Search the digital library catalog

Abstract: The integration of artificial intelligence (AI) into soil research presents significant opportunities to advance the understanding, management, and conservation of soil ecosystems. This paper reviews the diverse applications of AI in soil health assessment, predictive modeling of soil properties, and the development of pedotransfer functions within the context of agriculture, emphasizing AI’s advantages over traditional analytical methods. We identify soil organic matter decline, compaction, and biodiversity loss as the most frequently addressed forms of soil degradation. Strong trends include the creation of digital soil maps, particularly for soil organic carbon and chemical properties using remote sensing or easily measurable proxies, as well as the development of decision support systems for crop rotation planning and IoT-based monitoring of soil health and crop performance. While random forest models dominate, support vector machines and neural networks are also widely applied for soil parameter modeling. Our analysis of datasets reveals clear regional biases, with tropical, arid, mild continental, and polar tundra climates remaining underrepresented despite their agricultural relevance. We also highlight gaps in predictor–response combinations for soil property modeling, pointing to promising research avenues such as estimating heavy metal content from soil mineral nitrogen content, microbial biomass, or earthworm abundance. Finally, we provide practical guidelines on data preparation, feature extraction, and model selection. Overall, this study synthesizes recent advances, identifies methodological limitations, and outlines a roadmap for future research, underscoring AI’s transformative potential in soil science.
Keywords: artificial intelligence, machine learning, agriculture, soil health, soil parameter modeling, regional data bias
Published in DKUM: 17.10.2025; Views: 0; Downloads: 1
Full text (4,22 MB)

Abstract: This paper introduces a novel framework for augmenting explainability in the interpretation of point cloud data by fusing expert knowledge with counterfactual reasoning. Given the complexity and voluminous nature of point cloud datasets, derived predominantly from LiDAR and 3D scanning technologies, achieving interpretability remains a significant challenge, particularly in smart cities, smart agriculture, and smart forestry. This research posits that integrating expert knowledge with counterfactual explanations – speculative scenarios illustrating how altering input data points could lead to different outcomes – can significantly reduce the opacity of deep learning models processing point cloud data. The proposed optimization-driven framework utilizes expert-informed ad-hoc perturbation techniques to generate meaningful counterfactual scenarios when employing state-of-the-art deep learning architectures. The optimization process minimizes a multi-criteria objective comprising counterfactual metrics such as similarity, validity, and sparsity, which are specifically tailored for point cloud datasets. These metrics provide a quantitative lens for evaluating the interpretability of the counterfactuals. Furthermore, the proposed framework allows for the definition of explicit interpretable counterfactual perturbations at its core, thereby involving the audience of the model in the counterfactual generation pipeline and ultimately, improving their overall trust in the process. Results demonstrate a notable improvement in both the interpretability of the model’s decisions and the actionable insights delivered to end-users. Additionally, the study explores the role of counterfactual reasoning, coupled with expert input, in enhancing trustworthiness and enabling human-in-the-loop decision-making processes. By bridging the gap between complex data interpretations and user comprehension, this research advances the field of explainable AI, contributing to the development of transparent, accountable, and human-centered artificial intelligence systems.
Keywords: explainable AI, point cloud data, counterfactual reasoning, information fusion, interpretability, human-centered AI
Published in DKUM: 06.03.2025; Views: 0; Downloads: 5
Full text (186,97 KB)

Abstract: The stay-at-home restrictions to control the spread of COVID-19 led to unparalleled sudden change in daily life, but it is unclear how they affected urban crime globally. We collected data on daily counts of crime in 27 cities across 23 countries in the Americas, Europe, the Middle East and Asia. We conducted interrupted time series analyses to assess the impact of stay-at-home restrictions on different types of crime in each city. Our findings show that the stay-at-home policies were associated with a considerable drop in urban crime, but with substantial variation across cities and types of crime. Meta-regression results showed that more stringent restrictions over movement in public space were predictive of larger declines in crime.
Keywords: criminology, crime, pandemic, restrictions, stay-at-home, analysis
Published in DKUM: 03.10.2024; Views: 0; Downloads: 8
Full text (1,63 MB)
This document has many files! More...

Keywords: cellulose acetate, chitosan, solution blow spinning, protein adsorption, food packaging
Published in DKUM: 10.04.2024; Views: 251; Downloads: 19
Full text (4,04 MB)
This document has many files! More...

Abstract: This paper presents and discusses the results of the “2022 International Computational Fluid Dynamics Challenge on violent expiratory events” aimed at assessing the ability of different computational codes and turbulence models to reproduce the flow generated by a rapid prototypical exhalation and the dispersion of the aerosol cloud it produces. Given a common flow configuration, a total of 7 research teams from different countries have performed a total of 11 numerical simulations of the flow dispersion by solving the Unsteady Reynolds Averaged Navier–Stokes (URANS) or using the Large-Eddy Simulations (LES) or hybrid (URANS-LES) techniques. The results of each team have been compared with each other and assessed against a Direct Numerical Simulation (DNS) of the exact same flow. The DNS results are used as reference solution to determine the deviation of each modeling approach. The dispersion of both evaporative and non-evaporative particle clouds has been considered in 12 simulations using URANS and LES. Most of the models predict reasonably well the shape and the horizontal and vertical ranges of the buoyant thermal cloud generated by the warm exhalation into an initially quiescent colder ambient. However, the vertical turbulent mixing is generally underpredicted, especially by the URANS-based simulations, independently of the specific turbulence model used (and only to a lesser extent by LES). In comparison to DNS, both approaches are found to overpredict the horizontal range covered by the small particle cloud that tends to remain afloat within the thermal cloud well after the flow injection has ceased.
Keywords: numerical simulations, computational fluid dynamics
Published in DKUM: 28.03.2024; Views: 456; Downloads: 476
Full text (8,63 MB)
This document has many files! More...