Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2024
Forfattere
Rune Andreassen Berit Hansen Liya Pokrovskaya Vladimir Zhakov Daniel Kling Cornelya Klutsch Ida Marie Bardalen Fløystad Hans Geir Eiken Snorre HagenSammendrag
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Sammendrag
Raman spectroscopy provides detailed information about the molecular composition of a sample. The classical identification of components in a multi-component sample typically involves comparing the preprocessed spectrum with a known reference stored in a database using various spectral matching or machine-learning techniques or relies on universal models based on a two-step analysis including first, the component identification, and then the decomposition of the mixed signal. However, although large databases and universal models cover a wide range of target materials, they may be not optimized to the variability required in a specific application. In this study, we propose a single-step method using deep learning (DL) modeling to decompose a simulated mixture of real measurements of Raman scattering into relevant individual components regardless of noise, baseline and the number of components involved and quantify their ratios. We hypothesize that training a custom DL model for applications with a fixed set of expected components may yield better results than applying a universal quantification model. To test this hypothesis, we simulated 12,000 Raman spectra by assigning random ratios to each component spectrum within a library containing 13 measured spectra of organic solvent samples. One of the DL methods, a fully connected network (FCN), was designed to work on the raw spectra directly and output the contribution of each component of the library to the input spectrum in form of a component ratio. The developed model was evaluated on 3600 testing spectra, which were simulated similarly to the training dataset. The average component identification accuracy of the FCN was 99.7%, which was significantly higher than that of the universal custom trained DeepRaman model, which was 83.1%. The average mean absolute error for component ratio quantification was 0.000562, over one order of magnitude smaller than that of a well-established non-negative elastic net (NN-EN), which was 0.00677. The predicted non-zero ratio values were further used for component identification. Under the assumption that the components of a mixture are from a fixed library, the proposed method preprocesses and decomposes the raw data in a single step, quantifying every component in a multicomponent mixture, accurately. Notably, the single-step FCN approach has not been implemented in the previously reported DL studies.
Forfattere
Frank T. Ndjomatchoua Ritter Atoundem Guimapi Luca Rossini Byliole S. Djouda Sansao A. PedroSammendrag
Life history traits have been studied under various environmental factors, but the ability to combine them into a simple function to assess pest response to climate is still lacking complete understanding. This study proposed a risk index derived by combining development, mortality, and fertility rates from a stage-structured dynamic mathematical model. The first part presents the theoretical framework behind the risk index. The second part of the study is concerned with the application of the index in two case studies of major economic pest: the brown planthopper (Nilaparvata lugens) and the spotted wing drosophila (Drosophila suzukii), pests of rice crops and soft fruits, respectively. The mathematical calculations provided a single function composed of the main thermal biodemographic rates. This function has a threshold value that determines the possibility of population increase as a function of temperature. The tests carried out on the two pest species showed the capability of the index to describe the range of favourable conditions. With this approach, we were able to identify areas where pests are tolerant to climatic conditions and to project them on a geospatial risk map. The theoretical background developed here provided a tool for understanding the biogeography of Nilaparvata lugens and Drosophila suzukii. It is flexible enough to deal with mathematically simple (N. lugens) and complex (D. Suzukii) case studies of crop insect pests. It produces biologically sound indices that behave like thermal performance curves. These theoretical results also provide a reasonable basis for addressing the challenge of pest management in the context of seasonal weather variations and climate change. This may help to improve monitoring and design management strategies to limit the spread of pests in invaded areas, as some non-invaded areas may be suitable for the species to develop.
Sammendrag
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Sammendrag
We address the question of nature-culture synergies in protected mountain landscapes with a specific focus on the Norwegian National Park of Hardangervidda. Fragile and complex ecosystems developed from long-lasting socio-ecological grazing processes that started approximately 4000 years ago in Scandinavia are facing manifold environmental challenges and societal issues that endanger both natural and cultural heritages. Our goals are to clarify the nature-culture synergies and relationships and investigate holistic management and preservation of natural and cultural values. Our results highlight an urgent need to develop holistic conservation frameworks and methodologies for protected landscapes that integrate cultural and natural heritages and enhance the potential of local communities to protect threatened semi-natural environments and experienced-based knowledge for the future.
Sammendrag
Ensiling is a common mode of preservation of animal feed. In this process, the feed undergoes lactic acid fermentation in an anaerobic environment, which decreases pH and inhibits degradation of the feed and its nutritive value. Common silos include top loaded tower silos, side loaded bunker silos (also called horizontal silos), underground pit and trench silos, and bales and tubes wrapped in plastic film. Previous studies have revealed that the type of silo often have an impact on silage properties and feed value, but these effects can vary between silage materials. Silage density is another key factor for silage nutritive value and losses. Generally, high density results in smaller losses than low density, both in bunker silos and bales, but the density effect can also be influenced by properties of the ensiled material. The objectives of this literature review were to identify factors and conditions that can modify the effect of i) silage density, and ii) silo type on dry matter losses, leaching of nutrients, fermentation characteristics, silage feed value and mycotoxins contamination. A systematic literature search was carried out in in the Web of Science core collection platform of databases. Most studies showed positive correlations between silage density, and fermentation and feed value, and negative correlations with DM losses. The majority of these studies were conducted at laboratory scale and there was also a great variation in the magnitude of these effects. Further investigations at farm scale may provide more information about the consistency of these effects across experimental scales. The silo type comparisons indicate that silage bales, bags and tubes can be favourable for silage quality and dry matter preservation compared to bunker silos, but information on silo type effects on important crops such as maize is missing.
Sammendrag
Sustainable source-based hydrogels are now paid much importance for managing water pollution due to their distinctive chemical and physical properties like hydrophilicity, biocompatibility, viscoelasticity, superabsorbancy, softness, fluffiness, and biodegradability. Alginate-based hydrogels can incorporate much water due to their hydrophilic nature.Water pollution often changes groundwater, resulting in the inability to use it. Alginate-based gels remove pollutants through adsorption/desorption, transport, and other conventional techniques. Alginatebased hydrogels can incorporate much water due to their hydrophilic nature. Their composites have been demonstrated to control different water pollutants like inorganic, organic, and pathogenic microbes from various water streams in unique structural forms like a flat membrane, hollow fibber, microspheres, gels, foams, nanofibers, Calcium and sodium alginate-based hydrogel along with other materials like activated charcoal, zeolite, bentonite, graphene, biochars and composites have been proved to be effective blend in managing heavy metal pollutants.This review summarizes the results obtained from the sustainable removal of contaminants from water through the alginate-based hydrogel and the challenges associated with it for practical application in the future.
Sammendrag
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Forfattere
Geir Wæhler Gustavsen Gry Alfredsen Philip Bester van Niekerk Jonas Niklewski Christian BrischkeSammendrag
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Sammendrag
The biological durability of ten wood species was determined on the basis of results from laboratory agar block tests. The experiment utilised two specimen formats: standard EN 113-2 specimens (15 × 25 × 50 mm) and mini-blocks (5 × 10 × 30 mm) exposed to two fungi (Coniophora puteana and Trametes versicolor) for varying incubation periods. Mini-block tests yield dissimilar outcomes compared to the European standard test at six, eight, ten or 16 weeks of incubation. This discrepancy extended to both durability classifications based on median percentage mass loss and those based on relative mass loss (x-values). It was therefore concluded that laboratory tests with miniaturised specimens are not advisable as a substitute for conventional durability classification assessments.