?

Key Topics Covered:

I. METHODOLOGY

II. EXECUTIVE SUMMARY

1. MARKET OVERVIEW

2. FOCUS ON SELECT PLAYERS (Total 61 Featured)

3. MARKET TRENDS & DRIVERS

4. GLOBAL MARKET PERSPECTIVE

III. REGIONAL MARKET ANALYSIS

IV. COMPETITION

For more information about this report visit https://www.researchandmarkets.com/r/pqk9i4

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Researchers Find Breakthrough on Quantum Computing With Silicon Chips – TechAcute

Researchers from Simon Fraser University were successful in making a breakthrough in the field of quantum technology development. Their study paves the way for creating silicon-based quantum computing processors compatible with the existing semiconductor manufacturing technology.

The researchers light up the silicon chips tiny defects with intense light beams. Stephanie Simmons, the principal investigator of the research, explains that the imperfections of the chips serve as an information carrier. Investigators point out that the tiny defect reflects the transmitted light.

Some of the naturally occurring silicon imperfections may act as quantum bits or qubits. Scientists consider these defects as spin qubits. Also, previous research shows how silicon produces long-lived and stale qubits.

Daniel Higginbottom, their lead author, considers this breakthrough promising. He explains that the researchers were able to combine silicon defects with quantum physics when it was considered to be impossible to do before.

Furthermore, he notes that while silicon defects have been studied extensively from the 1970s to the 1990s and quantum physics research being done for decades, its only now that they saw these two studies come together. He says that by utilizing optical technology in silicon defects[theyve] have found something with applications in quantum technology thats certainly remarkable.

Simmons acknowledges that quantum computing is the future of computers with its capability to solve simple and complex problems, however, its still in its early stages. But with the use of silicon chips, the process can become more streamlined and bring quantum computing faster to the public than expected.

This study demonstrates the possibility of making quantum computers with enough power and scale to manage significant computation. It gives an opportunity for advancements in the fields of cybersecurity, chemistry, medicine, and other fields.

Photo credit: The feature image is symbolic and has been taken by Solar Seven.Sources: Chat News Today / Quantum Inspire

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Quantum networking: Defining the next wave of networking and communications – TechRepublic

Ed Fox, CTO of MetTel, explains quantum networking and how it will transform communications.

For many, quantum computing remains an abstract concept a far-off dream of titanic supercomputers able to process and solve problems millions of times faster than your average machine. The reality is actually a lot closer than many of us would think.

In fact, early adoption of quantum computing has already begun with Google releasing its experimental quantum computer back in 2019, and a whole host of tech giants, enterprising start-ups, and cutting-edge labs pouring billions into the research and development of quantum. It shows quantum isnt a fad. The evolution of the quantum computer will continue to accelerate over the coming years, with a string of recent scientific and technological breakthroughs signalingits inexorable rise towards more widespread application across the mainstream business world.

SEE: Metaverse cheat sheet: Everything you need to know (free PDF) (TechRepublic)

There are exciting times ahead, with quantum unlocking a wealth of new opportunities across various industries, sectors and verticals. One such example is the communications industry, which is set to enjoy the emergence and subsequent rise of one of quantums most impressive offshoots quantum networks.

Currently, due to various limitations, its almost impossible to transport data stored in a quantum computer. This restricts all process to the quantum computer in question, with any quantum information generated only able to be shared between one other quantum computer.

The collaborative nature of our world, with instant access to information (anytime, anyplace) the absolute norm, means that information shared between only two machines could almost be called antiquated ironic considering the cutting-edge nature of quantum computers.

However, researchers in the Netherlands have recentlyengineered a significant breakthrough, enabling quantum information to move across vast distances between two (even three) quantum computers via the use of an intermediary node. This takes place by manipulating a quantum theory known as entanglement whereby entangled particles replicate and enable quantum information to be transported across vast distances instantaneously and between more than two machines.

So, while closely connected, quantum computing and quantum networking do operate as independent industries. However, its safe to say that quantum computing will struggle to realize its full potential without effective introduction and use of quantum networks but what will this actually look like in the world of communications?

At its essence, quantum networking will fundamentally change how data is sent and received. If managed effectively, quantum networks also known as the quantum internet could drive a metamorphic change to the way the internet operates, carving out the true network of the future.

Wed live in a world with the potential for almost zero latency it would only be present when processing at the send/receive end of the network. Added to this, quantum networks would almost entirely eliminate physical network infrastructure. In other words, the copper and fibre optic cables that traverse our planet (often underneath our seas and oceans) would become redundant.

Perhaps most tangibly, any quantum information transported between two quantum computers (or more) becomes completely inaccessible during its journey, meaning no-one can intercept it. Picture a world where your data is 100% (and we mean 100%) protected unconditional security. It could spell the end of cybercrime as we know it.

Researchers alsopredict quantum networks and computers will help accelerate the invention of new medicines and critical vaccines, as well as support even more advanced use of artificial intelligence (AI).

In communications building on the benefits of absolute data security and instant delivery of information quantum devices will enable higher performance for sensors, such as high-accuracy GPS. We may also witness the birth of the quantum cloud. By connecting distributed quantum computers, users will possess instant access to a crack-proof data cloud, with higher speeds and more capacity than any cloud thats come before.

Of course, its very early days for the quantum network and computer. Were only scratching the surface when it comes to the exciting opportunities and complex challenges they present. An end to cybercrime and other such predictions are exactly that: Predictions.

But its safe to say that quantum computing will only continue its relentless march, as our understanding of its potential and investment in its evolution grows. Those who stay educated and aware of its unceasing development with discoveries and breakthroughs happening monthly will be primed and ready to take advantage of its awesome power as it becomes more accessible and affordable over the coming years.

Ed Fox is CTO of MetTel, a leader in communications and digital transformation (DX) solutions for enterprise customers.

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Humble Named Director of the Quantum Science Center – HPCwire

Aug. 9, 2022 Travis Humble has been named director of the Quantum Science Center headquartered at the Department of Energys Oak Ridge National Laboratory. The QSC is a multi-institutional partnership that spans industry, academia and government institutions and is tasked with uncovering the full potential of quantum materials, sensors and algorithms.

Humble was named deputy director in 2020, when DOE established this five-year, $115 million effort as one of five National Quantum Information Science Research Centers. Following the departure of former QSC Director David Dean, Humble began serving as interim director in January.

I am excited to be working at the forefront of quantum science and technology with this amazing team of scientists and engineers, he said. The QSC provides a wonderful opportunity to leverage our nations best and brightest for solving some of the most interesting scientific problems of our time.

As interim director, Humble has overseen the QSCs three primary focus areas: quantum materials discovery and development, quantum algorithms and simulation, and quantum devices and sensors for discovery science. In his new role, he will continue collaborating with QSC partner institutions including ORNL, Los Alamos National Laboratory, Fermi National Accelerator Laboratory, Purdue University, Microsoft and IBM.

A distinguished ORNL scientist, Humble also directs the laboratorys Quantum Computing Institute and the Oak Ridge Leadership Computing Facilitys Quantum Computing User Program. The QSC leverages DOE user facilities, including the OLCF, to solve research problems.

Humble joined ORNL as an intelligence community postdoctoral research fellow in 2005, then became a staff member in 2007. He received a bachelors degree in chemistry from the University of North Carolina Wilmington and a masters degree and doctorate in theoretical chemistry from the University of Oregon.

As QSC director, Humble will prioritize the development of quantum materials for quantum computing and quantum sensing, as well as the application of these technologies to aid scientific discovery, improve the nations security and energy efficiency, and ensure economic competitiveness. Other goals include demonstrating the advantages of early quantum computers and advancing methods for probing the fundamental physics of quantum matter.

By addressing current quantum challenges and expanding workforce development activities focused on recruitment and training, Humble anticipates that the QSCs leadership role in the ongoing quantum revolution will continue to grow.

Humble also serves as an assistant professor with the University of Tennessee, Knoxvilles Bredesen Center for Interdisciplinary Research and Graduate Education, editor-in-chief for ACM Transactions on Quantum Computing, associate editor for Quantum Information Processing and co-chair of the Institute of Electrical and Electronics Engineers Quantum Initiative.

Now in his 17th year at ORNL and more passionate about the future of quantum than ever, Humble is positioning the QSC to shape quantum research and technologies at national and international scales.

Quantum science and technology are transformative paradigms, and we have only scratched the surface of what is possible, he said. The QSC will bring new discoveries in materials, computing and sensing that promote a deeper understanding of these ideas and prepare us for the next generation of quantum technologies.

The QSC, a DOE National Quantum Information Science Research Center led by ORNL, performs cutting-edge research at national laboratories, universities, and industry partners to overcome key roadblocks in quantum state resilience, controllability, and ultimately the scalability of quantum technologies. QSC researchers are designing materials that enable topological quantum computing; implementing new quantum sensors to characterize topological states and detect dark matter; and designing quantum algorithms and simulations to provide a greater understanding of quantum materials, chemistry, and quantum field theories. These innovations enable the QSC to accelerate information processing, explore the previously unmeasurable, and better predict quantum performance across technologies. For more information, visitqscience.org.

UT-Battelle manages ORNL for DOEs Office of Science, the single largest supporter of basic research in the physical sciences in the United States. DOEs Office of Science is working to address some of the most pressing challenges of our time. For more information, visithttps://energy.gov/science.

Source: ORNL

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Vitalik Buterin, The Future Of Ethereum (ETH) And The Challenge Of Quantum Computing – Nation World News

Vitalik Buterin believes that the future of the Ethereum blockchain and crypto ETH is good, but there are many challenges to be solved.

Not long ago the founder of Ethereum made public about the future of blockchain which is widely used for various crypto projects. Heres the gist of what he told BUIDL in Asia programahead of plan Sickness going to Ethereum 2.0 Which will be held in September 2022.

The ZK-Rollup project is considered the most important foundation Example The Ethereum blockchain is getting widespread.

There are ZK-rollups Crypto transaction protocol that allows indirect transactions via the Ethereum blockchain aka off-chain,

This method will radically speed up transactions and increase their volume. In the end this will increase efficiency and expand Example Ethereum blockchain itself, including adoption ETH As for its crypto.

This technique is similar to the technique power network Used to improve from 2018 Example Blockchain Litecoin and Bitcoin.

In the long term, ZK-rollups will outperform optimistic rollup techniques, Vitalik said.

Again according to Vitalik, Ethereum developers should be prepared to face the threat of quantum computing, which is expected to get exponentially better in terms of speed.

The discourse on quantum computing, which is considered a major threat to current blockchain technology, including bitcoin, has been going on since 4 years ago.

Because at that time quantum computing technology experienced significant development, after it was proved that it is capable of computing very complex calculations in just 10 minutes. If you use todays supercomputers, it could take up to thousands of years.

Quantum computing does not rely on the combination of 0 or 1 numbers, binary numbers, but on the concept of qubitwhere two states Can run at once, i.e. 0 or 1 and 0 and 1. This may be because the processor does not take advantage of the electrical dynamics of transistors, but particles at the subatomic level.

This means that the computational speed is millions of times higher than that of todays supercomputers and is expected to continue to increase in the future to make it easier for humans to do their jobs.

The problem is that the smarter quantum computers are, the more they threaten current human cryptographic security systems, including the bitcoin blockchain that uses SHA256.

Vitalik Buterin: Googles quantum computer failed

This huge growth in quantum computing was noted by Vitalik last year, that the power of new computers is not a threat now, but will be in the future.

This is because quantum computing promises a new world of derivative technology, but at the same time poses a threat to traditional technology. This is exactly what happened when the first supercomputer was developed.

You can read the Blockchainmedia.id archive Related to quantum computing on this page,

We are currently working with several artificial intelligence researchers to develop new algorithms that can compete with the high capabilities of quantum computing. This is still a long way off, between 10-30 years from now, said Vitalik. he said. [ps]

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Vitalik Buterin, The Future Of Ethereum (ETH) And The Challenge Of Quantum Computing - Nation World News

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Spintronics, 2D Materials, and the Future of Quantum – AZoNano

Spintronics is an emerging technology that exploits the intrinsic quantum properties of particles like the electron, and the associated particle angular moment, called spin, in addition to the particle's electric charge.

Image Credit:Jurik Peter/Shutterstock.com

The dynamic control of the electron spin offers possibilities for creating novel quantum-mechanical devices, such as spin transistors, spin valves, and high-density memory. Spintronic systems are of particular interest in the field of quantum sensing, computing, and data processing.

Conventional electronic devices rely on the generation, transport, manipulation, and detection of electric charge carriers, such as electrons and holes.

Spintronics employs the intrinsic angular momentum of the particles (spin) together with their electric charge. The electrons exist either in spin-up or spin-down states, which can represent 0s and 1s in logic operations.

However, to build spintronic devices, the properties of the materials are crucial. In most materials, the spin-up and spin-down magnetic moments cancel each other, making them unsuitable for spintronic applications. In ferromagnetic materials, particles with the same spin state can accumulate in a majority-up and majority-down domains.

The spin state in these randomly scattered domains can be easily manipulated by the application of external magnetic fields. In addition, the transfer of electron spin-states between different materials and their reliable detection is also of great importance for spintronics.

Spintronics: The Technology Revolution Youve Probably Never Heard OfPlay

Video Credit: Seeker/YouTube.com

Most of the existing spin-based devices, such as giant magnetoresistance-based memory and spin valves, are passive spintronic elements. In such spintronic devices, the material's resistance or tunneling current in the system depends on the spin direction controlled by local magnetic fields.

The goal of the engineers and physicists developing spintronics is to move the field beyond the passive spin devices and create applications based on dynamic spin control. The two physical principles underlying the current interest in spintronics are the quantum-mechanical nature of the spin and the extended coherence time of the spin states. In particular, the coherence, or the stability of the quantum states with time, is an essential factor for spintronic and quantum applications.

By actively manipulating the spin of the charge carrier particles with spin-dependent properties, researchers envisage that they can create spin transistors, spin filters, and new memory devices suitable for quantum information processing and computation.

In these devices, the spin polarization can be controlled via spin-orbit coupling (the interaction between the particle spin and its orbital momentum), and spin waves, or magnons, can carry spin current through the material without energy loss.

Twodimensional (2D) materials have been drawing tremendous attention in spintronics because of their distinctive spindependent properties, such as long spin relaxation times, diffusion lengths and strong spin-orbit coupling.

Furthermore, the rapid advance in nanotechnology has enabled scientists to combine several preferred properties in one superior material through van der Waals stacking of two or more 2D material layers.

Graphene, with its high charge carrier mobility, extended spin lifetime, and long diffusion length, emerged as an excellent platform for fundamental spintronic research and eventual spintronics devices.

Recently, several research groups demonstrated spin injection in graphene at room temperature using a cobalt electrode. Spin detection has also been achieved by comparing spin-up and spin-down local currents. However, because of its zero bandgap and weak spin-orbit coupling, the material has limitations in building advanced spintronic devices, such as logic gates.

In contrast, 2D transition metal dichalcogenides, transition metal carbides, nitrides, carbonitrides, and organometallic sheets exhibit tunable bandgap and strong spin-orbit coupling, enabling reliable spin logic and non-volatile data storage.

The development of magnetic 2D materials with high Curie temperature is of particular interest, such as organometallic layered structures with embedded transition metal atoms. Such materials have been used to fabricate magnetic tunnel junction devices (ferromagnetic layers separated by a nonmagnetic layer), where the current can be controlled by the relative magnetization of the outer layers. Combining spin valves and magnetic tunnel junctions with the spin-transfer-torque writing write mechanism enabled researchers to create non-volatile high-speed magnetic random access memory.

There are several outstanding challenges related to the stability of the 2D materials and their manufacturing. Due to their atomic thickness, 2D spintronic devices are highly susceptible to moisture, oxidation, and thermal damage, thus requiring operation in a protected environment. Besides, the Curie temperature of most of the 2D magnetic materials is far below room temperature.

Most 2D materials are fabricated through mechanical exfoliation and stacking, which is time-consuming and expensive. Developing 2D materials suitable for wafer-scale synthesis and operation at ambient conditions are prerequisites for their wider adoption in spintronic applications.

Spintronics is at the point of becoming a mainstream technology similar to semiconductor-based microelectronics. Ongoing research and development efforts aim to integrate spintronics and photonics into a common platform for light-based and spin-based quantum computing. By using specially designed photonic circuits, researchers are hoping to be able to control the electron spin dynamics in nanostructured 2D magnetic materials when excited by short laser pulses.

Ahn, E.C. (2020) 2D materials for spintronic devices. npj 2DMaterials andApplications, 4, p. 17. Available at: https://doi.org/10.1038/s41699-020-0152-0

Hu, G & Xiang, B. (2020) Recent Advances in Two-Dimensional Spintronics. Nanoscale Res Letters,15, p. 226. Available at: https://doi.org/10.1186/s11671-020-03458-y

Feng, Y.P., et al. (2017) Prospects of spintronics based on 2D materials. WIREs Comput Mol Sciences, 7, p. e1313. Available at: https://doi.org/10.1002/wcms.1313

Awschalom, D. D., et al. (2013) Quantum spintronics: engineering and manipulating atom-like spins in semiconductors. Science, 8, p. 339, 6124-1174-9. Available at: https://doi.org/10.1126/science.1231364

Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.

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How machine learning could help save threatened species from extinction – The Verge

There are thousands of species on Earth that we still dont know much about but we now know that they are already teetering on the edge of extinction. A new study used machine learning to figure out just how threatened these lesser-known species are, and the results were grim.

Some species of animals and plants are labeled data deficient because conservationists havent been able to gather enough information about them to understand how they live or how many of them are left. It turns out that those data deficient species are unfortunately even more threatened than other species that are more well known (to scientists, at least). The data from this study came from the International Union for Conservation of Nature (IUCN), which maintains a global Red List that ranks species based on how threatened they are.

More than half of the data deficient species included in this study, 56 percent, likely face the risk of extinction. In comparison, just 28 percent of better understood species on the Red List are at risk of extinction.

Things could be worse than we actually realize now, says Jan Borgelt, an ecologist at the Norwegian University of Science and Technology and the lead author of the study published today in the journal Communications Biology. More species are likely to be threatened than we previously thought.

Much of Borgelts work focuses on understanding how human activity like hydroelectricity generation or plastic pollution affects ecosystems and biodiversity. The Red List is an invaluable resource for those efforts. But more than 20,000 species are classified as data deficient. And that blind spot can potentially make research that relies on the Red List less accurate.

To try to solve that problem, Borgelt and his colleagues turned to machine learning. They trained an algorithm to predict the extinction risk of data deficient species. To do that, they used information on 28,363 different kinds of animals that the IUCN has already evaluated. That way, the algorithm could start to understand factors that often determine how threatened a species is including climate change, invasive species, and pollution.

Then the researchers turned their attention to 7,699 data deficient species. Thats a little over a third of all data deficient species, but Borgelt and his colleagues could only work with species for which they knew the geographic distribution of the animals. The algorithm determined that 56 percent of those species are likely at risk of extinction. But some animals are in deeper trouble than others; 85 percent of data deficient amphibians, for instance, are at risk of extinction. That includes the Mali screeching frog, spotted narrow-mouthed frog, and several species of robber frogs. The IUCN doesnt even have photos of these critters on its Red List, but with names like that, dont you want to see them?

Their research received some validation when the IUCN updated its Red List last year. One hundred twenty-three of the species in the update were species that the algorithm had made predictions about. More than two-thirds of the algorithms predictions, 76 percent, were correct.

That was reassuring, Borgelt tells The Verge. But he also understands the limitations of machine learning. For now, [these algorithms] certainly shouldnt replace expert assessments, he says, because the expert assessments are more accurate.

But such algorithms, theyre really quick. Theyre not so time intensive or labor intensive as if you were to assess the species individually, Borgelt says.

The creatures numbers out in the wild might have eluded researchers for plenty of reasons. The killer whale, for example, happens to be labeled data deficient. Even though the orca starred in my favorite 90s movie and lived on all my childhood notebooks in the form of Lisa Frank stickers, scientists arent even sure whether theres just one species of killer whale or several. Other animals might be found only in remote regions with a limited range, for instance. And the same characteristics that make them hard to study might also make them more vulnerable.

That makes it all the more important to give these species some well-deserved attention. Machine learning, Borgelt says, isnt a replacement for tracing down the animals on the ground. But its another tool in the toolbox, and it could help conservationists figure out which species need some extra TLC.

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Artificial Intelligence is giving drug discovery a great big leap | Mint – Mint

Last month, Alphabets artificial intelligence subsidiary, DeepMind, stunned the world of science by presenting something truly spectacular: a snapshot of nearly every existing protein on Earth 200 million of them. This feat of machine learning could speed the creation of new drugs. It has already upended my own scepticism about the role AI can play in the pharmaceutical industry.

AlphaFold, DeepMinds protein structure program, is impressive because it reveals so much fundamental information about living organisms. Proteins are the building blocks of life, after all, and as such they are essential to life and to the development of medicines. Proteins can be drug targets, and they can themselves be drugs. In either case, it is important to know the intricate ways in which they fold into various shapes. Their coils, floppy bits, hidden pockets and sticky patches can control, for example, when a signal is sent between cells or if a process is turned on or off. Until now, capturing an image of a protein has required painstaking work lasting anywhere from days to months to years.

Since the early 1990s, scientists have been trying to train computers to predict a proteins structure based on its genetic sequence. AlphaFold had the first taste of success in 2020, when it correctly predicted the structures of a handful of proteins. The next year, DeepMind put on its server about 365,000 proteins. Now, its put the entire universe of proteins up for grabsin animals, plants, bacteria, fungi and other living things. All 200 million of them. Much as the gene-editing tool Crispr revolutionized the study of human disease and the design of drugs to target genetic errors, AlphaFolds feat is fundamentally changing the way new medicines can be invented.

Anybody who could have thought that machine learning was not yet relevant for drug hunting surely must feel different," said Jay Bradner, president of the Novartis Institutes for BioMedical Research, the pharma companys research arm. Im on it more than Spotify."

Count me as a former sceptic. I hadnt discounted the possibility that AI might have an impact on the drug industry, but I was weary of the many biotech firms hyping often ill-defined machine-learning capabilities. Companies often claimed that they could use AI to invent a new drug without acknowledging that the starting pointa protein structurestill needed to be worked out by a human. And so far, people have had to first invent drugs for the computer to improve upon them.

Producing the full compendium of proteins is something entirely different. Its little wonder that executives at biotech and pharma companies are widely adopting AlphaFolds revelations.

Rosana Kapeller, chief executive officer of Rome Therapeutics, offers an example from her companys labs. Rome is probing the dark genome, the repetitive portion of the human genetic code that is believed to be largely a relic of ancient viruses. Romes team spent more than six months refining its first image of one protein embedded in that dark genome. Just one day after they captured an initial snapshot of a second protein, DeepMind dropped its complete load of images. Within 24 hours, Romes scientists had perfected their picture. So you see," she said, thats amazing."

None of this is to say that AlphaFold will solve every problem in drug discovery, or even that its 200 million protein images are perfect. Theyre not. Some need more work, and others are more akin to a childs scribbles than fleshed out images. Scientists in tell me that even when the snapshots are imperfect, they have enough information to provide a rough sense of where the important bits are. David Liu, a professor at the Broad Institute of MIT and Harvard, said the technology still allows researchers in his lab to achieve that Zen-like understanding state" to decide where to tinker with a protein to change its properties.

But proteins also dont exist as still snapshots. Depending on the job theyre performing at a given moment, they yawn and jiggle and twist inside a cell. In other words, AlphaFold gives us protein Instagram; scientists would love to have protein TikTok or, eventually, protein YouTube. Even if that becomes possible, this addresses just one step in the process of creating new drugs. The most expensive part is testing that new medicine in humans.

Nevertheless, AlphaFolds pictures can help drugmakers get to the testing stage faster. DeepMinds feat may have taken several years of exploration, but it produced something with major consequences. And it made that work freely available. Finally, we are getting a glimpse of AIs potential to transform the drug industry. And now its possible to consider which problems machine-learning might solve next for science and medicine.

Lisa Jarvis is a Bloomberg Opinion columnist covering biotech, health care and the pharmaceutical industry.

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Artificial Intelligence is giving drug discovery a great big leap | Mint - Mint

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This Teenager Invented a Low-Cost Tool to Spot Elephant Poachers in Real Time – Smithsonian Magazine

ElSa is a prototype of a machine-learning-driven software that analyzes movement patterns in videos of humans and elephants. Society for Science

When Anika Puri visited India with her family four years ago, she was surprised to come across a market in Bombay filled with rows of ivory jewelry and statues. Globally, ivory trade has been illegal for more than 30 years, and elephant hunting has been prohibited in India since the 1970s.

I was quite taken aback, the 17-year-old from Chappaqua, New York, recalls. Because I always thought, well, poaching is illegal, how come it really is still such a big issue?

Curious, Puri did some research and discovered a shocking statistic: Africas forest elephant population had declined by about 62 percent between 2002 and 2011. Years later, the numbers continue to drop. A wildlife lover, Puri wanted to do something to help protect the species and others still threatened by poaching.

Drones are currently used to detect and capture images of poachers, and they arent that accurate, the teenager explains. But after watching videos of elephants and humans, she saw how the two differed vastly in the way they movetheir speed, their turning patterns and other motions.

I realized that we could use this disparity between these two movement patterns in order to actually increase the detection accuracy of potential poachers, she says.

Over the course of two years, Puri created ElSa (short for elephant savior), a low-cost prototype of a machine-learning-driven software that analyzes movement patterns in thermal infrared videos of humans and elephants. Puri says the software is four times more accurate than existing state-of-the-art detection methods. It also eliminates the need for expensive high-resolution thermal cameras, which can cost in the thousands, she says. ElSa uses a $250 FLIR ONE Pro thermal camera with 206x156 pixel resolution thatplugs into an off-the-shelf iPhone 6. The camera and iPhone are then attached to a drone, and the system produces real-time inferences as it flies over parks as to whether objects below are human or elephant.

It's really amazing just to see all these kids coming together. And for the same purpose enjoying science and doing research, Puri says. I was honored just to be on that stage.

Puri first learned about the capabilities of artificial intelligence just after ninth grade, when she was selected to attend Stanford A.I. Labs summer program.

Initially, my enthusiasm for artificial intelligence was based off of this limitless possibility for social good, she says. But she soon discovered that because data is collected and analyzed by humans, it contains human biases, and so does A.I. as a result.

It really has the capability to reinforce some of the worst aspects of our society, she says. What I really realized from this is how important it is that women, people of color, all sorts of minorities in the field of technology are at the forefront of this kind of groundbreaking technology.

About a year later, Puri founded a nonprofit called mozAIrt, which inspires girls and other underrepresented groups to get involved in computer science using a combination of music, art and A.I.

At an A.I. conference where she held a workshop, Puri met Elizabeth Bondi-Kelly, a Harvard computer scientist who was working on a wildlife conservation project using drones and machine learning. Bondi-Kelly had also started a nonprofit, called Try AI, to increase diversity in the field.

Puri reached out to the computer scientist about her idea to catch elephant poachers using movement patterns, and Bondi-Kelly became her mentor for the project.

To create her model, Puri first found movement patterns of humans and elephants using the Benchmarking IR Dataset for Surveillance with Aerial Intelligence (BIRDSAI), a dataset collected by Bondi-Kelly and her colleagues using a thermal infrared camera attached to an unmanned aerial vehicle (UAV) in multiple protected areas in Africa. Sifting through the data, Puri identified 516 time series extracted from videos that captured humans or elephants in motion.

Puri used a machine learning algorithm to train a model to classify a figure as either an elephant or a human based on its speed, group size, turning radius, number of turns and other patterns. She used 372 series300 elephant movements, and 72 human movements. The remaining 144 were used to test her model with data it hadnt seen before. When tested on the BIRDSAI dataset, her model was able to detect humans with over 90 percent accuracy.

Puri's software is "quite commendable," saysJasper Eikelboom, an ecologist at Wageningen University in the Netherlands who is designing a system to detect poachers using GPS trackers on animals. It's quite remarkable that a high school student has been able to do something like this, he says. Not only the research and the analysis, but alsobeing able to implement it in the prototypes.

Eikelboom cautions that Puris model still needs to be tested on raw video footage to see how well it can detect poachersthe accuracy of Puris model was tested using figures already determined either human or elephant. He also says other barriers already exist to using drones in parks, such as the money and manpower to keep them flying.

ElSa, he notes, could be used broadly for other conservation goals, not just for spotting poachers, too.

In ecology in general, we like to track animals and see what they're doing and how it impacts the ecosystem, he says. And if we look, for example, on the satellite data, we can find a lot of moving patterns, but we don't know what species they are. I think it's a very smart move to look at these movement patterns themselves instead of only at the imageat the pixelsto determine what kind of species it is.

In the fall, Puri will attend the Massachusetts Institute of Technology, where she wants to study electrical engineering and computer science. She has plans to expand her movement pattern research into other endangered animals. Next up is rhinos, she says. And she wants to begin implementing her software in national parks in Africa, including South Africas Kruger National Park. Covid-19 restrictions delayed some of her plans to travel to these parks to get her project off the ground, but she hopes to explore her options after she starts college. Because drones only have a battery life of a few hours, she is currently creating a path-planning algorithm to ensure maximum efficiency in the drones flight course.

Research isn't a straight line, Purisays. That has made me more resourceful. It also helped me develop into a more innovative thinker. You learn along the way.

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