In complex physical systems, conventional differential equations fall short in capturing non-local and memory effects. Fractional differential equations (FDEs) effectively model long-range interactions with fewer parameters. However, deriving FDEs from physical principles remains a significant challenge. This study introduces a stepwise data-driven framework to discover explicit expressions of FDEs directly from data. The proposed framework combines deep neural networks for data reconstruction and automatic differentiation with Gauss-Jacobi quadrature for fractional derivative approximation, effectively handling singularities while achieving fast, high-precision computations across large temporal/spatial scales. To optimize both linear coefficients and the nonlinear fractional orders, we employ an alternating optimization approach that combines sparse regression with global optimization techniques. We validate the framework on various datasets, including synthetic anomalous diffusion data, experimental data on the creep behavior of frozen soils, and single-particle trajectories modeled by L & eacute;vy motion. Results demonstrate the framework's robustness in identifying FDE structures across diverse noise levels and its ability to capture integer-order dynamics, offering a flexible approach for modeling memory effects in complex systems.
Soil thermal conductivity (STC) plays a crucial role in regulating the energy distribution of both the surface and underground soil layers. It is widely applied in various fields, including engineering design, geothermal resource development and climate change research. A rapid and accurate estimation of STC remains a key focus in the study of soil thermodynamic parameters. However, the methods for estimating STC and their distinct characteristics have yet to be systematically reviewed. In this study, we used bibliometrics to comprehensively and systematically review the literature on STC, focusing on knowledge graph characteristics to analyze the development trend of calculation schemes. The main conclusions drawn from the study are as follows: (1) In recent years, most studies have been focused on soil thermal characteristics and their main contributing factors, the soil hydrothermal process in the Qinghai-Tibet Plateau, geothermal equipment and numerical simulations, and the exploration of geothermal resources. (2) A systematic review of various schemes indicates that no single scheme is universally applicable to all soil types. Moreover, a single parameterization scheme fails to meet the practical requirements of land surface process models. We evaluated the advantages and disadvantages of the traditional heat conduction schemes, parameterization schemes, and machine learning-based schemes and the findings suggest that a comprehensive scheme that integrates these three different schemes for STC simulations should be urgently developed.
The protection of medicinal plants has been effective as a key factor in preserving the environment of medicinal plants. As such, this paper aims to map the environment of medicinal plants based on biodiversity and indigenous knowledge, in which its role is constantly seen in environmental studies. The study method was based on field survey in the target areas: Morvarid, Heiderabad, Dehmoord, Fath al-Mubin in Darab city in Fars province. Based on the findings of the study, a total of 89 species belonging to 43 families in the target areas were identified, with the highest frequency belonging to the Mint family. According to the results of studies, Anghozeh, Baneh, Thyme Shirazi, Arjan, Kenar, Jashir (Prangos), Lemon balm, Myrtus, cumins, and Kakuti in need of protective measures. Combining indigenous plant ethnological knowledge with new technologies along with high genetic diversity will be the way to control damage and protect the effective genes of medicinal plants. Ultimately, the elimination of the inheritance of desirable plant genes will lead to the erosive growth and acceleration of the regression of plant cover, which is considered as a rich chain and preserver of soil sanctity and stability of nature in the environment.
The Green Revolution has significantly contributed to agricultural development. However, it has also caused environmental damage due to excessive use of agricultural inputs and land exploitation. To promote sustainability in line with the Sustainable Development Goals (SGDs), organic farming has been recommended to improve and maintain agroecosystems. In Bali Province, Indonesia, the concept of Tri Hita Karana (THK), a form of local wisdom, has guided farmers in implementing the System of Rice Intensification (SRI) technique introduced by the government. This study explored how organic rice farming integrates local wisdom, assessed farmers' knowledge and attitudes towards SRI, and examined government support initiatives for organic rice farming. The results revealed that SRI farming aligns with the principle of nurturing the soil to enhance fertility, improving soil, water, and air in paddy fields and surrounding areas. THK also guides organic farming practices in SRI, helping farmers maintain a harmonious relationship with God, nature, and fellow human beings. Farmers generally have a high level of knowledge about SRI and agree with its implementation. While the government supports organic rice farming through policies and farmer subsidies for production inputs, it is recommended that efforts be intensified through improved agricultural extension programs that implement more demonstration plots based on the concepts of learning by doing and seeing is believing, increased SRI-related training activities for farmers, and more extension workers to enhance outreach across Bali's dispersed agricultural regions.
Forecasting landslide deformation is challenging due to influence of various internal and external factors on the occurrence of systemic and localized heterogeneities. Despite the potential to improve landslide predictability, deep learning has yet to be sufficiently explored for complex deformation patterns associated with landslides and is inherently opaque. Herein, we developed a holistic landslide deformation forecasting method that considers spatiotemporal correlations of landslide deformation by integrating domain knowledge into interpretable deep learning. By spatially capturing the interconnections between multiple deformations from different observation points, our method contributes to the understanding and forecasting of landslide systematic behavior. By integrating specific domain knowledge relevant to each observation point and merging internal properties with external variables, the local heterogeneity is considered in our method, identifying deformation temporal patterns in different landslide zones. Case studies involving reservoir-induced landslides and creeping landslides demonstrated that our approach (1) enhances the accuracy of landslide deformation forecasting, (2) identifies significant contributing factors and their influence on spatiotemporal deformation characteristics, and (3) demonstrates how identifying these factors and patterns facilitates landslide forecasting. Our research offers a promising and pragmatic pathway toward a deeper understanding and forecasting of complex landslide behaviors. (c) 2025 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/ 4.0/).
Ozone depletion, global warming, soil degradation, etc., could be, to a great extent, instrumental in making our Earth an unsafe place. Therefore, to prevent further damage, Article 6 of the United Nations Framework Convention on Climate Change (UNFCCC) emphasizes spreading awareness among the members of the planetary community to protect the planet. The study aims to identify teaching pedagogies that can effectively develop awareness and responsibility among university youth for a sustainable future. The study adopts an exploratory triangulation approach and uses three instruments: a closed-ended questionnaire, a focus group interview, and a comparative performance of control and experimental groups. Fifty-one faculties from two government universities of Saudi Arabia: Qassim University, Qassim, and Prince Sattam bin Abdulaziz University, Alkharj along with 47 students pursuing conversation courses at Level Three in Prince Sattam University participated in the study. JASP 0.9 open-source software was used for statistical analysis. The results revealed that constructivist inquiry-based approaches promoted sustainable development education.
The advancement of Geographic Information System (GIS) technology through 3D modeling has significantly improved disaster risk analysis, particularly for landslides. This study utilized Unmanned Aerial Vehicles (UAVs) and Agisoft Metashape software to produce accurate 3D models, which were used to identify the location, volume, displacement, and distribution of landslide impacts in Tawangmangu Sub-district, Karanganyar Regency. This area is characterized by hilly topography with slopes > 45% and frequent land-use changes that exacerbate landslide risks. The 3D modeling process involved several key steps: aerial image acquisition using UAVs at an altitude of 126 meters, photo processing with Agisoft Metashape to generate orthomosaic maps, Digital Elevation Models (DEM), and geospatial analysis. Camera calibration was performed to enhance accuracy, while risk analyze using overlay and scoring methods were applied to hazard, vulnerability, and community results revealed that most of Tawangmangu Sub-district falls into the medium-risk category for landslides, covering an area of 4023.45 hectares, with the highest risk levels identified in Sepanjang and Tawangmangu villages. The 3D models indicated translational landslides, with soil displacement volumes ranging from -5409.3 m(3) to -991, 808 m(3), causing infrastructure damage and road closures. Mitigation efforts integrated UAV technology for realtime monitoring and indigenous knowledge in the form of coping strategies passed down through generations. UAV data was also utilized for disaster simulation, community training, and evidence-based mitigation planning, such as designing retaining walls and evacuation routes. This study highlights the importance of combining UAV technology and indigenous knowledge to enhance community capacity for sustainable and independent disaster risk reduction in landslide-prone areas.
Restoration involves the recovery and repair of environments because environmental damage is not always irreversible, and communities are not infinitely resilient to such harm. When restoration projects are applied to nature, either directly or indirectly these may take the form of ecological, forestry or hydrological restoration, for example. In the current scenario of global climate change and increasing intensity of disturbances the importance of restoration in all types of ecosystems in order to adapt to the new conditions (so called prestoration) is evident. Whatever the objective of the restoration initiative, there is a lack of consensus as regards common indicators to evaluate the success or failure of the different initiatives implemented. In this study, we have carried out an extensive meta-analysis review of scientific papers aiming to evaluate the outcomes of restoration projects. We have done a review and selected 95 studies implemented in Europe. We explored the main pre-restoration land cover in which restoration initiatives have been implemented, the main causes of degradation, the objective of the restoration action and the indicators selected to analyze the success or failure of the action. We identified a total of 84 indicators in the analyzed papers and compared with the ones proposed for forest in the recent Nature Restoration Law. The analysis revealed five indicators commonly used for the evaluation of restoration initiatives (abundance, coverage, density, Ellenberg indicator, and richness), even where the initial objective has not yet been achieved. Our findings underscore both the benefits and challenges associated with a specific set of harmonized indicators for evaluating the success or failure of restoration initiatives.
Damage caused by pests and diseases is one of constraints on crop production for food security. Based on the use of questionnaire and interviews that were conducted in Kabare territory (SouthKivu), this study was carried out to (i) assess farmers practices, attitudes, and knowledge about pesticides use, and (ii) assess the human health and physical environment effects using pesticides. Data was collected from 300 small-scale farmers in study area. Results showed that majority of our respondents were men (59 %) rather than women (41 %) and local knowledge of pesticide use was low (60 %). Education level had a significant influence (p < 0.01) on level of knowledge about pesticide use, time and dose of treatment, method of control, and persistence time. In addition, education level influence significantly farmers' attitudes before and after pesticide treatment (p < 0.05). Pest management control, time of pesticide application, and packaging management method varied significantly with level of local knowledge (p < 0.01). Pesticides use by small-scale farmers has an effect on water, soil, and air quality. It also causes human pathologies such as vomiting, eye irritation, and even loss of life in event of heavy exposure. Inhalation and dermal exposure are main and most dangerous routes of pesticide exposure in our study area, which lacks protective strategies. Finally, use of pesticides disrupts biodiversity through the disappearance of pollinators, predators, parasitoids, and soil microorganisms. Therefore, broad continuity of this study with integration of other scientific aspects would effectively contribute to the improvement of environmental quality.
Using interviews and surveys of 212 households in villages situated at different elevations in the Everest National Nature Preserve (ENNP), correlations and comparative analyses were employed to reveal the residents' perceptions and understanding of climate change and its effects on the ENNP. Results showed that: (1) nearly all residents thought that climate warming and ice-snow landscape decrease were very significant, but there was an obvious difference between the residents' cognition and observations to the change of runoff; (2) higher altitude is, more obvious warming is, and stronger residents' perception of climate change and its impacts is in the ENNP, for which educational level and age were the main factors affecting their degree of perception; (3) especially, higher altitude is, more frequent the tourism participation of residents is and higher their income is; and (4) because the centralized pollutant treatment facilities have a low efficiency, and because the area receives a large number of tourists whose activities are spatially scattered, the potential risk of environmental pollution has been increasing in recent years. At present there is an urgent need for policy suggestions at the strategic level of national ecological security and interregional equity principles concerning the adaptation to climate and environmental changes in the ENNP.