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Simultaneous partial nitrification, anammox, denitrification, and fermentation (SNADF) is a novel process achieving simultaneous advanced sludge reduction and nitrogen removal. The influence of low temperatures on the SNADF reactor was explored to facilitate the application of mainstream anammox. When temperature decreased from 32 to 16 °C, efficient nitrogen removal was achieved, with a nitrogen removal efficiency of 81.9–94.9%. Microbial community structure analysis indicated that the abundance of Candidatus Brocadia (dominant anaerobic ammonia oxidizing bacteria (AnAOB) in the system) increased from 0.03% to 0.18%. The abundances of Nitrospira and Nitrosomonas increased from 1.6% and 0.16% to 2.5% and 1.63%, respectively, resulting in an increase in the ammonia-oxidizing bacteria (AOB) to nitrite-oxidizing bacteria (NOB) abundance ratio from 0.1 to 0.64. This ensured sufficient nitrite for AnAOB, promoting nitrogen removal. In addition, Candidatus Competibacter, which plays a role in partial denitrification, was the dominant denitrification bacteria (DNB) and provided more nitrite for AnAOB, facilitating AnAOB enrichment. Based on the findings from microbial correlation network analysis, Nitrosomonas (AOB), Thauera, and Haliangium (DNB), and A4b and Saprospiraceae (fermentation bacteria), were center nodes in the networks and therefore essential for the stability of the SNADF system. Moreover, fermentation bacteria, DNB, and AOB had close connections in substrate cooperation and resistance to adverse environments; therefore, they also played important roles in maintaining stable nitrogen removal at low temperatures. This study provided new suggestions for mainstream anammox application. [ABSTRACT FROM AUTHOR]

There is a noticeable research gap concerning the durability of recycled concrete, a pivotal resource for fostering urban sustainable development. This paper presents the results of an investigation into the bonding characteristics between complete carbonation recycled aggregate concrete (RAC) and reinforcing steel. Test specimens were meticulously prepared, incorporating five distinct replacement ratios (0%, 30%, 50%, 70%, and 100%) of recycled coarse aggregate (RCA). These specimens were subsequently categorized into two groups: one featuring stirrup reinforcement and the other devoid of it. Following accelerated carbonation, pull-out tests were diligently conducted. The outcomes of these tests revealed that specimens with stirrup reinforcement exhibited pull-out failure, whereas those without stirrup reinforcement underwent splitting failure. As the replacement ratio of RCA increased in complete carbonation pull-out specimens, there was an observed decrease in bond strength, slip value, and steel strain. Relative to natural concrete (NC), RAC70 exhibited a decrease in ultimate bond strength of 1.46 MPa, whereas RAC100 showed a decrease of 1.63 MPa. Additionally, the bond-slip curve displayed a diminished slope with higher replacement ratios. Furthermore, the peak value of the bond stress distribution curve shifted toward the free end with an increasing RCA replacement ratio. Complete carbonation recycled concrete exhibited heightened compressive strength, improved bond strength, and a steeper ascending segment in the bond-slip curve. The ultimate bond strength of NC-confined specimens increased by 26.76%. Conversely, for specimens with 70% and 100% replacement rates, the ultimate bond strength only increased by 9.55% and 3.89%, respectively. Furthermore, a constitutive relationship model for the bond slip between complete carbonation recycled concrete and reinforcing steel was formulated based on the empirical findings. Ultimately, the bond stress distribution curve reveals two peak points, observed near the loading end and the free end, respectively, with a more pronounced gap between the two peaks after complete carbonation. As the replacement rate of recycled aggregates increased, the maximum bond stress tended to shift toward the free end. When the replacement rate reached 100%, the gap between the two peak points significantly diminished. [ABSTRACT FROM AUTHOR]

Carbon dioxide corrosion is a pervasive issue in pipelines and the petroleum industry, posing substantial risks to equipment safety and longevity. Accurate prediction of corrosion rates and severity is essential for effective material selection and equipment maintenance. This paper begins by addressing the limitations of traditional corrosion prediction methods and explores the application of machine learning algorithms in C O 2 corrosion prediction. Conventional models often fail to capture the complex interactions among multiple factors, resulting in suboptimal prediction accuracy, limited adaptability, and poor generalization. To overcome these limitations, this study systematically organized and analyzed the data, performed a correlation analysis of the data features, and examined the factors influencing corrosion. Subsequently, prediction models were developed using six algorithms: Random Forest (RF), K-Nearest Neighbors (KNN), Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), XGBoost, and LightGBM. The results revealed that SVM exhibited the lowest performance on both training and test sets, while RF achieved the best results with R2 values of 0.92 for the training set and 0.88 for the test set. In the classification of corrosion severity, RF, LightGBM, SVM, and KNN were utilized, with RF demonstrating superior performance, achieving an accuracy of 99% and an F1-score of 0.99. This study highlights that machine learning algorithms, particularly Random Forest, offer substantial potential for predicting and classifying C O 2 corrosion. These algorithms provide innovative approaches and valuable insights for practical applications, enhancing predictive accuracy and operational efficiency in corrosion management. [ABSTRACT FROM AUTHOR]

The Lomax distribution is one of the well-known distributions that is used to fit heavy-tailed data. In this paper, we investigate the estimation of Shannon entropy of the Lomax distribution using noninformative priors. Some important priors including Jeffreys prior, reference priors and probability matching priors are presented. We demonstrate that the reference and matching priors for the Lomax entropy do not match the reference and probability matching priors of the Lomax parameters, regardless of which parameter of interest is considered. The propriety and the existence of the expectation of the posterior under each prior are validated, respectively. A simulation study is conducted to assess the frequentist performance of the proposed Bayesian estimates in terms of the mean squared error and coverage probability. Finally, the approach is applied to three real data sets for illustrative purposes. [ABSTRACT FROM AUTHOR]

Featured Application: The developed prototype provides a more efficient and accurate solution for classifying dynagraph cards, meeting the requirements of oil field operations and enhancing economic benefits and work efficiency. The dynagraph card plays a crucial role in evaluating oilfield pumping systems' performance. Nevertheless, classifying dynagraph cards can be quite difficult because certain operating conditions may exhibit similar patterns. Conventional classification approaches mainly involve labor-intensive manual analysis of these cards, leading to subjectivity, prolonged processing times, and vulnerability to human prejudices. In response to this challenge, our study introduces a novel approach that leverages transfer learning and the Swin Transformer model for classifying dynagraph cards across various operating conditions in rod pumping systems. Initially, the Swin Transformer model undergoes pre-training using the ImageNet-22k dataset. Subsequently, we fine-tune the model's weights using actual dynagraph card datasets, facilitating direct classification analysis with dynagraph cards as input variables. The adoption of transfer learning significantly reduces the training time while enhancing the accuracy of condition diagnosis. To assess the effectiveness of our proposed method, we conducted a comparative evaluation against conventional models like ResNet50, DenseNet121, LeNet, and ViT. The findings demonstrate that our approach outperforms other methods, achieving an accuracy of 96%, thereby improving classification accuracy by 3–4%. Therefore, our approach, based on transfer learning and the Swin Transformer model, provides a better solution for practical problems involving similar dynagraph cards. It meets the requirements of oil field operations, enhancing economic benefits and work efficiency. [ABSTRACT FROM AUTHOR]

When an intelligent vehicle changes lanes, the state of other vehicles may change, which increases the risk of collision. Therefore, real-time local path replanning is needed at this time. Based on model predictive control (MPC), a lane-change trajectory replanning strategy was proposed, which was divided into a lane-change trajectory correction strategy, a lane-change switchback strategy and forward active collision avoidance strategy according to collision risk. Based on the collision risk function of the rectangular safety neighborhood, the objective functions were designed according to the specific requirements of different strategies. The vehicle lateral controller based on MPC and the vehicle longitudinal motion controller were established. The longitudinal velocity was taken as the joint point to establish the lateral and longitudinal integrated controller. The trajectory planning module, trajectory replanning module and trajectory tracking module were integrated in layers, and the three trajectory replanning strategies of lane-change trajectory correction, lane-change switchback and forward active collision avoidance were respectively simulated and verified. The simulation results showed the trajectory replanning strategy achieves collision avoidance under different scenarios and ensures the vehicle's driving stability. The trajectory tracking layer achieves accurate tracking of the conventional lane-change trajectory and has good driving stability and comfort. [ABSTRACT FROM AUTHOR]