
These pictures show India's Shubhanshu Shukla is loving it in space
The Ax-4 crew is also contributing to the Wireless Acoustics project. (Photo: Axiom)
Insights from this work are crucial for developing interventions to protect astronaut health during long-duration missions, such as those planned for Mars.SCIENCE UNDERWAYShux has also redeployed a culture bag for the Space Micro Algae experiment. Microalgae are being studied for their remarkable potential to support life beyond Earth—they can generate food, oxygen, and even biofuel.Success in cultivating microalgae aboard the ISS could make them indispensable for future lunar and Martian outposts, providing sustainable life support and resource recycling.
Ax-4 Pilot Shubhanshu Shukla carries out operations for the myogenesis study in the Life Sciences Glovebox aboard the International Space Station. (Photo: Axiom)
The Ax-4 team, including Commander Peggy Whitson, Mission Specialists Sawosz 'Suave' Uznaski-Winiewski, and Tibor Kapu, has maintained a relentless pace of scientific activity:Tibor Kapu observed the Fruit Fly DNA Repair study, which examines how space radiation affects genetic integrity.
Shubhanshu Shukla looking out of the cupola of the Space Station. (Photo: Axiom Space)
By analysing how fruit fly DNA responds to the harsh conditions of orbit, researchers hope to develop strategies to shield human DNA from similar dangers during interplanetary travel.The crew contributed to the Wireless Acoustics project, evaluating a wearable acoustic monitor that measures sound levels throughout the station. This device is being assessed for comfort and accuracy, with its readings compared to those from traditional fixed sound meters.
Shux testing his photography skills in space. (Photo: Axiom)
Axiom added that a crewmember underwent an ultrasound scan as part of a project tracking cardiovascular and balance system changes. This study aims to enable real-time, AI-driven health monitoring for astronauts, with the potential to revolutionize healthcare both in space and on Earth.As the Ax-4 mission progresses, the crew's dedication to research, technology demonstrations, and outreach continues to expand humanity's understanding of living and thriving beyond our home planet.- EndsTune InMust Watch
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New Indian Express
2 hours ago
- New Indian Express
Watch the skies, Shux is passing by
BENGALURU: Be alert, look up at the clear sky and track the live movement of the International Space Station (ISS). This has become the latest trend among many stargazers and astronomy enthusiasts, especially after India's first astronaut, Group Captain and mission pilot of Axiom-4 Shubhanshu Shukla, boarded the ISS. Ever since Shukla docked into the ISS on June 26, there has been heightened excitement among astronomy enthusiasts and scientists. It has now caught the attention of citizens who are keen to watch and track the movement of the ISS overhead. There was a lot of enthusiasm among Bengalureans and people across South India on July 5, for the ISS was clearly visible over the horizon and many even videographed it. "It was like a bright moving star slowly passing by," they said. The same was seen on Monday, when the ISS crossed the Indian Ocean around 7.07-7.10pm and Bengalureans were able to see it with the naked eye in the southern direction at a 15-degree elevation. Experts from ISRO and Jawaharlal Nehru Planetarium said the enthusiasm among citizens to see and track ISS has increased. There are many mobile applications that can be downloaded through Play Store on mobile phones to track the ISS' live location.


Mint
2 hours ago
- Mint
ISS with Indian astronaut Shubhanshu Shukla to fly over THESE cities. Check date and time
The International Space Station, carrying the Indian astronaut Shubhanshu Shukla as part of the Axiom-4 mission, will fly over India repeatedly in the next few days. Last Thursday, Shukla became the Indian astronaut with the longest stay in space, surpassing the record of his mentor Rakesh Sharma, who spent seven days, 21 hours, and 40 minutes in space as part of the Soviet Interkosmos programme in 1984. Shukla is on a 14-day mission to the International Space Station as part of a joint ISRO-NASA project. NASA's Spot the Station and ISS Detector apps can be used to check the ISS's current location. While the ISS Detector app shows you where the ISS is at the moment, NASA's app will tell you when you can next see it from your location. The app also allows you to set a reminder or alarm for the next sighting time. According to NASA's Spot the Station app, the ISS will be visible from India at 4:59 am on July 8 next. After that, it will be visible at 7:59 pm on the same day and then again at 9:38 pm. On July 9, it can be seen at 4:10 am and then at 8:48 pm. On July 10, ISS will be visible from the country three times: 3:22 am, 4:58 pm and 9:59 pm. On July 11, it can be seen at 2:34 am and 4:09 am. The ISS carrying Shukla will be last visible from India on July 12 at 7:59 pm. Last month, Shukla interacted with Prime Minister Narendra Modi from space and said that India looks 'very big and grand' from Space. 'Jab pehli baar Bharat ko dekha, Bharat sach mein bohat bhavya dikta hain, jitna ham map pe dekhten hain, usse kahin jyada bada (When we saw India for the first time, we saw that India looks very grand, very big, much bigger than what we see on the map),' Shukla said.


Time of India
5 hours ago
- Time of India
AI might now be as good as humans at detecting emotion, political leaning, sarcasm in online conversations
When we write something to another person, over email or perhaps on social media, we may not state things directly, but our words may instead convey a latent meaning - an underlying subtext. We also often hope that this meaning will come through to the reader. But what happens if an artificial intelligence (AI) system is at the other end, rather than a person? Can AI, especially conversational AI, understand the latent meaning in our text? And if so, what does this mean for us? Latent content analysis is an area of study concerned with uncovering the deeper meanings, sentiments and subtleties embedded in text. For example, this type of analysis can help us grasp political leanings present in communications that are perhaps not obvious to everyone. Understanding how intense someone's emotions are or whether they're being sarcastic can be crucial in supporting a person's mental health, improving customer service, and even keeping people safe at a national level. These are only some examples. We can imagine benefits in other areas of life, like social science research, policy-making and business. Given how important these tasks are - and how quickly conversational AI is improving - it's essential to explore what these technologies can (and can't) do in this regard. Work on this issue is only just starting. Current work shows that ChatGPT has had limited success in detecting political leanings on news websites. Another study that focused on differences in sarcasm detection between different large language models - the technology behind AI chatbots such as ChatGPT - showed that some are better than others. Finally, a study showed that LLMs can guess the emotional "valence" of words - the inherent positive or negative "feeling" associated with them. Our new study published in Scientific Reports tested whether conversational AI, inclusive of GPT-4 - a relatively recent version of ChatGPT - can read between the lines of human-written texts. The goal was to find out how well LLMs simulate understanding of sentiment, political leaning, emotional intensity and sarcasm - thus encompassing multiple latent meanings in one study. This study evaluated the reliability, consistency and quality of seven LLMs, including GPT-4, Gemini, Llama-3.1-70B and Mixtral 8 x 7B. We found that these LLMs are about as good as humans at analysing sentiment, political leaning, emotional intensity and sarcasm detection. The study involved 33 human subjects and assessed 100 curated items of text. For spotting political leanings, GPT-4 was more consistent than humans. That matters in fields like journalism, political science, or public health, where inconsistent judgement can skew findings or miss patterns. GPT-4 also proved capable of picking up on emotional intensity and especially valence. Whether a tweet was composed by someone who was mildly annoyed or deeply outraged, the AI could tell - although, someone still had to confirm if the AI was correct in its assessment. This was because AI tends to downplay emotions. Sarcasm remained a stumbling block both for humans and machines. The study found no clear winner there - hence, using human raters doesn't help much with sarcasm detection. Why does this matter? For one, AI like GPT-4 could dramatically cut the time and cost of analysing large volumes of online content. Social scientists often spend months analysing user-generated text to detect trends. GPT-4, on the other hand, opens the door to faster, more responsive research - especially important during crises, elections or public health emergencies. Journalists and fact-checkers might also benefit. Tools powered by GPT-4 could help flag emotionally charged or politically slanted posts in real time, giving newsrooms a head start. There are still concerns. Transparency, fairness and political leanings in AI remain issues. However, studies like this one suggest that when it comes to understanding language, machines are catching up to us fast - and may soon be valuable teammates rather than mere tools. Although this work doesn't claim conversational AI can replace human raters completely, it does challenge the idea that machines are hopeless at detecting nuance. Our study's findings do raise follow-up questions. If a user asks the same question of AI in multiple ways - perhaps by subtly rewording prompts, changing the order of information, or tweaking the amount of context provided - will the model's underlying judgements and ratings remain consistent? Further research should include a systematic and rigorous analysis of how stable the models' outputs are. Ultimately, understanding and improving consistency is essential for deploying LLMs at scale, especially in high-stakes settings.