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In corn pest and disease prevention, traditional blanket pesticide spraying has led to significant pesticide waste and environmental pollution. To address this challenge, research into precision agricultural equipment based on computer vision has become a hotspot. In this study, an integrated system named the FGA-Corn system is investigated for precision pesticide application, which consists of three important parts. The first part is the Front Camera Rear Funnel (FCRF) mechanical structure for efficient pesticide application. The second part is the Agri Spray Decision System (ASDS) algorithm, which is developed for post-processing the YOLO detection results, driving the funnel motor to enable precise pesticide delivery and facilitate real-time targeted application in specific crop areas. The third part is the GMA-YOLOv8 detection algorithm for center leaf areas. Building on the YOLOv8n framework, a more efficient GHG2S backbone generated by HGNetV2 enhanced with GhostConv and SimAM is proposed for feature extraction. The CM module integrated with Mixed Local Channel Attention is used for multi-scale feature fusion. An Auxiliary Head utilizing deep supervision is employed for improved assistive training. Experimental results on both the D1 and D2 datasets demonstrate the effectiveness and generalization ability, with mAP@0.5 scores of 94.5% (+1.6%) and 90.1% (+1.8%), respectively. The system achieves a 23.3% reduction in model size and a computational complexity of 6.8 GFLOPs. Field experiments verify the effectiveness of the system, showing a detection accuracy of 91.3 ± 1.9% for center leaves, a pesticide delivery rate of 84.1 ± 3.3%, and a delivery precision of 92.2 ± 2.9%. This research not only achieves an efficient and accurate corn precision spraying program but also offers new insights and technological advances for intelligent agricultural machinery.

Although supercritical CO2 (SC-CO2) fracturing has shown promise in oil and gas development with demonstrated potential, its application in shale gas extraction remains in its infancy globally. In this study, fracturing experiments were conducted with water, liquid CO2 (L-CO2), and SC-CO2, as well as SC-CO2 at varying pump rates. The results reveal that SC-CO2 fracturing produces a highly complex fracture network characterized by fractures of varying numbers, deflection angles, and tortuosity. Analysis of CO2 temperature and pressure data showed a sharp drop in injection pressure and temperature at breakdown, followed by fluctuations until injection stopped. Acoustic emission (AE) monitoring demonstrated that energy released during main fracture initiation significantly exceeded that from CO2 phase transition-driven fracture extension, underscoring the dominant role of main fractures in energy dissipation. Compared to hydraulic fracturing, SC-CO2 fracturing created a seepage area 2.2 times larger while reducing the breakdown pressure by 37.2%, indicating superior stimulation performance. These findings emphasize the potential of SC-CO2 to form intricate fracture networks, offering a promising approach for efficient shale gas extraction.

Petroleum has become a strategic resource to the national economy, and forecasting its demand is a critical step to supporting energy planning and policy-making for carbon reduction. We first conducted a characteristic analysis of end consumption for petroleum products, and the key affecting factors are identified through an extended logarithmic mean Divisia index (LMDI) method. Afterwards, the long-range energy alternatives planning system (LEAP) was adopted to predict the petroleum products demand by considering the potential impacts of different policies on the identified key factors. Through comparative analysis of three scenarios including five sub-scenarios, the findings show that the dual carbon constraints are crucial to petroleum demand control. Under the enforcement of existing carbon peaking policies, the petroleum products demand will peak around 2043 at 731.5 million tons, and the impact of energy intensity-related policies is more significant than that of activity level. However, even if the existing policy efforts are continued, supporting the carbon-neutral target will not be easy. By further strengthening the constraints, the demand will peak around 2027 at 680 million tons, and the abatement contribution will come mainly from industry (manufacturing), construction, and transportation. Additional abatement technologies are necessary for the petroleum industry to achieve carbon neutrality.

Natural fractures (NFs) have been recognized as the dominant factors that increase hydraulic fracture complexity and reservoir productivity. However, the interactions between hydraulic and natural fractures are far from being fully understood. In this study, a two-dimensional numerical model based on the displacement discontinuity method (DDM) has been developed and used to investigate the interaction between hydraulic and pre-existing natural fractures. The inelastic deformation, e.g., stick, slip and separation, of the geologic discontinuities is captured by a special friction joint element called Mohr-Coulomb joint element. The dynamic stress transfer mechanisms between the two fracture systems and the possible location of secondary tensile fracture that reinitiates along the opposite sides of the NF are discussed. Furthermore, the model results are validated by a series of large tri-axial hydraulic fracture (HF) tests. Both experimental and numerical results showed that the displacements and stresses along the NFs are all in highly dynamic changes. When the HF is approaching the NF, the HF tip can exert remote compressional and shear stresses on the NF interface, which results in the debonding of the NF. The location and value of the evoked stress is a function of the far-field horizontal differential stress, inclination angle of the NF, and the net pressure used in fracturing. For a small approaching angle, the stress peak is located farther away from the intersection point, so an offset fracture is more likely to be generated. The cemented strength of the NF also has an important influence on the interaction mechanism. Weakly bonded NF surfaces increase the occurrence of a shear slippage, but for a moderate strength NF, the hybrid failure model with both tensile and shear failures, and conversion may appear.

Hydrologic exchange between river channels and adjacent subsurface environments is a key process that influences water quality and ecosystem function in river corridors. Predictive numerical models are needed to understand responses of river corridors to environmental change and to support sustainable watershed management. We posit that systematic hydromorphic classification provides a scaling construct that facilitates extrapolation of outputs from local-scale mechanistic models to reduced-order models applicable at reach and watershed scales. This in turn offers the potential to improve large-scale predictions of river corridor hydrobiogeochemical processes. Here we present a new machine-guided hydromorphic classification methodology that addresses the key requirements of this objective, and we demonstrate its application to a segment of the Columbia River in the northwestern United States. The resulting hydromorphic classes form spatially coherent and physically interpretable hydromorphic units that exhibit distinct behaviors in terms of distributions of subsurface transit times (a primary control on critical biogeochemical reactions). This approach forms the basis of ongoing research that is evaluating the formulation of reduced-order models and transferability of results to other river reaches and larger scales.

Temperature-based methods have been developed to infer 1D vertical exchange flux between a stream and the subsurface. Current analyses rely on fitting physically based analytical and numerical models to temperature time series measured at multiple depths to infer daily average flux. These methods have seen wide use in hydrologic science despite strong simplifying assumptions including a lack of consideration of model structural error or the impacts of multidimensional flow or the impacts of transient streambed hydraulic properties. We performed a “perfect-model experiment” investigation to examine whether regression trees, with and without gradient boosting, can extract sufficient information from model-generated subsurface temperature time series, with and without added measurement error, to infer the corresponding exchange flux time series at the streambed surface. Using model-generated, synthetic data allowed us to assess the basic limitations to the use of machine learning; further examination of real data is only warranted if the method can be shown to perform well under these ideal conditions. We also examined whether the inherent feature importance analyses of tree-based machine learning methods can be used to optimize monitoring networks for exchange flux inference.

Hydrologic exchange is a crucial component of the water cycle. The strength of the exchange directly affects the biogeochemical and ecological processes that occur in the hyporheic zone and aquifer from micro to reach scales. Hydrologic exchange fluxes (HEFs) can be quantified using many field measurement approaches, however, in a relatively large river (scale > 103 m), these approaches are limited by site accessibility, the difficulty of performing representative sampling, and the complexity of geomorphologic features and subsurface properties. In rivers regulated by hydroelectric dams, quantifying HEF rates becomes more challenging because of frequent hydropeaking events, featuring hourly to daily variations in flow and river stages created by dam operations. In this study, we developed and validated a new approach based on field measurements to estimate shallow water HEF rates across the river bed along the shoreline of the Columbia River, USA. Vertical thermal profiles measured by self-recording thermistors were combined with time series of hydraulic gradients derived from river stages and inland water levels to estimate the HEF rates. The results suggest that the HEF rates had high spatial and temporal heterogeneities over the riverbed, with predicted flux rates varied from +1 × 10−6 m s−1 to −1.5 × 10−6 m s−1 under different flow conditions.

Addressing groundwater contamination, this study applies machine learning (ML) algorithms to explore the spatiotemporal dynamics of hexavalent chromium (Cr[VI]) at the Hanford 100-Area. The research uses an extensive long-term monitoring dataset focused on groundwater wells and aquifers to enhance the understanding and management strategies of this complex environmental issue and predict the impact on aquifers due to the contamination in groundwater wells. The challenging nature of the task is due to various factors, such as the geological nature of the soil, pipeline leaks, and mobility of the particles that impact the speed of contamination. The findings demonstrate a random forest (ML)-based approach to predict the contaminant distributions accurately, thus significantly reducing uncertainties in contamination assessments and refining conceptual site models. This approach advances groundwater quality management and sets a precedent for future AI-driven environmental studies.

Although supercritical CO[sub.2] (SC-CO[sub.2]) fracturing has shown promise in oil and gas development with demonstrated potential, its application in shale gas extraction remains in its infancy globally. In this study, fracturing experiments were conducted with water, liquid CO[sub.2] (L-CO[sub.2]), and SC-CO[sub.2], as well as SC-CO[sub.2] at varying pump rates. The results reveal that SC-CO[sub.2] fracturing produces a highly complex fracture network characterized by fractures of varying numbers, deflection angles, and tortuosity. Analysis of CO[sub.2] temperature and pressure data showed a sharp drop in injection pressure and temperature at breakdown, followed by fluctuations until injection stopped. Acoustic emission (AE) monitoring demonstrated that energy released during main fracture initiation significantly exceeded that from CO[sub.2] phase transition-driven fracture extension, underscoring the dominant role of main fractures in energy dissipation. Compared to hydraulic fracturing, SC-CO[sub.2] fracturing created a seepage area 2.2 times larger while reducing the breakdown pressure by 37.2%, indicating superior stimulation performance. These findings emphasize the potential of SC-CO[sub.2] to form intricate fracture networks, offering a promising approach for efficient shale gas extraction.

Abstract This study investigates the evolution of elastic responses of fluvial unconsolidated sandstones during the water flooding process. Experimental study were conducted on rock samples obtained from the Bohai Bay basin, focusing on the coupled effects of varying fluid saturation, pore pressure, and confining pressure conditions on the elastic responses. The results show that water flooding process can lead to a decrease in P-wave velocity (3%–4%) and S-wave velocity (2%–3%), primarily driven by the weakening of the rock framework. The effects of fluid substitution were also observed, with brine gradually replacing oil. However, this mechanism has a limited impact on elastic wave velocity due to the similarity in density and elastic modulus between brine and oil. Furthermore, variations in pore pressure (10–20 MPa) significantly influence both the Vp/Vs ratio (increasing by ∼3%–8% per 10 MPa increment) and P-impedance (decreasing by ∼3%–4% per 10 MPa increment), offering experimental insights for quantitative time-lapse seismic monitoring of underground pore pressure variations. Analysis of the flushed-out solid particles indicates that the removal of framework particles significantly decreases the elastic modulus, which in turn is the dominant factor influencing elastic wave velocity. These findings contribute to a deeper understanding of the physical mechanisms governing elastic wave responses during water flooding enhanced oil recovery processes, offering practical guidance for seismic interpretation in reservoir development processes.