{"type": "FeatureCollection", "features": [{"id": "oai:helvia.uco.es:10396/24059", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:32:17Z", "type": "Report", "title": "Spatial crop-water variations in rainfed wheat systems: From simulation modelling to site-specific management", "description": "Open AccessEn campos en pendiente, los cultivos de secano experimentan diferentes grados de estr\u00e9s h\u00eddrico causados por variaciones espaciales de la humedad en el suelo, y los rendimientos var\u00edan espacialmente dentro del mismo campo. Esta variabilidad supone una oportunidad para la agricultura de precisi\u00f3n a trav\u00e9s del manejo espacialmente variable. Sin embargo, si bien se han logrado avances significativos en los aspectos de la ingenier\u00eda de la variaci\u00f3n espacial, como el aumento de la resoluci\u00f3n espacial de los sistemas de datos y la automatizaci\u00f3n, se ha avanzado mucho menos en relaci\u00f3n a la simulaci\u00f3n de las respuestas de los cultivos a las variaciones espaciales de la humedad y los flujos h\u00eddricos. La mayor\u00eda de los estudios sobre las brechas de rendimiento de secano ignoran la variabilidad dentro de la parcela. Sin embargo, el uso de modelos de simulaci\u00f3n de cultivos como medida de apoyo a los sistemas de gesti\u00f3n espacialmente variable, requiere que los enfoques de modelaci\u00f3n espacial del agua sean capaces de representar y simular con precisi\u00f3n la variaci\u00f3n dentro del campo de los factores relacionados con el agua disponible y la respuesta de los cultivos. Esta tesis doctoral representa una nueva contribuci\u00f3n a la agronom\u00eda de los sistemas agr\u00edcolas de secano, con \u00e9nfasis en el papel que juegan los flujos de agua en zonas de topograf\u00eda ondulada en la determinaci\u00f3n de las variaciones espaciales del rendimiento del trigo. La tesis se ha desarrollado en cap\u00edtulos que se complementan siguiendo un enfoque integrador. La presente tesis doctoral revis\u00f3 algunos de los modelos hidrol\u00f3gicos y de cultivo m\u00e1s ampliamente adoptados y explor\u00f3 nuevas oportunidades para simular variaciones espaciales del agua a nivel de campo mediante la incorporaci\u00f3n del flujo lateral de escorrent\u00eda superficial y sub-superficial en las zonas de menor elevaci\u00f3n del campo. Desde este punto de vista, se evaluaron las variaciones espaciales de las brechas de rendimiento en trigo de secano, en C\u00f3rdoba, Espa\u00f1a, que son causadas por flujos laterales de los puntos altos a los bajos. Desde una perspectiva agron\u00f3mica, las entradas laterales del agua contribuyen a las variaciones de rendimiento en los sistemas de producci\u00f3n de trigo de secano como el que se ha estudiado en el \u00e1mbito de esta tesis. La contribuci\u00f3n neta de estos flujos a las variaciones espaciales de los rendimientos potenciales de secano se mostr\u00f3 relevante pero altamente irregular entre diferentes a\u00f1os. A pesar de la variabilidad interanual, t\u00edpica de las condiciones mediterr\u00e1neas, la existencia de dichos flujos hizo que los rendimientos de trigo simulados variaran un +16% desde las \u00e1reas m\u00e1s elevadas de un campo hacia abajo. El rendimiento medio observado oscil\u00f3 entre 1.3 y 5.4 Mg de rendimiento de grano (GY) ha\u22121. Las respuestas de rendimiento neto al flujo lateral, cuenca abajo, fueron en promedio 383 kg de rendimiento de grano (GY) ha\u22121, y la productividad marginal de agua de LIF alcanz\u00f3 24.6 (\u00b113.2) kg GY ha\u22121 mm\u22121 en a\u00f1os de m\u00e1xima capacidad de respuesta. Dichos a\u00f1os de m\u00e1xima capacidad de respuesta se asociaron con bajas precipitaciones durante las etapas vegetativas del cultivo en combinaci\u00f3n con flujos laterales en las etapas posteriores a la floraci\u00f3n. En condiciones de campo, estas diferencias solo fueron visibles en uno de los dos a\u00f1os experimentales. Las implicaciones econ\u00f3micas asociadas con m\u00faltiples escenarios de tasa de aplicaci\u00f3n variable de nitr\u00f3geno se exploraron a trav\u00e9s de un caso de estudio y se propusieron varias recomendaciones. Tanto el tama\u00f1o de la finca (el \u00e1rea sembrada anual) como la estructura topogr\u00e1fica afectaron la din\u00e1mica de los rendimientos de la inversi\u00f3n. Bajo las condiciones actuales de pol\u00edtica agr\u00edcola, y de precios, la adopci\u00f3n de la tasa de aplicaci\u00f3n variable tendr\u00eda una ventaja econ\u00f3mica en fincas similares a la del caso de estudio con un \u00e1rea sembrada anual superior a 567 ha a\u00f1o\u22121. Sin embargo, las tendencias actuales en los precios de la energ\u00eda, los costes de transporte y los impactos tanto en los precios de los cereales como en los costes de los fertilizantes mejoran la viabilidad de la adopci\u00f3n de esta tecnolog\u00eda para una poblaci\u00f3n m\u00e1s amplia de tipos de fincas. La rentabilidad de la adopci\u00f3n de aplicaci\u00f3n variable de nitr\u00f3geno mejora bajo dichos escenarios y, en ausencia de apoyos adicionales, el \u00e1rea m\u00ednima para la adopci\u00f3n de aplicaci\u00f3n variable disminuye hasta un rango de 68-177 ha a\u00f1o\u22121 de \u00e1rea de siembra. La combinaci\u00f3n de aumentos de precios con la introducci\u00f3n de un subsidio adicional asociado al \u00e1rea de cultivo podr\u00eda reducir sustancialmente el umbral de adopci\u00f3n hasta 46 ha a\u00f1o\u22121, lo que hace que la tecnolog\u00eda sea econ\u00f3micamente viable para una poblaci\u00f3n mucho m\u00e1s amplia de agricultores.", "keywords": ["Agricultural crops", "Water management", "Artificial Neural Network", "Precision agriculture", "Crop modelling", "NDVI", "Spatial modelling", "Machine learning", "Water balance"], "contacts": [{"organization": "Roquette Tenreiro, Tom\u00e1s", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/oai:helvia.uco.es:10396/24059"}, {"rel": "self", "type": "application/geo+json", "title": "oai:helvia.uco.es:10396/24059", "name": "item", "description": "oai:helvia.uco.es:10396/24059", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/oai:helvia.uco.es:10396/24059"}, {"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": "10.1007/s00521-020-05253-3", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:14:33Z", "type": "Journal Article", "created": "2020-08-03", "title": "Source localization in resource-constrained sensor networks based on deep learning", "description": "Source localization with a network of low-cost motes with limited processing, memory, and energy resources is considered in this paper. The state-of-the-art methods are mostly based on complicated signal processing approaches in which motes send their (processed) data to a fusion center (FC) wherein the source is localized. These methods are resource-demanding and mostly do not meet the limitations of motes and network. In this paper, we consider distributed detection where each mote performs a binary hypothesis test to detect locally the existence of a desired source and sends its (potentially erroneous) decision to FC during just one bit (1 indicates source existence and 0 otherwise). Hence, both processing and bandwidth constraints are met. We propose to use an artificial neural network (ANN) to correct erroneous local decisions. After error correction, the region affected by the source is specified by nodes with decision 1. Moreover, we propose to localize the source by deep learning in FC which converts the network of decisions 1 and 0 to a black and white image with white pixels in the locations of motes with decision 1. The proposed schemes of error correction by ANN (ECANN) and source localization with deep learning (SoLDeL) were evaluated in a fire detection application. We showed that SoLDeL performs appropriately and scales well into large networks. Moreover, the applicability of ECANN in delineation of farm management zones was illustrated.", "keywords": ["Artificial neural network (ANN)", "Internet of things (IoT)", "0202 electrical engineering", " electronic engineering", " information engineering", "Deep learning", "Target tracking", "Error type II", "02 engineering and technology", "Decentralized detection", "15. Life on land", "Wireless sensor networks (WSN)", "Error type I", "Source localization"]}, "links": [{"href": "https://link.springer.com/content/pdf/10.1007/s00521-020-05253-3.pdf"}, {"href": "https://doi.org/10.1007/s00521-020-05253-3"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Neural%20Computing%20and%20Applications", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s00521-020-05253-3", "name": "item", "description": "10.1007/s00521-020-05253-3", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s00521-020-05253-3"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-08-03T00:00:00Z"}}, {"id": "10.1007/s10994-020-05918-z", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:14:50Z", "type": "Journal Article", "created": "2020-10-28", "title": "Incremental predictive clustering trees for online semi-supervised multi-target regression", "description": "Abstract

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.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.

", "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": "10.1111/avsc.12195", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:18:25Z", "type": "Journal Article", "created": "2015-08-21", "title": "What Factors Determined Restoration Success Of A Salt Marsh Ten Years After De-Embankment?", "description": "AbstractQuestions

How successful was the restoration of a salt marsh at a former summer polder on the mainland coast of the Dutch Wadden Sea 10\uffc2\uffa0yr after de\uffe2\uff80\uff90embankment? What were the most important factors determining the level of restoration success?

Location

Noard\uffe2\uff80\uff90Frysl\uffc3\uffa2n B\uffc3\uffbbtendyks, northwest Netherlands.

Methods

The frequencies of target plant species were recorded before de\uffe2\uff80\uff90embankment and monitored thereafter (1, 2, 3, 4, 6 and 10\uffc2\uffa0yr later) using permanent transects. Vegetation change was monitored using repeated mapping 14\uffc2\uffa0yr before and 1, 7 and 10\uffc2\uffa0yr after de\uffe2\uff80\uff90embankment. A large\uffe2\uff80\uff90scale factorial experiment with 72 sampling plots was set up to determine the effects of distance to a breach point, distance to a creek and grazing treatment on species composition. Abiotic data were also collected from the permanent transects and sampling plots on elevation, soil salinity and redox potential.

Results

Ten years after de\uffe2\uff80\uff90embankment, permanent transect data showed that 78% to 96% of the target species were found at the restoration site. Vegetation mapping, however, showed that the diversity of salt marsh communities was low, with 50% of the site covered by the secondary pioneer marsh community. A multivariate analogue of ANOVA indicated that the most important experimental factor determining species composition was the interaction between distance to the nearest creek and livestock grazing. The combination of proximity to a creek and exclusion from livestock grazing always resulted in development of the high marsh community. In contrast, the combination of being located far from a creek, grazed and situated at low elevation with accompanying high salinity resulted in development of the secondary pioneer marsh community.

Conclusions

Using target species as criteria, restoration success could be claimed 10\uffc2\uffa0yr after de\uffe2\uff80\uff90embankment. However, the diversity of communities in the salt marsh was lower than desired. Variable grazing regimes should be applied to high\uffe2\uff80\uff90elevation areas to prevent dominance by single species of tall grasses and to promote formation of vegetation mosaics. Low\uffe2\uff80\uff90elevation areas need lower grazing pressure. Also, an adequate soil drainage network should be preserved or constructed in low\uffe2\uff80\uff90elevation areas before de\uffe2\uff80\uff90embankment.

", "keywords": ["0106 biological sciences", "Salinity", "LAND", "Managed realignment", "Artificial saltmarsh", "NETHERLANDS", "Soil redox", "WADDEN SEA", "Soil drainage", "15. Life on land", "01 natural sciences", "6. Clean water", "Long-term study", "COLONIZATION", "Grazing", "Halophytes", "Elevation", "14. Life underwater", "MANAGED REALIGNMENT", "ELEVATION", "SCALE"], "contacts": [{"organization": "R.M. Veeneklaas, Petra Daniels, Jan P. Bakker, E. R. Chang, Peter Esselink,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1111/avsc.12195"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Applied%20Vegetation%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/avsc.12195", "name": "item", "description": "10.1111/avsc.12195", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/avsc.12195"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2015-08-20T00:00:00Z"}}, {"id": "10.1371/journal.pone.0125404", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:19:20Z", "type": "Journal Article", "created": "2015-05-06", "title": "The Contribution Of Mangrove Expansion To Salt Marsh Loss On The Texas Gulf Coast", "description": "Landscape-level shifts in plant species distribution and abundance can fundamentally change the ecology of an ecosystem. Such shifts are occurring within mangrove-marsh ecotones, where over the last few decades, relatively mild winters have led to mangrove expansion into areas previously occupied by salt marsh plants. On the Texas (USA) coast of the western Gulf of Mexico, most cases of mangrove expansion have been documented within specific bays or watersheds. Based on this body of relatively small-scale work and broader global patterns of mangrove expansion, we hypothesized that there has been a recent regional-level displacement of salt marshes by mangroves. We classified Landsat-5 Thematic Mapper images using artificial neural networks to quantify black mangrove (Avicennia germinans) expansion and salt marsh (Spartina alterniflora and other grass and forb species) loss over 20 years across the entire Texas coast. Between 1990 and 2010, mangrove area grew by 16.1 km(2), a 74% increase. Concurrently, salt marsh area decreased by 77.8 km(2), a 24% net loss. Only 6% of that loss was attributable to mangrove expansion; most salt marsh was lost due to conversion to tidal flats or water, likely a result of relative sea level rise. Our research confirmed that mangroves are expanding and, in some instances, displacing salt marshes at certain locations. However, this shift is not widespread when analyzed at a larger, regional level. Rather, local, relative sea level rise was indirectly implicated as another important driver causing regional-level salt marsh loss. Climate change is expected to accelerate both sea level rise and mangrove expansion; these mechanisms are likely to interact synergistically and contribute to salt marsh loss.", "keywords": ["Satellite Imagery", "0106 biological sciences", "Science", "Climate Change", "Marshes", "Poaceae", "01 natural sciences", "333", "Image Interpretation", " Computer-Assisted", "11. Sustainability", "14. Life underwater", "Mangrove swamps", "Ecosystem", "0105 earth and related environmental sciences", "Gulf of Mexico", "Artificial neural networks", "Winter", "Q", "R", "15. Life on land", "Texas", "Habitats", "13. Climate action", "Wetlands", "Medicine", "Avicennia", "Seasons", "Research Article"], "contacts": [{"organization": "Armitage, Anna R., Highfield, Wesley E., Brody, Samuel D., Louchouarn, Patrick,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1371/journal.pone.0125404"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PLOS%20ONE", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1371/journal.pone.0125404", "name": "item", "description": "10.1371/journal.pone.0125404", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1371/journal.pone.0125404"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2015-05-06T00:00:00Z"}}, {"id": "10.1126/sciadv.adg9644", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:18:59Z", "type": "Journal Article", "created": "2023-07-12", "title": "Artificial intelligence\u2013coupled plasmonic infrared sensor for detection of structural protein biomarkers in neurodegenerative diseases", "description": "

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.

", "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": "2026-05-23T16:20:52Z", "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": "

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.

", "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": "10.1890/10-0808.1", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:19:45Z", "type": "Journal Article", "created": "2010-09-27", "title": "A Brown-World Cascade In The Dung Decomposer Food Web Of An Alpine Meadow: Effects Of Predator Interactions And Warming", "description": "Top-down control has been extensively documented in food webs based on living plants, where predator limitation of herbivores can cascade to facilitate plant growth (the green-world hypothesis), particularly in grasslands and aquatic systems. Yet the ecosystem role of predators in detrital food webs is less explored, as is the potential effect of climate warming on detritus-based communities. We here show that predators have a 'brown-world' role in decomposer communities via a cascading top-down control on plant growth, based on the results of an experiment that factorially manipulated presence and size of two predator species as well as temperature (warmed vs. unwarmed). The inclusion of predatory beetles significantly decreased abundance of coprophagous beetles and thus the rate of dung decomposition and productivity of plants growing surrounding the dung. Moreover, the magnitude of these decreases differed between predator species and, for dung loss, was temperature dependent. At ambient temperature, the larger predators tended to more strongly influence the dung loss rate than did the smaller predators; when both predators were present, the dung loss rate was higher relative to the treatments with the smaller predators but comparable to those with the larger ones, suggesting an antagonistic effect of predator interaction. However, warming substantially reduced dung decomposition rates and eliminated the effects of predation on dung decomposition. Although warming substantially decreased dung loss rates, warming only modestly reduced primary productivity. Consistent with these results, a second experiment exploring the influence of the two predator species and warming on dung loss over time revealed that predatory beetles significantly decreased the abundance of coprophagous beetles, which was positively correlated with dung loss rates. Moreover, experimental warming decreased the water content of dung and hence the survival of coprophagous beetles. These results confirm that the 'brown-world' effect of predator beetles was due to cascading top-down control through coprophagous beetles to nutrient cycling and primary productivity. Our results also highlight potentially counterintuitive effects of climate warming. For example, global warming might significantly decrease animal-mediated decomposition of organic matter and recycling of nutrients in a future warmed world.", "keywords": ["0106 biological sciences", "China", "predator", "Qinghai-Tibetan Plateau", "nutrient cycling", "biodiversity and ecosystem function", "15. Life on land", "beetles", "01 natural sciences", "630", "trophic cascade", "13. Climate action", "food webs", "dung decomposers", "artificial warming", "top-down control", "alpine meadow", "coprophagy", "biodiversity"]}, "links": [{"href": "https://doi.org/10.1890/10-0808.1"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Ecological%20Monographs", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1890/10-0808.1", "name": "item", "description": "10.1890/10-0808.1", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1890/10-0808.1"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2011-05-01T00:00:00Z"}}, {"id": "10.20944/preprints202008.0113.v1", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:19:47Z", "type": "Report", "created": "2020-08-05", "title": "Convolutional Neural Networks with Deep Supervised Feature Learning for Remote Sensing Scene Classification", "description": "

State-of-the-art remote sensing scene classification methods employ different Convolutional Neural Network architectures for achieving very high classification performance. A trait shared by the majority of these methods is that the class associated with each example is ascertained by examining the activations of the last fully connected layer, and the networks are trained to minimize the cross-entropy between predictions extracted from this layer and ground-truth annotations. In this work, we extend this paradigm by introducing an additional output branch which maps the inputs to low dimensional representations, effectively extracting additional feature representations of the inputs. The proposed model imposes additional distance constrains on these representations with respect to identified class representatives, in addition to the traditional categorical cross-entropy between predictions and ground-truth. By extending the typical cross-entropy loss function with a distance learning function, our proposed approach achieves significant gains across a wide set of benchmark datasets in terms of classification, while providing additional evidence related to class membership and classification confidence.

", "keywords": ["0211 other engineering and technologies", "0202 electrical engineering", " electronic engineering", " information engineering", "artificial_intelligence_robotics", "02 engineering and technology"]}, "links": [{"href": "https://doi.org/10.20944/preprints202008.0113.v1"}, {"rel": "self", "type": "application/geo+json", "title": "10.20944/preprints202008.0113.v1", "name": "item", "description": "10.20944/preprints202008.0113.v1", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.20944/preprints202008.0113.v1"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-08-05T00:00:00Z"}}, {"id": "10.3390/rs13214195", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:20:50Z", "type": "Journal Article", "created": "2021-10-20", "title": "Sentinel-2 Recognition of Uncovered and Plastic Covered Agricultural Soil", "description": "

Medium resolution satellite data, such as Sentinel-2 of the Copernicus programme, offer great new opportunities for the agricultural sector, and provide insights on soil surface characteristics and their management. Soil monitoring requires a high-quality dataset of uncovered and plastic covered agricultural soil. We developed a methodology to identify uncovered soil pixels in agricultural parcels during seedbed preparation and considered the impacts of clouds and shadows, vegetation cover, and artificial covers, such as those of greenhouses and plastic mulch films. We preserved the spatial and temporal integrity of parcels in the process and analysed spectral anomalies and their sources. The approach is based on freely available tools, namely Google Earth Engine and R Programming packages. We tested the methodology on the northern region of Belgium, which is characterised by small, fragmented parcels. We selected a period between mid-April to end-May, when active agricultural management practices leave the soil bare in preparation for the main cropping season. The spectral angle mapper was used to identify soil covered by non-plastic greenhouses or temporary soil covers, such as plastic mulch films. The effect of underlying soil on temporary covers was considered. The retrogressive plastic greenhouse index was used for detecting plastic greenhouses. The result was a high quality dataset of potential bare uncovered agricultural soil that allows further soil surface characterisation. This offered an improved understanding of the use of artificial covers, their spatial distribution, and their corresponding crops during the considered period. Artificial covers occurred most frequently in maize parcels. The approach resulted in precision values exceeding 0.9 for the detection of temporary covers and non-plastic greenhouses and a sensitivity value exceeding 0.95 for non-plastic and plastic greenhouses.

", "keywords": ["Technology", "SURFACE", "Science", "Environmental Sciences & Ecology", "TEXTURE", "artificial cover", "ALMERIA", "0203 Classical Physics", "soil", "Remote Sensing", "SUPPORT", "0909 Geomatic Engineering", "Geosciences", " Multidisciplinary", "Imaging Science & Photographic Technology", "agriculture", "2. Zero hunger", "plastic mulch", "Science & Technology", "IDENTIFICATION", "soil; agriculture; Sentinel-2; artificial cover; plastic mulch", "Q", "Geology", "04 agricultural and veterinary sciences", "15. Life on land", "CLOUD", "REFLECTANCE", "RESOLUTION", "13. Climate action", "Physical Sciences", "0401 agriculture", " forestry", " and fisheries", "4013 Geomatic engineering", "Sentinel-2", "GREENHOUSE", "0406 Physical Geography and Environmental Geoscience", "Life Sciences & Biomedicine", "3701 Atmospheric sciences", "Environmental Sciences", "3709 Physical geography and environmental geoscience"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/21/4195/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/21/4195/pdf"}, {"href": "https://doi.org/10.3390/rs13214195"}, {"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/rs13214195", "name": "item", "description": "10.3390/rs13214195", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13214195"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-10-20T00:00:00Z"}}, {"id": "10.26434/chemrxiv-2022-cr5ws-v2", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:20:25Z", "type": "Journal Article", "created": "2023-04-10", "title": "Quantitative image analysis of microplastics in bottled water using Artificial Intelligence", "description": "

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.

", "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": "2026-05-23T16:20:27Z", "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": "2026-05-23T16:20:27Z", "type": "Report", "created": "2025-02-24", "title": "Toward a More Efficient Generation of Structured Argumentation Graphs", "description": "

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.

", "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": "2026-05-23T16:20:27Z", "type": "Report", "created": "2025-02-20", "title": "Viewpoints using ranking-based argumentation semantics", "description": "

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.

", "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": "2026-05-23T16:20:27Z", "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": "10.3389/fmats.2021.624631", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:20:15Z", "type": "Journal Article", "created": "2021-05-07", "title": "When Beneficial Biofilm on Materials Is Needed: Electrostatic Attachment of Living Bacterial Cells Induces Biofilm Formation", "description": "

Bacterial attachment is crucial in many biotechnological applications, but many important bacterial strains cannot form biofilms. Biofilms can damage materials, and current strategies to manage biofilms are focused on inhibition and removal of biofilm. Biofilm formation is inevitable when materials are exposed to microbes and instead of biofilm prevention, we propose management of microbial composition by formation of biofilms with beneficial microbes. Since bacteria need to overcome a high repulsive force to attach to the surface and later to grow and multiply on it, electrostatic modification of the surfaces of cells or the material by polyelectrolytes (PE) was used in our approach, enabling efficient attachment of viable bacterial cells. Since highly positively charged PEs are known to be bactericidal, they were acetylated to reduce their toxicity, while preserving their net positive charge and ensuring cell viability. In our study bacterial strains were selected according to their intrinsic capability of biofilm formation, their shape variety and cell wall structure. These strains were tested to compare how the artificially prepared vs. natural biofilms can be used to populate the surface with beneficial bacteria. Using an artificial biofilm constructed of the potentially probiotic isolate Bacillus sp. strain 25.2. M, reduced the attachment and induced complete inhibition of E. coli growth over the biofilm. This study also revealed that the modification of the surfaces of cells or material by polyelectrolytes allows the deposition of bacterial cells, biofilm formation and attachment of biofilm non-forming cells onto surfaces. In this way, artificial biofilms with extended stability can be constructed, leading to selective pressure on further colonization of environmental bacteria.

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.

", "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/ijerph17010271", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:20:42Z", "type": "Journal Article", "created": "2020-01-03", "title": "Designing Electric Field Responsive Ultrafiltration Membranes by Controlled Grafting of Poly (Ionic Liquid) Brush", "description": "

Electric responsive membranes have been prepared by controlled surface grafting of poly (ionic liquid) (PIL) on the commercially available regenerated cellulose ultrafiltration membrane. The incorporation of imidazolium ring on membrane surface was evidenced by FTIR (Fourier transformed infra-red) and EDX (energy-dispersive X-ray) spectroscopy. The PIL grafting resultedin a rougher surface, reduction in pore size, and enhancement in hydrophilicity. The interaction of the electric field between the charged PIL brush and the oscillating external electric field leads to micromixing, and hence it is proposed to break the concentration polarization. This micromixing improves the antifouling properties of the responsive membranes. The local perturbation was found to decrease the water flux, while it enhanced protein rejection. At a higher frequency (1kHz) of the applied electric field, the localized heating predominates compared to micromixing. In the case of a lower frequency of the applied electric field, more perturbation can lead to less permeability, whereas it will have a better effect in breaking the concentration polarization. However, during localized heating at a higher frequency, though perturbation is less, a heating induced reduction in permeability was observed. The electric field response of the membrane was found to be reversible in nature, and hence has no memory effect.

", "keywords": ["localized heating", "electric responsive membrane", "local perturbation", "Ionic Liquids", "Ultrafiltration", "Water", "Membranes", " Artificial", "Electrochemical Techniques", "02 engineering and technology", "poly (ionic liquid)", "01 natural sciences", "Article", "0104 chemical sciences", "Cellulose", "0210 nano-technology", "Hydrophobic and Hydrophilic Interactions"]}, "links": [{"href": "http://www.mdpi.com/1660-4601/17/1/271/pdf"}, {"href": "https://doi.org/10.3390/ijerph17010271"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/International%20Journal%20of%20Environmental%20Research%20and%20Public%20Health", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/ijerph17010271", "name": "item", "description": "10.3390/ijerph17010271", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/ijerph17010271"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-12-30T00:00:00Z"}}, {"id": "10.3390/ijms24076573", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:20:42Z", "type": "Journal Article", "created": "2023-04-03", "title": "A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation", "description": "

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

", "keywords": ["Deep Learning", "Artificial Intelligence", "Drug Discovery", "Review", "Neural Networks", " Computer", "drug discovery; drug design; artificial intelligence; machine learning; deep learning; biological evaluation; animal model; in vivo", "Algorithms", "3. Good health"]}, "links": [{"href": "https://www.mdpi.com/1422-0067/24/7/6573/pdf"}, {"href": "https://doi.org/10.3390/ijms24076573"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/International%20Journal%20of%20Molecular%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/ijms24076573", "name": "item", "description": "10.3390/ijms24076573", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/ijms24076573"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-03-31T00:00:00Z"}}, {"id": "10.3390/ijms25168599", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:20:43Z", "type": "Journal Article", "created": "2024-08-07", "title": "Design of Novel Membranes for the Efficient Separation of Bee Alarm Pheromones in Portable Membrane Inlet Mass Spectrometric Systems", "description": "

Bee alarm pheromones are essential molecules that are present in beehives when some threats occur in the bee population. In this work, we have applied multilevel modeling techniques to understand molecular interactions between representative bee alarm pheromones and polymers such as polymethyl siloxane (PDMS), polyethylene glycol (PEG), and their blend. This study aimed to check how these interactions can be manipulated to enable efficient separation of bee alarm pheromones in portable membrane inlet mass spectrometric (MIMS) systems using new membranes. The study involved the application of powerful computational atomistic methods based on a combination of modern semiempirical (GFN2-xTB), first principles (DFT), and force-field calculations. As a fundamental work material for the separation of molecules, we considered the PDMS polymer, a well-known sorbent material known to be applicable for light polar molecules. To improve its applicability as a sorbent material for heavier polar molecules, we considered two main factors\u2014temperature and the addition of PEG polymer. Additional insights into molecular interactions were obtained by studying intrinsic reactive properties and noncovalent interactions between bee alarm pheromones and PDMS and PEG polymer chains.

", "keywords": ["0103 physical sciences", "Animals", "Membranes", " Artificial", "Dimethylpolysiloxanes", "Bees", "01 natural sciences", "Article", "Pheromones", "Mass Spectrometry", "Polyethylene Glycols", "0104 chemical sciences"]}, "links": [{"href": "https://doi.org/10.3390/ijms25168599"}, {"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/ijms25168599", "name": "item", "description": "10.3390/ijms25168599", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/ijms25168599"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-08-07T00:00:00Z"}}, {"id": "10.3390/rs9111155", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:20:50Z", "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": "

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 (\uffcf\uff83\uffc2\uffb0). For this purpose, three distinct radar-based disaggregation methods are tested by linking the spatio-temporal variability of \uffcf\uff83\uffc2\uffb0 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 \uffcf\uff83\uffc2\uffb0 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 \uffcf\uff83\uffc2\uffb0 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\uffe2\uff88\uff923).

", "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/s20154127", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:20:51Z", "type": "Journal Article", "created": "2020-07-24", "title": "Smart Multi-Sensor Platform for Analytics and Social Decision Support in Agriculture", "description": "

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.

", "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": "2026-05-23T16:20:51Z", "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": "

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.

", "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": "2026-05-23T16:21:30Z", "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": "

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 (2018–2022) 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 (2018–2022) derived from Sentinel-2 bands (B02–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 R² = 0.62 for clay and outperforming XGBoost for SOC (R² = 0.63) and pH (R² = 0.76) in the agroforestry site. However, XGBoost performed better for SOC (R² = 0.54) and total nitrogen in croplands, as well as for sand, silt, clay, and total nitrogen in the agroforestry site (R² = 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 R² 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.

", "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": "2026-05-23T16:21:35Z", "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": "

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.

", "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": "10396/24059", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:24:25Z", "type": "Report", "title": "Spatial crop-water variations in rainfed wheat systems: From simulation modelling to site-specific management", "description": "Open AccessEn campos en pendiente, los cultivos de secano experimentan diferentes grados de estr\u00e9s h\u00eddrico causados por variaciones espaciales de la humedad en el suelo, y los rendimientos var\u00edan espacialmente dentro del mismo campo. Esta variabilidad supone una oportunidad para la agricultura de precisi\u00f3n a trav\u00e9s del manejo espacialmente variable. Sin embargo, si bien se han logrado avances significativos en los aspectos de la ingenier\u00eda de la variaci\u00f3n espacial, como el aumento de la resoluci\u00f3n espacial de los sistemas de datos y la automatizaci\u00f3n, se ha avanzado mucho menos en relaci\u00f3n a la simulaci\u00f3n de las respuestas de los cultivos a las variaciones espaciales de la humedad y los flujos h\u00eddricos. La mayor\u00eda de los estudios sobre las brechas de rendimiento de secano ignoran la variabilidad dentro de la parcela. Sin embargo, el uso de modelos de simulaci\u00f3n de cultivos como medida de apoyo a los sistemas de gesti\u00f3n espacialmente variable, requiere que los enfoques de modelaci\u00f3n espacial del agua sean capaces de representar y simular con precisi\u00f3n la variaci\u00f3n dentro del campo de los factores relacionados con el agua disponible y la respuesta de los cultivos. Esta tesis doctoral representa una nueva contribuci\u00f3n a la agronom\u00eda de los sistemas agr\u00edcolas de secano, con \u00e9nfasis en el papel que juegan los flujos de agua en zonas de topograf\u00eda ondulada en la determinaci\u00f3n de las variaciones espaciales del rendimiento del trigo. La tesis se ha desarrollado en cap\u00edtulos que se complementan siguiendo un enfoque integrador. La presente tesis doctoral revis\u00f3 algunos de los modelos hidrol\u00f3gicos y de cultivo m\u00e1s ampliamente adoptados y explor\u00f3 nuevas oportunidades para simular variaciones espaciales del agua a nivel de campo mediante la incorporaci\u00f3n del flujo lateral de escorrent\u00eda superficial y sub-superficial en las zonas de menor elevaci\u00f3n del campo. Desde este punto de vista, se evaluaron las variaciones espaciales de las brechas de rendimiento en trigo de secano, en C\u00f3rdoba, Espa\u00f1a, que son causadas por flujos laterales de los puntos altos a los bajos. Desde una perspectiva agron\u00f3mica, las entradas laterales del agua contribuyen a las variaciones de rendimiento en los sistemas de producci\u00f3n de trigo de secano como el que se ha estudiado en el \u00e1mbito de esta tesis. La contribuci\u00f3n neta de estos flujos a las variaciones espaciales de los rendimientos potenciales de secano se mostr\u00f3 relevante pero altamente irregular entre diferentes a\u00f1os. A pesar de la variabilidad interanual, t\u00edpica de las condiciones mediterr\u00e1neas, la existencia de dichos flujos hizo que los rendimientos de trigo simulados variaran un +16% desde las \u00e1reas m\u00e1s elevadas de un campo hacia abajo. El rendimiento medio observado oscil\u00f3 entre 1.3 y 5.4 Mg de rendimiento de grano (GY) ha\u22121. Las respuestas de rendimiento neto al flujo lateral, cuenca abajo, fueron en promedio 383 kg de rendimiento de grano (GY) ha\u22121, y la productividad marginal de agua de LIF alcanz\u00f3 24.6 (\u00b113.2) kg GY ha\u22121 mm\u22121 en a\u00f1os de m\u00e1xima capacidad de respuesta. Dichos a\u00f1os de m\u00e1xima capacidad de respuesta se asociaron con bajas precipitaciones durante las etapas vegetativas del cultivo en combinaci\u00f3n con flujos laterales en las etapas posteriores a la floraci\u00f3n. En condiciones de campo, estas diferencias solo fueron visibles en uno de los dos a\u00f1os experimentales. Las implicaciones econ\u00f3micas asociadas con m\u00faltiples escenarios de tasa de aplicaci\u00f3n variable de nitr\u00f3geno se exploraron a trav\u00e9s de un caso de estudio y se propusieron varias recomendaciones. Tanto el tama\u00f1o de la finca (el \u00e1rea sembrada anual) como la estructura topogr\u00e1fica afectaron la din\u00e1mica de los rendimientos de la inversi\u00f3n. Bajo las condiciones actuales de pol\u00edtica agr\u00edcola, y de precios, la adopci\u00f3n de la tasa de aplicaci\u00f3n variable tendr\u00eda una ventaja econ\u00f3mica en fincas similares a la del caso de estudio con un \u00e1rea sembrada anual superior a 567 ha a\u00f1o\u22121. Sin embargo, las tendencias actuales en los precios de la energ\u00eda, los costes de transporte y los impactos tanto en los precios de los cereales como en los costes de los fertilizantes mejoran la viabilidad de la adopci\u00f3n de esta tecnolog\u00eda para una poblaci\u00f3n m\u00e1s amplia de tipos de fincas. La rentabilidad de la adopci\u00f3n de aplicaci\u00f3n variable de nitr\u00f3geno mejora bajo dichos escenarios y, en ausencia de apoyos adicionales, el \u00e1rea m\u00ednima para la adopci\u00f3n de aplicaci\u00f3n variable disminuye hasta un rango de 68-177 ha a\u00f1o\u22121 de \u00e1rea de siembra. La combinaci\u00f3n de aumentos de precios con la introducci\u00f3n de un subsidio adicional asociado al \u00e1rea de cultivo podr\u00eda reducir sustancialmente el umbral de adopci\u00f3n hasta 46 ha a\u00f1o\u22121, lo que hace que la tecnolog\u00eda sea econ\u00f3micamente viable para una poblaci\u00f3n mucho m\u00e1s amplia de agricultores.", "keywords": ["Agricultural crops", "Water management", "Artificial Neural Network", "Precision agriculture", "Crop modelling", "NDVI", "Spatial modelling", "Machine learning", "Water balance"], "contacts": [{"organization": "Roquette Tenreiro, Tom\u00e1s", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10396/24059"}, {"rel": "self", "type": "application/geo+json", "title": "10396/24059", "name": "item", "description": "10396/24059", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10396/24059"}, {"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": "10.5281/zenodo.3463412", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:22:58Z", "type": "Journal Article", "title": "Prediction of liquefaction damage with artificial neural networks", "description": "The survey of the damage occurred on land, buildings and infrastructures
extensively affected by liquefaction, coupled with a comprehensive investigation of the subsoil
properties enables to identify the factors that determine the spatial distribution of the phenomenon.
With this goal, a database was created in a Geographic Information platform merging
records of local seismicity, subsoil layering evaluated by cone penetration tests and
groundwater level distribution for the relevant case study of San Carlo (Emilia Romagna-
Italy) struck by a severe earthquake in 2012. Here liquefaction phenomena were observed on a
portion of the village in the form of sand ejecta, lateral spreading and various damages on
buildings and infrastructures. The location of damage allows to test possible relations with the
factors characterizing susceptibility, triggering and severity of liquefaction. The relation
among the different variables has been herein sought by training a specifically implemented
Artificial Neural Network. A relation has thus been inferred between damage and thickness of
the liquefiable layer and of the upper crust, seismic input and soil characteristics.", "keywords": ["Liquefaction", "Liquefaction", " Artificial Neural Networks", "0211 other engineering and technologies", "02 engineering and technology", "Artificial Neural Networks"], "contacts": [{"organization": "Paolella Luca, Erminio, Salvatore, Spacagna Rose Line, Modoni Giuseppe, Ochmanski Maciej,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3463412"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earthquake%20Geotechnical%20Engineering%20for%20Protection%20an%20Development%20of%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3463412", "name": "item", "description": "10.5281/zenodo.3463412", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3463412"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.8089856", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:23:22Z", "type": "Journal Article", "created": "2020-08-03", "title": "Source localization in resource-constrained sensor networks based on deep learning", "description": "Source localization with a network of low-cost motes with limited processing, memory, and energy resources is considered in this paper. The state-of-the-art methods are mostly based on complicated signal processing approaches in which motes send their (processed) data to a fusion center (FC) wherein the source is localized. These methods are resource-demanding and mostly do not meet the limitations of motes and network. In this paper, we consider distributed detection where each mote performs a binary hypothesis test to detect locally the existence of a desired source and sends its (potentially erroneous) decision to FC during just one bit (1 indicates source existence and 0 otherwise). Hence, both processing and bandwidth constraints are met. We propose to use an artificial neural network (ANN) to correct erroneous local decisions. After error correction, the region affected by the source is specified by nodes with decision 1. Moreover, we propose to localize the source by deep learning in FC which converts the network of decisions 1 and 0 to a black and white image with white pixels in the locations of motes with decision 1. The proposed schemes of error correction by ANN (ECANN) and source localization with deep learning (SoLDeL) were evaluated in a fire detection application. We showed that SoLDeL performs appropriately and scales well into large networks. Moreover, the applicability of ECANN in delineation of farm management zones was illustrated.", "keywords": ["Artificial neural network (ANN)", "Internet of things (IoT)", "0202 electrical engineering", " electronic engineering", " information engineering", "Deep learning", "Target tracking", "Error type II", "02 engineering and technology", "Decentralized detection", "15. Life on land", "Wireless sensor networks (WSN)", "Error type I", "Source localization"]}, "links": [{"href": "https://link.springer.com/content/pdf/10.1007/s00521-020-05253-3.pdf"}, {"href": "https://doi.org/10.5281/zenodo.8089856"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Neural%20Computing%20and%20Applications", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8089856", "name": "item", "description": "10.5281/zenodo.8089856", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8089856"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-08-03T00:00:00Z"}}, {"id": "10.5281/zenodo.8300533", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:23:26Z", "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": "2026-05-23T16:23:26Z", "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": "2026-05-23T16:23:55Z", "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": "

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.

", "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": "11567/1235396", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:24:38Z", "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": "10459.1/60556", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:24:27Z", "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": "

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).

", "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": "11353/10.2156897", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:24:36Z", "type": "Journal Article", "created": "2025-02-05", "title": "Preferential use of organic acids over sugars by soil microbes in simulated root exudation", "description": "Sugars and organic acids, primary components in plant root exudates, are thought to enhance microbial decomposition of organic matter in the rhizosphere. However, their specific impacts on microbial activity and nutrient mobilisation remain poorly understood. Here, we simulated passive root exudation to investigate the distinct effects of sugars and organic acids on microbial metabolism in the rhizosphere. We released 13C-labelled sugars and/or organic acids via reverse microdialysis into intact meadow and forest soils over 6-h. We measured substrate-induced microbial respiration, soil organic matter mineralization, metabolite concentrations, and substrate incorporation into lipid-derived fatty acids. Our results reveal a pronounced microbial preference for organic acids over sugars, with organic acids being removed faster from the exudation spot and preferentially respired by microbes. Unlike sugars, organic acids increased concentrations of microbial metabolic byproducts and cations (K, Ca, Mg) near the exudation spot. Our results challenge the prevailing assumption that sugars are the most readily available and rapidly consumed substrates for soil microbes. Microbial preference for organic acids indicates a trade-off between rapid biomass growth and ATP yield. Our findings underscore the significant role of exudate composition in influencing microbial dynamics and nutrient availability, and emphasize the importance of biotic and abiotic feedback mechanisms in the rhizosphere in regulating root exudation.", "keywords": ["106022 Mikrobiologie", "Short-chain fatty acids", "Microbial metabolites", "Artificial root exudate", "Cation mobilization", "Growth yield trade-off", "106022 Microbiology", "Biogeochemical feedback", "Rhizosphere processes"]}, "links": [{"href": "https://doi.org/11353/10.2156897"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Biology%20and%20Biochemistry", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "11353/10.2156897", "name": "item", "description": "11353/10.2156897", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11353/10.2156897"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-04-01T00:00:00Z"}}, {"id": "11573/1722782", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:24:39Z", "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": "11585/1012565", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:24:09Z", "type": "Report", "title": "Dynamics of microbiome colonization on artificial support in a natural gradient of water pH at Panarea Island (Italy)", "description": "This study investigates the dynamics of microbiome colonization on artificial support in a natural gradient of water pH at Panarea Island, Italy. Our focus looks at the influence of the acidification on benthonic invertebrates in the Mediterranean Sea and the study of the microbiome dynamics and composition of the artificial supports.", "keywords": ["Artificial supports", " microbiome", " core", " pioneer microorganisms"], "contacts": [{"organization": "Leuzzi D., Foresto L., Scicchitano D., Palladino G., Mancuso A., Goffredo S., Rampelli S., Candela M.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/11585/1012565"}, {"rel": "self", "type": "application/geo+json", "title": "11585/1012565", "name": "item", "description": "11585/1012565", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11585/1012565"}, {"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": "14109037", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:24:44Z", "type": "Journal Article", "created": "2011-11-29", "title": "Reconstruction of the Eye Socket", "description": "Contracture or poor development of the eye socket is one of the most perplexing problems in ophthalmic plastic surgery. In general the best treatment for enlargement of the socket is expansion treatment by graduated conformers or expanders over a prolonged period. Considerable variation exists in the size, shape, consistency, and technique of applying pressure to encourage growth of the socket. Cutting surgery is never indicated, provided expansion of the socket is possible by a nonsurgical means. Surgery for this condition is difficult, unpredictable, and at times unreliable. Unfortunately, certain deformities of the socket are not amenable to expansion treatment and must be treated by surgery if an artificial eye is to be retained. The normal lining of the socket is composed of conjunctiva. This moist smooth surface is a most efficient type of covering. Other sources of mucous membrane are available if insufficient conjunctiva becomes a problem. Grafts of mucous", "keywords": ["Eye", " Artificial", "Humans", "Skin Transplantation", "Plastic Surgery Procedures", "Orbit"], "contacts": [{"organization": "B, SMITH", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/14109037"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Archives%20of%20Ophthalmology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "14109037", "name": "item", "description": "14109037", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/14109037"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "1964-04-01T00:00:00Z"}}, {"id": "1885/202815", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:24:51Z", "type": "Journal Article", "created": "2018-08-07", "title": "Quantization effects and convergence properties of rigid formation control systems with quantized distance measurements", "description": "Summary

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.

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).

", "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": "2966009560", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:25:30Z", "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": "

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.

", "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": "2997818980", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:25:32Z", "type": "Journal Article", "created": "2020-01-03", "title": "Designing Electric Field Responsive Ultrafiltration Membranes by Controlled Grafting of Poly (Ionic Liquid) Brush", "description": "

Electric responsive membranes have been prepared by controlled surface grafting of poly (ionic liquid) (PIL) on the commercially available regenerated cellulose ultrafiltration membrane. The incorporation of imidazolium ring on membrane surface was evidenced by FTIR (Fourier transformed infra-red) and EDX (energy-dispersive X-ray) spectroscopy. The PIL grafting resultedin a rougher surface, reduction in pore size, and enhancement in hydrophilicity. The interaction of the electric field between the charged PIL brush and the oscillating external electric field leads to micromixing, and hence it is proposed to break the concentration polarization. This micromixing improves the antifouling properties of the responsive membranes. The local perturbation was found to decrease the water flux, while it enhanced protein rejection. At a higher frequency (1kHz) of the applied electric field, the localized heating predominates compared to micromixing. In the case of a lower frequency of the applied electric field, more perturbation can lead to less permeability, whereas it will have a better effect in breaking the concentration polarization. However, during localized heating at a higher frequency, though perturbation is less, a heating induced reduction in permeability was observed. The electric field response of the membrane was found to be reversible in nature, and hence has no memory effect.

", "keywords": ["localized heating", "electric responsive membrane", "local perturbation", "Ionic Liquids", "Ultrafiltration", "Water", "Membranes", " Artificial", "Electrochemical Techniques", "02 engineering and technology", "poly (ionic liquid)", "01 natural sciences", "Article", "0104 chemical sciences", "Cellulose", "0210 nano-technology", "Hydrophobic and Hydrophilic Interactions"]}, "links": [{"href": "http://www.mdpi.com/1660-4601/17/1/271/pdf"}, {"href": "https://doi.org/2997818980"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/International%20Journal%20of%20Environmental%20Research%20and%20Public%20Health", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2997818980", "name": "item", "description": "2997818980", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2997818980"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-12-30T00:00:00Z"}}, {"id": "3044974791", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:25:39Z", "type": "Journal Article", "created": "2020-07-24", "title": "Smart Multi-Sensor Platform for Analytics and Social Decision Support in Agriculture", "description": "

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.

", "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": "3047587766", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:25:39Z", "type": "Journal Article", "created": "2020-08-03", "title": "Source localization in resource-constrained sensor networks based on deep learning", "description": "Source localization with a network of low-cost motes with limited processing, memory, and energy resources is considered in this paper. The state-of-the-art methods are mostly based on complicated signal processing approaches in which motes send their (processed) data to a fusion center (FC) wherein the source is localized. These methods are resource-demanding and mostly do not meet the limitations of motes and network. In this paper, we consider distributed detection where each mote performs a binary hypothesis test to detect locally the existence of a desired source and sends its (potentially erroneous) decision to FC during just one bit (1 indicates source existence and 0 otherwise). Hence, both processing and bandwidth constraints are met. We propose to use an artificial neural network (ANN) to correct erroneous local decisions. After error correction, the region affected by the source is specified by nodes with decision 1. Moreover, we propose to localize the source by deep learning in FC which converts the network of decisions 1 and 0 to a black and white image with white pixels in the locations of motes with decision 1. The proposed schemes of error correction by ANN (ECANN) and source localization with deep learning (SoLDeL) were evaluated in a fire detection application. We showed that SoLDeL performs appropriately and scales well into large networks. Moreover, the applicability of ECANN in delineation of farm management zones was illustrated.", "keywords": ["Artificial neural network (ANN)", "Internet of things (IoT)", "0202 electrical engineering", " electronic engineering", " information engineering", "Deep learning", "Target tracking", "Error type II", "02 engineering and technology", "Decentralized detection", "15. Life on land", "Wireless sensor networks (WSN)", "Error type I", "Source localization"]}, "links": [{"href": "https://link.springer.com/content/pdf/10.1007/s00521-020-05253-3.pdf"}, {"href": "https://doi.org/3047587766"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Neural%20Computing%20and%20Applications", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3047587766", "name": "item", "description": "3047587766", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3047587766"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-08-03T00:00:00Z"}}, {"id": "3095352776", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:25:41Z", "type": "Journal Article", "created": "2020-10-28", "title": "Incremental predictive clustering trees for online semi-supervised multi-target regression", "description": "Abstract

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.Bacterial attachment is crucial in many biotechnological applications, but many important bacterial strains cannot form biofilms. Biofilms can damage materials, and current strategies to manage biofilms are focused on inhibition and removal of biofilm. Biofilm formation is inevitable when materials are exposed to microbes and instead of biofilm prevention, we propose management of microbial composition by formation of biofilms with beneficial microbes. Since bacteria need to overcome a high repulsive force to attach to the surface and later to grow and multiply on it, electrostatic modification of the surfaces of cells or the material by polyelectrolytes (PE) was used in our approach, enabling efficient attachment of viable bacterial cells. Since highly positively charged PEs are known to be bactericidal, they were acetylated to reduce their toxicity, while preserving their net positive charge and ensuring cell viability. In our study bacterial strains were selected according to their intrinsic capability of biofilm formation, their shape variety and cell wall structure. These strains were tested to compare how the artificially prepared vs. natural biofilms can be used to populate the surface with beneficial bacteria. Using an artificial biofilm constructed of the potentially probiotic isolate Bacillus sp. strain 25.2. M, reduced the attachment and induced complete inhibition of E. coli growth over the biofilm. This study also revealed that the modification of the surfaces of cells or material by polyelectrolytes allows the deposition of bacterial cells, biofilm formation and attachment of biofilm non-forming cells onto surfaces. In this way, artificial biofilms with extended stability can be constructed, leading to selective pressure on further colonization of environmental bacteria.