This paper aims at describing some relevant aspects related to the classification, labelling and packaging of nanomaterials. Concerns have been raised about potential adverse effects to humans or the environment as result of impacts of nanomaterials. The new Regulation (EC) no. 1272/2008 on classification, labelling and packaging of substances and mixtures (CLP) does not contain any specific definition or provision related to nanomaterials nevertheless they are covered by the definition of substance set in the Regulation. It is recognized that different particle sizes or forms of the same substance can have different classification. Thus, if substances are placed on the market both at nanoscale and as bulk, a separate classification and labelling may be required if the available data on the intrinsic properties indicate a difference in hazard class between the two forms. CLP Regulation requires the manufacturer or importer to ensure that the information used to classify relates to the forms or physical states in which the substance is placed on the market and in which it can reasonably be expected to be used. Moreover, CLP demands testing relating to physical hazards to be performed if such information is missing or not adequate to conclude on classification. Further developments of the CLP guidance documents and implementation tools are needed in order to cover nanomaterials more specifically.
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http://dx.doi.org/10.4415/ANN_11_02_05 | DOI Listing |
Sensors (Basel)
January 2025
Department of Environmental Remote Sensing and Geoinformatics, Trier University, Universitätsring 15, 54296 Trier, Germany.
Assessing vines' vigour is essential for vineyard management and automatization of viticulture machines, including shaking adjustments of berry harvesters during grape harvest or leaf pruning applications. To address these problems, based on a standardized growth class assessment, labeled ground truth data of precisely located grapevines were predicted with specifically selected Machine Learning (ML) classifiers (Random Forest Classifier (RFC), Support Vector Machines (SVM)), utilizing multispectral UAV (Unmanned Aerial Vehicle) sensor data. The input features for ML model training comprise spectral, structural, and texture feature types generated from multispectral orthomosaics (spectral features), Digital Terrain and Surface Models (DTM/DSM- structural features), and Gray-Level Co-occurrence Matrix (GLCM) calculations (texture features).
View Article and Find Full Text PDFSensors (Basel)
January 2025
Space Robotics Research Group (SpaceR), Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, L-1855 Luxembourg, Luxembourg.
Malaria remains a global health concern, with 249 million cases and 608,000 deaths being reported by the WHO in 2022. Traditional diagnostic methods often struggle with inconsistent stain quality, lighting variations, and limited resources in endemic regions, making manual detection time-intensive and error-prone. This study introduces an automated system for analyzing Romanowsky-stained thick blood smears, focusing on image quality evaluation, leukocyte detection, and malaria parasite classification.
View Article and Find Full Text PDFSci Rep
January 2025
Faculty of Engineering & Information Systems, University of Technology Sydney, Sydney, NSW, 2007, Australia.
Fundus imaging, a technique for recording retinal structural components and anomalies, is essential for observing and identifying ophthalmological diseases. Disorders such as hypertension, glaucoma, and diabetic retinopathy are indicated by structural alterations in the optic disc, blood vessels, fovea, and macula. Patients frequently deal with various ophthalmological conditions in either one or both eyes.
View Article and Find Full Text PDFPediatr Res
January 2025
Department of Pediatrics, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
Objective: To evaluate the label accuracy of commercial infant probiotic products and identify potential microbial contamination.
Methods: DNA was extracted from seventeen infant probiotic products purchased from a large online vendor. Samples underwent 16S ribosomal RNA gene sequencing, QIIME analysis, and bacterial taxonomic classification.
Neural Netw
January 2025
National Key Laboratory of Space Integrated Information System, Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
Vision-language models are pre-trained by aligning image-text pairs in a common space to deal with open-set visual concepts. Recent works adopt fixed or learnable prompts, i.e.
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