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An Osaka Metropolitan University research team has found a deicing mixture with high effectiveness and low environmental impact after using machine learning to analyze ice melting mechanisms of aqueous solutions of 21 salts and 16 organic solvents. The research appears in Scientific Reports on June 7, 2024.

The dangers of frozen roads, airplane engines, and runways are well known, but the use of commercial products often means short-term safety over long-term environmental degradation. Seeking a better product, Osaka Metropolitan University researchers have developed a deicing mixture offering higher performance than deicers on the market while also having less impact on the environment.

The team, made up of graduate student Kai Ito, Assistant Professor Arisa Fukatsu, Associate Professor Kenji Okada, and Professor Masahide Takahashi of the Graduate School of Engineering, used machine learning to analyze ice melting mechanisms of aqueous solutions of 21 salts and 16 organic solvents. The group then conducted experiments to find that a mixture of propylene glycol and aqueous sodium formate solution showed the best ice penetration capacity.

Because of the mixture's effectiveness, less of the substance needs to be used, thereby also lessening the environmental impact. It is also not corrosive, preventing damage, for example, when used for airport runways.

"We are proposing an effective and environmentally friendly deicer that combines the advantages of salts and organic solvents," said Dr. Fukatsu.

The results of this research also provide new insights into the ice melting process.

"The development of highly efficient deicers is expected to make deicing and anti-icing operations easier," Professor Takahashi added. "This will also lessen the environmental impact by reducing the amount of deicer used."

More information: Machine learning-assisted chemical design of highly efficient deicers, Scientific Reports (2024). DOI: 10.1038/s41598-024-62942-y

Journal information: Scientific Reports

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Machine learning helps find advantageous combination of salts and organic solvents for easier anti-icing operations - Phys.org

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Hydrogen storage, heat conduction, gas storage, CO2 and water sequestrationmetal-organic frameworks (MOFs) have extraordinary properties due to their unique structure in the form of microporous crystals, which have a very large surface area despite their small size. This makes them extremely interesting for research and practical applications. However, MOFs are very complex systems that have so far required a great deal of time and computing power to simulate accurately.

A team led by Egbert Zojer from the Institute of Solid State Physics at Graz University of Technology (TU Graz) has now significantly improved these simulations using machine learning, which greatly accelerates the development and application of novel MOFs. The researchers have published their method in npj Computational Materials.

"To simulate certain properties of MOFs, it is necessary to simulate huge supercells. This applies, for example, to the calculation of heat conduction in MOFs, which is highly relevant for almost all applications," says Egbert Zojer, describing the challenge that had to be solved.

"The simulated supercells often contain tens of thousands or even hundreds of thousands of atoms. For these huge systems, it is then necessary to solve the equations of motion five to 10 million times. This is far beyond present day computational possibilities using reliable quantum mechanical methods."

Thus, until now, transferrable force fields often parametrized on the basis of experiments were often used for such calculations. However, the results obtained with such force fields turned out to be generally not sufficiently reliable.

This is now fundamentally changed by the use of machine-learned potentials. These are adapted to quantum mechanical simulations by utilizing a newly developed interplay of existing algorithms, including approaches developed at the University of Vienna. For the necessary material-specific machine learning of the potentials, the quantum mechanical simulations need to be carried out only for comparatively few and significantly smaller structures.

As a result, the calculations run many orders of magnitude faster and it is possible to simulate the forces in the huge supercells many millions of times on modern supercomputers. The decisive advantage here is that there is no relevant loss of accuracy compared to doing the simulations using quantum mechanical methods.

For the example of heat conduction of MOFs, this means that the newly developed simulation strategy will make it possible to simulate the relevant material properties even before the MOFs are synthesized, thus allowing researchers to reliably develop customized structures on the computer.

This represents a major leap forward for research into complex materials, which for heat transport will, for example, allow researchers to optimize the interaction between the metal oxide nodes and the semiconducting organic linkers. Using the new simulation strategy will also make it easier to overcome complex challenges. For example, MOFs must have good or poor thermal conductivity depending on their application.

A hydrogen storage system, for instance, must be able to dissipate heat well, while in thermoelectric applications good electrical conduction should be combined with the lowest possible heat dissipation.

In addition to simulating thermal conductivity, the new machine-learned potentials are also ideal for calculating other dynamic and structural properties of MOFs. These include crystallographic structures, elastic constants, as well as vibrational spectra and phonons, which play a decisive role in the thermal stability of MOFs and their charge transport properties.

"We now have tools that we know are incredibly efficient at providing us with reliable quantitative figures. This enables us to systematically change the structures of the MOFs in the simulations, while at the same time knowing that the simulated properties will be accurate. This will allow us, based on causality, to understand which changes in the atomistic structure generate the desired effects," says Egbert Zojer, who knows that research groups in Munich and Bayreuth have already taken up the new simulation strategy despite its recent publication.

More information: Sandro Wieser et al, Machine learned force-fields for an Ab-initio quality description of metal-organic frameworks, npj Computational Materials (2024). DOI: 10.1038/s41524-024-01205-w

Journal information: npj Computational Materials

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Scientist advance simulation of metal-organic frameworks with machine learning - Phys.org

By Michael Leedom

The modest stethoscope has joined the Artificial Intelligence (AI) revolution, tapping into the power of machine learning to help health-care providers screen for diseases of the heart and lung.

This year, NCH Healthcare in Naples, Fla., became the first health-care system in the U.S. to incorporate AI into its primary care clinics to screen for heart disease. The health technology company Eko Health supplied primary care physicians with digital stethoscopes linked to a deep-learning algorithm. Following a 90-day pilot program involving more than 1,000 patients with no known heart problems, the physicians discovered 136 had murmurs suggestive of structural heart disease.

Leveraging this technology to uncover heart valve disease that might otherwise have gone undetected is exciting, says Bryan Murphey, President of the NCH Medical Group, which signed an annual agreement in January with Eko to use stethoscopes with the AI platform. The numbers made sense to help our patients in a non-invasive way in the primary care setting, says Murphey.

Ekos AI tool the SENSORA Cardiac Disease Detection Platform enables stethoscopes to identify atrial fibrillation and heart murmurs. The platform added another algorithm,clearedby the U.S. Food and Drug Administration (FDA) in April, for the detection of heart failure using the Eko stethoscopes built-in electrocardiogram (ECG) feature.

AI-enhanced stethoscopes showed more than a twofold improvement over humans in identifying audible valvular heart disease, according to astudypublished inCirculationin November 2023. The AI showed a 94.1 per cent sensitivity for the detection of valve disease, outperforming the primary care physicians 41.2 per cent. The findings were confirmed with an echocardiogram of each patient.

Stethoscopes join the growing number of AI health-care applications that promise increased efficiency and improved diagnostic performance with machine learning. In recent years, the FDA has cleared hundreds of AI algorithms for use in medical practice. But as the health-care field employs AI for more services, skeptics point to risks posed by over-reliance on this black box, including the potential biases built into AI datasets and the gradual loss of clinician skills.

Since its adoption more than 200 years ago, the stethoscope has served as both a routine exam tool and a visible reminder of the doctors training. It is recognizable worldwide and, for most clinicians, has remained an analog instrument. The first electronic stethoscopes were created more than 20 years ago and feature enhancements to amplify sound and allow for digital recording.

Analog and digital stethoscopes both rely on the ability of the health-care provider to hear and interpret the sounds, which may be the first indication a patient may have a new disease. However, this is not a skill every health-care practitioner masters. The faint, low-pitched whooshing of an incompetent heart valve or the subtle crackling of interstitial lung disease may go unnoticed even by the ears of experienced physicians.

Enter AI, which can mimic the human brain using neural networks consisting of algorithms that, in the case of stethoscopes, are trained with thousands of heart or lung recordings. Instead of relying on explicit program instructions, an AI system uses machine learning to train itself through advanced pattern recognition.

The effectiveness of artificial neural networks to diagnose cardiovascular disease has been demonstrated in controlled clinical trials.

AI improved the diagnosis of heart failure by analyzing ECGs performed on more than 20,000 adult patients in a randomized controlled trial published inNature Medicine. The intervention group was more likely to be sent for a confirmatory echocardiogram, resulting in 148 new diagnoses of left ventricular systolic dysfunction.

A neural network algorithm correctly predicted 355 more patients who developed cardiovascular disease compared to traditional clinical prediction based on American College of Cardiology guidelines, according to a cohortstudyof nearly 25,000 incidents of cardiovascular disease.

These machines are very good at finding patterns that are even beyond human perception. But theres both the power as well as the risk, says Paul Yi, Director of the University of Maryland Medical Intelligent Imaging Center.

The risks include limitations in performance if AI models are not properly trained. The accuracy of the AI algorithm depends on the collection of sufficient data that is representative of the population at large.

These AI models require a large amount of data, and these data are not easy to come by.

The generalizability is a big issue, says Gaurav Choudhary, Director of Cardiovascular Research at Brown University. These AI models require a large amount of data, and these data are not easy to come by. Choudhary notes that once an algorithm is approved by the FDA, it cannot be simply revised as new recordings become available. Changes to a particular AI algorithm require a new submission to the FDA before use.

In January 2024, the World Health Organization published newguidelinesfor health-care policies and practices for AI applications. Its authors warned of several risks inherent in the use of AI tools, including the existence of bias in datasets, the transparency of the algorithms employed and the erosion of medical provider skills.

AI algorithms that interpret heart and lung recordings may not have been trained on the full spectrum of possible sounds if the data does not include a wide range of patients and ambient noises.

This technology has to be validated across a variety of murmurs in a variety of clinical environments and situations, says Andrew Choi, Professor of Medicine and Radiology at George Washington University. Many of our patients are not the ideal patients, he adds, noting that initial validation typically involves patients with clear heart sounds. In real world practice, there will be older patients, obese patients and noisy emergency departments that may compromise the precision of the AI model.

Another complication is the inscrutable nature of the algorithm. Without a clear understanding of how these systems make decisions, it may be difficult for health-care providers to discuss a management plan with patients, particularly if the AI output appears incompatible with other clinical information during the examination.

Explainability is sort of a holy grail, says Paul Friedman, Chair of the Department of Cardiovascular Medicine at Mayo Clinic and one of the developers of the AI tech that Eko Health uses. Over time, he says, more studies may elucidate how these systems process information. AI uncertainty is similar to our incomplete understanding of how certain medications actually work, he suggests. Both are used because they are consistently effective.

Im not dismissive of the importance of trying to crack the black box, but I think thats a subject for research, he says.

The introduction of AI in the exam room could both enhance diagnostic performance while disrupting the relationship between health-care provider and patient. The provider may become complacent and gradually dependent on AI for answers to clinical questions, while the patient may feel that the care is becoming depersonalized and lose confidence in the doctor.

The subconscious transfer of decision-making to an automated system is called automation bias, one of many cognitive biases the health-care provider must confront. There are many reasons providers may forgo medical training and uncritically accept the heuristics of AI, including inexperience, complex workloads and time constraints, according to a systematicreviewof the phenomenon.

It is still unclear how AI will ultimately influence the physician-patient interaction, says Yi. I think thats kind of the last mile of AI in medicine. Its this human-computer interaction piece where we know that this AI works well in the lab, but how does it work when it interacts with humans? Does it make them second guess what theyre doing? Or does it give them false confidence?

The number of AI-enhanced devices submitted to the FDA hassoaredsince 2015, with almost 700 AI medical algorithmsclearedfor market. Most applications are for radiology. AI is already being integrated into academic medical centres across North America for a variety of tasks, including diagnosing disease, projecting length of hospitalization, monitoring wearable devices and performing robotic surgery.

At Unity Health in Toronto, more than 50 AI-based innovations have been developed to improve patient care since 2017. One of these is a tool used at St. Michaels Hospital since 2020 called CHARTWatch, which sifts electronic health records, including recent test results and vital signs, to predict which patients are at risk of clinical deterioration. The algorithm proved to be lifesaving during the COVID pandemic, leading to a 26 per cent drop in unanticipated mortality.

I think AI is really going to transform health care, says Omer Awan, Professor of Radiology at the University of Maryland School of Medicine. He is not concerned that AI will take over physician jobs, instead predicting that AI will continue to improve efficiency and help reduce physician burnout.

Research continues on how best to incorporate AI into the primary care setting, including ethical issues such as data privacy, legal liability and informed consent. The adoption of AI may infringe on patient autonomy if medical decisions are made using algorithms without regard for patient preferences, according to a literaturereview.

Murphey says he is eager to see Eko Healths AI-paired stethoscopes improve the screening for early heart disease but remains cautious about too much use of technology.

I want to stay connected to the patient. I take pride in my patient examinations, he says. I think thats one of the important things we provide to patients in the primary care setting, and Im not looking to sever that part of the relationship.

This post was previously published on HEALTHYDEBATE.CA and is republished under a Creative Commons license.

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AI Stethoscope Demonstrates 'The Power as Well as the Risk' of Emerging Technology - The Good Men Project

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From reshaping technology to altering industries, Artificial Intelligence (AI) is set to impact the world more profoundly than any previous innovation. Not just in tech but automation in finance, healthcare, retail, gaming and almost every other domain is driving ahead AI's growing intervention in our surroundings.

Yet, concerns about automation have long overshadowed the future of work. While AI will enhance certain jobs, it is arguable that this may lead to job displacement for others. According to a recent report by Goldman Sachs, around 300 million full-time jobs globally could be vulnerable to automation by AI This includes various sectors where repetitive and data-driven tasks are more common.

But proponents of AI believe otherwise. Professor Fei-Fei Li, co-director of the Stanford Institute for Human-Centered Artificial Intelligence remarks that "Artificial intelligence is not a substitute for human intelligence; it is a tool to amplify human creativity and ingenuity."

As AI continues to open new doors, upGrad's Mayank Kumar points out the future potential: According to the World Economic Forum (WEF), AI will generate 12 million new jobs by 2025 - an opportunity we cannot miss." This shift necessitates a change in how young professionals are trained, particularly in India, where a large number of youth are at the cusp of joining the workforce.

To identify the most sought-after AI skills in the current Indian job market, Midday.com roped in experts from various sectors including health, education, gaming, filmmaking and finance. Here are the key takeaways from the interactions:

Top AI skills in the healthcare ecosystem In the contemporary landscape, where data availability is abundant, Harshit Jain, MD, Founder & Global CEO, Doceree shares that the skill of data extraction and analysis emerges as a paramount AI proficiency gaining popularity within healthcare. Through smart analysis of healthcare provider (HCP) data, AI can identify patterns in HCP behaviour, and enable pharmaceutical manufacturers to empower HCPs with knowledge or information relevant to their clinical workflow, thereby optimising patient outcomes.

Additionally, AI when used to study patient-level data in a compliant manner, offers invaluable insights into individual health histories/trajectories. This can help with personalised interventions and preventative measures.

When asked about developing a knack for AI, Jain remarked that equipping with AI skills requires practice. In his opinion, hands-on experience with real-world datasets and industry-standard tools is invaluable for translating theory into practicality However, for beginners to understand the nitty-gritty of technology, it is ideal to go for courses that are a comprehensive blend of theoretical knowledge and practical applications.

"A few online courses that I feel can offer such a blend include, AI in Healthcare Specialization Course by Stanford University; Artificial Intelligence in Pharma and Biotech by MIT or AI for Healthcare by University of Manchester. Key components of such courses include understanding AI fundamentals, machine learning algorithms and deep learning techniques relevant to healthcare data analysis. Having said that, an emphasis on ethical considerations, privacy concerns and regulatory compliance is crucial due to the sensitive nature of medical data," he added.

(L-R) Harshit Jain, MD, Founder & Global CEO, Doceree and Mayank Kumar, Co-founder & MD, upGrad

Top AI skills in business administration Recently, upGrad has introduced a Doctorate of Business Administration in Digital Leadership with Golden Gate University, San Francisco. In less than a month, over 1,500 individuals with over 10-12 years of work experience have enrolled in the program, while overall enrollments in the GenAI program crossed 100 in just April 2024.

Other top AI skills in business involve data analysis, proficiency in machine learning to automate processes and understanding natural language processing (NLP) to enhance customer interactions and streamline communication. An industry-first pedagogy to strengthen AI leadership in India, is in the making, informs Mayank Kumar, co-founder & MD of upGrad.

Today, ed-tech companies offer AI courses for professionals looking to upskill at all stages - from freshers to seasoned experts. While free courses, certifications and boot camps are a quick way to skill up, many long-format courses in partnership with top universities have also enabled professionals to build on their expertise and become thought leaders in GenAI.

"There's always chatter about new technologies disrupting education, but instead of just disrupting, we should focus on leveraging these technologies to enhance our skilling and lifelong learning," outlines the Mumbai-based ed-tech expert.

Top AI skills in technology Harnessing AI's potential, there is a surge in demand for professionals skilled in machine learning and AI ethics, informs Siddharth Shahani, executive president, Atlas Skilltech University, Mumbai. "It's not just about coding algorithms; it's about understanding AI's profound impact on society."

For those entering this field, he advises starting with the fundamentals. A growing number of universities are offering tech-enabled education like developing chatbots, image classifiers or predictive models and participating in hackathons. This blend of theory and practice equips graduates to drive innovation in any sector.

"Many of the university's students have gone on to roles at leading tech companies like Google, IBM and TCS, demonstrating the value of a comprehensive, industry-aligned education in today's AI-driven world," adds Shahani.

(L-R), Siddharth Shahani, Executive President, Atlas Skilltech University and Venkat Malik, co-founder and CEO, Tidal7

Top AI skills in gaming As gaming becomes more immersive, intelligent, intuitive and responsive mimicking humans, the need for AI skills and capabilities is bound to grow substantially, outlines Venkat Malik, co-founder and CEO of a Mumbai-based digital marketing agency - Tidal7.

He informs that "Machine Learning, Deep Learning, NLP, Simulation & Modelling are all specific skills that are likely to be useful in the gaming industry as games become more realistic, human-like, agile and responsive." When asked about specific courses to acquire these skills, he directed us to Coursera and Udemy. "There are beginner's guides and specialised courses which offer introductory and advanced courses which can help in game design and development."

In various aspects of game development, such as character behaviour, procedural generation and adaptive difficulty systems, AI programming and decision-making algorithms are emerging as useful skills when building these models. Courses also equip professionals with skills used in the creation of game worlds, terrains, textures, etc, customising the level of difficulty to the player's skill level and defining their characters.

The game development platforms or engines that are worth experimenting with include Unity, and Unreal Engine for building AI-led games, adds Malik.

Also Read: AI simplifies decision-making in real estate operations, here's how

Top industrial AI skills Adding on to the integration of AI in industrial growth, Deepak Verma, the COO of Images Bazaar informs that manufacturing industries are increasingly employing AI-powered tools and systems to save costs and enhance patient outcomes. Robotics and computer vision amongst other skills mentioned above are the most in-demand AI capabilities at the moment, he adds.

An excellent option for novices to learn these in-demand AI skills is to start with online classes and resources. Before moving on to more complex areas, he advises starting with introductory courses that give a strong foundation in AI ideas. Additionally, when you start your AI adventure, looking through beginner-friendly AI networks and forums or attending meetups relevant to AI can offer helpful networking possibilities and support.

Additionally, participating in real-world projects and practical experiences is essential to developing and demonstrating competency in AI abilities including deep learning and data science. Sentiment analysis, image identification and recommendation systems are a few useful project ideas. Working on these projects will provide you with invaluable practical experience using AI techniques to solve real-world issues, explains Verma.

When asked about the relevance of degrees in AI, he says "In the AI job market, degrees, self-taught abilities and certifications become crucial. Achieving a balance between these techniques entails employing online courses, workshops and real-world projects; along with self-taught skills. The goal is to develop a broad skill set that can satisfy the needs of the artificial intelligence sector."

(L-R), Deepak Verma, the COO of Images Bazaar and Anubhav Srivastava, head of AI at Stupa Sports Analytics

Top AI skills in sports management Reportedly, AI has enabled sports companies to enhance performance analytics and player training. "The focus is on transforming raw data into actionable intelligence, enabling informed decision-making for players, coaches and sports organisations," shares Anubhav Srivastava, head of AI at Stupa Sports Analytics.

The integration of AI in sports demands a specific skill set, particularly for roles centred on data analytics, predictive modelling and real-time performance tracking. "The most sought-after AI skills in the sports industry include data analysis and statistics, machine learning and deep learning, computer vision, programming, and real-time data processing," informs Srivastava.

For those aspiring to entre the field of AI in sports, a combination of educational background, certifications and practical experiences is essential. A bachelor's degree in computer science, data science, artificial intelligence or a related field provides a strong foundation.

Top AI skills in broadcast and filmmaking When it comes to the moving image, Gen AI is transforming the filmmaking and broadcast industries in a variety of ways. It brought with it efficiency in colour correction, animation, environmental effects and more labour-intensive tasks which took days to render, informs Abir Aich, executive vice president, Arena Animation, MAAC & The Virtual Production Academy by Aptech.

These technologies not only boost creativity, increase efficiency and streamline content creation production processes, but they are also being used to automate and improve various aspects of production, such as scriptwriting, creative visualisation, storyboarding and concept design, thereby driving innovation and efficiency in these creative fields, he adds.

For example, Disney's "The Lion King" (2019) used advanced AI techniques for creating photorealistic animal characters and environments, blending live action with CGI seamlessly. Then you have Warner Bros.' "Gemini Man" (2019) where AI was utilised to de-age actor Will Smith, creating a younger version of his character through deep learning and CGI. The other example is how by leveraging AI, Netflix can predict what shows or movies a user might enjoy based on their viewing history, enhancing the personalised viewing experience.

When asked about AI-integrated courses for film producers, Aich shares that it is critical to specialise in Gen AI applications for media, such as Midjourney, Runway ML, Stable Diffusion, Adobe Firefly and so on, and apply them at various stages of the creative visualisation process. Practical experience gained through industry events/projects, internships and other opportunities is critical.

Recommended courses include Andrew Ng's "Deep Learning Specialisation" and IBM's "Introduction to Artificial Intelligence (AI)" on edX. Specialised programs include CG Spectrum's "Real-Time 3D and Virtual Production", MAAC's specialised programs like Gen AI-powered AD3D Edge Plus and ADVFX Plus, Virtual Production Specialist from The Virtual Production Academy and community resources like Kaggle and GitHub also offer valuable learning opportunities.

AI tools for aspiring filmmakers:

Lastly, he notes that AI experts with filmmakers and broadcasters make for a very interesting collaboration. To integrate AI successfully, filmmakers should identify the scope of usage, invest in training, leverage existing tools, start with pilot projects and collaborate with AI enablers.

(L-R) Abir Aich, Executive Vice President, Arena Animation, MAAC and Sarvagya Mishra, Co-founder & Director of Superbot

Why agencies are priortising employees adept in AISarvagya Mishra, co-founder & director of the AI-powered voice agent startup - Superbot, shares that sectors spanning manufacturing and logistics to finance and communications are leveraging AI to catalyse efficiency, drive innovation and propel growth.

The capacity to cleanse, explore and extract insights from vast datasets is paramount. A robust mathematical foundation, fluency in programming languages such as Python, and hands-on experience in constructing, testing and optimising AI models are primary requisites. Fortunately, numerous pathways exist to cultivate these skills as elucidated by Mishra.

Academic institutions are rapidly expanding AI and data science degree programs to meet the soaring demand. One can pursue master's curricula or specialised short-term courses and boot camps. Moreover, immersing oneself in projects and AI coding challenges is invaluable for practical application.

The exigency is palpable - a striking 60 per cent of public IT professionals identify the AI skills gap as their paramount impediment, according to one study. With AI poised to yield hundreds of millions of hours in savings and billions in cost reductions for the public sector annually, agencies will prioritise hiring and training employees adept in these cutting-edge capabilities.

Irrespective of the chosen path, continuous upskilling and practical application will be critical to maintaining industrial AI skills commensurate with this rapidly evolving domain. Those who master these in-demand skills will be optimally positioned for long-term career growth.

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MIDDAY EXPLAINS: Surge in demand for AI ethics, machine learning and data analysis, must-have AI skills revealed - mid-day.com

By Chad Cox

Production Editor

Embedded Computing Design

June 10, 2024

News

London, United Kingdom / Toronto, Canada. In 2023, Alphawave Semi joinedArm Total Design and leveraged its collaboration with Arm to design an innovative compute chiplet built on Arm's Neoverse Compute Subsystems (CSS). The platform is ideal for artificial intelligence/machine learning (AI/ML), high-performance compute (HPC), data centers, and 5G/6G networking infrastructure applications.

According to the company, its chiplet-based custom silicon design solution adds a differentiator in its portfolio including IO extension chiplets, memory chiplets, compute chiplets, and Alphawave Semis ultra-high-speed connectivity IP and advanced packaging proficiencies.

Our Arm-based compute chiplet is a critical component in Alphawave Semis custom silicon platform and a demonstration of both our IP, SoC and packaging capabilities and our successful strategic partnership with Arm, said Mohit Gupta, Senior VP & GM, Custom Silicon & IP, Alphawave Semi.

Alphawave Semis portfolio features an Arm Neoverse N3 CPU core cluster and the Arm Coherent Mesh Network (CMN) ensuring efficient, scalable performance. Accessible on industry-leading process nodes, the SoCs allow quick deployment of high-performance digital infrastructure in order to create custom silicon solutions.

Eddie Ramirez, Vice President of Go-to-Market, Infrastructure Line of Business, Arm offered, Alphawave Semis new advanced compute chiplet is a fantastic example of how industry-leading companies are leveraging the performance-optimization and power efficiency benefits of Neoverse CSS to get to market faster and power the next-generation AI and HPC workloads.

For more information, visitawavesemi.com.

Chad Cox. Production Editor, Embedded Computing Design, has responsibilities that include handling the news cycle, newsletters, social media, and advertising. Chad graduated from the University of Cincinnati with a B.A. in Cultural and Analytical Literature.

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Alphawave Semi Leverages Arm's Neoverse Compute Subsystems for AI/ML Applications - Embedded Computing Design

This relation is not linear from food to physical activity. Unhealthy food can disrupt someone's sleep patterns and physical activity. Conversely, healthy food choices can improve the quality of sleep and physical activity.

So how does nutrition affect sleep? A new study looked at the relationship between fruit and vegetable intake and sleep duration.

The research, conducted by a team from the University of Helsinki in Finland, in collaboration with the Finnish Institute for Health and Welfare, and Turku University of Applied Sciences, Finland, was published in Frontiers in Nutrition i> on May 16, 2024. Anupa Thapa from the University of Helsinki was the first author of the report.

Thapa and his team stated that sleep gives our body a chance to rest and recover from wakeful activities. The heart, blood vessels, muscles, cells, immune system, cognitive abilities, and memory all depend on regular and sufficient sleep in order to function optimally.

A 2019 study showed that sleep is important for repairing DNA damage that occurs while awake.

Deep sleep occurs in 35 night cycles, each lasting an average of 90120 minutes. During each cycle, we begin with the non-rapid eye movement (REM) sleep stage. Next, we will go through two periods of progressively deeper non-REM sleep before exiting that stage.

A homeless person slept under a concrete drainage structure that had not yet been installed in the drainage construction project on KH Wahid Hasyim Street, Medan, North Sumatra on Wednesday (11/8/2023). Despite the ongoing government construction efforts, the number of poor residents in North Sumatra is still quite high, reaching 1.24 million people or 8.15 percent as of March 2023.

Our non-REM sleep becomes progressively lighter until we reach the REM stage, after which a new cycle begins or we wake up. Adults should aim to sleep for 7 to 9 hours per night.

However, recent research shows that insomnia and shorter sleep duration have become more common in adults. This can be caused by factors such as stress, consumption of fast food, and a sedentary lifestyle.

Lack of sleep is now becoming a public health issue, related to cardiovascular diseases, decreased cognitive ability, and increased mortality rates due to various causes.

In this new study, researchers aimed to explore how sleep duration can affect fruit and vegetable consumption, and vice versa. They also investigated the role of an individual's chronotype (activity time preference, such as morning or evening) in food choices and sleep duration.

The World Health Organization recommends that people consume at least 400 grams of fruit and vegetables every day. While the latest advice from the Nordic Council of Ministers recommends higher intakes, encouraging between 500 grams and 800 grams of vegetables and fruit. In simple terms, half of our daily consumption should come from vegetables.

Also read: Half of Indonesians Don't Sleep Well

However, research shows that many adult individuals in various countries do not meet the minimum intake. According to Thapa and his team's research, only 14 percent of Finnish men and 22 percent of Finnish women consume a minimum of 500 g of berries, fruits, and vegetables as recommended daily.

The research team reviewed details from the 2017 National FinHealth Study. A total of 5,043 adults, aged 18 and over, submitted detailed responses to a 134item questionnaire about the composition and frequency of their daily food intake in the past 12 years.

From these responses, three categories of sleep duration emerged: short (less than 7 hours per day; 21 percent), normal (7-9 hours per day; 76.1 percent), and long (9+ hours per day; 2.9 percent).

Individuals who sleep for a short duration have an average sleep duration of six hours; for those who sleep normally, the average duration is 7.7 hours, and for those who sleep for a long duration, the average duration is 10.1 hours.

The majority of participants (61.7 percent) categorize themselves as intermediate chronotypes, while 22.4 percent stated that they are morning types, and 15.9 percent identified themselves as night types.

Researchers included chronotype as a co-variate in the study, and noted that many studies did not include chronotype as a potential confounding factor. However, some studies have shown that it can affect eating behavior.

Researchers state, "Studies have shown that night chronotype is often associated with unhealthy eating behavior, including a tendency towards eating habits that are linked to obesity."

Also read: Remember, the body needs enough rest

Among the important findings of this research, individuals who sleep normally show higher intake of fruits and vegetables compared to those who sleep short or long in all subgroups of fruits and vegetables. However, the intake of various types of fruits and vegetables yields different results.

The study explains, "In the subgroup of vegetables, significant differences are seen in the consumption of leafy green vegetables, tubers, and fruit vegetables (such as tomatoes, cucumbers), between those who sleep normally and those who have short sleep."

Similarly, in groups of people who have normal and long sleeping habits, significant differences can be seen in green leafy vegetables and fruit vegetables. However, fresh and canned vegetables such as cabbage, mushrooms, red onions, peas, and beans do not show significant differences.

"In the fruit subgroups, a significant difference in average consumption was observed for berries and other fresh and canned fruits between individuals with normal and short sleep. Conversely, for individuals with normal and long sleep, the only significant difference is seen in the consumption of apples," stated Thapa.

A seller shows an acai bowl consisting, among other things, of various fruits and nuts, Friday (3/5/2024). Acai bowl is one of the food menus that is popular with those who live healthy lives.

Researchers also observed that the category of sleep duration can indicate, at a small level, the expected level of fruit and vegetable intake.

This is in line with the results of research by Eleanor M Winpenny from the University of Cambridge and the team at International Journal of Behavioral Nutrition and Physical Activity in 2023 which found a decrease in fruit and vegetable intake among teenagers during the day. day after night with short sleep duration.

According to Winpenny and his team, they show the causal role of sleep on teenagers' eating patterns. Shorter sleep duration at night causes a slight decrease in the quality of their eating patterns the next day.

Avoid high-fat cheese, chicken wings, or fried fish. These take longer to digest and keep us awake.

This finding supports experimental evidence that suggests the inclusion of sleep duration as one of the intervention components designed to improve the quality of dietary patterns and body weight status in adolescents.

A new study by Thapa and his team also found that chronotype plays a minimal role in the relationship between fruit and vegetable intake and sleep duration. The Winpenny study in 2023 found no relationship between fruit and vegetable intake and chronotype.

Researchers observe that overall, a decrease in the intake of certain fruits and vegetables is associated with the length and duration of sleep. In addition to the quantity, it is important to pay attention to the choice of fruits and vegetables.

A study in The Journal of Clinical Sleep Medicine in 2026 also found that consuming high fiber and low saturated fat foods results in better sleep and body recovery. However, in addition to choosing the right foods, it is also important to avoid others.

Researchers provide a number of food options to help start the journey towards better sleep. For carbohydrate choices, choose complex ones, such as brown rice or root vegetables.

Avoid simple carbohydrates, including bread, pasta and sweet foods such as cookies, cakes, pastries and other sweet foods as these tend to lower serotonin levels and do not improve sleep quality. .

For protein, choose those that are lean. Lean protein options include low-fat cheese, chicken, turkey, and fish. These foods are rich in the amino acid tryptophan, which tends to increase serotonin levels.

A variety of non-rice foods processed from the TPI Lewoleba Senja Market, Lembata Island, NTT.

Tryptophan can also be found in egg whites, soybeans, and pumpkin seeds. On the other hand, avoid high-fat cheese, chicken wings, or fried fish. These take longer to digest and can keep us awake.

Heart-healthy fats are also important. Unsaturated fats will not only improve heart health, but also increase serotonin levels. Examples include peanut butter and nuts such as walnuts, cashews, and pistachios.

Avoid foods that contain saturated and trans fats, such as French fries, potato chips, or other high-fat snacks. This reduces your serotonin levels.

Mothers in Tumbang Lawang Village, Katingan District, Central Kalimantan, on Wednesday (10/4/2019), cook using bamboo and forest spices. They do not use any instant factory-made flavoring and prefer to use natural spices. For them, the forest is a source of life, and local food is the key to food security.

Like tryptophan, foods that are rich in magnesium are also associated with better sleep quality. When choosing vegetables for dinner, try adding leafy green vegetables such as spinach, which are rich in magnesium. Nuts, seeds, avocados, and black beans are also magnesium-rich foods.

As for beverages, avoid certain types that can prevent sleep. A good and soothing drink to be consumed before sleep is warm milk or simply warm water.

Meanwhile, for caffeinated beverages, try to consume them before 2 pm. Caffeine can affect people differently, and even the smallest amount of stimulant can keep someone awake.

Also read: School Lunch with Local Food

Originally posted here:
The Close Relationship between Food, Exercise, Sleep and Our Health - kompas.id

Chinese scientists are one step closer to a future large-scale quantum computer after building the worlds largest quantum simulation machine based on the trapped-ion technique, praised by one academic journal reviewer as a milestone to be recognised.

The breakthrough was achieved under the leadership of Duan Luming, a quantum physicist renowned for his pioneering research, who returned to China in 2018 after 15 years of teaching in the United States.

Duan received his doctorate in 1998 from the University of Science and Technology of China, the countrys premier institute for quantum research, before joining the University of Michigan in the early 2000s.

Since his return, he has been a full-time professor at Tsinghua Universitys Institute for Interdisciplinary Information Sciences.

Duan and his colleagues, along with several research groups at universities and hi-tech companies around the world, have been chasing the trapped-ion approach to qubits.

Quantum bits, or qubits, are the building blocks of quantum computers, just as bits are in regular computers.

However, qubits are extremely difficult to harness in a controlled and repeatable way because of what is called their hazy nature.

Regular bits can be described as switches that are either on or off. But because uncertainty and probability hold sway in quantum physics, qubits can be both on and off at the same time, and also exist in a variety of in-between states.

Ions, or charged atomic particles, can be trapped and suspended in free space using electromagnetic fields. The qubits are stored in stable electronic states of each ion, and quantum information can be transferred through the collective motion of the ions in a shared trap.

But scalability remains a key challenge for this system.

This is where the trapped-ion approach comes in, as it offers one of the most promising architectures for a scalable, universal quantum computer.

Researchers earlier achieved quantum simulations with up to 61 ions in a one-dimensional crystal. Ion crystals are solids made up of ions bound together in a regular lattice the symmetrical three-dimensional structural arrangements of atoms, ions or molecules inside a solid.

But Duan and his teams quantum simulator was able to achieve the stable trapping and cooling of a two-dimensional crystal of up to 512 ions, in a first for science.

The feat holds great significance for the future of quantum computing, given that scalability is a major hurdle. The teams scaling up of the ions in a stable simulation system is seen as likely to pave the way to building more powerful quantum computers.

The findings of their study were published on Wednesday in the peer-reviewed journal Nature.

This is the largest quantum simulation or computation performed to date in a trapped-ion system, commented one reviewer.

Quantum simulators are devices that actively use quantum effects to answer questions about model systems and, through them, real systems. They are increasingly popular tools in the world of quantum computing for their role in advancing scientific knowledge and developing technologies.

Duan and his team also managed to perform a quantum simulation calculation using 300-ion qubits. They found the computational complexity of 300-ion quantum bits working simultaneously to be astronomical, far exceeding the direct simulation capability of classical computers.

Link:

US-returned Chinese physicist and team achieve world first in quantum computing - South China Morning Post

(TNS) Gov. Jared Polis signed new legislation at the University of Colorado Boulder on Tuesday to further support the quantum industry in Colorado.

The new tax credit bill, which aims to strengthen the quantum industry in the state, was signed at CU Boulder's JILA Research Institute. JILA is a joint institute between CU Boulder and the National Institute of Standards and Technology. JILA, which stood for Joint Institute for Laboratory Astrophysics when it began in 1962, has expanded into a world-renowned and award-winning physics institute delving into cutting-edge research including quantum information science & technology.

"This bill will support the construction of a state-of-the-art quantum technology incubator, a facility that is poised to be unique in the world, and that will set our state apart," Massimo Ruzzene, CU Boulder vice chancellor for research and innovation, said in a statement. "It will foster the translation of technology and catalyze innovation, expanding educational and workforce opportunities while also creating jobs and economic benefits for all of Colorado."

In 2023, Colorado was designated as a Regional Technology and Innovation Hub, a designation that positions Colorado to apply for and secure federal funding opportunities to advance the industry.

"Quantum technology is the future of computing," Polis said in a release. "Today we proved that quantum is bigger and better in the West. As home to four Nobel Prize winners for quantum science, more than 3,000 quantum workers, and five of the top 20 quantum companies, Colorado is the clear future of quantum. I am thrilled to invest in this innovative sector and am excited for the bright quantum future in Colorado."

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Colorado Bill Aims to Strengthen Quantum in the State - Government Technology

PALO ALTO, Calif., May 29, 2024--(BUSINESS WIRE)--D-Wave Quantum Inc. (NYSE: QBTS) ("D-Wave" or the "Company"), a leader in quantum computing systems, software, and services and the worlds first commercial supplier of quantum computers, today announced that it has been featured in a Wall Street Journal article on quantum computing, which highlighted its technologys strengths in tackling real-world optimization problems.

The article, titled "Quantum Computing Gets Real: It Could Even Shorten Your Airport Connection," showcases how recent technological advances are enabling businesses and researchers to explore quantum computing for practical use cases. It specifically notes how D-Wave customers have used its annealing quantum computing technology to address optimization problems including grocery store driver delivery scheduling, cross-country promotional tour routing, and cargo-handling at one of the United States busiest ports. The article also highlights recent research from D-Wave, citing it as an example of a computational supremacy claim that, according to a source interviewed for the article, is "actually the strongest" of all the computational supremacy claims so far.

The story comes as D-Wave continues to be a leader in the commercialization of quantum computing. D-Waves Advantage quantum computer, currently the worlds largest quantum computer (5,000+ qubits), and its Leap real-time quantum cloud service are in market today, helping customers accelerate the adoption and deployment of quantum and hybrid-quantum applications. D-Wave has already taken a customers commercial application into production, meaning its systems are used to facilitate its customers daily operations. D-Wave is also the only company commercially offering annealing quantum computing, which is uniquely suited to solve optimization problems, challenges that are pervasive within commercial enterprises.

"This acknowledgment by The Wall Street Journal of quantums growing relevance and importance reflects what were seeing with our customers a steadily increasing appetite and enthusiasm to harness the power of quantum to solve their most computationally complex problems," said Dr. Alan Baratz, CEO of D-Wave. "We believe there is no other company right now in the world delivering the same level of commercial-grade, production-ready quantum technology as D-Wave. Its an incredibly important moment for the industry, and this recognition of D-Waves leadership is gratifying."

Story continues

About D-Wave Quantum Inc.

D-Wave is a leader in the development and delivery of quantum computing systems, software, and services, and is the worlds first commercial supplier of quantum computersand the only company building both annealing quantum computers and gate-model quantum computers. Our mission is to unlock the power of quantum computing today to benefit business and society. We do this by delivering customer value with practical quantum applications for problems as diverse as logistics, artificial intelligence, materials sciences, drug discovery, scheduling, cybersecurity, fault detection, and financial modeling. D-Waves technology has been used by some of the worlds most advanced organizations including Mastercard, Deloitte, Davidson Technologies, ArcelorMittal, Siemens Healthineers, Unisys, NEC Corporation, Pattison Food Group Ltd., DENSO, Lockheed Martin, Forschungszentrum Jlich, University of Southern California, and Los Alamos National Laboratory.

Forward-Looking Statements

Certain statements in this press release are forward-looking, as defined in the Private Securities Litigation Reform Act of 1995. These statements involve risks, uncertainties, and other factors that may cause actual results to differ materially from the information expressed or implied by these forward-looking statements and may not be indicative of future results. These forward-looking statements are subject to a number of risks and uncertainties, including, among others, various factors beyond managements control, including the risks set forth under the heading "Risk Factors" discussed under the caption "Item 1A. Risk Factors" in Part I of our most recent Annual Report on Form 10-K or any updates discussed under the caption "Item 1A. Risk Factors" in Part II of our Quarterly Reports on Form 10-Q and in our other filings with the SEC. Undue reliance should not be placed on the forward-looking statements in this press release in making an investment decision, which are based on information available to us on the date hereof. We undertake no duty to update this information unless required by law.

View source version on businesswire.com: https://www.businesswire.com/news/home/20240528062466/en/

Contacts

D-Wave Alex Daigle media@dwavesys.com

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D-Wave Quantum Featured in The Wall Street Journal - Yahoo Finance

PALO ALTO, Calif., May 28, 2024 D-Wave Quantum Inc., a leader in quantum computing systems, software, and services and the worlds first commercial supplier of quantum computers, today announced it is set to join the broad-market Russell 3000 Index at the conclusion of the 2024 Russell US Indexes annual Reconstitution, effective at the open of US equity markets on Monday, July 1st, 2024, according to a preliminary list of additions posted on Friday, May 24th, 2024.

The annual Russell US Indexes Reconstitution captures the 4000 largest US stocks as of Tuesday, April 30th, 2024, ranking them by total market capitalization. Membership in the US all-cap Russell 3000 Index, which remains in place for one year, means automatic inclusion in the large-cap Russell 1000 Index or small-cap Russell 2000 Index as well as the appropriate growth and value style indexes. FTSE Russell, a prominent global index provider, determines membership for its Russell indexes primarily by objective, market-capitalization rankings, and style attributes.

Its an honor for D-Wave to join the Russell 3000 Index, an important benchmark for the US stock market, said Dr. Alan Baratz, CEO of D-Wave. This recognition reflects D-Waves leadership in ushering in the era of commercial quantum computing and will greatly increase visibility among the global investor community for the innovative quantum solutions were bringing to market.

Russell indexes are widely used by investment managers and institutional investors for index funds and as benchmarks for active investment strategies. According to the data as of the end of December 2023, about $10.5 trillion in assets are benchmarked against the Russell US indexes, which belong to FTSE Russell.

Russell indexesnow in their 40th yearcontinue to evolve to reflect the dynamic US economy. Annual rebalancing plays a vital role in establishing accurate benchmarks, ensuring they correctly mirror their designated market segments and remain unbiased in terms of size and style, said Fiona Bassett, CEO of FTSE Russell, an LSEG Business.

For more information on the Russell 3000 Index and the Russell indexes Reconstitution, go to the Russell Reconstitution section on the FTSE Russell website.

About D-Wave Quantum Inc.

D-Wave is a leader in the development and delivery of quantum computing systems, software, and services, and is the worlds first commercial supplier of quantum computersand the only company building both annealing quantum computers and gate-model quantum computers. Our mission is to unlock the power of quantum computing today to benefit business and society. We do this by delivering customer value with practical quantum applications for problems as diverse as logistics, artificial intelligence, materials sciences, drug discovery, scheduling, cybersecurity, fault detection, and financial modeling. D-Waves technology has been used by some of the worlds most advanced organizations including Mastercard, Deloitte, Davidson Technologies, ArcelorMittal, Siemens Healthineers, Unisys, NEC Corporation, Pattison Food Group Ltd., DENSO, Lockheed Martin, Forschungszentrum Jlich, University of Southern California, and Los Alamos National Laboratory.

Source: D-Wave

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D-Wave Quantum Set to Join Russell 3000 Index - HPCwire