A new study published in Big Earth Data develops explainable Random Forest models to predict stream total nitrogen (TN) and total phosphorus (TP) concentrations using upstream catchment characteristics derived from openly accessible environmental datasets. The results show that the models provide robust predictions for TN, while reduced and spatial covariates help mitigate overfitting and improve model interpretability, demonstrating the potential of explainable machine learning for regional nutrient monitoring.

Artificial intelligence is increasingly being used in finance, from investment management to financial analysis. However, recent research suggests that highly accurate market predictions do not necessarily result in better investment outcomes. In two complementary studies from Pusan National University, researchers investigated how financial AI can be improved to make better decisions and how the performance of these systems can be evaluated using a proposed framework. The findings support the development of reliable financial AI.

A recent study published in National Science Review has revealed global inequalities in lake water quality through the development of GlakeWQ, the first satellite–machine learning framework enabling consistent assessment across countries. Mapping 16,409 lakes in 145 countries, the framework shows that only about two-thirds of lakes meet sustainable water quality standards, leaving approximately 1.15 billion people exposed to potential water quality-related health risks and highlighting the urgent need for equitable freshwater management.

With the growing demand for marine exploration and environmental monitoring, underwater sensing technology faces severe challenges due to high humidity and the need for simultaneous detection of multiple physical parameters, such as temperature and pressure. Traditional underwater sensors typically rely on combinations of various functional materials to achieve dual-mode temperature–pressure detection, often requiring additional waterproof encapsulation, which complicates system integration. Achieving dual-mode temperature–pressure detection within a single material underwater remains particularly challenging. Herein, a waterproof dual-mode sensor based on Bi₂Se₃/carbon paper (CP) composite films is developed by electrochemically depositing pyramid-interface-structured Bi₂Se₃ layers on both sides of a flexible CP substrate. The film exhibits enhanced thermoelectric properties, with a power factor of 106.0 μW m⁻¹ K⁻² (9 times that of CP). Sensors constructed from vertically stacked pyramid-interface-structured films enable high-precision detection of temperature and pressure underwater, featuring a temperature-sensing response time of 0.9 s and a pressure sensitivity of 0.94% kPa⁻¹, while allowing synchronous decoupling of the two signals. Moreover, the sensor exhibits excellent hydrophobicity, with a contact angle of 143.7°, along with robust stability, overcoming key limitations for underwater applications. This advance in flexible underwater sensing technology offers a reliable strategy for underwater human–machine interaction and monitoring.

A combination of satellite imagery, environmental data and habitat surveys carried out by citizen scientists could help transform the way endangered habitats are mapped, according to new research from the University of Surrey and Surrey Wildlife Trust. 

As artificial intelligence becomes part of how people work, learn and solve problems, a new University of Phoenix survey conducted by The Harris Poll finds that recent working learners see meaningful opportunities for AI to support accessibility. The survey was designed to understand the impact of AI in the workplace and learning environments on accessibility, defined as ensuring digital content, tools and resources, including AI tools and output, are usable by people with different abilities through inclusive design, use of assistive technology or conformance with accessibility standards, such as the Web Content Accessibility Guidelines (WCAG). The findings were released ahead of the 36th anniversary of the Americans with Disabilities Act (ADA) on July 26.