{"type": "FeatureCollection", "features": [{"id": "10.1007/s10994-020-05918-z", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:15:09Z", "type": "Journal Article", "created": "2020-10-28", "title": "Incremental predictive clustering trees for online semi-supervised multi-target regression", "description": "Abstract<p>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.</p", "keywords": ["semi-supervised learning", "multi-target regression", "Classification and discrimination; cluster analysis (statistical aspects)", "Linear regression; mixed models", "predictive clustering", "Artificial Intelligence", "Learning and adaptive systems in artificial intelligence", "0202 electrical engineering", " electronic engineering", " information engineering", "Online algorithms; streaming algorithms", "02 engineering and technology", "Software", "data-stream mining"]}, "links": [{"href": "https://doi.org/10.1007/s10994-020-05918-z"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Machine%20Learning", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s10994-020-05918-z", "name": "item", "description": "10.1007/s10994-020-05918-z", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s10994-020-05918-z"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-10-28T00:00:00Z"}}, {"id": "10.1016/j.eja.2022.126569", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:16:09Z", "type": "Journal Article", "created": "2022-07-08", "title": "Mixing process-based and data-driven approaches in yield prediction", "description": "Yield prediction models can be divided between data-driven and process-based models (crop growth models). The first category contains many different types of models with parameters learned from the data themselves and where domain knowledge is only used to select the predictors and engineer features. In the second category, models are based upon biophysical principles, whose structure and parameters are derived primarily from domain knowledge. Here we investigate if the integration of the two approaches can be beneficial as it allows to overcome the limitations of the two approaches taken individually - lack of sufficiently large, reliable and orthogonal datasets for data-driven approaches and the need of many inputs for process-based models. The applications of the two categories of models have been reviewed, paying special attention to the cases where the two approaches have been mixed. By analysing the literature we identified three major cases of integration between the two approaches: (1) using crop growth models to engineer features and expand the predictors space, (2) use data-driven approaches to estimate missing inputs for process-based models (3) using data-driven approaches to produce meta-models to reduce computation burden. Finally we propose a methodology based on metamodels and transfer learning to integrate data-driven and process-based approaches.", "keywords": ["Process-based", "0106 biological sciences", "2. Zero hunger", "Artificial intelligence", "Crop growth models", "04 agricultural and veterinary sciences", "Data-driven", "01 natural sciences", "Yield prediction", "Dynamic crop growth models", "Surrogate models", "0401 agriculture", " forestry", " and fisheries", "Crop models", "Metamodels", "Neural networks"]}, "links": [{"href": "https://doi.org/10.1016/j.eja.2022.126569"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/European%20Journal%20of%20Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.eja.2022.126569", "name": "item", "description": "10.1016/j.eja.2022.126569", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.eja.2022.126569"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-09-01T00:00:00Z"}}, {"id": "10.1038/s41598-019-56868-z", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:17:52Z", "type": "Journal Article", "created": "2020-01-09", "title": "Modelling photovoltaic soiling losses through optical characterization", "description": "Abstract<p>The accumulation of soiling on photovoltaic (PV) modules affects PV systems worldwide. Soiling consists of mineral dust, soot particles, aerosols, pollen, fungi and/or other contaminants that deposit on the surface of PV modules. Soiling absorbs, scatters, and reflects a fraction of the incoming sunlight, reducing the intensity that reaches the active part of the solar cell. Here, we report on the comparison of naturally accumulated soiling on coupons of PV glass soiled at seven locations worldwide. The spectral hemispherical transmittance was measured. It was found that natural soiling disproportionately impacts the blue and ultraviolet (UV) portions of the spectrum compared to the visible and infrared (IR). Also, the general shape of the transmittance spectra was similar at all the studied sites and could adequately be described by a modified form of the \uffc3\uff85ngstr\uffc3\uffb6m turbidity equation. In addition, the distribution of particles sizes was found to follow the IEST-STD-CC 1246E cleanliness standard. The fractional coverage of the glass surface by particles could be determined directly or indirectly and, as expected, has a linear correlation with the transmittance. It thus becomes feasible to estimate the optical consequences of the soiling of PV modules from the particle size distribution and the cleanliness value.</p>", "keywords": ["Photovoltaic Arrays", "Cleanliness", "Particle", "PV", "02 engineering and technology", "Oceanography", "7. Clean energy", "soiling; experimental; transmittance; spectrum", "Turbidity", "Size", "Materials Science and Engineering", "\u00c5ngstr\u00f6m turbidity equation", "Transmittance", "0202 electrical engineering", " electronic engineering", " information engineering", "Photovoltaic system", "Ultraviolet", "Microscopy", "Soiling", "Energy", "Ecology", "Physics", "Q", "R", "Imaging and sensing", "Geology", "Particle size", "6. Clean water", "Photovoltaic Efficiency", "Chemistry", "Physical chemistry", "Particle (ecology)", "Physical Sciences", "Sunlight", "Medicine", "Infrared", "570", "Particle-size distribution", "PV System", "Energy science and technology", "Science", "Optical spectroscopy", "Partial Shading", "530", "Modelling", "Article", "Environmental science", "Techniques and instrumentation", "Optical physics", "Meteorology", "Artificial Intelligence", "Machine Learning Methods for Solar Radiation Forecasting", "Optical techniques", "Optoelectronics", "Aerosol", "Biology", "Renewable Energy", " Sustainability and the Environment", "Electronics", " photonics and device physics", "Building Integrated Photovoltaics", "Optics", "Photovoltaic Maximum Power Point Tracking Techniques", "FOS: Earth and related environmental sciences", "Materials science", "Photovoltaics", "Optics and photonics", "13. Climate action", "FOS: Biological sciences", "Computer Science", "Solar Thermal Energy Technologies"]}, "links": [{"href": "https://iris.uniroma1.it/bitstream/11573/1625670/2/Smestad_Modelling_2020.pdf"}, {"href": "https://www.nature.com/articles/s41598-019-56868-z.pdf"}, {"href": "https://doi.org/10.1038/s41598-019-56868-z"}, {"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/s41598-019-56868-z", "name": "item", "description": "10.1038/s41598-019-56868-z", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/s41598-019-56868-z"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-09T00:00:00Z"}}, {"id": "10.1126/sciadv.adg9644", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:19:10Z", "type": "Journal Article", "created": "2023-07-12", "title": "Artificial intelligence\u2013coupled plasmonic infrared sensor for detection of structural protein biomarkers in neurodegenerative diseases", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Diagnosis of neurodegenerative disorders (NDDs) including Parkinson\u2019s disease and Alzheimer\u2019s disease is challenging owing to the lack of tools to detect preclinical biomarkers. The misfolding of proteins into oligomeric and fibrillar aggregates plays an important role in the development and progression of NDDs, thus underscoring the need for structural biomarker\u2013based diagnostics. We developed an immunoassay-coupled nanoplasmonic infrared metasurface sensor that detects proteins linked to NDDs, such as alpha-synuclein, with specificity and differentiates the distinct structural species using their unique absorption signatures. We augmented the sensor with an artificial neural network enabling unprecedented quantitative prediction of oligomeric and fibrillar protein aggregates in their mixture. The microfluidic integrated sensor can retrieve time-resolved absorbance fingerprints in the presence of a complex biomatrix and is capable of multiplexing for the simultaneous monitoring of multiple pathology-associated biomarkers. Thus, our sensor is a promising candidate for the clinical diagnosis of NDDs, disease monitoring, and evaluation of novel therapies.</p></article>", "keywords": ["Artificial Intelligence", "Alzheimer Disease", "Humans", "Physical and Materials Sciences", "Neurodegenerative Diseases", "Parkinson Disease", "Biomarkers"]}, "links": [{"href": "https://doi.org/10.1126/sciadv.adg9644"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science%20Advances", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1126/sciadv.adg9644", "name": "item", "description": "10.1126/sciadv.adg9644", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1126/sciadv.adg9644"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-07-14T00:00:00Z"}}, {"id": "10.3390/su12062170", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:11Z", "type": "Journal Article", "created": "2020-03-12", "title": "Argumentation Corrected Context Weighting-Life Cycle Assessment: A Practical Method of Including Stakeholder Perspectives in Multi-Criteria Decision Support for LCA", "description": "<p>Despite advances in the data, models, and methods underpinning environmental life cycle assessment (LCA), it remains challenging for practitioners to effectively communicate and interpret results. These shortcomings can bias decisions and hinder public acceptance for planning supported by LCA. This paper introduces a method for interpreting LCA results, the Argumentation Corrected Context Weighting-LCA (ArgCW-LCA), to overcome these barriers. ArgCW-LCA incorporates stakeholder preferences, corrects unjustified disagreements, and allows for the inclusion of non-environmental impacts (e.g., economic, social, etc.) using a novel weighting scheme and the application of multi-criteria decision analysis to provide transparent and context-relevant decision support. We illustrate the utility of the method through two case studies: a hypothetical decision regarding energy production and a real-world decision regarding polyphenol extraction technologies. In each case, we surveyed a relevant stakeholder group on their environmental views and fed their responses into the model to provide decision support that is relevant to their perspective. We found marked differences between results using ArgCW-LCA and results from a conventional analysis using an equal-weighting scheme, as well as differentiation between stakeholder preference groups, indicating the importance of applying the perspective of the particular stakeholder group. For instance, there was a rank reversal of alternatives when comparing between an equal weighting approach for all environmental and economic dimensions and ArgCW-LCA. ArgCW-LCA provides opportunity for both public and private sector incorporation of LCA, such as in developing enlightened stakeholder value measures. This is achieved through enabling the LCA practition to provide public and private actors\uffe2\uff80\uff99 interpreted LCA results in a manner that incorporates educated stakeholder perspectives. Furthermore, the method encourages stakeholder multiplicity through participatory design and policymaking that can enhance public backing of actions that can make society more sustainable.</p>", "keywords": ["[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]", "decision-support", "Environmental management", "330", "[SDE.IE]Environmental Sciences/Environmental Engineering", "02 engineering and technology", "/dk/atira/pure/sustainabledevelopmentgoals/responsible_consumption_and_production; name=SDG 12 - Responsible Consumption and Production", "multi-criteria decision analysis", "Decision-support", "01 natural sciences", "7. Clean energy", "[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]", "12. Responsible consumption", "environmental management", "Life cycle assessment", "/dk/atira/pure/sustainabledevelopmentgoals/affordable_and_clean_energy; name=SDG 7 - Affordable and Clean Energy", "Analyse cycle de vie", "life cycle assessment", "Multi-criteria decision analysis", "0202 electrical engineering", " electronic engineering", " information engineering", "participatory design", "[SDE.IE] Environmental Sciences/Environmental Engineering", "10. No inequality", "Participatory design", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2071-1050/12/6/2170/pdf"}, {"href": "https://www.mdpi.com/2071-1050/12/6/2170/pdf"}, {"href": "https://doi.org/10.3390/su12062170"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sustainability", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/su12062170", "name": "item", "description": "10.3390/su12062170", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/su12062170"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-03-11T00:00:00Z"}}, {"id": "10.26434/chemrxiv-2022-cr5ws-v2", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:20:41Z", "type": "Journal Article", "created": "2023-04-10", "title": "Quantitative image analysis of microplastics in bottled water using Artificial Intelligence", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The ubiquitous occurrence of microplastics (MPs) in the environment and the use of plastics in packaging materials result in the presence of MPs in the food chain and the exposure of consumers. Yet, no fully validated analytical method is available for microplastic (MP) quantification, thereby preventing the reliable estimation of the level of exposure and, ultimately, the assessment of the food safety risks associated with MP contamination. In this study, a novel approach is presented that exploits interactive artificial intelligence tools to enable the automation of MP analysis. An integrated method for the analysis of MPs in bottled water based on Nile Red staining and fluorescent microscopy was developed and validated, featuring a partial interrogation of the filter and a fully automated image processing workflow based on a Random Forest classifier, thereby boosting the analysis speed. The image analysis provided particle count, size and size distribution of the MPs. From these data, a rough estimation of the mass of the individual MPs, and consequently of the MP mass concentration in the sample, could be obtained as well. Critical materials, method performance characteristics, and final applicability were studied in detail. The method showed to be highly sensitive in sizing MPs down to 10 \u00b5m, with a particle count limit of detection and quantification of 28 and 85 items/500 mL, respectively. Linearity of mass concentration determined between 10 ppb and 1.5 ppm showed a regression coefficient of (R2) of 0.99. Method precision was demonstrated by repeatability of 9 - 16% RSD (n = 7) and within-laboratory reproducibility of 15 - 27 % RSD (n = 21). Accuracy based on recovery was 92 \u00b1 15 % and 98 \u00b1 23 % at a level of 0.1 and 1.0 ppm, respectively. The quantitative performance characteristics thus obtained complied with regulatory requirements. Finally, the method was successfully applied to the analysis of twenty commercial samples of bottled water, with and without gas and flavor additives, yielding results ranging from values below the limit of detection to 7237 (95% CI [6456, 8088]) items/500 mL.</p></article>", "keywords": ["Fluorescence microscopy", "Artificial intelligence", "Bottled water", "Method validation", "Artificial Intelligence", "Microplastics", "Drinking Water", "Microplastic", "Nile red", "Reproducibility of Results", "Plastics", "6. Clean water"]}, "links": [{"href": "https://doi.org/10.26434/chemrxiv-2022-cr5ws-v2"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Talanta", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.26434/chemrxiv-2022-cr5ws-v2", "name": "item", "description": "10.26434/chemrxiv-2022-cr5ws-v2", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.26434/chemrxiv-2022-cr5ws-v2"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-04-10T00:00:00Z"}}, {"id": "10.3208/jgssp.v10.os-10-04", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:20:44Z", "type": "Journal Article", "created": "2024-06-16", "title": "A new Graph Neural Network (GNN) based model for the evaluation of lateral spreading displacement in New Zealand", "description": "The increased availability of high quality data from post disaster field reconnaissance, enabled the use of deep learning algorithms in the field of geotechnical earthquake engineering. The 2010-2011 Canterbury earthquake sequence in New Zealand caused significant damage due to abundant manifestation of liquefaction induced lateral spreading. The data available from this sequence is an ideal case study for deep learning analyses due to the amount and quality of information available through the New Zealand Geotechnical Database (NZGD). A dataset of about 7500 datapoints was collected and organized by the authors to develop a new Graph Neural Network (GNN) algorithm for lateral spreading in the Canterbury area. The comparison between predicted and observed data is performed using feed forward Neural Network. Several GNN models with different hyperparameters are explored and the best model is presented in this paper, and Explainable Artificial Intelligence is applied to the model that provides the best performance. These computationally expensive analyses were carried out utilizing cloud based computing capabilities offered by the Texas Advanced Computing Center (TACC) available to the natural hazard community through the cyberinfrastructure DesignSafe.", "keywords": ["lateral spreading; liquefaction; artificial intelligence; geotechnical earthquake engineering"], "contacts": [{"organization": "Giovanna Durante, Maria, Terremoto, Giovanni, Adornetto, Carlo, Greco, Gianluigi, M Rathje, Ellen,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.3208/jgssp.v10.os-10-04"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Japanese%20Geotechnical%20Society%20Special%20Publication", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3208/jgssp.v10.os-10-04", "name": "item", "description": "10.3208/jgssp.v10.os-10-04", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3208/jgssp.v10.os-10-04"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-01T00:00:00Z"}}, {"id": "10.3233/978-1-61499-906-5-205", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:20:44Z", "type": "Report", "created": "2025-02-24", "title": "Toward a More Efficient Generation of Structured Argumentation Graphs", "description": "<p>To address the needs of the EU NoAW project, in this paper we solve the problem of efficiently generating the argumentation graphs from knowledge bases expressed using existential rules. For the knowledge bases without rules, we provide a methodology that allows to optimise the generation of argumentation graphs. For knowledge bases with rules, we show how to filter out a large number of arguments and reduce the number of attacks.</p>", "keywords": ["[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]"]}, "links": [{"href": "https://doi.org/10.3233/978-1-61499-906-5-205"}, {"rel": "self", "type": "application/geo+json", "title": "10.3233/978-1-61499-906-5-205", "name": "item", "description": "10.3233/978-1-61499-906-5-205", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3233/978-1-61499-906-5-205"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-01-01T00:00:00Z"}}, {"id": "10.3233/978-1-61499-906-5-381", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:20:44Z", "type": "Report", "created": "2025-02-20", "title": "Viewpoints using ranking-based argumentation semantics", "description": "<p>To address the needs of the EU NoAW project, in this paper we introduce a new modular framework that generates viewpoints (i.e. extensions) based on ranking argumentation semantics by considering a selection function, a ranking on arguments and a lifting function as its input parameters. We study the different combinations of the input parameters and introduce a set of postulates investigated for the framework's different classes of output.</p>", "keywords": ["[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]"]}, "links": [{"href": "https://doi.org/10.3233/978-1-61499-906-5-381"}, {"rel": "self", "type": "application/geo+json", "title": "10.3233/978-1-61499-906-5-381", "name": "item", "description": "10.3233/978-1-61499-906-5-381", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3233/978-1-61499-906-5-381"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-01-01T00:00:00Z"}}, {"id": "10.3233/faia200166", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:20:44Z", "type": "Report", "created": "2025-02-21", "title": "Gradual Semantics for Logic-Based Bipolar Graphs Using T-(Co)norms", "description": "In this paper we consider a bipolar graph structure encoding conflicting knowledge with logic formulas. Gradual semantics provide a way to assign strength values in the unit interval to nodes (i.e. logical inference steps) in the bipolar graph. Here, we introduce a new class of semantics based on the notions of T-norms and T-conorms and show that they handle circular reasoning and satisfy desirable properties such as provability and rewriting.", "keywords": ["[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]", "[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]"], "contacts": [{"organization": "Jedwabny, Martin, Croitoru, Madalina, Bisquert, Pierre,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.3233/faia200166"}, {"rel": "self", "type": "application/geo+json", "title": "10.3233/faia200166", "name": "item", "description": "10.3233/faia200166", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3233/faia200166"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-01T00:00:00Z"}}, {"id": "10.3390/rs9111155", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:09Z", "type": "Journal Article", "created": "2017-11-10", "title": "Disaggregation of SMOS Soil Moisture to 100 m Resolution Using MODIS Optical/Thermal and Sentinel-1 Radar Data: Evaluation over a Bare Soil Site in Morocco", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The 40 km resolution SMOS (Soil Moisture and Ocean Salinity) soil moisture, previously disaggregated at a 1 km resolution using the DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) method based on MODIS optical/thermal data, is further disaggregated to 100 m resolution using Sentinel-1 backscattering coefficient (\u03c3\u00b0). For this purpose, three distinct radar-based disaggregation methods are tested by linking the spatio-temporal variability of \u03c3\u00b0 and soil moisture data at the 1 km and 100 m resolution. The three methods are: (1) the weight method, which estimates soil moisture at 100 m resolution at a certain time as a function of \u03c3\u00b0 ratio (100 m to 1 km resolution) and the 1 km DISPATCH products of the same time; (2) the regression method which estimates soil moisture as a function of \u03c3\u00b0 where the regression parameters (e.g., intercept and slope) vary in space and time; and (3) the Cumulative Distribution Function (CDF) method, which estimates 100 m resolution soil moisture from the cumulative probability of 100 m resolution backscatter and the maximum to minimum 1 km resolution (DISPATCH) soil moisture difference. In each case, disaggregation results are evaluated against in situ measurements collected between 1 January 2016 and 11 October 2016 over a bare soil site in central Morocco. The determination coefficient (R2) between 1 km resolution DISPATCH and localized in situ soil moisture is 0.31. The regression and CDF methods have marginal effect on improving the DISPATCH accuracy at the station scale with a R2 between remotely sensed and in situ soil moisture of 0.29 and 0.34, respectively. By contrast, the weight method significantly improves the correlation between remotely sensed and in situ soil moisture with a R2 of 0.52. Likewise, the soil moisture estimates show low root mean square difference with in situ measurements (RMSD= 0.032 m3 m\u22123).</p></article>", "keywords": ["soil moisture and ocean salinity satellite (SMOS)", "Atmospheric Science", "Artificial intelligence", "Environmental Engineering", "550", "Science", "Soil Moisture", "0211 other engineering and technologies", "Aerospace Engineering", "FOS: Mechanical engineering", "02 engineering and technology", "01 natural sciences", "Environmental science", "[SDU] Sciences of the Universe [physics]", "Engineering", "Meteorology", "DISPATCH", "Image resolution", "Arctic Permafrost Dynamics and Climate Change", "14. Life underwater", "Moisture", "0105 earth and related environmental sciences", "Soil science", "Water content", "Radar", "Geography", "soil moisture and ocean salinity satellite (SMOS); DISPATCH; radar; Sentinel-1; disaggregation; soil moisture", "Soilmoisture and ocean salinity satellite (SMOS)", "Synthetic Aperture Radar Interferometry", "Q", "FOS: Environmental engineering", "Geology", "FOS: Earth and related environmental sciences", "Remote sensing", "Remote Sensing of Soil Moisture", "Surface Deformation Monitoring", "Computer science", "Earth and Planetary Sciences", "Groundwater Extraction", "Geotechnical engineering", "[SDU]Sciences of the Universe [physics]", "disaggregation", "Environmental Science", "Physical Sciences", "Sentinel-1", "soil moisture", "radar"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/9/11/1155/pdf"}, {"href": "https://doi.org/10.3390/rs9111155"}, {"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/rs9111155", "name": "item", "description": "10.3390/rs9111155", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs9111155"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-11-10T00:00:00Z"}}, {"id": "10.3390/drones9020129", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:20:57Z", "type": "Journal Article", "created": "2025-02-11", "title": "Unmanned Aerial Vehicle-Based Hyperspectral Imaging and Soil Texture Mapping with Robust AI Algorithms", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>This paper explores the integration of UAV-based hyperspectral imaging and advanced AI algorithms for soil texture mapping and stress detection in agricultural settings. The primary focus lies on leveraging multi-modal sensor data, including hyperspectral imaging, thermal imaging, and gamma-ray spectroscopy, to enable precise monitoring of abiotic and biotic stressors in crops. An innovative algorithm combining vegetation indices, path planning, and machine learning methods is introduced to enhance the efficiency of data collection and analysis. Experimental results demonstrate significant improvements in accuracy and operational efficiency, paving the way for real-time, data-driven decision-making in precision agriculture.</p></article>", "keywords": ["precision agriculture", "UAV-based hyperspectral imaging", "TL1-4050", "soil texture mapping", "artificial intelligence (AI) in agriculture", "Motor vehicles. Aeronautics. Astronautics"]}, "links": [{"href": "https://www.mdpi.com/2504-446X/9/2/129/pdf"}, {"href": "https://doi.org/10.3390/drones9020129"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Drones", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/drones9020129", "name": "item", "description": "10.3390/drones9020129", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/drones9020129"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-02-11T00:00:00Z"}}, {"id": "10.3390/ijms24076573", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:00Z", "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": "<?xml version='1.0' encoding='UTF-8'?><article><p>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.</p></article>", "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": "10.3390/s20154127", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:10Z", "type": "Journal Article", "created": "2020-07-24", "title": "Smart Multi-Sensor Platform for Analytics and Social Decision Support in Agriculture", "description": "<p>Smart agriculture based on new types of sensors, data analytics and automation, is an important enabler for optimizing yields and maximizing efficiency to feed the world\uffe2\uff80\uff99s growing population while limiting environmental pollution. The aim of this paper is to describe a multi-sensor Internet of Things (IoT) system for agriculture consisting of a soil probe, an air probe and a smart data logger. The implementation details will focus of the integration element and the innovative Artificial Intelligence based gas identification sensor. Furthermore, the paper focuses on the analytics and decision support system implementation that provides farming recommendations and is enhanced with a feedback loop from farmers and a social trust index that will increase the reliability of the system.</p>", "keywords": ["330", "decision support system", "[SPI] Engineering Sciences [physics]", "Social IoT", "Internet of Things", "TP1-1185", "01 natural sciences", "7. Clean energy", "630", "data logger", "Article", "gas sensor", "[SPI]Engineering Sciences [physics]", "Soil", "sensor", "Artificial Intelligence", "social feedback", "data analytics", "agriculture", "2. Zero hunger", "Chemical technology", "Reproducibility of Results", "Agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "0104 chemical sciences", "3. Good health", "13. Climate action", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/20/15/4127/pdf"}, {"href": "https://www.mdpi.com/1424-8220/20/15/4127/pdf"}, {"href": "https://doi.org/10.3390/s20154127"}, {"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/s20154127", "name": "item", "description": "10.3390/s20154127", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/s20154127"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-07-24T00:00:00Z"}}, {"id": "10.3390/s22114207", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:10Z", "type": "Journal Article", "created": "2022-06-02", "title": "Agrobot Lala\u2014An Autonomous Robotic System for Real-Time, In-Field Soil Sampling, and Analysis of Nitrates", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>This paper presents an autonomous robotic system, an unmanned ground vehicle (UGV), for in-field soil sampling and analysis of nitrates. Compared to standard methods of soil analysis it has several advantages: each sample is individually analyzed compared to average sample analysis in standard methods; each sample is georeferenced, providing a map for precision base fertilizing; the process is fully autonomous; samples are analyzed in real-time, approximately 30 min per sample; and lightweight for less soil compaction. The robotic system has several modules: commercial robotic platform, anchoring module, sampling module, sample preparation module, sample analysis module, and communication module. The system is augmented with an in-house developed cloud-based platform. This platform uses satellite images, and an artificial intelligence (AI) proprietary algorithm to divide the target field into representative zones for sampling, thus, reducing and optimizing the number and locations of the samples. Based on this, a task is created for the robot to automatically sample at those locations. The user is provided with an in-house developed smartphone app enabling overview and monitoring of the task, changing the positions, removing and adding of the sampling points. The results of the measurements are uploaded to the cloud for further analysis and the creation of prescription maps for variable rate base fertilization.</p></article>", "keywords": ["2. Zero hunger", "0106 biological sciences", "precision agriculture", "Nitrates", "Chemical technology", "soil sampling", "TP1-1185", "Robotics", "04 agricultural and veterinary sciences", "artificial intelligence", "01 natural sciences", "Article", "UGV; precision agriculture; artificial intelligence; soil nutrient analysis; soil sampling", "Soil", "soil nutrient analysis", "Robotic Surgical Procedures", "Artificial Intelligence", "0401 agriculture", " forestry", " and fisheries", "UGV"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/22/11/4207/pdf"}, {"href": "https://doi.org/10.3390/s22114207"}, {"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/s22114207", "name": "item", "description": "10.3390/s22114207", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/s22114207"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-31T00:00:00Z"}}, {"id": "10.5194/egusphere-egu25-13513", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:49Z", "type": "Report", "created": "2025-03-15", "title": "Integrating Remote Sensing and AI modelling in Mediterranean Agroforestry and Croplands systems: A Methodological Perspective for spatial SOC monitoring in the MRV4SOC project, Spain", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>This study presents a robust framework for spatially explicit monitoring of soil properties and Above Ground Biomass (AGB) estimation in Mediterranean agroforestry and cropland systems by integrating remote sensing (RS) and artificial intelligence (AI). These variables are critical for assimilation into process-based models for Soil Organic Carbon (SOC) dynamics monitoring within a Monitoring, Reporting, and Verification (MRV) system. The framework was developed as part of the MRV4SOC project in Spain, aimed at designing a comprehensive, robust, and cost-effective Tier-3 approach. The primary goal is to produce high-quality geospatial layers of topsoil properties and AGB estima tion, which serve as key inputs for SOC dynamics modeling.The methodology was tested at two long-term demonstration sites in Spain: Quercus ilex Dehesas in Extremadura (SW Spain) and rainfed cereal crops at La Canaleja experimental farm in central Spain. These agroecosystems provide diverse testing grounds for scalable and transferable SOC assessment methodologies within an MRV framework. The approach integrates multi-temporal remote sensing data (2018&amp;#8211;2022) from Sentinel-2 and Landsat satellites with machine learning models to predict essential soil properties (SOC, Sand, Silt, Clay, pH, and Total N) and AGB. Ground truth data for AGB estimation were sourced from the Spanish National Forest Inventory (SNFI), while soil property predictions utilized the LUCAS 2018 topsoil libraries due to limited site-specific datasets for model training. A bare soil reflectance composite (2018&amp;#8211;2022) derived from Sentinel-2 bands (B02&amp;#8211;B12) at 20-meter resolution was employed for geospatial soil property mapping.Given the limited availability of ground truth data, simpler models like Quantile Regression Forests (QRF) and XGBoost were selected. QRF achieved better accuracy for soil texture properties, with R&amp;#178; = 0.62 for clay and outperforming XGBoost for SOC (R&amp;#178; = 0.63) and pH (R&amp;#178; = 0.76) in the agroforestry site. However, XGBoost performed better for SOC (R&amp;#178; = 0.54) and total nitrogen in croplands, as well as for sand, silt, clay, and total nitrogen in the agroforestry site (R&amp;#178; = 0.61 for clay). For AGB estimation in the Dehesas area, a machine learning approach was implemented using SNFI data and remote sensing-derived transformation features. A gradient boosting algorithm (LightGBM) resulted in an R&amp;#178; value of 0.8. In La Canaleja, a bare soil reflectance composite was similarly employed for soil property mapping. Further analysis will be carried out to develop a bottom-up approach for monitoring SOC using these products and process-based modelsUncertainty analysis using Prediction Interval Ratio (PIR) assessment was conducted separately for landscape (L) and sub-landscape (SL) levels. While most properties showed medium to low uncertainty, sand and silt exhibited higher variability in croplands, and SOC displayed the highest uncertainty in the agroforestry site across L and SL levels.This methodology contributes significantly to improving MRV systems by delivering high-quality geospatial layers for SOC dynamics monitoring in complex environments. Increasing ground truth data availability is essential for enhancing model accuracy and minimizing prediction uncertainties further.</p></article>", "keywords": ["Cropland management", "Artificial Intelligence", "Remote Sensing Technology", "Agroforestry"]}, "links": [{"href": "https://doi.org/10.5194/egusphere-egu25-13513"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/egusphere-egu25-13513", "name": "item", "description": "10.5194/egusphere-egu25-13513", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/egusphere-egu25-13513"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-03-18T00:00:00Z"}}, {"id": "10.5194/isprs-archives-xlii-3-w6-9-2019", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:53Z", "type": "Journal Article", "created": "2019-07-29", "title": "EVAPOTRANSPIRATION AND EVAPORATION/TRANSPIRATION RETRIEVAL USING DUAL-SOURCE SURFACE ENERGY BALANCE MODELS INTEGRATING VIS/NIR/TIR DATA WITH SATELLITE SURFACE SOIL MOISTURE INFORMATION", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Evapotranspiration is an important component of the water cycle. For the agronomic management and ecosystem health monitoring, it is also important to provide an estimate of evapotranspiration components, i.e. transpiration and soil evaporation. To do so, Thermal InfraRed data can be used with dual-source surface energy balance models, because they solve separate energy budgets for the soil and the vegetation. But those models rely on specific assumptions on raw levels of plant water stress to get both components (evaporation and transpiration) out of a single source of information, namely the surface temperature. Additional information from remote sensing data are thus required. This works evaluates the ability of the SPARSE dual-source energy balance model to compute not only total evapotranspiration, but also water stress and transpiration/evaporation components, using either the sole surface temperature as a remote sensing driver, or a combination of surface temperature and soil moisture level derived from microwave data. Flux data at an experimental plot in semi-arid Morocco is used to assess this potentiality and shows the increased robustness of both the total evapotranspiration and partitioning retrieval performances. This work is realized within the frame of the Phase A activities for the TRISHNA CNES/ISRO Thermal Infra-Red satellite mission.                     </p></article>", "keywords": ["Technology", "Environmental Engineering", "550", "Ecosystem Resilience", "Soil Moisture", "Evaporation", "Energy balance", "Biochemistry", "Environmental science", "Transpiration", "Meteorology", "Artificial Intelligence", "Soil water", "Thermal Infrared", "Applied optics. Photonics", "Machine Learning Methods for Solar Radiation Forecasting", "Photosynthesis", "TRISHNA", "Water balance", "Biology", "Soil science", "Global and Planetary Change", "Water content", "Evapotranspiration", "Geography", "Ecology", "Global Forest Drought Response and Climate Change", "T", "FOS: Environmental engineering", "Geology", "FOS: Earth and related environmental sciences", "Remote sensing", "15. Life on land", "Engineering (General). Civil engineering (General)", "Remote Sensing of Soil Moisture", "6. Clean water", "TA1501-1820", "[SDE.MCG] Environmental Sciences/Global Changes", "Chemistry", "Geotechnical engineering", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Physical Sciences", "Computer Science", "TA1-2040", "Water cycle"]}, "links": [{"href": "https://doi.org/10.5194/isprs-archives-xlii-3-w6-9-2019"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/The%20International%20Archives%20of%20the%20Photogrammetry%2C%20Remote%20Sensing%20and%20Spatial%20Information%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/isprs-archives-xlii-3-w6-9-2019", "name": "item", "description": "10.5194/isprs-archives-xlii-3-w6-9-2019", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/isprs-archives-xlii-3-w6-9-2019"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-07-26T00:00:00Z"}}, {"id": "10.5281/zenodo.8300533", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:23:50Z", "type": "Journal Article", "title": "AI technology: what it is and what it's not, and how it can (potentially) help us solve the climate crisis", "description": "AI (Artificial Intelligence) technology, with the launch of OpenAI\u2019s ChatGPT (the fastest growing app ever) and similar, is now a buzz: a new technological jump of the human race, but potentially a Pandora box for information manipulation and misuse. AI could soon replace thousands of jobs and revolutionize how we travel (self-driving cars), purchase items, do admin/office work, communicate with computers (and people), but also how governments fight wars and control people. AI is making a lot of people enthusiastic, but even more nervous. We review the potentials and perils of AI tech; how it can also help us with extremely important things such as solving the climate crisis and better monitoring and conservation of natural resources. Links and references are extensive and hopefully will motivate you to read more on the topic.", "keywords": ["Machine Learning", "Artificial intelligence", "Consciousness", "13. Climate action", "Climate crisis"], "contacts": [{"organization": "Hengl, T., Consoli, D., Bagi\u0107, M., Brocca, L., Herold, M.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8300533"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/OpenGeoHub", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8300533", "name": "item", "description": "10.5281/zenodo.8300533", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8300533"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.8300534", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:23:50Z", "type": "Report", "title": "AI technology: what it is and what it's not, and how it can (potentially) help us solve the climate crisis", "description": "AI (Artificial Intelligence) technology, with the launch of OpenAI\u2019s ChatGPT (the fastest growing app ever) and similar, is now a buzz: a new technological jump of the human race, but potentially a Pandora box for information manipulation and misuse. AI could soon replace thousands of jobs and revolutionize how we travel (self-driving cars), purchase items, do admin/office work, communicate with computers (and people), but also how governments fight wars and control people. AI is making a lot of people enthusiastic, but even more nervous. We review the potentials and perils of AI tech; how it can also help us with extremely important things such as solving the climate crisis and better monitoring and conservation of natural resources. Links and references are extensive and hopefully will motivate you to read more on the topic.", "keywords": ["Machine Learning", "Artificial intelligence", "Consciousness", "13. Climate action", "Climate crisis"], "contacts": [{"organization": "Hengl, T., Consoli, D., Bagi\u0107, M., Brocca, L., Herold, M.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8300534"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8300534", "name": "item", "description": "10.5281/zenodo.8300534", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8300534"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-01T00:00:00Z"}}, {"id": "10.60692/t1jsz-vm842", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:24:18Z", "type": "Journal Article", "created": "2019-07-29", "title": "EVAPOTRANSPIRATION AND EVAPORATION/TRANSPIRATION RETRIEVAL USING DUAL-SOURCE SURFACE ENERGY BALANCE MODELS INTEGRATING VIS/NIR/TIR DATA WITH SATELLITE SURFACE SOIL MOISTURE INFORMATION", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Evapotranspiration is an important component of the water cycle. For the agronomic management and ecosystem health monitoring, it is also important to provide an estimate of evapotranspiration components, i.e. transpiration and soil evaporation. To do so, Thermal InfraRed data can be used with dual-source surface energy balance models, because they solve separate energy budgets for the soil and the vegetation. But those models rely on specific assumptions on raw levels of plant water stress to get both components (evaporation and transpiration) out of a single source of information, namely the surface temperature. Additional information from remote sensing data are thus required. This works evaluates the ability of the SPARSE dual-source energy balance model to compute not only total evapotranspiration, but also water stress and transpiration/evaporation components, using either the sole surface temperature as a remote sensing driver, or a combination of surface temperature and soil moisture level derived from microwave data. Flux data at an experimental plot in semi-arid Morocco is used to assess this potentiality and shows the increased robustness of both the total evapotranspiration and partitioning retrieval performances. This work is realized within the frame of the Phase A activities for the TRISHNA CNES/ISRO Thermal Infra-Red satellite mission.                     </p></article>", "keywords": ["Technology", "Environmental Engineering", "550", "Ecosystem Resilience", "Soil Moisture", "Evaporation", "Energy balance", "Biochemistry", "Environmental science", "Transpiration", "Meteorology", "Artificial Intelligence", "Soil water", "Thermal Infrared", "Applied optics. Photonics", "Machine Learning Methods for Solar Radiation Forecasting", "Photosynthesis", "TRISHNA", "Water balance", "Biology", "Soil science", "Global and Planetary Change", "Water content", "Evapotranspiration", "Geography", "Ecology", "Global Forest Drought Response and Climate Change", "T", "FOS: Environmental engineering", "Geology", "FOS: Earth and related environmental sciences", "Remote sensing", "15. Life on land", "Engineering (General). Civil engineering (General)", "Remote Sensing of Soil Moisture", "6. Clean water", "TA1501-1820", "[SDE.MCG] Environmental Sciences/Global Changes", "Chemistry", "Geotechnical engineering", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Physical Sciences", "Computer Science", "TA1-2040", "Water cycle"]}, "links": [{"href": "https://doi.org/10.60692/t1jsz-vm842"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/The%20International%20Archives%20of%20the%20Photogrammetry%2C%20Remote%20Sensing%20and%20Spatial%20Information%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.60692/t1jsz-vm842", "name": "item", "description": "10.60692/t1jsz-vm842", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.60692/t1jsz-vm842"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-07-26T00:00:00Z"}}, {"id": "2966009560", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:26:05Z", "type": "Journal Article", "created": "2019-07-29", "title": "EVAPOTRANSPIRATION AND EVAPORATION/TRANSPIRATION RETRIEVAL USING DUAL-SOURCE SURFACE ENERGY BALANCE MODELS INTEGRATING VIS/NIR/TIR DATA WITH SATELLITE SURFACE SOIL MOISTURE INFORMATION", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Evapotranspiration is an important component of the water cycle. For the agronomic management and ecosystem health monitoring, it is also important to provide an estimate of evapotranspiration components, i.e. transpiration and soil evaporation. To do so, Thermal InfraRed data can be used with dual-source surface energy balance models, because they solve separate energy budgets for the soil and the vegetation. But those models rely on specific assumptions on raw levels of plant water stress to get both components (evaporation and transpiration) out of a single source of information, namely the surface temperature. Additional information from remote sensing data are thus required. This works evaluates the ability of the SPARSE dual-source energy balance model to compute not only total evapotranspiration, but also water stress and transpiration/evaporation components, using either the sole surface temperature as a remote sensing driver, or a combination of surface temperature and soil moisture level derived from microwave data. Flux data at an experimental plot in semi-arid Morocco is used to assess this potentiality and shows the increased robustness of both the total evapotranspiration and partitioning retrieval performances. This work is realized within the frame of the Phase A activities for the TRISHNA CNES/ISRO Thermal Infra-Red satellite mission.                     </p></article>", "keywords": ["Technology", "Environmental Engineering", "550", "Ecosystem Resilience", "Soil Moisture", "Evaporation", "Energy balance", "Biochemistry", "Environmental science", "Transpiration", "Meteorology", "Artificial Intelligence", "Soil water", "Thermal Infrared", "Applied optics. Photonics", "Machine Learning Methods for Solar Radiation Forecasting", "Photosynthesis", "TRISHNA", "Water balance", "Biology", "Soil science", "Global and Planetary Change", "Water content", "Evapotranspiration", "Geography", "Ecology", "Global Forest Drought Response and Climate Change", "T", "FOS: Environmental engineering", "Geology", "FOS: Earth and related environmental sciences", "Remote sensing", "15. Life on land", "Engineering (General). Civil engineering (General)", "Remote Sensing of Soil Moisture", "6. Clean water", "TA1501-1820", "[SDE.MCG] Environmental Sciences/Global Changes", "Chemistry", "Geotechnical engineering", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Physical Sciences", "Computer Science", "TA1-2040", "Water cycle"]}, "links": [{"href": "https://doi.org/2966009560"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/The%20International%20Archives%20of%20the%20Photogrammetry%2C%20Remote%20Sensing%20and%20Spatial%20Information%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2966009560", "name": "item", "description": "2966009560", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2966009560"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-07-26T00:00:00Z"}}, {"id": "10459.1/60556", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:24:50Z", "type": "Journal Article", "created": "2017-11-10", "title": "Disaggregation of SMOS Soil Moisture to 100 m Resolution Using MODIS Optical/Thermal and Sentinel-1 Radar Data: Evaluation over a Bare Soil Site in Morocco", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The 40 km resolution SMOS (Soil Moisture and Ocean Salinity) soil moisture, previously disaggregated at a 1 km resolution using the DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) method based on MODIS optical/thermal data, is further disaggregated to 100 m resolution using Sentinel-1 backscattering coefficient (\u03c3\u00b0). For this purpose, three distinct radar-based disaggregation methods are tested by linking the spatio-temporal variability of \u03c3\u00b0 and soil moisture data at the 1 km and 100 m resolution. The three methods are: (1) the weight method, which estimates soil moisture at 100 m resolution at a certain time as a function of \u03c3\u00b0 ratio (100 m to 1 km resolution) and the 1 km DISPATCH products of the same time; (2) the regression method which estimates soil moisture as a function of \u03c3\u00b0 where the regression parameters (e.g., intercept and slope) vary in space and time; and (3) the Cumulative Distribution Function (CDF) method, which estimates 100 m resolution soil moisture from the cumulative probability of 100 m resolution backscatter and the maximum to minimum 1 km resolution (DISPATCH) soil moisture difference. In each case, disaggregation results are evaluated against in situ measurements collected between 1 January 2016 and 11 October 2016 over a bare soil site in central Morocco. The determination coefficient (R2) between 1 km resolution DISPATCH and localized in situ soil moisture is 0.31. The regression and CDF methods have marginal effect on improving the DISPATCH accuracy at the station scale with a R2 between remotely sensed and in situ soil moisture of 0.29 and 0.34, respectively. By contrast, the weight method significantly improves the correlation between remotely sensed and in situ soil moisture with a R2 of 0.52. Likewise, the soil moisture estimates show low root mean square difference with in situ measurements (RMSD= 0.032 m3 m\u22123).</p></article>", "keywords": ["soil moisture and ocean salinity satellite (SMOS)", "Atmospheric Science", "Artificial intelligence", "Environmental Engineering", "550", "Science", "Soil Moisture", "0211 other engineering and technologies", "Aerospace Engineering", "FOS: Mechanical engineering", "02 engineering and technology", "01 natural sciences", "Environmental science", "[SDU] Sciences of the Universe [physics]", "Engineering", "Meteorology", "DISPATCH", "Image resolution", "Arctic Permafrost Dynamics and Climate Change", "14. Life underwater", "Moisture", "0105 earth and related environmental sciences", "Soil science", "Water content", "Radar", "Geography", "soil moisture and ocean salinity satellite (SMOS); DISPATCH; radar; Sentinel-1; disaggregation; soil moisture", "Soilmoisture and ocean salinity satellite (SMOS)", "Synthetic Aperture Radar Interferometry", "Q", "FOS: Environmental engineering", "Geology", "FOS: Earth and related environmental sciences", "Remote sensing", "Remote Sensing of Soil Moisture", "Surface Deformation Monitoring", "Computer science", "Earth and Planetary Sciences", "Groundwater Extraction", "Geotechnical engineering", "[SDU]Sciences of the Universe [physics]", "disaggregation", "Environmental Science", "Physical Sciences", "Sentinel-1", "soil moisture", "radar"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/9/11/1155/pdf"}, {"href": "https://doi.org/10459.1/60556"}, {"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": "10459.1/60556", "name": "item", "description": "10459.1/60556", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10459.1/60556"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-11-10T00:00:00Z"}}, {"id": "11567/1235396", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:25:03Z", "type": "Report", "title": "AI-based solutions for autonomous underwater observing systems and science discovery", "keywords": ["Artificial Intelligence", " Intelligent Observing Systems", " Edge Computing", " Science Discovery", " Biodiversity"], "contacts": [{"organization": "Simone Marini, Daniele Lagomarsino Oneto, Mattia Cavaiola, Jacopo Aguzzi, Daniele D\u2019Agostino,", "roles": ["creator"]}]}, "links": [{"href": "https://iris.unige.it/bitstream/11567/1235396/1/DAgostino-Biochange.pdf"}, {"href": "https://doi.org/11567/1235396"}, {"rel": "self", "type": "application/geo+json", "title": "11567/1235396", "name": "item", "description": "11567/1235396", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11567/1235396"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-01T00:00:00Z"}}, {"id": "11573/1722782", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:25:04Z", "type": "Journal Article", "created": "2024-06-16", "title": "A new Graph Neural Network (GNN) based model for the evaluation of lateral spreading displacement in New Zealand", "description": "The increased availability of high quality data from post disaster field reconnaissance, enabled the use of deep learning algorithms in the field of geotechnical earthquake engineering. The 2010-2011 Canterbury earthquake sequence in New Zealand caused significant damage due to abundant manifestation of liquefaction induced lateral spreading. The data available from this sequence is an ideal case study for deep learning analyses due to the amount and quality of information available through the New Zealand Geotechnical Database (NZGD). A dataset of about 7500 datapoints was collected and organized by the authors to develop a new Graph Neural Network (GNN) algorithm for lateral spreading in the Canterbury area. The comparison between predicted and observed data is performed using feed forward Neural Network. Several GNN models with different hyperparameters are explored and the best model is presented in this paper, and Explainable Artificial Intelligence is applied to the model that provides the best performance. These computationally expensive analyses were carried out utilizing cloud based computing capabilities offered by the Texas Advanced Computing Center (TACC) available to the natural hazard community through the cyberinfrastructure DesignSafe.", "keywords": ["lateral spreading; liquefaction; artificial intelligence; geotechnical earthquake engineering"], "contacts": [{"organization": "Giovanna Durante, Maria, Terremoto, Giovanni, Adornetto, Carlo, Greco, Gianluigi, M Rathje, Ellen,", "roles": ["creator"]}]}, "links": [{"href": "https://iris.uniroma1.it/bitstream/11573/1722782/2/Durante_A-new-Graph_2024.pdf"}, {"href": "https://doi.org/11573/1722782"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Japanese%20Geotechnical%20Society%20Special%20Publication", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "11573/1722782", "name": "item", "description": "11573/1722782", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11573/1722782"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-01T00:00:00Z"}}, {"id": "1885/202815", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:25:18Z", "type": "Journal Article", "created": "2018-08-07", "title": "Quantization effects and convergence properties of rigid formation control systems with quantized distance measurements", "description": "Summary<p>In this paper, we discuss quantization effects in rigid formation control systems when target formations are described by interagent distances. Because of practical sensing and measurement constraints, we consider in this paper distance measurements in their quantized forms. We show that under gradient\uffe2\uff80\uff90based formation control, in the case of uniform quantization, the distance errors converge locally to a bounded set whose size depends on the quantization error, while in the case of logarithmic quantization, all distance errors converge locally to zero. A special quantizer involving the signum function is then considered with which all agents can only measure coarse distances in terms of binary information. In this case, the formation converges locally to a target formation within a finite time. Lastly, we discuss the effect of asymmetric uniform quantization on rigid formation control.</p", "keywords": ["0209 industrial biotechnology", "Digital control/observation systems", "Agent technology and artificial intelligence", "formation control", "Lyapunov and other classical stabilities (Lagrange", " Poisson", "  (L^p", " l^p )", " etc.) in control theory", "Systems and Control (eess.SY)", "02 engineering and technology", "Decentralized systems", "quantization effect", "Electrical Engineering and Systems Science - Systems and Control", "binary measurement", "0203 mechanical engineering", "Quantization", "FOS: Electrical engineering", " electronic engineering", " information engineering", "rigid formation control"]}, "links": [{"href": "https://openresearch-repository.anu.edu.au/bitstream/1885/202815/5/01_Sun_Quantization_effects_and_2018.pdf.jpg"}, {"href": "https://openresearch-repository.anu.edu.au/bitstream/1885/202815/8/quantization-effects-convergence.pdf.jpg"}, {"href": "https://doi.org/1885/202815"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/International%20Journal%20of%20Robust%20and%20Nonlinear%20Control", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1885/202815", "name": "item", "description": "1885/202815", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1885/202815"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-08-07T00:00:00Z"}}, {"id": "2767588274", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:25:55Z", "type": "Journal Article", "created": "2017-11-10", "title": "Disaggregation of SMOS Soil Moisture to 100 m Resolution Using MODIS Optical/Thermal and Sentinel-1 Radar Data: Evaluation over a Bare Soil Site in Morocco", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The 40 km resolution SMOS (Soil Moisture and Ocean Salinity) soil moisture, previously disaggregated at a 1 km resolution using the DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) method based on MODIS optical/thermal data, is further disaggregated to 100 m resolution using Sentinel-1 backscattering coefficient (\u03c3\u00b0). For this purpose, three distinct radar-based disaggregation methods are tested by linking the spatio-temporal variability of \u03c3\u00b0 and soil moisture data at the 1 km and 100 m resolution. The three methods are: (1) the weight method, which estimates soil moisture at 100 m resolution at a certain time as a function of \u03c3\u00b0 ratio (100 m to 1 km resolution) and the 1 km DISPATCH products of the same time; (2) the regression method which estimates soil moisture as a function of \u03c3\u00b0 where the regression parameters (e.g., intercept and slope) vary in space and time; and (3) the Cumulative Distribution Function (CDF) method, which estimates 100 m resolution soil moisture from the cumulative probability of 100 m resolution backscatter and the maximum to minimum 1 km resolution (DISPATCH) soil moisture difference. In each case, disaggregation results are evaluated against in situ measurements collected between 1 January 2016 and 11 October 2016 over a bare soil site in central Morocco. The determination coefficient (R2) between 1 km resolution DISPATCH and localized in situ soil moisture is 0.31. The regression and CDF methods have marginal effect on improving the DISPATCH accuracy at the station scale with a R2 between remotely sensed and in situ soil moisture of 0.29 and 0.34, respectively. By contrast, the weight method significantly improves the correlation between remotely sensed and in situ soil moisture with a R2 of 0.52. Likewise, the soil moisture estimates show low root mean square difference with in situ measurements (RMSD= 0.032 m3 m\u22123).</p></article>", "keywords": ["soil moisture and ocean salinity satellite (SMOS)", "Atmospheric Science", "Artificial intelligence", "Environmental Engineering", "550", "Science", "Soil Moisture", "0211 other engineering and technologies", "Aerospace Engineering", "FOS: Mechanical engineering", "02 engineering and technology", "01 natural sciences", "Environmental science", "[SDU] Sciences of the Universe [physics]", "Engineering", "Meteorology", "DISPATCH", "Image resolution", "Arctic Permafrost Dynamics and Climate Change", "14. Life underwater", "Moisture", "0105 earth and related environmental sciences", "Soil science", "Water content", "Radar", "Geography", "soil moisture and ocean salinity satellite (SMOS); DISPATCH; radar; Sentinel-1; disaggregation; soil moisture", "Soilmoisture and ocean salinity satellite (SMOS)", "Synthetic Aperture Radar Interferometry", "Q", "FOS: Environmental engineering", "Geology", "FOS: Earth and related environmental sciences", "Remote sensing", "Remote Sensing of Soil Moisture", "Surface Deformation Monitoring", "Computer science", "Earth and Planetary Sciences", "Groundwater Extraction", "Geotechnical engineering", "[SDU]Sciences of the Universe [physics]", "disaggregation", "Environmental Science", "Physical Sciences", "Sentinel-1", "soil moisture", "radar"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/9/11/1155/pdf"}, {"href": "https://doi.org/2767588274"}, {"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": "2767588274", "name": "item", "description": "2767588274", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2767588274"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-11-10T00:00:00Z"}}, {"id": "3044974791", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:26:14Z", "type": "Journal Article", "created": "2020-07-24", "title": "Smart Multi-Sensor Platform for Analytics and Social Decision Support in Agriculture", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Smart agriculture based on new types of sensors, data analytics and automation, is an important enabler for optimizing yields and maximizing efficiency to feed the world\u2019s growing population while limiting environmental pollution. The aim of this paper is to describe a multi-sensor Internet of Things (IoT) system for agriculture consisting of a soil probe, an air probe and a smart data logger. The implementation details will focus of the integration element and the innovative Artificial Intelligence based gas identification sensor. Furthermore, the paper focuses on the analytics and decision support system implementation that provides farming recommendations and is enhanced with a feedback loop from farmers and a social trust index that will increase the reliability of the system.</p></article>", "keywords": ["330", "decision support system", "[SPI] Engineering Sciences [physics]", "Social IoT", "Internet of Things", "TP1-1185", "01 natural sciences", "7. Clean energy", "630", "data logger", "Article", "gas sensor", "[SPI]Engineering Sciences [physics]", "Soil", "sensor", "Artificial Intelligence", "social feedback", "data analytics", "agriculture", "2. Zero hunger", "Chemical technology", "Reproducibility of Results", "Agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "0104 chemical sciences", "3. Good health", "13. Climate action", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/20/15/4127/pdf"}, {"href": "https://www.mdpi.com/1424-8220/20/15/4127/pdf"}, {"href": "https://doi.org/3044974791"}, {"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": "3044974791", "name": "item", "description": "3044974791", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3044974791"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-07-24T00:00:00Z"}}, {"id": "3095352776", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:26:16Z", "type": "Journal Article", "created": "2020-10-28", "title": "Incremental predictive clustering trees for online semi-supervised multi-target regression", "description": "Abstract<p>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.</p", "keywords": ["Artificial Intelligence", "0202 electrical engineering", " electronic engineering", " information engineering", "02 engineering and technology", "Software"]}, "links": [{"href": "https://doi.org/3095352776"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Machine%20Learning", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3095352776", "name": "item", "description": "3095352776", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3095352776"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-10-28T00:00:00Z"}}, {"id": "37487270", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:26:34Z", "type": "Journal Article", "created": "2023-04-10", "title": "Quantitative image analysis of microplastics in bottled water using Artificial Intelligence", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The ubiquitous occurrence of microplastics (MPs) in the environment and the use of plastics in packaging materials result in the presence of MPs in the food chain and the exposure of consumers. Yet, no fully validated analytical method is available for microplastic (MP) quantification, thereby preventing the reliable estimation of the level of exposure and, ultimately, the assessment of the food safety risks associated with MP contamination. In this study, a novel approach is presented that exploits interactive artificial intelligence tools to enable the automation of MP analysis. An integrated method for the analysis of MPs in bottled water based on Nile Red staining and fluorescent microscopy was developed and validated, featuring a partial interrogation of the filter and a fully automated image processing workflow based on a Random Forest classifier, thereby boosting the analysis speed. The image analysis provided particle count, size and size distribution of the MPs. From these data, a rough estimation of the mass of the individual MPs, and consequently of the MP mass concentration in the sample, could be obtained as well. Critical materials, method performance characteristics, and final applicability were studied in detail. The method showed to be highly sensitive in sizing MPs down to 10 \u00b5m, with a particle count limit of detection and quantification of 28 and 85 items/500 mL, respectively. Linearity of mass concentration determined between 10 ppb and 1.5 ppm showed a regression coefficient of (R2) of 0.99. Method precision was demonstrated by repeatability of 9 - 16% RSD (n = 7) and within-laboratory reproducibility of 15 - 27 % RSD (n = 21). Accuracy based on recovery was 92 \u00b1 15 % and 98 \u00b1 23 % at a level of 0.1 and 1.0 ppm, respectively. The quantitative performance characteristics thus obtained complied with regulatory requirements. Finally, the method was successfully applied to the analysis of twenty commercial samples of bottled water, with and without gas and flavor additives, yielding results ranging from values below the limit of detection to 7237 (95% CI [6456, 8088]) items/500 mL.</p></article>", "keywords": ["Fluorescence microscopy", "Artificial intelligence", "Bottled water", "Method validation", "Artificial Intelligence", "Microplastics", "Drinking Water", "Microplastic", "Nile red", "Reproducibility of Results", "Plastics", "6. Clean water"]}, "links": [{"href": "https://doi.org/37487270"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Talanta", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "37487270", "name": "item", "description": "37487270", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/37487270"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-04-10T00:00:00Z"}}, {"id": "PMC10095548", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:28:05Z", "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": "<?xml version='1.0' encoding='UTF-8'?><article><p>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.</p></article>", "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": "PMC10337894", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:28:05Z", "type": "Journal Article", "created": "2023-07-12", "title": "Artificial intelligence\u2013coupled plasmonic infrared sensor for detection of structural protein biomarkers in neurodegenerative diseases", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Diagnosis of neurodegenerative disorders (NDDs) including Parkinson\u2019s disease and Alzheimer\u2019s disease is challenging owing to the lack of tools to detect preclinical biomarkers. The misfolding of proteins into oligomeric and fibrillar aggregates plays an important role in the development and progression of NDDs, thus underscoring the need for structural biomarker\u2013based diagnostics. We developed an immunoassay-coupled nanoplasmonic infrared metasurface sensor that detects proteins linked to NDDs, such as alpha-synuclein, with specificity and differentiates the distinct structural species using their unique absorption signatures. We augmented the sensor with an artificial neural network enabling unprecedented quantitative prediction of oligomeric and fibrillar protein aggregates in their mixture. The microfluidic integrated sensor can retrieve time-resolved absorbance fingerprints in the presence of a complex biomatrix and is capable of multiplexing for the simultaneous monitoring of multiple pathology-associated biomarkers. Thus, our sensor is a promising candidate for the clinical diagnosis of NDDs, disease monitoring, and evaluation of novel therapies.</p></article>", "keywords": ["Artificial Intelligence", "Alzheimer Disease", "Humans", "Physical and Materials Sciences", "Neurodegenerative Diseases", "Parkinson Disease", "Biomarkers"]}, "links": [{"href": "https://doi.org/PMC10337894"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science%20Advances", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "PMC10337894", "name": "item", "description": "PMC10337894", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PMC10337894"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-07-14T00:00:00Z"}}, {"id": "PMC7436003", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:28:11Z", "type": "Journal Article", "created": "2020-07-24", "title": "Smart Multi-Sensor Platform for Analytics and Social Decision Support in Agriculture", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Smart agriculture based on new types of sensors, data analytics and automation, is an important enabler for optimizing yields and maximizing efficiency to feed the world\u2019s growing population while limiting environmental pollution. The aim of this paper is to describe a multi-sensor Internet of Things (IoT) system for agriculture consisting of a soil probe, an air probe and a smart data logger. The implementation details will focus of the integration element and the innovative Artificial Intelligence based gas identification sensor. Furthermore, the paper focuses on the analytics and decision support system implementation that provides farming recommendations and is enhanced with a feedback loop from farmers and a social trust index that will increase the reliability of the system.</p></article>", "keywords": ["330", "decision support system", "[SPI] Engineering Sciences [physics]", "Social IoT", "Internet of Things", "TP1-1185", "01 natural sciences", "7. Clean energy", "630", "data logger", "Article", "gas sensor", "[SPI]Engineering Sciences [physics]", "Soil", "sensor", "Artificial Intelligence", "social feedback", "data analytics", "agriculture", "2. Zero hunger", "Chemical technology", "Reproducibility of Results", "Agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "0104 chemical sciences", "3. Good health", "13. Climate action", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/20/15/4127/pdf"}, {"href": "https://www.mdpi.com/1424-8220/20/15/4127/pdf"}, {"href": "https://doi.org/PMC7436003"}, {"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": "PMC7436003", "name": "item", "description": "PMC7436003", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PMC7436003"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-07-24T00:00:00Z"}}, {"id": "PMC9185546", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:28:14Z", "type": "Journal Article", "created": "2022-06-01", "title": "Agrobot Lala\u2014An Autonomous Robotic System for Real-Time, In-Field Soil Sampling, and Analysis of Nitrates", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>This paper presents an autonomous robotic system, an unmanned ground vehicle (UGV), for in-field soil sampling and analysis of nitrates. Compared to standard methods of soil analysis it has several advantages: each sample is individually analyzed compared to average sample analysis in standard methods; each sample is georeferenced, providing a map for precision base fertilizing; the process is fully autonomous; samples are analyzed in real-time, approximately 30 min per sample; and lightweight for less soil compaction. The robotic system has several modules: commercial robotic platform, anchoring module, sampling module, sample preparation module, sample analysis module, and communication module. The system is augmented with an in-house developed cloud-based platform. This platform uses satellite images, and an artificial intelligence (AI) proprietary algorithm to divide the target field into representative zones for sampling, thus, reducing and optimizing the number and locations of the samples. Based on this, a task is created for the robot to automatically sample at those locations. The user is provided with an in-house developed smartphone app enabling overview and monitoring of the task, changing the positions, removing and adding of the sampling points. The results of the measurements are uploaded to the cloud for further analysis and the creation of prescription maps for variable rate base fertilization.</p></article>", "keywords": ["2. Zero hunger", "0106 biological sciences", "precision agriculture", "Nitrates", "Chemical technology", "soil sampling", "TP1-1185", "Robotics", "04 agricultural and veterinary sciences", "artificial intelligence", "01 natural sciences", "Article", "UGV; precision agriculture; artificial intelligence; soil nutrient analysis; soil sampling", "Soil", "soil nutrient analysis", "Robotic Surgical Procedures", "Artificial Intelligence", "0401 agriculture", " forestry", " and fisheries", "UGV"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/22/11/4207/pdf"}, {"href": "https://doi.org/PMC9185546"}, {"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": "PMC9185546", "name": "item", "description": "PMC9185546", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PMC9185546"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-31T00:00:00Z"}}, {"id": "e5881b1be1e5784815c1f757ed4127aa", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:29:50Z", "type": "Report", "title": "Measuring and controlling knowledge diversity", "description": "Assessing knowledge diversity may be useful for many purposes. In particular, it is necessary to measure diversity in order to understand how it arises or is preserved; it is also necessary to control it in order to measure its effects. Here we consider measuring knowledge diversity using two components: (a) a diversity measure taking advantage of (b) a knowledge difference measure. We present the general principles and various candidates for such components. We discuss how these measures may be used to generate populations of agents with controlled levels of knowledge diversity.", "keywords": ["[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]", "[INFO.INFO-DM] Computer Science [cs]/Discrete Mathematics [cs.DM]", "Diversity measure", "Knowledge diversity", "Entropy", "Diversity control", "Ontology dissimilarity"], "contacts": [{"organization": "Bourahla, Yasser, David, J\u00e9r\u00f4me, Euzenat, J\u00e9r\u00f4me, Naciri, Meryem,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/e5881b1be1e5784815c1f757ed4127aa"}, {"rel": "self", "type": "application/geo+json", "title": "e5881b1be1e5784815c1f757ed4127aa", "name": "item", "description": "e5881b1be1e5784815c1f757ed4127aa", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/e5881b1be1e5784815c1f757ed4127aa"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "oai:iris.unige.it:11567/1235396", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:32:29Z", "type": "Report", "title": "AI-based solutions for autonomous underwater observing systems and science discovery", "keywords": ["Artificial Intelligence", " Intelligent Observing Systems", " Edge Computing", " Science Discovery", " Biodiversity"], "contacts": [{"organization": "Simone Marini, Daniele Lagomarsino Oneto, Mattia Cavaiola, Jacopo Aguzzi, Daniele D\u2019Agostino,", "roles": ["creator"]}]}, "links": [{"href": "https://iris.unige.it/bitstream/11567/1235396/1/DAgostino-Biochange.pdf"}, {"href": "https://doi.org/oai:iris.unige.it:11567/1235396"}, {"rel": "self", "type": "application/geo+json", "title": "oai:iris.unige.it:11567/1235396", "name": "item", "description": "oai:iris.unige.it:11567/1235396", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/oai:iris.unige.it:11567/1235396"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-01T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Artificial+Intelligence&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Artificial+Intelligence&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Artificial+Intelligence&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Artificial+Intelligence&offset=35", "hreflang": "en-US"}], "numberMatched": 35, "numberReturned": 35, "distributedFeatures": [], "timeStamp": "2026-07-26T06:20:15.767133Z"}