19 Mar 2026 On 18 March, Xi’an Jiaotong-Liverpool University (XJTLU) hosted the Intelligent Engineering 2026: XJTLU-XJTU-UoL Joint Symposium alongside Xi’an Jiaotong University (XJTU) in China and the University of Liverpool in the UK. The event brought together experts from the three universities and other Chinese institutions to discuss advances in intelligent engineering. It also showed

Key takeaways Satellite imagery combined with artificial intelligence has led to new, high-resolution statewide maps that detect changes in kelp forests previously invisible to traditional monitoring tools. The maps provide critical data-driven insights into how kelp survived or declined following the record-breaking 2014–2016 marine heatwave, allowing managers to target specific areas for recovery and develop

UCLA Key takeaways Satellite imagery combined with artificial intelligence has led to new, high-resolution statewide maps that detect changes in kelp forests previously invisible to traditional monitoring tools. The maps provide critical data-driven insights into how kelp survived or declined following the record-breaking 2014–2016 marine heatwave, allowing managers to target specific areas for recovery and

AWS News Blog We’re thrilled to celebrate three exceptional developer community leaders as AWS Heroes. These individuals represent the heart of what makes the AWS community so vibrant. In addition to sharing technical knowledge, they build connections, forge genuine human relationships, and create pathways for others to grow. From pioneering cloud culture in mountain villages

A systematic review and meta-analysis published in Frontiers in Oncology found that ultrasound characteristics–based artificial intelligence–assisted diagnostic systems “demonstrated high clinical potential” in distinguishing benign from malignant thyroid nodules, with an ensemble deep learning classification model showing the highest diagnostic accuracy, according to Zhan et al. In addition, the researchers wrote, “For thyroid nodules in

Artificial intelligence (AI) models analyzing imaging, clinical, and molecular data may improve the diagnosis and prediction of polycystic ovary syndrome, according to a systematic review of 80 studies that found some tools consistently outperform traditional diagnostic approaches and identified potential molecular biomarkers. The review, conducted by Mustafa Ghaderzadeh, PhD, of Urmia University of Medical Sciences