Artificial intelligence can dramatically speed up the painstaking work of tracking wildlife with remote cameras, cutting analysis time from months or even a year to just days while producing nearly the same scientific conclusions as humans. That’s according to a new study led by researchers at Washington State University and Google, published in the Journal of Applied Ecology. The team tested whether a fully automated AI system could replace humans in processing hundreds of thousands to millions of camera trap images collected in Washington, Montana’s Glacier National Park, and Guatemala’s Maya Biosphere Reserve.

With the rapid advancement of the information era, the demand for device integration and intelligent sensing has grown significantly. Traditional three-dimensional (3D) materials are constrained by lattice mismatch and interfacial defects, and their limited functionalities often require bulky auxiliary components. In contrast, the rich family of two-dimensional (2D) materials eliminates lattice-matching constraints and offers unique light-matter interactions, paving the way for compact and novel intelligent sensing technologies. However, large-area fabrication and precise layer alignment in all-2D systems remain major challenges that hinder device scalability. Given that the performance and manufacturing capabilities of 2D materials cannot replace traditional semiconductors (such as Si), they are more likely to be heterogeneously integrated with conventional 3D semiconductors. 2D/3D heterojunctions combine the distinctive optoelectronic properties of 2D materials with the mature electronic functionalities of 3D semiconductors. In this work, we present recent advances in 2D/3D heterojunction photodetectors, with a particular emphasis on the underlying physical mechanisms, including band structure design, interface optimization, external-field coupling, and novel topological configurations. Meanwhile, we also explore emerging opportunities for CMOS-compatible and intelligent sensing optoelectronic systems. Finally, the challenges and future research directions toward the integrated development of 2D/3D heterojunctions are discussed.

The approach tackles a long‑standing challenge in materials science: many promising coatings require high temperatures or harsh processing, making them unsuitable for delicate surfaces such as living tissue, soft plastics or emerging electronic materials.

The field unites principles in biology, engineering and earth sciences to develop scalable solutions to urgent environmental, social and economic challenges.

Simon Fraser University researchers have received nearly $1 million in special funding from the Digital Research Alliance of Canada to develop an artificial intelligence–powered system that forecasts whale movements in busy shipping corridors.

The Humans and Algorithms Listening for Orcas (HALLO) project aims to help the Port of Vancouver and vessel pilots make more informed decisions about when and where to slow down for the Salish Sea’s endangered Southern Resident killer whales.

The system integrates real-time acoustic and visual data, vessel tracking, and citizen-scientist whale spotting reports to track not only where the Southern Resident J, K, and L pods currently are, but forecast where they’ll be over the next few hours. 

New multiplexed imaging technology using standard clinical MRI systems can simultaneously map more than 20 biomarkers in high resolution, providing a comprehensive view of the brain with a single scan. Researchers demonstrated the multiplexed MRI technology, or MRx, by characterizing brain tumors and multiple sclerosis lesions — revealing different structural, physiological and molecular changes within the diseases.