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The intensive use of chemical fertilizers in China to maintain high crop yields has led to significant environmental degradation and destabilized crop production. Returning straw to soil presents a potential alternative to reduce chemical fertilizer requirements and enhance soil fertility. This study investigates the effects of different nitrogen (N) input levels and straw additions on crop phosphorus (P) uptake and soil P availability based on a long-term N-fertilizer trial. The treatments included no fertilizer input (CK), conventional (NPK), reduced NPK (0.75NPK), and straw-amended (SNPK) treatments. Results indicate that SNPK significantly enhances shoot P uptake and crop yields by 43.7–61.9% and 29.3–39.6%, respectively. The SNPK treatment improved rhizosphere P availability and increased the phosphorus activation coefficient (PAC) by 1.72-fold compared to NPK alone. The enhanced soil P availability under SNPK was primarily attributed to an abundance of functional microbes, leading to higher P storage in the microbial biomass P pool and its turnover. Additionally, SNPK promoted root exudate and phosphate-mobilizing microbes, enhancing P mobilization and uptake. Nitrogen fertilization primarily influenced root functional traits related to P acquisition. These findings provide valuable insights for developing effective fertilizer management strategies in maize–oilseed rape rotation systems, emphasizing the benefits of integrating straw with chemical fertilizers. [ABSTRACT FROM AUTHOR]

Imitation learning has shown promise for autonomous driving, but suffers from covariate shift, where the policy performs poorly in unseen environments. DAgger is a popular approach that addresses this by leveraging expert demonstrations. However, DAgger's frequent visits to sub-optimal states can lead to several challenges. This paper proposes a novel DAgger framework that integrates Bayesian uncertainty estimation via mean field variational inference (MFVI) to address this issue. MFVI provides better-calibrated uncertainty estimates compared to prior methods. During training, the framework identifies both uncertain and critical states, querying the expert only for these states. This targeted data collection reduces the burden on the expert and improves data efficiency. Evaluations on the CARLA simulator demonstrate that our approach outperforms existing methods, highlighting the effectiveness of Bayesian uncertainty estimation and targeted data aggregation for imitation learning in autonomous driving. [ABSTRACT FROM AUTHOR]

Autonomous driving has the potential to revolutionize transportation, but developing safe and reliable systems remains a significant challenge. Reinforcement learning (RL) has emerged as a promising approach for learning optimal control policies in complex driving environments. However, existing RL-based methods often suffer from low sample efficiency and lack explicit safety constraints, leading to unsafe behaviors. In this paper, we propose a novel framework for safe reinforcement learning in autonomous driving that addresses these limitations. Our approach incorporates a latent dynamic model that learns the underlying dynamics of the environment from bird's-eye view images, enabling efficient learning and reducing the risk of safety violations by generating synthetic data. Furthermore, we introduce state-wise safety constraints through a barrier function, ensuring safety at each state by encoding constraints directly into the learning process. Experimental results in the CARLA simulator demonstrate that our framework significantly outperforms baseline methods in terms of both driving performance and safety. Our work advances the development of safe and efficient autonomous driving systems by leveraging the power of reinforcement learning with explicit safety considerations. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) miscible fracturing huff-and-puff technology now plays a pivotal role in enhancing crude oil recovery rates, particularly in reservoirs with challenging physical properties, strong water sensitivity, high injection pressure, and complex water-injection dynamics. In this study, the oil-increasing mechanism and huff-and-puff effect of CO2 miscible fracturing fluid are investigated through a comprehensive experimental approach. Specifically, experiments on PVT gas injection expansion, minimum miscible pressure, and CO2 miscible fracturing fluid huff and puff are conducted on the G fault block reservoir of the J Oilfield. The experimental findings demonstrate that injecting CO2 into reservoirs leads to an expansion in oil volume, a reduction in viscosity, and an increase in saturation pressure. Crude oil extraction is further enhanced by the addition of solubilizers and viscosity reducers. The use of solubilizers not only increases oil recovery rates but also reduces the minimum miscible pressure required for effective CO2 dispersion. We also found that shut-in times, permeability, and the huff-and-puff method used all have considerable impacts on huff-and-puff recovery rates. This study offers valuable technical insights, supporting the application of CO2 miscible fracturing huff-and-puff technology to enhance oil recovery rates in low-permeability reservoirs. [ABSTRACT FROM AUTHOR]

CO2 flooding technology is an effective technical means for tertiary oil recovery in high water cut reservoirs. Using an existing well pattern to inject CO2 is an economical and feasible development method under low contemporary oil prices. Although the particle migration caused during the displacement process will block the pore throat of the rock, the injection of CO2 can effectively reduce the CO2 injection pressure, and the dissolution that occurs during the injection process improves the seepage capacity of the fluid as a whole. In this study, assessments of particle migration and plugging after CO2 flooding and evaluations of rock dissolution by CO2-aqueous solution were carried out, and the variation characteristics of the relative permeability curve before and after displacement were evaluated, further explaining the influence of CO2 flooding on reservoir seepage capacity. The results show that, in the process of CO2 injection, the injection pressure decreases under different injection rates. After CO2 flooding under oil conditions, the core permeability loss was 43.8%, and after cleaning with toluene and anhydrous ethanol, the core permeability recovery was 24.3%, indicating that the particles were bound by crude oil during the migration process and accumulated into clusters, resulting in blockages. Although there was a certain degree of blockage, the CO2-aqueous solution mainly reacted with chlorite and released Ca2+, Mg2+, and Fe2+ ions. The concentration of Ca2+ increased by 157.43%, the concentration of Mg2+ decreased by 43.33%, and the concentration of Fe2+ decreased by 50.47%, indicating that, although the generated MgCO3 and Fe2O3 blocked the pore throat of the rock, the overall dissolution effect of CO2 on CaCO3 was stronger, and the overall seepage capacity of the fluid was improved. To a certain extent, this technique can improve the water injection capacity and the effect of subsequent water flooding. [ABSTRACT FROM AUTHOR]

After the oil field enters the high water content period, the oil–water mixed fluid in the mixing system will gradually change into the water-in-oil mixed fluid, while the dissolved CO2 causes the pH value of the mixed fluid to decrease. There is also a certain amount of bacteria in the output fluid, with many factors leading to the intensification in the corrosion of the oil–water mixed system pipeline in the high water content period. To clarify its corrosion law, through the mixed transmission pipeline material, 20# carbon steel, in high water conditions under the action of different single factor dynamic corrosion rate experiments, along with the use of the SPSS method, were used to determine the corrosion of the main control factors. The results show that in the high water content period, the corrosion rate of the mixed pipeline 20# steel gradually increases with the increase in temperature pressure, CO2 partial pressure, SRB content, Ca2+ + Mg2+ content, and Cl− content. The corrosion rate with the CO2 partial pressure and SRB content changes show a strong multiplicative power relationship; with Ca2+ + Mg2+ content, Cl− content changes show a logarithmic relationship, the relationship degree R2 is above 0.98. Through SPSS data analysis software combined with experimental data for correlation degree analysis, it is concluded that the correlation magnitude relationship between each factor and corrosion rate is CO2 partial pressure > SRB content > Cl− content > Ca2+ + Mg2+ content > temperature pressure, which provides a theoretical basis for the corrosion protection of an oil gathering pipeline. [ABSTRACT FROM AUTHOR]

Cadmium is one of the most toxic heavy metals and can be easily absorbed by plants, affecting root growth. Root border cells (RBCs), that are located in the periphery of the root cap and originate from the root cap meristem, represent a convenient tool to study the toxic effects of Cd on root performance. In this work, vegetables with contrasting types of root apical meristem (RAM) organizations were used. The open RAM organizations included pea and cucumber, and the closed RAM organizations included tomato, chili, and eggplant. The number of RBCs were significantly higher in the species possessing open RAM organization: pea (11,330 cells per root) > cucumber (8200) > tomato (2480) > eggplant (1830) > chili (1320). The same trend was observed for cell viability: pea (61%) > cucumber (59%) > tomato (49%) > eggplant (44%) > chili (42%). Pea and cucumber had higher relative radicle elongation rates and a lower increase in stress-induced accumulation of malondialdehyde (MDA), making them more resistant to Cd stress than the vegetables with close RAM organization. Under Cd treatment, the number and viability of RBCs in vegetables with both types of RAM organization were significantly decreased. However, the decreasing ratio of the number and viability of RBCs in pea and cucumber was higher than in tomato, chili, and eggplant. Taken together, the plants with the open-type RAM are more tolerant to Cd, and it can be speculated that the cadmium tolerance of the vegetables may be correlated with the number and viability of RBCs in response to cadmium stress. [ABSTRACT FROM AUTHOR]

Pseudo-response regulators (PRRs) are the important genes for flowering in roses. In this work, clock PRRs were genome-wide identified using Arabidopsis protein sequences as queries, and their evolutionary analyses were deliberated intensively in Rosaceae in correspondence with angiosperms species. To draw a comparative network and flow of clock PRRs in roses, a co-expression network of flowering pathway genes was drawn using a string database, and their functional analysis was studied by silencing using VIGS and protein-to-protein interaction. We revealed that the clock PRRs were significantly expanded in Rosaceae and were divided into three major clades, i.e., PRR5/9 (clade 1), PRR3/7 (clade 2), and TOC1/PRR1 (clade 3), based on their phylogeny. Within the clades, five clock PRRs were identified in Rosa chinensis. Clock PRRs had conserved RR domain and shared similar features, suggesting the duplication occurred during evolution. Divergence analysis indicated the role of duplication events in the expansion of clock PRRs. The diverse cis elements and interaction of clock PRRs with miRNAs suggested their role in plant development. Co-expression network analysis showed that the clock PRRs from Rosa chinensis had a strong association with flowering controlling genes. Further silencing of RcPRR1b and RcPRR5 in Rosa chinensis using VIGS led to earlier flowering, confirming them as negative flowering regulators. The protein-to-protein interactions between RcPRR1a/RcPRR5 and RcCO suggested that RcPRR1a/RcPRR5 may suppress flowering by interfering with the binding of RcCO to the promoter of RcFT. Collectively, these results provided an understanding of the evolutionary profiles as well as the functional role of clock PRRs in controlling flowering in roses. [ABSTRACT FROM AUTHOR]

Biochar is an effective method for increasing soil carbon (C) sequestration and nitrogen (N) supply under continuous monocropping. To investigate the impact of biochar placement methods on soil C and N, a one-year field experiment with five treatments was conducted including control, mineral fertilizers only (F), biochar hole placement (BFH; biochar applied to the soil layer at 5–10 cm) + F, biochar band placement (BFB; biochar applied to the soil layer at 15–20 cm) + F, and biochar band and hole placement + F (BFBH). The results showed that, regardless of the placement method, biochar application increased soil total organic C (TOC) and C pool management index by 6.9–39.7% and 4.1–36.1%, respectively, especially for dissolved organic C (DOC; 6.9–51.3%), readily oxidizable C (ROC; 2.4–46.4%), and microbial biomass C (MBC; 10.4–41.7%). Single biochar placement methods significantly influenced DOC, MBC, and ROC contents of both soil layers in the rank order of BFH ≈ BFBH > BFB at 0–15 cm and BFB ≈ BFBH > BFH at 15–30 cm. Soil TN and microbial biomass N (MBN) mainly accumulated at the site of biochar placement. The increased soil TOC:TN and MBC:N ratios under biochar treatments promoted inorganic N immobilization and reduced the loss of ammonium N and nitrate N (NO3−-N) through leaching at the early stage of tobacco growth. Biochar-adsorbed N was remobilized at a later period (vigorous growth stage and maturity), possibly causing the slow decrease in NO3−-N content. Additionally, soil C and N pools were significantly influenced by the main effects of soil layer and growth stage. Overall, biochar application increased soil C and N pools and inorganic N supply through N remobilization. However, the increased labile organic C content and microbial activity may prevent C sequestration in biochar-amended soils. [ABSTRACT FROM AUTHOR]

The simulation of landscape pattern optimization allocation (LPOA) to achieve ecological security is an important issue when constructing regional ecological security patterns. In this study, an LPOA model was developed by integrating a binary logistic regression model and a nonlinear programming model with a particle swarm optimization algorithm in order to consider the complexity of landscape pattern optimization in terms of the quantitative structure and spatial layout optimization, integrating the landscape suitability and factors that influence landscape patterns, and under constraints to maximize the economic, ecological, and comprehensive benefits of landscape patterns. The model was employed to simulate the LPOA in the Longquanyi District of Chengdu City, Sichuan Province, China. The model successfully obtained an appropriate combination of the landscape quantitative structure and spatial layout, as well as effectively integrating the landscape suitability and factors that influence the landscape pattern. Thus, the model addressed the problems of previous studies, such as neglecting the coupling between quantitative structure optimization and spatial layout optimization, ignoring the macrofactors that affect landscape patterns during optimization modeling, and initializing particles without considering the suitability of the landscape. Furthermore, we assessed and analyzed the accuracy and feasibility of the landscape pattern spatial layout optimization results, where the results showed that the overall accuracy of the optimization results was 84.98% with a Kappa coefficient of 0.7587, thereby indicating the good performance of the model. Moreover, the simulated optimization allocation scheme for the landscape pattern was consistent with the actual situation. Therefore, this model can provide support and a scientific basis for regional landscape pattern planning, land use planning, urban planning, and other related spatial planning processes. [ABSTRACT FROM AUTHOR]