Recently, large language models have gained considerable interest due to their impressive performance on different tasks. OpenAl's ChatGPT is a revolutionary language model that has quickly become popular among early adopters. Many users have gone so far as to call it a disruptive technology in many different domains. Its large, pre-trained model provides powerful capabilities already being applied to various use cases. Gaining an understanding of the thoughts and feelings of those who are quick to embrace new technologies is essential, as it can offer valuable insights into the technology's potential successes, failures, and other characteristics. In this paper, a mixed-method study was developed using 463,983 tweets from early ChatGPT users. We first apply topic modeling to identify the maj or topics and then conduct an extensive qualitative sentiment analysis. We observed that most people were pleased with the tool, but some expressed apprehension about its potential ethical and legal implications. After its launch, however, the fears were largely alleviated as people began to use it and recognize its benefits for personal and business purposes. It is important to be aware of any new technology's possible drawbacks and use it responsibly.

This study presents an innovative, automated deep learning-based technique for near real-time satellite monitoring of river ice conditions in northern watersheds of the United States and Canada. The method leverages high-resolution imagery from the VIIRS bands onboard the NOAA-20 and NPP satellites and employs the U-Net deep learning algorithm for the semantic segmentation of images under varying cloud and land surface conditions. The system autonomously generates detailed maps delineating classes such as water, land, vegetation, snow, river ice, cloud, and cloud shadow. The verification of system outputs was performed quantitatively by comparing with existing ice extent maps in the northeastern US and New Brunswick, Canada, yielding a Probability of Detection of 0.77 and a False Alarm rate of 0.12, suggesting commendable accuracy. Qualitative assessments were also conducted, corroborating the reliability of the system and underscoring its utility in monitoring hydraulic and hydrological processes across northern watersheds. The system’s proficiency in accurately capturing the phenology of river ice, particularly during onset and breakup times, testifies to its potential as a valuable tool in the realm of river ice monitoring.

Our study explores the role of extended reality (XR) in supporting cancer patients. A systematic search was conducted across six electronic databases. We adhered to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) reviews. We included 62 studies. XR technologies were mainly applied during the treatment phase (64.5% of studies), with additional applications in palliative care and during survivorship (33.9%). One study addressed the diagnosis phase, and none supported prevention or early detection. XR showed effective results supporting patients in seven areas: anxiety and stress reduction (n = 40), pain management (n = 19) and symptom tracking (n = 14). Challenges identified related to physical discomfort, technical issues, and user compliance. XR interventions demonstrate significant potential in improving patient outcomes, particularly in anxiety reduction and symptom management. However, their application remains unevenly distributed across the cancer care continuum.