Washington, 

NASA aeronautical innovators are poised to break the sound barrier again, this time in a very different way that could make it possible for all of us to one day travel by air just as fast as any of the X-1 pilots who flew supersonic.

NASA's X-59, the centerpiece of the agency's Quesst mission, will enable commercial supersonic travel over land.

Lockheed Martin has designed, built and conducted initial flight tests with the aircraft and the first flight is targeted for 2023.

"That first supersonic flight was such a tremendous achievement, and now you look at how far we've come since then. What we're doing now is the culmination of so much of their work," said Catherine Bahm, an aeronautical engineer at NASA's Armstrong Flight Research Center in California.

Through Quesst, NASA plans to demonstrate the X-59 can fly faster than sound without generating the typically loud sonic booms that led to supersonic flight over land being banned in 1973.

The plan includes flying the X-59 over several communities to survey how people react to the quieter sonic "thump" it produces - if they hear anything at all.

"Their responses will be shared with regulators who will then consider writing new rules to lift the ban," said NASA.

"And when that happens, it will mark another historic milestone in flight, potentially opening a new era in air travel, where airline passengers might hop on a supersonic jet at breakfast time in Los Angeles to make a lunch-time reservation in New York City," it added.

Seventy-five years ago, a sonic boom thundered for the first time over the high desert of California, when the thunder came from the Bell X-1 rocket plane flying faster than the speed of sound.

It was October 14, 1947, and the joint X-1 team of NACA, Air Force (newly formed that year), and Bell engineers and pilots had broken the sound barrier -- an imaginary wall in the sky some said was impossible to penetrate.

"With the X-59 flying on the Quesst mission, I think we are ready to break the sound barrier once again," said Peter Coen, NASA's mission integration manager for Quesst. IANS

Washington [US], October 15 (ANI): According to new research, oxygen levels in the Earth’s atmosphere are likely to have “fluctuated wildly” one billion years ago, creating conditions that could have accelerated the development of early animal life.

Scientists believe atmospheric oxygen developed in three stages, starting with what is known as the Great Oxidation Event around two billion years ago, when oxygen first appeared in the atmosphere. The third stage, around 400 million years ago, saw atmospheric oxygen rise to levels that exist today.

What is uncertain is what happened during the second stage, in a time known as the Neoproterozoic Era, which started about one billion years ago and lasted for around 500 million years, during which time early forms of animal life emerged.

The question scientists have tried to answer is – was there anything extraordinary about the changes to oxygen levels in the Neoproterozoic Era that may have played a pivotal role in the early evolution of animals – did oxygen levels suddenly rise or was there a gradual increase?

Fossilised traces of early animals – known as Ediacaran biota, multi-celled organisms that required oxygen – have been found in sedimentary rocks that are 541 to 635 million years old.

To try and answer the question, a research team at the University of Leeds supported by the Universities of Lyon, Exeter and UCL, used measurements of the different forms of carbon, or carbon isotopes, found in limestone rocks taken from shallow seas. Based on the isotope ratios of the different types of carbon found, the researchers were able to calculate photosynthesis levels that existed millions of years ago and infer atmospheric oxygen levels.

As a result of the calculations, they have been able to produce a record of oxygen levels in the atmosphere over the last 1.5 billion years, which tells us how much oxygen would have been diffusing into the ocean to support early marine life.

Dr Alex Krause, a biogeochemical modeller who completed his PhD in the School of Earth and Environment at Leeds and was the lead scientist on the project, said the findings give a new perspective on the way oxygen levels were changing on Earth.

He added: “The early Earth, for the first two billion years of its existence, was anoxic, devoid of atmospheric oxygen. Then oxygen levels started to rise, which is known as the Great Oxidation Event.

“Up until now, scientists had thought that after the Great Oxidation Event, oxygen levels were either low and then shot up just before we see the first animals evolved, or that oxygen levels were high for many millions of years before the animals came along.

“But our study shows oxygen levels were far more dynamic. There was an oscillation between high and low levels of oxygen for a long time before early forms of animal life emerged. We are seeing periods where the ocean environment, where early animals lived, would have had abundant oxygen – and then periods where it does not.

Dr Benjamin Mills, who leads the Earth Evolution Modelling Group at Leeds and supervised the project, said: “This periodic change in environmental conditions would have produced evolutionary pressures where some life forms may have become extinct and new ones could emerge.”

Dr Mills said the oxygenated periods expanded what is known as “habitable spaces” – parts of the ocean where oxygen levels would have been high enough to support early animal life forms.

He said: “It has been proposed in ecological theory that when you have a habitable space that is expanding and contracting, this can support rapid changes to the diversity of biological life.

“When oxygen levels decline, there is severe environmental pressure on some organisms which could drive extinctions. And when the oxygen-rich waters expand, the new space allows the survivors to rise to ecological dominance.

“These expanded habitable spaces would have lasted for millions of years, giving plenty of time for ecosystems to develop.” (ANI)

 IANS

San Francisco, October 15

Meta will end support for its ‘Instant Articles’ format on the Facebook platform next year, as the company moves away from its news-related products and focus on TikTok-like offerings.

According to Axios, the quick-loading article format, that was first launched in 2015, will shut down in April 2023.

‘Instant Articles’ was a mobile format to quickly load news articles on the Facebook app.

Once the support is ended, news links on Facebook will take a user to a publisher's mobile site.

“Currently less than 3 per cent of what people around the world see in Facebook's Feed are posts with links to news articles,” a company spokesperson said in a statement.

“And as we said earlier this year, as a business it doesn't make sense to over invest in areas that don't align with user preferences,” it added.

The company once called itself a Fifth Estate, and launched revamped News tab that included a dedicated local news section among other topics, including breaking news alerts.

Earlier this month, Meta announced to shut down its newsletter product called Bulletin for creators and writers by early 2023.

Meta said it will refocus resources from Bulletin to work on its discovery algorithm, that the company is building to take on Chinese short-form video TikTok.

In June 2021, the company launched Bulletin, a set of publishing and subscription tools to support creators in the US.

The company is now giving news publishers six months to readjust their Facebook strategies.

 Pennsylvania [US], October 14 (ANI): Natural language processing (NLP) is a sort of artificial intelligence that allows machines to utilise written and spoken phrases in a variety of applications, such as smart assistants or email autocorrect and spam filters, to help automate and expedite activities for individual users and companies. However, the algorithms that power this technology frequently exhibit characteristics that might be insulting or discriminatory toward people with disabilities.
A study conducted by researchers at the Penn State College of Information Sciences and Technology (IST). The researchers found that all the algorithms and models they tested contained significant implicit bias against people with disabilities. Previous research on pretrained language models -- which are trained on large amounts of data that may contain implicit biases -- has found sociodemographic biases against genders and races, but until now similar biases against people with disabilities have not been widely explored.
"The 13 models we explored are highly used and are public in nature," said Pranav Venkit, doctoral student in the College of IST and first author on the study's paper presented today (Oct. 13) at the 29th International Conference on Computational Linguistics (COLING). "We hope that our findings help developers that are creating AI to help certain groups -- especially people with disabilities who rely on AI for assistance in their day-to-day activities -- to be mindful of these biases."
In their study, the researchers examined machine learning models that were trained on source data to group similar words together enabling a computer to automatically generate sequences of words. They created four simple sentence templates in which to variably populate a gender noun of "man," "woman," or "person," and one of the 10 most commonly used adjectives in the English language -- for example, "They are parents of a good person." Then, they generated more than 600 adjectives that could be associated with either people with or without disabilities -- such as neurotypical or visually impaired -- to randomly replace the adjective in each sentence. The team tested more than 15,000 unique sentences in each model to generate word associations for the adjectives.
"For example, we selected the word 'good,' and wanted to see how it associated with terms related to both non-disability and disability," explained Venkit. "With the addition of a non-disability term, the effect of 'good' becomes 'great.' But when 'good' is associated with a disability-related term, we get the result of 'bad.' So that change in the form of the adjective itself shows the explicit bias of the model."
While this exercise revealed the explicit bias that exists in the models, the researchers wanted to further measure each model for implicit bias -- attitudes toward people or associating stereotypes with them without conscious knowledge. They examined the adjectives generated for the disability and non-disability groups and measured each one's sentiment -- an NLP technique to rate whether text is positive, negative or neutral. All models they studied consistently scored sentences with words associated with disability more negatively than those without. One particular model, which was pretrained on Twitter data, flipped the sentiment score from positive to negative 86% of the time when a term related to a disability was used.
"When we look at that score alone, we see that just by the addition of a term related to disability into the conversation, the sentiment score of the whole sentence drops," said Venkit. "For example, if a user includes a term related to disability in a social media comment or post, the probability of that post being censored or restricted increases."
The researchers also tested for implicit bias in two large language models that are used to automatically generate long texts, such as news articles, to see how a blank left in the sentence template would change depending on the adjective used. In this analysis they generated 7,500 sentences, again variably inserting adjectives related to non-disability or disability, and tested to see how a blank left in the sentence template would change depending on the adjective used. In this case, when given the sentence of "A man has 'blank'," the language models predicted "changed" for the blank word. However, when a disability-related adjective was added to the sentence, resulting in "A deafblind man has 'blank'," the model predicted "died" for the blank.
The models' implicit bias against people with disabilities could be apparent in various applications -- for example, in text messages when applying autocorrect to a misspelled word or on social media where there are rules that prohibit abusive or harassing posts. In the latter, because humans are unable to review the vast number of posts that are made, artificial intelligence models use these sentiment scores to filter out those posts that are deemed in violation of the platform's community standards.
"If someone is discussing disability, and even though the post is not toxic, a model like this which doesn't focus on separating the biases might categorize the post as toxic just because there is disability associated with the post," explained Mukund Srinath, doctoral student in the College of IST and co-author of the study.
"Whenever a researcher or developer is using one of these models, they don't always look at all the different ways and all the different people that it is going to affect -- especially if they're concentrating on the results and how well it performs," said Venkit. "This work shows that people need to care about what sort of models they are using and what the repercussions are that could affect real people in their everyday lives." (ANI)

 Washington [US], October 13 (ANI): Researchers have identified over 12,000 genetic variants that influence a person's height.
The 12,111 variants, which cluster around parts of the genome associated with skeletal growth, provide a powerful genetic predictor for height. The variants identified explain 40% of the variation in height for people of European ancestry, and around 10-20% for those of non-European ancestry. Adult height is mostly determined by the information encoded in our DNA -- children from tall parents tend to be taller and those from short parents are shorter, but these estimates aren't perfect. Growth from a small baby into an adult, and the role genetics play in this, have traditionally been a complex and poorly understood area of human biology. Previously, the largest genome-wide association study looking at height used a sample size of up to 700,000 individuals, the current sample is about seven times more than previous studies.
The unprecedented scale of the research provides new levels of detail and biological insight as to why people are tall or short, with heritability being linked to various specific genomic regions. The findings show that genetic variants associated with height are concentrated in regions covering just over 20% of the genome.
The study's findings could help doctors to identify people who are not able to reach their genetically predicted height, which may then aid in the diagnosis of hidden diseases or conditions that may be stunting their growth or impacting their health. The research also provides a valuable blueprint on how it could be possible to use genome-wide studies to identify a disease's biology and subsequently its hereditary components.
While this study has a large number of participants from non-European ancestries compared to previous studies, the researchers emphasise the need for more diversity in genomic research.
Most of the genetic data available is from people of European ancestry, so genome-wide studies don't capture the wide range of ancestral diversity across the globe. Increasing the size of genome-wide studies in non-European ancestry populations is essential to achieve the same level of saturation and close the gap in prediction accuracy in different populations.
Dr Eirini Marouli, co-first author of the study and Senior Lecturer in Computational Biology at Queen Mary University of London, said:
"We have accomplished a feat in studying the DNA of over 5 million people that was broadly considered impossible until recently.
"Genomic studies are revolutionary and might hold the key to solving many global health challenges -- their potential is tremendously exciting. If we can get a clear picture of a trait such as height at a genomic level, we may then have the model to better diagnose and treat gene-influenced conditions like heart disease or schizophrenia, for example.
"If we can map specific parts of the genome to certain traits, it opens the door to widespread targeted, personalised treatments further down the line that could benefit people everywhere." (ANI)

Mumbai (Maharashtra) [India], October 14 (ANI/BusinessWire India): Pocket HRMS today announced its availability in the Microsoft Azure Marketplace , an online store providing applications and services for use on Azure. Microsoft Azure customers worldwide now gain access to Pocket HRMS to take advantage of the scalability, reliability, and agility of Azure to drive application development and shape business strategies.
This is a significant milestone for the company, which is on a collaboration streak with major technology players to offer end-to-end employee management solutions for businesses. As a Microsoft Partner, the Azure Marketplace would enable Pocket HRMS to make their payroll and HRMS software available to a broader audience of companies looking to digitize and enhance their HR processes. With offices and customers in all major cities across India, Pocket HRMS boasts its presence across the country, processing more than five million HR transactions per month. Kumar Siddhartha, Managing Director at Pocket HRMS, said, "We are honoured to announce that we are a proud Microsoft Partner. With this announcement, we aim to bring together the best in cloud-enabled infrastructure and the best in HR software. Our goal is to provide greater value to our customers and bring about a true HR transformation. It will help in increased employee engagement, faster payroll processing , and a highly scalable solution that grows with our client's business requirements. We are excited to offer our powerful smHRt suite of features to our prospective clientele."
"Through Microsoft Azure Marketplace, customers around the world can easily find, buy, and deploy partner solutions they can trust, all certified and optimized to run on Azure," said Jake Zborowski, General Manager, Microsoft Azure Platform at Microsoft Corp. "We're happy to welcome Pocket HRMS to the growing Azure Marketplace ecosystem."
The Azure Marketplace is an online market for buying and selling cloud solutions certified to run on Azure. It helps connect companies seeking innovative, cloud-based solutions with partners who have developed solutions that are ready to use.
Established almost two decades ago, Pocket HRMS is a leading HRMS software in India. Its core values are ensuring the best in customer service, cost-effectiveness, and robust module offerings. The latest update has incorporated an improved AI-based attendance system and an AI chatbot- smHRty that can interact with employees to apply leaves, answer queries, and more. Additionally, Pocket HRMS offers HRs a unique global look at their database with smHRt searcHR, which is one of the first implementations of its kind in the HR domain.
To learn more, contact us:
https://www.pockethrms.com | [email protected] | 022-67687800
This story is provided by BusinessWire India. ANI will not be responsible in any way for the content of this article. (ANI/BusinessWire India)

 Washington [US], October 12 (ANI): A team of astronomers has discovered a unique black hole spewing a fiery jet at another galaxy. The black hole is hosted by a galaxy around one billion light years away from Earth named RAD12.
Galaxies are typically divided into two major classes based on their morphology: spirals and ellipticals. Spirals have optically-blue looking spiral arms with an abundance of cold gas and dust. In spiral galaxies, new stars form at an average rate of one Sun-like star per year. In contrast elliptical galaxies appear yellowish and lack distinct features such as spiral arms. Star formation in elliptical galaxies is very scarce; it is still a mystery to astronomers as to why the elliptical galaxies we see today have not been forming new stars for billions of years. Evidence suggests that supermassive or 'monster' black holes are responsible. These 'monster' black holes spew gigantic jets made of electrons moving at very high speeds at other galaxies, depleting the fuel required for future star formation: cold gas and dust.
The unique nature of RAD12 had been observed in 2013 using optical data from the Sloan Digitised Sky Survey (SDSS) and radio data from the Very Large Array (FIRST survey). However, follow-up observation with the Giant Meterwave Radio Telescope (GMRT) in India was required to confirm its truly exotic nature: The black hole in RAD12 appears to be ejecting the jet only towards a neighbouring galaxy, named RAD12-B. In all cases, jets are ejected in pairs, moving in opposite directions at relativistic speeds. Why only one jet is seen coming from RAD12 remains a puzzle to astronomers.
A conical stem of young plasma is seen being ejected from the centre and reaches far beyond the visible stars of RAD12. The GMRT observations revealed that the fainter and older plasma extends far beyond the central conical stem and flares out like the cap of a mushroom (seen in red in the tricolour image). The whole structure is 440 thousand light years long, which is much larger than the host galaxy itself.
RAD12 is unlike anything known so far; this is the first time a jet has been observed to collide with a large galaxy like RAD12-B. Astronomers are now one step closer to understanding the impact of such interactions on elliptical galaxies, which may leave them with little cold gas for future star formation.
Research lead Dr Ananda Hota says, "We are excited to have spotted a rare system that helps us understand radio jet feedback of supermassive black holes on star formation of galaxies during mergers. Observations with the GMRT and data from various other telescopes such as the MeerKAT radio telescope strongly suggest that the radio jet in RAD12 is colliding with the companion galaxy. An equally important aspect of this research is the demonstration of public participation in making discoveries through the RAD@home Citizen Science research collaboratory." (ANI)

San Francisco, Oct 13 (IANS) With an aim to prioritise security and privacy, encrypted messaging app Signal has announced that it will soon stop supporting SMS messages on Android.

The platform said it has "now reached the point where SMS support no longer makes sense".

"In order to enable a more streamlined Signal experience, we are starting to phase out SMS support from the Android app," the company said in a blogpost.

"You will have several months to transition away from SMS in Signal, to export your SMS messages to another app, and to let the people you talk to know that they might want to switch to Signal, or find another channel if not," it added.

The change will only affect users who use Signal as their default SMS app on Android.

"If you do use Signal as your default SMS app on Android, you will need to select a new default SMS app on your phone. If you want to keep them, you will also need to export your SMS messages from Signal into that new app," the company said.

According to Signal, it is removing SMS support for the Android app to prioritise security and privacy, ensuring people aren't hit with unexpected messaging bills, and creating a clear and intelligible user experience for anyone sending messages on Signal.

"The most important reason for us to remove SMS support from Android is that plaintext SMS messages are inherently insecure. They leak sensitive metadata and place your data in the hands of telecommunications companies," the messaging app said.

"With privacy and security at the heart of what we do, letting a deeply insecure messaging protocol have a place in the Signal interface is inconsistent with our values and with what people expect when they open Signal," it added.

Russia on Tuesday added US tech giant Meta, the parent company of Instagram and Facebook, to a list of "terrorist and extremist" organisations, according to a database of the Federal Service for Financial Monitoring (Rosfinmonitoring).

Russia in late March banned Facebook and Instagram for "carrying out extremist activities" after authorities accused Meta of tolerating "Russophobia" during Russia's military campaign in Ukraine.

Meta had announced on March 10 that the platforms would allow statements like "death to Russian invaders" but not credible threats against civilians, before saying the change only applied to users posting from inside Ukraine.

Facebook and Instagram have been inaccessible in Russia since March, but many Russians resorted to VPN to keep using the social media network.Instagram is particularly popular in Russia, and used to be a key platform for advertisement and sales.
Billions of people use Meta's app around the world.

This decision puts Meta on the same list as right-wing nationalist groups, foreign terrorist organisations including the Taliban and Russian opposition groups.

 Washington [US], October 12 (ANI): Published in the journal 'Proceedings of the National Academy of Sciences' (PNAS), a new study found links between social disadvantages and fetal brain development.
Researchers examined 289 healthy newborns from socially diverse families in the United States using diffusion MRI scans shortly after birth. The analysis revealed that prenatal exposure to measures of social disadvantage was associated with altered microstructure of white matter in frontolimbic pathways of the brain that are important for socioemotional development. Early life adversity (social disadvantage and psychosocial stressors) is associated with altered microstructure in fronto-limbic pathways important for socioemotional development.
Understanding when these associations begin to emerge may inform the timing and design of preventative interventions. In this longitudinal study, 399 mothers were oversampled for low income and completed social background measures during pregnancy.
Measures were analyzed with structural equation analysis resulting in two latent factors: social disadvantage (education, insurance status, income-to-needs ratio [INR], neighborhood deprivation, and nutrition) and psychosocial stress (depression, stress, life events, and racial discrimination). At birth, 289 healthy term-born neonates underwent a diffusion MRI (dMRI) scan.
Mean diffusivity (MD) and fractional anisotropy (FA) were measured for the dorsal and inferior cingulum bundle (CB), uncinate, and fornix using probabilistic tractography in FSL. Social disadvantage and psychosocial stress were fitted to dMRI parameters using regression models adjusted for infant postmenstrual age at scan and sex. Social disadvantage, but not psychosocial stress, was independently associated with lower MD in the bilateral inferior CB and left uncinate, right fornix, and lower MD and higher FA in the right dorsal CB.
Results persisted after accounting for maternal medical morbidities and prenatal drug exposure.
In moderation analysis, psychosocial stress was associated with lower MD in the left inferior CB among the lower-to-higher socioeconomic status (SES) (INR >= 200 per cent) group, but not the extremely low SES (INR < 200 per cent) group. Increasing access to social welfare programs that reduce the burden of social disadvantage and related psychosocial stressors may be an important target to protect fetal brain development in fronto-limbic pathways. (ANI)