{"type": "FeatureCollection", "features": [{"id": "10.1016/j.agwat.2023.108391", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:15:35Z", "type": "Journal Article", "created": "2023-06-02", "title": "Optimizing relative root-zone water depletion thresholds to maximize yield and water productivity of winter wheat using AquaCrop", "description": "Determination of relative root-zone water depletion (RRWD) thresholds to trigger irrigation is crucial to create optimal irrigation schedules targeting maximum yield and/or water productivity with limited water supply for a crop. In this study, a numerical procedure to determine RRWD thresholds was developed through coupling AquaCrop software with genetic-simplex algorithms. Using a two-year field lysimetric experiment for winter wheat conducted in the North China Plain (NCP), AquaCrop adequately simulated canopy cover, final aboveground biomass, grain yield, seasonal evapotranspiration, and soil water storage, with the normalized root mean squared error (NRMSE) smaller than 15 % and determination coefficient (R2) larger than 0.84. The global optimum range of RRWD thresholds was preliminarily determined using the genetic algorithm, and subsequently final RRWD thresholds were optimized by fine tuning using the simplex algorithm. The RRWD threshold combinations (composed of the RRWD thresholds to trigger different sequential irrigation events) for varying number of irrigation events (i.e.1\u20134) were optimized based on 39 years of historical meteorological data, and the effects of climate change on the optimal crop yield (Ya, opt), water productivity (WPopt), and the combinations of optimized RRWD threshold (RRWDopt) were investigated. The results indicated that both Ya, opt and WPopt generally increased with time showing a tendency of gradually elevated annual CO2 concentration and seasonal average effective temperature. Irrespective of the number of irrigation events during the winter wheat growing season, the differences of RRWDopt for different combinations of irrigation sequence and event in the same kind of hydrological year were relatively small, with a coefficient of variation consistently less than 23 % and a mean of 8 %. When combinations of mean RRWDopt were applied into AquaCrop to trigger irrigation for winter wheat in various hydrological years, the simulated yield (Ya, sim) and water productivity (WPsim) under 1\u20134 irrigation events were found to be comparable to their respective optimums (Ya, opt and WPopt), with all the values of Ya, sim (WPsim) falling in the range of 92 %Ya, opt (90 %WPopt). Therefore, the mean RRWDopt should be helpful to formulate rational irrigation management strategies of winter wheat under changing climatic conditions in the NCP.", "keywords": ["HD9000-9495", "2. Zero hunger", "0106 biological sciences", "Evapotranspiration", "Agriculture (General)", "04 agricultural and veterinary sciences", "Agricultural industries", "15. Life on land", "01 natural sciences", "Irrigation scheduling", "6. Clean water", "S1-972", "Optimization algorithm", "13. Climate action", "Climate change", "0401 agriculture", " forestry", " and fisheries", "Crop model"]}, "links": [{"href": "https://doi.org/10.1016/j.agwat.2023.108391"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agricultural%20Water%20Management", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agwat.2023.108391", "name": "item", "description": "10.1016/j.agwat.2023.108391", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agwat.2023.108391"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-08-01T00:00:00Z"}}, {"id": "10.1016/j.envpol.2021.118128", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:16:06Z", "type": "Journal Article", "created": "2021-09-09", "title": "Diagnosis of cadmium contamination in urban and suburban soils using visible-to-near-infrared spectroscopy", "description": "Previous studies have mostly focused on using visible-to-near-infrared spectral technique to quantitatively estimate soil cadmium (Cd) content, whereas little attention has been paid to identifying soil Cd contamination from a perspective of spectral classification. Here, we developed a framework to compare the potential of two spectral transformations (i.e., raw reflectance and continuum removal [CR]), three optimization strategies (i.e., full-spectrum, Boruta feature selection, and synthetic minority over-sampling technique [SMOTE]), and three classification algorithms (i.e., partial least squares discriminant analysis, random forest [RF], and support vector machine) for diagnosing soil Cd contamination. A total of 536 soil samples were collected from urban and suburban areas located in Wuhan City, China. Specifically, Boruta and SMOTE strategies were aimed at selecting the most informative predictors and obtaining balanced training datasets, respectively. Results indicated that soils contaminated by Cd induced decrease in spectral reflectance magnitude. Classification models developed after Boruta and SMOTE strategies out-performed to those from full-spectrum. A diagnose model combining CR preprocessing, SMOTE strategy, and RF algorithm achieved the highest validation accuracy for soil Cd (Kappa = 0.74). This study provides a theoretical reference for rapid identification of and monitoring of soil Cd contamination in urban and suburban areas.", "keywords": ["DIFFUSE-REFLECTANCE SPECTROSCOPY", "HUMAN HEALTH", "PREDICTION", "POTENTIALLY TOXIC ELEMENTS", "Boruta algorithm", "01 natural sciences", "Visible-to-near-infrared spectroscopy", "NIR SPECTROSCOPY", "Soil", "ORGANIC-CARBON", "Machine learning", "11. Sustainability", "Soil Pollutants", "Least-Squares Analysis", "0105 earth and related environmental sciences", "Spectroscopy", " Near-Infrared", "RANDOM FOREST", "Urban and suburban soil Cd contamination", "04 agricultural and veterinary sciences", "15. Life on land", "QUANTITATIVE-ANALYSIS", "6. Clean water", "RIVER DELTA", "13. Climate action", "Earth and Environmental Sciences", "Synthetic minority over-sampling technique", "0401 agriculture", " forestry", " and fisheries", "HEAVY-METAL CONCENTRATIONS", "Cadmium"]}, "links": [{"href": "https://doi.org/10.1016/j.envpol.2021.118128"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Pollution", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.envpol.2021.118128", "name": "item", "description": "10.1016/j.envpol.2021.118128", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.envpol.2021.118128"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-01T00:00:00Z"}}, {"id": "10.1007/s10533-021-00759-x", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:14:45Z", "type": "Journal Article", "created": "2021-01-26", "title": "How much carbon can be added to soil by sorption?", "description": "Abstract

Quantifying the upper limit of stable soil carbon storage is essential for guiding policies to increase soil carbon storage. One pool of carbon considered particularly stable across climate zones and soil types is formed when dissolved organic carbon sorbs to minerals. We quantified, for the first time, the potential of mineral soils to sorb additional dissolved organic carbon (DOC) for six soil orders. We compiled 402 laboratory sorption experiments to estimate the additional DOC sorption potential, that is the potential of excess DOC sorption in addition to the existing background level already sorbed in each soil sample. We estimated this potential using gridded climate and soil geochemical variables within a machine learning model. We find that mid- and low-latitude soils and subsoils have a greater capacity to store DOC by sorption compared to high-latitude soils and topsoils. The global additional DOC sorption potential for six soil orders is estimated to be 107 $$ pm$$ \uffc2\uffb1 13 Pg C to 1\uffc2\uffa0m depth. If this potential was realized, it would represent a 7% increase in the existing total carbon stock.In many application settings, labeling data examples is a costly endeavor, while unlabeled examples are abundant and cheap to produce. Labeling examples can be particularly problematic in an online setting, where there can be arbitrarily many examples that arrive at high frequencies. It is also problematic when we need to predict complex values (e.g., multiple real values), a task that has started receiving considerable attention, but mostly in the batch setting. In this paper, we propose a method for online semi-supervised multi-target regression. It is based on incremental trees for multi-target regression and the predictive clustering framework. Furthermore, it utilizes unlabeled examples to improve its predictive performance as compared to using just the labeled examples. We compare the proposed iSOUP-PCT method with supervised tree methods, which do not use unlabeled examples, and to an oracle method, which uses unlabeled examples as though they were labeled. Additionally, we compare the proposed method to the available state-of-the-art methods. The method achieves good predictive performance on account of increased consumption of computational resources as compared to its supervised variant. The proposed method also beats the state-of-the-art in the case of very few labeled examples in terms of performance, while achieving comparable performance when the labeled examples are more common./=3 for Cd and Cu, and 1-4 for Pb and Zn), the Ramallosa marsh is highly polluted with Cd and moderately to highly polluted with Cu, Pb and Zn. The enrichment factors obtained confirm that the salt marsh is highly polluted (especially with Cd) as the primary result of anthropic activity.", "keywords": ["Industrial Waste", "Reproducibility of Results", "Agriculture", "Pentetic Acid", "15. Life on land", "Waste Disposal", " Fluid", "01 natural sciences", "6. Clean water", "Ion Exchange", "13. Climate action", "Metals", " Heavy", "Wetlands", "Linear Models", "Potentiometry", "Water Pollution", " Chemical", "Soil Pollutants", "Oxidation-Reduction", "Algorithms", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.jhazmat.2009.05.074"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Hazardous%20Materials", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.jhazmat.2009.05.074", "name": "item", "description": "10.1016/j.jhazmat.2009.05.074", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.jhazmat.2009.05.074"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2009-10-01T00:00:00Z"}}, {"id": "10.1093/nar/gkz378", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:18:09Z", "type": "Journal Article", "created": "2019-05-06", "title": "Caver Web 1.0: identification of tunnels and channels in proteins and analysis of ligand transport", "description": "Abstract

Caver Web 1.0 is a web server for comprehensive analysis of protein tunnels and channels, and study of the ligands\uffe2\uff80\uff99 transport through these transport pathways. Caver Web is the first interactive tool allowing both the analyses within a single graphical user interface. The server is built on top of the abundantly used tunnel detection tool Caver 3.02 and CaverDock 1.0 enabling the study of the ligand transport. The program is easy-to-use as the only required inputs are a protein structure for a tunnel identification and a list of ligands for the transport analysis. The automated guidance procedures assist the users to set up the calculation in a way to obtain biologically relevant results. The identified tunnels, their properties, energy profiles and trajectories for ligands\uffe2\uff80\uff99 passages can be calculated and visualized. The tool is very fast (2\uffe2\uff80\uff9320 min per job) and is applicable even for virtual screening purposes. Its simple setup and comprehensive graphical user interface make the tool accessible for a broad scientific community. The server is freely available at https://loschmidt.chemi.muni.cz/caverweb.

", "keywords": ["0301 basic medicine", "Internet", "0303 health sciences", "Binding Sites", "BINDING; STABILITY; MECHANISM; MYOGLOBIN; MIGRATION; DYNAMICS; KINETICS; PATHWAY; ENZYMES; SERVER", "Computational Biology", "Ligands", "Protein Structure", " Tertiary", "3. Good health", "Molecular Docking Simulation", "Benchmarking", "User-Computer Interface", "03 medical and health sciences", "Web Server Issue", "Animals", "Humans", "Protein Interaction Domains and Motifs", "Amino Acid Sequence", "Carrier Proteins", "Protein Structure", " Quaternary", "Algorithms", "Protein Binding"]}, "links": [{"href": "http://academic.oup.com/nar/article-pdf/47/W1/W414/28880050/gkz378.pdf"}, {"href": "https://doi.org/10.1093/nar/gkz378"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Nucleic%20Acids%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1093/nar/gkz378", "name": "item", "description": "10.1093/nar/gkz378", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1093/nar/gkz378"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-05-22T00:00:00Z"}}, {"id": "10.1029/2022je007190", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:17:27Z", "type": "Journal Article", "created": "2022-01-25", "title": "InSight Pressure Data Recalibration, and Its Application to the Study of Long-Term Pressure Changes on Mars", "description": "Abstract

Observations of the South Polar Residual Cap suggest a possible erosion of the cap, leading to an increase of the global mass of the atmosphere. We test this assumption by making the first comparison between Viking 1 and InSight surface pressure data, which were recorded 40\uffc2\uffa0years apart. Such a comparison also allows us to determine changes in the dynamics of the seasonal ice caps between these two periods. To do so, we first had to recalibrate the InSight pressure data because of their unexpected sensitivity to the sensor temperature. Then, we had to design a procedure to compare distant pressure measurements. We propose two surface pressure interpolation methods at the local and global scale to do the comparison. The comparison of Viking and InSight seasonal surface pressure variations does not show changes larger than \uffc2\uffb18\uffc2\uffa0Pa in the CO2 cycle. Such conclusions are supported by an analysis of Mars Science Laboratory (MSL) pressure data. Further comparisons with images of the south seasonal cap taken by the Viking 2 orbiter and MARCI camera do not display significant changes in the dynamics of this cap over a 40\uffc2\uffa0year period. Only a possible larger extension of the North Cap after the global storm of MY 34 is observed, but the physical mechanisms behind this anomaly are not well determined. Finally, the first comparison of MSL and InSight pressure data suggests a pressure deficit at Gale crater during southern summer, possibly resulting from a large presence of dust suspended within the crater.

", "keywords": ["Atmospheric sciences", "550", "Astronomy", "Atmosphere (unit)", "FOS: Mechanical engineering", "Library science", "Oceanography", "01 natural sciences", "CO2 ice", "pressure", "Mars Exploration Program", "Engineering", "Surface pressure", "Storm", "Martian Climate", "Space Suit Design and Ergonomics for EVA", "Martian Atmosphere", "Earth and Planetary Astrophysics (astro-ph.EP)", "Climatology", "Global and Planetary Change", "Geography", "Martian Surface", "Physics", "Geology", "Impact crater", "Condensed matter physics", "Anomaly (physics)", "World Wide Web", "Algorithm", "Satellite Observations", "Residual", "Physical Sciences", "Exploration and Study of Mars", "Astrophysics - Instrumentation and Methods for Astrophysics", "Research Article", "FOS: Physical sciences", "Mars", "Aerospace Engineering", "Pressure gradient", "Environmental science", "[SDU] Sciences of the Universe [physics]", "atmospheric mass", "Meteorology", "Orbiter", "0103 physical sciences", "Instrumentation and Methods for Astrophysics (astro-ph.IM)", "Formation and Evolution of the Solar System", "0105 earth and related environmental sciences", "Pressure system", "CO 2 ice", "Astronomy and Astrophysics", "FOS: Earth and related environmental sciences", "Astrobiology", "Computer science", "Physics and Astronomy", "[SDU]Sciences of the Universe [physics]", "13. Climate action", "Global Methane Emissions and Impacts", "Environmental Science", "cap sublimation", "Water on Mars", "Astrophysics - Earth and Planetary Astrophysics"]}, "links": [{"href": "https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2022JE007190"}, {"href": "https://doi.org/10.1029/2022je007190"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Geophysical%20Research%3A%20Planets", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1029/2022je007190", "name": "item", "description": "10.1029/2022je007190", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1029/2022je007190"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-25T00:00:00Z"}}, {"id": "10.1038/s41598-020-58025-3", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:17:36Z", "type": "Journal Article", "created": "2020-01-28", "title": "Building de novo reference genome assemblies of complex eukaryotic microorganisms from single nuclei", "description": "Abstract

The advent of novel sequencing techniques has unraveled a tremendous diversity on Earth. Genomic data allow us to understand ecology and function of organisms that we would not otherwise know existed. However, major methodological challenges remain, in particular for multicellular organisms with large genomes. Arbuscular mycorrhizal (AM) fungi are important plant symbionts with cryptic and complex multicellular life cycles, thus representing a suitable model system for method development. Here, we report a novel method for large scale, unbiased nuclear sorting, sequencing, and de novo assembling of AM fungal genomes. After comparative analyses of three assembly workflows we discuss how sequence data from single nuclei can best be used for different downstream analyses such as phylogenomics and comparative genomics of single nuclei. Based on analysis of completeness, we conclude that comprehensive de novo genome assemblies can be produced from six to seven nuclei. The method is highly applicable for a broad range of taxa, and will greatly improve our ability to study multicellular eukaryotes with complex life cycles.

", "keywords": ["0301 basic medicine", "Evolutionary Biology", "0303 health sciences", "Genome", "Fungi", "Computational Biology", "Eukaryota", "Genomics", "Article", "Workflow", "Evolutionsbiologi", "03 medical and health sciences", "13. Climate action", "Algorithms"]}, "links": [{"href": "https://www.nature.com/articles/s41598-020-58025-3.pdf"}, {"href": "https://doi.org/10.1038/s41598-020-58025-3"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Reports", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1038/s41598-020-58025-3", "name": "item", "description": "10.1038/s41598-020-58025-3", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/s41598-020-58025-3"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-28T00:00:00Z"}}, {"id": "10.1038/s41598-021-02302-2", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:17:36Z", "type": "Journal Article", "created": "2021-11-30", "title": "Predicting sensitivity of recently harvested tomatoes and tomato sepals to future fungal infections", "description": "Abstract

Tomato is an important commercial product which is perishable by nature and highly susceptible to fungal incidence once it is harvested. Not all tomatoes are equally vulnerable to pathogenic fungi, and an early detection of the vulnerable ones can help in taking timely preventive actions, ranging from isolating tomato batches to adjusting storage conditions, but also in making right business decisions like dynamic pricing based on quality or better shelf life estimate. More importantly, early detection of vulnerable produce can help in taking timely actions to minimize potential post-harvest losses. This paper investigates Near-infrared (NIR) hyperspectral imaging (1000\uffe2\uff80\uff931700\uffc2\uffa0nm) and machine learning to build models to automatically predict the susceptibility of sepals of recently harvested tomatoes to future fungal infections. Hyperspectral images of newly harvested tomatoes (cultivar Brioso) from 5 different growers were acquired before the onset of any visible fungal infection. After imaging, the tomatoes were placed under controlled conditions suited for fungal germination and growth for a 4-day period, and then imaged using normal color cameras. All sepals in the color images were ranked for fungal severity using crowdsourcing, and the final severity of each sepal was fused using principal component analysis. A novel hyperspectral data processing pipeline is presented which was used to automatically segment the tomato sepals from spectral images with multiple tomatoes connected via a truss. The key modelling question addressed in this research is whether there is a correlation between the hyperspectral data captured at harvest and the fungal infection observed 4 days later. Using 10-fold and group k-fold cross-validation, XG-Boost and Random Forest based regression models were trained on the features derived from the hyperspectral data corresponding to each sepal in the training set and tested on hold out test set. The best model found a Pearson correlation of 0.837, showing that there is strong linear correlation between the NIR spectra and the future fungal severity of the sepal. The sepal specific predictions were aggregated to predict the susceptibility of individual tomatoes, and a correlation of 0.92 was found. Besides modelling, focus is also on model interpretation, particularly to understand which spectral features are most relevant to model prediction. Two approaches to model interpretation were explored, feature importance and SHAP (SHapley Additive exPlanations), resulting in similar conclusions that the NIR range between 1390\uffe2\uff80\uff931420\uffc2\uffa0nm contributes most to the model\uffe2\uff80\uff99s final decision.Extensive application of clustering and classification algorithms shows the potential of a CNT-based sensor array in breathomics.

", "keywords": ["electronic nose", "Linear discriminant analysis", "Principal component analysis", "Breath analysis", "02 engineering and technology", "sensors", "Supported Vectror Machine", "01 natural sciences", "nanotubes", "Ammonia; Biomarkers; Carbon nanotubes; Classification (of information); Clustering algorithms; Molecules; Nitrogen oxides; Principal component analysis; Sulfur compounds; Support vector machines", "0104 chemical sciences", "3. Good health", "breathomics", "Chemistry", "SWCNTs", "COPD", "ta318", "e-nose", "0210 nano-technology", "ta215"]}, "links": [{"href": "https://iris.cnr.it/bitstream/20.500.14243/536855/1/RSC%20Adv._2021.pdf"}, {"href": "https://boa.unimib.it/bitstream/10281/517427/2/d1ra03337a.pdf%3b"}, {"href": "https://publicatt.unicatt.it/bitstream/10807/190102/1/d1ra03337a.pdf"}, {"href": "http://pubs.rsc.org/en/content/articlepdf/2021/RA/D1RA03337A"}, {"href": "https://doi.org/10.1039/d1ra03337a"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/RSC%20Advances", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1039/d1ra03337a", "name": "item", "description": "10.1039/d1ra03337a", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1039/d1ra03337a"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "10.1080/05704928.2022.2128365", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:18:00Z", "type": "Journal Article", "created": "2022-10-03", "title": "Mathematical techniques to remove moisture effects from visible\u2013near-infrared\u2013shortwave-infrared soil spectra\u2014review", "description": "This is an Accepted Manuscript of an article published by Taylor & Francis in Applied Spectroscopy Reviews on 03 October 2022, available at: https://doi.org/10.1080/05704928.2022.2128365", "keywords": ["EJP Soil", "Proximal Sensing", "ProbeField", "Soil Moisture", "04 agricultural and veterinary sciences", "algorithms", "01 natural sciences", "diffuse reflectance spectroscopy", "field-moist conditions", "EJPSOIL", "0401 agriculture", " forestry", " and fisheries", "indices", "Soil moisture", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://www.tandfonline.com/doi/pdf/10.1080/05704928.2022.2128365"}, {"href": "https://doi.org/10.1080/05704928.2022.2128365"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Applied%20Spectroscopy%20Reviews", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1080/05704928.2022.2128365", "name": "item", "description": "10.1080/05704928.2022.2128365", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1080/05704928.2022.2128365"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-10-03T00:00:00Z"}}, {"id": "10.1101/2023.12.16.572011", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:18:15Z", "type": "Journal Article", "created": "2023-12-18", "title": "Open Soil Spectral Library (OSSL): Building reproducible soil calibration models through open development and community engagement", "description": "Abstract

Soil spectroscopy is a widely used method for estimating soil properties that are important to environmental and agricultural monitoring. However, a bottleneck to its more widespread adoption is the need for establishing large reference datasets for training machine learning (ML) models, which are called soil spectral libraries (SSLs). Similarly, the prediction capacity of new samples is also subject to the number and diversity of soil types and conditions represented in the SSLs. To help bridge this gap and enable hundreds of stakeholders to collect more affordable soil data by leveraging a centralized open resource, the Soil Spectroscopy for Global Good has created the Open Soil Spectral Library (OSSL). In this paper, we describe the procedures for collecting and harmonizing several SSLs that are incorporated into the OSSL, followed by exploratory analysis and predictive modeling. The results of 10-fold cross-validation with refitting show that, in general, mid-infrared (MIR)-based models are significantly more accurate than visible and near-infrared (VisNIR) or near-infrared (NIR) models. From independent model evaluation, we found that Cubist comes out as the best-performing ML algorithm for the calibration and delivery of reliable outputs (prediction uncertainty and representation flag). Although many soil properties are well predicted, total sulfur, extractable sodium, and electrical conductivity performed poorly in all spectral regions, with some other extractable nutrients and physical soil properties also performing poorly in one or two spectral regions (VisNIR or Neospectra NIR). Hence, the use of predictive models based solely on spectral variations has limitations. This study also presents and discusses several other open resources that were developed from the OSSL, aspects of opening data, current limitations, and future development. With this genuinely open science project, we hope that OSSL becomes the driver of the soil spectroscopy community to accelerate the pace of scientific discovery and innovation.Analysis of differential expression of genes is often performed to understand how the metabolic activity of an organism is impacted by a perturbation. However, because the system of metabolic regulation is complex and all changes are not directly reflected in the expression levels, interpreting these data can be difficult.

Results

In this work, we present a new algorithm and computational tool that uses a genome-scale metabolic reconstruction to infer metabolic changes from differential expression data. Using the framework of constraint-based analysis, our method produces a qualitative hypothesis of a change in metabolic activity. In other words, each reaction of the network is inferred to have increased, decreased, or remained unchanged in flux. In contrast to similar previous approaches, our method does not require a biological objective function and does not assign on/off activity states to genes. An implementation is provided and it is available online. We apply the method to three published datasets to show that it successfully accomplishes its two main goals: confirming or rejecting metabolic changes suggested by differentially expressed genes based on how well they fit in as parts of a coordinated metabolic change, as well as inferring changes in reactions whose genes did not undergo differential expression.

Availability and implementation

github.com/htpusa/moomin.

Supplementary information

Supplementary data are available at Bioinformatics online.

", "keywords": ["0301 basic medicine", "570", "[SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]", "Metabolic networks; omics data", "Genome", "[INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS]", "0206 medical engineering", "610", "Computational Biology", "[INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS]", "02 engineering and technology", "[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]", "Original Papers", "Models", " Biological", "03 medical and health sciences", "[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]", "Algorithms", "Metabolic Networks and Pathways", "[INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]"]}, "links": [{"href": "https://iris.uniroma1.it/bitstream/11573/1321358/5/Pusa_MOOMIN_2020.pdf"}, {"href": "https://academic.oup.com/bioinformatics/article-pdf/36/2/514/48991611/btz584.pdf"}, {"href": "https://doi.org/10.1093/bioinformatics/btz584"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Bioinformatics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1093/bioinformatics/btz584", "name": "item", "description": "10.1093/bioinformatics/btz584", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1093/bioinformatics/btz584"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-08-22T00:00:00Z"}}, {"id": "10.1093/toxsci/kfae051", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:18:10Z", "type": "Journal Article", "created": "2024-04-18", "title": "Parameter grouping and co-estimation in physiologically based kinetic models using genetic algorithms", "description": "Abstract

Physiologically based kinetic (PBK) models are widely used in pharmacology and toxicology for predicting the internal disposition of substances upon exposure, voluntarily or not. Due to their complexity, a large number of model parameters need to be estimated, either through in silico tools, in vitro experiments, or by fitting the model to in vivo data. In the latter case, fitting complex structural models on in vivo data can result in overparameterization and produce unrealistic parameter estimates. To address these issues, we propose a novel parameter grouping approach, which reduces the parametric space by co-estimating groups of parameters across compartments. Grouping of parameters is performed using genetic algorithms and is fully automated, based on a novel goodness-of-fit metric. To illustrate the practical application of the proposed methodology, two case studies were conducted. The first case study demonstrates the development of a new PBK model, while the second focuses on model refinement. In the first case study, a PBK model was developed to elucidate the biodistribution of titanium dioxide (TiO2) nanoparticles in rats following intravenous injection. A variety of parameter estimation schemes were employed. Comparative analysis based on goodness-of-fit metrics demonstrated that the proposed methodology yields models that outperform standard estimation approaches, while utilizing a reduced number of parameters. In the second case study, an existing PBK model for perfluorooctanoic acid (PFOA) in rats was extended to incorporate additional tissues, providing a more comprehensive portrayal of PFOA biodistribution. Both models were validated through independent in vivo studies to ensure their reliability.

We lack strong empirical evidence for links between plant attributes (plant community attributes and functional traits) and the distribution of soil microbial communities at large spatial scales.

Using datasets from two contrasting regions and ecosystem types in Australia and England, we report that aboveground plant community attributes, such as diversity (species richness) and cover, and functional traits can predict a unique portion of the variation in the diversity (number of phylotypes) and community composition of soil bacteria and fungi that cannot be explained by soil abiotic properties and climate. We further identify the relative importance and evaluate the potential direct and indirect effects of climate, soil properties and plant attributes in regulating the diversity and community composition of soil microbial communities.

Finally, we deliver a list of examples of common taxa from Australia and England that are strongly related to specific plant traits, such as specific leaf area index, leaf nitrogen and nitrogen fixation.

Together, our work provides new evidence that plant attributes, especially plant functional traits, can predict the distribution of soil microbial communities at the regional scale and across two hemispheres.

In precision agriculture (PA) practices, the accurate delineation of management zones (MZs), with each zone having similar characteristics, is essential for map-based variable rate application of farming inputs. However, there is no consensus on an optimal clustering algorithm and the input data format. In this paper, we evaluated the performances of five clustering algorithms including k-means, fuzzy C-means (FCM), hierarchical, mean shift, and density-based spatial clustering of applications with noise (DBSCAN) in different scenarios and assessed the impacts of input data format and feature selection on MZ delineation quality. We used key soil fertility attributes (moisture content (MC), organic carbon (OC), calcium (Ca), cation exchange capacity (CEC), exchangeable potassium (K), magnesium (Mg), sodium (Na), exchangeable phosphorous (P), and pH) collected with an online visible and near-infrared (vis-NIR) spectrometer along with Sentinel2 and yield data of five commercial fields in Belgium. We demonstrated that k-means is the optimal clustering method for MZ delineation, and the input data should be normalized (range normalization). Feature selection was also shown to be positively effective. Furthermore, we proposed an algorithm based on DBSCAN for smoothing the MZs maps to allow smooth actuating during variable rate application by agricultural machinery. Finally, the whole process of MZ delineation was integrated in a clustering and smoothing pipeline (CaSP), which automatically performs the following steps sequentially: (1) range normalization, (2) feature selection based on cross-correlation analysis, (3) k-means clustering, and (4) smoothing. It is recommended to adopt the developed platform for automatic MZ delineation for variable rate applications of farming inputs.

", "keywords": ["Agriculture and Food Sciences", "2. Zero hunger", "Spatial Analysis", "precision agriculture", "ACCURACY", "Chemical technology", "management zone delineation", "TP1-1185", "04 agricultural and veterinary sciences", "15. Life on land", "Article", "VARIABILITY", "Soil", "YIELD", "FUSION", "feature selection", "ATTRIBUTES", "clustering; feature selection; management zone delineation; precision agriculture", "Remote Sensing Technology", "Cluster Analysis", "0401 agriculture", " forestry", " and fisheries", "FIELD", "SOIL-PHOSPHORUS", "Algorithms", "clustering"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/22/2/645/pdf"}, {"href": "https://www.mdpi.com/1424-8220/22/2/645/pdf"}, {"href": "https://doi.org/10.3390/s22020645"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/s22020645", "name": "item", "description": "10.3390/s22020645", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/s22020645"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-14T00:00:00Z"}}, {"id": "10.3390/rs13183789", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:20:34Z", "type": "Journal Article", "created": "2021-09-22", "title": "Optimizing the Sowing Date to Improve Water Management and Wheat Yield in a Large Irrigation Scheme, through a Remote Sensing and an Evolution Strategy-Based Approach", "description": "

This study aims to investigate the effects of an optimized sowing calendar for wheat over a surface irrigation scheme in the semi-arid region of Haouz (Morocco) on irrigation water requirements, crop growth and development and on yield. For that, a scenario-based simulation approach based on the covariance matrix adaptation\uffe2\uff80\uff93evolution strategy (CMA-ES) was proposed to optimize both the spatiotemporal distribution of sowing dates and the irrigation schedules, and then evaluate wheat crop using the 2011\uffe2\uff80\uff932012 growing season dataset. Six sowing scenarios were simulated and compared to identify the most optimal spatiotemporal sowing calendar. The obtained results showed that with reference to the existing sowing patterns, early sowing of wheat leads to higher yields compared to late sowing (from 7.40 to 5.32 t/ha). Compared with actual conditions in the study area, the spatial heterogeneity is highly reduced, which increased equity between farmers. The results also showed that the proportion of plots irrigated in time can be increased (from 40% to 82%) compared to both the actual irrigation schedules and to previous results of irrigation optimization, which did not take into consideration sowing dates optimization. Furthermore, considerable reduction of more than 40% of applied irrigation water can be achieved by optimizing sowing dates. Thus, the proposed approach in this study is relevant for irrigation managers and farmers since it provides an insight on the consequences of their agricultural practices regarding the wheat sowing calendar and irrigation scheduling and can be implemented to recommend the best practices to adopt.

", "keywords": ["[SDE] Environmental Sciences", "2. Zero hunger", "0106 biological sciences", "evolutionary algorithm", "grain yield", "Science", "Q", "04 agricultural and veterinary sciences", "seeding date", "15. Life on land", "water resources", "01 natural sciences", "630", "6. Clean water", "irrigation scheduling", "wheat", "[SDE]Environmental Sciences", "0401 agriculture", " forestry", " and fisheries", "seeding date; irrigation scheduling; evolutionary algorithm; optimization; water resources; wheat; grain yield", "optimization", "water re- sources"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/18/3789/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/18/3789/pdf"}, {"href": "https://doi.org/10.3390/rs13183789"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs13183789", "name": "item", "description": "10.3390/rs13183789", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13183789"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-09-21T00:00:00Z"}}, {"id": "10.31224/osf.io/r8xau", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:20:11Z", "type": "Journal Article", "created": "2021-09-02", "title": "Multi-Axial Hybrid Fire Testing based on Dynamic Relaxation", "description": "

This technical note presents the experimental validation of a hybrid fire testing coordination algorithm recently developed by some of the authors. For the first time, the algorithm is applied to solve the static response of a multiple-degrees-of-freedom hybrid model.

", "keywords": ["Coordination algorithms", "Fire test", "Dynamic relaxation", "Computer science", "bepress|Engineering", "General Physics and Astronomy", "02 engineering and technology", "0201 civil engineering", "Control theory", "engrXiv|Engineering|Civil and Environmental Engineering", "0202 electrical engineering", " electronic engineering", " information engineering", "General Materials Science", "Safety", " Risk", " Reliability and Quality", "Partitioned time integration", "Hybrid fire testing", "engrXiv|Engineering|Civil and Environmental Engineering|Structural Engineering", "Technical note", "General Chemistry", "Experimental validation", "Hybrid fire testing; Dynamic relaxation; Partitioned time integration", "engrXiv|Engineering", "bepress|Engineering|Civil and Environmental Engineering", "Static response", "bepress|Engineering|Civil and Environmental Engineering|Structural Engineering", "Multi axial", "Hybrid model"]}, "links": [{"href": "https://doi.org/10.31224/osf.io/r8xau"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Fire%20Safety%20Journal", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.31224/osf.io/r8xau", "name": "item", "description": "10.31224/osf.io/r8xau", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.31224/osf.io/r8xau"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-09-02T00:00:00Z"}}, {"id": "10.3390/ijms24076573", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:20:27Z", "type": "Journal Article", "created": "2023-04-03", "title": "A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation", "description": "

The discovery and development of new drugs are extremely long and costly processes. Recent progress in artificial intelligence has made a positive impact on the drug development pipeline. Numerous challenges have been addressed with the growing exploitation of drug-related data and the advancement of deep learning technology. Several model frameworks have been proposed to enhance the performance of deep learning algorithms in molecular design. However, only a few have had an immediate impact on drug development since computational results may not be confirmed experimentally. This systematic review aims to summarize the different deep learning architectures used in the drug discovery process and are validated with further in vivo experiments. For each presented study, the proposed molecule or peptide that has been generated or identified by the deep learning model has been biologically evaluated in animal models. These state-of-the-art studies highlight that even if artificial intelligence in drug discovery is still in its infancy, it has great potential to accelerate the drug discovery cycle, reduce the required costs, and contribute to the integration of the 3R (Replacement, Reduction, Refinement) principles. Out of all the reviewed scientific articles, seven algorithms were identified: recurrent neural networks, specifically, long short-term memory (LSTM-RNNs), Autoencoders (AEs) and their Wasserstein Autoencoders (WAEs) and Variational Autoencoders (VAEs) variants; Convolutional Neural Networks (CNNs); Direct Message Passing Neural Networks (D-MPNNs); and Multitask Deep Neural Networks (MTDNNs). LSTM-RNNs were the most used architectures with molecules or peptide sequences as inputs.

", "keywords": ["Deep Learning", "Artificial Intelligence", "Drug Discovery", "Review", "Neural Networks", " Computer", "drug discovery; drug design; artificial intelligence; machine learning; deep learning; biological evaluation; animal model; in vivo", "Algorithms", "3. Good health"]}, "links": [{"href": "https://www.mdpi.com/1422-0067/24/7/6573/pdf"}, {"href": "https://doi.org/10.3390/ijms24076573"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/International%20Journal%20of%20Molecular%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/ijms24076573", "name": "item", "description": "10.3390/ijms24076573", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/ijms24076573"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-03-31T00:00:00Z"}}, {"id": "10.3929/ethz-b-000278733", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:20:43Z", "type": "Journal Article", "created": "2018-07-06", "title": "Cost\u2013benefit optimization of structural health monitoring sensor networks", "description": "

Structural health monitoring (SHM) allows the acquisition of information on the structural integrity of any mechanical system by processing data, measured through a set of sensors, in order to estimate relevant mechanical parameters and indicators of performance. Herein we present a method to perform the cost\uffe2\uff80\uff93benefit optimization of a sensor network by defining the density, type, and positioning of the sensors to be deployed. The effectiveness (benefit) of an SHM system may be quantified by means of information theory, namely through the expected Shannon information gain provided by the measured data, which allows the inherent uncertainties of the experimental process (i.e., those associated with the prediction error and the parameters to be estimated) to be accounted for. In order to evaluate the computationally expensive Monte Carlo estimator of the objective function, a framework comprising surrogate models (polynomial chaos expansion), model order reduction methods (principal component analysis), and stochastic optimization methods is introduced. Two optimization strategies are proposed: the maximization of the information provided by the measured data, given the technological, identifiability, and budgetary constraints; and the maximization of the information\uffe2\uff80\uff93cost ratio. The application of the framework to a large-scale structural problem, the Pirelli tower in Milan, is presented, and the two comprehensive optimization methods are compared.

", "keywords": ["Stochastic Processes", "structural health monitoring", "structural health monitoring; Bayesian inference; cost\u2013benefit analysis; stochastic optimization; information theory; Bayesian experimental design; surrogate modeling; model order reduction", "Chemical technology", "Cost-Benefit Analysis", "Bayesian inference", "Bayesian experimental design", "Uncertainty", "Bayes Theorem", "TP1-1185", "02 engineering and technology", "stochastic optimization", "Bayesian experimental design; Bayesian inference; Benefit analysis; Cost; Information theory; Model order reduction; Stochastic optimization; Structural health monitoring; Surrogate modeling; Algorithms; Monte Carlo Method; Nonlinear Dynamics; Stochastic Processes; Uncertainty; Bayes Theorem; Cost-Benefit Analysis; Analytical Chemistry; Atomic and Molecular Physics", " and Optics; Biochemistry; Instrumentation; Electrical and Electronic Engineering", "Article", "surrogate modeling", "0201 civil engineering", "Nonlinear Dynamics", "model order reduction", "cost\u2013benefit analysis", "Monte Carlo Method", "Algorithms", "information theory"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/18/7/2174/pdf"}, {"href": "https://re.public.polimi.it/bitstream/11311/1085132/1/Sensors_2018b.pdf"}, {"href": "https://doi.org/10.3929/ethz-b-000278733"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3929/ethz-b-000278733", "name": "item", "description": "10.3929/ethz-b-000278733", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3929/ethz-b-000278733"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-07-06T00:00:00Z"}}, {"id": "10.5061/dryad.7465c1j", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:20:55Z", "type": "Dataset", "title": "Data from: An alternative approach to reduce algorithm-derived biases in monitoring soil organic carbon changes", "description": "unspecifiedQuantifying soil organic carbon (SOC) changes is a fundamental issue in ecology and sustainable agriculture. However, the algorithm-derived biases in comparing SOC status have not been fully addressed. Although the methods based on equivalent soil mass (ESM) and mineral-matter mass (EMMM) reduced biases of the conventional methods based on equivalent soil volume (ESV), they face challenges in ensuring both data comparability and accuracy of SOC estimation due to unequal basis for comparison and using un-conserved reference systems. We introduce the basal mineral-matter reference systems (soils at time zero with natural porosity but no organic matter) and develop an approach based on equivalent mineral-matter volume (EMMV). To show the temporal bias, SOC change rates were re-calculated with the ESV method and modified methods that referenced to soils at time t1 (ESM, EMMM, EMMV-t1) or referenced to soils at time zero (EMMV-t0) using two datasets with contrasting SOC status. To show the spatial bias, the ESV and EMMV-t0 derived SOC stocks were compared using datasets from six sites across biomes. We found that, in the relatively C-rich forests, SOC accumulation rates derived from the modified methods that referenced to t1 soils and from the EMMV-t0 method were 5.7-13.6% and 20.6% higher than that calculated by the ESV method, respectively. Nevertheless, in the C-poor lands, no significant algorithmic biases of SOC estimation were observed. Finally, both the SOC stock discrepancies (ESV vs EMMV-t0) and the proportions of this unaccounted SOC were large and site-dependent. These results suggest that although the modified methods that referenced to t1 soils could reduce the biases derived from soil volume changes, they may not properly quantify SOC changes due to using un-conserved reference systems. The EMMV-t0 method provides an approach to address the two problems and is potentially useful since it enables SOC comparability and integrating SOC datasets.", "keywords": ["2. Zero hunger", "soil organic carbon", "basal mineral-matter reference systems", "soil volume change", "reference systems", "15. Life on land", "algorithm-derived biases", "SOC comparability", "equivalent mineral-matter volume"], "contacts": [{"organization": "Zhang, Weixin, Chen, Yuanqi, Shi, Leilei, Wang, Xiaoli, Liu, Yongwen, Mao, Rong, Rao, Xingquan, Lin, Yongbiao, Shao, Yuanhu, Li, Xiaobo, Zhao, Cancan, Liu, Shengjie, Piao, Shilong, Zhu, Weixing, Zou, Xiaoming, Fu, Shenglei,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.7465c1j"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.7465c1j", "name": "item", "description": "10.5061/dryad.7465c1j", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.7465c1j"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-06-03T00:00:00Z"}}, {"id": "10.5194/bg-3-571-2006", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:21:09Z", "type": "Journal Article", "created": "2010-04-29", "description": "

Abstract. Eddy covariance technique to measure CO2, water and energy fluxes between biosphere and atmosphere is widely spread and used in various regional networks. Currently more than 250 eddy covariance sites are active around the world measuring carbon exchange at high temporal resolution for different biomes and climatic conditions. In this paper a new standardized set of corrections is introduced and the uncertainties associated with these corrections are assessed for eight different forest sites in Europe with a total of 12 yearly datasets. The uncertainties introduced on the two components GPP (Gross Primary Production) and TER (Terrestrial Ecosystem Respiration) are also discussed and a quantitative analysis presented. Through a factorial analysis we find that generally, uncertainties by different corrections are additive without interactions and that the heuristic u*-correction introduces the largest uncertainty. The results show that a standardized data processing is needed for an effective comparison across biomes and for underpinning inter-annual variability. The methodology presented in this paper has also been integrated in the European database of the eddy covariance measurements.

", "keywords": ["european database of the eddy covariance measurements", "550", "net ecosystem exchange", "Molecular Biology/Biochemistry [q-bio.BM]", "[SDU.STU]Sciences of the Universe [physics]/Earth Sciences", "[SDU.ASTR] Sciences of the Universe [physics]/Astrophysics [astro-ph]", "[PHYS.ASTR.CO]Physics [physics]/Astrophysics [astro-ph]/Cosmology and Extra-Galactic Astrophysics [astro-ph.CO]", "Life", "QH501-531", "[SDV.BBM.BC] Life Sciences [q-bio]/Biochemistry", " Molecular Biology/Biochemistry [q-bio.BM]", "QH540-549.5", "eddy covariance technique", "[SDU.OCEAN]Sciences of the Universe [physics]/Ocean", "QE1-996.5", "algorithm", "[SDU.ASTR]Sciences of the Universe [physics]/Astrophysics [astro-ph]", "Ecology", "Atmosphere", "[SDU.OCEAN] Sciences of the Universe [physics]/Ocean", " Atmosphere", "500", "Geology", "15. Life on land", "terrestrial ecosystem respiration", "gross primary production", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "[SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry", "[PHYS.ASTR.CO] Physics [physics]/Astrophysics [astro-ph]/Cosmology and Extra-Galactic Astrophysics [astro-ph.CO]", "13. Climate action", "[SDU.STU] Sciences of the Universe [physics]/Earth Sciences", "co2", "measurement", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "environment"]}, "links": [{"href": "https://doi.org/10.5194/bg-3-571-2006"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Biogeosciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/bg-3-571-2006", "name": "item", "description": "10.5194/bg-3-571-2006", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/bg-3-571-2006"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2006-11-27T00:00:00Z"}}, {"id": "10.5281/zenodo.3268521", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:22:34Z", "type": "Report", "title": "Decision-Support System for Optimisation of Crop Configuration based on Artificial Intelligence", "description": "The work is the first official outcome of the collaboration between MSU and BS. Here we analysed the potential of multi-objective evolutionary algorithms in smart seed selection.", "keywords": ["Portfolio Optimisation", "Data analytics", "Evolutionary algorithms", "DSS"], "contacts": [{"organization": "Marko, Oskar, Pavlovi\u0107, Dejan, Crnojevi\u0107, Vladimir, Kalyanmoy Deb,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3268521"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3268521", "name": "item", "description": "10.5281/zenodo.3268521", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3268521"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-06-29T00:00:00Z"}}, {"id": "10.5281/zenodo.3268522", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:22:34Z", "type": "Report", "title": "Decision-Support System for Optimisation of Crop Configuration based on Artificial Intelligence", "description": "The work is the first official outcome of the collaboration between MSU and BS. Here we analysed the potential of multi-objective evolutionary algorithms in smart seed selection.", "keywords": ["Portfolio Optimisation", "Data analytics", "Evolutionary algorithms", "DSS"], "contacts": [{"organization": "Dejan Pavlovic, Oskar Marko, Vladimir Crnojevic, Kalyanmoy Deb,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3268522"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3268522", "name": "item", "description": "10.5281/zenodo.3268522", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3268522"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-06-29T00:00:00Z"}}, {"id": "10.60692/5feqz-9r143", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:23:26Z", "type": "Journal Article", "created": "2021-01-26", "title": "How much carbon can be added to soil by sorption?", "description": "Abstract

Quantifying the upper limit of stable soil carbon storage is essential for guiding policies to increase soil carbon storage. One pool of carbon considered particularly stable across climate zones and soil types is formed when dissolved organic carbon sorbs to minerals. We quantified, for the first time, the potential of mineral soils to sorb additional dissolved organic carbon (DOC) for six soil orders. We compiled 402 laboratory sorption experiments to estimate the additional DOC sorption potential, that is the potential of excess DOC sorption in addition to the existing background level already sorbed in each soil sample. We estimated this potential using gridded climate and soil geochemical variables within a machine learning model. We find that mid- and low-latitude soils and subsoils have a greater capacity to store DOC by sorption compared to high-latitude soils and topsoils. The global additional DOC sorption potential for six soil orders is estimated to be 107 $$ pm$$ \uffc2\uffb1 13 Pg C to 1\uffc2\uffa0m depth. If this potential was realized, it would represent a 7% increase in the existing total carbon stock.

Extensive application of clustering and classification algorithms shows the potential of a CNT-based sensor array in breathomics.

", "keywords": ["electronic nose", "Linear discriminant analysis", "Chemistry", " Multidisciplinary", "Principal component analysis", "02 engineering and technology", "VOLATILE ORGANIC-COMPOUNDS", "sensors", "Supported Vectror Machine", "01 natural sciences", "nanotubes", "E-NOSE", "breathomics", "THIN-FILMS", "SWCNTs", "RANDOM NETWORKS", "COPD", "ta318", "e-nose", "ta215", "WALLED CARBON NANOTUBES", "Science & Technology", "Breath analysis", "SWCNT SENSOR", "34 Chemical sciences", "Ammonia; Biomarkers; Carbon nanotubes; Classification (of information); Clustering algorithms; Molecules; Nitrogen oxides; Principal component analysis; Sulfur compounds; Support vector machines", "0104 chemical sciences", "3. Good health", "Chemistry", "ROOM-TEMPERATURE", "AMMONIA SENSOR", "Physical Sciences", "NO2 DETECTION", "03 Chemical Sciences", "0210 nano-technology", "RESISTIVE SENSORS"]}, "links": [{"href": "https://iris.cnr.it/bitstream/20.500.14243/536855/1/RSC%20Adv._2021.pdf"}, {"href": "https://boa.unimib.it/bitstream/10281/517427/2/d1ra03337a.pdf%3b"}, {"href": "https://publicatt.unicatt.it/bitstream/10807/190102/1/d1ra03337a.pdf"}, {"href": "http://pubs.rsc.org/en/content/articlepdf/2021/RA/D1RA03337A"}, {"href": "https://doi.org/10807/190102"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/RSC%20Advances", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10807/190102", "name": "item", "description": "10807/190102", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10807/190102"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "11858/00-001M-0000-0014-F719-F", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:24:11Z", "type": "Report", "title": "Approximation Algorithms for the Unsplittable Flow Problem on Paths and Trees", "description": "We study the Unsplittable Flow Problem (UFP) and related variants, namely UFP with Bag Constraints and UFP with Rounds, on paths and trees. We provide improved constant factor approximation algorithms for all these problems under the no bottleneck assumption (NBA), which says that the maximum demand for any source-sink pair is at most the minimum capacity of any edge. We obtain these improved results by expressing a feasible solution to a natural LP relaxation of the UFP as a near-convex combination of feasible integral solutions.", "keywords": ["Scheduling", "Unsplittable Flows", "Integer Decomposition", "ddc:004", "Linear Programming", "Approximation Algorithms", "004"], "contacts": [{"organization": "Elbassioni, Khaled, Garg, Naveen, Gupta, Divya, Kumar, Amit, Narula, Vishal, Pal, Arindam,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/11858/00-001M-0000-0014-F719-F"}, {"rel": "self", "type": "application/geo+json", "title": "11858/00-001M-0000-0014-F719-F", "name": "item", "description": "11858/00-001M-0000-0014-F719-F", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11858/00-001M-0000-0014-F719-F"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2012-01-01T00:00:00Z"}}, {"id": "1854/LU-8720112", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:24:18Z", "type": "Journal Article", "created": "2021-09-09", "title": "Diagnosis of cadmium contamination in urban and suburban soils using visible-to-near-infrared spectroscopy", "description": "Previous studies have mostly focused on using visible-to-near-infrared spectral technique to quantitatively estimate soil cadmium (Cd) content, whereas little attention has been paid to identifying soil Cd contamination from a perspective of spectral classification. Here, we developed a framework to compare the potential of two spectral transformations (i.e., raw reflectance and continuum removal [CR]), three optimization strategies (i.e., full-spectrum, Boruta feature selection, and synthetic minority over-sampling technique [SMOTE]), and three classification algorithms (i.e., partial least squares discriminant analysis, random forest [RF], and support vector machine) for diagnosing soil Cd contamination. A total of 536 soil samples were collected from urban and suburban areas located in Wuhan City, China. Specifically, Boruta and SMOTE strategies were aimed at selecting the most informative predictors and obtaining balanced training datasets, respectively. Results indicated that soils contaminated by Cd induced decrease in spectral reflectance magnitude. Classification models developed after Boruta and SMOTE strategies out-performed to those from full-spectrum. A diagnose model combining CR preprocessing, SMOTE strategy, and RF algorithm achieved the highest validation accuracy for soil Cd (Kappa = 0.74). This study provides a theoretical reference for rapid identification of and monitoring of soil Cd contamination in urban and suburban areas.", "keywords": ["DIFFUSE-REFLECTANCE SPECTROSCOPY", "HUMAN HEALTH", "PREDICTION", "POTENTIALLY TOXIC ELEMENTS", "Boruta algorithm", "01 natural sciences", "Visible-to-near-infrared spectroscopy", "NIR SPECTROSCOPY", "Soil", "ORGANIC-CARBON", "Machine learning", "11. Sustainability", "Soil Pollutants", "Least-Squares Analysis", "0105 earth and related environmental sciences", "Spectroscopy", " Near-Infrared", "RANDOM FOREST", "Urban and suburban soil Cd contamination", "04 agricultural and veterinary sciences", "15. Life on land", "QUANTITATIVE-ANALYSIS", "6. Clean water", "RIVER DELTA", "13. Climate action", "Earth and Environmental Sciences", "Synthetic minority over-sampling technique", "0401 agriculture", " forestry", " and fisheries", "HEAVY-METAL CONCENTRATIONS", "Cadmium"]}, "links": [{"href": "https://doi.org/1854/LU-8720112"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Pollution", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1854/LU-8720112", "name": "item", "description": "1854/LU-8720112", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1854/LU-8720112"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-01T00:00:00Z"}}, {"id": "1854/LU-8746428", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:24:18Z", "type": "Journal Article", "created": "2022-01-16", "title": "Clustering and Smoothing Pipeline for Management Zone Delineation Using Proximal and Remote Sensing", "description": "

In precision agriculture (PA) practices, the accurate delineation of management zones (MZs), with each zone having similar characteristics, is essential for map-based variable rate application of farming inputs. However, there is no consensus on an optimal clustering algorithm and the input data format. In this paper, we evaluated the performances of five clustering algorithms including k-means, fuzzy C-means (FCM), hierarchical, mean shift, and density-based spatial clustering of applications with noise (DBSCAN) in different scenarios and assessed the impacts of input data format and feature selection on MZ delineation quality. We used key soil fertility attributes (moisture content (MC), organic carbon (OC), calcium (Ca), cation exchange capacity (CEC), exchangeable potassium (K), magnesium (Mg), sodium (Na), exchangeable phosphorous (P), and pH) collected with an online visible and near-infrared (vis-NIR) spectrometer along with Sentinel2 and yield data of five commercial fields in Belgium. We demonstrated that k-means is the optimal clustering method for MZ delineation, and the input data should be normalized (range normalization). Feature selection was also shown to be positively effective. Furthermore, we proposed an algorithm based on DBSCAN for smoothing the MZs maps to allow smooth actuating during variable rate application by agricultural machinery. Finally, the whole process of MZ delineation was integrated in a clustering and smoothing pipeline (CaSP), which automatically performs the following steps sequentially: (1) range normalization, (2) feature selection based on cross-correlation analysis, (3) k-means clustering, and (4) smoothing. It is recommended to adopt the developed platform for automatic MZ delineation for variable rate applications of farming inputs.

", "keywords": ["Agriculture and Food Sciences", "2. Zero hunger", "Spatial Analysis", "precision agriculture", "ACCURACY", "Chemical technology", "management zone delineation", "TP1-1185", "04 agricultural and veterinary sciences", "15. Life on land", "Article", "VARIABILITY", "Soil", "YIELD", "FUSION", "feature selection", "ATTRIBUTES", "clustering; feature selection; management zone delineation; precision agriculture", "Remote Sensing Technology", "Cluster Analysis", "0401 agriculture", " forestry", " and fisheries", "FIELD", "SOIL-PHOSPHORUS", "Algorithms", "clustering"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/22/2/645/pdf"}, {"href": "https://www.mdpi.com/1424-8220/22/2/645/pdf"}, {"href": "https://doi.org/1854/LU-8746428"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1854/LU-8746428", "name": "item", "description": "1854/LU-8746428", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1854/LU-8746428"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-14T00:00:00Z"}}, {"id": "1871.1/270d8bb4-64f4-4f60-b44e-492fcf327fc8", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:24:18Z", "type": "Journal Article", "created": "2024-02-09", "title": "Improving the fire weather index system for peatlands using peat-specific hydrological input data", "description": "

Abstract. The Canadian Fire Weather Index (FWI) system, even though originally developed and calibrated for an upland Jack pine forest, is used globally to estimate fire danger for any fire environment. However, for some environments, such as peatlands, the applicability of the FWI in its current form, is often questioned. In this study, we replaced the original moisture codes of the FWI with hydrological estimates resulting from the assimilation of satellite-based L-band passive microwave observations into a peatland-specific land surface model. In a conservative approach that maintains the integrity of the original FWI structure, the distributions of the hydrological estimates were first matched to those of the corresponding original moisture codes before replacement. The resulting adapted FWI, hereafter called FWIpeat, was evaluated using satellite-based information on fire presence over boreal peatlands from 2010 through 2018. Adapting the FWI with model- and satellite-based hydrological information was found to be beneficial in estimating fire danger, especially when replacing the deeper moisture codes of the FWI. For late-season fires, further adaptations of the fine fuel moisture code show even more improvement due to the fact that late-season fires are more hydrologically driven. The proposed FWIpeat should enable improved monitoring of fire risk in boreal peatlands.

", "keywords": ["CARBON SINK", "Environmental technology. Sanitary engineering", "01 natural sciences", "G", "4406 Human geography", "Geography. Anthropology. Recreation", "Meteorology & Atmospheric Sciences", "GE1-350", "ALGORITHM", "Geosciences", " Multidisciplinary", "TD1-1066", "0105 earth and related environmental sciences", "QE1-996.5", "Science & Technology", "CLIMATE-CHANGE", "Strategic", " Defence & Security Studies", "CONSUMPTION", "Geology", "04 agricultural and veterinary sciences", "15. Life on land", "Environmental sciences", "SEVERITY", "0403 Geology", "0911 Maritime Engineering", "13. Climate action", "Physical Sciences", "Water Resources", "0401 agriculture", " forestry", " and fisheries", "0406 Physical Geography and Environmental Geoscience", "3709 Physical geography and environmental geoscience"]}, "links": [{"href": "https://nhess.copernicus.org/articles/24/445/2024/nhess-24-445-2024.pdf"}, {"href": "https://doi.org/1871.1/270d8bb4-64f4-4f60-b44e-492fcf327fc8"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Natural%20Hazards%20and%20Earth%20System%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1871.1/270d8bb4-64f4-4f60-b44e-492fcf327fc8", "name": "item", "description": "1871.1/270d8bb4-64f4-4f60-b44e-492fcf327fc8", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1871.1/270d8bb4-64f4-4f60-b44e-492fcf327fc8"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-02-09T00:00:00Z"}}, {"id": "1959.7/uws:46474", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:24:21Z", "type": "Journal Article", "created": "2018-04-19", "title": "Plant attributes explain the distribution of soil microbial communities in two contrasting regions of the globe", "description": "Summary

We lack strong empirical evidence for links between plant attributes (plant community attributes and functional traits) and the distribution of soil microbial communities at large spatial scales.

Using datasets from two contrasting regions and ecosystem types in Australia and England, we report that aboveground plant community attributes, such as diversity (species richness) and cover, and functional traits can predict a unique portion of the variation in the diversity (number of phylotypes) and community composition of soil bacteria and fungi that cannot be explained by soil abiotic properties and climate. We further identify the relative importance and evaluate the potential direct and indirect effects of climate, soil properties and plant attributes in regulating the diversity and community composition of soil microbial communities.

Finally, we deliver a list of examples of common taxa from Australia and England that are strongly related to specific plant traits, such as specific leaf area index, leaf nitrogen and nitrogen fixation.

Together, our work provides new evidence that plant attributes, especially plant functional traits, can predict the distribution of soil microbial communities at the regional scale and across two hemispheres.

This technical note presents the experimental validation of a hybrid fire testing coordination algorithm recently developed by some of the authors. For the first time, the algorithm is applied to solve the static response of a multiple-degrees-of-freedom hybrid model.

", "keywords": ["Coordination algorithms", "Fire test", "Dynamic relaxation", "Computer science", "bepress|Engineering", "General Physics and Astronomy", "02 engineering and technology", "0201 civil engineering", "Control theory", "engrXiv|Engineering|Civil and Environmental Engineering", "0202 electrical engineering", " electronic engineering", " information engineering", "General Materials Science", "Safety", " Risk", " Reliability and Quality", "Partitioned time integration", "Hybrid fire testing", "engrXiv|Engineering|Civil and Environmental Engineering|Structural Engineering", "Technical note", "General Chemistry", "Experimental validation", "Hybrid fire testing; Dynamic relaxation; Partitioned time integration", "engrXiv|Engineering", "bepress|Engineering|Civil and Environmental Engineering", "Static response", "bepress|Engineering|Civil and Environmental Engineering|Structural Engineering", "Multi axial", "Hybrid model"]}, "links": [{"href": "https://doi.org/20.500.11850/510230"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Fire%20Safety%20Journal", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.11850/510230", "name": "item", "description": "20.500.11850/510230", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.11850/510230"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-09-02T00:00:00Z"}}, {"id": "20.500.14243/532031", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:24:34Z", "type": "Journal Article", "created": "2022-10-03", "title": "Mathematical techniques to remove moisture effects from visible\u2013near-infrared\u2013shortwave-infrared soil spectra\u2014review", "description": "This is an Accepted Manuscript of an article published by Taylor & Francis in Applied Spectroscopy Reviews on 03 October 2022, available at: https://doi.org/10.1080/05704928.2022.2128365", "keywords": ["EJP Soil", "Proximal Sensing", "ProbeField", "Soil Moisture", "04 agricultural and veterinary sciences", "algorithms", "01 natural sciences", "diffuse reflectance spectroscopy", "field-moist conditions", "EJPSOIL", "0401 agriculture", " forestry", " and fisheries", "indices", "Soil moisture", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://www.tandfonline.com/doi/pdf/10.1080/05704928.2022.2128365"}, {"href": "https://doi.org/20.500.14243/532031"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Applied%20Spectroscopy%20Reviews", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.14243/532031", "name": "item", "description": "20.500.14243/532031", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.14243/532031"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-10-03T00:00:00Z"}}, {"id": "2117/83996", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:24:39Z", "type": "Report", "title": "Sistemas TCM-8PSK en canales con interferencia intersimbolica", "description": "Peer Reviewed", "keywords": ["Algorismes", ":Enginyeria de la telecomunicaci\u00f3 [\u00c0rees tem\u00e0tiques de la UPC]", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria de la telecomunicaci\u00f3", "Algorithms"], "contacts": [{"organization": "Femenias Nadal, Guillem", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/2117/83996"}, {"rel": "self", "type": "application/geo+json", "title": "2117/83996", "name": "item", "description": "2117/83996", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2117/83996"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "1991-01-01T00:00:00Z"}}, {"id": "2750197518", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:24:50Z", "type": "Journal Article", "created": "2017-08-24", "title": "Sparse Recovery in Magnetic Resonance Imaging With a Markov Random Field Prior", "description": "Recent research in compressed sensing of magnetic resonance imaging (CS-MRI) emphasizes the importance of modeling structured sparsity, either in the acquisition or in the reconstruction stages. Subband coefficients of typical images show certain structural patterns, which can be viewed in terms of fixed groups (like wavelet trees) or statistically (certain configurations are more likely than others). Wavelet tree models have already demonstrated excellent performance in MRI recovery from partial data. However, much less attention has been given in CS-MRI to modeling statistically spatial clustering of subband data, although the potentials of such models have been indicated. In this paper, we propose a practical CS-MRI reconstruction algorithm making use of a Markov random field prior model for spatial clustering of subband coefficients and an efficient optimization approach based on proximal splitting. The results demonstrate an improved reconstruction performance compared with both the standard CS-MRI methods and the recent related methods.", "keywords": ["Mice", "Image Processing", " Computer-Assisted", "0202 electrical engineering", " electronic engineering", " information engineering", "Animals", "Brain", "Humans", "02 engineering and technology", "Magnetic Resonance Imaging", "Algorithms", "Markov Chains"], "contacts": [{"organization": "Marko Pani\u0107, Jan Aelterman, Vladimir Crnojevi\u0107, Aleksandra Pi\u017eurica,", "roles": ["creator"]}]}, "links": [{"href": "http://xplorestaging.ieee.org/ielx7/42/8053927/08016375.pdf?arnumber=8016375"}, {"href": "https://doi.org/2750197518"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IEEE%20Transactions%20on%20Medical%20Imaging", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2750197518", "name": "item", "description": "2750197518", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2750197518"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-10-01T00:00:00Z"}}, {"id": "28858789", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:24:53Z", "type": "Journal Article", "created": "2017-08-24", "title": "Sparse Recovery in Magnetic Resonance Imaging With a Markov Random Field Prior", "description": "Recent research in compressed sensing of magnetic resonance imaging (CS-MRI) emphasizes the importance of modeling structured sparsity, either in the acquisition or in the reconstruction stages. Subband coefficients of typical images show certain structural patterns, which can be viewed in terms of fixed groups (like wavelet trees) or statistically (certain configurations are more likely than others). Wavelet tree models have already demonstrated excellent performance in MRI recovery from partial data. However, much less attention has been given in CS-MRI to modeling statistically spatial clustering of subband data, although the potentials of such models have been indicated. In this paper, we propose a practical CS-MRI reconstruction algorithm making use of a Markov random field prior model for spatial clustering of subband coefficients and an efficient optimization approach based on proximal splitting. The results demonstrate an improved reconstruction performance compared with both the standard CS-MRI methods and the recent related methods.", "keywords": ["Mice", "Image Processing", " Computer-Assisted", "0202 electrical engineering", " electronic engineering", " information engineering", "Animals", "Brain", "Humans", "02 engineering and technology", "Magnetic Resonance Imaging", "Algorithms", "Markov Chains"]}, "links": [{"href": "http://xplorestaging.ieee.org/ielx7/42/8053927/08016375.pdf?arnumber=8016375"}, {"href": "https://doi.org/28858789"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IEEE%20Transactions%20on%20Medical%20Imaging", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "28858789", "name": "item", "description": "28858789", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/28858789"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-10-01T00:00:00Z"}}, {"id": "2954562561", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:24:57Z", "type": "Report", "title": "Decision-Support System for Optimisation of Crop Configuration based on Artificial Intelligence", "description": "The work is the first official outcome of the collaboration between MSU and BS. Here we analysed the potential of multi-objective evolutionary algorithms in smart seed selection.", "keywords": ["Portfolio Optimisation", "Data analytics", "Evolutionary algorithms", "DSS"], "contacts": [{"organization": "Dejan Pavlovic, Oskar Marko, Vladimir Crnojevic, Kalyanmoy Deb,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/2954562561"}, {"rel": "self", "type": "application/geo+json", "title": "2954562561", "name": "item", "description": "2954562561", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2954562561"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-06-29T00:00:00Z"}}, {"id": "3122165360", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:25:11Z", "type": "Journal Article", "created": "2021-01-26", "title": "How much carbon can be added to soil by sorption?", "description": "Abstract

Quantifying the upper limit of stable soil carbon storage is essential for guiding policies to increase soil carbon storage. One pool of carbon considered particularly stable across climate zones and soil types is formed when dissolved organic carbon sorbs to minerals. We quantified, for the first time, the potential of mineral soils to sorb additional dissolved organic carbon (DOC) for six soil orders. We compiled 402 laboratory sorption experiments to estimate the additional DOC sorption potential, that is the potential of excess DOC sorption in addition to the existing background level already sorbed in each soil sample. We estimated this potential using gridded climate and soil geochemical variables within a machine learning model. We find that mid- and low-latitude soils and subsoils have a greater capacity to store DOC by sorption compared to high-latitude soils and topsoils. The global additional DOC sorption potential for six soil orders is estimated to be 107 $$ pm$$ \uffc2\uffb1 13 Pg C to 1\uffc2\uffa0m depth. If this potential was realized, it would represent a 7% increase in the existing total carbon stock.Tomato is an important commercial product which is perishable by nature and highly susceptible to fungal incidence once it is harvested. Not all tomatoes are equally vulnerable to pathogenic fungi, and an early detection of the vulnerable ones can help in taking timely preventive actions, ranging from isolating tomato batches to adjusting storage conditions, but also in making right business decisions like dynamic pricing based on quality or better shelf life estimate. More importantly, early detection of vulnerable produce can help in taking timely actions to minimize potential post-harvest losses. This paper investigates Near-infrared (NIR) hyperspectral imaging (1000\uffe2\uff80\uff931700\uffc2\uffa0nm) and machine learning to build models to automatically predict the susceptibility of sepals of recently harvested tomatoes to future fungal infections. Hyperspectral images of newly harvested tomatoes (cultivar Brioso) from 5 different growers were acquired before the onset of any visible fungal infection. After imaging, the tomatoes were placed under controlled conditions suited for fungal germination and growth for a 4-day period, and then imaged using normal color cameras. All sepals in the color images were ranked for fungal severity using crowdsourcing, and the final severity of each sepal was fused using principal component analysis. A novel hyperspectral data processing pipeline is presented which was used to automatically segment the tomato sepals from spectral images with multiple tomatoes connected via a truss. The key modelling question addressed in this research is whether there is a correlation between the hyperspectral data captured at harvest and the fungal infection observed 4 days later. Using 10-fold and group k-fold cross-validation, XG-Boost and Random Forest based regression models were trained on the features derived from the hyperspectral data corresponding to each sepal in the training set and tested on hold out test set. The best model found a Pearson correlation of 0.837, showing that there is strong linear correlation between the NIR spectra and the future fungal severity of the sepal. The sepal specific predictions were aggregated to predict the susceptibility of individual tomatoes, and a correlation of 0.92 was found. Besides modelling, focus is also on model interpretation, particularly to understand which spectral features are most relevant to model prediction. Two approaches to model interpretation were explored, feature importance and SHAP (SHapley Additive exPlanations), resulting in similar conclusions that the NIR range between 1390\uffe2\uff80\uff931420\uffc2\uffa0nm contributes most to the model\uffe2\uff80\uff99s final decision.

The discovery and development of new drugs are extremely long and costly processes. Recent progress in artificial intelligence has made a positive impact on the drug development pipeline. Numerous challenges have been addressed with the growing exploitation of drug-related data and the advancement of deep learning technology. Several model frameworks have been proposed to enhance the performance of deep learning algorithms in molecular design. However, only a few have had an immediate impact on drug development since computational results may not be confirmed experimentally. This systematic review aims to summarize the different deep learning architectures used in the drug discovery process and are validated with further in vivo experiments. For each presented study, the proposed molecule or peptide that has been generated or identified by the deep learning model has been biologically evaluated in animal models. These state-of-the-art studies highlight that even if artificial intelligence in drug discovery is still in its infancy, it has great potential to accelerate the drug discovery cycle, reduce the required costs, and contribute to the integration of the 3R (Replacement, Reduction, Refinement) principles. Out of all the reviewed scientific articles, seven algorithms were identified: recurrent neural networks, specifically, long short-term memory (LSTM-RNNs), Autoencoders (AEs) and their Wasserstein Autoencoders (WAEs) and Variational Autoencoders (VAEs) variants; Convolutional Neural Networks (CNNs); Direct Message Passing Neural Networks (D-MPNNs); and Multitask Deep Neural Networks (MTDNNs). LSTM-RNNs were the most used architectures with molecules or peptide sequences as inputs.

", "keywords": ["Deep Learning", "Artificial Intelligence", "Drug Discovery", "Review", "Neural Networks", " Computer", "Algorithms", "3. Good health"]}, "links": [{"href": "https://www.mdpi.com/1422-0067/24/7/6573/pdf"}, {"href": "https://doi.org/PMC10095548"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/International%20Journal%20of%20Molecular%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "PMC10095548", "name": "item", "description": "PMC10095548", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PMC10095548"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-03-31T00:00:00Z"}}, {"id": "PMC11199918", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:27:06Z", "type": "Journal Article", "created": "2024-04-18", "title": "Parameter grouping and co-estimation in physiologically based kinetic models using genetic algorithms", "description": "Abstract

Physiologically based kinetic (PBK) models are widely used in pharmacology and toxicology for predicting the internal disposition of substances upon exposure, voluntarily or not. Due to their complexity, a large number of model parameters need to be estimated, either through in silico tools, in vitro experiments, or by fitting the model to in vivo data. In the latter case, fitting complex structural models on in vivo data can result in overparameterization and produce unrealistic parameter estimates. To address these issues, we propose a novel parameter grouping approach, which reduces the parametric space by co-estimating groups of parameters across compartments. Grouping of parameters is performed using genetic algorithms and is fully automated, based on a novel goodness-of-fit metric. To illustrate the practical application of the proposed methodology, two case studies were conducted. The first case study demonstrates the development of a new PBK model, while the second focuses on model refinement. In the first case study, a PBK model was developed to elucidate the biodistribution of titanium dioxide (TiO2) nanoparticles in rats following intravenous injection. A variety of parameter estimation schemes were employed. Comparative analysis based on goodness-of-fit metrics demonstrated that the proposed methodology yields models that outperform standard estimation approaches, while utilizing a reduced number of parameters. In the second case study, an existing PBK model for perfluorooctanoic acid (PFOA) in rats was extended to incorporate additional tissues, providing a more comprehensive portrayal of PFOA biodistribution. Both models were validated through independent in vivo studies to ensure their reliability.Soil spectroscopy is a widely used method for estimating soil properties that are important to environmental and agricultural monitoring. However, a bottleneck to its more widespread adoption is the need for establishing large reference datasets for training machine learning (ML) models, which are called soil spectral libraries (SSLs). Similarly, the prediction capacity of new samples is also subject to the number and diversity of soil types and conditions represented in the SSLs. To help bridge this gap and enable hundreds of stakeholders to collect more affordable soil data by leveraging a centralized open resource, the Soil Spectroscopy for Global Good has created the Open Soil Spectral Library (OSSL). In this paper, we describe the procedures for collecting and harmonizing several SSLs that are incorporated into the OSSL, followed by exploratory analysis and predictive modeling. The results of 10-fold cross-validation with refitting show that, in general, mid-infrared (MIR)-based models are significantly more accurate than visible and near-infrared (VisNIR) or near-infrared (NIR) models. From independent model evaluation, we found that Cubist comes out as the best-performing ML algorithm for the calibration and delivery of reliable outputs (prediction uncertainty and representation flag). Although many soil properties are well predicted, total sulfur, extractable sodium, and electrical conductivity performed poorly in all spectral regions, with some other extractable nutrients and physical soil properties also performing poorly in one or two spectral regions (VisNIR or Neospectra NIR). Hence, the use of predictive models based solely on spectral variations has limitations. This study also presents and discusses several other open resources that were developed from the OSSL, aspects of opening data, current limitations, and future development. With this genuinely open science project, we hope that OSSL becomes the driver of the soil spectroscopy community to accelerate the pace of scientific discovery and innovation.Tomato is an important commercial product which is perishable by nature and highly susceptible to fungal incidence once it is harvested. Not all tomatoes are equally vulnerable to pathogenic fungi, and an early detection of the vulnerable ones can help in taking timely preventive actions, ranging from isolating tomato batches to adjusting storage conditions, but also in making right business decisions like dynamic pricing based on quality or better shelf life estimate. More importantly, early detection of vulnerable produce can help in taking timely actions to minimize potential post-harvest losses. This paper investigates Near-infrared (NIR) hyperspectral imaging (1000\uffe2\uff80\uff931700\uffc2\uffa0nm) and machine learning to build models to automatically predict the susceptibility of sepals of recently harvested tomatoes to future fungal infections. Hyperspectral images of newly harvested tomatoes (cultivar Brioso) from 5 different growers were acquired before the onset of any visible fungal infection. After imaging, the tomatoes were placed under controlled conditions suited for fungal germination and growth for a 4-day period, and then imaged using normal color cameras. All sepals in the color images were ranked for fungal severity using crowdsourcing, and the final severity of each sepal was fused using principal component analysis. A novel hyperspectral data processing pipeline is presented which was used to automatically segment the tomato sepals from spectral images with multiple tomatoes connected via a truss. The key modelling question addressed in this research is whether there is a correlation between the hyperspectral data captured at harvest and the fungal infection observed 4 days later. Using 10-fold and group k-fold cross-validation, XG-Boost and Random Forest based regression models were trained on the features derived from the hyperspectral data corresponding to each sepal in the training set and tested on hold out test set. The best model found a Pearson correlation of 0.837, showing that there is strong linear correlation between the NIR spectra and the future fungal severity of the sepal. The sepal specific predictions were aggregated to predict the susceptibility of individual tomatoes, and a correlation of 0.92 was found. Besides modelling, focus is also on model interpretation, particularly to understand which spectral features are most relevant to model prediction. Two approaches to model interpretation were explored, feature importance and SHAP (SHapley Additive exPlanations), resulting in similar conclusions that the NIR range between 1390\uffe2\uff80\uff931420\uffc2\uffa0nm contributes most to the model\uffe2\uff80\uff99s final decision.