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Russia’s diversified design bureau for marine engineering, Rubin, unveiled a project for a modified version of a submarine that the company is working on.


MOSCOW, ($1=76.79 Russian Rubles) — Russia’s diversified design bureau for marine engineering, Rubin, has unveiled a project for a modified version of the Guardian submarine that the company is working on. Rubin is the greatest designer of Soviet and Russian submarines – 85% of them are the work of the company.

According to preliminary data, the submarine is designed to reach a maximum speed of 21 knots. If it travels at 10 knots, it can travel a maximum of 4,000 miles. Ruby achieves this speed by integrating a more powerful power plant, as well as reducing the resistance to movement in the surface position.

Urban air pollution exposure is an ongoing critical challenge for humanity today because the majority of us live in cities. A just-published study in The Lancet Planetary Health reports that 2.5 billion live in urban areas where particulate matter levels exceed World Health Organization (WHO) safety guidelines causing more than 1.8 million attributed deaths annually. The study reports that “the global health burden of ambient fine particulate matter is increasing annually” and is associated with mortality from cardiovascular, respiratory, and lung diseases including cancer. And in terms of chronic diseases, particulate matter in the air contributes to asthma, bronchitis and emphysema, and seen as the leading environmental risk humans face today.

What is particulate matter? The Canadian government defines it as airborne particles that can be solid or liquid. Particulate matter comes from natural and human sources. The natural ones can be seen when volcanoes erupt spewing ash and other aerosols high into the atmosphere. But far more dangerous because of the sheer volume, is human-produced particulate matter coming from smokestacks, tailpipes, power plants, recently tilled fields, tires running across asphalt and concrete roads, and other human activities that release fine particles into the wind. The lighter and smaller the particle, the greater the threat. That’s because fine particulate matter is easier to inhale. It’s also easier to enter the pores of leaves in plants, and easier as liquid aerosols to bind to our buildings and bridges and other infrastructure where its acidic nature causes corrosion.

A particle of 2.5 micrometres (equivalent to 0.00009843 inches) or less is a public health threat. The U.S. Environmental Protection Agency tracks aerosol pollution at this size and on its site notes that particulate matter smaller than 2.5 micrometres has been declining for two decades. The Lancet study contradicts this finding noting that globally levels of airborne particulate matter have changed very little in twenty years. And where’s the greatest rise? In the cities of Southeast Asia.

Please welcome Samantha Higgins, who defines herself as a professional writer with a passion for research, observation, and innovation. She resides in Portland, Oregon with her husband and her two twin boys. When she’s not writing about artificial intelligence and other technology subjects, Samantha loves kayaking and reading creative non-fiction. In this her first contribution to 21st Century Tech Blog, she talks about the progress being made by those who create the neural networks that make computers learn about the patterns in human existence. That’s what machine learning is all about.

Machine learning is a technology that gives us language translation applications, word prediction when composing emails and texts, and suggestions on the order presentation within social media feeds. It is a technology used by many industries from healthcare where it can aid in medical diagnosis and interpretation of radiology images, as well as in the operation of autonomous vehicles.

Machine learning is a subcategory of artificial intelligence (AI), software tools that learn without explicitly relying on programming. Many companies deploying AI today are primarily using machine learning to help reduce labor costs and increase productivity.

Recent advances in AI are using deep learning to identify areas within human organs that surgeons can safely dissect before operating, machine learning to predict if patients with memory issues will develop Alzheimer’s within two years, and deep learning to analyze eye scans during routine examinations to identify patients at short-term high risk for a heart attack.


AI is being used for surgical guidance in the OR, for predicting early-onset Alzheimer’s, and through eye exams who may have a heart attack.