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.

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.

This case study aims to evaluate the impact of deep excavation on the adjacent short floating pile and lateral deformation control strategies using capsule expansion technology (CET). Two control strategies, i.e., real-time control (RTC) and one-time control (OTC), were applied to control the lateral displacement of piles. In this case, the wall lateral deflections (delta hm) range between delta hm=0.075%He and delta hm=0.11%He, which are relatively small and less than the specified protection levels. Although the wall deflection was controlled to a relatively small level through reasonable excavation and support schemes, the maximum horizontal displacement of the short floating pile reached 13.2 mm (0.054%He). Therefore, reasonable deformation control measures are necessary. After three stages of RTC treatment, the maximum lateral displacement of P2 was reduced by 49.2%, while P1 was decreased by 22.7% treated by OTC. Meanwhile, multiple RTCs can always control the pile deformation within the cracking limit, which avoids the dilemma of protecting the pile after it has been damaged. It confirms the feasibility and efficiency of CET in controlling pile deformation in real-time. In addition, RTC for pile lateral displacement mainly includes two aspects: (1) expansion directly induces lateral displacement of piles; and (2) expansion compensates for the soil stress loss in front of the pile to reduce the impact of the next excavation on the pile. Therefore, as external influence sources have long-term adverse effects on adjacent piles, RTC as an efficient control method should be given priority consideration for controlling pile lateral displacement.

In this study, a high-confining pressure and real-time large-displacement shearing-flow setup was developed. The test setup can be used to analyze the injection pressure conditions that increase the hydro-shearing permeability and injection-induced seismicity during hot dry rock geothermal extraction. For optimizing injection strategies and improving engineering safety, real-time permeability, deformation, and energy release characteristics of fractured granite samples driven by injected water pressure under different critical sliding conditions were evaluated. The results indicated that: (1) A low injection water pressure induced intermittent small-deformation stick-slip behavior in fractures, and a high injection pressure primarily caused continuous high-speed large-deformation sliding in fractures. The optimal injection water pressure range was defined for enhancing hydraulic shear permeability and preventing large injection-induced earthquakes. (2) Under the same experimental conditions, fracture sliding was deemed as the major factor that enhanced the hydraulic shear-permeability enhancement and the maximum permeability increased by 36.54 and 41.59 times, respectively, in above two slip modes. (3) Based on the real-time transient evolution of water pressure during fracture sliding, the variation coefficients of slip rate, permeability, and water pressure were fitted, and the results were different from those measured under quasi-static conditions. (4) The maximum and minimum shear strength criteria for injection-induced fracture sliding were also determined (m = 0.6665 and m = 0.1645, respectively, m is friction coefficient). Using the 3D (three-dimensional) fracture surface scanning technology, the weakening effect of injection pressure on fracture surface damage characteristics was determined, which provided evidence for the geological markers of fault sliding mode and sliding nature transitions under the fluid influence. (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/).

Deep engineering disasters, such as rockbursts and collapses, are more related to the shear slip of rock joints. A novel multifunctional device was developed to study the shear failure mechanism in rocks. Using this device, the complete shear-deformation process and long-term shear creep tests could be performed on rocks under constant normal stiffness (CNS) or constant normal loading (CNL) conditions in real-time at high temperature and true-triaxial stress. During the research and development process, five key technologies were successfully broken through: (1) the ability to perform true-triaxial compression-shear loading tests on rock samples with high stiffness; (2) a shear box with ultra-low friction throughout the entire stress space of the rock sample during loading; (3) a control system capable of maintaining high stress for a long time and responding rapidly to the brittle fracture of a rock sample as well; (4) a refined ability to measure the volumetric deformation of rock samples subjected to true triaxial shearing; and (5) a heating system capable of maintaining uniform heating of the rock sample over a long time. By developing these technologies, loading under high true triaxial stress conditions was realized. The apparatus has a maximum normal stiffness of 1000 GPa/m and a maximum operating temperature of 300 degrees C. The differences in the surface temperature of the sample are constant to within +/- 5 degrees C. Five types of true triaxial shear tests were conducted on homogeneous sandstone to verify that the apparatus has good performance and reliability. The results show that temperature, lateral stress, normal stress and time influence the shear deformation, failure mode and strength of the sandstone. The novel apparatus can be reliably used to conduct true-triaxial shear tests on rocks subjected to high temperatures and stress. (c) 2024 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting 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/).

Forest fires pose a catastrophic threat to Earth's ecology as well as threaten human beings. Timely and accurate monitoring of forest fires can significantly reduce potential casualties and property damage. Thus, to address the aforementioned problems, this paper proposed an unmanned aerial vehicle (UAV) based on a lightweight forest fire recognition model, Fire-Net, which has a multi-stage structure and incorporates cross-channel attention following the fifth stage. This is to enable the model's ability to perceive features at various scales, particularly small-scale fire sources in wild forest scenes. Through training and testing on a real-world dataset, various lightweight convolutional neural networks were evaluated on embedded devices. The experimental outcomes indicate that Fire-Net attained an accuracy of 98.18%, a precision of 99.14%, and a recall of 98.01%, surpassing the current leading methods. Furthermore, the model showcases an average inference time of 10 milliseconds per image and operates at 86 frames per second (FPS) on embedded devices.

This paper proposes a novel data-driven framework for scour detection around offshore wind turbines (OWTs), where damage features are derived from wind and wave-induced acceleration signals collected along the tower. A numerical model of the NREL 5 MW wind turbine, which considers aerodynamic and hydrodynamic loading with soil-structure interaction (SSI) and servodynamics, is developed. The model is used to simulate the acceleration responses along the tower for a healthy structure, and a structure affected by progressive scour. A data segmentation process is initially performed on the collected data, which is followed by a feature selection scheme based on the analysis-of-variance (ANOVA) algorithm, to eliminate irrelevant characteristics from the time domain feature set of responses. The proposed framework consists of two main components: (a) offline training, and (b) real-time classification. The acceleration responses collected from the healthy structure and the structure subjected to three different damage scenarios (different scour depths) and under various load conditions, are used in the offline training mode. The selected feature vector from the feature extraction process is used as input to a Naive Bayes classifier (NBC) algorithm to train the model. In the real-time classification, a prediction of the scour depth affecting the structure is performed using a new dataset simulated from unseen load cases and scour conditions of the OWT. The results show that the model trained in the offline stage can predict the scour depth in the real-time monitoring stage with performance measures over approximately 94%.

Development of digital twins is emerging rapidly in geotechnical engineering, and it often requires real-time updating of numerical models (e.g., finite element model) using multiple sources of monitoring data (e.g., settlement and pore water pressure data). Conventional model updating, or calibration, often involves repeated executions of the numerical model, using monitoring data from a specific source or at limited spatial locations only. This leads to a critical research need of real-time model updating and predictions using a numerical model improved continuously by multi-source monitoring data. To address this need, a physics-informed machine learning method called multi-source sparse dictionary learning (MS-SDL) is proposed in this study. Originated from signal decomposition and compression, MS-SDL utilizes results from a suite of numerical models as basis functions, or dictionary atoms, and employs multi-source monitoring data to select a limited number of important atoms for predicting multiple, spatiotemporally varying geotechnical responses. As monitoring data are collected sequentially, no repeated evaluations of computational numerical models are needed, and an automatic and real-time model calibration is achieved for continuously improving model predictions. A real project in Hong Kong is presented to illustrate the proposed approach. Effect of monitoring data from different sources is also investigated.

With the escalation of global warming, the shrinkage of mountain glaciers has accelerated globally, the water volume from glaciers has changed, and relative disasters have increased in intensity and frequency (for example, ice avalanches, surging glaciers, and glacial lake outburst floods). However, the wireless monitoring of glacial movements cannot currently achieve omnidirectional, high-precision, real-time results, since there are some technical bottlenecks. Based on wireless networks and sensor application technologies, this study designed a wireless monitoring system for measuring the internal parameters of mountain glaciers, such as temperature, pressure, humidity, and power voltage, and for wirelessly transmitting real-time measurement data. The system consists of two parts, with a glacier internal monitoring unit as one part and a glacier surface base station as the second part. The former wirelessly transmits the monitoring data to the latter, and the latter processes the received data and then uploads the data to a cloud data platform via 4G or satellite signals. The wireless system can avoid cable constraints and transmission failures due to breaking cables. The system can provide more accurate field-monitoring data for simulating glacier movements and further offers an early warning system for glacial disasters.