Plant-parasitic nematodes pose a silent yet devastating threat to global agriculture, causing significant yield losses and economic damage. Traditional detection methods such as soil sampling, microscopy, and molecular diagnostics are slow, labor-intensive, and often ineffective in early-stage infestations. Nano biosensors: cuttingedge analytical tools that leverage nanomaterials like carbon nanotubes, graphene, and quantum dots to detect nematode-specific biochemical markers such as volatile organic compounds (VOCs) and oesophageal gland secretions, with unprecedented speed and accuracy. The real breakthrough lies in the fusion of artificial intelligence (AI) and nano-biosensor technology, forging a new frontier in precision agriculture. By integrating AI's powerful data analysis, pattern recognition, and predictive capabilities with the extraordinary sensitivity and specificity of nano-biosensors, it becomes possible to detect biomolecular changes in real-time, even at the earliest stages of disease progression. AI-driven nano biosensors can analyze real-time data, enhance detection precision, and provide actionable insights for farmers, enabling proactive and targeted pest management. This synergy revolutionizes nematode monitoring, paving the way for smarter, more sustainable agricultural practices. This review explores the transformative potential of AI-powered nano-biosensors in advancing precision agriculture. By integrating these technologies with smart farming systems, we move closer to real-time, costeffective, and field-deployable solutions, ushering in a new era of high-tech, eco-friendly crop protection.

The real-time monitoring of fracture propagation during hydraulic fracturing is crucial for obtaining a deeper understanding of fracture morphology and optimizing hydraulic fracture designs. Accurate measurements of key fracture parameters, such as the fracture height and width, are particularly important to ensure efficient oilfield development and precise fracture diagnosis. This study utilized the optical frequency domain reflectometer (OFDR) technique in physical simulation experiments to monitor fractures during indoor true triaxial hydraulic fracturing experiments. The results indicate that the distributed fiber optic strain monitoring technology can efficiently capture the initiation and expansion of fractures. In horizontal well monitoring, the fiber strain waterfall plot can be used to interpret the fracture width, initiation location, and expansion speed. The fiber response can be divided into three stages: strain contraction convergence, strain band formation, and postshutdown strain rate reversal. When the fracture does not contact the fiber, a dual peak strain phenomenon occurs in the fiber and gradually converges as the fracture approaches. During vertical well monitoring in adjacent wells, within the effective monitoring range of the fiber, the axial strain produced by the fiber can represent the fracture height with an accuracy of 95.6% relative to the actual fracture height. This study provides a new perspective on real-time fracture monitoring. The response patterns of fiber-induced strain due to fractures can help us better understand and assess the dynamic fracture behavior, offering significant value for the optimization of oilfield development and fracture diagnostic techniques. (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/).

Offshore wind turbines (OWTs) empoly various foundation types, among which Jacket-type offshore wind turbines (JOWTs) are often used in shallow waters with challenging soil conditions due to their lattice framework foundations and multiple anchoring points. However, prolonged exposure to harsh marine environments (e.g. storms) and age-related degradation issues like corrosion, fatigue cracking, and mechanical damage increases failure risks. To address these issues, this paper introduces a Digital Healthcare Engineering (DHE) framework, which provides a proactive strategy for enhancing the safety and sustainability of JOWTs: (1) Real-time health monitoring using IoT; (2) Data transmission via advanced communication technologies; (3) Analytics and simulations using digital twins; (4) AI-powered diagnostics and recommendations; as well as (5) Predictive analysis for maintenance planning. The paper reviews recent technological advances that support each DHE module, assesses the framework's feasibility. Additionally, a prototype DHE system is proposed to enable continuous, early fault detection, and health assessment.

Relevance. The increasing recreational load on the ecosystems of the Lake Teletskoe basin, its related obvious damage and the necessity to quantify the ongoing transformations. Aim. To assess the current state of a soil cover of the adjacent territory of Lake Teletskoe in terms of tourism and recreation impact. Methods. Comparative geographical and chemical as well as soil-ecological monitoring of studied indicators of early, short- and long-term diagnostics. Results and conclusions. Because of the tourist activities in the coastal zone of the mountain-forest belt of Lake Teletskoe, a developed path network transforming its natural ecosystems appeared. Some parameters of soil properties and composition on this path and in the sites not affected by recreation differed significantly. The analysis of water extract showed the decrease in acidity, the reduced content of ammonium and nitrate nitrogen, phosphates, a change in the concentration of calcium cations, potassium and magnesium in the top soil layer on the path, as compared to the undisturbed places. Soil trampling by recreants has damaged litter, decreased its thickness or completely destructed the promenade area. Reduction in litter reserves on the moderately developed paths exceeded by more than 2.7-4.0 times, whereas on the well-defined ones (as in the Altai State Biosphere Reserve with the established systemized movement across the territory), litter was absent at all on a few or even single paths. On weakly developed (fresh) paths, litter reserves turned out to be even higher than on the undisturbed areas or on the paths located next to a gravel site. Recreational loads were responsible for the 1.2-1.7 times increase in soil density of the upper (0-5 cm) layer, accompanied by a decrease in soil porosity and air supply. The hardness of soil was 1.3-1.5 times higher on the path than around it. The tourist effect on general physical properties of soil was traced to a depth of 20-30 cm, but maximum changes were noted in its upper (0-5 cm) layer. Recreational loads did not impact essentially on the aggregate soil composition. No significant changes, caused by tourist and recreational activities, were found in the elemental chemical composition of soils. The content of nutrients and lead were within the background and did not exceed the standardized values. The detected high concentrations of arsenic were not associated with the influence of tourist and recreational activities.

Armillaria is a soil-borne genus of basidiomycetes whose species can cause stem and root rot in woody plants. The effects of plant-pathogenic Armillaria species are well known in forests, but are underestimated in urban areas, where cases causing damage to trees and shrubs in green spaces have been steadily increasing in Switzerland since the 1980s. In this study, we present a simple, rapid, and cost-effective protocol for high-throughput diagnostics of the two primary pathogens A. mellea and A. ostoyae based on partial PCR amplification of the RPB2 gene. The specificity and sensitivity of the presented duplex PCR-I and single-plex PCR-II were evaluated using different methods: (i) testing both PCRs on tree pathogenic or soil-borne fungi of genera other than Armillaria, (ii) using dilution series of Armillaria-DNA to determine a minimum detection limit, and (iii) sequencing the selected RPB2 region to verify the primer sequences and positions. The utility of PCR-I and PCR-II as a high-throughput method was successfully tested on 65 DNA samples of Armillaria from Switzerland. Finally, an uninvolved person compared both classical methods, pairing test and sequencing, with PCR-I and PCR-II in a blind test. This study provides a reliable and alternative protocol for the rapid diagnosis of A. mellea and A. ostoyae causing root rot of woody plants.

Soil moisture is an important driver of growth in boreal Alaska, but estimating soil hydraulic parameters can be challenging in this data-sparse region. Parameter estimation is further complicated in regions with rapidly warming climate, where there is a need to minimize model error dependence on interannual climate variations. To better identify soil hydraulic parameters and quantify energy and water balance and soil moisture dynamics, we applied the physically based, one-dimensional ecohydrological Simultaneous Heat and Water (SHAW) model, loosely coupled with the Geophysical Institute of Permafrost Laboratory (GIPL) model, to an upland deciduous forest stand in interior Alaska over a 13-year period. Using a Generalized Likelihood Uncertainty Estimation parameterisation, SHAW reproduced interannual and vertical spatial variability of soil moisture during a five-year validation period quite well, with root mean squared error (RMSE) of volumetric water content at 0.5 m as low as 0.020 cm(3)/cm(3). Many parameter sets reproduced reasonable soil moisture dynamics, suggesting considerable equifinality. Model performance generally declined in the eight-year validation period, indicating some overfitting and demonstrating the importance of interannual variability in model evaluation. We compared the performance of parameter sets selected based on traditional performance measures such as the RMSE that minimize error in soil moisture simulation, with one that is designed to minimize the dependence of model error on interannual climate variability using a new diagnostic approach we call CSMP, which stands for Climate Sensitivity of Model Performance. Use of the CSMP approach moderately decreases traditional model performance but may be more suitable for climate change applications, for which it is important that model error is independent from climate variability. These findings illustrate (1) that the SHAW model, coupled with GIPL, can adequately simulate soil moisture dynamics in this boreal deciduous region, (2) the importance of interannual variability in model parameterisation, and (3) a novel objective function for parameter selection to improve applicability in non-stationary climates.