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Water scarcity is the most obstacle faced by irrigation water requirements, likewise, limited available meteorological data to calculate reference evapotranspiration. Consequently, the focal aims of the investigation are to assess the potential of machine learning models in forecasting irrigation water requirements (IWR) of snap beans by evolving multi-scenarios of inputs parameters to figure out the impact of meteorological, crop, and soil parameters on IWR. Six models were applied, support vector regressor (SVR), random forest (RF), deep neural networks (DNN), convolutional neural networks (CNN), long short-term memory (LSTM), and Hybrid CNN-LSTM. Ten variables including maximum and minimum temperature, Relative humidity, wind speed, precipitation, root depth, basal crop coefficient, soil evaporation, a fraction of surface wetted and, exposed and soil wetted fraction were used as the input data for models with their combination, 8 input scenarios were designed. Overall models, the best scenario was scenario 4 (relative humidity, wind speed, basal crop coefficient, soil evaporation), however, the best scenario for DNN and RF model was scenario 7 (root depth, basal crop coefficient, soil evaporation, fraction of surface wetted, exposed and soil wetted fraction). While the weakest one was the group of climatic factors in scenario 6 (maximum temperature, minimum temperature, relative humidity, wind speed, and precipitation). Among the models, the hybrid LTSM & CNN was the most accurate and the SVR model had the lowest estimation accuracy. The outcomes of this research work could set up a modeling strategy that would set in motion the improvement of efforts to identify the shortages in IWR forecasting, which sequentially may support alleviation strategies such as policies for sustainable water use and water resources management. The current approach was promising and has research value for other similar regions., Validerad;2023;Nivå 2;2023-04-18 (joosat);Licens fulltext: CC BY License

Water scarcity is the most obstacle faced by irrigation water requirements, likewise, limited available meteorological data to calculate reference evapotranspiration. Consequently, the focal aims of the investigation are to assess the potential of machine learning models in forecasting irrigation water requirements (IWR) of snap beans by evolving multi-scenarios of inputs parameters to figure out the impact of meteorological, crop, and soil parameters on IWR. Six models were applied, support vector regressor (SVR), random forest (RF), deep neural networks (DNN), convolutional neural networks (CNN), long short-term memory (LSTM), and Hybrid CNN-LSTM. Ten variables including maximum and minimum temperature, Relative humidity, wind speed, precipitation, root depth, basal crop coefficient, soil evaporation, a fraction of surface wetted and, exposed and soil wetted fraction were used as the input data for models with their combination, 8 input scenarios were designed. Overall models, the best scenario was scenario 4 (relative humidity, wind speed, basal crop coefficient, soil evaporation), however, the best scenario for DNN and RF model was scenario 7 (root depth, basal crop coefficient, soil evaporation, fraction of surface wetted, exposed and soil wetted fraction). While the weakest one was the group of climatic factors in scenario 6 (maximum temperature, minimum temperature, relative humidity, wind speed, and precipitation). Among the models, the hybrid LTSM & CNN was the most accurate and the SVR model had the lowest estimation accuracy. The outcomes of this research work could set up a modeling strategy that would set in motion the improvement of efforts to identify the shortages in IWR forecasting, which sequentially may support alleviation strategies such as policies for sustainable water use and water resources management. The current approach was promising and has research value for other similar regions., Validerad;2023;Nivå 2;2023-04-18 (joosat);Licens fulltext: CC BY License

Due to the limited available amounts of components, especially of low water-soluble drugs, formulation development is often impeded by a careful characterization. The use of small batch sizes might solve this problem but requires also adequate analytics. Concentration of nanoparticulate formulations lack straightforward evaluation techniques. In this work, a precise and straight-forward method is established to individually count nanoparticles. A microfluidic chip with known dimensions was used to visualize single particles flowing through the channel (single-particle tracking (SPT)). A sequence of 10,000 images was analyzed to determine the mean particle concentration. The proposed method is independent of the particular flow rate through the microfluidic chip as long as there is no particle overlap and a continuous exchange of particles. Monodisperse Rhodamine B labeled poly (methyl methacrylate) (PMMA) nanoparticles (267.03 +/- 9.79 nm) were used as a model and reference particle system for the evaluation process of SPT allowing for a gravimetric determination based on density analysis using analytical ultracentrifugation (AUC) and gas pycnometry. The SPT method was evaluated and compared to other techniques used for concentration measurement. Both approaches (SPT and gravimetry) provide very similar and comparable results indicating the applicability of this novel quantification approach. In contrast, multi angle dynamic light scattering (MADLS) could not yield a precision as good as SPT (number 11.67\%; SD nMADLS = 49.45\%). Finally, the measured particle number concentrations can be realized in low concentration ranges (0.8249 mu g mL-1 - 0.08249 mu g mL-1) not accessible for MADLS (0.08249 mg mL-1 - 0.008249 mg mL-1) and gravimetric analysis.

Due to the limited available amounts of components, especially of low water-soluble drugs, formulation development is often impeded by a careful characterization. The use of small batch sizes might solve this problem but requires also adequate analytics. Concentration of nanoparticulate formulations lack straightforward evaluation techniques. In this work, a precise and straight-forward method is established to individually count nanoparticles. A microfluidic chip with known dimensions was used to visualize single particles flowing through the channel (single-particle tracking (SPT)). A sequence of 10,000 images was analyzed to determine the mean particle concentration. The proposed method is independent of the particular flow rate through the microfluidic chip as long as there is no particle overlap and a continuous exchange of particles. Monodisperse Rhodamine B labeled poly (methyl methacrylate) (PMMA) nanoparticles (267.03 +/- 9.79 nm) were used as a model and reference particle system for the evaluation process of SPT allowing for a gravimetric determination based on density analysis using analytical ultracentrifugation (AUC) and gas pycnometry. The SPT method was evaluated and compared to other techniques used for concentration measurement. Both approaches (SPT and gravimetry) provide very similar and comparable results indicating the applicability of this novel quantification approach. In contrast, multi angle dynamic light scattering (MADLS) could not yield a precision as good as SPT (number 11.67\%; SD nMADLS = 49.45\%). Finally, the measured particle number concentrations can be realized in low concentration ranges (0.8249 mu g mL-1 - 0.08249 mu g mL-1) not accessible for MADLS (0.08249 mg mL-1 - 0.008249 mg mL-1) and gravimetric analysis.