Perceptions of cultural and provisioning ecosystem services and human wellbeing indicators amongst indigenous communities neighbouring the greater limpopo transfrontier conservation area.

Nature plays a crucial role in providing ecosystem services (ESs) essential for human wellbeing and biodiversity conservation in rural areas. However, existing paradigms often lack an integrative approach towards rural livelihoods and wellbeing, highlighting the need for a comprehensive understanding of the relationship between human wellbeing (HWB) and ESs. The area around the Greater Limpopo Transfrontier Conservation Area (GLTFCA) offers such ESs to indigenous people who rely heavily on these natural resources.

Influence of Different Measuring Backgrounds on the Classification of Multilayer Polyolefin Films Using a Near-Infrared Handheld Spectrometer.

The low thickness of plastic films poses a challenge when using near-infrared (NIR) spectroscopy as it affects the spectral quality and classification. This research focuses on offering a solution to the challenge of classifying multilayer plastic film materials with a focus on polyolefin multilayer plastics. It presents the importance of spectral quality on accurate classification.

Targeting CD206+ macrophages disrupts the establishment of a key antitumor immune axis.

CD206 is a common marker of a putative immunosuppressive "M2" state in tumor-associated macrophages (TAMs). We made a novel conditional CD206 (Mrc1) knock-in mouse to specifically visualize and/or deplete CD206+ TAMs. Early depletion of CD206+ macrophages and monocytes (Mono/Macs) led to the indirect loss of conventional type I dendritic cells (cDC1), CD8 T cells, and NK cells in tumors.

Deep learning approaches for classification of copper-containing metal scrap in recycling processes.

Separating copper from iron scrap is a critical operation in metal recycling and achieving this with low cost sensoric equipment like RGB cameras instead of XRF/XRT is becoming increasingly attractive. In this article, the groundwork for creating an image classification model to separate copper from iron scrap has been performed. Twenty of the most common and most easily available CNN architectures were trained on 2200 metal scrap specimens and evaluated inline on a sensor-based sorting rig for their prediction accuracy and their inference latency to mimic real circumstances in an industrial setting.