Results 291 to 300 of about 1,221,513 (328)

Recognising Seaweeds: Addressing Gaps in International Biodiversity Frameworks for Global Seaweed Conservation

open access: yesSustainable Development, EarlyView.
ABSTRACT As anthropogenic pressures increasingly impact marine ecosystems and the biodiversity they support, governance mechanisms for international biodiversity conservation have emerged. Seaweed habitats are important repositories for marine biodiversity, and they provide crucial ecosystem services that support both ocean and human health.
Shaun Beattie   +7 more
wiley   +1 more source

Occlusion Handling using Semantic Segmentation and Visibility-Based Rendering for Mixed Reality

open access: green, 2017
Menandro Roxas   +4 more
openalex   +2 more sources

Driving SDG15: The Role of HEIs in Biodiversity Conservation Through Digitalization and Reporting

open access: yesSustainable Development, EarlyView.
ABSTRACT Education, research, and public engagement are key strategies guiding European higher education institutions (HEIs) in advancing the United Nations Sustainable Development Goals (SDGs). Through stakeholder, legitimacy, and resource‐based view theories, this study examines the contributions of HEIs to saving Life on Land (SDG15), focusing on ...
Assunta Di Vaio   +4 more
wiley   +1 more source

Road surface semantic segmentation for autonomous driving. [PDF]

open access: yesPeerJ Comput Sci
Zhao H   +5 more
europepmc   +1 more source

Chalcogenide Materials in Water Purification: Advances in Adsorptive and Photocatalytic Removal of Organic Pollutants

open access: yesSmall, EarlyView.
Chalcogenide materials emerge as efficient agents for water purification, enabling adsorptive and photocatalytic removal of dyes, pharmaceuticals, and pesticides. This review highlights recent advances in synthesis, structural tuning, and pollutant interaction mechanisms, while addressing challenges of toxicity and scalability. Insights into the future
Damilola Caleb Akintayo   +2 more
wiley   +1 more source

Self‐Driving Microscopes: AI Meets Super‐Resolution Microscopy

open access: yesSmall Methods, EarlyView.
This review examines the use of machine learning to automate super‐resolution optical microscopy, enabling the microscope to autonomously make decisions on what, when, and how to image. By eliminating the need for human intervention, this approach has the potential to enhance the versatility and accessibility of super‐resolution microscopy.
Edward N. Ward   +3 more
wiley   +1 more source

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