Found 9883 publications. Showing page 57 of 396:
2015
On behalf of the Norwegian Ferroalloy Producers Research Association (FFF) NILU has assessed possible airborne emissions of HCB, PCB, and PCDD/F from Norwegian silicon and ferrosilicon production.
The report concludes with that the Norwegian silicon and ferrosilicon melting process does most certainly not lead to a 'de novo' formation of organochlorines. The report summaries all performed emission measurements. It states that, even in worst case, the annual contribution from Norwegian silicon and ferrosilicon production does not exceed one percent of the total annual load. Thus the contribution is regarded as negligible.
2011
2002
Survey of emissions of volatile organic chemicals from handheld toys for children above 3 years
NILU
2020
2003
Surface-Bioengineered Extracellular Vesicles Seeking Molecular Biotargets in Lung Cancer Cells
Personalized medicine is a new approach to modern oncology. Here, to facilitate the application of extracellular vesicles (EVs) derived from lung cancer cells as potent advanced therapy medicinal products in lung cancer, the EV membrane was functionalized with a specific ligand for targeting purposes. In this role, the most effective heptapeptide in binding to lung cancer cells (PTHTRWA) was used. The functionalization process of EV surface was performed through the C- or N-terminal end of the heptapeptide. To prove the activity of the EVs functionalized with PTHTRWA, both a model of lipid membrane mimicking normal and cancerous cell membranes as well as human adenocarcinomic alveolar basal epithelial cells (A549) and human normal bronchial epithelial cells (BEAS-2B) have been exposed to these bioconstructs. Magnetic resonance imaging (MRI) showed that the as-bioengineered PTHTRWA-EVs loaded with superparamagnetic iron oxide nanoparticle (SPIO) cargos reach the growing tumor when dosed intravenously in NUDE Balb/c mice bearing A549 cancer. Molecular dynamics (MD) in silico studies elucidated a high affinity of the synthesized peptide to the α5β1 integrin. Preclinical safety assays did not evidence any cytotoxic or genotoxic effects of the PTHTRWA-bioengineered EVs.
American Chemical Society (ACS)
2024
Surface warming in Svalbard may have led to increases in highly active ice-nucleating particles
The roles of Arctic aerosols as ice-nucleating particles remain poorly understood, even though their effects on cloud microphysics are crucial for assessing the climate sensitivity of Arctic mixed-phase clouds and predicting their response to Arctic warming. Here we present a full-year record of ice-nucleating particle concentrations over Svalbard, where surface warming has been anomalously faster than the Arctic average. While the variation of ice-nucleating particles active at around −30 °C was relatively small, those active at higher temperatures (i.e., highly active ice-nucleating particles) tended to increase exponentially with rising surface air temperatures when the surface air temperatures rose above 0 °C and snow/ice-free barren and vegetated areas appeared in Svalbard. The aerosol population relevant to their increase was largely characterized by dust and biological organic materials that likely originated from local/regional terrestrial sources. Our results suggest that highly active ice-nucleating particles could be actively released from Arctic natural sources in response to surface warming.
Springer Nature
2024
2004
Surface ozone and crop damage in Norway - Estimates for the year 2010. SFT rapport, 99:05
1999
2016
2016
2003
2023
Supporting the improvement of air quality management practices: The “FAIRMODE pilot” activity
Academic Press
2019
2010
2016
2013
2014
2006
2006
Supervised Anomaly Detection in Univariate Time-Series Using 1D Convolutional Siamese Networks
In time-series data analysis, identifying anomalies is crucial for maintaining data integrity and ensuring accurate analyses and decision-making. Anomalies can compromise data quality and operational efficiency. The complexity of time-series data, with its temporal dependencies and potential non-stationarity, makes anomaly detection challenging but essential. Our research introduces ADSiamNet, a 1D Convolutional Neural Network-based Siamese network model for anomaly detection and rectification. ADSiamNet effectively identifies localized patterns in time-series data and smooths detected anomalies using a quantile-based technique. In tests with physical activity data from Actigraph watches and MOX2-5 sensors, ADSiamNet achieved accuracies of 98.65% and 85.0%, respectively, outperforming other supervised anomaly detection methods. The model uses a contrastive loss function to compare input sequences and adjusts network weights iteratively during training to recognize intricate patterns. Additionally, we evaluated various univariate time-series forecasting algorithms on datasets with and without anomalies. Results show that anomaly-smoothed data reduces forecasting errors, highlighting our approach’s effectiveness in enhancing time-series data analysis’s integrity and reliability. Future research will focus on multivariate time-series datasets.
IEEE (Institute of Electrical and Electronics Engineers)
2025